P2M1M2/h8dsft_Milestone2P2.ipynb

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      "name": "h8dsft_Milestone2P2.ipynb",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Perkenalan\n",
        "\n",
        "Nama: Sivi Almanaf Ali Shahab\n",
        "\n",
        "Batch: 006\n",
        "\n",
        "#### **Abstract**\n",
        "\n",
        "Explore Hotel aspects and Predict the rating of each review.\n",
        "\n",
        "#### **About this dataset**\n",
        "\n",
        "Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.\n",
        "With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!\n",
        "\n",
        "\n",
        "#### **How to use**\n",
        "\n",
        "* Predict Review Rating\n",
        "* Topic Modeling on Reviews\n",
        "* Explore key aspects that make hotels good or bad\n",
        "\n",
        "\n",
        "#### **Acknowledgements**\n",
        "\n",
        "**Citation**\n",
        "\n",
        "Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.\n",
        "\n",
        "\n",
        "**License**\n",
        "\n",
        "CC BY NC 4.0\n",
        "\n",
        "**Splash banner**\n",
        "\n",
        "Photo by Rhema Kallianpur on Unsplash.\n",
        "\n",
        "**Splash icon**\n",
        "\n",
        "Logo by Tripadvisor.\n"
      ],
      "metadata": {
        "id": "mH1VX87xNf1i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import pustaka yang dibutuhkan"
      ],
      "metadata": {
        "id": "wVRCqv6-NfzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "import tensorflow as tf\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "from nltk.probability import FreqDist\n",
        "from nltk.corpus import stopwords\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow import keras\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "id": "SnoEde0YLNKt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b10e7df4-ca0c-4fbe-c55a-9dfa696aa01b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Loading"
      ],
      "metadata": {
        "id": "AHd6rn0_Nfwp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RDRdBeCCCBqT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3a31f1e-62f6-4310-8b5f-9d20bd41dd54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.10.8)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n"
          ]
        }
      ],
      "source": [
        "## Install Kaggle library\n",
        "!pip install kaggle\n",
        "\n",
        "## Make a directory named `.kaggle` \n",
        "!mkdir ~/.kaggle\n",
        "\n",
        "## Copy the kaggle.json into this new directory \n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "## Change permission for this directory : \n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download andrewmvd/trip-advisor-hotel-reviews"
      ],
      "metadata": {
        "id": "mjs0bxrRJp87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ae39bd3-e3e2-4a55-be4d-333787cbda34"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading trip-advisor-hotel-reviews.zip to /content\n",
            "\r  0% 0.00/5.14M [00:00<?, ?B/s]\n",
            "\r100% 5.14M/5.14M [00:00<00:00, 84.2MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip ./trip-advisor-hotel-reviews.zip"
      ],
      "metadata": {
        "id": "o9u_i3SeKZ0W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b179773-ff52-4bf2-f212-f803abab7f1c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ./trip-advisor-hotel-reviews.zip\n",
            "  inflating: tripadvisor_hotel_reviews.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_tripAdvisor = pd.read_csv('./tripadvisor_hotel_reviews.csv')\n",
        "df_tripAdvisor.head()"
      ],
      "metadata": {
        "id": "vkJtjoJNKgFu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cd1b09fb-bbf7-4cd1-db9f-35aceac22b18"
      },
      "execution_count": 5,
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              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nice hotel expensive parking got good deal sta...</td>\n",
              "      <td>4</td>\n",
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              "      <td>5</td>\n",
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              "      <td>great stay great stay, went seahawk game aweso...</td>\n",
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            "text/plain": [
              "                                              Review  Rating\n",
              "0  nice hotel expensive parking got good deal sta...       4\n",
              "1  ok nothing special charge diamond member hilto...       2\n",
              "2  nice rooms not 4* experience hotel monaco seat...       3\n",
              "3  unique, great stay, wonderful time hotel monac...       5\n",
              "4  great stay great stay, went seahawk game aweso...       5"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Missing Values \n",
        "\n",
        "df_tripAdvisor.isnull().sum()"
      ],
      "metadata": {
        "id": "d-juQsUNLYg7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15b35cf9-3e73-485c-838b-f2eaf3a04080"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Review    0\n",
              "Rating    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No missing values in data"
      ],
      "metadata": {
        "id": "UQdgq0FdL61q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews = df_tripAdvisor.copy()"
      ],
      "metadata": {
        "id": "348Fa_jfLnIt"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check ukuran data\n",
        "tripAdvisor_reviews.shape"
      ],
      "metadata": {
        "id": "OGmPzGD6LxTe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "804d7d3d-f24f-4e99-dd2d-1768ee221282"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20491, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data memiliki 20491 rows and 2 columns"
      ],
      "metadata": {
        "id": "fs3ES6-1MKBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat sample dari data column review\n",
        "sample_reviews = tripAdvisor_reviews.loc[20000]['Review']\n",
        "\n",
        "print(sample_reviews)"
      ],
      "metadata": {
        "id": "S28mQnpKL2BR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8983acad-3dbd-4829-f7d8-5ce04d1a76a9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "best hotel remember, booked hotel internet just going pictures reviews read site, glad did, hotel lovely modern tasteful excellently equipt clean, location n't perfect sites barcelona gaudi buildings museums shops bars resaurants catalyuna square litterally corner bus metro services bring ride away changes bus metro-line, extremely easy need holiday hotel staff helpful request needed emergency dental treatment receptionist boss got dentist straight away, impressive service excellent customer care.i not recommend hotel highly, schools door effects little, not issue hotel wonderful,  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning"
      ],
      "metadata": {
        "id": "ueDcosy3NfuF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Di bagian Data Cleaning kita akan membersihkan text untuk digunakan sebagai data preprocessing, seperti mengubah text dari Uppercase menjadi lowercase, remove Hastag, remove Punctuation dan lain sebagainya"
      ],
      "metadata": {
        "id": "3TJinRv5gMQP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita ingin mengecek jumlah upper di setiap kata yang dimiliki pada setiap baris"
      ],
      "metadata": {
        "id": "PWPokDGbMjrO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Uppercase\n",
        "tripAdvisor_reviews['upper'] = tripAdvisor_reviews['Review'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
        "tripAdvisor_reviews[['Review','upper']].head()"
      ],
      "metadata": {
        "id": "BzUJ-2YvSk8t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "daf26538-0ba5-435a-fb5e-003eb015b851"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca9a57f8-c695-4789-8bb5-6972628fd33f 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-ca9a57f8-c695-4789-8bb5-6972628fd33f');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                              Review  upper\n",
              "0  nice hotel expensive parking got good deal sta...      0\n",
              "1  ok nothing special charge diamond member hilto...      0\n",
              "2  nice rooms not 4* experience hotel monaco seat...      0\n",
              "3  unique, great stay, wonderful time hotel monac...      0\n",
              "4  great stay great stay, went seahawk game aweso...      0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of Uppercase: \", tripAdvisor_reviews['upper'].sum())"
      ],
      "metadata": {
        "id": "JZPmRhZKZYUN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "beae8d27-57d7-4bf0-c214-06139c0c9c57"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of Uppercase:  23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita ingin mengecek jumlah Hastag di setiap kata yang dimiliki pada setiap baris"
      ],
      "metadata": {
        "id": "FIxx3J-iNIfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hastag\n",
        "tripAdvisor_reviews['hastags'] = tripAdvisor_reviews['Review'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
        "tripAdvisor_reviews[['Review','hastags']].head()"
      ],
      "metadata": {
        "id": "aG5Q9N4XQ9MO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "40046e73-5ec5-4216-f0e0-162ebdfa6a39"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-357d113f-2c6b-4b87-81fd-a597670c3c41\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>hastags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nice hotel expensive parking got good deal sta...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ok nothing special charge diamond member hilto...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nice rooms not 4* experience hotel monaco seat...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>unique, great stay, wonderful time hotel monac...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>great stay great stay, went seahawk game aweso...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-357d113f-2c6b-4b87-81fd-a597670c3c41')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-357d113f-2c6b-4b87-81fd-a597670c3c41 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-357d113f-2c6b-4b87-81fd-a597670c3c41');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                              Review  hastags\n",
              "0  nice hotel expensive parking got good deal sta...        0\n",
              "1  ok nothing special charge diamond member hilto...        0\n",
              "2  nice rooms not 4* experience hotel monaco seat...        0\n",
              "3  unique, great stay, wonderful time hotel monac...        0\n",
              "4  great stay great stay, went seahawk game aweso...        0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of Hastags: \", tripAdvisor_reviews['hastags'].sum())"
      ],
      "metadata": {
        "id": "tnC599EaZvM4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80b03a94-4a1f-42f4-d334-95d2f9e0eccb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of Hastags:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita ingin mengecek jumlah uppercase di setiap kata yang dimiliki pada setiap baris"
      ],
      "metadata": {
        "id": "UUDzSLOINhrP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Punctuation\n",
        "def count_punct(text):\n",
        "    count = sum([1 for char in text if char in string.punctuation])\n",
        "    return count\n",
        "\n",
        "tripAdvisor_reviews['punctuation'] = tripAdvisor_reviews['Review'].apply(lambda x: count_punct(x))\n",
        "tripAdvisor_reviews[['Review','punctuation']].tail()"
      ],
      "metadata": {
        "id": "6bDKahJaPEb_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7df0061e-ac04-43c1-ff41-77624fd07d4a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c87ffdef-10a9-4580-91ad-927ba26b1641\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>punctuation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20486</th>\n",
              "      <td>best kept secret 3rd time staying charm, not 5...</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20487</th>\n",
              "      <td>great location price view hotel great quick pl...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20488</th>\n",
              "      <td>ok just looks nice modern outside, desk staff ...</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20489</th>\n",
              "      <td>hotel theft ruined vacation hotel opened sept ...</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20490</th>\n",
              "      <td>people talking, ca n't believe excellent ratin...</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c87ffdef-10a9-4580-91ad-927ba26b1641')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c87ffdef-10a9-4580-91ad-927ba26b1641 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-c87ffdef-10a9-4580-91ad-927ba26b1641');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  Review  punctuation\n",
              "20486  best kept secret 3rd time staying charm, not 5...           18\n",
              "20487  great location price view hotel great quick pl...            5\n",
              "20488  ok just looks nice modern outside, desk staff ...           10\n",
              "20489  hotel theft ruined vacation hotel opened sept ...           94\n",
              "20490  people talking, ca n't believe excellent ratin...           15"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of Punctuation: \", tripAdvisor_reviews['punctuation'].sum())"
      ],
      "metadata": {
        "id": "OuUcNE_1aLZy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6da3c80-09f8-4927-8872-917c58def64e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of Punctuation:  357539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita ingin mengecek jumlah numerik di setiap kata yang dimiliki pada setiap baris"
      ],
      "metadata": {
        "id": "Yppi2HWuOQG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Numbers / Digits\n",
        "tripAdvisor_reviews['numerics'] = tripAdvisor_reviews['Review'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
        "tripAdvisor_reviews[['Review','numerics']].head()"
      ],
      "metadata": {
        "id": "hmH5Gj83RVhj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bc78f3c9-c6e1-4278-de05-2f39b1266f57"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-32e9052f-34e1-4681-85a8-c28a6f87f80e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>numerics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nice hotel expensive parking got good deal sta...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ok nothing special charge diamond member hilto...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nice rooms not 4* experience hotel monaco seat...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>unique, great stay, wonderful time hotel monac...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>great stay great stay, went seahawk game aweso...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32e9052f-34e1-4681-85a8-c28a6f87f80e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-32e9052f-34e1-4681-85a8-c28a6f87f80e 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-32e9052f-34e1-4681-85a8-c28a6f87f80e');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                              Review  numerics\n",
              "0  nice hotel expensive parking got good deal sta...         1\n",
              "1  ok nothing special charge diamond member hilto...         6\n",
              "2  nice rooms not 4* experience hotel monaco seat...         7\n",
              "3  unique, great stay, wonderful time hotel monac...         1\n",
              "4  great stay great stay, went seahawk game aweso...         2"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of Numerics: \", tripAdvisor_reviews['numerics'].sum())"
      ],
      "metadata": {
        "id": "2bIq3RASakpQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8c41332-8917-4929-cda0-4cd31f1deff7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of Numerics:  38610\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Method untuk cleaning data\n",
        "\n",
        "def clean_data(data, name):\n",
        "    # Convert words to lowercase\n",
        "    data[name]=data[name].str.lower()\n",
        "    # Remove the Hashtags from the text\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'\\B#\\S+', '',x))\n",
        "    # Remove the links from the text\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r\"http\\S+\", \"\", x))\n",
        "    # Remove the twitter handlers\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'@[^\\s]+','',x))\n",
        "    # Remove the Special characters from the text \n",
        "    data[name]=data[name].apply(lambda x:' '.join(re.findall(r'\\w+', x)))\n",
        "    # Remove all the single characters in the text\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'\\s+[a-zA-Z]\\s+', ' ', x))\n",
        "    # Substitute the multiple spaces with single spaces\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'\\s+', ' ', x, flags=re.I))\n",
        "    # remove text in square brackets\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'\\[.*?\\]', '', x))\n",
        "    # remove punctuation and remove words containing numbers\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'[%s]' % re.escape(string.punctuation), '', x))\n",
        "    # remove words containing numbers\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'\\w*\\d\\w*', '', x))\n",
        "    '''Get rid of some additional punctuation and non-sensical text that was missed the first time around.'''\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'[‘’“”…]', '', x))\n",
        "    data[name]=data[name].apply(lambda x:re.sub(r'\\n', '', x))\n",
        "    return data[name]"
      ],
      "metadata": {
        "id": "VIaAVCdkBUMx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_data(tripAdvisor_reviews, 'Review')"
      ],
      "metadata": {
        "id": "QyZgBGT1Q2FA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d17ad6f2-51a1-4459-f0f7-7b7bc466f388"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        nice hotel expensive parking got good deal sta...\n",
              "1        ok nothing special charge diamond member hilto...\n",
              "2        nice rooms not  experience hotel monaco seattl...\n",
              "3        unique great stay wonderful time hotel monaco ...\n",
              "4        great stay great stay went seahawk game awesom...\n",
              "                               ...                        \n",
              "20486    best kept secret  time staying charm not  star...\n",
              "20487    great location price view hotel great quick pl...\n",
              "20488    ok just looks nice modern outside desk staff t...\n",
              "20489    hotel theft ruined vacation hotel opened sept ...\n",
              "20490    people talking ca t believe excellent ratings ...\n",
              "Name: Review, Length: 20491, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data sudah clean selanjutnya data ini kita akan gunakan pada data preprosessing"
      ],
      "metadata": {
        "id": "nu3X61RlOndk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews = tripAdvisor_reviews[['Review',\t'Rating']]"
      ],
      "metadata": {
        "id": "wSe0t0XHDGfx"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews.tail()"
      ],
      "metadata": {
        "id": "cjyxTbAXhdvy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "51a305cc-47da-49c5-e8ca-082233136262"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-852c6ae0-c014-4dc1-910e-65ad93a4b1fe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20486</th>\n",
              "      <td>best kept secret  time staying charm not  star...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20487</th>\n",
              "      <td>great location price view hotel great quick pl...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20488</th>\n",
              "      <td>ok just looks nice modern outside desk staff t...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20489</th>\n",
              "      <td>hotel theft ruined vacation hotel opened sept ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20490</th>\n",
              "      <td>people talking ca t believe excellent ratings ...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-852c6ae0-c014-4dc1-910e-65ad93a4b1fe')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-852c6ae0-c014-4dc1-910e-65ad93a4b1fe 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-852c6ae0-c014-4dc1-910e-65ad93a4b1fe');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                  Review  Rating\n",
              "20486  best kept secret  time staying charm not  star...       5\n",
              "20487  great location price view hotel great quick pl...       4\n",
              "20488  ok just looks nice modern outside desk staff t...       2\n",
              "20489  hotel theft ruined vacation hotel opened sept ...       1\n",
              "20490  people talking ca t believe excellent ratings ...       2"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Eksplorasi Data"
      ],
      "metadata": {
        "id": "g-lcyMolNfrk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita akan melihat jumlah kata yang paling sering muncul pda dataset"
      ],
      "metadata": {
        "id": "hskJwC2TPfSe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reviews = tripAdvisor_reviews['Review']\n",
        "\n",
        "# Sentence Tokenization\n",
        "\n",
        "reviews_sentences = []\n",
        "for review in reviews:\n",
        "  \n",
        "  sentences = sent_tokenize(review)\n",
        "  reviews_sentences.append(sentences)\n",
        "\n",
        "reviews_sentences[0]"
      ],
      "metadata": {
        "id": "GxMvA7S4Ieuh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8746394f-4234-4504-91c6-8107eae31712"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['nice hotel expensive parking got good deal stay hotel anniversary arrived late evening took advice previous reviews did valet parking check quick easy little disappointed non existent view room room clean nice size bed comfortable woke stiff neck high pillows not soundproof like heard music room night morning loud bangs doors opening closing hear people talking hallway maybe just noisy neighbors aveda bath products nice did not goldfish stay nice touch taken advantage staying longer location great walking distance shopping overall nice experience having pay  parking night']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_token = []\n",
        "for review in reviews:\n",
        "  \n",
        "  word_token = word_tokenize(review)\n",
        "  words_token.extend(word_token)\n",
        "\n",
        "words_token[0:10]"
      ],
      "metadata": {
        "id": "6ZkyugKMZDPl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83b04358-64c8-41bb-93b6-4970870ce655"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['nice',\n",
              " 'hotel',\n",
              " 'expensive',\n",
              " 'parking',\n",
              " 'got',\n",
              " 'good',\n",
              " 'deal',\n",
              " 'stay',\n",
              " 'hotel',\n",
              " 'anniversary']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lima kata yang sering mucul pda dataset adalah:"
      ],
      "metadata": {
        "id": "_8LA6kDCP7_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fdist = FreqDist(words_token)\n",
        "fdist.most_common(5)"
      ],
      "metadata": {
        "id": "c5zrB9ghbp_3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c667c4de-73d4-4b0a-9053-569872c20713"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('hotel', 49820),\n",
              " ('room', 35332),\n",
              " ('not', 31710),\n",
              " ('great', 21475),\n",
              " ('t', 18742)]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lima puluh kata yang sering mucul pda dataset bisa dilihat pada gambar dibawah ini:"
      ],
      "metadata": {
        "id": "7PwDnvj0QGoF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "fdist.plot(50, cumulative=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HW2sMAuCdj9I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "outputId": "4458bce8-ebb8-4459-be71-183c32919589"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Untuk kata yang muncul kurang dari 10 pada dataset kita bisa lihat dibawah ini:"
      ],
      "metadata": {
        "id": "eQXGRCENQQ5X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_common = list(filter(lambda x: x[1]<=5,fdist.items()))\n",
        "min_common[0:10]"
      ],
      "metadata": {
        "id": "ivS1dClrlJG9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ff8f283-fb13-4d0c-f1c7-bdb2ac7573d7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('bangs', 4),\n",
              " ('hood', 4),\n",
              " ('gee', 5),\n",
              " ('chating', 1),\n",
              " ('pillowsattentive', 1),\n",
              " ('staffnegatives', 1),\n",
              " ('brandi', 2),\n",
              " ('pattens', 1),\n",
              " ('palatte', 1),\n",
              " ('seahawk', 2)]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita juga bisa melihat kata dari yang paling sering muncul sampai dengan yang paling jarang muncul dengan wordCloud_generator"
      ],
      "metadata": {
        "id": "jTkv4jFUQw_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def wordCloud_generator(data, title=None):\n",
        "    wordcloud = WordCloud(width = 800, height = 800,\n",
        "                          background_color ='black',\n",
        "                          min_font_size = 10\n",
        "                         ).generate(\" \".join(data.values))\n",
        "    # plot the WordCloud image                        \n",
        "    plt.figure(figsize = (8, 8), facecolor = None) \n",
        "    plt.imshow(wordcloud, interpolation='bilinear') \n",
        "    plt.axis(\"off\") \n",
        "    plt.tight_layout(pad = 0) \n",
        "    plt.title(title,fontsize=30)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Z1xwtEm-QhTQ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wordCloud_generator(tripAdvisor_reviews.Review, title=\"Most used words in reviews\")"
      ],
      "metadata": {
        "id": "xGntqVltz-ot",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "778930cb-06ee-423f-f4f0-15ea91fc59f1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mpTpfq3cBUD6TVH3rLhZC1Au2L9VMB2tcZqrvSJyq1vRG/7PWp7CSbr0QYqu/vZx/U4X3o2Y47675rmKh+BxwSgjxT0KI+sFvwMtEoHE6D6iL6fShmkM/OE2VbO3oPtE0VZXajmaJZsAfxfyyEOKjKMfH61BaqJtRmppe1EyxncB/PI1yng1WHBRIKY8Ad/gz366len03oEw0I6ip3RuklH9Y89Pajv5mKWWzBm8lavOJryL9au7patmF0iTFUH4bP2Kxf1NtOoB1QohtKC1NxdTxaJNZcBlUPJnVlnfZ5/A0WPP6lFL+EBVKogt13deino8rUc/YdcCDqwyn8ONIbZ1/RErZcCbcafKPqMkQoMIOfAwWZqD+hP/9E1JKeYb515Z5k98OvBR21WzfhNJGVt6lx2sGIU+hnu+kf/wAVQGrhG8yr8fXqn0O+JwQYhOqjboO1Q5vRflOvhc12eNKKeXZmuH5qiXQOJ0/fMH/vJiq4/EXGqZszljN9tamqRqnaep0K6W0pJSPSCk/LaV8B0pl/BGqZoGfFyrq9ctN7eyZRiO/WrpXOL6AlLIopbxfSvkHUso3oQTCj1Gd4fNxv+OsUGu+WL/a8zQgRVXr0TA6dx2rSbNaFvk5+aNV0eBYpbGHasyiimlhV5O8K89hvxCiPmjfInxt5WZ/d2YZM91qqH2G17Q+pZQzUspvSCk/JqW8BhVw8p/8wyGq0/FfbazVu1DLPaio7KBmG1c02j9D1QT5UiZKrHWZn6QqjN1U97nwLtVpem/y/ZsqkyyaDUIWIaU8LKX8Bynlf5JSbkP5SD7jHx5i8czKgJeJQHA6f6jEdKqw6thNNdQ6d9+6ivS1s1oeb5qqDill2R9Z/kXN19fXJas1KayVKW++Zrs+GnU9KzkaN0VKmfXjpdzlfxVhsePxD2u2F80MOs3zeFSjDfcKtfTDctTPyHsp1AtEtea3hZk9fmP/QE26G2vyaKZpqzxLGkqLuhzXUdU4rfoZXOG8oEbjTfEFxUbOyavCnz12B9UgkK8RKhr9q40fUR1gnPG7UIv/zFWWGBqi+sxVzHS1x8+ENXl/K9QJRNf6WqFt/n594N/KO3MjyhWive770z330ywOBhrEcjoLBILTeYI/m+IzwGP+32dOdwab75dUCaJ5iRCiqfDkB62rdC5HUR3p6TJas11v9q1Vh6+Viak2lkzTjtGfOv/WZsdPg9Ga7drre5Kqv8G7VyHwLEetcPzRZol8Z9NG/lZnRF1jfw3VyQiNRr61jX1FwCrSfIbXXTXbv7GCD1xt3LG7mqZaHQ+hIoUDvE2oJVOa8WsoE8cZ49dhrSn9Vef64Jvz/93ffa0QYk2EJxb7L71fCLGBqlBwz0t0hv4OVSf7/7xGvkGVdyRCNUZUI/NbJV0/ix3pz9TcD8u3wwEvA4HgdB4hpfyElPIa/+93zzCb/1Oz/UUhxPb6BEItSvsvVO//J2sD/AkhBoQQn6qN3dMgjzjVdfRALVVQS63fwOWrLfxySBW4szKL6XVCiCXCkT8r8assnvZcn+YyIcT/9IWRZmm6gcp6Zh5qMddKOTzgt/zdEPAdIUR9PKT6/C4QQvxVg0NfpNqIf1io9b7qf9uCul+ty53jDNjlf0aoRjRv1IBXvutDLZ0B8MgygQe/TVWwvB74pG+WWIQQ4repCrjHUQEQzxh/huef+7th4CtCiM76dP7086ZCqp/mZ4UQH15OiyTUWnyVuGmHV5pB+GPM76CcoAH+RQhx+3KJhRAb/Palt1kaKeUTQMWH6Z2oZYcqAvhLimfm+xz9L3+3E/j3GmftZmW+WgixXCT2XTXblXbxkQaDkMqyVbXpCqjBcqPzflwIcWuj96eG/1yzXd8OB7wMBNLpjxlSyq8IId6B6ggHgKeFEF9AjXxq16qrdMLfY/E6aaA60v+KWoD1CZSpZi/KVNaG8oV5H9X4Jg+wNJ7KC6jRfy9qxDiF0lBUtGgF3/H2dPkUylES4C4hxN+jzAUaqhP7MEr9/a9UBZ962lCRvz/hTyV+GBWaIYNqSC/yr6/S6X6pPhqylPJuIcTvoWbgDQOPCSG+hwpjcAIlbHWhHOdvRE1Ndqibri2lnBdC/BJqCRAN+LwQ4l3Av6EaWIG6X8Mo7dRarVkHi4Uks8F3FZ5B3fv2FdIBCyEG3o+q1xjqWbpJCPElVN30ofxVKhoEC/jganw8VsEnUXF+LkUJ7HuEEH+LEuRq16qbR3UyNzbJZysq1MRfCCG+jwrhcBylRehFOYq/narW6g8bZfJqQEr5tFCLVP8tyv/tu/579V3UAMpCvUvbUfe8EmG74cLZNdyJirjdRlUzmaE66+6llPmz/mDngyif0j1CiH9DtSVjqPvag2oLbkEtT3UINaO4EU9RXT6r6TsipXSEED9CDRgq6ZYbhNyMEvLGhRD3oJa0GUcNeteh1v+rTNYo0Xyx+IA1JBCcfjz5AMpU9nOojqtZfJWvojqs+lgotftXsjS6dC33Az9dn4eU0hZqLbK/QWll6hucoyyOp7JaPo9yqLzDz/cX/L8KZX/fprngVCmrQTVGVTO+TJMZg1LKTwghjqOmpreiZqktt9RFwxmSvrDbjYoKbaJi1by5LtldKC3XWgpOT1Nt7EEJtUvMb74g9CNUI11hWdOClPJZIcQtqHIPoISYRprHWeB9azUjzY+hcxvVhX77UBqRWuZRgttyy3pUnpEE1ThfjbBQ68Z9rsnxVwVSys/5Ma7+FlXn17PU77GWGZS5dznuRA1wNKqTQb66hkF4P4Sa2fY7qMHiT7F89PqmM5x9gehBqrP+oPk7cj+LXQmWe5cqz2E/qs27o0m6aeBnpZS7mxwPWEMCU92PIVJKW0r586ip059DTSHPoTrGI6gG6RYp5U83aoR8k9gWVJDIf0Jpj1IojUke1dj8M/BWKeXNsvk6bf8PNcL/BqrROa01pZrk6aG0Su9FOV7O+fmOogI0XiEbrPdWl8cPUSPJ30B17PtQgqbrf+7x87pBSvme5Rpq/1wbUFqV76FmdpX8v3HUCPaTqFHrpibZIKX8S5Rg8QWUZqOMWjj2XpRg8S6q5pA1wTfP1kbjfniZWW21jXueVThySykfQWluPopyyJ1CXcMMSgP6P4DNUsp7Tr/0y553EjU54JdRgmDaL7NErVF3qZTy3hWy+QOUNur3UbO8RlHvj4165h5HmcUvkFL+n8ZZvLqQUt6N0sz8J5TG9Diqzsqoe/8IakLJW4F1Usplg3n6Ppv1muy1Wnaosr7b//bL/HHUMzrul7eIarPuRT0D10opb1why9p3pKn5jaWC0q5l8nwbSpj7M1RdjKPeobK//QNUO7ZVSrma1SIC1gDN884k8GpAQEBAQEBAwKuPQOMUEBAQEBAQELBKAsEpICAgICAgIGCVBIJTQEBAQEBAQMAqCQSngICAgICAgIBVEghOAQEBAQEBAQGrZNk4TpqmBVPuAgICAgICAl5VeJ7XdKmoIADmmqLRbvbSZvZyqnQAy1uLQMgBAecvmmHSvvFisqcOYuXTK/8gYM1paRukZ92loEG5mGHyxFNY5dyiNGY4Tt/6KwhHk+B5TJ16nmyqaTzHAKA9NkhHfJhjc0/huM3Cm700NDSS0T66EiOE9CiWU2QiI8lbc6vOIxZqZ7DtQgw9jO2WOJV6kYKVWvmHAWdMYKpbQ3R0esMjbI5fRlRfq3VtAwLOX8ItHXTtuBYz1nKui/KqxbYKFPIzROOd9AxeghmOL0njuQ7FvOqMewYvJZ5sukzj2qHpdA9cTP/w1S//uV4GkpFe1rdfgqE1XfbyJdMS6eGC/tvojA+j6ybRUBJNW25N7KU4rkXemidkRFjXupOomXyZShtQIdA4rSEuLlPWMcpekaKbW/kHa4Suw803R1g3aHDwoM0Tj5ex1jTGdMArHt2gbfgCinPjlFJThBJtJAe3kZ8+QXF2DCMcpWVwK/mJo1iFDOFEG9HOAcyYaoTL6Rlyk8fwHPVgGZEY8Z5hkoNbiXX20775UhJ9G/Fch9zEKMW58eqpzTDx3mEirV24tkVh5iTF+UmoBN/VdOI969F0g+LcBIm+EULxJHapQG78CHbh1bp27uoo5mcZP6pWyuldf1nDNI5dYmb8BYr5Gdq6Np+Vcum6QUfvNkqF+bNyvlci8XAHUTPJ7rF/J1Oa9L89PQ+ZspPjVOpFLCdPMvLyC8ThZJiei/uI9cQpzRcZe/wUeB49F/cR70tQzpSZfHYcPGjd0EasO05uLEusK0YpXWJm9xRG1KT3kj4i7VFSR+aY2z+La7sve9nXikBwWlM85qwx5qyxs3pW04Q7PhznhhsifOXLBV543sKyfjzc00Ih1b/a9hlmoGvoIQO3dKYZrIwRDbHuzRcy+cP9lKaXCsyh1ij9t+5g4n5JeTa/ukw1DT28inJrGq3b++h7w3asVJHRf3y0Ybur6Tpd268ifVxSSk0R793A+uveztTuhxibHSPa0U/PztdyIjWN61h0X/g64j1DOOUCRiiCEYkzs/cxpvc+Ap6LEY4R7x4k2tGHboaJtvdiRlvwHJtSaqpaN+EYPRe/nuTgNpxiDj0UATwmnr2fzMn94HloukHbhgsIJ7uwcikird2gaaBpWPn0eSU4meEE3QMX0tI2iOe6pOdGmZ3Yi2Or1YQSrevoXX85sxN7SM0cBiAUTjC4+QZyqVNMjz2P57kLeXX2bqelbRDDDOPYJeanDjA7KancRDMUo6t/J8n2IUAjM3+M6bEXcewGbgCet4o+d/kEhhmls3c7rZ0b0HSTbOoEM2MvLpj+IrF2+oauYH76IPGWPlUPuMxNHWB+cj+ua6EbITp7t9PWtYmOHoFVzhGNdwEemfkTTBx/EtdpbPoy9Qgbu65hMnOAVPEU7bFB1rVdxHh6L7P5o8RC7axvv4QT889SsFJEzBb6koKWSA+e5zKXP8Zk9iCup94bZca6iKnsQdpigyQj3biey1T2IDO5UTwqnbVGe2wdfcltmHqUdHEc04guqS9Tj9DTspn22CC6ZpIujjOekVhO9b1ujQ7QnRhhLL2XzvgwbbF1eJ7DZOYAM/lRdM2gp2ULnfFhktE+TD3Mlp7XYjsl8tYcR2efxHZLtMfW09OymRPzz1Gw5hfOv6XndczkjjCVPbTk9r/cGFGTre/YQaQtQurIHOFkBM1vY+O9CTzbpefCXloGWph4cgzxzh2kj6fZ+MbNzO2fIdIZo5wu0XtJH/G+FvITWba8TXDknkMLwtYrgTUTnDTTJNTVTXlqEtzzU3KM6UlCepScPUdEjxPRlUq76OYpuhm8RXdNI2l04ng2BTdDVG8hosfR0LC9EjknjYt6OXUMkmYnpqbWoXQ9h7Qzg+M1V/uEtRgxowUDEw9wPIuSm6PkLV4WzdBCxPVWTD0MnkfJK1B0Mricn3W8lsQTGu94R5R02uPufzszf7HEcCcdlw1x4uvPrnHpqmghndYdA8w8MYpaEnAxejRE644Bph85vOo8o/2t9L5+K8e+/OTyCT2P7JEZQo+P0n/rDiVwNGpBXZdSeoZwsgM9FCHa3kMxNUU42QFApK0Hp1zAys7h2hYz+x5nevfDOFYRTdMZuPJNtI1cSPrYHsrZOcrZeaZ2P0QpNU2idwOTz+2iMDsOeHiOL+xpGq0bLqBt5ELGn7yH7PgRjHCM/stvpfeSGynMnFokFCUHtzL1wo84/uBdeK6Dphu41vnjJxiKtLBB3EYs3kl6/jiGEWbdxtcSS3Rx4tADuE6ZYmEWMxRlcNPrKebnsEoZ+oavpLVjA1Mnn10QmgwzygbxRlraBsnMHaOUnycSayMcbUXTNDzPwwzFGN52Ky2tA2TmjwMa/cNXEUt0c/zgrsbC00vAMCMMbbmJtq6NZOaP45Tz9A5eTiLZz1H5fWwrrwSrvgto69pEuZimkJshnuxjRLyRUc9ldmIveB6OXaKQm8bzHMqFeTJzxwCPQm5moQ4aoWk6rZE+ilaadHGMNl+YKds5ZvNHaY320Rrtw/M8omYrovdGQkaM+eIYumYw3PEa4uEOjs4+ieNZhIwofUlBe2yQop2maKVJRvvY1nsDeycs5vLHAWiL9rOt9ybKTo5McZLWaC3ERaMAACAASURBVD9tsQE0qmYzQw+zqftaOmJDpAqnsNwCA207aYl0c3D6QSxHtd1Rs4Xe5DaioVY8z6NopQmbcQxdmfw8wHIKZEtTGHqYRLiTTHGSkp2l7OQW6icWaqUrMcJERlLwuxJdM+lObKRQPjdavNYNbbRv7uCFzz9L5lgKzdBwLRfd1JnePUU4GcaMh+m5qJeJp8YoZ8uc+NFRWtYlOfXYSYZv2UjLYJJ11w5x6tET5CZydO3oof81A8zsmcIpOefkuk6XNROczPYOOm+5ncmvfxm3sFaLV68dGhqD0W0MhLcwWnyegchW4norhmaQsWcZLT7PZPnoQnodnW2Jq8k7KebtCYaiF5A0OtE1g5Q9xZ7sA2Qd5TNgaiGGohfQZvYQ0RN4eDyZ+jYZZ+natxoanaF1DEUvoN3sI6RH8PCw3CInivs4VHh6IW1ETzASu4je8AhhLYoHFJw0J0uSE8V9uLwyHrIzZfMmgzs+FGffHntFwallczcdlw+jh00y+yeYe+Y4bRcO0n/rdhIbugi1x8kfn2Xi3n1EB9roumoEsyVC/vgsM48fJdrTQnyog+lHj4DrER/uJNrfytzTx4j2tdJ11QiaqTP75DFyozOAR+eVIyS39lKayKAZy7sLaqZOx2s20HtjhPzxOWafPIpTsIgPd9J15QbQNWYeHyV/fI7W7f3037qD1u39GPEwxck0Y99+ES1k0PmaYVo29VCcSDP9yGGcfBm3aFGey+M5zYdrnudSmpugZd0WQrEk4WQn2ZMHiHYOYETihFu7sPIZnLKq59L8xKLfZ8cO0bPztRjhGDAHnotrlXBtpTlw7fISIccIRUmu30ZpbpLsqUM45QJOMUfmxD4Gr307oUTbIsHJdSzmj7xwXmmYqmi0d22hvXsL+5/9CunZI2iaTv+Ga1i/+QZmxveQS4/hWEVOHXmYrZe8i57By8ilTtI7eBknjzxELn1qIbfOvu20d23i8O5vMTe1n8pQW9P0hY4z2bGBzr4dHHrxm8z5WqjewcsY2fETzE5K0rNH1vQKE60DdK+7mKPyHqZOPQ+eS1f/TjbtfBtzU/uVUATouknZLnF497ewyllCkSTbL38fnb3bmZ3Yi+vazE3tJz13jN7By8ikTjB29JFVlcF1bfLWPNFQK6YRJRHuJF0cIxZuAzTi4U5Kdg7LUUJLMtrHC2PfJlVQddvfegHbel7PXP44cwXl/G7oJiU7w76J+7DdIlEzyWXr30l3YiNz+ePomkl/63Y0NPaN/4CinSZsxLlw4CdojfYvlK010kt/cjsHpn7EeHovHh69ya3s6LuV6dwRprIHF9LGQ+3M509wcPoB7AXHciWEeZ7DbP4Ys/nj2K5FR3w9Y+k95MoN10o/rwi3hHFKNuVUEc/18Fz13HZf2MvwzSPkTmWIdicwIgZoGnbBxik5WNkyTskGz8OMhoj3JUgOtRLtjJGbzDGzd3rZ9ut8Y2XByTDQjFUkSyTQI2Hg9BzbzjYxI0lfeBPHC7vJOSliRpKR2MVsil1GwckuEXa6w+uJ6UnGSgfZb89iaCYhLULJrQqHZa/EgfwThLQIQ9Gd9Ec2NT1/R2iA7YnrcHE4WHiKnD2Hhk7CbCfvVGdChLQIm2KX0hka4Ej+OTLONAYhBqJb2RC7CNuzOFXav/YVdB4htpv09xvs27O8uSrS3cK6N19Eeu84VqaI59vKCyfnyJ+YRw+bzDx2BDujOnY9pGOlixTH03RdPQKaRu7INN3XbyY3OkNhPE3XNRtxChbRvlbWveUicoen8VyPoXdextF/epxIdwu9N25j+qFDJEa6CLVFly1jtK+VaF+SjJyg57VbcC2Hwsl5ht55Gel94+B5DL3jMo7+yxMUx1Pkj88S6Uow89gRnLxqeLuuHqFtxwDzz50gKfoYfNvFnLjrGVxrFQK051FMTdG+5TJCiTbMaAupIy8Q7ewn2tFHuKWd4qwyMWu6Qbx7PS3rtxFuaUc3w0Rau9AM1RiuFs0MEW7pIJLsZOQNH6iaqCJxPNfGCC+uMyubwrVKq87/bKIbIVo7N1AupcHziCW6AbCtPGYoRiTeQS6t6i+fGefUkYdYv/n1uAM7Sc8eZWbsxYW8NM2gtWMDpWKa+ZlD1NonKnVUSWNbeVzHIpboUuezixhmmGiii/Tc6JraZ1o7NuDYJWyrSCzeCSjfKE3XiSW60TQ1OHBdi9TsEaxyFgCrlMEqZzHDL31CjOs55MoztMcGiZgthI0EM7ljdMaHiJgJ4qE2CtY8Hh6d8SHSxQlypdmF36cLp3A8m2S0n3lfmLLdMrP5Y9iuev+LdgbLLRI2lMUhZERpifSQKp6i7Jvcyk6B+cJJktGqz1B7bBDbLWM7JWJhpal1XAtN00mEO5lGXzD92W7JP2etSfKVIxg0o5wpY0RMIh1RSqkSuqnjOi7rrhkkezLDga/vY/C1Q7QM+JNFvMq/6rU7JZv00RTHdx1lZs8UekjHtd0fLx+nxLYdxDZtWTEjI9mKkTj/vfldXCbLo4yVlX047UyjobOj5Tq6woNkCrPU3uSwFkWWHmWqfHyRPXzxS+BRcvNYlLC85to2UwvRH95MWI/xXOZeZq2xhXxm7eo2QNLoZCCyhUP5pzlZkgvnLuaztLV20xfeyGT5CHadOdA7G4bus0AsBjt3hkgmV+6oPc8DTSPUGiNzYJL8yXk826U0laUwliLcFiO9p+p3Vp7NY3XnCbXF0EyD+GA7s0+MUprKktzai1Oyifa2Mn7PbhLDnbRs7KY4kcYt2UT7W0lu6yM+3El2/yTTjxwme3iatp0Dy5axPJ9n+sFDpOUEsYE2EsOdmIkIruUw+cMDuCWL+HAXbTvXMfGDfeRPzJPY0EV679jCY9Fz/WasdJFwZwJN12m/qJ+J+ySlydVpaOxCFjyPSFsPaBrF+UmsXJpE3whGJKYctoFE3wbWv/ad5CePkzkhsQoZkuu2kBzasarz1NwZJbDNTzJ/+Dlcp0YA9lxK85OLU7t2nbn87GP2dhEa6kfTNbyyRXHfEbxSGU3TCUVaiLf0sunCt9UIOJpyfvY8Ku2C57nMTu6jf/gqovEOZib2YFtVHxjdCGGEYljlLJ7bROjVNMKRJNF4F5t2vgXXT6fOl8JzbTS0Na2vUCRJNN7ByPbbcV11rzQ07HJe7ftCs+c62HXhDjzPRdONl1wGD5d8eY6+5DaiZhJTD5EqnqIjPkRLuJuwmWAmrywDYTNBrjS74M8E4HgOllMkYsQXBD3Pcyk7i9tldf/U9eiagalHKduFmvr0sN3SIrNixGwhFmpja+8NuJ5/PwDLLuBU6sf/ue2WsZyXx8ysnUPlRPpYirkDM2z9SUH6WBrP8zh23yjpY2l6Lupl409soX1jB5rRvIylVInxJ08x8sZNdG7rQg/rnHrkBKkjqzM/6vEYEbERzdTxHJfy6Emc2bMbfmFFwSk6son41m2Up6eWTWfE4mp613mO69lk7MVapZQ9iee5xPU2DAwcqi9izkmRtedqhCY405GDQYg2s5uMPU3WnqNe+KqgoZEwOwjpEXrCw7SaXQtHdXQiehzHswlp0SWCk+Moh+oLLzK5/voIQ0Pqnhw+7PDgA2UOHbJxVlBQxGIaYrvJNdeGWb9ex9A1pqZcnn22zFNPWqRSy1+/acLmzSZXXR1iZMQk2arhOjA35zI66vDccxajR2yKde1KZ6fGxZeE2LTJZJswef3rI+g6XH5FiD/5TNuS89zz3SL33FOiPJvj5N3P0331RobfcwVzTx9j4j65oHmqZ+D2nYTaouSOzCjnZEPHKVhk9k+S3NaLlS7iWTaF8TTxoQ40XaNy+8fv2UP20BSt2/spjqfA9XByJdzy8pXq5MtY2SJ4Hq7lYMRDmIkwTsHCtWw8x8PJlzATkcYZ6BpmIkJ5JgcaFCfTpOU4dnb1GhqnXMC1SsS6B7ELGax8mnJmhkT/CLoRopSaBqBl3RZc22Ly+V0Ls+PivcMN8/Q8z+80l777rmVRSs9gxlpIje7GLmZXXdZzgmGQuPZSWm9/HegazlyayU9/HntKDaYcu0wuPc7xA/ctEoQAf6p/ZZagRnv3ZkKRBJaVp6NnG+nZ0QWfJM9z8BwbM5JYOgZbwMNxShRyUxzbfy9WaXHdlQrzy/oKnQmuU6aYm+HYgR8smQlXLqYXCXlLBmiLlQoviYrWpyXSheUWKZRTlO2c75BtLGiYbKeEoZtKQPLPrWs6hhbC9soLBVpJuPQ8D9ezVV41l6GxWMNqu2Xy5XkOzTxE0Vocs6xkZ/G82jZgDSukRlDSNB1de+kC6pniFG0OfkPSdUE3kY4YpfkiTtHm+A9HKc4WMOMhDv6bJJQIkT2VYfTeQ+Sn8hz+7kFy41lGv3eY7KkMcwdnyJxIE+uOY+ctinOrd+8Jb1pP18/9NJqp4xbLzN35TfLnm+AEkHn2KdJPPLqsVjjSP0DHDbesVbleVhzPrtu38PAwtMUvIYDtWWvmS6RpGqYeJmel6wSxJSkJ+Y7mGtqSOCJz1jh5J7Uw6ll8Dnj3e+K8730xOjp1DANCIQ2r7PGe9zp8/u/zfOPrBZq5oQ0PG3zk5+LcdluU9nbNbzfUZy4X4/HHLD77F1n27LEbzgFobdP4wAfivPNdMfr6dExTOboa/gikVPTIZD0++xdZ/uWfC4uEuCuvCvMrv9pCe5tOLK7R2qp+09dncPPNSztmuc9G00pgGBQn0py8+3naLlxH/xt2MP3QIWy7jFe20SMmesTEsx08D9p2DnDia8+SHZ0mKXxVvAeZ/RN0XbWBtgvXkTk0hZ0tUp7LU5rOMvvUUYqTGYxoCCdfpjSdJdrbih4xifa3YkRXiPXiAW7Ng+V6lKaVhivcFscp2US6WsgcVAMU13LQQwZGJLQgWOVPzFMYTzNxv1T3xNAWzHirwSkXsfJp4j1DzB95Hs+xKcyN033BdRRmx7BLqsOySwV0wySc7MC1y4RbOmhdLxoKR3Yxh13I0bJuC3apAJ6HXczjWkVcq0jq6G4GrriNjq2Xkz6+D1wXPRxFD4XJTx5fCG9wPmC0xIlsGkKPKROiGw4vdJyuY5NLnSTZPoRVzi3yV9J0A6/mZUgk+1m38XqmTz1PLj3O8LZbyMzvYOrkM35eFvnsBL0dryGW6KGQrdG8aTp4Lp7rkk2dpKNH4NglsqmT1SSaseZCE0A2dYLudZfgujbZ1CmqflfGGWu2HMdCN8J+mVfXjpbtvG9u6yNfnqPs5ClYKTriQ+C5FKwUnueSKo7R27KVqJlc8A+KhdoJm3GypemG7WMjbLdEwZojGenF0MO4TgFTD9MS6UKvCXWYLo4z0LoD17VJFydYqB901k5IWlwuUw8TNmIL37VGVRnPJeVsWYUgqOPUo0sDqs6m1cBuZo8alM3um144NvnM+JL0K2LoRDYPYyT8OnE9WANN5+myouBUHjuJnZ7HyS0fl8jOpPHOeM742UTD1CPUykKmFkFDVwLUkgZp7V4Iz/Ow3DJhPeq/bE1TUvbUA3ek8BzT1vFVn+OKK8O84VadfXstvva1IqmUS1eXzi23RNi+w+Q3frOF1LzLd76zVFPR16fzG/+thTe/Ocr4uMtddxUZPWLjeTC8weCmmyLcdnuEnl6d3/6tNHLf4vutaXD77VF+7ddbsG2PXbvK7H7RIpPxiMU0+gd0tm832bzZ5ORJZ4ngtWe3zWf/PIthKKHp/R+Mc8EFJs8/Z/H5v186jV/ut/A8iHYn6L1JYESVFid7eBrXUpnnT8zTd0uUkQ9cTUZOMPXAQbJHZuh7w3baZ3JEuluw0r5D9HSWwqkUSdHH2Hde9IWpSVq397P+py7DzpZwyzYn736e6UcOM/L+q9h4x7VoGrj26QnXni+otWzuYfg9VwBgZUukdiuTYvFUCk3XGfngNWQPTTHxg31M7pKse8vFxAba8ByX9L5xZp8+RtcVG2i/ZIjESBfD77qc+RdOkpETC46bFVyrRCk9Q9uGnZTmlPN3OT2LboaxC9mF2XCZY3tpXb+NgStuxy4V8Byb7Nhhkuu3LbmO0vwks/ufoFNcSfvIxdilPFMv/JDMyQMqrxMSMxyjU1xFx9bXgOuAppE9dYjCzKnzSnDS21oIDa9reMzzHGYnJZ39FzC89Ramx17AccqEIglMM8rkiWewylkMI8zAyHW4js34scexSjniyT4GRq4hO3+CQk4JxjPju+no3c7GC97M9MnnsK08oUgLjmMxM/YCnucyP32I7oGLGdpyE1OnnldpwgnMUJzpU89TKiqtUCTWgRmKEo13YIQiJJL9aJqOVcouhBEwQ3HCkSTxlj4MM0ws0UmidQDHLlLMzwMeqdmj5NKnWL/59USibZRLGcxQjFC4hZmJPRRz0w3rphmea5NNn6S9ewv5wQmscg6rlCWXHltWiLKcAmU7R1tsHdPZI7ieTbY0zYbOK5jJjeJ6Fh4uk5kD9LZsZWPX1UxlD6JpBoNtFzGXP0aqsPqQMLZbYjJzkG29NzHSeSWpwhiJSBcd8aFFwtdc4QTp4iQbu64mGkpSsnOE9ChhM85k9gD58uqjfa8GZYZ0Wd9+CaYextDD9LVuX+JnGDFbCBkxEuFODD1EItKF5ZawnAIl+zzX8p4mmmEQ2TZyrouxsuCU3f38qsIL2PPzzN7/fdzS+encWcHQTDpC/cxb47g4aOh0hVRjmXXmFpnp1hobi5Q9wUBkC+2hPqbLxxe0WToGGhoOys8ja89ScnL0hUdI2VMLy7doaOiaqab8Nijrli0GX7uryKc+mWFszMV1lens/vtKfOJ/tXLRRSbv/0CcRx+1mJ2tsd9H4F0/HePWN0Q4eMDmj/8ow2OPWeTzqvONxuAH3y/xe/+7lUsvDfHud8f49Key5HLVzlnX4Y1vjBCJaHz3u0X++A9VGSqaykgEenp0enoM9h+wl2gwjx93OH5c1Ud3j84bb4+wY4fJyRMO3/pWc3+B8lyemUePEEoqn6H8yXncsqqbwsl5jnzxUUJtMcpzSvg6+c3niA914NoO49/bs+BgqxkaVrpA9vA0pRnV4di5Eifvfp7YYDtGxMTKFHHyZex0kdE7HyfcmcBKqXyLE419jcpzeUbvfGwhxtPUgwdB0yjN5Dj5zeeIDbaDplE4Nb/gwF6aynDkHx8l3JnA9gW7zMEpjv3zE4S7W/Bcl+JYGs9yyR+fx0oXmXroIJ6jNFnNfN3mDj5NbvwIRV9wKqVnOHTP53AKuYUZcqXMDMcf+Crhlg5/ZkwWK58hNfoipfTiztO1y0zvfYTMyQMY4SiuYy2Y/AA8x2b20DPkJkYx40lAw7VLWLkUblm1FZ5rM7PvceYPv4BTXGWcq7VG0wgN9GK0NY+AXszPcujFf6N/6Er6hq9E0zQcu0Rq5siCT1Bb1yYisTZOHvoRpUIK8Bg/+hjxZC/d6y7i5OEHlUksP8vo3u/QN3QFvUOvQdM0XMdiusaJvFxMc2TPt+hdfwU9g5ei6waOXSY9O4rrKoFT000GN7+eWLwLMxxH10wGRq7DsUvMTu7zA2NqdPZtp2fdJeiG0la09wha2oYoFec5suc7OHYRu5xjdO936Rm8jK7+C9GNEI5TIjt/ciHukuc6lEsZHHuxptMq55b4OLmuzfjRxzGMCH1Dr8FzHWYm9pDPTuAt4y/geBaZ4iTxcAfZkhI0C1aKopUmU5pceLbz5Vn2TtzL+vaLGe54DR4eqcIYJ+afpeyod831HEp2FsddLKCX/Zl5FaZzRwjNxOhv3UF7bJBMaYpjc0/Tldi4oG2znAL7p3axrm0nfUmBrpk4nkWqMLYof8ezKNrZVWm8HNeiaGUaCpIFK8XBqR+xvv0ShjuuoOzkmMrsx3aKC07nGjqDbRfTER9UCgE01rVdSF/LNlLFcQ5OP7BiGV5JmF3thHq7znUx0JZzJv5xWuRXQ2Nz/HI2xS4jZU8xVT5K1pkjqrcwFN1B2S2yO/sAeVfZSnUMLmu9DQ14MfvDZSOBRzQVE8rQQgxGBX3hEfblHlaCmOcos5ovILWZvVyQuB5DC3GqdIC8k0bTNGJ6Etsrc6y4G1Dxm0ZiF7E+sp0Z6ySz1hiu5xDWoySMduat8QUH93AYPvf5Dm64IUIm43LHB+Z48snFDYVpwvveF+N3Pt5KOu3y3z+W5t7vV4Xc4WGDv/1cOxs2mPzZZ7L8zV/nlsjLug6f+N0kH/hgnFOnHN7/vjlGR51F5/jSP3dw7bUR7rwzzx/8fmaRYHU6dPfofOrTrdxwQ4S7v1nkV37l5bVhR/uSdFw2TFL0cepbz5M9dHqj64BXOKZBx3veTOsbrlv4yp5NMfHH/w97crFPpK6baLrpx1tycV17wf9H00103cRxylCjvTYMFXakPvCjrofQdGMhdpPrWkscxit5Vs/n4LnVQZNhRhsu0+G6Nq6v0dP1EHqD2dGe5y2JB6XphiqXXybPtRcEQ9AwzPCiawYWBLJGgS11I4SmGaApQdp1Vx6c6pqJrhm+gOAplwU9jOs5i5zBa9MCS46r34VwXHuRe4Thx8WrjbWnoWPoJqDjeg6e56h7WbdOna4ZavDqe0S5nr1ISKrkU3/ORmiagaGZC9fZIAWGHkLzZ+y5ro2uGWrbP6ehh9BZaq5ycV+2NfbOFYnrL6fzfW9F9011bqHIzBe+Tv6x59b8XGu/yK9hYMQTaIaBk80oE12zwHtN0MIhtEgIN1s4OyFPfWzPYrx0iO7wEEPRnWiaTsqa5Gjh+QWhqYJ66LUVjXX9kU0MRtQIJKxH0DWDLfErsD0LyystivmUtqfYm3uYoegOhqI7CGkRXFxKbo4TxX0LeTqexbHCbiy3xEBkM73hDWgYWF6RlD3VVJA7eNDh1Kmloxfbht27bcZOOawfMti+3eQH95YWqn7DBoNt20xSKY+nniqj6419/Q8eVL5N3d0G69YZiwQn14V9e22uuSbMzTcrzdXd/1ZkZsY9X2OiVtE1PNdl/Ht7yI3Orpw+4McKLRQismloVWld14Ymnb/n2mqGVR2O01gT77oWuMubK5vluZD3KgJhuq61oKVaCc91cJrN9sNbiJK+KP8mkcDVMQs4PZOsEkaq1+z5s9xWk3Zxab26kACKRgKFh7skbaN0Sjhrrk1qlE/TtJ6DvaxmyltSBqfOncRxLZzTrN9XJJpGeGQ9WvTc+njB6QpOhkF88zbarr4Os7MTJ5Vi+rt3Y81Mkdh5EW6pTH7/3lWZ9sIjA3Td8Rbyz+yn8KzEmpzFzeRfdiHKw2POGmesdMiPxg22V6LsLW58XBz2ZB8EoOwubz4YKx1ixjrZ8JiHR8HJLNqftyfI5uYIaVF0TVcjUc/BcheXwfJKHC/uZaJ8BAM1AnQ9F9srY3uNG5GxMYdCsXEdTk+7TM+4jGw06e01iESgWFQy7+Ytpu9bBJ/6dBvlUuM8Wlt1TFPd4s7OxZKV68JXvlzgkktD7NwZ4r/+Rgvv+ukY936/xP33lTh61GZuzjubcvKqKY6lGR9Lr5ww4MeS0LpejI7Wc12MgFcChk5koIPy+Dzeafo2Bpw5RnsrocHe82L2/mkJTtHhEbre+CbKU5PkD+wnvmkzmmng2TZGooXEjhEKhw/ilVf2c9LCISIbBwmPrKP1jddQlKMUnj9Acc8RrPFpcF4+FYWHR9krLIntUU/RXZ1j3WryqkcJPyuPSjxcSisIbrUU8h5Ok8FpsehRLHpoGsTjarZdZb+jQz2MmgbJFg033jwOx8y0S6kMjrtUAtq92+ajv57i3e+OcdvtUS64wGTnzhAf+nCchx8q8627i+zaVSKbPQ+lp4BXLZEtwwuz6QIClsOIhen5qWsZ/+J92Klz5JP3KsTs6yLU193QLH3Wy3I6iRPbdlCeGGfq29/EiMeJbRhZOGbPz2NsV4H5Vt0leh6apmG0xIhfvp3Yzk1Y47OUDh4j/9Q+ykdO4uSLEEj1q0bXmgd3rjW/ed5i5V7F1y2V8vjt30ozPb284Oq6cGS08X05fMjhM5/J8p1vl7jq6hC33R5FCJM33hbhyqtCfOfbYf7vZ7OMj5/v9jvUIsFxFSizgmc7uLl8Y5cEw0BPRAkN9BLesA6ztwujJY4WDuG5Ll6hhD2XxpmapXx8DHtmHq9UxiuvkapdAy0cRk8mCK/vI7S+H7OrAz0RQzNNcB3cfBF7NoU1NkX56Cmc+TReqfyya3u1kIkej2H2dmH2dxPq60JPqhUHVP14qi5KZZxUBnt6Tv1NzeIWinhlmxWDkK2qIBqYBpppoJkmRjJBVGxCC9eZAHQNo7UFr3x6fiKe7eBm16BDNU30SAg9mSA00Euovxujsw0jmUALh0DT8BwHr1jGyeRwZlNYY5NYY1O4ucKa3FO9Jb7o2QfwXA83kzuv8tZiEX/lipp8cvnGA3BNQ49FMbrbiYysx+zvxmxPokXUAtRuycJJZ3Cm5iifnMCemFbPX8kCTy0zYqdyaJGQanAbDCAbF1JDC5lo0QhmZzuhdb2YvZ0Y7a3o8ShaSE368Swbt1DESWWxp2exTk1iT83hFopgvcTJS4aOkVwc4d0tlfEKS5UdWiSM0dpCeMMgofV9mJ1t6PEoGIYqY66AMztP+cQE5RNjuOmceubWCsNAC6l3VI9GiO7YhNFWF2Rb09ATMYz20wy+7YGTzZ2xgua0BCc9Hseam8XN5zDi8cUHNU5rOQY3m6d06ARmf5dq1DUNLRohMjJAeH0viasvwjo5Se6pvRT3HsEen8HNnz8Lfq4ZGsR7W/A8j8Lk8iEfVkNLUsM0G0fVi8U1YjHl8JnNepTLfoA4D+Zm/W0XTpxweOGFl/aCFvLw3HMWe/ZYfOPrRa66Ksw7firK614f4b3vizE+7vA3f53jfI9gYXS00fHetxDdUg0AWTp0nJnP37W4c9Q0jM42YpduJ3H1JYQGepUfNiDDCQAAIABJREFUn2EslmZdD1wHz3bxyhb2zByFZ/eS2fU4bvqlTR3WYlEim4eJXbKd2IVb0FuTquExDBUfqPJ6up6a1WQ7uLkCRXmY/DN7KO0fVZ3WGqOFQ4RHBoleuJXYzm2YvZ0LQstC3VTqx5foPccFx8FzXNxMlvLoSUqHjlMaPYHtCwarRtdV49qSQE8mMNqThPq6MAd6CA30Yna0qTZIX9x+Ga0t9PzKB0570fLysTGmPvul0xa4ANURJBOEBnqIbNlAdPsmQv8/e28eXOd1nnn+zrfe/WLfdwIkSIo7KYraN1uW5E12e0mctbs6XTWdnkons2Tpykxluis1k+qZnu6e1Ew66cRxuhzHdmxLtmXLlCVLFClR3DcQ3ECCAIgdd1++df74LgBeXoDEBcBVeqq04AL3u+d+yznPed/nfd7mOiRd98jejfcTFO4pz/vJtSycZIbc2Utkj/aRvzS4omta/U+/iNbVWuRVbU3HGf8//3pl5FBA1W+8gq+7vehlO5Zk7N//17LGLHSNyItPEn5y1/xxUhmm/ss/YFwp9huSgn703i6Cj2xF72lH0nWPvMlS8T3oOJ7vm2Vhx1PkL1wh9eb7GIMjuKaFnchS908eJXN2CCdvepKPZJb0ySuUQJZRaipQWxrxrW1HX9uJUlNZ+FzZ63N5/fM5u6udG4ONNTZJ9ng/2dPnMa9ew10mgVIbaqn7vd8s8mRL7T9K7Nuvz/u3qgpaVyuBhzfj39iDHAmCoszfe1Dw93S859S0sJNpsif6SX9wHHPwGq5Z/kZQBHzIoQBSKIgcDaPUVnobhsZalNpKpGAp0Ra6RuUXX6Dis8+W9VmOYTLxn76BeXUZXlKUSZzsRBy1th7JX0yahKbha23HTiZKKkIWgzE4yvh/+hb6unb8GzrxbehCqalASBJCkZHDAeTeDvSeNqyJGXL9V8iePE/u9CXsePkLjIvLpDFE3smSv0mF3O1EwyNtTBwd8ZodFiDJEvW7W3FMmyuvr7z3XEuLTCAgmFqgX6RnBSBhGDA6ajM7r7uuJ/q2LBfdBw9tUldMnGZhmjAx4fCjH+U4ctTg3/67CJ/4hI9PfELnr/7y1sRJCO5q+0MhCeRQALliXv8iV0eRQoH5hUMI9J52oi8/jW/DGoR6EzNMWYAsIVTAryNHQ0ihAOkDx25Rf3NzKHXVhD/xKMFdm72S+pttYmThTdaaihTwEardgX/berJH+0j+bD/G4MiqRZ+U2ipCT+0i+PBmlKWUERfGLWQZ8M6jHAqgNtYR2L0Va2oG48Ig8R++hTk8dpMDzUNtrCX66WdQ6qtRaiq9SMcSdBJCkpAji9sTLAZpOr6se1aKhAhsXY9/yzr0Ne1I0dDS0hKz9xSAriEHA6j11QR2PkT2WB+J199Z8rkqGVMwgBwNF43DtexV0ZnIweLnCrxIEVKZJ0/gRZCuP5aqeD9fR5yU2ioin3qC4CNbkIKBBQ40ezwBkuQRe3TkUBCtqY7cyXPes1EohHJyBv7O+V52xlishDipbY0Edj6Er3cNWntTUVTspp8PhYhL4RkIB9HXtBHcs5XUOx+SevdQeZuHWciydz3leQKidbQwWyEoBfyEntxJ6NlHUGqrFr//BECByGsqUtCLJPu3rCP11kGSb3+Am1u6NZHw61R89jm0zmaU6krkinDh/N/ifUIggn7Af8u/LXpfLl90DspFWcQpdeo4NS9/nvov/zLm1CRyMER4yw7kUAitrp6pN368dKbpuFgTM1gTM2QO96FUhtF72gjsWI/W1ogcDRZ27BJqQzVKXRWBHb3YU3Eyx8+TOdKHOTrlVeUtcUcYs8aIWQtPIFpER0iCfMyLaqlhHUkWGMk8elUANaDi2A65yQx2zlvt1ZCGXuntVI14jnw8h+JX0av8SLKEmTTIx7JIikSorYLOz/ZiZgyMRJ70cALHsvFVB0hemSE3dX30AnxVAdSQhpWzyE2mcR0Xf00Qx3bQQjpW3iI3lSlpK9K1RqF3vTLnhzQLXYdduzQaGmTGxx1OnChmLAOXLY4fM9m6TeWll32884s8w8MLn9fZAMGNp32x12cxNelw6pTJJz7hIxAQhYdy4QXatlyyWe940aiEz0dJi5a7BUnX5ydeRca/pZfKL3wSpbF2SQvyjTCvjmJNLa1PUwkUGf+GbiKfeRa9sxnKbMQ7CzkYILhnK3pXK7Hv/YzM0TMrTpHra9qo+MpL6J3NS5oEbwUhCdTaKq9XVxnnWY6G8W9bj+RbpJ3NPQApFKDi888TfGQLwr+wvUBZEB7hD+7ZilJbxcy3foQxMHxHK5jvJoQkz6dvhEBrb6LilU/g29DtpcTKhDUZwyiQT9ewmPzRofnn7LpI7vVQ2xqp/vVX0NqbVuX+RwiUxlqin3sOpa6a2A/exIkvrU/lzaBURpAK6bvoZ58l9Nh271kp8x4UkkCtryH6mWeQoiHir/4cN7u0SVuoKv7N61Cb6soe/91AWVfTGB9j8vVXiex8BL2pBdc08XV0YU6OM/XTH5G5dGFZD6abzWNm85gjk6T2HUNrqce/qRt9XTt6VzNKZWRu5y8F/URbG4g89zC581fIHDtHvv8KxvD48id6SVC3s4WKtdX0/fVh7LxN1+fWY6YNht8eoP2FHvy1QdSwzuSxawy82ode5afny5vRK/04hsXUqTGGfn6JqvV1ND3RgVC80O+Fb5/Ezpi0vdBDpKuKthfWYmdNzn3zOEYiT/3uVlqeW8Pw2wOkhjwPp2h3NV2f2+DlxAWMvDPAxPFrbP29J8iMJpEUCTWoMfDDs0wcKa7m0zTBv/gXQSbGbU6ftjBNz+fpmWd1vvJVP5IEx46ZHDtanEYYG3X45jezdHYpPLJb4/f/IMzf/HWGvj6LTMZFkiASETQ3y6xbpzAyYnPokFkUMaqqlnjmGZ0rly0uXbKZnp43v1RV2LRJ5fHHvMWrv9/Cthe/VzIZl8FBC9fVWdMt89TTOm++mccq8PJZvdbdSPUJn+ZZ/ksS/s29VH7lpZvvzmZPwiK/z/VdKDsdNHu8wNb1VHz5xUU/33Vd3FweJ5vHtWxPZ+TXEbpW8vdCllEaa6n8pZcByBw5vXwNQG2VR5p62hcdF5aNk8niGJ52RCiKp3fy64sSUNd1yZ4+V+gf9+DAyeQwx6a8KMMi94lbSN/M6m1c2/bOWdDvbTIXeJ+QJPS1HVR84ZNM/c33sCdXy936HidgsjSnh1Gb6qj85c949+Jif3+LZzR/8cp8Kl2AWh0muLENya/NHdOcShI/0D/3HmvMk5fcjDS5BU2Tm83jFFIAkq6V6CpnMStpCT21CyuWIPHaWysmw8KnobU34evtIvTEzpKomDt7z2XzuI6DUFWkkB9pkci6CPgIP7ULO5Yg+eb7d2eSvs0ojwa7Lsa1EaZ+8hpKtAJJ03FtCyuZwMmsUnWB7WBcuYYxOIr87lHU1np8vR34N/egNtZ4F1WWkII+/FvW4tvQhTU6Re7CVbJHzpK7cNXTQpVDohyXxMA09Q+34KsOYGUtQq0VXPzuKcxUnqE3L2KbNhU9NbR9ai0Dr/ZRv7MFJaBy+v87iJk1kGQJx7SJXZgifS2BkCR6vrqZ6Joqht68yPlvnSTYGOH0XxzEiM+z8Cuvn0MN6/PPqwStz3cTOz/J1TcuULmhjrZP9pAaiiNrMvELUwy/dYm2T/VQt72JqZOjzPaPsW2XffsM2tpk/uzfRznbZzETc6muEmzdptHUJHH5ss3ffj1T0qvOtuH113O0tcn86q8FeOllHxs3qlwZtMikXRRFEAwKauskmppk/stfpDlypDi6GI0I/uVvB5EkGB62GR9ziMcdhAQ1NRI93QqdXZ4T+He+k+Vmwcl8HvbvM3jpRR8trTJ/8IdhXnrZx/SUg88nCIUEr72W442f3nmn+tmIk9bWSMUrz8+RFtdxcPMG5tgUxuUhzNFJnEQK17QQmoIUCqHWV8+JxiWfjmtZ5C+V9ni6JYRA7+3ydp83kCbXcTwN4fkr5M5e8sStecO7yLKM5PehNtXi29CN3tVaFOEQQiBXRqn4/PM4qTS5swPlC3V1jdDTD6N3thSPy3Vxs3nyA0Pk+i5iXRvHzmRxTbvQbFlGaCpyJIhSV4Xa0oje3oQUCiI0BSQJJ5Emd+pCWWkAa2Ka+GtvLbgQgafpCGzfWLLbdbI5Uu8e9kTGZcCeSZRfpu44ZI+dIfT4dtSWhrnXXNPT2BiXhzCHxzDHJnFSGW+xtR2EIiMFfKhN9fg3r0Vrby4mxUIgAF9vF4HtG7yFbDUE9nczf74ECMkjTiLgJ/rpp9G72+YNPQ0TeyaOcWnIE17HEt79JMteyqmmEq2lAbW1EcnvRV7yl4bmNLaSrlL9wnaQBYGeJtJnrhLoaSJ55GLRGNy8Qfr9Y176XpbnNwuGgTU2jXFlGHN4DGtqBjdnzOmWhKogRULoa1rxb+5Fqan0UrHXX1NZJvToNnLHznrpwxVACviJfOoJ1OZ6JM0jQ67tYCeS5PsHvDlkcsYr3HBchOoVVGhdrQS29KLUVRc9W0IIRMBP+Ond5M5cWJKOyM0bJPfun4t8lQ5SoHe349/YU/w+0yJzrA9zqDytkmvZ2DPLt59ZVvzQNU3MyYllf+jSPsTFjqc8YV7/FZJvHkTvbMa/ZS36mhaUukqkgB9JU9HaGlCbagnu2oB5bZLM4bPkzlzCHCuk8paAzGgKM20Qao7iOg5mKk9mLImvOkjLs2vQojpq2Iev0g8CQq1R4henyU17k6oNCFmi+qF6arY0IGSJ6Joqpk+P3/yDb4DiVwnUhRh++xJW1iR1NY6kSOhVAcxkntj5Kcy0QXYiQ6g5iqRIcyRxZsbh29/yGuf+s38e4LHHNYJBr9FvIuGw/z2D//pXGQ59uLBoNZlw+S9/kebqVZtXvuCjs1Phscd0VNVbO3NZl5mYy5kzFmf7rJL5N5l0OXXSS/dt2qTi2ym8nqUumKZLPO7y3j6Dv/u7DAf2G7dcjw8cMPjzP0/ztV8J0N4u8/LLPhwHLMslkXDZ9+7dccUVuorSUIN/23rUZk/j4OTy5M9fJrXvCPlzA16Ex7S83mwu87oJVfHeX1uNf9Na5FAAexlpOqW2iuhnn0Vtri8mJ6ZJrv8yyTf2kb84iJMzFlwos6fOkXr3MP6tvURfegqlvmYuyiOEQGmsI/LiU5hj09jT5Y1Pqa8hsG19MVFxXezpOPHX3iJz5HRhc7PITlQITzyuqUgBP1pHM/5Na9Ham7Cm4uT6L5U1HmtyhuQb+xb9vfD7UJsbSolTLk/qFwdLnMNvBdd1l0VOrPFpssfOotRUYs8kyF8cJHvqPMbAUIEsmYuKgrMnz5Pef4TA7i1EPvk4cmWk6L4QikLw4S2k3z++4iIED/d+xEmprSL8zG78W9bPVSAag9fIfHCczLE+nEQKx7S8+XN2MpI9bZNQFeSKMPq6LrS2JoyLg/OtmRQZOeJn9BtvU/elR5l89SBqdZjoY+tLhpHrH8AYGEJpqMW6Nk7u3GVyp89jDI97RMQ0F638yx7rI7XvCJFPPErg4c2IGyJBSnUl/h0bMYZGlxexnv0oTcXXu2au6MA1TbInz5F4Yx/m4DVvDlng+NkT/aTfO0LkpScJ7HioJFKl1FcT2LaR+Mj4LSPXbt4g+c6Hi0ftFZnI83YpcbJtssf6yBw8Ud6XhhX11i2LOGl1DTi5LFaitP2FHAgi+f2Y01Ornkd3TQt7Jklm5iyZI2dR6qrQ17ahd7Wgr2lBa61D0jXkcNAT0XW3Yc8kyPUNkD1+juzJC9iJWzQpzppMnxmnamMdVs4ieTmGmTJof2Etaljn7N8eJdgUYf1v7gAEjmmj+ItPnxpSaX9xHRf/8RQz/RNs+M2d123MZkPBN/+ujukUju0xfyELhOx9nuu4OOZ1E3LhWLYN3/6HLG9EBR9+aDA66nDsmMG27RoNDd5iODRkc/SIydjYzW/geNzlW3+f5c29eTZtUmhrUwgEBbbtEo+5DA7a9PebTE+XXuOJCYc//IME69crtLTIVFZJ6LrAcTziNjhoc/qUyczMwveHQBDVGrBcg5Q5hWHAt/4+y4cHTTZvUampEUiSIJ12GB11OHHi7rjlClkm9PiOOZGxk8mS2Luf5JsHcBYrXCgspl75eB4jnsK4cMXzQSs3HSYEwT1b8fV2lYh2M0f7iH3np7de7C0bJ5km/d5R7JkEVV/7LErDvEeKkLyIVmDnRpJvvFfW2NSGGpT6mqK0h5M3SL55gNS+Q7eOBs+mLwolz9bENJlDp1Bqq5B8evlVYoXjLQrFLGqPMv8+5sZxR+C6pN49hB1Pkj9/2VsQl3pvWBb2TILk3gO4eYOKL76AHCoWQCt1VWitjeROn1+Fwd6uiNPqrB1CCPTuNk+QHfB5z8aR08Rfewvz6k0aANsOru1ZYjipDObQWImeznU9ywKhytipHMGNbdipHEpFabTEnokTf/XnSKEAub5L2DNLbB3letYc5uAIM9/6sUd892wt/o6qgt7VilwRxp5efksqIYRXYIB3v6cPniT2nZ/cMiLj5g3MoVFi3/4JQlEI7HjIKzqZPa4k4d+yjuTe95ZWEW/Zi199x114nnRdr/LwTj2jBZRFnCK7dpMfHiJ57HDJ77SmZsIPbWHi9Vdxb3OjX2t8GmsmQf7cIFpHE4Et3QS2r0eKBL0woSRQqqMEH9uCf+tazJEJUu8eI/PhGeybeIRMHrtG81Od5KazXHm9H9d2sQ0bSZEIt1VQv7sVNeix6okjI3R9YSONj7WTj2VxLIfsWArHtNGiPmo2N1K1oY74RU+LYaYMrIxB3Y5mkpdnSF6Nez3q6oLoUR+2TyFQHyI3nWH88DANe9pwTIeqh+pJXY2THllcBGjb8OoPim/M4WGH4eHlq6knJx3eessAyovqJJMuBw+aHDy4HFIjEVCjGHaOlOkt/I4DE4PV7BsOMp4tL9JwO6FUVwDg5PMkfrJv6ZPDDViO87DaXE/wka0lu7P85SFi310CaboejkPuzEUSb+yj8ksvIgLzJpBCVQjs2kTm8OmlR8VkySu1vqFixY4lyPVdXL4O0XXLjvzcj7DGp0i+eWD5m0/LIr3/KP6H1uLftr5IKyZ0T8uyOsTp3ocU8CqtXNsmc/iUt6FYjjbuhmiLmzeJvXsG17BIHLpIzWd2IWkKsXdOl77XdsiePLeiYIKTypDYux99fRfKDVWISl0VcmVkRcRpFq7jkOsfIP69n5WVxrJnEiR+ug+9swW5prJoXpKroqhNdeQvDK54fPcSyvNx8gcQ2sKVKUKSUapqyjPAXAakgA+tvRF9bTv+jZ1oHYUSz8JEPZtHRvLywHIogNTThtbWSGDrWuI/2kfu3OCCocfcVIbxQ8MgIHnFWyhG3r1M6/PdND3ZydSp0YI+yWXm7ARXftxP3c5mhCwxcXiYxMAMAz88S8PuVvLTWS7+42kyYx7hsXMWA6+dpXFPG5H2Si589xRaRKfl6S4Uv4riV2h9vpsrPz3H8C8GcF1ofqqTzGiSi987h5UxmDh2DTPpkdLMaJLpMzKOtZIi9tsLVfJRoTciEOTtDLZrYjo5/EqURH4MB5uoVo9hZ8jZaap8zQgkstb8JFChN9IU7EWVfCiSTtqcIW6MIguVqFaPJgfIWnESxiQud9Yo1ZuQz5D6xcE75zEmSwQe3oRSU1E8Fssi9fP3scaWQS4ch+yRMwT3bMO3tmPuZSEEalMdvnWdpPcfXdKhhBClRpJ4O1mnDF3SRxorjNi7eYPM4VP4N68ripYIRUGprVrp6G4zVj+SZQ6PEX/t5ysuKPBscvxY8TTZS6O4eQsrkWHo//mxV01sLBL1WIUMjHVtgtyZi4Qe3Vb0uhwNI9/MWqEMOOksyb37sZZRQGBcukr+8jCBmsqi1yVdQ22q/2gSJ6HrCCEhZBlJU5F813kmCJBUDV9HpxfuXqqLalmj9LwnApu78W/qRutqnvN8gllNgYOdTJM7fYns6YvIkSCBnRvQ2hq8KgBdJbC9F7k6Suzbe8kcW3gXMPBqX9HPuakM579Vmj91LIfxQ8Me0boOE4eHmTi8cN+6qZOjBTG3ByOe4+w3Fl6Qrr5xnqtvFO8MB35wZu7/Y+cmiZ2bXPC99wIkIdMU7CWkVpMyp2gJPUTcGCOeH6UhuJZ+cxrHsWkMriOWHyGXuYRAUOPvACBtzT68AllcX73hIpCo9XcS1evJWgmqfM2omUtM5ga5k9oLO5Ei9c6H2KtQErxUKFUV+NZ2lnhFGVeukesfWPZx7XSW7In+kio4KeBH72olc/j0klyBXdf1Is6uW5Sqk4J+lNoqrGu3WRv5MQDPhNPJG8jXl95LnstyuQ3ZF8Y9rnEqwHVd0geOeSm3FUKpClHx2Hqm3zxBxePrmfrxYS+dmzdv+9lwsjnMoVGvIEAujiKKVbLZMC5dJX9xmQTHdclfuEJgx0PF3FdRHsgekEsiTlXPvYCvuRWtvgF/5xrC23cV/0FB/Bp/fx+OtUq6E8lz0NWaar12LJu6UaqjXgVQwSDNdV1PAzE2Teb4OTKH+7DGZ7zdvyyR2n8C37oOQo9vwbeuA8mvFyqhnsGaSWBcvkmu+2OsCIrQiOoNDCaPkzQmUWU/kljccMzFYTo/TFSvL3o9nh8lqtUhkBlJe6RWkXTqAl3M5IZJmzP45DA1/nZm8iPY7p3TPRmXrt7xnZTaVIfa2lD0mhdiv7Qy0a9ledoP0wJtnpQJIbzqomAAeyntFGwHa2JmrlR+FnI0QujRbZjDY8sSw3+M8uDk8jiZbJHOSRSqsZClVWhjdW9X1c3Cmpwhe6L/1n+4BEiqgt5ajd5YSXBjK4mDxYbFrmnfvt51roudTOOaJkKeJ0qi0MZlpWTYdRyyJ/pxMssw1SzAGp2kQAbmxyfLSKHViYjdS1gScYq9+zZ6UzOVTzyDlUqSG7xc9HvXtDAmxsgPDa681FUIr1R6Yxf+h9bgW9/peeZc/3muiz2TJNc3QOb4OXKnLmLHbtj1Wzb2VJz0/uNkT5wj8sk9RF9+3CNPnc0EH9mMOTK5ej3CPkYRhJCRhELOTmG7JnkrhU8udWEWSJQ7CUvIBJQKLC2PT4ng4hLLj+Le4V1w9vSFO+tRoiiorQ0loXk3my+0OVjZWOxkGiueRL0hnaPUVSMFfNhLyXS4LubYBOa1CbTWxrmXhSwR2LUJXJfEz/ZjXBm+rY28P/JwnIUtG4RACOmOp7XvFvIXrqyo7Px6WIkMdipH7Rcewd9RR8OvPF38WSPTjP9DGYUUZcI1jAU1kcsx3b0RdiLlGXyu4Jm0F2rBI4llGY7e61jSN7KTCTL9CfSGJqx4bEFx+IpQaIyqdzQR2LUBvacNta6yyOnXM+HKY03GyBzqI3v8XME5/NYM30llSfz0AHJlmPDTOxCyjG99B1I4gD21sKhOSNDQ6WfscpaFushU1Gu09AQ4te/j3fNCcF0Hx7XRpSCGyKDJAUDg4iAQSEJBlXxo8q2t8l3XQZY0BBIuDg42GWuG0cx5ZnIj3kLgund0MXAte9l9jpYLSVPRmhtKXrfTWazYyhcH1zAXXGzlcLCkFPpmsMamyB4/i9pQWzRpClkm8PBmtI4WsqfPk/ngBObImNe89Hak+D/KcFn4nN7lFkZ3Eq7jYA6PrZq2zk5lmfjuAfSWGmpe3sHkax8W/d7J3WZ7FMddhRTrwrCnYiuWHCzWNWQ1iN29hvJarvSdWtVdotBU1OZafGvb8W/vRe9s8gzHpHmzL9d2sKbjGJeGyRztJ3vqInYiVXao2UlnyRzuI7B1HUp1FLWhBsmnL7rUqrrE5/5VG/u+O0bfgTi2VfDwkKC5J8CL/7yFbNL6mDgtAss1SORHaQquJ2XWEtUbSObHydtpXNehKdiLi4MuB3Fx0aQAFXoDIbUaXQ6StZPE82PYrkHaitEcXE9TsJekMUnSnGQie4WGQA8BOYokZGL5ayTMO6efsZMpr7v2HYRQFZT60n5vQpa8ipbFzOOWCLkquiBBErLkdUVfIlzDJPX2QfTudvR1ncV+QrKM0lBDuLaK4K5N5PoHyB476xk8jk+tQgrpAYYQSH4fIuDzHNZVZa7xr5Akz4NIlkGSvB6IwfL6d5WHe5/oujnDqzZbgcdR8QHBimewc9eI7T9L9uIqbJwU2fMj9OkIXfU8pGab/85e08J/9TVtC7qQr8aVsBOpFaXpvIGUjuRB5ehlESdz/AaB3QryqkptJdHPPIGvtwO1saaofNkteDOYY1NkDveRPXEB4/LIiiuXrMmYF6GqjiIF9CKR3Y2wLZfRgSyv/E47kjTIqX0xXNdl/SMVfPa3W8kkLH7+3z7WSC0Gx7UYSZ+lslApF89fQyDIWkkuJ48S0eownRwX8u+TtRJIQkISClO5qwCokub1JANiee88e6k+FxeH8cwF8nYavxzBcLIYzgof+nK/XzZ/5xf5QoPOG6FUV1D5lZdu3+fO6ShY8ixtTc4Q+/5eKr/6MlpbYyFFNO9OPlvwEXx4M/4tvZjDYxgXB8mcPEf+3ABu3vzI9FVbFEIg/DpaayNaWxNKfTVyNIwUCngLraZ6hqqyPL/YypL3X2V5PQvLGNxtPPbqwLUszzF/tY+bN4nv67v1Hy4ESUKujKB3tqA21XlNpyMhpIDPu6aq6pHhWUJc0KQJWfZeX6itzgq/D3jzmXu7I2YPEMpOPgpFQa2qRqtvRA6FcYw8xvgY5sQ4Tm7pi5dSW0n4mV1z5GWuMi6ewrhyjfTB0+T6L2PPJFdNh+QaFm5Bg+U6zk3XANt0+dnXR7ANhxd/qxl/REYPyDzxxXouHUvy5t9dY+Lq6pSgKxE/gTWlzQ3zo3Hy1+5iREsSaNUhJL24gsvJmRjTqVumVwwny1jmAgAtoY345DAuLilzas6n6XqMZs6js00CAAAgAElEQVSVvAZguyZTucEbXrOYLpCsuwE35/VtupOQdLWslNlqYjnh9vy5Aab+8tuEn9uDf+t6r+P5AhO/pGvoXa1o7U0EHt6MOTJO5vBpsqfPY0/O3HFzu7sNEfChNdXj37YB38Zujyz5C0TpAUx7FGP1yLJrWh4Bv9sQArkigr6mjcCODWgdLQXyq4GirLih82qcMdc0l+Up91FFWcRJKCqRXbuJPvI4CIGTzyEpKpLuI332NDO/2IsVX6IRlyTmSZNpYQyNkz11gczRfoyLQ7dlshSKPOdr4mZuHTFIxyxe/6thchmbL/xOO0KCn339Gm9/cxQzv3qLZmh9E71/8k9KXh/++/cZ+tt3y3eWXiUoYR+tv/kkkU2tRa+n+ke58hc/xxhfuq4maUySlZK4Czk034dwHeeOR0SEVtqY9859+Oy/yvjOjos5NMr0f3sV//E+gnu243uo22tgutDOuRBRk6Nh9HWdhMemyBw6SfrgyZu7PT9A0DpbCD39MIHtG72KuLt1vR8EFJoi300ITcW/bQOhJ3fhW9tx7wqlHWdh9/yPsSDKuor+NT2ENm0l/v575AYv45gGQpLRamuJ7n6c8LZdxN59G9deAumxHexEmvzFq6Q/PEP+wlXMsWmvHPo2QWiK1+AxkcIYHl8wjBut1VD14snq9HsxfCGF3Z+uYfxKjmitF4Exsg6JqXtgR3ObMEvY9LroDa+76LWRsohT3Fi5j8pHHrK0YFzedZzbvlt0HZdl720tm+yxs+QvXkXvbiOwazO+dZ3IkeCiO24hSaiNtURefArfpnWk9x0m8+HJO+qZdUehyPi39BL99DNobU0LyghmzX29Br+F9j2G6aVZ8nkvom4YXoRekvFv6kHyL12bVh5WumlYjBA+OERxtnlu6KldSJFQyX3uuu58OyCrcD0tGyeX9yLahjn3j2OYKNUVBZ3T4rYuy8YKHu+PIsoiToE1PeRHhogf3F9kO2CMXUNoOqFNWxDv71sScTKvTTL+n/4e48qop126AzsDc3SKyb/8AUKVPeHgjRYGwFf/sIO29cUi29nAQiCs8Et/1IldcOs++36cb/yv904bkNWGa9iY06UCaK0qiFZbqrX5GLcZtr3g5GZPxUh/cHxJBpXLhTkyvuKJ1UmmyR47S+7sJdSGWgLbN6D3dqE21Hp9/xYiUIqM1t6E2lCD3t1G/Edv3/FqxtsOSSKwpZeKr7yEUlM151M3C69AJoY1OokxOII5NIY1OY0dT3qVXK5bSJu7c4uxUlXhdVW4bcRphQRHiAc6miYF/EReforwM7vnWr/MwXVxDBNrfApzZBxj8BrmtXGsqRhOMu01n52toHPnCVZwzza09qYS4vTgnsV7F+Wl6nQdKx4r9WpyXex0CiErS34Y7FhyQeJyO+HmjFs6yL73j+Mcr1Rv+jezmBl9sFtIOJaNlSjVrUm6il4XQSgS7lJavghKGiK7toud/zinXg5cw/Qm0RtgzSRI7t1/x5+nZcF1cbN5jIEhjMvDnlB2bQf+jT34NvYgV0ZKCJQQAuHTCezajPDpzPy311bcPuNeglJbReTlp1HrSismnWyO9AfHyRw8SX5gCDe7NF2lk8vf0+J6IUsgPaBLviTh39pL6KmHS0kTYI5Oknz7ILkz5zFHJpbsfbhYuf/HuPMor6puYhxf5xrUunqsWMEdWEjIwSCB7rVzr93POPXux/YCc3BcrGQOx7KRbtjl6A1RJE3Btm4d5Qg3R3jyT58t2klP9U3ywf+xH8e4M/eLr8rPtv9uJ1pk3hts8vQE/d8+g5W5PyYkJ2/iZHMlXe8lTUX4fXA/EKfr4brY03Ey7x8ne7wfpboC/9b1BB/ehFJX7bWTKLIykPBv7MF+6Uli3/kpTvrOVlLeFkgSoce3o7Xc4AbvutixBPFXf+5FE8utKL7HIzpC10oaQT8okCvChJ/ZXfKcuo6DMTDEzLd+7LU2KVe7eg9fz48ayvNxOn0cX0cX9V/+GrnLA9jpFJKmoTe1IPkDTP7oBw8kK5ZVQbRGQ/NJZFM2ySnjbmsO7xjsjIGTM5FCxZOcVhspCB1vTZwkTSbaVYl0nW4jH8/f8XmgdnM94db5vknh5jCXfnT+viFOrmVhz8RLnL1FwIccCd3XfeDcQi8uc3iM9P4jBB/ZSujxHaiNtUULhlBkgru3kDl0mtzp8zc54v0BpaYS3/puuFE0bDukfnGQ1C8OLsscVAiBpC0tcr48rCyaJQUDiNs6vrsHfU0b6nWu+bOwpmLEvvtT8ucuL+u4s9YTN+LejSs+uCiLOFmxGFM//SGhjZvxdXTha+/ANfLkhoZInTxG/trwPR0eLheSIujaEuLRz9VR2+pDVgVmzmH4Qob93xtn+HzmgS9EcPImjlkaFVIjfoRy/5RGu7ZDdjpLpG1e6O6vCeCv8ZOdvE39pVYZrmlhXZuEtZ1Fr8uhIEp1JXmW3+T3nkEhCpX46T6MwREqXvkEWmfrfLRSCKSAH99DPeTOXlp5i6e7DLWhBrmmoiQ9aU3HSB88uWxHdaEpCH0Vmr8uMp+v1BZBrggXdYZ4kKB3lxpVurZNvn+A3DJJEwLPHHoBYfjHcag7j/JqI10Xc2Kc2L63EQcPeA+P6+KYBq6xisLUlYaZVyEcJASs3Rnhld9pIzllcvFYklzGJlyp0r4hSNP/0ME//O+XGblwfyy6y4WTtxa0hlAi/psaiN5rcGyX/ExxukNSZcItEabPlnpK3YtwDQNjcKS0Q7pfR2tvInPo5IPTe9G2yZ25QFxRqP6NLyBHQ0Vzgt7VipCk+14aIFdESlI6AMbAEM4KKgiVumqvCnOFWPD8SqJAylYwvtoqpOCD1/wVQKmrKTn3rmGRP3952aa5QtNQqisXLKB4cEIV9w+WZSrhWpan/F8lCF1Fa2tAa2tAjoYLRm/LI06uZRN//T2c5MoIjaIJ9ny2lsunUvzgPw6SSczf8JUNGr/0h53s/FQ1r/7nB5w4WfaCAnDJt/xrdDfgOi75eDFxErIg2HgfVQfaDsbVUexYAqW6Yu5lIQS+Dd3Ie/c/UKJpHJfsqXOYY5NI0VDRzlqOhECRYDV4osvCkRVJ3PbtvPBpC7bRsGbiK7KY0LvbVmVj4+aMGxveF/y2Qlhjk8s6plBVtNZGhH+hiNN9TgMkyTMrvdF6wLaxppfocbjQYUMBtPamlY7uPoa7SO/Fu6PlK6+qTtMJrt9IcP1DyMHSvlh2Ksn497+Nm19itZkk4VvbRuSFPWidzcjhgNevZwUnw8nmSf7iyIqJkyQLqhp03v770SLSBDAzanD5dJqGztvZC+oegeMuWMklqUuvoLwX4NoORrI4KipJgkDN/bXrNa+Nk7901esrd935V5tq8W/tJbn3wAOVLseycZIL9AQUwos4rcJHuGYhquq6Rfe0dCcEzIVy85JIwgoMVuXKKL7eNXNmvyuBHU8WxnGdzkxTUVsalq3VkWsq8PV23T0z19uKgjfTYtd0mdA7WtAW0E19ZOC4OPl8yTMqFKWks8WdQFlPVrB3A1XPfhJJUTAnxjDGrhX9Y06Ml3Vz+Na1U/2bnyGwawNqXaXH1CVp+Quy63qmlqsgPHIdMPI2kRq1ZP6RVUGkWiWXvr/TBEuBUKWFd653YDe+mnBdMNM3pJMlgV5xf+ksnGSa7OHTJRVlQpYJP70bte0Bm1wVGWmB5sVu3lg900/bxskZJTxFaCpybeXqfMYicPPGgjotORJeUM9yS6gKwT1bUZvrV+XxNEcnS+ZToWv41nUilqNRkiT8m9ah3lBFeN3Ryz/mvQTH9by1boCQpAX7TC4FUjhI6Kldi7qO3+dnbGlw3YXbXMkSck1laXHFbUZ5zuGdXeSHrzLxw+/jZFbWGV6ujBB58VHU5jpv51iwx3cte87QTWgKyPK8u+rszkcw38wSQAiMwVEyR/vJXxrCjqdWNDYAy3TpP5jgsS/UYZkul67TOG18rII1W8O89ud3r1fanYLs1xZk9N51ugsDWi5cF/sG6wMhCZTA/VfZkz3ZT/DSVXyb1hbtapWGGiq/+AIz337d8ytbSeRJkgpVPFJ5zbVlCSngw8nkV0W47evpQKmtKlkczNGJVW1FZE/N4FpWcaWXEPgfWkfuxLnbFsWzEymcdLZkUdXam5GDAawyz71/QzehZ3avmvDaGBzBteyidKKQJPTudny9XWSPn136uRECvafdIwH3kT6yXFiTM57VwHXEV2gKWlcr6fePlxVcEAEf4ef2oK/tWDSgcD9NwyuBnczgJDPIFfPPihACX28XqbcOYk/fOSuhsmmaOTW5YtKEAK2jEf9Da+ZIkz0ZJ33oNNnj57Gmvfx+xSvPEHp8K+a1KWa+9QbW2JTXVT0S8t6/cQ1aZxNy0I9jmGQO95G/OLQqk5xju3zwwwki1SpPfqme536lESEVRMZpm33fHaP/g+XnrO8LCIFeF0UJl7oP21njjje5XRHc2bYhxZDV+89LxklnSbyxD7WtEaVi3l5ByDK+Dd1U/9rnib/+LvlzAzjpzNJnVkVGDgU9U8qeDvwbu8kcOkXq3UNLHptcFaX6N76AMXiN7PGzWONTnhtyua2UFBmto5nIp5/2JsrrFg3XcbyKOmf1Ir75y8O4uTzcSJy29pI93keu72L5vjtLgDU+hTUd9zRb15Pg+moCOzeR+Nl7sAQ9qfDr+DetI/rZZ1FqVi9KZo1NYQ6NoXe3Fb0uV0aJvPwUdjKNcenqLedc4dPw9a4h+tlnPYuJBxjGwBDuEzuKHb4Lz6a+toN8/8CS1ii5ppLw0w8Tenr3TZt7fyQiToA1OY01OVNEnAD0jmaCj2wmuffAHSuOKYs45a5eJdCzDjkcwU4uvU/ZjRCKgm9d+9yuyJ6MMf3Nn5L+8HTR5OQk0gXbeRdrYgbj6rzrd/b4OZJvHiT01A6iLz+O3tlM5ZeeZ+pvXsO8tjzR4o2IT5h87z8M0rIuQFN3AN0vkYpZXDmdYnI4/8BbESghnfDGlhLzSwArnl2aa/i9Akmg+O6/6NJiyPdfJvmTfUQ+8zTyddVJQpHRutup/mf15E6dI3fmIsbVa9hTMex0xqvqkQvRJE1DDgeQK6PIlVHUxlq01ga01iakaAjXtMiduVjWuIQso7U04N/YQ/iph8lfHsK8MuK1ChmdnGsrsWiZe8CH1lyPr7eLwO4tJcaQAObQmEdkllmqvxCMKyNYE9MegZkdixAoNZVU/tKnSf7sPbJH+xbvlScEwqd5FXJuIeqwBJhjUxiXhzyx9PURClUh/MJjuLZF+sCxhXVeAIqCvqaV4O4tBHY+hBwOghBe37O8saBzdTmwkykyR06hdTTdEHUS6N3tVP/a50n+/AC5Mxe973zDdZVCAbTWRvzb1hPYtRml0iP6rmV7ET79Ljauvk3IX7iCPR1Haqqbe00IgdpYS+WXPkXs+3u952qRiKwUCeHf2E1wzzZ8G7rn7gsnl/fE5w+o/9WtYE3FyF8ZRutoLialikLkxacQmkbmg+OF9PIi84umeu2dNBVrYmbZUfFbEidfRyd6UwsAkt+PWl1Dw1d+hdzgZex0uqjbvZPLkTpx9NYVd7KM2uTtOlzLJnO0n8yRsyU7Ote0rkvZlYZ2nVSW5N6D4ELFK0/jW99B5IU9zPzDz8pLL9wEtuUyeCbNyIUskuz9bBkfjeBocF0T0e0dC/4uPxbHNW5fQ+bVhqRIBBtCxS+6YOfvn+9wPVzTJPXOQaSgj9AzjxSVtAshkIN+gg9vxr95HXY8hZPN4uZNXMfxdISShFBkhK4h+XUknw/h04sqJVd6l0sBH/4N3fh61+BksjipNE4mh51Ie/+fy3saH0lC0jWkSAg5GkKOhr1I2g26Bdd1cRIpkm8e8Ca9VYSTypA+cKzQC+w6giAEWnM9Ff/kU4Se2Ik17vWI89JX152/YADJpyFUlVz/JWa++aOlfbBtkz5wDP/GtSh188amQgiUaJiKzz9PYPtG8heuYI5N4WayIMtIAT9KbRVaawNqYy1ydcWct5JrWaQ/OI4dSxJ58cmVeS5ZNpmjffi39KL3dBbdH0II1LZGKr/yEtbENNbEDFYsgWtYSD4NKRhAro6iVESQKyJz6TnXtske78NOpAg9vvOO61NuN+yZOKn9R6n43HNFuiQhBHpXK9W//gr5gasYA8NY0zEwPAIphYOoTXVoLfUoDTWeSagQcy7yiZ+8S2DHRvSejgeObC4JpkXmg+MENq9DqS1+VuRwkMiLTxLY8VAhihvDNUzv3tdUJL+OHAx4c5yu4iTSTP3X7yy7TdUt71itvpHghk1zP7uW5UWM2js97dF1f2snE6ROn7hlaFlIAqXKMyJ0cnly/VcWDLHN6pqEqi7aEdrNG6R+cZjAlh58D60h+MgmUu8dJ39+8FZfbUmI1qh0b4/Qsi6ALyiTnLEYPJPi0vFkSbXdAwNJEOiopfmX9yCHSrUSruuSHZy8r0iHHtaoXl/cC8x1XHKx+7ffoJPJkXj9HZycQfi5PaV93oRA8vuKG73O7sTu4MQrJIEcCiCHAvMVmrNNTLluPIV/FlsUnFSG5M/fJ/3+sdU3vnQcMkdO4+vtwr91fcl8I4cCSMFWtM6WuYa6s3rLuXHjPRvmWHm+YMbAEMm3PyD6mWeKr1XB7FNf14ne3eZpuq67fnM6z+vOl2OYZI+eIfa9vaiNtYQe21GS2igX1ugkiZ/so7Iiilpf/AwJIRABP2pbk+eWXcgQLHY9Xcclf/Eq8dfeQq6MEHxk26Ki5/sVbmGB1zua8W9bX1yZKQRydQWBqiiBLeuvkzsIzx9LLmxqrr+myTSJn7xL8s0DKJVR9K5WuI7cfzS28R7yA0Ok9h0m8tJTSDekLyVdQ22pR22uL+jIFphbCj+boxOefnqZuOUdmzp5nOyFc0s6mOs4SzPCLIS1oeCGvIi/hWt4O2RvJ7f4UJ10luzJC/jWdyIFdHy9HatCnKqbdT7/37fRsz1CfNIgn3UIRhWe/FI9h9+Y4kf/7xCZxCLkoYx1adHdw9ykXPbQlwHvc5SQj8pHuql7aSuhtQ0Ljs3OGmQuT94/EScBTY+1Em6NFr3s2g7p0ZUXEtxNOJkcyZ/txxydJPrik97CLi1OPpZCmObIjWV7JcDlwHVxCxYWs5NU8ccXfpaXeFMXSrvtyRkSP9tPet9hL0p1G2BPx4n94E2EX8fX21WygM3ZpNwsgLMMfaVrWqTe+RAp4EUPpYCv9HMVBXGT2dp1XVzDJL3/CPEfvo09FUMoMtbUTIl5aPkDdMmeOIsU9FHx+U8gFzzEFjw3Nxkfrkv+whVi330D4/IwSt7wWnQt6Od0f8OamCb2g70ITcX3UE8RgZw7V5p006nddV2syRkSr79Det9hsGxPP2VYxVHR2/xd7imYFsm3PkCOhgk+uq20n+XseindXu3qLYmTk0mvXAx+I8TSLPudTA5sB+HTkAKlAuXrYVy55qUhFBmttX7FQ5RVweNfrCdYofBXv3+eiaEcju2iqIK2DSE++RtNbP9kNfu+M1byXq0uQuShliX7SwQ6FxZLBrvqqHth04Ki5tWEUGSUkI6vqZJAVy16fRQ55Ftw8XVdl+zlCTID47d1TKsFxa/Q9EgLvV/agOIvvt0dyyF24f43jHQNg+zR05hXrxF4eBOB7RsLzsz+JadpZqtavVRaCuPSVTKHT3tux2XAnkkQ+8c3CO7egtpYixQOeBHjMhdu13Vx8wZ2PEXu9HlS73yIOTxWvsi8TJhD15j++vcJP78H/9b1KJXR8iIijrOsfp1OKkP8h29jjk4Sfn4Pan2Nl1a41XlzXZxsHvPaOKl3D5E5fHpOD2VPxTDHptC6Wle+uNoO6fePY03MEH7+UfSeduRwcEk+V65lYU3FyB49Q/IXH2KNehpUN5PzxL6R61PoD078xLw6yvTf/YDwc4/i37YepSq6oNlpEQobDyeVJnfuMsm9+8lfugqF+z5/ZRgnmytaDx+cM7Y0OIkUse/+FHNknNBTu7yq2zK0cl6Ax1yRRvLuxEhd1xO64dkKyMGFBYx2MoNr2UiShNpQ45V3LuLdYqcycyFiORpa8G/KgawIujaH2P/9cc4fLhbCT1+bpn1DkO5t4QWJU3BNHW2/9SxaZan/TDmo2NVFxa6uFR1jteEYFrHDl8mNxJZczq8u8DdCltBCGpa6uikXIbxWKlpYI9QcoXF3E52fWIOvqvQey05mmbkFcXIyOdIfHPcmr+tgTcVwsvdQms9xscanSPzwbVLvHMJXSO94+pdKb5Hz6V4KqmDv4ZoWTjqLHU9ixxKYY1OYgyPkLw9jL1HcfCNcwyT97iHS7x9Da2vyGp421KBUVyBHw0ghP8Ln87oDKPKchsO1PDGzk8pgzSSwJqYwroyQP3vJC6vf5s3D/BfwKt1mvvVjUu8dIbB1PVp7E0ptFXIk7Bn0qipQGHMu753DRAp7Jo45Pk3+7KXlfXQuT3rfYXKnz+Pfuh69pwO1qRa5Iork0z0C57q4pomTzmLNxLHGpsifv0z2eD92rHieck2L9IGjXpl2gUCbI+PLb1Njef3WjIEh9N4ufL1daG2NKDWVSKGgN8ZCGxwnl8dJpDDHpzAuD5M90Y9xubiXqZPNkXzzgFchOTvmbA43Xx7xdC2vPc+NpNrJZLGmVlcLVy6s8WlmvvUj0u8fw791PVpbE2pDNVI45KWaZAlsxztfyTTWdMwrfjhzgVz/QEl01Z6Jk3j9F8hV850DzKHRJUc6nUSKxE/eLYkO5i8Orrga3U6kSbz+TsmxjYGhFR13ITjpLMk395M51od/yzr0Ne1e38eKiOcHqaogzRdJOOkcTiqNPZPAnJzGvDKCk8ne+oMWwV0hTq7jYs8koKMJoaoodQuXz1qTsTntk97dgqRrONbCX9Yr15xNA6w8TDd76Re6l4RYlXZ49x9cyA5OMfX2GXBcot1V7PzXj9zybYpfKWnPUtFVweP/9tnbEk2TFK+Czlfpw1ftR1qgsMB1XEbeHyIfu3kRgZPOknrrg1Uf40pQERU8+qjOW2/nyWZLz5+TSJH58CSZo2eQw8H5RU1T5t2kbdtb+A3TE25nct5EslrXw7QwLg6iXLvK48+GOHFQYTyhei1GZjWLhVTYnIebaXlRpnTWszC4TSm5JcF2MK+MEL86ihQOIIdDSH4fQpXn5xfbiy45eRM3m8NJZzwyvdIFaCZB6q0PyBw84ZHNoN9bCGTJCy/YXgp1lrC5NyHwuZPnyJ1cmtRiMUgSBIKCTNr1DM0NE86fQ524hB0IEbP8HilXFY84Fa6lk83hxFPoTgbJKnVClx0Tvf8Yoh9iM87yZWuWTfZoH9mjfSgRP45l42Tu4r1zIxwXY2AIY3DEex6jXoNjoSiekbDj4loWTjbvLe6J9KI6YTdvet0Blgk7liD27Z8s+/03gxNP3rZjLwgX7MkZUm++T+b940iREFLA71UdyrK3iBfOrZs3PHKaznrz3AqtRe5OxMmyMUcmYFsvQldRW+oXjCZZ49PYqSxKTQV6dytaewO5Mwt3gNc7mxAF3YSbW3kkwLZdRgeybHuuitFLWSaHclimi+aXaO4J0L09zLE37/80TzkwExlG//EQuRHPaEwLaTTsWJ5TtRbWqd+2mHvwbYYLqeEkV34+gJW9T3Ra12HbVo1f/VqAgx8aCxKnOVg29kzC26TcJXR1yvzTr6n86Z8lGT57HzYgdhyceApnFUx1y/7odLbEIf5uoKJS4lf/aZC//0aasVEHSYIND6l86WsBHMflj3535Kbv/8JvBkkmHF77XrZow1nfIPOr/yzI2vUqf/w/xRgaXGH0WZbo/Ncvkj43ysg396/sWLcDtoMdSy67kutjLI47/azcnYiTZZO/fG2uNFprqUOprvAMLq+Dk8uTvzSE3tGIHAkSfelxrOkE1mjx36mNNQQf2eSxTMfFmly5g6hluOz//ji//G+6+PX/rZvRgQxGziUQkWnuCTBwMsXRvQsTJztr4mQN3IrAA1M2aiWzXPvuh0wfOHfn0ia3CWbW5MKr/Uz3rY7f152EALZtVamtke6LVoE93SqdncpqtE174KB3tePbuJb0B0exxu/dezGTcTmwL08q6T33jgNHPjSwbZcvf+3WcoRAUGCaouR+HR6y+fpfpvndPwivRpIAAShhP7J/cbPIj/ExVgN3TeNkXh3DGp1CbapFa6lHX9OMNT5dHM51XLKH+wg9tgVJ1/Bv6qbuX32VzOG+ghmmi9ZST2DHerQ2rwLMMQ1yZy+vyjCvnk3zV79/ns1PV9K5KUykRiExZfDan1+l7/04iamFd9CpvhH6/+R7hHoaCG9oJtjTgFoZQA76kHTlviJTjmmTG5nh2j9+yNTbfTi5+e9sZU0Sg3G0iI4aVO95F27XdbEyJue/38+57/WVtGC5W+hol/nC5/3s2K4RiUhksw6XBmx+/HqW9w4Y2DZEIoKvfjnA7oc1dmzXqIgKvvHXVZim97ycOGny7/+vFNMz3pZ+3VqFP/lfIvzf/zmF48AXX/GztkfBsuCNvTn+5utpZjNhug47tml8/nPe3zgOnDxl8oPXspw4aRZlDUIhwZOP63zm0z6ammRyOZfDR0y++70MFy9659PvE3zqBR/PP6ezfZtGY4PMn/1pBamUN7bLV2z+w39McvHSvXH+7xbU1kbCTz1K/sLlVSdOqgp7ntB54hkfui7o7zN5/dUsjz6hIyT48Q+y2DZ88asBUkmHQx8YvPLlAG0dMrkc/PjVLMcOGbS0yfzGb4Woa5D4d/8mTjq9+KYpEBQ894KPbTs1ZBk+2G+w9ydeFGDjZpXudVE0DV5/Ncv77y2cShMC1vQovPx5P1XVEpcuWPz41SwTYwukViSBGvEjh3ye/CO5cMRB0lXUyiBClXENCzOWxrnRSkWWUKMB5HsE9+YAACAASURBVKDubb6TWaxE8fHkoI4c0DFjaY+gBXUvpRtLY99LqcEHFIqkUxFqRZY04plhcsbd69xx1ww0zIkZsn0DKA3VIARKTcXcjX09cheukjlyluDuhxCqgtbRiNbeSFEtwWypp+uSv3B11YiTY8P4lRx7//YaQlybe/1WjuFO3iQ7MEF2YIKJvaeQ/RrBrjqC6xoJdtXha63G11yJErx3y3AdyyY3OEXsyGUm954ic2WyJNI0fW6avb/9OtXra6jeUEO0s5JoRwXBxtA9R6Ic2yE+EOPia+e4+MNzmOl7I20U8Av++I8ibFiv8u6+PKf7TCJhibY2mZ07Nfa/Pz8hT005HDpk0NQooygyP3szRyrlXZPhEZtcfv76+P2C3l6Vr3wpQEuLzPi4zZk+k8pKCVWZt7hQFXj5RT//4++GuTxocfSYia7Dnj0aTz+p84d/HOe9/cbcMX/jVwP8+q8FOXbM5NAhk2hU8NlP+3h4p8bv/c8xrlzxyFAq7XDipEkoJGhqlHnvQJ6rV73fTU87xOP3d9TyXodtw6ULFhfPp9A0wb/83TCnjhtMjNs8/yk/+97KY9kuW3dqfPPrabIZl7ffzDExZvPEMz4+84qfY4cMhv5/9t48upLrvvP73Nrf/vCwAw30vrObZHOnRJNaKFG7LVuLR7Yc2zOO7Xiciec4Jx5PJuPYEycTe7zM8XGc8XE83iRHpjaLkixSlERS3Nlk7yvQQGMH3r7WfvNHvQYaBLobINFUM+rvOTxsPNSrevVQdet7f/f7+34vBvzFn9b5tX+TRjeuPuHzXMmJox7Pf99h05DKT/9ckpefj2QT6YzC7/9vVbZs0/j4p+KcOOZRq668BhJJwUd/PMbpEz6vvOjw0z+f5L2PWPzD3zVZ1qwoILGzj02feQdGVxq/YdM4N4sWXz6mqimLvo/cQfae7ZGjuhdQeXWMuX98Bb8SESOhq2Tv3k7PB25DS0dNJPZEgZlHX6Q5utQ9nL17O50P7qX49Gmyd+/A7E2DUJh59EWKT516I3+mm1gHkrFe9mx6BFU1OD3xzR9O4iRbDs3DpxGaSuv4CPbpMaS3cgYa1ptUvv79SOe0bTBqf40MYpbvLwzxphaoPPYMQXVj9AiaIQh8iVypa0Rp66nC4BoPgFASNByqxyaoHptAjRsY3Wms/gzx7b0k9wyQ3N2PnomveGvgeNHM6DoFjC5CQuj5+HUbL1+nNVGgMTpPc2Se1mTxyn5NUtKcb9CcbzD59EXMjEmiP0V6OE1uTxfdB3pIDqbR4zqKvtwTR4aScJW/9wadDoTRNRE4AZXxMjPPTzH9wiTlkRKhd+Mo+7u6FHbu1HjqGYff+p0qjYZEVaGnWyEIljSi1ark0S+1UFXYuVMjmRT81d80WVi4+rncdafO7/xujaeedmg0JImEQFXhkt1af7/KL/5CghMnPX7nd6uMjQeoKrzjfoPf/vcZfvozcV562cV14daDOj/5qQSPfd3mD/9zjXJZYhrwwQ9a/LvfzPDxj8X44z+p07Iljz/hIISDEHD/vSZf/mqLl1++McjqDwOEiLRJd99nkutU2LxFxYoJRs/5mBZ09Spkswr1WsjkRR9Fhd17dR75cIzePpVMNlpakzLS0co1jEFCgZ4+hYc/YNHRqTA4rKLp0T1/9FWX6amAYiHkoz8eZ2CTypmTK8eVeFxw930m/YMad91rMDgcTcB0QyxWVyEKHx/4xL0gBOP/5UlC2yP3zl3oXZcFwGoq3e89QPaeHcx84XmcuQrWYAe9H7uT0PaY/dJLyCAksaOXTT/1AKVnz1J+aQShKnS//1YGP/MOxv/s27jz1cUvNb6tF7/ukP/OCbx8DSVuLP3+Jq4jBAmrE8tI44c/+OreD9SytXXsPM6Z8cia4Eq6GQnu6BTFv/46mQ+2/UOyycu6g0KCWgNnZIrqN5/FPjO2IRoczRA8+Mk+jj1VYv7iys6r3Xen0QyFY99bX7tr0HRpjedpjecpHx5DS1h0v/8Awz/74IptF751jJlHX9rQFPgrIpRRN4wfEro+oeuv63uUocQu2dglm8KpBSa+N44W10n0Jtjy8Hb2fGr/Mq7bmK0z8thZwuuQdyd9idfysAstapNVWsUWXsMjvEGW5i5HvhAty917j8GnPxXnhRccxi8GzMxuzPfy2mvesu67SxWqS9i5Q+OW/TrPPe+yZYvG8FD0oIrHFWxbsmO7Rl+vysxswKHbdAYHFWZnAw7esmQxIcOoOWjfPp1MRqFYvHGI6ZuFMA30gT78fAE1k0ExDbzZqKVf7+sGoeDPLywTpirxGGpHBiUe+WiFrkdQKq+wC1gVioLW04maTuHNzhNePgnUNLSuXJRHhyBsNPELxVW7D3t6VT778wm++TWbp5706R9UEQLy+ZDR8z4HbzNIpgTnTvs0m5Kf+Mk4A4Mqj36uyY7dOh/46Poz7vbdovPxT8X53H9t4nmSzVu0y3RNl938EsQVnKWEgGIh5NHPN5hqVygbdUmrufy6jW3uxuzPMvOFF6i+NgYSvEqDjvt2LW5j9KTpuH8X5RfPU3w20mY2R+cxezPk3rGL/JPH8astsvfsxKs0WHj8KM5sVMUI/YCdv/mjJHf1U1yoLU5e1bhB5aURyi9ubFbiTVwdmmKQjvcjFBVugOHlB+t17weEV/BlWob2Elz+z78SddcN96GkojDNsN7AnVrAOXtxQ1X1qia47T05Lp5urEqchvcm6NsaWzdxuhzSDfDcBs0LC4Suj2Is/3MEDRd3vvrWEKeNhAS/5eO3fOxCCxlIdn18D6q5dH6N2Ton/+YYgXPjkZm3Es2m5P/8/Ro/+zNxfvkXkvzsZ6Nuuce+YfPU0w5vwEtxGcYngmUz9cshBGzapKKqgs/8ZJxP/sTKh+X5ER/TEhiGYGBAxbIE/8O/Sq1aaQ1CeV1ix7SkSXw4R/X07Jt6WKkJg9T2bqpn5lZqXK507M4Oun7u07ReO4G1bxdqR4bGC4cJimVSD92PMAyq33mG2hPPIF0XNZ0i86H3YO3ZgZqOqh/S93FGxqh84zu441f3tDG3babjxz9EaNuUv/h13DZxUuIxEvfdQeLeO9A6O0AIgnKV1pETVJ94mrC+3KTYtASJpMKFEZ9kUjC8JSLEMoTnn3H4uV9MMj8X8tLzLQI/qjwuzIXk8yHv/7CG+QZUBOmMggwFkxM+t95u0Nm11BFw4Dadnl6F4S0aCJiZWv37bzQkkxd9hjZrnDjqRfuUK4vuZl8GGYTY06VF1YY7X8WvL43VeiaO2ZehMTK3eN3IIKR1sUDugb2YfVmCukN8Szf2dAmv3Fx8r1dq4BUbWEOdCE1ZXA3xSg3s2fJN0vQWQ1VNMonBKxLutxrrGuaEYUR+K6t5TKgqQlGR3vUro4VNm9bRc7SOnovsC9qeJhsOsRQnIAQrPO1VTWAlNRRtY/6IXrmJW6xj9WWvvfHbEG7NoTHXID2cufbGPyAIRaNjy0HcRpn63BszMATQrAS+vT6n/eMnPH7rt6v85eYG77jP5AOPWPz2/5Lhv/5Ng7/4y8abIk/BNZaSXVciJfzef6rxzLMrbTxsByYmfFRV4HnQakn+9a+XGRtfOQbUapLCeqpNikBPWyiait90CV0fPWUhVAUZhLjlFooRudpfIjpCVdDT0TZePfJMEqpC0HRR40Y7zw20lBk1oVSjB6mejmF2JcgcGKQxVlgzcYKItOj9vZQefYz4oQMk7jlE6/hpip/7MvE7DpC441aaLx/Fn88Tuh5hvUn18afxZmaRQYAxvInMB95N6kfupfToY68z3mv/fRQFa9c2Mh9+L36+SOXrT0TGn4DQNZLvvJvUg/dRf/ZlWifPIISCtX83yXfchQwCKl97Yhm7yM8HvPKiyz//5SQz0wGvvezSbFdtRs/71GqSej1kYtxHSnj8GzYf/3ScX/uNNOMXfCbaWrV3vc/iwXeb9PWr/OKvpnj5BYdnvuvwvg/GuO0Ond5+ld/8XzM88c0WI+d8ZqZ9/uW/TnFh1OfUCQ/Pk5TLIWdP+/zsLySJJxS++miTSkXy7vdZ/Eh737/wKylefM7hiW/aPPr3TT70sTj/479L06jBl7/QpPS660qoSmQGevn4L1k+yVRE5Cv1uqp2tE103SBAaAoEcjkZCiUyCKPfXZ4b5wUr9nejQiBQFQNV1VGE1s5qE0gZEsqQMPTwQxcpN+YZqggVTbVQFB1FKEhkdKzQxw8cQvnGrV/iZgdxM3ftDd8irIs4pQ/djZdfoHn+zIrfmX0DWEObqb78AtJ/C7QMa6lUvQHEkiqHHu7ETKhkew0OPpSjd8vSTFwISGY1bn2og5e+sTGdMH61hTtfXTdxincMEng2Tn19oaKXw4hn0WMpmsUp5LVU72uEle5BCIVWdS56eLV8ahcrNzRxAonvNAi8qxtiXg1CKPQdeA/Tr36T0F/fBKJakxw77nPylM8Xv9Lij/8gy4c+YPH4EzajF5au9Uuzb0Usy/l8Q5ASRi8E2HZINqtwfsTHvsLpq6pk9IJPEEQ6lOMn1jYIynYWrr7KJCM2kKXnR3Yg/RC/4VA+Ps2mj91K82IRsyfFxKOvosZ0Ou/eipmL05qtEB/I0nnvVgLHxy02aIwVSO/pY/prx+h9aBfVc/PE+tJYfWm0uEHhxTEAuu7dRmB76KmrRzetCiGwz41inx1FGAbWnh0458donTyLEreIHdzfNuAFaduUv/7EZSHG4E7OENu7A623GyURX0acLj34Y/t2kfnIw3iTM5S/9jhBaUn4qmbSJO+/i9apc9S+8/3F93sLBYyBXuKHDlD77nOEtaVlvXpd8ld/XkfTBWEQkeNLHMN14f/4rQpSskjKTxzzOHemghAC35eIiJfw9JM2zz3tRAHGQOBLfB/+4XMNvvj/isXXPU8SBPAn/6mGqkXaUCGi/X/x800QS76hl+bWTz1p8+zr9u15cPaUz4XzVRQ1MkddbS7uV5ptEr2kDVVMfZkVQdBw8CtNzO7lIcd6RyIKei01kKHEna+iZeMoMT2SKLT3pWfjeIX6cjK2Rr1pzMjSkdwcfd9Bk3L9In6wcmKiCI1scghLT0e7J6RUv3hF4XMq1ksy1oNAwfaqFGsrfQ1VxSBpdZOK95GO90faID2NqpoIBEHo4foNmk6RamOGYn2MWnMGeY3wFiEUMvFB4mYOKUOqzRkaTh4hFBJWN93pnXSkNhM3c+iqhUTi+zYNp0C5MUmhOkK1OcNaQmJUxcDUkxh6ElNP0pPZjaLo7e9MJZfajKpcPbUiXz2P629wXFwb6xp6zU1Dl6U5L4eaSJLYs4/aa6+siThpvTkSd+/HnZzHm5wnqDaiQSQMf6BlUEUVdA9b7DiUItdncu9HuvCcpXOWEux6wOkXKrz42MYRJydfi4JR12FVkBs+iF2df1PEKdExSLpvB5PVeQJvYyJEMr07UXQDu5ZHSp+g5VObrK77/K6KS07ka7xWhKKS7N2Kle1F1Uxa5TninQOULhzBqZfo3nUPVqaHwsjLi+/p3Hk3oecQzw0S+A6lC6/h2w1y2w5RvngMr1Uj1bcd1YhhV+bp2nk32aG9KLpB6DnMHvsOvt0g1b+DdP8OZBhQGj9Gq7TUobl1i8q2rRqnzviUyyECSCUFhi7w/JXzgzCMuusSSYW77zL49pNO5OYswbbluvsIRkZ8vv0dh499NMb4xYAnnrRptSQd2Ui03mhIXngxskR44UWXk6c8/pvPJigUQw4fdglC6Mwp7N6tMTYWcPrMEqGSEsrlyBH63nsMTpxsWxtIcHxI7egmsTlH/UIBqy+NPl7ErztMfvkIQ584hNERp35+gbw3Qu9796BoKvHNOWpn56ienaf/kX3UTs+BECS2Rksqfs2m4wP78cot1LhBfFMHCKienaM+ssDAhw6s7wtqI6jWIrd1247czau1yBzT9RajY4Ao8imVwty5Fb23CyUWQxg6+kAfYctmhaGVH2BsHSZ++/4oZPixJ5aRJgCtuxM1m8YYHqTjkx9dengrAn2gN0pf6Mjg1pY3xfg++P7qF8Trs9ilhCjPefn2V9pH9P6Vr3seK5aGLy1QvP6xcKV9S3nl/V9Cc2wBGYSkb9tM/dws0vPJHNqCdlmTjTtfpXZqiszd2ym9cB6/ZqNnE2QObaU1UcBdqCH9kMqrY2z6mQdI7OyndvQiCMjcvjkKAR+df0Mu0wmrmz1D70dTLRp2gWNjX2yThuWwjDQ7Bt5FNrEJgDD0OTP1OBMLL62638GuQ2zqOoQiVKYKr1GsjS37njpT2+jPHSCTGCRmdKCsEnarE8My0qTj/fRm91K380zmX2ameGxVcncJqqIz2HUbg523E8qA0dlnGJt7lt7sHjb33Esy1oMilh/P0OLErRxd6e30Zvdycf4FZssnCcPVOYJAYUvv/WQSAxhaok2cEihiKe9SU02Guu9iaPWI10W8cOb/uTGIE7B6FLOqoiaT64o60TqzZD/+7rYVuktQqOBOzOFenMWbyRNU6lEMRKN13YM9L0ej4vP1P5sk3aXzz/7tNp798jwXji4fkMJA0qoH2I2NqXoFTQd3rhqRgLWmxreh6hbxjkEAnHqRwGthpbpwW1VC30XVLTQzgdMooagaZiKHohntSlVk4CmESizdi5QhTqOE7zQQioqZyKEaMcLAw6kXCH0XM9mJDAN0K0kY+NHrgYeiGVjJyFpC1c1lsxff8alN1ZCBRGzE8qYiiG/rIWg6ONOrm50aXSmEpuLMltvnqJDs2YJQVFQjjhHP4DYrpAd3M3/yGSpTp7GyvZjJHI2FiwCkB3YR+i6F8y+R7NlK1657WDjzHMm+rdRmzuG1apjpbvRYmtrsKKXxY8RyAyycfpbQcwjcFomuIbJDeylPnEQz4vTse4DJl79G4ER6iq4uhf/ul5N05hRarYj4xGIC15X8xV82mJ5aeY1981s2999n8m9/I80v/YuQIISXXnb5gz+urbvNv1AM+f0/qPHf/0qKX/7FBL/0i4nFcbjZlPzd55u88GL0lB0Z9fmPv1fjV38lyW/9u3TkAt0uF+QLIX/0n+vLiBNEn+u55x1+6ifjfPD9Fo4Loxd8fu8P61SLDZpTFeojC3hVm6Dl4bf9cKQfrsi8kmGIV7WxupMkWl7UBFBtUTszR+9795L//nm8qo09V8PJ1/CqNs2JEsmtXZg9KYKWh6K9MSdOeYnBXir5XSrfLH7d0Wc1d24l++GHEZaJN7tAUKkia3XCdnTU68dPJREnec/tUfyGBDWVjLLlLt8mHou+CyEWK1uX4E7PRVEvzg2Um/gWwC02WPino/R+5BDWQAeB7aJYBl5xaawOmg5zX32FTT/zI2z7Vx/AzdfRO5MousrkXz1N2M7Eq742TmlnHwOfvBf7gT1R1mV3mtmvvLLMjmA9cLwatlslGbOw9BS6trJrGsDQEiQuW4ISQiEdWz1RQREaMSOLILqG683Iy/ByJGM99HbsR1WiR7uUIX7g4ocOYRjNWhShoWsxVMWIxkWrm+39DyEQTOYPE65h6U6gkLQ6Gey8jS2992PpaSQSx6sTBC6SsH2cOKqiI4RCKtbLzsF3E0if+dLJVStcQij0duwlFeu7of0O10Sc0nffh5ZKY/YPoMYTaMnlpU+hG8S278SZmkAGayQ5iohCDk0DNQ16Tw5r71YgCqf0Fkr4c0W8mTzebAFvvoA/W8QvVq7bMt0luHZIec7lxPfLzI3bFKav86AkwZ4uEbRctOTalxKEEGT6d0XLbVaSVmWe2dPfY/CWh5k98zSN4iTxjkG6tt7B5JFvkOnfRbJrC4FnE3g2C6PRrCbeMUAuuBXNiOHZNaZPPImiGeSGD6JqJnosRXV+lPzoSwzd+gF8zybwWhixDOWpk5QmT9C5+XbSvdtxmxUSHYNU584vO7/GXB2n6hBbJWx3EYogvrkLa7ADoarUz8zg5quk9m3C6EnRmijSODODNdCB1Z+lcXYGVEF8SzfWYA6hKjRG5ghqNt0P34KWilF+ZZTKK2PRxwh8nGoeoWjosRSNwgSpvh2AxGtWCdzlzQUy8KhOn6U+d4HAc+k78BCqtppqVhIGHm6jQug5OLUCYbt6l+zbhpnuJtW7DQnEsr3EMr3U56MS+/HjPn/wRzX27NZJpwVSRj5Hrxx2OXHSXzUT8fCrHr/+P5V5x/0mmUxkmHnsuL8sfmVuLuD//vM6rx3xrpqrKCWcPOXz679R5t67DXbu0DBMQaUScn7E58iRpZlhEMAzz7qcGylz/70GQ5siR/BiKeTkSY9jJ1bOIkcvBPzP/77KA+8wGRhQ8TzJuXM+pUJAY24OxdIxOxNIP8SerVA5EcV3VE/P4hbbs0UBQhFIP6R6cgbltiGs3jSF50fxqjZa0kS6PvZcjaDpMvedM2RvGcDsTNC8WKRycoYOU8PIxii8cIFgHfqm9UDELFIP3oeay1L4qy/gnLvQXldV6OrpQutaRaOhKDRefBV3Yprsj32Q7Icfpvj5Ly8Lpw0bUYB586XXqD7x9FselGmkDDa/c5DqZI25Y2+Nw3nP/k5yO7Kc+doocjWdXhCy8MRx7NkyiR19hLZH9cg4Zl92ma1NazzP+J8+TuaObegdCepnZ6i+Nr44oYIoFWHqb79P+tZh4lu7kUHI/NdfpXZyallFuzW2wPw3juCVrm134/p1bLdCMtaDqhqLhEde1hImEMStTnQtfpndgyBhdaEqBsHr2u4tI42pJRbzHWv2yoD5QnWU4e67MPQE1cYsleYU9dY8LbeMF7SQMkRXY6TiffRl95FNDiGEgqHFGeq+m0JtjIa9cM3zE0KQTW4ml9yGrsVw/TqzpZOUamO03Aqh9NHVGOn4AP25A1FHnBCYeootPfdSrl/E8VZGz0gZMl04iqkv15lmEoN0JIcRQiEIPQrVURr21a9Fx7t+NhFrIk5Bs4E5OIQaT6CYFkp8OXuWnkdr9DzVl59HrlHJGlTqtI6eQ+vJtdOMtSj9W1Mjo8uBboyBbuRtu6Ig0nqLoN4kqNTxpuajytTkPH6pinQ8pOttaGXK9yTPf3UB135rBqmIOHnrIk7IqMo0dfwJzGSOwf3vQTNXj0AQqoaV7sauLVC8eITQ9/Dbep7AazF37llC32P40IeJZftoFCcpjL8GSFJdW0j37STfJlp2ZZb5kRdJ9+6gY3AfjdI06d7tzJ9/nlZ5lk0HH1mxJNGcb+KU7asSJ6EqJPcOIFQFZ75Kx307qJ+aJrG7j8qr42QObcEr1AmaDkZ3KtKGlRqkbhmCMMQt1ske2kL+O6fwazZSysX2Yoi8aKQMIQyi2dc1lg4jH6hLnTZtsY4Q7X+2mwcUFSGUy7ZZDkXVcBslGvkJZBhQnx2hVVka8Fq25OlnXJ5+Zn2aqFOnfU6dvvL1PjMb8id/uvYydbUq+dYTDt964tqThLm5kC99Ze1asMnJgM/9fXOV3/gU2xqkS6gcaxOnU7Oolk73AzuID+doXixGhCeU5J9ZIuXxoQ46791G9dQsXik6hjNfY+7J5TrM+e++uaDbtUAxTZRkIur0HZ+MSJMQaF059P7eVcdG6Xo4oxexz46CqpL96PtJf+DdVP7xcYJKNPB783m8+Tyx/btpHjkZuYy3hW5C1wGBvI4VJzNtsPuj25n4/tRbRpx6D3az8wNbOf9PY/hXaACSrk/18BjVw2OLr7XGV34+d6HGwjePXPV4oeNRfrFtM3AFNEfn11yBcv0mLbeyKE9ImJ0oikpwGekVQiUdi7I+/cCh6RTIJAbRtThxM0etNbtsn5aRWaxcOX4dx1tJ4FpuibPT3yYMA+qtORyvtmoFqdyYpFy/yM7B99KZ2oYQAstIk0tuXhNxArD0FFJKGnaekZmnyFfPErxuCa7SmKTSnGL34MNkEpsQQhA3O8mltjJTPLpin5KQiwsvrnh9S+/9ZBNDCAFB6DFbOs5s6eQ1PuH1k/ysiTg1ThyjeeYU8pEP4+Xz1F5dvv4q26XrVbvtrgBvap65P/wcSsxA6+nE6O9CH+hC68mhZpOo6SRqOoGImSimgWIaaJ0ZpJTE9m1DttPdg1INb3oed2IOb3oBv1DFvTC1ISSqUXnrlgjt6RJhy118Pq8FEkmzPEvoO/h2u21ZW5nTJBCEnkNp4gRd2+6kf++7qMydozoTPUycRnlRGO07TYxYmiDVTefm2xBCoFspNCMiPGHgtY/p4tt1hKqhaiaKquHUi/huE7uWR9GWC/eaCw3sYgu2dVz9nEKJV6zRulggsa0HoyuJX7Npnp8jfWAILROjdbGw6PoL0aDnzJSjzsT+LNLzcQs11JaLPfXG7SJgpRZUSgkyRI+l8Z0m8Y4+vHYXnQyj60WzEniBjwwD7GoezUrSLE4TOE1UwyJwfvDBrW8HBI5H4cUxii+PR2apq+jZWtMVpr92lNANkD/gFvGw2cRfKBDbv5vkA/fiTc+ippLEDu5FaOrVJ5VBQOvISdRUkswj7yJstah+4zuEzRZBpUbtO8+S/cjDdPzEh2m+dhxpO1G3X18PfqlM7Ymn37oTvYlrQsqQhlMgCD001SBudSGECixdA4qikY5Hy3INJ0+5frFNnGIkrK6VxEnPoGvRONxo5fGDlZOXIPSYL51uL4Nd7X6Q1FpzTOVfJR3vx9DiKEIlHV9faLsfOIzPv8B8efWlN4mk0phiuniURKwbXbVQVZ1MYnBV4nTpXdd6LarQ/eDu97VpnKRsV5VGCB2b8EqtN+tBKJG2Q2A7BKUazpmx6HVdQ+tIoXWkUTvSaD0d6P1d6L2daL051FQchBJVqAwdJW5hDHaTuGs/Mgjw5kvM/e9/uSFBv28lvHITr9LEGsqty6vi0sN6+WtBZBSGQDMiEz6JpFGcwK4tkO7dTvfWO3Eb0Xek6ma7cqKiaDpB4JEbPkjgO8yefpp0s1lCAAAAIABJREFUzza6t999ae/I8PIZTGQvLKWMyJJQ2qRp+Tk4ZZuLT45Rm6yCgNrF6qoleKEqpA4OEdvchTNfpXF+js6H9tL3o3egaArObIXULZtI3bIJv2ZH3ithGDUtyIh4ScCv2aQPDhPaHsXvn20PI2F0w4lIpyKJbkAr20tuy62k+rYTy/aixVIURw+3uwzbn1HKSAzsOdQXxunefS9eq4ZqxLFrkTjfd1o0S9MM3PowbqPM3MmnqU6dxUp3M3Dbw4RBgFsvMn/y6TU5Mf/QQ3JN2wAZhASt61gVllyyzl78WQbBZT/LxfZ26XrUvvscwtBJv+cdkTt+rU7ztRM4I+Mk7jy4fKwP21qpS6+FIY0XDqNmUiTvvQO/UKL+vecgCGgePop0HBL33UnHxx5BGDqhbePN5XFGx9/0aab6Ewy9Y4D0phQylNSm6lz8/hT12aWKq2ZpbHloEz37uwCYPbLA1Euzy7zYtJjG5ncO0rU3R+hL5o7MR9u4y/9GiZ44Q/cPkNmcQiCozzYYf2aK2tTqy2CqqbLlwU2kN6U4/ZXztAob8Ay6zmjYeYLQjYiT2YEqNC6/mk09hWVEnca15ix1O08Q+miqRcLqZFFASKQpipkZVMWIqjxOHj9YvUot1+EQWW5M4PlNDC0OCAw9ua5zrLfmWaicvUZHnqRYG8MPbHTViqw09DSKol9RJH6jY13i8ObZU2tux3zD8Hz8+RL+fLtSoCoopoEwdYRpoKbiaL2d6L25iFAN9qB3dyBiJkLT0DrS6xKp3zAIJdVjE8uW6rxS4w1xarteJDuwF1W3yA7uIwwDVNUg2T/cfmCLqF2+bT9gpbrJDuxBiGjZqVWaIZbqRo+lSHYNkxnYg6Je+VLxvRZOrUDn8K00S9MkuzZTz48t20YGkpHHzqL8k7L482qu4aHj0TgzQ+3kNH6tReh4zH/jKKqlEzQdgoZD8/wczkw5Cu2ttHALNaQXIANJ/onjhC2P+tkZnLnqoqhXBh6F869EpBIBioIMPOzyXCQAHzlM8cIRLumVAtdm5rXHF+0J7OoCk688hteqUTj/MtWps9G2vksYBsggGgDmTz6NZqVAhoSujZRh9FosGa3Pu60Ns324iesPb6HA7O/9KUElco92xibI/5e/JWh3sNlnR5j9j39CUIgaLbzJaUp//1WURLydjeZFNgGqSuvoSfzy0tJx85Wj2GdHCS57TTou1cefovHia0jbWeLtjkvz1ePY5y6gxKxoKTwIog6/5mrLoGuEiJbF7vpvDyJDSXGkgmqp9BzoZv5EYZE4CUVh+J2DdO3JUZ2qkxpIsO3dw7z0Z0cY+VZE3Kysye0/ewvde3MUzpfRLZU7/sVBslsynPiHs4sEq3N3B/f8yu0omqA4Ep17zy1dLJwsrEqcVENlx/s2c8un93D878/g1t4eD9uGnccPHEw9ia5aWGYGx186v3S8D1WJVgjqrXlsp4zj1dqeRZ3oqoUXRNVpVTWIm7koyD4MaNqFFRqoNwLXaxCE/uJETlU0hFDWPEblayP4wbUr6I5XI2gTvchfSkdTDNwfBuK0Vv3ShiIICZs2NG2EaUQRBo0WQa2JMCsIVUUxdTTLWNGF83bD9OeeY+bRpWVQ6fpXbYWtzY/iO9EyUeC7lKZOErgtFkZfpHP4VhK5QcpTJwjDgCBwow6KrshSYn7kBVrVeaQMmTr+OEYsjW4lmT3zDG6rSn7sMLnhW0n1bKM8dXJRO1WePoPbjAY7t1WlPH0Kz64xd+45ckMHMFNdLIy8iGfXV9x8gRNc1SlchiHNsTxhy8VdWBL2+ZUm/mUd2n7Nxq+tPuP0q22i5AbL9hEdf+UDJmz3SDu1ldoIr7X0fhn4eM1LcQzuqtsDBK5N4C7/bJfE+DfxNoTv488taT6k6+Lni0s/2w7+7HLdS9hoRoLuy+H5+PZyHVLYbL3ODPOyfa6mM5FRBSusbUwWJ4CR0Dn4z/YQBpKn/sPzS0RJFcuqY5Gvk+T7v/cy9dkGVtbkR37zHra9dzMj3xpHKIKBO3vZ8tAQ3/vt55l+eRZFU9j/iV3c8pN7mHxhhuL5MqqhcMsnd6MaKk/9h+epXKwtHe/1p0vUFLDlwU3s/+RuTnzhLOe/OUZwA0YnrQbHq+F4NRJWJ4qikTA7qTSmFn+fivWhKjqhDKjbC9huBcerEjc7SFhd6Fp8kThpqknc7AQiX6im8+YkCJewWIXnkunzKo7PV0AoA+qt+TV14YFcpn+KJulvwwJHG+uzIxAKei6HuWkI1YytICqhbVM/fmTtnXVX/FTq4lKc3pXF2NyPMdyH3teJkk5E+qdUHKGp0axOyii+xXYJ6s23vOtkoxC6PlwpUHcVVGaWBLCh71AYO7z48+yZlZqH8vQpytPLU7xblbllYuVL8J0G8+eeXfF68eKSyNJtlimOvxb923eZPfPUmj/7qggkzfMrP8sPA1RFR8Jiy/BN3MRbAavDIre9g2N/f5r63BLZe/0yugwkc6fy1GejiZpddqhNN+jeF3UKqoZC78FuGvMNnKpDvCvS4tRmG1hpk2R/kuJIGSNp0LUnx8i3xqlO1q94vOhFyab7BjjwmT2cfWyU89+88LYhTRARzbq9QC61BSFU4lbn4u80xSRhdSKEgu1WcP0mjlfHdiM/v7jZgaElaDqFxe3jZmSQ7HkNbPdaUpSoqqOrFrG267ahJ9DVyIZAUVRUoaEoGnHz6rrTK8EP7EVitxZcvpy3dnp2Y2J9BpiDg3R98GMopon0PLRMFr9aiTrtTJPG6ZPUTx6D9V7bqoKaSkSi8I40+mA35uZ+9KE+tGwSoetRx52qLHZBST/Ar9QJynWCclsgPj6Le3Emsiy4iZt4m8DQk2zpeyehDJgtHKXe2hjyKFCwzAymkUZTDEAQhC6OW6PlljcsauEm3r6wMpFmplWwr8rXpWSFrigMwsXxWNEUEl0xslsyPPBv7lmMJVF0hVbRXmyyNdMGQhE0C61rivljOYvbPrsPPaaTP1XEt99u16tcvJcVobYjQyLdUszMYulphBA0nSJ+YBNKn6ZTIJQBqmKQsDqpNCaRhFhGGk2NRZ3CfoPWFZzFAXQtTkdimK7MTjqSw+haDKUtwxDtzmDxOuryRjyTgsB7nd71hwfrIk6Jvbcgw5D8178KQtDxwLsoPfUkim6QOnQXzZFza+6sE6YeVZE29WIM9qD1RbolrbsDRddWmt9JSdi0I1+niTncqfnI52mugL9QQjpvz7XSm7gJXYsz3HsPYejTcoobQpw01aKv8wC9HftJxnrQtBgC8Hybpp2nUB1hpnCE1gaV/G/i7YnACxGKQNGvbQx6tbBxKaN9lUYrHP3bU7j15eNxeawSif39iGyt5XhIGPnWOAN39rLvE7uoTNRoFd9eS971Vp5QhihCwdSSGFoc128QMzswjShmpekUFjvk6vYCQeiianFSsd623ki2q1OCUAa0nNIV9U1xs5Ntfe+kO7MLTbWWESIpo+Uy37cJQpcg9AmlT9LqQVNXdmNfC5JwXUL0/z9hXcRJS6Wxxy/QGjmHluskdB38ShmvkEdYJsm9+2meO70mTxFjcz89//JTKHFrUbsEkc4lbDmEtkvYsvEXSrhjM7jjM7gzecJGi7BpI233+gvVb0CkNu2me9870cwYQtWoTpxh5pVvbNj+VStBbvvtpDftRTUsQs+hOPoqpZFX3/wS7E1cFUIoWEa2/W8VTTUXIwzC0McPnTWJNjU1xraBBxnsPtR2B14aPA09jq4NkUr0k0tv49zk41TqE9fnhDYaIhL1R+GsSuRj1HbUjv5b3PAH+SnfNpCuSzPfwq27dO/NMf7UJH6rfY9f+grXOMQGbkD+TJHOnR1Up2qURpYqIooW5eUBtIo2rZJN995ORh8fX0awhLLYrwKAXXE49aVzTL8yx/2/dge3/vQ+Dv/Fcdza9QuS32hcMsKMmx3oWgxTT+H5rcU8tyD02t100fdQb81FImotTireh6KohEFAwuoBaPszre4lFTdz7Bl6hFxqC4pQkTLEdquUG5OUauPU7QW8oBU1s8iI9AgUbt/+aZKxa+SXrIIfvqfvEtZFnELXQVzqWAuCKF4jmcLLLxDU66jJVLv1/doQho7WGT0kpJR4CyXci7NRFWl6AXdqHm8mT1hvsRhzcBM45XmK514mlusjt/MujOQbW5++Erp230PPLQ/SXLhIfXYEoeoEdmP5iAYIVcdM5XCqC1fML7yJ9UEIhbiVIxnrpSu7k3R8EFNPtMvzdaqNaYrVUeqtuasSqN6OfQx03Y6mLrmcX9peiGi5WxU62eQwOwbfw/ELX8Rxr5/L7rqgKCjxGGoyjhKPRxMry4pMck0TxTIRloliGJEOUmub5ipK1GkmxNu9R+QtQ+PwcVovvsLZxy5w4NN7sMsO88fzKKqClTUpnCtRGl2b7CH0QiaenWbLg0Pc9tn9jDwxjt8KsLIGRspg7DsT2GUH3w44+7VRDv3zAxz8zF5mXo1IQKzDonShQuHsZRXQtr1I4WyJI399knt+9RDV6TqnvnhudU3UDQg/cGjY+Yg4qRFxarllklY3Qii4XnWZA7btVrG9arSUZ2QwtARB6LXtCSCUPjV7JXFSFZ1NXXeQS26OHMplSKk+zvnp71FpTl1xWV60CdZNrA/rIk7u/ByxrdsRhkHoOAT1Gpl73oGWyRLfvhPpeW/4ISo0FYIQv1jBXygRtKNVhKZGLeU3iRMAbr2EWy9hl2dJb9q7ofsWmk6yfztuvcjEc1/GrRWI3LJZ8f3Huwbp3H0PUy/846rdahsOsXwdXsIPNAz6+kDQkdpCfGtnFJipKFxePenJ7qXReZDJ+ZeYKRxdtVxvaAl6OvYukibHqzNbOEq1MRWZx1o5ejv2kYz1oigqmeQg/Z0HGZ997q3VPKkqQtNQLBOtpxO9rwetuxOtswMlGUeJWSiWhbAMFNNEGHpEjm6yog2DOz1Hwws5/80LyCBky4NDbH5gEzIMacw1qVyMyLQMJW7dxX9dR6zf8pdVjCoXazz3h6+w98d2ctvP7AcgsANmj8wv6plkKLnw3QkUTbDlXcMM3tUX6aeKLWp/uyQW990Ap+5Gc+ZQMvXSLKe/dJ6tDw6RP1Vk/vhb42D+ZnHJEVzKHWiahamnFvVLUkpcr0nTXgppD2VIvTVPR3IYVdFJWF34gYOhJdr7s2k5K4XhMSNLV3o7SjujznarjMw8Rblx8aqfTxErZTE3cW2s08fpNEGzEbXFOja1I6+Se+i9dDzwLkLPpfzM99ZsWSBtF2++GLmDmwZqNkX8jj3EDu5Auj6h4+Lny3iTc7jjl4J/awTVJmGjdZNIXQeouoWqm/h2A7d+qeVarlqTTfZuxcp0XxY3srHQ4zpm1sJIGehxHT1poMV1VEMFKQmcAK/p4TW86P81B7vi4Dffvlo3IUSUCN7Oo3o9FEUlGetlx6b3IITC5PxLK4zn4rEu4m09hEQyOf8SF+eeW0w9V4RKvnyOnZveQ2dmB4rQ6cnuZb50muY1sp/e9PkZOmpHBq0rhzE0iLl1CH2gFyUWW149ujmQv6Vwqi6nvzrC6JMX2/dXZB3i1CNi3phv8vTvvriCOB37/BlU/dzizzKU5E8VeWHiMHpMA0UQ+iFe08e3l5b5vYbH2ccuMPa9SVSzfTw3wK0vTQTOf+MCY9+dWLQv8e2Ak188y/lvjS3b7kZHKH2adpFQ+ihCwzJSGFq8bS0g2xYES5ltUoZUmzMAqEIjafXgeHVUJUpiuGSq+XoYenLRrgCgWB+ntgat5CXH8JtYH9ZFnPxyCb+8VEq1L4wwXy6hd+QirVOpuGYrAHdyjvz/9Sj6QDf6YDf6YA/GYA9qRxrFMlFJoHd3EGsH/4ZNG2+ugDdTiJbyJtsRK3PFDc2ouxI0SyW7JY2VMVcd2GuzDcpjN8hyx2Uw010k+7ejx9OEvkezMEVj7sKSXkkIkr1biHVtwkjmMFI5Qt+j/45HAAhdm9LoEdx6MapI9W0j1tFPdtttGIkMfbe9h8CPbmSnskD5wtHIXPMNQGgK2W0ddB/oIbe7k9RwhkRvAqsjhmZqK6QrUkoC28cu2jTm6lQnqpTOFcgfm6d8vkR4FTHrm4aioGWzCNPELxY3LCvs0rXlBy4tp4jntxBCWXQZVhQVTY0x1HsP5foEtfYgewmWnsbQozyrIHApVM4vkia45L0yy/jssyRi3cTMDpLxXhJW13UjTko6ibVzG9bOrRjbhtH7e6OA75u4YRB6IXZp9WtYBnJVUfaVtEZu3VshDl9xPD/ELl/5nvGaPl5z+bju2wG+/faLK2q5ZTy/FYX06ikSVieaahLKkFpr7nWTH7lIjhRFI27laDrFxUpSvTXftiy5HAJdjaEoSwSo5ZRW2W4lku0YlBsdK5IWfsCTq/X5OF0ORUUI8EtF/FLx2tu/DtJ2sU+PYZ8eQ+gaSjKGkoyjd3dgbBnA3DaI1teJmoghLANhmZhbBzG3DiKDgLBhR0aY5Rru5BzOhWnc8RmCSmRPsJHLOMn+BHf/0kF69nUiVIFqqARuiKJG3SFOxeXMP47cWMRJKGSG99Fz4CFUw0IGHkLV6dx1N+Xx48wd+Tah5yAQWNlekj1bUIwYimYghEosF2UWBXYTRY8ecqpukurbhpnpQY+lUDQdK9u7eIPKIFgU+a8HZsak65Yetr5/O7m9XcS64miWds3KgxACLaaTHNRJDqboub2PwPZp5puUzhQYe2KUhSNz2KUN7MQRAnPzZtI/8k6M/n6k71P40pdxRi9gbNqEtXMHjcOvElTWbomxqMOVkjD0WKicZWrhMLZTIpB+22nXIJPcxHDPPSTjvcSMDvpyt9BoLRDKpQFSVQ2U9uzU9eqr5lkBVBpTlGpjWEYWVdFJxfsoVM4v29ebgdA1tO4u4nccILZvF1pPF0oijlBuVpNuYg0QCrqVXGZCuxGIdfQzdOdHmXjpK7TKs9d+wwag6ZRw/cYicYry4ARSBlRbMyu2d/0GTadEMtaDqadIxXtRhYaUIfXWwiqGk3LRyHJpzLz2OKypJt2ZnWjalYPXbxREVbboma4IFV0xr/6G64x1EyclniA2vAW9uxvpedRPHCNo1NFznYSuS1Bdv4eS9HyCUi0K7J2Yo3nkLEJRUJIxjKE+jC396APdaF3ZdoZdCiUVj+JX+joxd20mFYZI18OdXmD+jz5HUNy4G277e4cZvLOPyRdmsCsO29+7mQtPXgQhGL6/n9kjec7909iGHW8jEMv10Xfre3AbJSae+QJuvYRqWHTvf4DcjkM4lUhkLmVI4eyLFM69jB5Ps+VdP0XgNLnw7b+KdiRZ9OrwW3VmDn8LoWoMP/BJrGwPF5/+Ap7dLjXL1+fYXR2KptC5r4tdH9/L4DuH0eP6Ci3TenCJSKU2pUkNphm4f4jp5yc584WT5E8sEG6AeZ7e20vuRz+CYsUIqlWMTYMoRrt6IiB5262E1Sr1Vw5ffUeX4VJbsiRkoXyG0xe/gec3VmzXtAvYboU9mz9M3MyRSQ5hGmlaztLERREqoj1o+oFzRVffIHSpNKbo6diHppokYt3tDp43SZyEQO/tJn7oFhL33YHWmVvmv3YTN7EWWKlOBm9/hNGn/25DvYJU3SLRNbRqGPr1QrQcV0dKiaknF5fGvMBeJgy/BM9v0WgtkIr1Ymhx0vF+FEXD9Ztt/6aVRQHPby0GCgOkYt2oinHF+1kIlZ7sHroze94W96btVRcrc6pikIz3opb0ZW7kbyXWRZzURJLce95HfPc+hKIStJrYE+MEzQaJPftRDJPSU99es5fTFRGEUXhnqUarVKN19BxCU1GzKdRsCq0zE+XVDXRFeXX9XSiJGIquYWzqRej6mzv+69Czr5P8mRLP/uFhkr0JBg71cPbrFyiNVZl6eZZbP7OXeFeMZv4G8RgRgmTfdsx0J7OvPYFbLwOCwLUpXzhKbscdJPu2Uh47Rug5bUF/GC3ftQN7r2Q9IMMgKpu2/wsD7w3ZFOgJnS3v287uT+wjvTmDom6cVkq0W9P1uM7wQ1vI7ezkzD+cZOSxs3jXWEK4FuIH9iM0neJXvkrYbNH7Cz+/+Dsvn0fKEL2vb1377EzvAARB4DKdf3VV0gQRsSrXLlKqjhLv7iBmZjG0+DLiBEvEU8pwZYn7MjTtAkHgoqlm24zvzf0NhKYRO7iX1Hveibl1KNIs3cTbFkJpJzO072+h6sDS2CAUjctDv4WitsPFicaG0F+mRb20PVK2u7MFMgyXB5ULEcWTdG/GiGdQdXPx4R8G3uu0rQKhtivTqxxvaRt1KX/tKiRBqFq7M5MoODzYmKakUPq0nBJShlhGBkuPJkoNO4/vr1yu9AOHhlNY3N7UUyBE22F89bHB8Wo07AXS8QGEEGSTw3RldjBbOrEsrFygoKkmfR372Nx7XxTu2w48v5EJVNMu4vpNYkYGIQQ9mV2UauPkq+cIw4B2QE+7fB8ZfK61ei4UFTWRwq9dy419CeszwNx/EKO3n4WvfQnpunS+/0PRL4KAoFEntm07QtXePHF6HYSho8RMaHfYhbaDtB3Cpr3k6RS3rtu6p57Qacw3cSouia4oakY1VGQgmXpxloOf3sOme/rJn37jZoJaykJLxfCqTYLGm/OoUlQdK9ONasYZvOcjy2YdQlFQDQtFt1BUndDbGG3OemCkDPZ95gA7f3QPxhU0YxsFoQhSQ2kO/NxtJPqSnPjrI9hvwkTP6O/Hz+dpnTmL3tOzbPInWzYyCFHi6yt9X/JQ8fzmqjPQyxFKn3prgSD0oviEqxjXXesKcrzaYkVK12KLlao3AjWbIfWu+0jcewdqNn1DD8I3sTZ0bb+LWEc/Ey9/FSEUttz/CWQYMnn46/hui8Hb3odTK7Jw9jliHQN0bb8TK92NoukEnk1p/Cil8WOEgYeiGfTtfwgQBF6LVO92VN3CqReYP/MszcIUIEl0baZr2yFSvdswEh1sf/CzkV1Nq8bsie/QKkVLW0LVyA7uJbf1NjQzQeC2KE2cpDR+lLBNRoSikuzZSveue9HMBG69GGVMvn5sVRSswSHSh+7GHBhCKCruwizVV1+ideH8hjzP6vY8ofTR1aWxodacIZArJ3KSkIZdwA+cxe0For3kt3oHc8utMF8+Q9LqRlUNdDXGzoF3k0kMUq5P4PktVDXq0utMbYty8lSDamsGz2/Smdr+ps/xesLxasyXTzPUfSeKUDH1NLs3vY/u+i7qzTl86aEIFU0xMbQEqmowMvNd7Ks4rF+CGk+QveVO8s89sebPs77IlYFB7ItjNE+dQO/qXnYBBq0mwjDhzWoYBCipRLQs15lF686i9+RQOzPRa7kMSiJ2Ra2E9Febdbw5+C0PI6GhaGJR2xTLRYK60JcEXkCs482tuXY+uJfuRw7SGs/TODdL4+wszQsLBK03ILQWCkLVCAOP4uirq9oFuNXC4gDzVsJIGez76YPs/ol90dLcGiBDSeD4+E4QRTmIaJlPtTRUY21dWGbGYueP7UE1VF77s1dwq2/83GUYrtoEIYwoTmK9Ydhq2zogCN01BWYGQWSEqarmm6oSBYG76OESde28sXtXzWbIfux9JO674w1p3G7ixkTgO8SyvQhVQ9NjxDsGCHwPzYq6PmPZfhr5yDxVtxKEvktx7DXCwCe7aS8Dt74fu1agsTAOQqBZSTqG9lOfv0Dxwqsoukn3znsYOPhexp79Ar7TwG2UKFw4DEKQ7lOZOfZtwtBHBj5ufWlimh3cy8CtD1OeOEGzNEO8Y4D+A+8GKSmMvgyAlell+K6P0ixOM3/mWYxYms7td6Koyx97Rlc3PR/9BGZv/+Lk2+zrxxrawtyXPk9r9Dxv1u6x1g7D1dr7D8Mgeu0KS01Np4DrN9Hb+iMpQ1pO6YqaRSkDpoqvkYr30pvd2zbTTTPcfRfD3Xetsn1IpTHF2aknMPQEnalt3MjGsUHoMpk/TCrWQ0dy8+L5DeQOQm7l9n5gMz63lLWqpbKkdx1Ydd+qFUdLZdb1edZFnGQQLJUzL4cQaKl09MBYqyhbVRZdw7WOdLTkNtCN3t8VaZgSMdS4FW2jL/+YMgwJbQ/peVGwb6GMe3EOd2IOb2aBoLSxgsLCuTKb7u3//9h7r+dIsuzM8+c6dAQCGkgkUmtVlSW6slRXs7tJtmA3e4bkkGMcsWNrtmtja/u2D/uw87Z/wM6Yrc3u2AxnOVRN0SR72KK6u7q0yBKZWaklNAJAIHSEa/e7D44EEolAJgJAlmjWZ5ZmCY9wDw8P93vPPec730dmOI1dc6hPNzn83b3YNQcjpZPZkWbuQhs38w1CScVIHx0mua+f1P4B8s8fxCs1cRbqNK7OUH79Ouad9mqx7SACD7dVRQQ+zdlbNAu3N31uD4bUUZZPMRT2fusAB753uG3QJISItGHqDrWxCuUbZRrTdcxiC9/yCf1w+f66axOhJXUSfUnSI1nyB/NkdmbR0waKsTagUmMqe76xj9Zck6t/dik6XofwCnPEjxxC3zEcBehLHyFpGvEjh5HjcdyZmQcf5D7crdPfqxT+IKhqbEW4bk2zycqz+bBf5t4U/mYHTbU3T/bbXyNx+sQXQdOvGJz6IiChxzPoqTye3STwHLR4OlK21+PY9ShDWp+7TWP+znLZzmmWiGX7SOQGo8BpCUKEFC79ErM8Ey3wJImhk19H0eP4TgvPrOGZNVJ9uwl8d9Ux70I1kuR3n6K1OMXsxVcQgUdt9jp6Mkf3nscpjX2EJElkBqIKyNzlV7Gqc0iygqxqJHt2rBxMkonv2ot+T9B0F1o2R3L/Ieyp8Y4XQ/fDcqo0rSJxPZqgHb+J6azfVGU6ZZp2cXk88EOHhj3PgwI412tya/aXOF6DvuxBNDWBLKvLmWRBSBj6eIFNqXaLycX3aVpFEkaOujmHriYC03h8AAAgAElEQVRwvOa6xxdCRDyrJR0px60vlckeDiEEjtdc2ddvdmzX0rIXuTb9U0b7vkR3Zi+qbCDLyqrvJ0RIGAZ4fovwHmFPPddNcnQ/5tSdtQfexLjVUeBkT4yRfeY5UiceI7TMaCWS7UJJpUmdeAzr9k2Ev7EbTBvqJf8Hv4k20I2ciiOpSmSlICtrxQ7DkNC0CaoNgmoz0neaWcCdLeLNFAkaLQiWshGPQMV64s0ZEt1xJBmcusPtn0/y9P9yim/9+68gyVCdbDD1ztruiI3C6MuQ2Nu//J0VQ0MZ6sIYzJEY7cGaKHUWOIUBrflx/H2n6T741LJoJoCsGmiJDIFj4tvrPyQP+QBC30XWdPRUF77VePg+QN/JAQ797lH01Nrykmd6lK4UmX5zksJ7M5gLLQIvRPjhQ81AJUVCVmRkXSE9nGbozAjDZ0bo2p9Hja2+xdW4xv7vHWbxSpH5Dzv/zVoXLxE/coj8d7+DMzGJpCjE9u3F2L2b1GOn8IpFrGs3OjrmXdVuTY2TSvQ/ML0syxqpeB+KHHXZxIzcMn9DllR0Lb38XkmSHpiNi1qco9cD4dPpqlpOJUh/9QWST5xccRT4Ar8ysJslROhjpLuJZfuwqpFifSzbhyOXCUOfwF2RB9CTOfRkDkU10JO5aNJWVy+QrOo8rrl0f4sQz2pE7+tg8tKTOYx0D1alQG7kyPJ2WTPQ4mm0WJLAc4nl+rFrxeXmFREGmOWZ1SLNsoTW3dv+OZGWXlPULQdOrt/k/O0/vyc4E5G1yjrwA4dL43+7aiG0ESK06ZS5OfMKhdJFcqmRJemDWLQoDWxaTolqcwrTKS/rQZlOlQ9v/TFRp1+4rhBuELrcKbzOVP19EsM56nfm8R/wHe5FKPxV30eIcBPEbkHTWuDa1E9IxfvIJXcQN7pQZQMQ+KGL55uYToWWvYjtrnCWAqtF9fIHNG9dXnNUNZUle/R0R2fSoQDmVfSBQfK/9nWE66Lmusi/9DUkXceZnaZx4aOIULcBKNkUiZP7l/9eJrEKAYEguKvbNL2AO7uAv1DFL1UJSjWCeusTFcAs3ajy7r8/t6wrMv76NKEfMvhYH4EXMvVugdKtjRPL7ofek8YYWJsqlCQJr27RuDwNRANDsn83WiJNLNODlogmyZ7DzxB4Lm6jRGt+HIDW/DiL196l7+hz6C90YdeiwEvR4yh6nOLlN6hPXd30OTfn7pAZOczwk9+ktTiFBNi1IpXb59rqOMXyMfZ/7xDxvsSq7UIIzIUW1//yChM/H6M13+w4Ky4CQRAEBG5A+XqJ8s0yE78YY9fX9nDwnxzG6FptdpnsS7D/u4eo3al0LFXgLSxQ+fFPyX7ly2SefxZJUci+9GWE52PdukX1pz8jaGwskLyLhjnPYHfULTLU8xj11ixum5WfhERXapR8es+SdYrMSN9T2G6Vcn0MQ8+QT48uv19VYg/MYOlqalkfxg/sNWKaD0Py6cdJfenxL0jgv6IIHBPfMTHSPcTSPZjVApKkEMv0IsIQ324ulfsl8rtO0b3nMUQY4tlNFEVD0dfqA4W+szpw2cQwLikasqqTyA+jxdOrXmuVppbb8mVVJ/TdVZ8XBv5qMnp0xM5PYhPww87oAesZ+T4MofCpW4W2UgftIVZpvT34nDyMXIrur+yifO3BquRr990e4dKoI3iaWmt6w/s45QXcyuJ98YkECPxmnfrV8x2dQ2dedbZN5bVfYF6/SmznKEoqg3Ad7Nlp7IlxQrM9478tBBHR27IjU9+mhTdXwp2cw52aw18oE5oOoeMibOdTtdcQoVglxubbAXd+OcXkOwUIxRpF3U4gaQrx0R5kvf1PUf94Eq8cXVdFj9O15yR6qgtZVvCsBiDo2vMYQoS05sdpLUxEHSa+y+LVt7HLBXK7jxPL9oEk4Zt16pOXMRfX3nQiDLBrC4Tuw4OJ6vhFJEkmt+s4qb5dBL6zioOw6jvKEkNf2kH/44OruueEEDQma3z0H95n9r2ZbZELACCMjnvlTy7SmKrx2L99kkTfihq3pMgMPjVM78l+pl6foKOMcRhi37yFV5hD6+1B7e4GIfBKJbziImGrg2dgCZXGGK7fQteS9GT3c2TXbzFT/JCWvbikkSWhyBpd6VFG+p4mZuSWV2zJWA9Hdv0WTauIpsRIJwaXj2voWWJG1zqEc4lMcnjZmiXq+tn49dd37yT1/FNIsU9XT+ULPFo4jUUSXYMoegKrcg5ZNcgMHYgMZOuLBH6U2ek7+Ayt0jTzl18j8B1imV6MdPfaA250GF+uIK8NakLPIXAtatNXWbjxzn37hfiuhaxoBI6Fke5e6fSDSKdOvmesFQKvvHifBtLdlwRucX5L5uZKInI8kGQZNW3gllu4FQslrmH0ppAVGads4tUtEFGTkNGdBAT2QoPQC9EyMdS4Fo2PS8bW9kIDsY7Ar2yoxPrSyLpCYHnYxQbCC1ESOrHeFEgSzmITv+kgKTJGT5LA9jG6EwSWj1NqYXQnsOYjPURJUzC6k7jlFkpMQ03qFN9cTf+QNIVYbwoloRO6PnahTugFaNk4Rj5BGIQ4C00C+1NydgjD5dKgms6SHInKuNVL7yMbMQK3s6C246WicBzsiTHsibFOd10Fv1Sl+oNX8GYXcWeL+AsVhPPZl9KXZIm72dPQiyYaWZUQIQ8tKbU9nqoQ39HVNlUsQkH9wuRylOy1qky+/hcbPnboOdSnr1GfvkZi3wAgMG+tL8Pv2622x0+fGsVdqOPMrgRGoedE+k83zj70PPSMzsiLuzAyqydZa9Hk/H/8iJm3px6JaWdg+0y+Mo4SU3nsf36CWNdKR4uRNdjx/ChzHxTwOrVwEIKg0YgyS3e29hxAJAswV7rISN+TKLJGb+4g3Zl9OF4d1zeRkDC0NLqWXCrLCRy3wVTxLIP5EyTjfcT0lYylECGu18LQ0wz1PEajNbumjTlu5OjrOoQiR2XThjm3IaVhADkRJ/3C02gDfV90z/2Kw6oVyQ4fwjXruK0Kkhxxm+RsH6Xx84jARzUSyKqOWZrGNasgycQyvWiJzKY/N1hqXFH1JJ61unTtNMtY1XkS3TtACHxn6d6WZBRVX5JJ8TErM+RGjmKk8vh2E0nRSPftXl0WDEOssVu4C3MYfQPLgdrdoKl19dKWynRdJ4cZ+o0j1G8V0bNxyh9OUrkwzdBvHCG1txfhh9E49VcfIYKQke+eRMvGkRSZ1mSZ0nvjjHzvFAB6VwJrro6WNpj8y3M076xdEEmqTN/z+8idHCa0fbyGzcw/XCK0PYa+cZT4YBZkCa9mM/lX51B0hQP/9kWqlwoY+QROucX8L66z9384w+0/ehdrpkpyZ57R332c23/0Dlo6xtA3jhLrTXP+44jLKSkyfS/so/v0TgLLI7A9Jv/6PAjByPdOocQ1ZF2hNV5m+u8+Xjfg+yQgx+LkH38OJZ5ETaapXv6AWN8Q8cGdj66rTlIjgcKt1nsB/LkS1R+8uuXjfBJQDIXhJ/rZ/dIIyd72HX3jr09z+S9vttn7wZBUGb23/QDjlprY0+WtNnQAkD42EpXFHhA4rYeuZw5QPz+xKnDqBPGeBL2n+ldtC7yAqVcnmH3n0QRNdxH6IZO/GKP/sUF2fW0PsroyaPad6ieWMzoPnNrgrnbYZro6g9BluvgBiVg33Zk9yLKKLCvEjS7iRteq90ZcBYfp4gdMzb+HaZfZO/wScaMrCqrCgFprhmL1OnuHvkxPdj9i5NcplC7QtIoIQpKxHnb0PUk2tQNJknC8FvXWzIY6+gC04QHiJ458QQb/RwC7XkRPdmHXFwl9j8BrEvouWiKL14oCGqdexG1V6d7zOKqRQNHjxLsGtySm2ipO0rPvSXac/gZWpUDg2VSnr+KZNULfoXjzXXY89puMful7WJVIAVxL5jBLUxRvvAsipDF3B2t0juFTv05j/g6qkSSRH8Z3Vtu2uKUixR/9LdnTT2MM7QBJxpmZpPb+O9iFjZeD2kFSZGRNZeGXN7CLTRCC+ECW3LFhxv7bWexig12//yT5x3cSuj5KQmfsj99DCNj3b84sZXlUZn96laHfOELtSoHEUJb4cK5t4CTrKsld3bTGS8y/ehPhh/hNh+yxQVK7e5j+4UUC22PPv3iazIE+WuMllLhG/foc9WvzSLJEYHk0x0vkjg9hFWqk9nRjFWo4xSZ2oc70319k9PdWOEGxgTS9z+xm5r9fonalgKyr+KbL4NcPIykyMz+8iJo2GP29Jyi9P4E5tXnZnq1CMRLIRpzFt39G7/ORrVhgNlESqY6O01HglD75OJJhUHvnjX9UJrvDp/t57n97AkmSMBctAm9txHzvhNwJJEVGyyXavubMVvCb7ctmkiJjDObQezOIUGBPlfDKES/GGM5j9GchFNiFCu786hWb3ptBTcexJotRxmu0ByVh4C7UseeqEIRo+RTxnd0ElotsRMJ3m0X+QDdGenW2yS7bjP30Nr716H0GvZbH2E9vMfTMDmK5Fd5FLBcjtzdPY3rjnCQll0Pr68WdmiK0ot9GHx4mcfwYkqZhXrqMMz7e8fNh2ovcmPoJuwaepTu7D0NLr5EaCEOfll1kdvE8s4vnCUKPYvUallOhN3cAQ8tgOmWK1esEoUtXepTe3EH688foyR3ADxwEAlUxljv4QhFSqt2kYW7QfkJVSJw6ipxqf89+gV8tuK0q1ekrNBfGCXwHEfpUp69ipLuxG9HE7VkNZi+8TH7XKRL5YVyzxtzFV0j17cZplICowceqFHD12KouOc9uUJu9TuitXryYlVlmz/+U3MhR4l0Dy917d9FanGTy7N/RNXocI90dZVlb1WV5BACnUWLq/R/SvefxiNBeX2T24s/pGjm6itROEGCN3cKZmVrm6wnfJ3SdbZnnrLka1lw9al4C9O4EgeNjFWoEloc5XSUxnI18NxcauBULEYS4FZP4YBa/5eI37KWSnk3QnUTW2nMXA8tj8Z07DH79MKO/+/hShmsGI58kfbCfHbKECMLlaglAYHs0bxUJnZWxuHpxlv4v72fx3THSe3tZfG98+fzvh55NIAKBOV0hdIOopChBYjhH9uggWjbK9HtVE2mT8+R2QYQBiBAtm0dSNbRUlvjwbvxmZ44nnek47RwldOx/VEETwO6XRvDtgA/+n4vMX1xcduy+F/4m+TmSJKHE24sYOgs1ArN9NkQ2NFJHRtC6U2i5BEHLpfCX76AkDfq/8wReuRl1VenKSuAkQMun6P/uk1jjCzjzVbJP7iV1aBi/aaE9l6L44/O4xTr9v/0ksqoQmA7xnd1U37u1qe8HkNubX8O/bEzVqN7u3ONws6hcL9GcbawKnGRVJru7i6nXJh6w52okjh4h9cRpSn/117gzs6j5PF3f/E2M0Z0QhsT27Kb4p3+OX+xcnsK0S1yf/Cm59FVyqR0kYr1oagwJCcdr0jALlOt3aJoLy/V6IUIaZmGN2S/AdPEDMokhDD2DpsaXNWHuQghByyoyOf8e3jrCevdDSaeIHTnw6ZfohECEAuH7y/8Il+QqltTvv8CDEbYe/pv7doPxt7+/atvCtTfXvM8sz0QSA/egVVoJYkTgUbyfj0QUAN1540/WfrAIqc1cozZzrf2JCYFVLWBVH0SAFti1eWbO/XjV1kahTWVAiGhuewTSdiIQq2gcge0jazKyFnGQlLhKYPsEloeaNpAUCRGAElNxq9ZdsfVltwZgfT67ENQuF2hNlMmf3snwt4/jlE1CL6B2ucDt//QWXtNZfq+RT4JgjTSLOV1BCEHuxDBqyqB+bf2FVegHSKocdcUvnwcEjk/xzdtM/+2FFcP1T5GrDFF2yZy6Q+7EU8QHRuj/ynfwm3XKH73R0XE6I4c7zra5wG8WkqwsdUms/wNE71kJZCRFjQQhN0B6bodkX5zF62XGXn0EZSUpIta1g1ezCN32GZnAdqmfG0OO6+i9Gfq+9ThyTEPNxFGTBqWfX8RZqBNaK7+X1pVk4LefwhxfoPzqFdR0nNzT+6i+dwt7pkzPrx0ne3o3rVtzaF0pZv6/1xB+QHxX75ZU2VPD6TXbytdLhG0yd48KvhNQuVGi50jv8jZJkUkOJjs6jj40RGhZeKUyyDKJo0fQ+vso/eVfE5oWXb/1LRKHD1HfROAEEIQOpdpNyvU7qEpkuAwSYehFgpUdMNnL9TFuTL/MroFnSRj5JZXxqOXYD2wa5hxjhdfbBl3rwdi9EyW79vfcDgghIBQIx4nGGtslaLbwF8sE1TpBvUHQaCEsK3INcN3lYGlF00qAWNLVOnEUb3oGbz7qKJUMg9STj4Oi0Hz3/U99LPu0EZrWw9/0BbYdVqGGW7Xo+/J+nGKT9L5eZn54idD1Gf72cXq+tBtJU5A0ldrVAvGBjXPFZEMld3SQMAgJHZ/AjuaP5u1Fup8apffZPbQmK2iZGJUL6+vNeQ0bc7rK4FcPUfpgksDyQJYwupPEBzOoSZ3Ezi68qrVcxhv4tUNULkwvlf4WKL8/wfC3j9P91Che3UY2VCrnHi0142EQgU/j5iWswiRKPBF1hzbrBGZn0jwdyxGkTzyGmu/GL5c6+qDtQnJgN3ZlDt9q/0UlWSG14wCNyZVWe9VIoCYyWG06yTYCu+psivi9IQgQXvtsVWi766dHe9L0/sYpJFlGUqNynyRLWBNFaufG6fv2abxyk9KrV7DGFpAUmfSJnQQth+LLFxBegBzTiI/2IryA1JElvst8DSVhEDoefsMmtFy8qslWSnX3ZnnuojXXfHTXtA1CP6Q1v5ogLckSRptzexCUZILQNBG2jZxOET96GPvOONb1G4S2TbbZROvt2fL5ChHg+Vub2IQImC9fpt6cJp/ZSzLegyyreL5Fo1Wg0pzYcKYJAEnC2DuKHOvsmj38PAVhs4U3M4c7PYc7U8ArzOMvlKLJ/V6pkgdA2zGEmu8itG282TmE66Jks4hA4E7PEC6UaAYhsf178YtlhG2j9vWi9fcSmhbOxBSsY68hSwq9ib0YahRo+6FLyRzHCTrvotwMFEmnO7GTUIQsmm1E/L7AZxJWoRZxYu+5d72qxdTfnKf3ub1kDw9Q+OlV6jfmEX7I7I8v0/30LggFU39zDrdiUfl4Bq9mUbtcwClGci3+eo4SQqDEdboO9yMCQeEnV2hNlBFewOT3P6L3ub30PpePeEZCENheVIa7j7AtvJDKuWm0dIzyB1FGXtYUuh4bIbW7G2u2xuDXD1N+f4LqpVkmvv8hfc/vo+/5fTilFo2bC9SvzyOpMt1PjiKpCvXr8596sUo2YsR6hzCn7+DVooqHEksQGxjBnpt6yN4r6CzjZLYQYUDfd/4p9swkQaMJ96hzho5N8+KFtt4+ib6dxHt2oKVymPPjONUFkoN7APCtBo3pm6SG9xPL9eG1qtTGLxPvHiLRPxqlH8cvoehxuo+ewaku0Ji+QauwegCRVI30jgP0HHkGI52nNn4JEQSkR48QWE2sxWlSw/sxcn3R+yWojV9BS6RJ9O1EhD61sUtrgrKpdwsc+e19DBzvYeFqOUpr3ncDRMa3nVzNlf3CdVo0xd0VdBsk9w2gd6eZ/qNXUbMJYjuXJmsB5deu0Lg4Sf6Fw/R89RhT/+8rCKB5dQav3KT7y0co/NV7CC/Aniwx//cfYo0tgCwhQkHmxE5kTUE2VBACObY102RZX5tR80xvOzjvG0co8O377ksJ1HVkINaDCIIlI1AJracHfXCIyvs/JLSjErbwffhM6RoJLLfKzOKHS39H2iWbgZyIo/b1rE7Jb/ashIAwJKjUMM9fxrp8A39uAb9ag0123UiSBGFI4sQxms0Wsq4T2A5KNk08fYDWB+ejr3632mEYJE4dx5tfIHZofxRwzbTPvkkopI0+srEBkno3EhKWV/vEAiddiTGaO00Qug8InCTS2WFAolFbfxJIJHvp6tlPtXSHVnOD3LYvsCk0by/SvL2WxG3N1pj8/kdrttevzVO/trqBZ+G1qLRYLEbzkjm9vmZg6AYU37pN8a21bhHmdJWJP/9wzfbZf7jU9lit8RJj4ysJktDxmXu5vfafWzaZ/ruP12yvXS5Qu7x5cejthppIkzl0EnN65RlSUxkyh049usApcfAIem8/sm6Q2HNgjRR+0KjTvHKp7aot3ruD0HMIfYfQc5A1AyPbQ+naWXyriZbMkN5xgMbUDVLDe7HL8wSOiV0ukBk5hJHpobUwgVsv0Zi+gbW4Ns0ofB+rOIXbOET19gUC14raShsljExUotGSWUQYoMaS+FaD1OAejK4+7FKBWNcA8Z4dNKauc+/k0pht4bY8Xvjfn2LijRlqUw1CP1wVPVfH6yxc3kQWLghxqybtqLayriIpctusk990kFSZ1LERYoNdqJmIvxIbzpPY009ou1F3Q91e+ZxindLPLzLwvafo+dpxyq9eoXlthvwLh2n1Z5FjOs2r09izFYQfkH/xCKHjoXent9TZ1679VFakLUzhm4AUfeb96JQL483Pkzh6lMSxoyROHMdbLGJPTkVRrqogxWKI0qeTjd0YtmAenU6iZFLbYqYdmhbW+cs0Xn0Hb25hy1IkkqFj7NqJEAK1J4+kaQg/wJ0pRFyOPbvWqJvLyQT6cCTTQRg+0HohEC5jlbPIksLu/JMMpo6s+95PC5IkYRhZ9Fg0xrluC9epYRhZNCOF71nYVgXbqhIGHpoeZc80PYWuL+n72DV8r4Ms5Bf4Ap8TaJku4sOjaJkcyV0Hoo2SRKx/R8c0no4Cp/r779L8+Ny6r4sgQLjtB8DAsVDjKcyFSczFGYx0Hs9s4LVqCN9DTmZRY0nUWAJzYQohQjI7D+M7FkosiayqCN8jDHwCxyT02vETBIHrEIb+KjuRqDtihUwbWBFxOnAt1HgaNZZEiSVxakXcRoX7J5dT/+IIO58dQlZk8ntzbb/fxb+4vqnAKfQDnEL7FYSajnz6RBsl7taNWeSYRmy4i+b1WazpUkQkF1GGR+/txilUqX0U6Qw1r0wjgKDlsPDj86QODyNCweLPPyZzchfGQA6/YRE6Hl65ydzffUDmxCih6TL/N2expjYfDHjm2oyanjE+UU9JSZaiz7wXgo67+sxLV0gcP0bvH/5zhONQ/ocf4S8FSlpvH7Jh4Jc+OdL7JwkllUROdsYJa4eg3qD+s9dpvP4ewtoc7/B+SLqO0t2FN18ktKyIBylB7MA+APziIpKuEztyAH1okFilgn3rDs7YBMKLyOXBQ363QLgEYmPWF58WZEUlnoi6zFRFZ75wnt7Bk7hOg1g8z9zM+7hOY9nuQ5IU8j0HiCd7cO06AsHc9AcdiaF+gS/weYCSTBMbGEHL5kkfOLG0VeA36tSvrh/XtENHgVPQbBA0O7OTgIicLWsGWiIKUiRkPLNO4JjLtV+nXqYxfQMtlSVwbQLHRIQhWjKDb7fw7WgVZJdmyO45iWIk1pTqIGo39M0Gvae+QvXmh0iKSmb0CGo8Raq+HxEEhL5D4KiEnoPrebiNMvH8IIpmELY55vv/8QIf/+mD7Ulaxc3xUYQbYI4XEaFYow9l9GUiJdY29ezQ9qidvcX9TZSh5bL4s4trz+/GSrrUW2xQeWOlW6Xy1vU177cnFrEn2ilOdw5rce21yezMIsnSJ0YUlDWFzM7VtjYiCLEWO1tdu3NzLP7Z99GHh/CrVZyJyWV/REmSaH50DvPq5q1sPsuQ43HkxNb4TUGzRe3Hv6T5xtl1F1mbQdhs0XzjXVAUnDvjBJUqzVp9mY8V1OqIIMC6dBX72k1C00LYDq33z6GkU1HJ3N2+gEhCRlPiqHJU5vZDDy+w2pL7ZUlFk2NL/oNLnluhzUayg7KkYCgpQhHghTZhGNCoTVItjzEwfJpEspd0doRGbQpF0VDVGK6zegwXQtCsF2jUphgYehxVNfDuzTpJMmo6jaTd2/0rCJrNqBPtfigKSiKJlu8mNjSC1tOLms4ix2JIshy1+tsWXq2KW5zHLczgVSsElrntXqNyPI6SSLK5VZrAr9e2Rbdwy1AUlFgcJZlC7+lD6+1DW/KJlXU98tMTYWR877qEZhOvVsWvVnAXiwStBuFS08W99JrthByLo6TS6L39GANDaF15lGQKSdeRgNB1CVpNvGoZZ24Wd2GeoFmPaA6fAOzCJEGrgVctUb3w7vL2MAhgg2bFd9FR4KTmughME7GePLmioqZS0erNXPGTU4w4im5glWZRjCSyHsMp3MGuzK9kggKPys2PIlE9ASL0KV19d0XJdemL1cYvU5+8ttrv6B6IwKd44TWQpIiPIsH8Rz9fOsbdfQQrD1J0js2ZW0v7r72ApRub96F7GEQQYt5ZwKu00LtXi3DFR7pR0zG80ibNeD8jaEzXV19yoOtAN3pK79grbrOI5WN07c+v2hYGgvpkZ/odhCHu7CxuobCGrOwWCrhzc9sw+EvoWpKYlkFRDJR7zHg7RRC6VBoTbEdRVNLULfvSmeev0Hzz/W0NmoBIQfq+jJFwPcL66iDBn1/d7Ri2zA215XcCVdYZSB1iMH2YhNaFBLS8CrP1y8y3buDf49llKCmGMkfpT+4npqYJCWg4C8zUL7Fojj1QlFSRNAbTRxjNPU6hcZWp+oUld3iASJLBdRq06rNY5iKtwMe2q8STPSRS/ehGGsssRWKriW5kWcH37TXms0oySe+3/ymJPfuWt4lQsPjTv6f+wXvce2+puTzJQ8dIHzuJ3j+IrKrLnMDVWGqtDwWh52FPT9C8fAHz1nX82vaNt5nHniL/4lc3dd+KIKTwF/8V63Znpt3bCUlV0XsHiO/ZS2LvAYzBHchGFIAiSeuUzZf4tmKp29T38SplnPlZrMlxnNkZvOJ8VInZBiiJJLHRPaQOHSW+9wBKIrn++S1JKogwJGg1MO/cpHX1EtbEGKH16EvEXr1K5fzbyEYcLZmJStrV0hra0cPQ0Y9JAr0AACAASURBVN2UefJLWGO3cecKSJpGYLaiAVAI5FiM7DPPE9u5CxEENM5/SOvSBQB8q0Vj6jpqPI1vNrAr84igTRQvwlV8mLZfRoiHeget2k+wzvtXTyQb8SOSFKmtanj0mWLT2RN7tkLzRoGuL+1bpY+j96RJjPZgTZQ+19pZlRulyAvqnsk/NZii//QgEz/fumXJQyHB8DMjxPKrdYwCN6B8Y5MlyHa/x706K5uAJMkkY73054+SSQ4R07KRUOUWAifLKfPB9T8i3IbykqQqSOr6psEPQ1Bv0nrnw19pGQAJmaH0Ufbkn6FkjjNdvwhCkE/sZH/3cyiyxnT9Y0IRoMoGu7ueoj91gLnmNWp2AUXW6U/u50DPi4hFQbG1luQLUdA0nDnOaO40hcYVJmvn8EOHZn0GkPA9m1LxKrZVpjh/kVi8K7oHRAhCUK9MRJMqkbBq4Du4ToNq+c5a6x1JQlZVZH11qVvv7o2oBJ6HpKok9hwg98wLxEZ3I6nqQ7S+pOiWlkFRVZL7DxHfuRtr/BaVt17DGr+9LWOepChIuo6sdt7gIoLg09MrkyT0nj7Sjz1Jcv9h9N6+NRy9B+y8PFxIKKBqGIPD6ANDpI49hre4QPEffhBd461Alonv3E32qTMk9uxHTiQffr2W/UIV5FyezGNPkTxwBPPmNWpn38aemXpkGbHonCUSw7vJHHkcSVaQZBm3VqZy7m38xsYD9o4CJy3fTeLAYe6mD4J6lcobr2JPjBHfs5/kwSO0rl1GzebInXked2EObyHKKtnlOeDz2cEhqzK9R/IMPtZHIh9rGzwVzi1w55WNs/LvhVcxKb95g8yxEdT0SilEUmS6nj1A5extQuszkC7eJOoTNVqFJukdK3oketpgzzf3s3i5SKvwaDNq2d1d7PraHtTY6tu9ertCc7az0rM+NPhQnk9Qry9rB20UsqwxkD/KaP8Z4rE8svSwiWdj8AJz+6hksvxAAvXD4Nwex5tb/7rIuoKeiWEvfjKdao8ChppiR/YEVXuWm6U3sf06AGV7ClXWGcmeYtEcx/QqpPRehjJHma5/zFj5vaXynETNLnCk72vszJ6ibE2tcpUPRIAi6YxkTzCUPsp0/QJTtQv4YRSMOvZKBtVsRt1ZlrmIZa6U3e/9W5JkXKeBbVWoVzcuBAugdfciazohkD55mvwLX0XNtffd3AhkwyCx/zBqLk/5ly/TvHYJ2lQAftUhGzGSh46SO/Ni1Iylba2r+S4kSYoyb0JEZdGtnGM8TubUk2SfehYt371p+yVJklBTadInHscY2kH1rVdpXLqA8B6Nb62aSJE+eIL61XM4pQUkSSK9/xjZQ6covf/qxo/T0adKMhJQ//B9Qscmvnsv2WeewysuoHX34C4Wqb79OgD9v/MHxHftjQKnzzn6j/fw5f/jaeJdMZyGS6I7hrloo8VVYjmDuQtF5i9vgQ8kBJV3b9L9wkFyT+1dNfBkTuwkfXSE2gefX+0Wq2xRODtDaji9/N0kWWLg9BCH/9kxPv5PH+E2Hs2DEu+Jc/xfnyJ/qGfVdQ2DkJk3JnHrnWU/ur75DRJH1++oEkFA4613KP3NDzZ8TEmS6e86wr7hr2Loj0Zccjsg7ipzb2KMFGGIOzVL2Fo/KIr1phh8cS+3/3Rtm/bnBRmjF02OU7EuLQdNAI7fpGRO0hUfIWP0Y3oVcrFBAMrm5FLQBCBouovU7Xl6krtJaXlqzuoF587cY4xkTzJWOctM/eKGPQbbQYiQanlzmQe9uwc5niBx4DDdL/06ajqz5Y5LSZYx+gfp+fq3CJoNrImtjXsiDFcywZ+22v0GIMfi5M68SO6Z51Fi8Yfv0CFEGGCO3cIrbX6+knSDrmdfIvfMC8h6e9eLjo+pKNHv/pvfQTZiVM++te18NwBJ1ZEkGWt2YrlUac6Mkz16+iF7rkZHgZPwPernP6T2biS578xO0/3r30LSDSR1qftrScvGL5dRM5t3x/4sYfdLOwicgNf/z7OIUPDE/3ics//3Beyaw4Fv7kHVFabf3Vo2LWg6FP76fRK7etH7MsuTvJaJM/Bbj2HPlNftvvusw7c8Zt6cYvjZERJ9K+lcRVfY8839AFz9s4uYC+a2iWLKqkxqOM2xf3WKkRdHV3kJCiGo3ChReH+mY/Xy2quv0Tp3fvVGVUXr7SFx5DDO1BT1t9/u6JgxPctI/9PoWmr5/LzAwnIqtKwinm9tenL0fJNwm1LfwvWWyjKdl+uE6xFUaw+1XEgMZdj9OycBmPrxVZLDWbpPDiMpEnNvjmF0J+g6MoAIQ+q3FjFn6ww8tweBwG+4LH40Te5wH4sfThPvT6NnY5QuzK4rJLvd0OQ4IJYzQPfCDx2ECNGVaELUlRhB6BGItdlkL7SQkFGV1WT8bGyAjNGPKuvIkvLJaqHdBzXbRWLvfnLPvIByT9AkluxLgmYDd34Ot7xIaFlAROnQct0Yg0Mo6SyyYbTNUGn5bnLPfRlnvkBob14ItnnxHE5hBiWeiIjisXjU5BBLoMSj/6vpDHpvP5K8+TL0dkCOJ+h6/iWyT55ZN2i6q38Wuk5EArfMiDLjuVFJVY+hJJMRD0rTkDUdFAVJkhBCELRamDevIfzNVTBkwyD3zAtknzqzNmi6y12yTPx6DXe+gN+oEdoOyBJKPIHW1Y3eP4CaTCO1+e2VeILccy8RWCaNi+c7Jm0/DKFrE/oe2WNPYBemkTSN1J5D2Avrq6i3Q2cCmLa9QvYLQyRNR47H0Xt6UBKJVTeeCPwtpfU/S8jsSFO6WeX2LybJ78niOz71mSalm1WacyYv/bsvMXS6jzu/2Fyp7i4aV2aY+bN32PGHz6LlU1FqVZHJPraLwe89ycyfvLWk4v05g4D583PMvjPNnm/uR7nHYkZLauz/7kFye7u49ffXWTg/j1lsbZrLLCkSqaE0A08Mse/bB+jan19jiOmbHnd+dIvqnc4DUftWm9W5JCHJMtaVq+R+49dR0hm8uY1nWrOpEdKJgaXBLaRhzjE+9xbl+p2o9X0LXA+B2LbW8tCyCU0LOd55Z53wfcINaDUFts/Uj64y+t1jxPvS+C2P+p1Feh7fQWpXF4qu4jVsyhdmGXxpP6EXomVj3P7Tj9j1vROoSR0tHSPenyY12oU11/jEgiYAL3QACUVauxJXZA1JkpbJ4V7oIEsqsrR2GI72F6vKdAChCLhVepPe5F5Gsqdw/BbzzRsdWfFsFyRdp/ur30COxZcnwND3sSfu0LjwEebYzahJKAyX9dIkSQJJRonHSR48QuaxpzCGR9qWeuI7d5M8eITGhbWijRuFX6/h12srRGVJWjqHlX+xwWEGf/9fL3XffTqQNI3sE8+QfeIMstH++RK+j7s4jzUxjjVxB6cwE3W5ixVNwWjOUFCWgsHY8Ah6/yB6Tx9qJou7MI81sUleqaKQOnaK7NPPIbcJ7Pxmndb1qzQvX8CenY66EcV9v70soyRSJA8cJn38FLEdo2uI+2omS9fzv4ZXq2JP3NlWfm9gtqheeJfM4cfoOv0cBAGtqVvUr6/tRH8QOgqc7Mlxcs+9iJJKEdo2xo6dIEnkXvgKSiJBYFooqTShbaP19GKNf37LS/dCViQCL0QEgjAQSLK8zJdpFCJxzJ6D+S0HTsILWHzlCnJcY/j3z0Q6TpKErKv0fv04wguY+f67+J/D4Mk3PW784Bq9x/vJ7smtlOwkCcVQGXhiiPzBbhYvF1m8tEDx4gLV2xWcmh1loe66biwdb3mdIkVcsERPgty+PL3H++g93kf+UM8aThNEK7bC2RnGfnq7rTDnQ7EOKVyEIfbYGMLziB/cj32zjZHoOsgmR5CQEUJgOVVuTr9MuT7OJygPuiGEjSZBo4Xa3bWJvdfrAFoNp2zit1wC20fPxMjs68ap2igJHVlTQQIRCIIlB/bIziIyFL1bkimdm6H36Z24NZvWTIddk1tE010kED5pow9VNpYzT4qkkzH6EULQcKPOvrq9gCKrpI0+KtbUclZRV5KkjB7cwKLlrQ7uTbfMXPM6dWeBw72/xr7uM9h+g6rd2Yp5OxAZlEfSvXezTI1zH1B5+zX8antNrLt3tO+51D54F3t6ku6vfZPE3gNrgic5niB54DCtG1e33nF1T+PG/U9V6Difuil0fOdusk8/ixJfG5DczRTVP3yX5uULOPOFdctYd79FYLZw5ws0L51HSSTR+weJDY/glkub5g8Zvf3knnkBNbWaTiCEwCsvRry0Kx+vK9+w/Nu7ZWpn38K8fZ38l79G+sTpVb+9JEnoff3knjrDwnxhm7vtBG5lkerF99HSWUQYInwPNZHEq22cttFR4NS6fgU5FiN19ASSpuHOFaj//GzUfqhrxEZG6fsn/wxJURCBj3V745PHZxnmokW8K4ae1vAtn9AP6TnYxeKNCrIioSW0dbvtOkXoeCz8+GNCy2Po95/B6M0gyRKyodL3zVPEdnQx94MPaN6YI2h9vrqTqrfKXPwv5zj9vz5NvCexJk2rpw0Gnx6m//FB3IaD1/JwqjZm0cSp2fhmdO2RQNZktIRGrCtOoi+BnjHQEjp6SoucuttM0mEQUrq6yMX/cqFjbtOGIMlIqoIcb6cDvz4SsSgQEYSUG2PbJh+w3fArNYJyBTE63DEBWFIVZOPBfIjQDXDK0SDpVi28poPXcon3p/DqNs5ii1hvkuzBPox8gtr1Il7dRu+Ks+d3TuI1Hcy5BqEboMQ03MkKbm17jGwVSUdTjGV9JkmSialp4mqGkBA3MBEixPLrFBqX2ZE5iZ07TbF1GyEEPYld9Cf3UWhew3QrANTdeeabN9mROU4QOFTtWRRZYzB1mLTew1jlLF7QftIwvQq3Sm9ysPcl9nc/z5WFl2l5n57wqnBdamffpvLGKxsvrQmBU5ih9PMfoWZzGH0Dq16WJAljeAStuwdnevIRnPVnA3I8Qe7MC6iZ7JrXhBB4pUVKv/gRretXNxX0BGYLa+wW1uTmO5glVSX79HPoPX1rz29xgcWX/zutG9c6Kq15pUVKr7wckeEPHl0TPCX2HSSx9wDNS+cfcJTOIOsx8k+8QKx/eFW50pqdoPzhGxs+TmccJ8ehfvYd6u8v6SvdF/Vad26TPHIMJZnCunMLd36TvB9NQUnGkQw94lPcM0gH9RZh/ZPtuimcL3Lsdw+QG81Qvl1j/uIiJ//5IdKDSfSkRnZHius/3L7sWmi5FF++iLNQZ/gPzpA+MowkSygxja6n95E6NETlnVtUPxyjdaOAM1/7LM6zayACwfTrk6gxlZP/0xPEu+NrJmBJklB0hXh3gng3sHPtYLIZhEHI4qUiH/1f71G5uXkVdClmtGkLlpB0nfi+vaj5PPbtzgYobYnHIsKARquw3Cb+WYOwbJyJaWLHDiJ1SAqVdA05vWTXss7q3i42mf5JJMw6+4to0VW/tUJilRSJ/md3s/jBFHNvRM9banee5kSZse9H0idqUqfniRFAULu2sG3PxWD6EEPpI6hKjJiaQpVjHOh5ATewCITHlYWf03SLCBEwWT2HEIKB9CFGc48D4Pom0/VLTNbOLXOavMDiVulNRnOn2dX1JJoSByGw/Brj1Q+Yqbf3ELuLmjPHjcXXONL3NQ70vMi14itY/iebYbsL8/Z1qm+9uik+klOYoXHhQ4yvfXPNa1ouj57vwZmZ+lxLsjwIqcPHiI3sWjMWCiHwKiUWf/J3UVCy1XFhCx2KsdG9JPYfWjP2Cdeh8s7rtK5d3tRx/UqJypuvovcNoHf3rnpNNmKkTzxO68aVbdN9UxJJ9K4e5n72N/jNzT8rm1Ozu1evRpKQiLgZoWXS+PBsZ8eSJeSYgdqTxdi/E2P3IGpPDjkeEc5R5FUSNvWX36Pxyw8fSjLdTky+NYvbcKlNNfAtn+v//Q5de7Ic+s7e5dcn357d1s8UYUj9wiRBy2Hwe0/QdWZ/VKoAtGyC3q8fp+uZfTiFKnahij1TwSnW8esWoeNvrgzVAayJRbxK5wFs4AaMvxxNesf+5SmSQynkbTCNXQ+RInRA4f1ZLv7n85SuFrc0mea+9lViu0bXvqCoqNlMRL68fKXjc4TotNqRij9LcK7fJnzxGeR8h4GTLKP19SDFjE3brIhQULm0ejFmzTWYe2MlUA39ALvYpDVdxWts37WsWNPYfmOVFtm9cPwVWQsvtBmvfsBC6zaGkgBJwg1MTLeyhghu+TVuld9itnEFXYkRihDHb2L5tVUNAU5gcqP0+pqSUtWe5dL8T5bKgo+mM/Vh8BsNah++t/kWdyEwb98gOPNllPukPiRFQcv3RFWMNh6on3coqTSJA0facoZCx6F29h3MWzcerbbRQyDpBqmDR6KuyftgT01sOmi6C2dmCmtiDC2XXxWY3e2wNAZ3RFynrWCJ+yVJMqFjI2sakqpxdzIQoegoW7YlGWBFjZHq3okkyZj1eQLPIvA2PigqmSSxI7tJfukYsf0jyIkYkh6Rz9crBSjZVNvt90NOxaP33nMc4fn4pRr4nUXeVtlm/PWZ5Y6v0s0Kr/y7d+janSHwQqpjNeza5gctSVfR80mUuI4c01DiOkrSQOtKonUlkWNaRHC9R85DkiW0bAI1Eyd5YADhh9G/5fbbTZ/OhjD2H16m9Nq1h7+xDQI3YOynt2nNtTj0e0fpPz2IltgerZJ7IYTAnG8x9vJtbv3tdZqFxpavi/DWITmHNub0NM0Pz+HOdhZEu34UgEqwpovqswZ3Zg5nfAo1396z8UHQdw6jZFL4m/WnEyyX8u4isDysezTOQiegcWf7TZZbXrmjUlgofJpukY0olPmhQ915cHY+FD41u73LfN359CRfhBBY47eiUtoWMkJBo467OE88uWfNa2om+ysbOOndvcR37Wk73zmzUzQufLghceZHCS2bI753/xoOmggCGpfOE7S2psMnAh/zxlVSR46jKKsDSCWTIbZjJ/bk2JbuLy3TtSx6qeW66X/pt7CLBcKlcp27OE/j5sYJ4lsKnBK5QZK5YRRVRzWSmLUCZnUDk4YkYewfIfvrXyJx6gBSon1L6lZg7Bkm950XVnlrBQ2T8p/8BHei8xLivW3yIox4T2YbD7bNILm7l5F/8yJqOo6ajqGmYsgxbUPX5G6HiKTLsD2SGhuCpG2tdTf0QuY+mKU2XmX013az5xv7ye3tWiUbsBV4LZfZszPc+YebFN7rXHZgPVRf/ll7kvPdgHUTaFpFenOHkCSFVKxni2f4aCFcD/PsOeJHDz6Us3Q/tME+9OFB/IXPtxL+F1iB8FysibGoe24LCF0Xr1wiPro2cFISiV+ZDu1VkBWMoR2oybXJABGGND4+R2B+2nZbElpP3xpuE4BXq+DMzW6L3pIzPxuV4+7LvMmqht43gKwb7X0RNwgRBMsk8+attRmy0OssO72lwEmSFFy7TiLTh6Jo6/rHrdonppN69iTZb55BG9i84ujD4M2VkOMG+q7B5QBEhCHxk/txZ4odZZ20hIqiKzh1d43OkBpTIs+1mrPpyVnNJciebFP++UcAa9Hkxl9fZer1CfpODrDjhZ107csvkb01ZG397ONdiFDgOz5ey8MuWcx/VGD6jUmqt8s4dWd7s2+PQJStXL/Nzv6nUGSDbGonyVgvLbv48B0/DQiBfXMc68IVEk+c6Oj5lRSF5JnT2NdvEba2Z9Hxq47ePpnf/qdx/tt/NTFbW7uRe3pl/tW/SfLHf9Rifm577uPAbGFPd6Y43g4i8NedGCVN+1yIV3YKSVXbBooAXqkYXddHMN50BEUmPrq77XPuzhe2zVcwNE38WrUtQV7P9yDH4lsKnPxmjcr5zvT1HoQtBU5WfY54ugdFixME87hm5YHvl2I62W+cIfubZ5BTa4nB2wl/oYJ9YxJ95wAsCfZJskzyicM0X/uIoLbxFdL+39hFz6E8H/3nSzTnVpcK8vtynPrDI1z88+sUznVms/EFIoR+SKvQZKxwi/Gf3SbRmyB/qJfsnhzJ/iRGNoaW1FB0BVmRo0qkH+I7Pm7TxS5bNGcaVG6VKd8o4TXdzwVZ/i4a5hzz5asM9pwkFe9l1+Cz3Jz+Oa73aa822yNsNGn88i30ncOo/T0dPcexfbtJnD5B8833P/1J4XMA1xFMTgQE/tZvaE2D4REFXd+ecVcIQWhZuMVtGPfCkHAdArAkK+tyyz7PkDUNva+/7WvO7DR+/dMXPJZkGWNwx5rtQgj8apXA3B6pACHCdbOWaia7bQrl24UtBU6+a1Kc/Ai1cJUw8Aj8B/B8JCnKNP3mGZT0g9u17yVAbiW4si7dJv3i46uUjrWBbrTB3o4Cp/7jPSR74wRtMkrNOZPMjhR9x7q/CJy2ASIQtOZatOZaTL26IjugxpeyT/Jdob2Q0AvwTB/f9LZNcfzTgB/YTM6/S9zIkkuN0p8/hiQpTM2fpWnNE4pg20QstwvO2BSN198h9+2vI3UgiCnFY6RfOoM3V8S5uTXewmcVhgHPPGfw5FM6sgLvv+fy5msOuiHxre/EmZkOePoZnVot5I//c4t8t8KLXzEoLgQ88ZTO1cseP/qhzehuld/7gzieB6/90gEEj53WGN2t8sMfWAQBPPUlnd4+mZ/8g83pJ3Wee9FAkuCVn9lcOOcRj0v81m/H2bNPpTAbsGGf2A3CbzYQ7tZJ+AJ+Je+FB0HN5NqSwkUY4pYWCe3NZ1i2C7IRa5sFuqteLseMbVmkyrEYYh1ythyPrxHJ/LSxpbNJdu2I9ExS3cQzA8zffhtnnayTsX+E7DejTFM7hK5HUI0E9oTlEDou+o5+tP78ps/PnZwnqLeQ4yuu3pKhYRwYwb42vuHj3PWosytrb2SrbCOCkHj+s03q/bwi9EKcmoPTgTjZJ41Mj0ZXr4brhCzOOCSzKukuFVmRqMy7NKs+3YMGyf+fvTcNjitL0/Oec+6Weyb2hQBIEODOKhbJ2quru6v3Zaa7x7OoJTs8I41GCskhRcghOewI//APhx0Oywo5NA4voZCsmfG0NK2e1vR0d3VNT1V1VdfKWlks7hv2Hcg9867n+EeCIEEAJEAAJKuabwQZJDLznnOR9577nu97v/fLGpTzIfMTKzcYVXeWK5OvsmfHl8imeuhoPkRTeiel6gSFyig1d54wctFaoTewUikVUq5NseUhOKWovvUBVlsryWceQ9rrE/cLIbC6Osh96yvkf/BT/KufPn8epWFhXvHC8y7NzZLv/FacC+dCajXNZ59z+PB9n5/8qI5hCHwfMlnBN349xn/8DzX+4s/rBAGEIYwOh7zwU5ff/bspLAvqQL2ueeIpm7ff8JifU3zuCw4ffxSw/6DFl78W48Wfu5gG/M3/IsH0VIWjxy32HTD5wZ/VefpZh1R6a6URUfne2B98GmBmMqtYm4DyPcJi/r4gko1ozyr3tpRkH3+a9JFjWzPQDWaqK16yrPtO47a5qjorjhVLYTpJ6uUZDDsOqxAnmUmS+coTDU3TzV4VYYh7aYza++fxLo0RTM41okFK0fr738L68uN3PD9Vc/GuTiwjX8Iycfq7GzYH6yzZ15rFthpiRWRDSACxrWnHB7h/EUtInvh6M4GnyLRYnDtRYufBJKkmk3opwnMVp98o8vjXmsnPBDR32vz1n0xTr1zfXVlmgp0dTxGPNWOaDQIuhUHMzhKzs7Q3HUDrRusUpcMNuRzXvAXePfdvUerOelPdCqpSpfizl5GJOInjD617VyikxBnsp/m3f438D5/Hu3wfaDm2EFEItarm+GONaFDfTpN4QlCraaIQXnvF4+zp5ZVSxYLilZc8Cvnr363nweysIgyu/2xyIqJW0/TtMrHtiOZmydkzAY8/4bD/oInvOwSBpn/AZO9+kyNHbd57x+fUyYBaVXP02BZWr2pNVH+gVbtTGMnUqoRAB8GmK9W2Co05riR311zj1yI7Wwkhjfvu+bop4uTXi2Ta+ilMncdyUquH2oQgtrevUT0nlzdaVeVaw5fp1Q8aNgFb7D2kvQB/dBqePHx9OlJiNmcwsimihdItPn0dhZESXY+00TzYxNz5hWV9P9oPt2LFTcoTd36hV85OcOaffe+OP38vUB/d+pLvTyIyrRaWI3njR3PsezRNe18MFWkuvFchP+XxyOeb6NmToP+hFPqjCnZcEk8Zy4iTbSbZ2fkM8hZNRoUQCGEiN3jLmqGzreqQKF8k/8PnQesGebLWGXmSAnt3Hy2/99uUX36T2rsniUrlu+rPtl3YucvgD/5hkl+86DE6GnL8cXtJ26yUplhceY6eB9XK7c+9VNScPxuw/4BFJiOYn1fMzShsB8ZGIl7/pYfvwS9e9BgZDvncFxyuSYc8T7PVFf2fRouAuwXpxBBiFeIUhUSbEEJvJaTjbFsB13ohbvj7zg8ikObaOimtog3ZPmxOHF6eYfzsi6goXNMRWMRsko8dQCaXp7JUpU7+h7+g/NJ76HU0/7wT6DAinM2jlVr25ctkHDOXXjdxGnl9goEv9nL89w9x9j9dpjxZRUhBtjfF4d/eiwoV4+/coUs6EJbqlD769KUsfhVQzodEgebgk1naeh2GTldp3eE0ehvqxgZhftJj+GyVyasubjWiMHvT9S4+2RHLaKFA/s+fR7keyccfaTQBXo+VhpSYrS3kvv1V4of2UnnzPdzzl1HF8m0/ez+jqVkST0g+fD+gq1uSydz0u1iDH62HMmoN778T8Lu/n6S5RXL6VEClohkZijhw0GJyQjE5EZLLSYoFzdhoxOBek9dfFew/aN5c7b1prKeS+gFWhzDNNaxN9Ia9BrcLwvx0VDTa2RZyR55s/EeAMMzG71nQMGAdH6J84aN1H29zPk7ZTtBQK06smY81Mkli+3YuezDoKKL24QXKv3h/20hTYyBNVKyi6h5G8vqKIZMxjNz6jDQBpk/NceYHlzj83X207W+mtuAipCDREkNIyTv/vZotdAAAIABJREFU90cUhtdHwu4WBHJRC/PJ38Hfz6iXI9776wU6dsa48J7P2IUac+MeblUReIqPXi0yO+YRBXly7TZ+Xa0wAQ7DOmOz721L5ZAXVFB3wXU4KhQp/OgFwvkF0l/4DEYus24fMuHYxA/tw97Zg3d1BPfj89RPXyDKFxu7wE9YFGpyImJhLuIf/uMkc3OK/IIiijRag+tq1E1rpVKNaNCNMAz48ldjPPM5m4FBk3/8X6d583WPX/7CY+hqiO9rOjokf/anjRTs++/67Npt8Hf+IEkUaSYnIv7o39Z48QWX3//7Sf6b/z5DpayYnVaftF/npxhr3R/3z7rduIdXzlNrDVF0d5oja73pcSLfxZ0eQ1o2qd37CUoF3JkJhJQk+gY3nHLcpHO4g2HGcKtzoHUj8nTTF273dSBvqqJTVZfyK++j69sv+FV1F1WpLydOjr3MGPN2COshp/79eaZPz9P3VBeZnjRaacbemmTkjUlmzszfS0f8FZAY9Fh7KKkFCtGDSr/txty4z9z49Q2Ae0Pz5fFLDQ3I2MU6YxdX14N4QYWLYz/fnsktaqPuBlSlRvnlNwgmpkl/+bM4u/sQ1vqMXAGMVJL44f3EBvtJf/Ez+CPjuOcv449OoEoVVLWGcr37QjR7K0xPKf73/61CKtXQNQnR0DBFEfzz/7nMwsLyxeLqlZB/8b+UuaHnKFEEJ97yOXM6QIgqSkG5rAiCxun/4b+sYFkwN9s4VqWi+f6/r9PU5GGYUK9q3LpmeCjiX/7zComEoFrVCKBYvI8Wq19hNKQtq1zLQrLl5Y93CBWGq95v2vMovvvmlnh4rQfBwtzt33QLRLUKpXMfYmWbiXXsYO7tl5bMRb2FWVK792/oeJsiTlpFZDv2EM+0o1XEwvgp/PryKgu7pwNhLR/GH5vBH7k7bQJ0EKH9m4SxpoFYZxXQNQT1kPETU4yfuPOU3N2CRlNWBTx1fwg37YxD/1d2L4V8q9MVpk5MELr3jz7CiJlYCRM0hF5EWNt6MfXa0ETR/Vs1uBFoP6B+6hz+6ASpzz5J6pnHMHOZdYf7hRCIeAwZj2G1t5J89Aiq7hJMzRBMzRLOzBHmi0QLBcJCEVWuomr1+4pMKQX5BUV+lQ4tszMrSUvgXydAN2JhQbGwRpeX/MLK99drmnptJUmen1M8UCTef9CBv7q8xTCQtrPKJ+4+dBCs2idPK4U3OU7l9PrTW/cDtFIY8SSJnt14sxMIwyTZN7DhXoCbIk5uZZ65kQ+W/r9anzqzJYO4qYmrPzSJ9u7Og0mHEfqmfLEw5Aoyt1mkZTMd5k5M4VBSc8wEIyRlhmazi7HgIhLBDmsvU+FVmo1OBJKETAMwEVymrBZwRJwua4C4SFHXFaaCq2g0vfY+Au2TkBnqqsJEcJFAe7QY3bSY3RjCpBjNMhk0mp3utA+SNpoZ8c9Sj8rYIkaPvY8R/yyh9omJJF3WAJPBZQxh0mn2Y4sYZbXAdDhMoLf2IR5viXP0v3psyYNp/uwc+YsLhJP3rnLEjJtk+rJ0Hu+i5WAbsZYEht3Y5akwwit6FK7kmT05Tf7iPG7eRUf3z8P5fkdUKFH62cu45y+Teup4o0VLOnlHQlMZj+H09+Hs6m1UF3o+qlZH1V2076PqHlGpjCpXiKo1VM1dfL2O9oPGn2DxT3RTa5xf4a9Uue4dN1x+gM0jqlZX1YgJ08JIJFf5xN1HVKusWvQlTAMZ32LB3BbDkjECtfz6jmplimfeJ73nMNmDx0ArvIVZCqfe2dCxN2lH4BBPtyGEREgDv1YgCpc/dGUyfq1mfwlhvoSO7pL4LVIriBNSriBzm4EtYuywBslH01RViZ32QQLtUYhmaBc76TR3YQoTRYirqiSsDI5IcMU/Sc5op8fexwX3HTqtfkAzHJyh3eylx97LVHCVdrOXUf8Co/45dtmHyBkdlKN5Oq1+poKr1HQZiUTT8PiZCC4xIB/BFo1dS6A9bOGQM9qYC8dJyRyOaFz03dYAVVVkKhyiz95PpCOmwisb8gq6LYRALrp+AyQ7U5jxrW/qu15k+3MMfmsffc/twmmKYVhGQzB4rTXPYpPk7id7iH7zAPlLeS7/6Dxjr4/ilz4dkaG7Ae0HeOcv4w+PEdu7m+TjR3H29mNk16d/WoFFEf21iFRjEH3dPHGxX2Dj+9PXtVFaL3KlxZ8vvqY30V/w04Dyq29R+ulL93oav7IIK6s/B6VjY2Y33kR7OxCWSyufnzTInZnOrFkUdq8hkOxufpILc6+iuU5OdRRRuXoed3YCw4mDBhUGKHdjDuibIk4qCgm8CkIapFt2YjpJAm95RYywzBXaMlXz7p5vixQrvTKU2lLiZosYzWY3prBp0gFJmSUtm5gLx5gILrE/9jiecrnkfYCiMW4hmqGqioQ6oMXcQUymaDF3LEaEUjgygUBginHqqspCNEVVFaiqIo6IUyDE0zXarF5mghEK0cwS2Ql1gLpB16LR5MMZckYHC+EUaaOFoppDIGgxd+BESTKylaTMERkh06GBZvvSaLHmeCMtdg/QtLeZo//wMboe37EUAbsZjcbJYNgGhm3Q/kgHTYNNtBxq48z/d4rqPYyUfRKhXY/6R2dxL14ltqef+COHSDx8AJlObb6aUCxK6sX1+/yTXwN0dyA34Pj+AFuPsJBvpOtugpAGVnMrwnHQ3r3dqEXVKmGljNW03IhaSImZbUI6zl1xODeERXtqD2mnlapfYLpygUh5ZGKdtCb6kcKk4I4zXxsmZqbpyT5Me2oPUpqEymdo4QSBchGGSXrvQ6QGDiANcyniXJsYYuHdV9c9n009vaLQxa3MAYJYsnUNT4pVQpGGXNQ8bD9TlY6FjC33b9BBhNrSVKHA13Umgyv42mU8uIiv62g0SkcIrhl4XT/fayTnWuWbQKC1YiYYoaoaOrFQB0ghUYQoHS69XyAItM+Qf5qc0U6ntYsms4Or3imiNQhPSc3TYnaRkk0kZJpJ7zICSaA9poKreLoGAfjaRW0jaYJGG5V4a2KxFHTxhwLMuIUZW4xMCYGOFJEXEdSDLUmTZfqyPPIPHqXzse41SdNqEEJgpx0Gv7WPWHOcD/6Pd6iM388l84KYncEy4ygVUXXnuB9yUtp18S4PE5UrGMkE8YcP3Dci2Ad4gLsN5boEC/NYTS3LXxACp2sHZjpD4N3jZt9RhDc1Trx3ZRN6u70TM5PFvwvEqSO1l4TdzGz1Ck3xXvqyjzBceA+tFUV3EhB0Zw7iRzUq3hxz1StknE7GiidRKiJUDYJqJtOkBg5QPP0eQfG6WbfyN3YOmyJOTqKZpq4DIAQq8AjclSX52vUaIXPj+oNKJmINF+67kK2TyThGZnm+WLkeqrI1zQmhkQrzdB0pDGpRCVvECHWIgUWPvY/ZcBRTWLRbfYz65wHIGq1MB0NkjBa0VriqSlnlcWSCuXAcKSSiEXNadUwDEwOThXCSQLvssg9jCJNIr056fFXHVTW6rd24qoqrq1jCoa4qWNImH0xjCXtN4rWVEEKQ7EwhpEBaBi37W2k50EqmL0u8LYGVtBBSEroh7kKd0nCR/KV55k7N3HHrFSNmMvjtfXQ/0bMh0rTsGLZBz7N9BGWf9//wBH55G600NgFDmgzseI7OloepufO8feb/2Rbn8HVDCqyONpyBXTh7d+MM7sJszt1zY70HeIB7CR2G1EeGSAzsXfGa096J07mDYH7unqbCtIpwr15GP/rUiuiw3daB3d7VaPK8zXNsT+3Bj2pkY904RpK4lWWifBovqhG3cjhmEsdMYRsJIh1QC4pEyqfiz6NvFH4LgXJd6hMjKO/Oi6c2RZy86jyl2UvY8Sy10jSBv7JxblgoN9JyN2iKzPYmME0Itpk5GRKrs2Wl+WbVJSpsXbrF13Um/Eu0Wb20m72EOmDUP0dcphFIpoIrSAx22odIyUbuWgrJYOwoBiaT4VVCfCaDy+yw9rIv9iiKiJlglJoqEWh/KU8b6QCFwhI2XdZuYrLhRzUbjhHqgLRsptPaTcZoJSaTxGWGyeAyvnYpqwX6rP1c9T8GINA+E8ElOq1+mo1OIh0yFlygorefEGT6snQ91k3/1wZpPdxOsiOJtFaPPqhI4S64LFyY4/KPLzL1zjhBZWNEILc7R98X+leQJq01KlSE9RAVKlC6QehsAzNuIuRyc0rDMuj53E5mP57hyk8v3qeCcYEhHaQwGvrDezWLmIO9o4vE8Ydw9vRjdTTSD59ks88HeICtgo5C3NEhlOchneVVdMI0SR1+hNrlC6j61m3yNwyt8aYnCRbmsFvalr0kLYvUgYfuwhwFUhjUgwI1P0/Nz+MvVoz3Nz1GPShSC4qLUaVbry3K90BA61NfpD45il70AAkrJdzpsXXPaFPEKZZuI9M2SOhXaek5wtzI+3jV5fWz4dQCOoyWVbE5u7qQcYdom32cZDxG/NDuFa1eomKFYGb1ZsR3Ao1mIZqkGM0hRcN4MtQ+blSjEM0S0fhyLnkfLEV0FsIpZsJRWHwvQFUVueJ9iBQGGk2kQzSKC+57hIvHmAguLyb3Ikb8c0vjXSNUVVVgyP+Y4cU8mEYR6MZn58JxCtEMoQ6WZl6IZqlEhRvmfXciEz2f3UnvZ3fiNMVWkJObIQ1Joi1BvKWX1oNtDL1wmbP/4fT6tUYCep7duaIRs9Yad77O6CvDTLw1RmmsROSG2Cmb3EAT3U/30nm8i3jLch8yJ+uw59v7mH5vgsomWu1sG4TANO5dObOwLezebpJPHif+yEGMZAKM+6/f1AM8wL2GPzuNOzZMYveeFZYdif4BUvsPU/rw3Q2Xy28lgkKe2sVzWE0tK6LEiYE9JHbvoXL65DbOQFP15wmVz3xtuFEggsA0YsStHGPFj1A6xBDXC46UDkEsVtZF7nWB+KKY3YgnSfYNck3C4E6N3T3iZMfSVAsTFKfP07rzUQxrZXmiNzTZ8FGKX1/IzY4WnN07qK2z5ckdz6+vg9j+m3KzkSKYmEWVt5YhazQh/jIpiSYCrkfVQvylVxSKQK/Mq4YEcBN5uf45lqXSbh4PQKFQqxz32qgrrQZWzvtuING68eaQQgpiTXH2/OYBrJTNh//Xe9Tnbv892mmbtofakfbyiFZYD/n4353k4l+cR/nXv6cqkL+4wOirI+z8Yj+Hf/cI6R2Zpc2MEILmA620HemkMnFpw+ex3RAIDGPtvkzbCSOXJfXs4ySfPIbZ2vyALD3AA9wCYalI7eI5Yr27kPbye1bGE+SeehZ/bgZ3dOjeTBDQvkf17GmS+w9j5ZqWvdaY42fxZ6fxZ7bP43CyfJbe7FFSrW1oIuZrwxTcCdygyK6mR/GjOjc6rgdRnaq/wJ7Wz1IPCowUPiBUHlGtwtSLf4Gda0HaToMsCclGH4CbEhl4tQKpph10Dn4GO5Yh9FbuvsO5Av7I8l+ojNukP39shaP4VkKmE2S/+gQydZNruetR/+jSPc0bD/tnmA1H79n4n3QYpsHOL+3m0H/5MHbm9gQh1Z0h1hJf8RCf+XCK4RevLiNNNyKsBVz92SU+/D/fpTa7PA0tDcnOL/QjrftTp3PXiZNpEju4l5a//TtkvvI5rLaWB6TpVwyOA005saKI+QFuAaUof/wh3tT4irYiQgjsji7avvkd4rv3ILaikEIajbTgBu/N+vAVquc+XtHUWUhJrKePtq9/G6e7Z2v62kmJkUrjdPcgFslkyZvh0vxrjBY/ZKx4kkJ9nCByubzwJkP59xgtnuTU9PPk643nqkZzdeEEw/l3mS5fIFrUeArTInfkCdo//01anvwCCEGit5/soeMbm+Jmzq9emmFm6F3Kc1eZHTqBX18ZQVI1j+q755aX/wtB/NBusl9/ChHb+gVeJmPkfv1Z4kf2NOwIboA/OkP9/N2xiV8Lnq4vpeceYDmuaY5UeIvQtADDMRj49b10P9mz4ju+GamuFLHcytLr6fcn8cu3TherQDHyiyGG/vrKitdyg01kerO3/Py9gBASU95F4mSZpJ4+Tsvv/Q6x/YMbduV/gE8HBgdN/tk/TfNbvxmnv9/AvDeOI584hMUChbdeQ9VXipWFlDjdvXR852+QfeIzWM2tGycnQmCk0sT7B2h65nPknnwWI7GxoIWOQgpvv4Y7sTKdJQyD+MBe2r/zO6QfPoaRXH8f2BthJFPE+vrJPfEZOn7zb9H13d/Dyl6LcGm8qELFn6Xizy8aW+pGFZ0/Sz0o4IXlpeo5gEDVqfhzVIOFpVSdmUgR7+hh9pc/a+ibhEC5dWLtOzY0101d2smmHaA11cI4mfZBZDWPV73J3D+KqJ+5ij80hb27u5GfFAIRd8h86XF0GFH6+QlUZQvaJkiBkUuT/cbTpJ87jowt13koP6DyxkeNsT5NEI2LVxgSYYjGjSXE0v2lNUvGf1ppdKTQ4f1h/qdChbtQZ/bjGfLn56lMVYgWW7GYcZNkV5rWg620HGrHyTpLeighBFbcYvBb+5h8e/yW1XZW0sJwll/qURBRGS+jgttrB3SkGf75FXZ9ZWBZitFOOTQfaKVw5dZ6OdOIE3eyaMD1CoTRzanUrdUk2WYKIe5Omb9wbFLPPEbma881Wqs8wK8sRkcjXvgrj4MHTP7O7yXJFxRvvOlz7mxApaJZxUdx+yFuWA+59m+WzFRBIJ3YmtFRaTvIWOy6ieo1A1VuMFpdNMzdjN6hdvEspQ/fIfvY0wjTXDYfIQRWUzMtX/waqUMPUx+6Qn34Cv7MVMNDSV9LUV07N4mMxbByzVitbcQ6d2C1tmHlmjGzOWqXL1D6YGNO2QDBwjz5X76E8bVfb/hM3TRHp3MHbd/4Du7YCLUrF3FHhgjy8w2CsvSsuf59GPE4ZlMzVlMrTkcXdls7ZrapkQ40jFWJ5KYhBFop1CJpEoaBmcqsiKTdDpsiTpadXMwPTuAkmgj91fUmwcQs5Vfep7mrBRG/HiY0Mklyv/YZ7J1dVF79AG9oolHttor3063PwsBsyRIb7CH9+eM4e3uRzk3eTUpRP3WZ+ocXNn78+xGGxMrGsVvSOO0ZYr3NxLpyWE1JzGwCw7EQi1VqOohQbkBQqhMUqnjTReqjC/gzJfz5CkGhit7uCseboLXGL/uMvjLMpb84R2m4SOiFDSJzwz0mLQMrbpLdlePA3zxM91M9y0hQbrCZjuNdjLw0tOZYRsxckVILKv6G7ATKYyWm351g11cHlhYMM2HSNNDUsNZYo+V8wmlhsOdLZFM9CAT5ygiXRn9O3b9OtuJOjkP9v7FlqS0pLGxz+9Lg1yAsi9TTj5L9+heQmTvbZT7ApwelkuaVVz3eecenr8/gmWds/t7fTZLPK179pccrr3gUinfDu8/Bbu/CSCQRjo20Y0jHaZAgx1n2b2E7GMkUMrZK+xApafnyN8lWnm209vE8lO+hfW/p39d/5qN8D39ulrCwRoPBW0C5LoXXX0E6MdIPH0VYKyPG0naI9e7C6e4h++iTKK+h2YnqNQgjMAykbSOdODIeQxgWwjSRltXolrHZ9UUpqhfPImMxWp77Klbzcv8pIQRGIkliz37iu3ajfB/lukSVMsprbBaFZSEsG+k4GE4MTBNhbOEcb4OoXiMo5Wl65GmcpnZaHv0cVraZ4pn3N3ScTREn3y3TvOMQ8Uw7hhmjNHt5jdkqKq+dJLZ/F8mnDi8rGJSJGKknDhE/vBvv0hje5TH88Vmi+SIyvfJiFpaJkUkiYw5GNoXZ3oTd24GztxenfwfSWZkm0FoTzZco/vQNwtnCHZ2rGTPI7coQy65eTl2eqlIY2l6xO4AwJcnBTrLHdpHa20lioAO7Nb1hbyKtNUGhRv3qLNXL0xQ/GKZ8egzlbn1VnbGKDiish5z//hnOfu9jgsoaBEaD8iM8P2Lm5DSVyTLH/tHj9D3XjzQbx7TTNp2PdjP6yvCa1gCGbSy9/8bxI2/9uwy/4jN3epa+53YtETdpSJJdaey0vWbEqzmzm9bsHgyjcV225fZSrIwyMv3m0nuktMilelc1kL2fETuwh8xXP4+RTd/rqTzAfQApYUe3wbOfsTl82CIINC+97HLlSsRnn7VJJgV/+r3tj/bbbR10/MbfwG5t35TmRgiB3dqG3dp2+zfT2JwvvPwCC7/4+R2NF5YKLLz0M1CKzNHHEKvkOoUQCNOCxX52Nzt6bzuiiPLJ90ArWr74jVXHF0Ig7AY5JZWGdf7+7gaU75I/+SbJnXtRXg0dReRPvoU7tTHN8aaIU700xVwUYMWS+PUyXm1tUqJqLoX/9AuM5jSxPb0rhG5GMk7iyB7ih3ej6h7K9ZGJlbqU9OePkTi2D2EaCNtCJmLIuHNLMz1VrVP4y1dxzw3d0XmmupI8/g8epv1gC8IQGLZB5CukIZCWxCv6nP/Ly9tKnIykQ2pfF21ffZjUvi7s1hRyE42KhRDYTUnspiSZI320PneQ2tAscy+dofjBEEG+tmWpvNzgyuqqybfHOf/9M2uTplVQm6lx+o9Pke7J0ry/IT6WhiS7M0eiLUl1anVrACFW2h1EQYTaSORRQ2m4iJt3SXZej67EmuPYGecWqcLrlR43zueTDrOjjcxXP4eRu/80Xg9wb3DooMnf+4MkZ8+G/PkP6wwNRywsKJSCYlHx1a/epRYvN6Tn7iq2YMywVGT+xecJFubIPPokVq55a0ThWwmtKX98krBUoumZzxPbuRvjDgTntx5CowJ/hWB+sxCmRWrXXpy2LoRhAoLMvodxmlo3FHXaFHHSWuFWZnEr67OF90enWfij52n67peJH+pf9YIQhoGRSmCkVk81mE0ZzKb1aSm01kSlKsWfvE75lyfvOEU38KU+djzaydjbk7hFj4Ev7eTqSyMgBH1PdzF1co6LLwzd0bFvCylI7m6n/etHaH52H2Y6dkN+fmsgDIndmsZqTpE+1EPpoxGmf/whpQ+HUf7mncSb97Ys8yWL/Igrz1/EK27cqj9/cZ7JE2PkdueWIj/xtgTx1sSaxGlVqI1bo1SnK3hFbxlxcrIOdmptIXa+PMxc6RK5ZC8Apdok88U1IrNAGHlU3blNLRhSGqRibUi5Pepc4diknn0cZ1fvHbuwP8CnD1eHIv7H/6nM/LzimmTEsiAWE1y5GvEf/uweGjl+ghBVK+TfeIX66BCZo4+TGNiDmdm8077WmqhcwpuaQAWbzCxEEfWrl/Bnp0k/fIzUoSM4XTtW6LPuYJKE5RLuxBi1KxeIylsbjDATKdJ7HqJ07kPC6vXnRVRfad59y+Ns6axuBw3elXEW/uRn5L79LInHDiKsTf6i1xpKacL5AoUf/ZLKayfRmzDbbD/Ywtz5PG/8y/dJdSTpPtbOhZ9eJT9UYvzdKY785wdItMapzW1tzx5hSpqf3U/3bz5GYnd7o8ffNkJIgRG3yT0+QKK/nZmffMDUjz4gqm3OqPRGogFQmShTGi7emZZSw+xHM+z5drhEnJyMg5PZmLha38Hgbt4lqC6PkFkpGzO+9m1UdWe5MPICiVgzAkHNy1P3VtdAaK2p1mc5M/SjZU2aNwrHSvHQ7t/CsVdPoUlpotRNZcXCQK9zTKu7k+SjR5aZ2j7Apxhao4IAdVPDWa0VOrp+HVUqmkpl+X01sNukv9/g+Z95jI/f+p7TUbRiDAAVBBu6W7VSDX3Nmg1ybxBRrn2UDb9Ha43eKgW8UrjDV/GnJnG6e4jv3kNyYC9WaxvCbLSkWjPCdYNoXWuFcl38mSnc0WHqw1fwJsa2zOk7qpQpvPVLqhfOkOgfJDG4n1hPHzIeX5yjvOUctW50a4jqVbyJcdzxEbzxUbzJcaLq1psLR75HUFwg0TdIVC0vtWPx5mfw8+vvC3hPVj5/ZIr5P/kZ7qUx0p8/ht3dtmWLsNYaXfNwL45Q/MnruOeH0ZuMmlhJi+pMDa/ok2yNNyoCbAMdacZPTPHwd/fT80QXc+e2zo3cTMdo//oROr51rKFhuothZyEETnuGrt9+Arstw8T338abKt7x8ZzsclJTGS/jbyBFdzPKYyWiG8TsZsK6JXnZKgQVn7AeorW+LhCPmSsq9m6G6xdw/fVp64KwRqU+vbl5hrWlptCrYeeuLzAy/CrRYnWflCY9Pc8wMXGCMLyNBsU0SB5/GKPpQYruVwVRtcrMX/4AebPNhG6IbdvaJN1dq6eTHn7IIpUWwG02X1FE4e1fUv7ovRUvKT/Y0IPen55k8nv/9u6nuBZ/H1sJ5bnUr17CHRumeOJ1zEwWu70Lu7UVM5PFSKQWiZRoEM8gQPseYaVMWMwTLCwQzM8S1Wsoz0VvNtK06iQVwdwsxYV5yqc/wkymFvvYdWLmmjBSKaRlI6REhSE6aJDasFIiXJjHn58nLOZR9RrK85aR8TuCEJhtWcxsijBfJpy7/uySlo3d1Ept7CpBpbQkRwmrG2vafs+2jFG+TOn5N3E/vkLyycMkHtmD1de5Kd2Ocj3ci6NU3zpN9cTpLXMHD+sBdtJEmmJJ23StfYcKNVEQEW/aunJyI2HT+RuP0fWfPYoRvzcO0ABmqkHejKTD6L95BW/mzsKmhrN8AfMr/rpsANaCX/aXCcGlKe+KEaVWmrAeLFX+wurC802MQLDCquAOjqIVUbQ2MbXtVGM3GAEITDNOItG6LgsDI5shdnjfPdFpaa0hCIlqNVTNRdfrKC9ABz7aDxtecVqDUluujfg0wh9apyBWK6JykbViKb/+3QRf+qLD3NzKe7q93eCNN9a3SVK1Gqq2+TVbhyFhYes2sfcDdBAQBUEj1TZ+n5onK4WqVfFrVfzZaTjz0b2ZhyGxe9rIfuEo/vgc89976fprWhG5dYSUSNNaIk5ygyT7nsfa/dFpgql5Kq+dxN7ZSezALpyBHsymdCMKZRqNRf5GLYVaXBzDCO0HhAsl3HPD1M9cJRibJlwoNd6zRZi/WKDnyS4yO9K4RY/SWIWvoB2lAAAgAElEQVQD3xnALXo4KZtMT5qpk+sP890K0jbp+LWjdPzaI7clTY3QsEJ5AcoPUV5IVPUIK3WietDwahKN6i8ZtzFTMYyUg7TNxh/HQpi3LgEVhqT5mb1EdZ/Rf/MKYXnjD/YV1W6bfOauKD4TW3DQdSLyo0bEaXE8aYiGd9YWQGtNGG5NuvdGI7hriMVy9PY9Sy7Xz75930GrxqNQSpNyZZJolc+sOMae/m2totNaQ6RQno92XcJCkXBimmBqlnChQFSuoD0fHQToIGykRlTUIE3qBm+dB7gtNupdsxaqVc2/+6Ma73+wMppx9BGL3t77TNz8APcnpEDGHIRtQqTQaISUROUa6IaxtXQsdBASVeqNhuwxGyPREKaruo+quxBG1N6/iJlLYbXnlg2hAh9vYQYzmcaIJ7mWbl2vTOEa7jlxAtBBSDA5RzA1j3vyCslcF65RwWjNYuZSyHSiESY2JDrS6CBAlWtEhTLB9AJRobK4iIbb0nNt+LVxEi1xhASv5HH5r0d44h89wq/9qy8gJBRGyoy+ObnpcYQhafrMXjq+dQwzs4qvyCK00gT5KvXReWqXZ6hensYdW8CbKzfsBPS1B8i1Ay9WcgmBjFk4rWlivS0kdreT6G8j3teC3ZxcU3woLIOWzx3Amy4y9cN3URso4wcI3eXvt5P2qhYF64WddpaRlVs6jQu2jNgAK/2abtOgeINHJ4g2X66t0UTRytSI6xYZHnoZw3CYmnyfMGy8RykfzyuibhGlAkBKnN07VxjLbgW01qhqjWBqBn94HO/qKMHIOGGxBFGEjhSo+8O09QGW44W/cvF9qNVWfjcfnw6Ymbl7vnkSk5hMYIs4lrCXLD6UVoTax9d1XFVFrRk/uzOYwiYhM9gihiHMhhCbEF+7uKqySo/QW0MgiMkkMZHCFDZSSCIdEWmfuqri6dr1xrUbhsARMRyZwBIxDGEgkCitUEtzrm54zpuFkUvR9PUnkHEbEbOJ8hVkOk7hx28BkPnCUWTMRoch5TfO4F0eJ3FwJ4mjexCWgaq4FJ5/m3D+FpkRIZCWjWHHlmmvpL2xis/7gjgtQWtMbdOaO8Dwxb/GH9uc1mOrMH+hwFv/6gOCWoMADL06hgoVXUfbiQLF6FuTzF+6M3+oG+F05ej+rcdx2lZWDV5LPYTFGguvXSD/9mWql6YI8tUNkcWo6hHMV6icn2x0j84lSQy00/T4AM2f3Y+Va1Qz3uwKayYdOr55lPKpMcqn199FGsAtLI+iJLtTWEmb+vydkYR0Twbjhoa9kbu2J5M0JGZitRYgd0Z2pCmXax01m04L+UGVK+O/QKPJl4c2dazGnPSqESfQ+H6F8fG3qJQnN7zLkqkkZlvLlmlHrv3eVKVK/dQ56h+fxx8aJZzPPyBInyAUCmt/V8Wixl+jF+TWQRATCVqsHeSMDpJGlphMYAkHiUmjvXlEoH1cVaEUzTPlX6UYzay7SCRrtNFp9SOFSTGcZSq4giJCYtBidtNp7yZjtBKTCQzMRtN3HeDpGrWoxJh/nvlwfB3jCZIyQ5c9QJPZQXyRjAnk4jl41KIShWiGCf8SNbV+7anEoMnsoNnsJm00E5cpbBHHEBYSgUIR6QBP16lGRebCMWaCkVWb0W8HhBCYrVnKv/yI9LMP40/MYWRTOH3tWD1t+ONzVE6cI/nIIJnPHWFubBb38iTu0BTCMGj97nPYO1pvSZwMO4aVbaF46gRhrbz07FT+xkjihomTEAZOPIth2ARBncArY5gxpDTwvYbAyrQSSGnieyVMK47tZACN5xaJQg87lm2QJCuO1grPbex2DdPBclIszJxFRY2wr5QmlpNudHw3G2P6bgnQWHYSy04hpAFa4bklwmDrS1610kukCSB0I668PMrIm5OgNKG3BQuDFLR/4xESu1Y3C9N+SPHDYSZ/8A7Vi1NEtS3odachyFcpvnuV8ulxFn55ns7vPEr2+K5V04R2W5qObx2jdmWGqL7+8SsT5WW6oHRPhmx/jtLoxivrhCFoO9KBGb9Ohsy4RfP+VibfmVhq13INRtwk2ZFccZw7SrGJhgv5jcxJBRE63NxDPgirXJ16DWCpymMz0GjqXp6au4DrFVcs1vXaArlcP46TWToXpQLmZs+sqLa7EWYug5HbmjSd1hpVq+OeuUjl1bfwRydQtU9ZK6RbQNoGiY40fsnFvwNbjk8K9u4x2b/f5Hv/fvu+2157H73OAWIyiYm9agRYYmAKm7hMkTXaaTN7GfbOMO6fJ+L2EfSEkWGHsxdLOMzKFLPhCFJLdjqH6HUOYIvlTcQFYAsDmxhJmWMquHrbpU5i0mHtZJfzEEkjhxRySRLQeF1iCou4TJEzO2izerninmQ2GLltBC0lcwzGjpMz27GEg7jp2AAGEkOY2MRJySZarB20mb1cdN+jqjYfGFgPVM0lLFYJ54qEhQrCNhExm/i+XtCaxKFdCMciKlaRcRunt534gZ3ImI3d19FI890Cke+i3BrNxz/TEIerxnrrzkxQPP3uuue5MeIkBLmW3bR2HkapECEMpsffx3ZS5JoHuHrhBVTk09nzKForZiY+oKvvSSw7gZAmbm2B8aHX6Bt4bpF41bDsFKWFq0yNvYPtpOnYcYxkupMz7/0xYVgnnmxl8NBvUCmNI6SBaTgMXfw5KvLp3vkMCEimO5HSZOj8C5SLd8krRDfcp7cKyYEOmp/Zu2rD2sgNmPvrjxn7k9cbEaZtgKr7lE6NUhueo/t3nqDjm0cxEsvJkxCC7CN9pB/qpXBibS+im1G4uLz83rAaDXpnTk7jFTb20GgabKb7yZ5lESdpSvZ8Zx+FSwuMvDy07P2xphgtB1eSUTNmLjvGemAlLOzU8oU5qIeEG0xdroaNRn9uBaUCRqbeZHLuQ5SKVpChrq5jpDM91OsLS2ZWYeTd1rlcJhPI5Na0cgnn85T/6hUqb7yH9n/1Gl5n97Ry9L99jtEXznPxTz6419PZFPbvN9m7x+TcuZBnn12+ZvT3m0xNbm/ESSBJyEwjRbYY6amrMq6qEupgkcTESBpN2CKGFJKEkWEgdoRA1xdJzfo3P45oRLM67d3sij2EKWwiHeFGZXztodFYwlpKs9VVmaoqcqtdosRghz3IQOwYjmzINCIdUo2KuKpCRLhImjKL52qQMVo4EH8SU1hM+JdumbrTQEwmcWTj/tVaUddV6qpCoFwUEYYwScgMCZlFComFTbvVhyLiTO11QrahIu/meaprNgXLtYpRqUrxpQ+onx5eOiOzJUP2y8fJ/+hNvJFp2n73q9wuk2DYMcxUmsJHJwiq1yNTyt0Ysd8QcTIMm9bOwxTmr1IpjdHWdYSW9gPMTn2ENG2cWJYwqBFPtTM58hbZ5t1YdoKp0QaT27X3y+RnzwMC3ysxcvllkukuunc+yezUKerVOcaH3mDX3q8sG1dIg7mpU1RKk+ze/3WS6U7CwMW0HIYvvkg620OudZBKefM6o9Ww89kd1PMuMx/Pr3xRQO9TXZTGKhRHNlbSuHQIU9L8zB7sltSK3ZIKQuZfPcvYn76xbaTpRoSlOhPfP4GwDDq+fqTB+G+Yk5mO0/z0Hkonh9etdSpcyeMVXGLN13VbHUc72ffbBzn//dN4hfWFSZOdKQ78rYfI7FxZCh9rjvPwHxzDsA0mT4wTuiHSNuj/2iDpnpWpTzu7ce+nRFsS+6bPNCwKtn9B2Si8oIwXrH49xhOtjAy/QqWysftFxBr9vTYDrTXh1AyFH/2c2skzsEUC5U8aIjfEna1ueONwP0IIMAwY3GPy9FM27753/X6wGubM24q5cIyuaDcSk7lwlHw4vajR8VGL0SRTWCRklj77AK1WL1JIbBmn297DfDiJr9f/4HRknDarjz7nABrNlH+FqWCImioR6QCNxsDEljGyRhsCiatu7UmUM9vpjz2MIxtZmLLKM+qdpRDO4GsXjUJi4Mg4rVYvvfZ+HJHAkQl2OQ9RjYoUorWlLTVVYjYYwRQOxWiGhXCCSlTE13UiHaDQGEhsmaDN7KHXOYgj4gghaTa7aba6mQmG1zz+tkJr6mdHSB7biyrXG1rnIETVfbTSCNMgNrgDp7+LyomzYEiMRKzRVSRmY2STqKqLDiNUFKKCgNTAAcJadWnj6M1PE5TWX4m5IeIkpUkq04NpJWhqHUBKi0ppAt8tE3hVEsk2wqCOiny8eoFcy27SuT4MMwZaEQT1JaO9SmmSKPQa6T1hIA2LtQitVy/g1haIQpfAr2GaMerVOYQwyLUMEEs04dau7563Gvu/NcDc+fzqxEnDQ7+zj/H3pjn5x2fv6PhOe5bMQ32IVcraa1dnmfiztwnmt94MbC2ExRqT3z9Bcnc76Yd6l70mpCC5r4tYTzO1yzPrOl5ttsb0+5P0fbH/hga5Fge+e4hkR5LLP7lI4XIev+yt3JSJhsFlbqCJfb9ziB1P92JY1yNF13QyQgiyu3I89k+fYv7cHOXREon2JO1HOpCrGIcatkFusJmJN8eI1qnByPbnSLQtj7i4+Tp+6ZMVMXHrC6TSXYSh1/B70g0VSOBXudWuWFjmpv3WomKZ4k9epPbhx5+OZtt3iPLVBd75H/5qRWr5k4jz50MuXgw5eNDi+z+o89JL1zdCjzxiMTiwvVLauqpwuvb6ovjbXTVtFWiPuqrgqSqmdGgyOxAIsmYbcZnE30BRhi1i9DsPITEYck8x6p9bXUitoBBOA3KJwK0GE4ed9mFiIoXWmnK0wNn6mxSj2RWRMC+qNQiPqrMn9iiWcEjIDD3OPkq1uTVTdhrFuH+RqWBoURwfrjh2CHhRnWpUQAODsaMIDGwRo8noYC4YRd2xIP32UH6IPzKDqnn443NExRpCCHSocK9OknrqIJkvHkP7AdV3LxBM5am8cZrkE/sJ54qUfvEBUb6CmUuR+ezDWN2tCEOS+8YTlF/9CH98DuV7lC98hJnMYMQT6CgiqBQJihtrzLyhK1prjVtfYPTyy1RKN+5WNeXiKKnsDgK/Rr06RxjUUFFIfvY8Y1deJYqCpfe262O3NOi7GUpHy7UfQuC5BbSKiCdbqFfnKMxf3hJ9yGowHHnLKjBhioYx5h3C6cqR2N22ItqklWb2r07hjt99TxJ/rszM8ydJHdyBMG9IaQlBrLuJeE/LuomTX/YYe22Ezse6cbKxxcMIrKTN7m/uofPRbmY/nqE0VKA6XSWoNbySrIRJoj1JbqCJtoc6iLcmVrT4cOfr1GZrtBxoRUiBnXboemwHXY/tuOWchBDseLqHy395gdrM7SN5Ztyk/Wjn0vyvoTZTw81/srQ5hunQ13WctraHFv2eNGFQ58qVF25phyCkAXJzwvD6+6eoffCrTZpgUTdZurtVS9sFtVjwePp0wNmzcKPH4vnzIRMT2/tdaxQVtb41sqKKzATDZI1WDGEiMUjKHMVobt3jCSGxcBjzzzPinyXUa2+cGkTj1uffYnWRMxvrf6QjxvzzFKK111ZFyIR/mQ5rFy1iB1JImowOMkbLLT/n6vUVEikipoOr9Nh7SRgZhBDEZQZTOBuKzG0Uqlyj+PNGdiqYahCZG++Q0s9XmqOWX/+Y8usfr/j5wg9fW3UMHQZEbp3U4GEMp1FZJ2cnqY1c2dBcN0ScosinlB+mresIhulgGDb12gL16iyl/BBtXQ9j2TWmx95Ha0Vx4QrdO5+muX0/Xr2IacXIz11a9dgCge2kiSeaMUyHeKoVt5ZnrTivaSUwTIekkyaeaCWR6mRi+PXFXfPmIWRDPCwWS/kbzX1XGgjFsjaxrEO0Vjn87caxDBK725CriLHro/OUTo5sqSfVRlD6eIza5RlS+7qW/Vw6Jon+NvJvXVxfuk7DxJtjTDw5xs4v7V5mGCmEINmZItmZQoWK0A3RkULrhnbJcIzFSrZVtF9BxPnvnyF/Oc/xf/w46d7MLa0BtNa4eRc7ZTciTgPNDH5rL6f/5NRtd/6th9rY+YX+ZcQt8iMKV/KbckG/F5iceJeZ6eXmdELIW5pmAo2IrlbAnZGncKFA5a330cHWRFnaHu2h7+v7mHj1Kn1f3UcUhFz984+xc3F2fvMAYc3n4p9+QPHiHOiG3UduXyvdnx8gu7cNaUrKQwuMv3iJhdPTqBsij00H2tn/dx/n3L8+gZm06fnyHlI9OaIgZOLlKwz96DRoaHm4iwN/8Dhn//UJsoOttD3Wi51xqM9WGPv5RWZOjC47bu/X9tHzpT2YiYbb8/CPzzL84+WRaiEF+3//ccy4ycjz5+n72j4ye1oRAvJnZhj+8VkqY4Wlh6AwBE0HO+j50h6SPVnMuIXhmA3D1qrP1BtDXP6z7TcjzOUk3/h6jL4+A8u8fp+8+LLL2Nh2V9atF5pKlCfQHoYwEQgsubFSdK01NVVm3L94S9K0HpjCotXswRKNOdSiInPh+G0/pwiZC8ZoMRsbREcmyJpttyROG0GgPcrRAgmjIXOwpI0UxrbY/awFaQj2PJHDq0bMXK1x8HMtXHmvyO6jGZp3xKgWQ9798TTde5MMHMth2oJ3fzKDZUsOPNtMFCjmxlzOvDq/lIwyEymyh45TvXoOb24apCQ9eJDswaMsvPfLdc9tYxEnFTI9/h7N7QdobttPGNSp1xrMMPCrzE+fxTAtqpUpAKrlKSZH3qa5bR+pzA4qpQk0mlJ+qJFaA6LQJT97AaUCMk39ZHK9uLV5WjoOU8oPUStPU5i7uBSxKhVGCEOXts6HKBVHmRl/HzT07fkSmaZdzE+f3sgprYmWvU30PdONaRtke9NYcZPjv//QsvcIQ9AymCPREmfu7MZCfdcgLYPEzpXRJoDy6TG82TvTTW0FwkKN0kcjpPZ2LeOvQgjifS1Ix1q3zskrepz93sdkdmZpObB65aA05S0b5t4IFSkm3hzj8k8v4i7UcXIOj/z9R1ek0q5Ba019tsap//dD9v3WQbL9OQzHYO9vHkCFiss/vkB9buVuShiC5v2tHPn7x4m1LI8qeiWPmQ+n7upishVw3ZW787a2w9TrC6v6P12DDkJ0EN6xHYF78QrhzPp39reDlbJpe6wXM2FTnynT/sROjvzTz1EeyuPOV2l/tIf+bx/i1B++QVQPaD3WzZF/8ln8skf+9BRREJEbbIi0z/2bdxh/8dKSH5iZsMgOtLDzWwdJ9eSoTZcpXprDzsaWEX8zadNypJv9f/sxEFC8OEfNkLQ+0s3D/+RZTv6vrzD91sjS+/NnZ1ChIrenld6v7cVpXuV6FZDoStN2bAfpXc34JZf8mWlizQl6v76PVF+Ok//iVdzZxiYxO9jK0f/uOerTFabeGEZKQc9X9hJvT3H1h6eYeWdj9iF3in17TR55xOI//qBOpXJ9Izk9dX9FF0Pto27IThh34MpTifKUo1WkGxuEIxJkjJal9b8YzRCo9eneyirPtXLla5EzA3NdVYK3g9Z6WfpRYiBZv/eelc6R3LEbDdTGrxCUN16VpyJNZSGg5/9n7z2j68rS9Lxn75Nuvhc5ECAIMBZTha7q6uoK3dVxWu3xBEn2jKzRsq0lKy0t+5/1w3/8y2tZlhzlNZJsa9YozGhGmtGkrp6unq7uSl2JVcyZBECAyLg5nLi3fxwQJAiQCARIVjffWncVeHDPvgfnnrPPu7/v/d7vqQyBq3BSBnZKsv/FNs6/s8je5wt07U7SqoZMXqxz5LV2OgcStGohdsLg7f8wTasWrnA2EZaDkAaN8SvLFgQNyyZ/5AubOrZNXzFh0IrJyhqYnz65alu9epN6dSWDXpg5s/xz4DeYnfwYgMXZsyzOrg67uZO3SUlx7gJCGOQLQxjSxDCc2JbAShJ426cDUqEikbfpf66bdHeSdFeStpHVomS/EXD5z64z8eHWhOnCkNjdq8XLKohwJ4so99FFM5Qf0posovwQeVc/Nrszi7A29wAtXSly+v/9jGf/7gvkhwu3jTk3iSiImDs5y+l/8SmthdhVdvzN6wghOPIbx0n3ZpBWHKXSWqMjTWuxyfl/c4bRN67S8VQX+eECQggSbUkO/8Zxup/pZfqjm5SuFvGKLlprkp0pep7tZdcru5fffyeqY2UWL2wfEbgTQkiEMBA8mMGm1nrZEdwwHLSO4nHvqqBra9tLuTx2X+KkWi6q5W7JAFNHEcHUbOzsu42QhmTuk0nG/uNZjvx9zeA3D3D1g5NMvz2K/jtfIr+3I66eVJq9f/U4QcPj7D99n9LZGbTSZHYXOPJ3X2Lfrz9D8dwsjck7fHGkoO2pbs7+3z+leGaayA0xU3bs4HDnZCwFRsLks//5x9TGSggBvV/ew9F/8DI9Lw2tIE718RL18RLNqSp9rw3f8+8SAsyMTfnyPFf+7WcEVS/WBf6tFxn8hYNkBvLLxKn7i4MYjsXV3znJ3McToMErtzj2375C/UaF2ujWFnWbxdR0hO9pvvVNh0pZLZ+i99/3mZt/uORJEJfb3/rvlu2GQGAIa0Ue4+6y/PWgUTRUeUuNwu+GJRIkjdsWH552l45vAyTlDkYghIh9q4RJtGEZjEByy5bg1nlaOi7su4jSxs+RMC0KTz2P4SRozT4YaV+caMURpefzXPppCRVpojB+XXiniNuIePrrnRSnXQxLLlvM1Es+vqdW2cGpwEMrRfbAcVrTN5CWTXb/UbzFzXlGbl21JwRGPoP2A1Rz45OhUcjG6vb6+rYBRiEbq+cbKyMBWkcszJyhs+8Yu/e9ThQFLM5doFZdO8RpdrVh9bQvl/pH1Qb+2P2JTul6mY9/8zRW2uKr/8OL1KYanP6diyvfpDWRr3Cr/tatCQyJlV+96oxaPv5i/ZFHM4JSg7DhYjuZFdutQmrTPdq00kx9MEnQCDj6N56m65kerORa5pT32F9r/IrH5PsTnP/XZ6iMlpbPT+RFjH7/KqXLiwx9fZiOw12YSRMVKCrjFcZ/eJ35M3NEbsj4m9fZ/fqe5eiWnbbp++Iuup7uIXJDVBS7rktLYiVNpG2sIi+RFzL6/auEze2tqDONBOlEJ7l0P5lkD7aVxjScjU2ka8D1K5wf+2M0msHBl6nWbtLZ+RSWlVqhCUwm29dsYn4nolodVW/AFhr8aj8gqtS23dhSK0Xt+mJMjqdruMUm9YkKYdPHKzYxj/eBEGT3tJHZXWD67VFK52aWHeDrE2Wm377O0X/wMoVD3SuJk9YUz84w/8nkcro8bKy9kJl5b4z6eClu+wKUL80T1Dyc9mTcgHUL6XZvscnUT64va6HCZkDx3Awjf+UYVvp2ZDbRlcavtHAXm8v3Q32ijLQN7HwinvceQrq/u0tSKEh+9JZHpXL72lpcfDikSWKQkOnlkvq0kcMSCSzhYAgTAwMpYj8nW2xdk6rReGp7tD62TGJy+7vcbT9Fv71vQ/vGc8IdFc/C2kBUSGCLBEmZIS3zpIw8CZnGEg6msDAwkcJYqgrc/DkyUxkSXQOk+oZoTF4jaFQJWw3sQifK9wibNYRhYhe68CsL2PkOdBRipnMo38MrzaHD23Oq24hw6xGpnMniZAut4cK7RXqGU7j1iBtna1QXfAo9DuVZj8qsRxRoyrPemvdc1KxTPvMR+cPPkdn7FEQRzZtjVC+e2tzfuekzswRhGiSP7yecK+FeHN3wfomj+1C1Bq1Tl9d9b/LYfsLFCu751Z5BreYiE9d+vKHPzLz6NG2/8tXliqDGiYvM/qN/fd99tIKgGRI0Q4pXy4RutGW7gftBLLVBWfX5fkjUfPTiUdUK1kzHSceCe7RouR90qJn7bIYPpmoMfWOE3V8Zov1Q57qeSqEbMndyhtHvX+XmexP4tdUPMBUoipcWKV5axEiYWGmLyIsI7tIgLV6YZ/KdcYa/vW9ZsySkwEpaGyJyWmumP57i5vvbmwJJJzoZ7H6RrrZDOFZ2W1q5NNwFhBAoFTI19TFRFNDZ+RTj4z8mCm9fX7uHvrIup4lKZcJSGXugj3VZ1l3QUbQjfk36Dj81FSpUEKH8pUpBpWOtooBEZ9xSqDVXX9k7UceVkZEbku67y9xTQ2NqY30vm9O1FRN15MdavVutjrayAgqbwXJUaXlcN4zHvOPea07X6H9thGR3mur1RdCQ29tB5IV45dZD00i6Hriupq0guTObm0jsfB/JtCzQZ4/QYQ6QNdoxNtCweqvQ6G1JhwnEUluY2+fHkg4WW7P8EKw2tbwTBibd1hC99jB5o2uVaed2wEikSXb1Y6ayMVkKfdz5KdqPvURrbpLKpc8wU1m6X/wms++/Qe8r3yWolYk8FytboHr1NNWrZwGNNGDoWI7BwxnO/niRMIgXtGd+tDLK//GfrI4WzY7eIzCjNd78FLNvTSJtJ56XtlBUtiniZI8MkDg0jDANmp+cQ1gWyWcP4RwYwhu9iX9tgtRzhzEKWcJimcYHZ3BGBnAODiEMg/q7sdGbsCwSR/aCEPjj06SeewqZTRHOl2ieOE/qucNLUaIOotLq1N1mIRP2msaSG8Xl748ht7Hf2Qrco5danGJ69NoArdSazF2Y8oH8WZqzDS79u3Pc+NEouaECnUe6yO8pkOpOYaVthBSEzYDGXIPy1SLzZ+aojJWXU3PrIXLDWPBtGFjtHYS1GjqIH9x+zefS750nO5Cj80j3qkq9+0ErzcL5ec7+1sltrabLpvrYP/BN2rJ7kA9YuXYv+H5M/G9Ovk+zubBiwigVr6DU/aNnqtnCH79J8tB+hL3xSCEspR7NHSpL1yuJ0FpQQQRo5BrpZWlIhBRr2lKsalB9D6gduFc3OgfMvD9G32sjHP5bL9L1wiBCCtqP9jLz7hjF0zvjbbcW5ucjTp4MkBIsi+XvYocuZyAmC13WICPOM2SMNgxhxF5h2ifQPpEO8FULX3tEOiQiwBIOXdYgptiYnnJtbAcZFav0Vb5yt0zKfN26pwlmUmYZdp6mxxrClon42kLhK59QBwTaXXoFqCUvqjazl73bEx4AACAASURBVIzRtqlj8IqzLNYr2IUOyhdO0JxaP6jSnB6jcuU0uX3HSO8aoT52CRX6KAWLky4f/8ksxSl3W065mc6RP/o85dMfErXiRUmyfwinq4/yqQ82Ps6G32lIUs8eovHT04TzxXgVJSXelRuE86WYCCmFvXcA99w1EgeG8K5Nknz2EPW3TxAVK3Encx1HnYLpeepvfYw9vAt7Tz/uhes4ewcJ54tYu7qo/fBDMq99YVvM04Rjbyk6cgvFbehDd09o0GtM2EKKlTYAjwjCNNb0QdKheuALWYWKxnSdxnSdmY9vIoylPnC3VkFaozVxld0GH2B3w+7oYtd/+beZ/YPfpXn10vL2xYsLnPxnJ3j27zxPx5G1xfmrjjdSLJyZ49Q/P8HCufltS6NaRpI9vS/Tnhte1h7d0iZ5QZ0o8rfc0NP1Kqt66TUaqytv5ufPrb/y0uCev0LmlS9i2ptM11nmjjQH3ijqkxUiLyKzu4CRNImWolTClGQG80jHpDb+ALYfj7C3Xmu2zvwnE3S/MIiZtIjckNE/PMvMe+P45YdnlbGwoDh9JmD/ARPbiqduzc6emjazhwOJ50nJPAiIdMBiOM1CMEk5nKWl6nd4G8Vp1ILRQ8HseUDitB3Qq3RS4945ZoMxtjK5RDpu0Hs3LOGwN/EMfdY+BAKNpqEqzAc3WAynlqsMbx8TWMLmYPJLmyZOG8Wt+Vb5Ht5inJ6LWk2EcUcmQ0N1wYdtlJFKy8bOt6PuSAeqMCDR2bupcTaxBBQrfxQC7XmoejMOwWsNYknIqhTN05dXapNu7S4FqtFCGAYyG/cQE0JAGNE6fQXV9OI3C7bnjjMk0rYeLCSp46iQYUmkZazJwUI/ImxtvuRWK020hgBc2hZm+tE9aG7BSNnINdJXkeuj1fatsnWk0dEOlCwLgbDWSCtqmPt0hk/+1w/Y/5efYteXB7HTdhz9WxKtx7b/MWHyKx7jb41x7Y8vUb5W2jbSBJBJ9dKR348QcpkwzZXOs1C+gutXiFSwZY8ypaNVnmnJVCc9PU/jOHlAI5D4QYPxsbfuKw4H8Cen8ccmMAr3t364G8IyMQr52GJ6J77nddCaqTH30QR9rw6z6+v7mX77OipQdBzrZeBbByidn6V6dWeE/ssQcTGIkCKOfAmBNCXSjsu8dbR2dHc9OO0pBr99kMk3r3D9358mdENAL6cqtzLmVjA8bPIbfz1FFMHwHoPLV0IOHjAZHd2Z79sSCQbsg6RkPk5Ha8WEf4kx9wyevp+G9tFH8iGeQm45jd9KsYXap6lW95d8EHSY/XSbQ8il+aUaLnDFPUExnL7ngiz2n9q+Y9Aqin3gACOZQhi3qIfe1nZTGzkODAMr10ZQXgAETns30Y41+Y0iWicvkXr2IAhB48QFomojrrTxQ6JSlWByFjefwerrIqrWcc9fo3XqEunnj4CA+k9PoSp1WrOL6CjC2TuAd3kc79oEVl8XYbmKe3GUYGqOzKvPIUyTqP5gKyZhbsztWJo2Wit0tDpMamcs9v/CHvZ+c4h0V3LN1M7l743yyT8/s2r7etCRIig2YO9dx5O0sLuycfTlUa1mhcDuzGKsQeCCUgMd3GcCEgIzl0faDv7iAqj45pCOg93Vgzc3i751sQqBkcliZrIIw0BHEWGjTlSrrqwcsSysQjvCcdBhSFgpo1orJ0hhmpiFNmQiifb9mDTd4/TdSrtVb1S49qeX6Xuhn7Z9HTgFB2FIdKhoLbYoXl5k+sObVMfKsTnnNqMtO4RpxOc4jFzGpt/l5sKnBOHO9F3s7XmWKPKQ0qRSGSeRaEOpYEOTtfZ96u9/QuLgXkRq4+JRIQRWXzcylUDVdr510N3QSnP9909j5xz2/WfHGf7lI+hIYyZMmnN1Lv/2CdzFnY3OpAfyDH77IKnuDMmeDImOFP2v7yU9kCdsBsx+MM7s+5tvayEEBDWPPb90mP7XR0CBCiOa0zWmfnyNqbevL0fYdhJ9vZKp6Yg33/T4zi84/OY/b/CNryfo69uZyHlSpmk3+5YJfDVaYMK7sA5pArnk3/ToofF0E60VYkmTlZQZJMa2aKhuocvajSnixW+ofW74FyiGU/e938UaacQHgbc4Q2rXMFGrQWrXyLaNu1mEzTre7E26XvoGfqWINC2MVJrSZ+9vapxNnRnv2gTetYnbBzE9v/xzYzFOZzXeW2lJ4F0ex7t8ezJoLd6uWvGvxeLa+jsr7Q2aH26egNwLwjLW7Zic6OwjM3iA1lxcytuYHlvRvmXPawN88e89TX2myeyZBUJvNUMuj1VXbdsIdBThza3eV5oGqT1dGBmHqPZo+lkZSYv03m7kGilDb7aKuo+RoTBMci+8RHLXIDP/4XeI6rG+xundRf9v/E1u/tY/w52My7QTuwZp+8o3MNKZeBVuWbhTk8z/yR8s65KEZVN46VUyR47H/xYSd2qC4o/fJCzHKRZhWmSPP0v+xZfjSJHnEhQX7k88dax5mvt0hrlPY/8xaUqkJVGBWvb12UlkUz3xoWhFuX6DyfmPCdeJ/DwILDvF9PjH9EqTSnmMWfckwyPfwpAWal0TTI13dYzmZ2dJf+kLiDXSuPeCM7Ibs62Av03EqTZe4tq/O4W7GI9XvjiHVhp3oYnWmsVT0wQ1b7mXYHOmxun/7V06jvWSHelAmpLGzQqLJ6fiirQ70JiucfX3TlE6d/8y5cZkmYu/9Umc5rvjMovckPE/u0DQ8G9HfJQmrHs0geZcncUzMyvGumWUqZVm6u1RnML0qj6I9Rvx59VvxNe8kTQZ/PYBvEqLhZNT+NVYC2I4BoVD3Rz5+1/Gr7grLBF2Cre0/y1Xk0pL9u8zyWYFD9il555wxO2mtQDFcBpXrX9tOSK5raTgQeArl5aqkzbi1HfW6MAQ1iYsBe4PE4uUzC5LADzdohjcnzQBS33xNtfQO+m0oQEvrFG9fn6Fd1P12jm01iS6+mlOj9GcHiNyG1SuniFsxM8Gv7JAbezCiuCFFCZCGutGwjcKHQaUTv2U1swEdns3OopoTY3tbMuVzyPiiNP9hazpXXtRgYeVacPK5GnO3lghyhx8qY/yWJX3/skJyuNVVLj6otvqA1YHEc3xebTWq1IfmcO7cLpzNB8RcbI6MuSO7161XWtNa2we5T549EUYBtlnXwAhWHjjj4jcFtJJxBUPt/LQQpA9+jSZw8covf0j/PkZzEI77V/9BoUXX2bhh29AFGH39NL22tepnfqU+vnTCNOk7ZXXN92YVoUPhzDdgmXGE5TSEeX6xI6SJgDXrWDZWZrNBbq7j9FsLmAaNhsNzatGk9qP38ce7Mca7N9wyk7msiSOHMC/ObMt6braaIna6G1dUunCHKULt/VbC5/eZOHTlRYlYcNn9oMb9yUSQkiaU1Wu/tvVvnR3oz5R4eL/89HK/Q0LHQrG/uj8iu2Nm1Wu/u7tsuc4Nbv2dTb11jWklUAaDgi1vJCr3yiv+LyO4330f3UvV3/3FJNvXkbdigKL2N/p+H/3KtmRjodCnK5dix94c3MRFy8G/O3/JkOlovj//uVORBjFchTlFnzdumevtluQGGSM9lX7Piq4ukktWiQl49R31mgnbeTxw+2Jfsbu6LcXN4poTR3U3YhtHe6tYzSNBFKaKBUSRi5SGAhhoJSPjkLqoxcxDBvLjG1PojCgee0SLSFQKor3kSbu6DXEklwgqJRQtQamsNGGQaQCsuk+Uol2FsqXCbbpnEjbQTpJlO9Su3wmft4kkih34+P/7BOnDUScVBhi2A52to3QbXL3A8TJ2pTGqsydW9x2XyUdKlrji0R1DzO70vrf6c7T9qV9tMYWHkmFXftL+3F6Vt88Uc2lOTqPDrYvNy1MM66GKRZXpUuNdIb0U0dxb05Qv3AGlMJfmCcxMETmqSMY77xF5LZI7t6DDnzq507hz8Wr+dqpE8tRqscVUsTXp9Yaz99a5HIzmJs9SRj5tJrzOIk8+cIe5ufP37dP3d3wb0xR+cFPaPvL38XcoK+TEIL0i8/SOn2B4ObM+js8IqR7hmnMjaPVFlb9QpDpHcYwHcrj94+cZ3pHqE2v3YIKIcj27SXTvYe5C+8RNNYuULHzCaRtoPxohZbJTFqk+/MYjom7sLOp0VxOoBQUS4rKZz6eB//+P7R44/suQQDN5k5IDVZbAhjExpH3K6RIGwU6rV2bttPYKQTaZSG8SYc1gIWNJWx2WQeohotE9+p6vwlERCuiSwKJIUzUfXRFAkmvNXJfctmW20Mq0YFh2IxPv49tpujrPE65PoHnV0knOujtPI7rVXD9Ci2vTH/Xs7heGcOwmZr/jLbcHhJ2bCy8WL6KEIKu9qfw/Cott0S9OUNHfi/pVBcazezi2QfuRytth7ZnvozT0YOwHWpXzpLsHcTp6qP4ydsbHudnnzhtQONUn7hMdvcBIimpT1xZJVJuLLQwbYk05e0V3TbCm63QHJ0je2xwxepdSEHn64epfDJK/dLDKysGSI100/n1I6tsHLTWNG8s0BzfHiGtjiJqpz6l89vfpfu7v0LrxhiNC2dxJ8fRYTwxGqkUZr6AkcnS9d1fXiavTm8/wraR6TSR52K2dRBWK0R36J6CSml5nMcVKyJMYuvVnxuF695+CE/ciPsz2XZ2c2sCrWmdPIfZlif/C68jUhvzhLG6O8l+7WXKf/j92EzzESPTO0KyrY/Id6lMXiCR76bnyGvU2q9SnbpM0KxSGDqKNB2ai5M05sbI7z6CEALTSVMaO03kr1ypSsMi0zuMlcziVuapzVwl27efZKEHt7pAY3aUZEc/3Udew8l1UZ26TOQ1ye06gOmkacyP01y8SWP+Bk62877ntXR+Dr/isu+vPUP+YBd+uYW0DNIDedqP9lK+PM/iyakdPYevvOxQrSmqFU1nZ2yA6fvg+zurzfRVi1AHyw/4nNGBKWyCe0RULJFgt/0UGdn2mGicYswHE/RZ+2g3exFC0m3tpq5KTHoXCdclT2LJrNKhpVZ3zgh1ENsULGU0LGGTNdophms/TwSSLnOQHnvPPc/RrbSfH9TpzBzEkBYtr0StMY1c+p0Qkkj5zCyeJow80sluoshjYvZDhvpeJpfuJ+m0MTV/kmyqm0yqm5ZXJoo8pudPLXc7KFZHCcImMwvb02vRSKaxcm3Mvft9ul7+FghB2KyTybdvapydn6UfMTYScUJFlC99xsKpd3EXVk8y1//iBunuNCNfGyTZ7mAmDAx75UuaW78R/bkq1TMTcYn/XUj0t7Hrr79Mck/ntlgzrAsByaEOBv/GKyQHO1ZN2tqPqJ66gTdbuccA640vVpEDd3Kcmd//N1ROfIjV0UnXd3+FtldeZ9lFT0iElCjPJWq1ll/N0WtUPvopqtWMCz3lUupjha+PXqFXexzR9OL8uhSS5GbL/LcJfX1fwDQ32ezUD6j95AMqb7yFqm7MHFaYJunnnyb7+peR6c1pKHYC6a4hQrdBqzwTm/VV5ghaNapTV/DrJbRSeNVFgkaZwu4j8T6dg0SBR+XmJVS4Oq0qpEHoNmks3CDTM0SqYxfpjgHKN86RyHfh5Dpwy7OEbp3qzUuErTrprt04uW5Ct0F+4CkMe2PC+8bNCqf+8dvMn7hJbriNni/tpuPpuCn3lX/1Kaf/yTu05revFdVa6OqSjAybHDoU96rr7pIrXqnUzkxcrmpSjRaWIyodVj991gi2SCw/9AUSSyQoGN08lfwSvfYIESFKq22tXHsQ+LrFmHcGVzfQaExpM5J4hkOpl+gw+0nKLI5IYosEjkiSkBnSskCnOcD+xHM8l/kmu+0j9xhdUwxnllOYtkiy2zkca6mW4yYCA4ukzLLbeYpDyRdxRIqIcM1z5Nh5cul+XL8aV6kBppnEtjPYVuaOQhdvObIlEJhmcilKZeEHdbSOSDpt2HZuafGoCSNvRVRJa4VpJkls17yoNVopDCeJkAZGIkWiu3/Z02mj+DmJON0/n53fd5zypc8IW2tPMKZjgIBX//sXmDk5T3WqvkrnNHNqntG3Jtbcfz3oSLH49iU6v3oYp39lTzRhSArPjyBNycRvv0v9ws6uHjMH+9j1X7xM4YWRNVe63myZxbfOb8CNWINSYJq3iZIQGOn0aiNEpQjLJaqffEDt9KfkX3iJ3DPP07h0Hm/6JsptETbq+DPTFH/4xtofJyVhrUpi1wDStpeVDkYihTAfDz3DvVCqjTHQ9QWEMChkBjGNBGG0vbo2ISRd3ccwjbXJUT4/xPT0J5seV7se1R++g2q55L/7dcz2wrr7yGSC3Le/grAsqm/+5JFU2d1C8dqnZPv2URg8zHyjTOg2UKFP0KygQp901xDZ/v2Ebh0zEdunqDDALc8RNNdePKgowKst4tfLKKUwE2mUCvDrRVToI63E8uf4jRIgMBMZnGwboVujWZqOK6028gdoqFxeoHJ5h60U7oPTpwN+46+nGNhlkM4IBnatLCb53hsuP/yL7dftebrBlH+NrNG+1FbFYn/yeTqsXdSiRSIdYgiLjCxQMHtwZJJQB0x6FymYPeSNtZuNPwoUw5tcdT9lX+K5pRYoNrvs/fRaIzRVFV81iYgwMLCEgyPTMUEUAq0Vreje5HguGKfXGqZgdiOFpNsaIiXzFMNpPNVAIEnINHmji4zRhhSScjhHOZxj0DmEIe4y6fRr1FvzOHaWhfIVosgnlexY6nVn4Ng5/LBJrTG9TIJuEbC27B4qtUlqzRnCyKeQ3U2kfMq1caS04sr2O1KtTXeRVLKDXGYXXrG2ZT+7WwibdVozE7Q/9wqJngF6vvqLaBVRPPHOpsbZEHFKf+koRvbRrw63ArO7bd1jD5s1Un178CuLqCjELy9wp5hp5Ou7KezOEgWKriMddB3pWDVGFERbJk4ArRsLlD68Su8vfQHuchIXUpB7ZojhbJK5N05R+uAqQaW5ZoRqKxBm3C+v7eUDdH/rGKmR7jVJkw4jFt66gDu1viGoXiJDVnsndlcXXuBjpNKkDx9bGRESArurh6hRX9I2CVSzGadLl44hrNVoXbtC+qmjJAaH8OdjAbCwLKRlEZSKoBTuxDj5L75EYmiEqBV7haUPH92Wc7STqNQnqTamyKV3kU310dN+hOmFk/fVIWwWUpr09DxNpbL2NSrkA6yhlKL+wQmiWp3ct76CMzSwbnpcWBbZr76E2dlO9YfvEExOof3tt3pYD9m+fVip3JLtR7zNb5TpPPgSlYlzaDSG5RD5Tfx6LESPQu/eWgsNOgpRwkdrhQo83PI8TraTnmOvo1WEW44r9YJmjZ4jX6E0fobG/DhWMothOviqAlqTHzxMqr0fHQUURz8j8h6emeVmcOZswP/5T+t84+sO/X0Gf/a9laT/5s2d8enRaGaDUTJGgV3WfkxhYwqLbms33dbKohatNb5ymfQvMeadZYjD5IzOHTmurUChmPGvE2qfYec4WaMdiYEhDLJGG9zDiFJrTUhAyL2rYV1V57p3kv3iedKygBSSrNEWj3vXWApFMZzhausEiohOa4CMsXIxpHTI7OJK/V6tMU2tsTL9d6deU2tFozXPzfkTy9ua7gJN907C31ql8QzCJtPz6xdpbBQ6DKhePIk3P4V5+TRaRfjFeYLK5sxvxd2uwit+KYQG6P8f/xb20OacNR8bSImwzBVE4O5edbmRo9iFLlARkdeidOlEHC1ZQrIjgblOLzW/EeBVH6wXV3Kok/3/8BdJDXevmZbTWqPcgPqlaYrvXaZ+aRp/rkJQ2UI/Kimw8imcnjzp/b10vHqQ9IFeZGJts1CtFNWTN7j+v38fb2ZjaTqrs5vOb/8nmLk8QakYC8DDkMTAEDO/+1u4kzcQpkn3L/1VzHwbUaMe+z9lc7RujFL8i+/f1jmlM7S//i2c3v6YZGmFkUjRuHqJ8vs/AaWQiSRtr75Oav8hwlJx+bgTuwaZ+9M/oHn5wubO0UOCQNLdfoT9A98gYedx/QrXp37MfPnStnk5SWnS3/9FpqY+WvP3Q3teZ3LiPYLgAT5PSqyeTrJfe4XUM0eQucy6uicdRYSLJRofnqR1+hzB9NxDJVDSSsQpXhWhgjgqIk0bIU1U6KG1xrAToFV8/wUe0rRRUXBPmwshY3NLHUVI00KFAdK0EIYZk6rQv/05hrncsV1aNkIYaBXG+1gOUhpoFcV9BR+hO/lGcOigSWen5N33tr8n4f1gCYc+a4Qee5iUzGEJB4lB3FQkxFcudVVmxr/OfDBBiE/B6GaPcwyAmWCUmeD6fT+j3exjwD6IISyUjpjwL9xTJ/QgEAgSMkufNUz7UprOlg4Sc8n1WxEREWp/ycqgSjGcZj6YwNX3jtwKBHmji0HnEHmjG1vGlgwCgVoaz1UNFsMpbvqXaaoalrAZco6QNTpoRhXGvLPremTd+/MlQhrrtnXaaUjLxunqxZ2dWtOz8U5ore85eW2IOO36n/4eznD/Vo/1scPdxMlIpJFWbL+vVUTY2PnKprUgDEnH1w6z+29+Fbstfd/36lDhzVdpjs7RmijizZTx52sElSZRw0N54XIlnjAkMmFipBNY+RR2V5ZEX4HEQDup4S6crtx9/Xi01njTZa7/H39O9eSNTU3gVnsHiaERpJMgLBfxZqdJ7h6mee1y7O0kBE7/AE5PH3LJ2DIoFWlNjKG9leF96SRIDI1gtbWBhrBexbs5SXjHakEmkiT3jGAW2ojqNdybEyQHh2hNjC+TqccRUpj0dTzNcP9rJJ0CQdhkoXyVYvUaleYUrlciesBJR0rrnhNXKt1Nq7m4LS6+IuGQPHqQ7Fe/jLNveEO9AHWkCGfnaV24gnvhCt7oBKq2s9qcJ/jZgiNSZI12EjKzomddS9Wpqzvbinw+YIskGaNAQqQxl5oBK62WBd8tVaelNpe+MjDJGu0kZRZLOAgEESGeatFQFVqq+thov3YCVqGDzhe/xuxbf4Ly7y+HeEKc7sKqiNPeozhtPZhOEhWFzH305rLoDSC/O4udvr9OplVyqc88eHRAOiZ9v/oCvb/6AlZuYwJRHSmUFxB5IToI0aGKSdOtr/ZWqwdTIi0T6VjIhInYSP8+rfEWakz81tssvnXhsWg8/HlCb/sx+jufvu97NBBFcVuVdLLrDkPMWCwZRC2iyCNSPlEUsJmWEa5f5eL491a0XZHSIp3uIZPpiT2LWovUa9OE2+STAsR6tvYCqeOHSb/4bOwannDuWwautQalUY0mUbVGMDWDNzZJMDFFWCqjvQAV+Gg/hMe8UvIJnmAzEEgcEst6okD7+Kx8sEskNkkC/G2xKvh5hJnJ0fbMlymf+YigWmb5IbkGD7ofcfqZF4dvBNVrZ0GcAwSdz7y2anJ/7R++QP/zPffcX4Wa0//2Ih/+Xw+ei1VeyPR/PIGwDHp/+fkN9asThsRIORip7e1tp7XGn68y+dvvsviTi59L0mSYsGtvktJcQK209sM2kZb0DiW4cbG5nKHNd5jsOZxi9obHzPjWV6pJp4323L4t7SuEwDITWGZiVaPejaLpLsbpsjt27+4+RkfnIVqtIlor2tr20mjMcePGO9sXSteaaLFE7a33aHx6mtQzR0k9fRhnZDcimVgzhSeEAENg5DLIbBprVy+p558GpYlqdcKFRcKFElGxTFipoupNVKuFarpo10W5ftw/0w+eEKufIxhtOYy2PP71rWtMHzV65CC7jYMYGCgi5tQkN6JLRHcYeqZFngPmM9yIrjCvJh/h0X5+ocIA6STofu0v4c5PL6fr/OIc9Wsbl3I8IU5AZuggTr4zLk+0He52uTz7+1cYe3ulA7HhGBQGswy82MvNE7NceWN0245HtXxm/vATlBfS+0vPYbWvrxXZbmilaN1YZPK336X8yfVtNbt8EFiOwFiyfgg8hYrATtyOnHkthWULTFugorig7/CLWa6fbRD4Ct9VaAVOUiIkhL4mkzd55rUci9M+vqvwXEUYavqGEyRSkplxD9MSaA1RqLEcQRRoNtrjeDu+uy2PscZuufxuJifeo1KJWyElEu3sGf46hmHviAZBVWrU3/6A1unzJA8foPCr38HIZu5/2Lf+XiFAgtmWj40298ebtVJo10N5Htr1Ub6P9oP4FSy9PB/lB2jfjyOxQQBhhI7iF2qpqa66w8LiZzdLsQrB9Cz+xOoqXWnG2qo7odFEvsvjeILMrjac/Xs+t8RJIOmXw7i6wXh0EY1eiiqtnHNbus618Cwt/TOUwhYS01pd6auicP32T1uB1njz0wSWs3Ju3ORlvSXipFwff3I2Dpk/5pBJB2tXF9K+d6rNLy8SuS3QmqBeXmWAOfb25OoMgxAYtsGuL3Rz9NcOYqwjHt8swprLzB9+gjdboe9Xnyc13I1cz49qG6C1Jmr61M5MMPV7H1C/OP3YRJpsR/DyL3bQ1mOhNVw73WDicotv/FoXpfkArxnx0ZtlvvQLbeQ7LbTSfPpWhURa8uxXCxx9KcelE3XGLzR58TvtZNtMqosBp9+r0rnL5iu/2oHlSN769wuU5wIWp/z45hJw4LkMliO4dKLOS99p59Q7FYqz65OMIGxSb82v+76dguuVV0WrXLeEZWdwnBwaSCQLhGEL04xTw2HYemCH3lsQtoVMJZHpFDKdRCTs7RlXSkQqiVyr0bBeUmkspf7QOj4Ht163fnfn/38OUf3Re6uIk5AGe579T8n3HlixPfQbXHrnt/Cb966oFZaJ2duJTCaIylXChTIiYWN2FAhn4u4HVl8nUbWOsG2EYSAzKYQhCabmUI1WXBzS3Y5RyKGaLsHMPAQhMpdBOhbCsZHpFOHsAlGljtlZQDgO7qWVC1eZz2J1t8dz+vQ8qtHC7O0EpTDyWXQUEUwvoFsuSInZkcdYcsAP5xaJyjVEwsHq7UTYJuFihahY2dbrxcQiJbIkRIqESFPRCxiYaDTuHSJsiUFOtC0L3tfSICVJI4VBs8R+cgAAIABJREFUoH2SIo3EIMCnqWur2tDYJJbfE+LTWOM9DwvJbBcHX/uvl001b2Fh7AQTZ/6c7SbqynMpn13DdmWT3+uWnsTBzCLzv/kHBLOPr9j2FhJ7B+j6O7+K7F1tIXAL0rLxq4uk+0cwnAT1Vn3FidTRWpeqRgWK8femOPbrhxh8qY/5C9t7PpQfsviTizSvz9H17WN0fOUpnK7ctn7GndBK0xydY/7Nsyy8dZ6wvD0VXdsGIUjlDC58XKNZizj2co6FKR9pCj79UZnibED/cIJcu8mf/6s5XvhmgaFDSbSCT98qo5XmwLMZpkddasWAwFcc+kKGM+9VqZcjfvBv5nj9r3TSO+RQnruDFGlYuOlx/LU8xZkAwxKU5jYWmZkrXaDSeHRhdaXCVbYGUloMD3+DZjO23UgmO4ginz3DXwM016+/ieeubzmxFmQ6hdnZvvLVUcBob8NsLyAce+ejp2LJ/nApWgUPxzv284Z7WUaYThonvbJUXRrWqofbChiSxPEDJI7sQzddZDpF/Z0TqEaT3Hdepf6Tj1GuT+5bL1P983dJHt6LvW+IYGoWs71AMD1P9c9+gjXYS+a151FNFyOdxL0yTvOTsySP7CP53FP449PIhENLKaJaA6MtT+qLR0FpilfjfnwylyH79RcRhoGwTFS9ReV7b5P71suIpEO4UMJsy+GPTVF760OsXT3kvvUyqt5EhyGts1dQLY/0l5/B6u1EhxEymaD6xtuEc9s3xydFhgFjL0kyOCJJgS4cI0VEwER0heKSA7qBSY/cTVa2kxIZLoefMaPGl8cRSHqNPbTJLjzdIikymFhIJDeiy0yp0WVilBI59hrHSIssGo1AMKPGGY8uPbBH0lYgDAM7VVgV4TSdHbQ/Ug9OErcWcWq66JYPj0n65n5QrThcfz+kd+3FK81iZQoYToLG1Oi6pYp3QlqSRH579UXL0DpOmf3r9yi+e5mOVw+S/8IwdmcWI2nHAu8tPhW01uhQETU93MkSi+9cpHJiFHeqtG0eUdsNwxTkOywsRxJ6mijSeM0ItxUfr9uKEFLQ3muRyhoxsTIEufY4AuV7moH9SQb2JZm4EouhhQDLkbT12DhpA7+lSKQl6XxsfJpIS0rzAbViyLNfzXPiL8obXqD4YQM/fPStRe7EwvxZarVJhDCWLx2tNb5fQ2tF4K9zvFIiMymkbWPks5g9XVh9PVh93Rj5bBxhSiSQSQcM46GnmZ/g4UMmE6SeO4J79jL+zTnSLz1D8ukDVN94h8YHp8m8+jza83HPXia4OUvy8F5UtU7tB+8jEzaF//wvYXa2kf7iMaJSldapS9iDvaSeP4J3ZRwMCZGi8d6nRPVmnF6NFN7lMWQ6QeLo7QhZ4shejHSK+vufgYb8L72OvasbiKNJ1T97G2ugl9y3X6Z54hyp5w7jT85Qf+ujJWdpjdlZIHF4L413PiUqV8l+62USh0aobyNxaugKV8Mz2MLhiPgSi2qaiegKGr2i1UqAx7XoDHnVyT7z3n03C6KLCXWFsegioBmQ+xg0DlDSczR0FYnBsHEYieRC+AmB9ijILnYbB2nqGnNPdFMbxtaJU/j4kyYAHYbr+8JoRbJrF42p66QHVgt5raSJMFevtsyEQe/xTtKdScaL2+v0fDdUK6B+YYrGlRmsPzpB/ukhMof6SQ62Y3flsNrSSMdc3zdHa6KGR1Bs4M1VaY7NUzs3Se3sJGHd3bwf1ENG6CsG9iepLASceb9KvRQyfqlFuNQXqzgbcOVknadfzVMrh1w93SAMNN2DDkLA2Z/WCLx4jHTe5OxPa7QaitGzDY6/nKM44zM95tE75JBtNwGxLBxv1SICX7Ew9XB9arYb6XQv2dzA0rWyZDIautwY/wlRtL4Q3uzuoP3XfhmruwOZzcTkXQqQt/pUPSFKP28QtoU92AtS4BzYA4aBNzMPSuOev0rm1S8gM0ncP7kOS6n/cKGEcj1Uy0UHIUZbDqu3C0wDs7MAQhBMzMTRfyEI5ktElTrriQvNtjz2UD8Z04i7EswVY01cFBFMzaP9AFVvghQIJyb/3tUbaO/2fS1TCezdfegXjsbauCgiamxvBF6h8HGXjSdDAjzWrmwNCQjwltJ0a8/RAT7TaoyGjn325tQEu4y9WMSL+ozIkxUFRqPzVHRsPNlSDXYZI3TIXubV1COJOn0esTXi1PIe+8aptxCLQu9/rNWxC1jpHK25m3G5812hvGf/qyO071vpnioAI2GQH8wStkImP3g4TXh1qPBnqyz86AILPzqP3ZnB7sxitaXjVz6FmUsgbWuZ7KkgQnshYd0jrHv4xTr+XBlvtkpQbsS94AwDIY2tdYTfIaSNAo5IUgpnl25ojdtQXDxR5vqZ25PYZ2/dYcip4cJHdS58dFtAefrd1b5c3/uXsyv+/e4fr1xJjp1vMXb+9iR24Nk0B76Q4eRPKvju53tyyWT7qFZuUK/PcGsS1lpt2CfKSKdJHt6/g0f4BJ87RBHBzAK1H7yHPz695MSuQGmc/UOgNarexN6zi9apSwCxvklKMGKTYtV0iSo1vCvjND6+w5n6Fk+4U8R/HyjXw71wncr33o7JkBRwa6F/94Jfa7QfILOpFdXUOggJbkxT+eO3CBdK8RiPidbzXikGVzcI9e17OEItp+MAUiKDJRz65TAd8rahtSNSuLqJgUH4hDhtCD8HEadoXRF7UC0SVOMHp7uwmgA5WYtU+1rKf8XMqXku/fF1Fq9uTROyFRi5HPlvfJXKj97GmynfdvKWAmEasWeTFEsTgY4nHsMgdfwYzp4RVFDBPTdLWKwj0ykyL72I1dVJMDNL/aMTqMa9UzVGextRqbzjolpT2AwlYtfaT2s/INAugaf54PslAu/h39wTV1rMTfrUyuHnXk/cbMzT0/M0+cKeZcPLMPSYuPHOhiJOT/AEd0M1Xbyr46S+eHxJyO3gT0yj/YD0y8/ReP8zlOeTfvFpguk42uEMD5B89inM9jxRuUYwu0Dz1EVSzx1GBSEohWq5eEvapVUwTayedszeLox8Bnt4gHC+iHvxOrlvfpnU80eIyjWkbdE8dXHNIXQU4V64RuqLxyBU6DCMLS9KVYLZRVIvHsMfn8JIp3AvXCMqb6yZ9WaxHTFaxXqNi2UsPKe5Qnx+M7pGQ1dRT0jThvEAEafPCXEKNpCqWwfv/i8nVrsfa41WsaD6ocOQGLk13L6VRvvhmreOkc/hDI9Q/+AjvBsTy1E4q6cbu6+X8hs/IKrW4rD0PT/XIP+N1yn90Z+uCGvvBCzh0Gb2IZbXSzFXa9YezXXXqita9Z+NiSWd6WVu/iz1+vQd/m/RI2+H8ASfX+ggpP7OpySP7sfe3Y9qtNBBiLBM3HNXcS9cRwchMuEgnLjC2RudjCvclKb2g3chjHDPXYUgxN47GHdHuDQKShHcmI4jP3esWqRtYQ8PIG2LYHKOxFMjtPyA4OYstR9+QOLwXow9OYKbs6A0rTNXiBbiLgOq0aT58VlUo4V77io6CLGHd0Gk4hRio0ntB++ROHYAe88uVLX+uXnm3QtxRMpnQU0xr26uv8MT3BObJk5aKVTTe4zClveHDqN1U3XrjhFpdBTfsMIQSFPGgZxHqAcSjkPmxReQmTTB9AyNE59hdnSQ2DtM7acfoj2f7Guv4E9MIEyL9AvP4QztRtgWzsRNqj9+m/SzXyB1/AhWVxeF73yL1sXLND47hdlWIP3cMxjZLP70DM1TZ+KO9q+9QvLQAaT1KyjPo/znP9yxthhJI0vKyNGKdmaF93hBYBpxRDNOmT141EdKa6m3lV5FiHyvSlvbXlKpriXbAU0U+kxNfUi0E94pT/BzAd1yaX58ZtX2cOZ2I9fWZ7HJYPLwXqJyjdpffAB32r9ECvfCddwLK3vH+eOr/aZUs0Xj3U/XPJbg5mxMmO6Ae/rS7X0bLZof3T5W99zVmLTdgahSv+f42437PUkEAolc7i0nMZZsC9SmokQ1XaKl6/TKIZq6jq9dBGBiExHh8ZhVUT/G2DxxCiO0+/kJ58cRpwfX7Vgpk479bbTvy5PuSqIiTWO2yeLVCsUrJaLg4RJJI5MhrFTxT58l95VXiMoVdBhi9XTHWiXA6u4iLBZpXbxMVK8jLYvqO+/jT8Wd6BufnUI1m6SOHaH85o+IKhWEaZB54TnCYpnmydNkvvwlUkcP0zjxGbX3forZ0U75e38eizrdtQXxEpOUkcWRKQxhIYBIRwTaw1UNPNXk7qlCILBlEkeksGWSHnsYKQwMYdFtDxHoux7+qkXpnk02BY5IkjDinL4h4kkmVHEjy5aqrxJBCgRpo420USDUHqVg5r7eJpZIUDC7kcKgEs7jqq0TSMfOcmDw2wgEDXeB0ekHc/CWwmSg6/mlnnctJuc+XlHZVyxdo9Uq4jg5EBLfq+H7VdQ2lOk+wRNsBMHMQrzw/LznvR8COmQfbaKblMiQEGn6jD1kRYGWrnNDXd7wOBEho9F5RoyjPGW+QKj9pYi+YCy6sOUGvj+P2BBx8samUV48kWvPJ5grrbPHY4QwQgUBWut7V/sIiZUtoAIP5bmrxOF2xuL4XzvEge8Ok+m+LSLUSlO7WefM717i/B9efagRqKhSwbs+SjA9gzd5E6uvF3/qLiJx688Nw7hyJYxQrrtMfLXvo9xY6K+aLbQfYORyJA8eJFhYwN7Vh9XTg5AiJllL742arVUNeG8hJXPsThyhzeolITOYwgIEkQ4JdItmVGM+uMGUd3mFkDEhs+xNPUvO6MSRKSwRV4I4MsXh9KurPqcYTvFJ9ba4+RaSMkufs5eC2UvKyOHIJHJpdRZoj1ZUY9YfY9K9uKrfU5vZw8H0l/BUizP1tyiHK1esd6LLGuRQ+iU0ijP1Hz8QcTKNBD1thxFCUq7dYHzmvQdSGwhh0JEbobNwAM+vUaqN4dduE6cwaJDtPkrCaQM0UcpnevqTbTO9fIInWA+tk2trjn4eEREwHl2gpdfWloY6oEWdlq6zqGaWtwfE0WGNYkFNUddlQm5HjF1d51L4KU19O2pf1UUuhp+Qle3Y2CgULd2gpj9Hz/THABsiTsXf+cFtPY3WcarucwTtBnGZvbE2ccqNHCHVN0Rz+gZmIkXp4scr3MNHvjbIoV/ay8zpBd7/J59SnaojDUnbcI793xnm2K8fpDRWYerE3MP6k5ZckZcedJFaLgVfhhAI07xvU9U1IQWq1aLx2SnC0pIeoNlC+z4kVwvk74QjUhxKf5kOq59AezSjCr5ykUJiyQT2UpSmqaqrQtMaRSuqES01o82bXeTNLkLtMxeML2+/hUZUYa0Ad9rIM+AcwhQ2vnaphUUC7WFgkjSy5M0uMkYbUhiMt84sR5U0mlI4Qz0qkTXa6bQGqITza5bnmsKm3erHFDbzwQ1q4eNmBKvwwpjIGdIileikVBtb/m1PzzO4bpnpqY/RWpPN9tPd8zTN5jxhuLO2Gk/wBE+wEgp1Xw+lsp6nrO/ffaCmS6vIj4/HlLq+6r0uTVz1JLr0INgQcVLVx8vAb7NQrheTjLvF1Euwc+00Jq5hJFIYiRR31zgMfrmf4rUy7/2jj2kVb5PGhYtFpj+b4xf+8VfY9ULvQyVORj6Hvasf5bpY/b14o+OolotMJDGyGcyOdsyO9k2Pqz2PYHERI53GvXoNmUjE4vqlsl2EwCzkCYsldBisbB5rD9Fm9eCqJpebHzHvjy8TE4lBxmgjZ3ZSj4pEd6XeXFXnWuuWnkAwknyWvNmFr1pcbnyIrzf2QC+Hc0y4cdi5HMzSVDVuHWTWaGdf6nm6rEF67REW/BvUotukpxGVKQUzy8RpyrtCU622MkjJHAWrB0VIMZh67ELcWmuCILZSkNIkYa90m3cSbcxNnKXViv/2IGzR3nEQIZ60rnyCJ3iCJ1gPW54pDWGStArU/UVuPZgcI0PaaqPoPl7NFpsnLhKVqstRmXBhpXWAX1kks3s/ZjJNffLaqpSFNCX12eb/z957xsyVpfl9v3Nz5ao3R5IvM5vdTXZPd0/q7ckzO7uzkIVd7XotS8ZKMmwI9gfDsGAIMGB/sD7ZsCUZMATYclLWCsKuZidpZ6Z3Uvd07mYHZr45Vo43nuMPt/iSxaqXfBPZnN3+f2CoW3Xr1K1b5zznef7P/98TNN1Gc72NW/PQrfvYERw2pCTYKmKOj+GcPU3UbOJeu44KAvy1NbJf/RKy0SSqVO90vymJ7HR6iZjEAqGy425zDaTr0Xr9TVLPPoM9dxTl+zRfexO/HTvRu9euk/3Kl4gadeo/+jPkXaJwjpZGw6Ajt6iHmz0cIUlEPSpSj4o8TITKZ969NDBT1IjKLLuXyRljXQ5WqidwUii2/EUm7OOkjSEyxght/07gBbG9Qc4cI6FlcGWTYvD4qe0qFJHsfu9Cw9B7Ve3b7ViOoFy6ilQR+dwxgqD9SVfdJ9gR9/odfoJP8BcZ+w6cDM1mNnuB9eYVat4aKbPAdOZJmn7psQuc/IV1/MUNevra70Jz+Tp+rYgwTIJmrU+ZtnStQmEuh5U28Zu9i0tiyEE3dco3ajwqRPUG5T/8tzFvyzC7TvBxUFd/+acIy4Io6lqqBNuvqXzn+3HJ7S54i0v4a2t3BVgKb2EJf2MzLvVJGWfsusear7yGsN/tlmx7VW491UYSkdYLDJnTbPrzBOrRl3Xvp37blnUiFWAJB31AhqUWbtEIS4xaRxi3jlIMlnqyY4YwGTFn0YRGNdykHT26730vuK3nIhBoWu/n3Fh/h4mJi0zPfBYhBO12kbXV1z/RcPoEO2Mn/psQmE4G005jWAmEHnd+KRkRhR6B1yLo1JHRow3KNcPGcjIYVhLNtLe90JSSyNAn9Dv4bp3IdzlsI9kdx6SbGN3rpJsOmm4gxJ1xKRkShT5R0CFwW0TBoxvbvdBNB8NOYZgJdNNGaHdcKZSMkDJChj5R6BL6HUK/s/M9cogQmt6931LoptO937T4e40CosAl8JqEbvOhcjb3HTj5UYfN1nWmMk+QtcfJ2WMU2wust3bP8n9kUPfv3khNHqO9uYTQdDJHTlO98nYPQfzGDxb41H/6FBf+6jluvbxEu9hBGBrZqRSnfn2O5nqLzfdLOIU7O3sVKbz6Q2rtVirOEgGK3sVO+X5fcHT7NWpQF1wU9fC5tp/bcQf+ZFUQ7Kj1VPSXmLJPkjPGOJV8niFzkqK/RClY2XWp7bCgYZDW8yT1HJbmoAsTHR1LS2AKGyHixt57ERGy4d9ixJwlb4yT1LI0otL28aSWJWeMoFBs+gsPEJz7eCCEhmWkuv9TqHs2AlHksrT0M3TdBDSkDO5v4PoJ/sJDyqg36yQEiew4Q9PnSQ8fxU4PYyUyaIaFQENGAaHfxmtXaFfXqK1fpbZxDRk+XLkLK5knP3GG9PARnOwYViKLaafQ9LhJRcqQKOjgd+q4jSKtyjLVtSt0ausPPPd+IDSDZGGK9NARkvkJ7NQQViIXL/yGjdC7gVM3EAkDl8Ct4zW7123zOu3qal/D0kMZq9BIFqbJjs6RzE9hp4YwE3HwqRtW3K2tFEqGyCgk9NuE3cDYbZZo19ZplhZxm6UDBlGqe6/dlenXDDIjx8hNnCZVmMZOFTCdNJoe+7UqGREFHoHXxGuVaVfXqK5doVGcfygB3Z4DJ03oWHo8KbeDKqX2AjPZJyl1Fql6q+jCRKrHx7ZjNwjaDQrnnkfTDZpLV/sWmtO/eZzJi2Mce2mG09+aI/KieKeVMEgUbLyGz8jZAndzoxqrTb7/d34aP/cvCNqyzketV5hLXCBvjDNhnWDUPEJHNtgKltj052mFVSIe3v1hCItxa44p+yQJLYMmdNT2DxE0oaEL8z5nUFSDTRpRmZSeY8w6SqNzJ3AasY5gCItGWKYe3p+w+XHBMbMUMkcBkCrqMxmenvkM5dJ1Wq14sbCsDBOTz7K68stdkcOVkg80zv4EvwLYgxZfbMXUpWSkCowd/zRDsxewU/luUNILXbPRTRs7VSAzcozh2adpFBdYv/ozGsX5Q7Z2EtipAkOzTzNy9Bns9HAclAxojNE1C92wsBI5UoUZCtPnGTv+aSqrH7F163U69c0DL7SaYWGnhsiNn6Qw9QROZhSjG7zt1NktdK2bkUrhpIfJjMwho4Bx93M0ivNsXP8FzdLiQwmgDCtJZuQYI8c+RWpothuQDB6rEICmoxs2pp2CzCjQ1Z8LPEKvRbu6xurlH9Ms7aD4/gAopbYzlEJoJPOTTJz+NbLjp7CcNKKbPewZV/f6mU6aZG6C/MQZRo4+S239KhvXX6FVXT3UAGrPgVPCyDNXeP7OA0oRSp+CM0PKGqbmrrNUf+fQBvgwodkO2bnzCE3DyhTQDBMjle36LN2Jdt2ax9Irq3vSxW+X3I9VIPPjQi3c5FLjZYbNKcaso+TNcdL6EBl9mGn7NMvuZZbcj/DVYDPLg8AQFscTF5m1zyGEoBZuUQnWack6gXSJVIitJTiT/AyOntrxPB3ZoBgskdYLDJtTrHhXcWUTSyQYMifQ0CgGy109qscHumaRToxxZPwzpJwRAKLIp9HplVVIJkbZij7Y/n8UeSQSw30lvZ0QlavUvv3vD2/gn6APQtNInD9H1GjizS/yMEo23q3dUypkFDeIJPOTzD79G+QmTiOEtitDZyE0DDtNYfo8qaEZVj/8IZs3Xz+k4EmQmzjF9Lkvkxmdi303d9lJLIRANyyczCiTZ0bJjZ9k5cMfUln5cN9jM50ME6deZOTYs1jJHCD2bXqt6SZWMs/wkYukR46y8v4PKC2+e4hlT0EiO8bk2ZcYnr2IZljxo/sYrxAahpXAMB00wzoQJ+52OVUIjcL0k8w+9Q2c7NiexiY0HSuZZ/T4C6SHj7D03neprH647zHdiz0HTm5YZ776xo7Ht0mpvwqQiqjTAiFozH8EQiADn3snqff+2eV+y5UHQSlk+PgGTsKIjTWFoaGZsb+d9ENUKFFBFHtF7XP4EQGbwQLlYJWUnqdgTjJpnyCtFziWeBpN6NzsvNPXWXdQxMHZGYTQWfOucbPzDh3Z5O4PktYL9xW2hJjIXvJXmLROkNIL5IwxXL9JzhglqefwZJtKsPbA89yLdGKM6dHn+sjapuFwOypPOsOcO/pbSLW3c4suCTxpD5Gwh7Y5HR2/Sr3ZS2APww7pzBRB0EIpRTI5Rpwe392OLKrWqX/v5T2N71FCINCEgdYtxSoUUsX+8/c+U7/reZKISEXcfb/owuxeF4EmdKQKkUh0EXMr4v9H95xV6x4XKOKsX/97P+AzmCbO3EnC1SL177/8sQtFyijAyYww9cSXyY6d3L6/oEscV3J7sRRC9AUwt/9tJXJMn/8aCijeeuNAQYDQdArTTzLz5NdIZMcHLqo9YxPxvbHT2JL5KY5e/BaGlaQ4/+a+x5YZO46VzO+4yG8HFarrLacGX7O7x2YnC8w89etEYUBl+dKB+TtC6GTHTzB9/mukh4/0fJ8HgQIaxfm4XLffc8g445SbPMPsU98gkRvvPd6l3mxfAyEGBvG3/5/ITTDz5NcJ/Q6N4q19j+tu7DlwilRA0x/cGaULk4w9Sifsb+F+HCEDj8bCZYxkBis3gmYYSL+/VHG35crDhJ60cKaH8NZrhI3Dz8gAaLZB4ugI2SdnSJ+fITEzhJ6wQBOoIMLfatC6vk79/WVa19bxt/ZveRISUIu2aEQlNv0FTiafZdyaY9I6yZp3nWa0k+ja3VyK3b2XQJAzRjE1G192WHIv05H9YzeFgyYePEnUwrhcN2rOMmxOUQnWKJgTWCJBOVyhFu5desIys4wXnsAy0zs+xzSSjA89uedz38bdk0cYuqxsvYUf9JbqisXLTE2/QD4/h5IRtpOjVLpCGP7qk8MtLcGodYQxZ46UkQcEgexQ8de41XqXoMu104XBsDXLpHOSlFEAoBmWWO1cpeyvIokQCJ7I/hqtsIqlJShYU5T9ZTbdeY4mnyJp5Nl0b3GrfWcTYAqHcec4E84JbC2JVBG1cJOV9uV93TOPCzTdZPLMS+TGTm6XSmQU4LUqeK0KfqdK6HVQSmLaSez0MHZqCDuZ7ymtiC6ZfPqJrxB06nEWYF9BoaAwdY4jF34TO1XoWzSjwMNrV/DbVfxOLSYvIzCsBFYih50qYKcKPWVGIQRWssDMk98gCj1KC++w191j4DaoLL9PujCDMO6cWymFDH0Ct0Hot/A7TUKvSRS4KCXRTQcrmcdK5nFSQ+hm7+ZKCIGVyDF17ou0q6u4jYPQBOIs3ZGL3yKRHRvIb7xdLovH20GG/rYhuOiW6m4TyO8ui8oooLLyYZfYvl8onMxoXObMjm4/KmVE0Kl377kygdcCJdEMGzuZx04PYaeG+sqMQgiS+Skmz76E+0aJwD14fHKowi2m7jCePEXV7fcVepyRO/k0Vm4YPZEiajdxyxuo6NHztHLPHefo3/oijQ+Wqb5xi9rb8wTlw9PQssayjP360wx/4RyJmcEaT85UgeyFI4x96xnq7y2y+e13qL01jzyAbY1E0pY11rwbDJszOHryATwjUHfpPwmh7WL+EhjCQiCQSuIP1FbqBlfCHnCsFxEhm/4Cw+YUw+Y06/pNhoxJFBGlYHVf3YKuX6VYu0YhcxTHyg/c5e03rX83lFIEYZulzdfYKL/f12VYqy3geXUymSmEprOx8S6t1sb2xPirCkNYHE99iunEGarBOuudG0gkjp7qWRwEGhPOSU6lP00t2GS1cxUBFKwpzue+yOX6L9j0YuFAR8uQS4yz5c3TDEvMJs6TNUaph0VUqJhMnKQSrFHyl9GFydHUU0wlzlD0Fil6ixiazZh9jEx2mA/qL9MM482CcBwSZ09hjo+jfA/3xjz+4v1LZ1rCIfXcs0S1Op0PL6PCRzdHJXLjJPOTCKGhlMJrlSnOv0ll9SPa1bW+0pZmWKSHZhk++iy1BvE1AAAgAElEQVQjRy6gm3fEc4UQ2Mk8k2e/QLu6itfau2p1Mj/F9BNfxUn3zmNKSlrVFYrzb1HfuEanUewbm9BNktlx8lPnGD32Kez08PbvLg7s0kw/8RU6tQ3a1b2vZZWVDxide55kfgIlJW6zSKO4QLO0SKe2htssEbj9mzohdOz0MPmps4wd/3Q3qOkNABLZcYZnn2blwx/ueVy3kRqaZuapb+yYpQsDl/rGNWrr12jX1vFaZUKvHWfghIi5RHYqDvLSwyTzU6SHZkgWpunU1mmVFw/EJ9J0g+HZp7sBUPy7DdwGxYW3qax+SKu8QhT0JhaEpm8HW2MnPo2THr7nuEZ+8iyZ0TnKS+9x0NL3ngOnjDXK8fynB5ZZdM1EHirp7xFB02jc+hAjncNMZR/8/IcyBsHwS2ewxrIMjz1B/vnjuGtVqm/covyzK3QWiqhwnzejgPSZKWb+2otkn5pBs+8ftADotkn+U8dJHR9n83vvsfHHbxJUd+b0CDQSWgZJSCC9PgK4jklKz6MJHb/LN7of3CguIxnCImeMsfXADjaFJ+PX6MIgo4901XG7pHB08sY4U/ZJNHaXli4Hq7SiOmk9z6g1S9oYwpNtisH+5Dbabolryz/AMlIk7AK59Cz59CxJZwTbzCBE3PUThJ19dOvFBPgwcqm1Vtgsf0C1ubRD6VzhumVc93FTPD8YhqxpJp2TrLnXuNl8u2uDo7p+iRphN9i19RRHk09RD7a40vjFtqTEpjfP2eznOZa6QDVYx5dthIBO1GChfQlLJMmZ40Qq4HrzNVJ6gQv5r5PQ4zkjb04w5Zxmuf0RC+33iFSAQKPmb/BE7iUmnVNcb76BlsuQ+8ZXsCbGCTa3EI5N4snzNF95jdbb7/ZprSEEWipJ7itfxJqdofrvvvtIuqzuxm3+m1KKZmmRpfe+Q6O4sCMXSIY+9c0btKtrdGrrTJ37MqaT7lmoU4UZRo49FwcBe1hoDTvF5NmXSOQne98zCikvvcfyh3+K1yjuWM5SUUCrsky7tk5j6yazT3+T9PDRnuApkRll4vSLLL7zJ4T+3javfqfG1vzr5CbOUF58l/rWLQK3/sAsjFIRbmOTjaslmsUFjj7zW6SHj/QE/Zpukh07ycaNXxJ6e7d5Mp0MU+e+TCo/1Rc0ySikUZxn/epPaRQXBp9fKWTo4YUeXqtMY+vmtsxCIjuKEPqBynQQ0w5uB9pKSTr1LZYvfY/q+lXkDllxJSM6tXXcRpF2bY0jT/8GidxEz2fUdJPh2QtUVz88ME9sz4GTqTn4ssN680rfMdtIMeTMHGhAHwc6G0tEbhtndCrexX0MfILEzBCpE3EtVwiBkUmQSjskj4+RPj3Jzb//PfzN/aUYk8dGOfqffZn0mck71jm7gNAE5lCKyd9+Ds3WWfnnrxA1B9+4hjA5mXyWhJahEq7TimoE0uP2wpUzRhm359DQWPMX8eT9J6NGVMKVTRwtzYnEMzhaGjdqgBDoGATK6wlgFGrbbNfRUhxPXMDSHFzZREMnYwwxaZ+MszHKw+TBWSdPttnyF8gkhpiwjqMJnXKw1uVN7QeKIOwQhB1abpFS/UbXV+4ET5/8XQQ6LbfI1cXv7ZkrKJUiijz8sEUkg1/57NF+MGRNESqfdfcG7l1l2ns3eTljBEtLsNK53KPD1YqqlLxljqUukDVHKHpxV5AXtQiki6bpBNKlFdW63ouxu7wu9K4w6ii2nkIXOhPO8e3zGsJCQydlFLDMJNZT50mcOknxn/8rvMUlhGGQffFz5L7yBfylZYKNu0p6SqElEmS//AXsE3NU//g7eAv761Y6DPjtCsuXvkd96+au5snQb7Nx/ReYdprJc1/oUafXdJPC9HnKy5d2Lwcg4sxBYepcT0ChpKS6foXF976D367e5wR3oGRIffMmS5e+x4kXfg87VbjrbfTtsVVXL7OXDIWMAtav/ZyN668iI3/P64lSEc3SAisf/pATL/wepnOntC9E3EGYyI7R2Nr7PJSbPEth6om+zjQpQ8rL77N06Xt4zTui1ruBjAL8dgW/XSHmVhzO+hlnNissvvNtqutXdhVcKxlSW7vCmpPt8tUSPcdTQ9NYiRxu82BCzHsOnNpBlZXG+9S9fgNUS09iavf3M9s3xO0/1KE3mITtBs7wJDLwidz2x6KSm3lyFjOf7EvNKqCzXCYo72+xNofTTP2HnyF1emJPQdPdY9Adi7FvPI2/1WDjO++g/P5F+TYBN2MMkzVGAYVUEQqF1l1YZFcjab7z3gNLXe2oznznPeYSF0nrQ5xJ3s5yCjShsRUs9mV+mlGVefcSc4kLZI1RzhpDhDLuztCFQTMsc73zJkec84yaRx742WW3LDdlnyahp4lUSDFYfmC2bLdQSqKUpNXZxA9aOFaWKPKpt1cJPxGj3DNsLUWofHx5f36gpSVRSuLL/gyAL10UCku7M+FK7mgYKdRd3//teSImjltaEg2NKedMX3k0UkF8L1oWzumTuPML+KvrEEaoMKJz5Rrpz38Gc2qSYHPrzvkFZD7/aZwTx6l9709xb87v48ocDqSMKC2919XG2f0cqWTE1q3XyU2cIj00u+2fKYQgmR0jOzJHp76xq3OadpKRo89gWMk751cKt1li7cpPdh003TU6msUFSovvMHHmpZ7yuWmnKEyfp755c8dMx45njULUAWVXmsUF6ls3GZ59uudxo1sm2yt0M8Ho3HNoeu+yr5SkvnmT5Q9+gHfAgOIwF2clQ7ZuvUltl0HT9uuUpLz8HmPdjrq711TddEjkJx594ORGDdxo8CIeRB3Wmh/tfzQChG2jpRy0hI1mmQjbRCRshKEjNIGKJMqPbUKUH6JcH9nxiFodCPe3y84cOxeroHZaREGcJXmU0ByT9OkJtITVdyxqutTeurWvMp0wdIa/cJahz55CMw7WNaGnHSb+8vO0rm/QeL/fZiRUPtc7b1IJ18gaozhaCkPEonOh8mlHNcrBGrVwc1dimJKIZe8qTVll1DxCSs+jC51IhbiyRWmA1YkkZNm9QiusMmYdJannEAh85VILN9nyF+nIBgktgynsXY2jFVVoRCUSepp6WKQRFjns+yOUPm231Ocp9zjBMAUTJ1PUNj0apcdTw0mqEIH2wFJspLpcjQFNArcf6wmO1fYfO0IphVQhEVGXy9S/gEtCoqSOnssSXL+xreoPID0P2Wpj5HN3jLmVwjl7BmGa+MvLcaZJPnx15p0QdGpUVvZX5vDaFUqL75DMT/Us3Jpukh0/QWnpXUL/wfIeyfw06ZFjPY8pGVFdv0KzuLDncUGX0Lz6EUOzF/o4U7mxk5hOGm+HTPvDRBS41DdvMDTzVO/ib9iYdvI+rxyM7NgJktnxvsf9Tp21yy/j1h8vXTq3UaS08Na+Oggjv0N96ybp4d4NsqYbOKnhHV61e+yTHK4QCDL2GEPODLp2Z8Fv+kU2Wtf2dDbhWNhHJ7COT2PNjGNODGGM5NGzKYRj9RPYlEJFEtl2CSsNwmKVYHULf3ED7+YKwVppzxOMkcgQCR00ncNMN+4G1nCaxNGRgUQ9b7NO86P9ke0TR4YZ/42LD+Y03d7p3YeYLITAmcgx9psXaV1bR3r9uylXNlnxrrLiHY56/G0T3XKw+88vCSmFK5TClR2fs+xdZtm7vKvz6cLAECZSRVQOVKbbGVEU0PZKDDF36Oc+LGRGLP7gfzrHD//vZV75w7WPuzt+IBphmRH7CFlzhEZY3JEn1ghjblfGGEbD2JYK0IVBxhhCoWiFeyMsSyJaURWlJAk9S9EfzIPTVBIVhQhd384oQ7cpQNe6Kv63f48aRCHtSx+QvPAkqWcu0Hj1dXiEpPDbUErhtiq0yvvj9ykZ0Sgu4LUqJO7qlEII0iPHMKzkrgKnwtQ5dL13gxkFLtUDaC8BtCrL+O1qX4eeYadIFWa65atHC6UkXquCDP2eLjuhaWi6xV7WKaHp5CZO9WTqbqO+cZ3axvVdn+tRobL6EX5n/5ZW7coK8We6q4qjGZhO5sBj23dXnaknmc0+jRs2yTtTFNvzDCVmaAe7nHA0DT2fJnnxFKnnn8CcGkUvZNCsBxOXEQJh6OjZFHo2hX10AvXsGWTbJSrVcK8v03rlEt7NVWTrwW39Skoit0XYaSL9R59xMofS2JODU6+N95eJ2nvf7QhTZ/RrT2JP5AYeV0oRVNvU3rhF7Z15gkoLI5Mg88Q0+eePY4/nBpb2sk8dIX12ivq7Hx/P4lEia4yS0YfxZIdisHxfH7z9IpI+bbf8iZHqAbHlLTCZOMnR5AUAyv4qCoWtJUnoaba8JULl0Y5qbLq3GLPnaIWV7SBnxDrCmHOMTXd+Hx6EirK/SiVYYzZ5fjvQjt8/QdoYphEWqQdVgpU1zIlxtGSSqBbzFvV8Dj2dJtjYAqm4nTRzb9yi/uOfID2PzOc/S1As4V7+GGytlKRdXTsQqdZtFHGbWziZ3k2iaaex00MPLJ/opkNqaKZvg+d3arRrB+vklqGP1yqRGTnWc35NN0jmJykvvXug8+97XFGAjPx+eQJN7xNqvh+sRJ5kbgKh9c7pqlt+fRQ+c3tBFLg0SwsHut/8Tj8nWAiBbvZXdvaK/Zv8ChOlFMv19zA1m6X6u9TcNYaTRx/4Wi3pkHjmNNmvPI89N4Wwrb0LTN4DIQR6KoGWdDCnR0k9d472W1eo/+h1vBur/Z0qd8Err2OkcrGRoR7xSDNOQmCPZjBz/TsBFUnql5ZQe7BGuI306Qnyzx9HDCjRKaXw1qos/38/o/LqdSI3iCdrIai8cp3STy4z89d/jeyTM33Bk5lPUvjsSRofLO+/y+9XBJZIMGEdxxAW5XDtIerwKGqtZdZK7+L6VeQDJrHps2l0Q7D4fgPdFJz4VI7Akyy+30BGimNPZ2nVAjZvdUBAdsRi8mQKJ6XTqgWsXG7RafTuzq2ExtSpFLkxm8CTrN9oUVnz7jsvp/ImcxezlFZc1m+0Pva5tx1Vudp4laOppzmeepZT6Re2BSjrwRZlf5VQxaW6+fa7CCE4knyKY6lngDhbueUtMt9+l0iFsVjiHtCJ6lxvvMax1EWOp59BF5+Oz6si2lGNVrOK8n3a777P0O/8JdIvPEfnyjW0hEPm85/BuzVPsLbWe1KpkJ5H85evYxTy5L76JcJyhXDz0ZZVlFK49X5e614Q+i28ZjlepMXd2k4aicwYtfX7B4ROZhTTyfRl5t3GFtEheOD5nTpKyR4PS6Hp2KnB0i2PArGh7sF/WFYyhz2gRNVpFGN+2WMGr13F2zNfrRcy9O5NOBEruR9c7HPfgZMkQqoITRhEKmQ0OReXNbT7R3P6cJbcNz9H5qVn0LM7217sF3HKO85GpV+6iH1imtp3X6H58/dQ3uAfV2vl5qGPY7cQhoY9mR8YOPrlJu5qdc8xnGYb5F84gTPVLwx3O9O0/E9/QeknV1B388KUQnoBjfeXWfo/X+bE3/kWznTvOTRTJ3VqEms0i7d2sBv7ccTtxdIUNrPOOUbMGULls+JeQT6EbNNtVBsLVBu742g8/eVhJk4k+b/+649I501+7787RW3L5//5Ox8R+pKv/a1Z3vtRic1bHcaPJfiN/+IYo7MJfE/ipHSuvFrl+/9ogXYtDp7spM4X/uo0F746QuBLTEejvuXzvf99gYX3GwPvPzup85U/mOHUC3m+/Q9usXHz40/0KxQlf5lmWCZrjmBp8WYklD6tqNpDBu9EDa42XiVrjpHQM9uP1YMtQuVvn2++9S6B9JAofNlhvvXudkeoLz2uNV+jGd4p49TDIh81fhbz/PQkILbfv9XlPXnzC1S/+wMyn/8syQtPAQp/dZ36yz8lqt+l7yPldiZAttrUf/wThn7nPyD3lS9S+XffRTYPv2y8E5SSB17IgLj0JEP0u7u6BNjpBwcnsTBkf/NRPC5xYKPq2ID43rs4Fs0Umv4QJCDEXX+JQUf6MkT7hemke7rzbqNTWyfyH47Y8kHgd+qE3sE0DO/1nN3GIWjl7TtwCiKXzfZ1Qumx1b7FkewFpJKsNC7t+BpzaoSh3/86iQunEOaham8OhNA0zJkxhn7/6+j5NLXv/ALlPl6WMMLQsccGl9M6C6V9KYhbo1mGXjwzuItOKso/u0LpJ5d7g6Z70Ly2TvFP32fmr7/Yd6MlZgokZob+XAVOAsGkfZIZ+ywSiSUcEno80Sy7l6mEaw84w6PD5nyHE5/KYToaI7MJolChm4JExkA3BE7aYP1GC90UfOVvzGIldP75/3CVRtFn9nyG3/jbxygtd/jJP1tFaHDhqyM8+81Rvv+PFrn1To1UweQ3/8s5vvo3Z/kX//1VWtVeonQia/DFvzbN8WeyfPsfznPttRqPWFbovvBkmy1v51Jyxhmj5ZUJVUDZX8HUE6SdUeruZp90waY3v/3vUPlsencsGyIVsOb28zkD6VLageMEoMKQ9qUP8eYX0RwHJSWq3cHwdTRhIFWICgKK//LfoIJguxwTliuU/uW/QRgGmh9hmhm8YP/K/nuDInAPHqj5bn1AACJ2xTsxkzl0o39jPnrsOfKT5ziwqKGVQtzj1yiEQNMMNM0kOsBNLjQDw0qgm04sHpkawkpkMOxU7O+mW2i6iaYbaJqB6P7bsJIDA549vbfQsJwMmt6faXGbpUPJ1h02Ir/dbdR6PLHv6EUTOnl7iqw1hhs2WKi9TSes40eDCX56NkXhr3yF5HPndq2OrMIQ2fGQbhCX2hSgCYRloDl23HH3gIhcCIGeSZL/1osoL6D+p6+hvMenK0joGtbw4B+Gu1Imau5duj737DGcqcLAY95Wnc3vvot6kBK4VJR/cY3x33oGa7h3UjNyyTgT9Za2rzLi4wodg7QxhIGBJKId1VnzbrDsXSY8ZF+9g2DjZgtNFwxNOkyeSrJ2vYVhaeQnbHRdIARU130mTqSYOZvmz/7pCksfxItebavE5357ktOfLvDKv1nHdDTOvzTE6rUWH/2sjNuKqG74fPByiW/+7WPkRu3twEkp0DR48femeOqLI/zR/3KTq69W9lWiyzoT6LqFZSSptpaJZEAuOY2umdQ763hhg1xiCsfM4oVNKq1FElaejDOOlCGV9hKm7pC0CuiahRvUiWT8HbX9MhlnHC9s4pgZklaBMPKptpexzQxHRz5NpbVItb1Mx6+SdkbRhLFNJs84Y6TsYfywTa2zRsoawrGyCKFRb6/TCQ5hwyAlUa2+zXEydIejE59jtfweza4pc1Tu54vefn4+NctI7hTXV3+0fUwTOrnkNJXWQ+AfKnUoC1nkdwZmAgaRlu+FaSX7AhvYOZtyWBBC23fmx7BTZEaOkR6aJTU0QyI7gZXIHlomaTcQmo7hpLk3q6WUJHCbh2S2fLiIQv8QzYwPH/sOnCIVUHaXsfUUtp4iZQ1jajatoMJSvZdIJ2yTzFdfIPnMmR2NGFUQIpsd/OVN/Fur+KtFoloD5QaxKKWMlZGFFpfihGmgJR2M0TzW9Bj28SmM0TyaY4Peb/inJR1yv/E5grUi7XeuxpyexwBCFxiZ/vSziiR+sTGwe+1+0GyDoc+dGujxpqSk/LOruCu7I/AH1RbNK+sMfa43cBJC4EwPISwD1Xk4uxXn6CjTf/AlNv/4dRpvzz90UVL7yDDp/+Q5bv7wJs035pEywlcd2lF9z2a+DxvFZRcZKoZnbMaOJVn6sEl+zGbyZAq/HdGsBAReRGEiTXrI4nO/M8n5l+7wG8aPJ9m41cZO6pi2RmHKIV0w+Y//3llk15MxPx47nCeyd6YIGSme/NIw43NJ3vnBFjffrO2b15RPzSJVQKOziVSSfGqWlD1EEHYYy55ms36Z0cxJGu4mYeShaxbj2bO4QY10YpJIBmiaTj45Q6l5iyByMXWbofQxlsvvMJY9xWLpTaSShJHPSOY4naBKKD0UCi9sEkZePKcgyCenqHfWMLAYTh+n1lkl60x2xzqDFzRRSjKUPsZq9b19tUgbmh2bAnfFSXXNip3gVYSuGWzVruH5dxNaBabuIISOUlE8diXRNYsg6rBVvSNCrAmDlDPC9MiztP0KUkUE4YO71HaLWHPs4L8DGQUDr51m3L8pSGg6mjGgw/pRYB9vadhp8pNnGDn6DMn8VFc1/dEFSz0QAt3oF/xVMkI+pnpxSkYHNjJ+mNh34CTQMITZdSLX0IRBKH06Qe3eJ2KfmCHzxWcRA9riVRDiL27QfvsKrTcvE25WUEG4HSw9ELqOMHU0x8aemyL5/DkST57AGO13p9aHsmS/8Rm8W6tElUeV4r4/YlJ7/00duQFBZe813vTZKRKzg3UqvI061V/eiDN4u0DU8mnf2KDw2ZN919Iez6JZOg/QGtw3jEyC3Asnqf7iyiPh6usZB/vZCbbe+oBiuPzYBNaD4HcktWKcUcqP21z+RQWvHTFzLk2jFFBadgm8eNKJAsnKlRYbt+4sojfeqlFd9/A7EaYdT+blVZeb79SIgjufOwokpZU7GU/NEGSGLZY+bHLqhQLv/bjE/Dv1fcW0CknT3aLWWUUTOgkrT9oepUUJL2wSyYCtxnWyiUk0YeCHLVL2MJpmErvKS0CnE9Sod9aQKiKMbKSMmMw9QcevIVVEIRk7GSSsApow8IISftii0dnEC+MsXCeokYlifRvTSMQq9O01bCONbaSJIp+GGzcG5JPTCPQ9d1dqwmB29HnaXomNaqxEfWz8c1SbS9Q7a8yOPk8hfZTLS98l6MQ/qmxygqnhiwihEYYdVsvv0XJLFNJHmBx6miDsUGvHshvpxDizo8+RS01zfOIl/LDFrfWfbgdpB4VS8lB+gzvxTh5I2BVan9r144rU0CyTZ79AfuIMuuk8MNhTSsWBggzjsq2KUDIOqDVN7xLiDxJ0iYHXTim5Mw/oY4fi42dN7oz9d9VpNrO5CyilaHibFNs3afolgnvUeIVlknr+HOZYf+lI+gHNP3uL2vd/SbCyub/rFEWoKCJyfdpvX6H9/g0ST54g/60Xcc4e7eH5CCFwTs6SeGKO5s/f28ebPQQIgWb1fw3SDwlbe9sNCF0j9+wxjHsUyCGesJofrdK6vktrA0CFEX6xgQqjPk6aWUgP7Nj7BA8fSilKyy5Hnsxg2Bobt9pEgeSZr49iOTrXXqsSeJLqhkenEbL4foNX/+3g793oRFTWXGQEv/jXa7jNnRdaAfzyjza49KMiv/N3T/LVP5jlX/2P16ht7C/reLt7UKoozvZoVrz58msoJUnZ8QYgaRdYr7lU2ssYmhn7V/k1Us5wPPl3zxe/tsrs8LNcXf8xmtBxzBxuUCOM3O1gJwxdJvPnKTZu4AZ1RjMnySWn8MIm5dYCKMXM0AUsPclm4xqmkYgznt0Afj85D6lC2l6JTHKSYv0GumaRtAssbb1OELZZ2PgljpndDiAEGoXUEYKgzcLmq+i6SRjF5OVi/TpKKcbyZ7fPX2+vsLSl0ITGR0t/sq/v4/4Q+/vgfafZ4SQPjL53Pu62ykR++6Gts53G1q6zH+mhI7HH3MjRHYOdKPRx65u4zRJeu0roNYkCtys9ECKjECVDpAxJFaaZOf+1gaT4P/d4fOOm/QdOftTmSuknWFoCx8xQSMwylT5Hwy+zWH9r+3l6PkPy4ume1yqlkG2X+ndfofb9V5GNw0spE4R03rlKsFZk6Pe/TupTZ3sWeJGwSD53jtZrH6KCx6C2KxhI4lZhtOcynT2eI/PU7ECV8LDpxdID7b0tckG9Q9Ty0fK9t4qRcfZl4fIJDg6lYONmmxd+a4xb7zboNELWb7SxUzpDlk151UVJWLveYunDJs9+c5TN+TblVQ+hQTJr4LuSrYUOnUbIpR+X+OrfmOXCV0e4+ssqSoKd1LCSOqtXWz1ZqNCPA7I/+Yfz/PZ/e4Kv/c0jfOd/m6dd39u9ul79cJuTBFDvrOEFDTShE0QekQwptxYQCMLIQ6qQjdpHWEYSgSCIOtTaqwih3VVCUlTai7T8Em4Qt5Yvlt5AEzrF5s1tIvVy5R1M3cEP23Fmq36NUvMWYeQThG1Wq5cwdYdIhvhRi45f2/YO7Pi1gQbnu0G1tcxI9iSWkSKXmqLlFvF3KKcpJJXWIkfGPs3cxIsU69eotQ6mVXQQ3LZNOvB5dGNgBkbKB1xTKVFRyID+cjau/Zzy8vsPLXuiZLArfpeTGePIxd/sGgb3XisZ+rRrG1RWP6C2fg2/U0eGHlHk94qe3gMh9EP4XKp77frPLfSH36T15xEHyDhZjKdOoglj+wfVDqs0/F6tG/v4FHruHuKelLTfvEz1T36O6jyEGqtShOslKn/4Q8yxAvbc1J1jQmBOjWBMDBEsPSxdnr1hZ+HDPYTcQpA8MbZtFHwvvLUqtXf2bkkg3QA5gEiuJ6xHFDgpEkdGcI6NoVkG/maN9tW1gaKg5nCG5OlJjFwS5Yd4q2XaNzZQQW8WRZg6qdNTWJMFUAp3sYgY0HHy2ELB1kKb7KhNbatI6Ena9ZBOIySVN6mX4kU+ChT//v9Y5Nf/86P8zt89id+RCAFSKl7+JytsLXRQEt770yLpgsnnf3eKz/+VKaJIYVqCq69V2ZzvEN1z/VCwerXFn/7jJf7Sf3Wcz//uFC//k2UCd/cTfBD11niVkrhBr2Bdx+8lYUfSp+PfFfgP+HlEMuh5nRf2l+SDqNPz/vfSC+49HtzV8CIHLEC7hRc0aHllhjJHSTmjFGvXBn+ILurtNS4vfY+R7AlmR19AE29RauwsnXJHKf0h1LY1gXYIwoG6mYgV0e9B6N+/CUYpSRR2OWl92fQIv1X5WDkxQtMZnXuOzOjxvvGFfoetW6+xduWn+/DSOziUlIRB//UVmo5hOjxqp4w/D9i/jpOKqHubBNLFj9r4UXtgPd2aHoN7MiCy2aHxozcfTtB0F4K1Eo2fvo01O76ddRJCoGdTmKOFxyNwUvQt7BBLKbdPWSwAACAASURBVOylFKYnTPKfmkN3BvDIlKL25q19deipMBrYOSeMwUFTvNMSh0IkBUg9MUvh157AGE6jOxYqktR+eY31f/ULonp3cROQOjvN+O98FufISJzF0zSitkfl5Q/Z/KPX7lxjXWP0t55j+OsX0GwD6QZELY/W1VUYoKUl0NCEdmjGvnuBJvSY/3APn0YTBpUlxf/731xn7WadwI/5J9/++7cwHZ3Skrv9vNoK/PH/vMjocYtUzkCGinrRZ3P+TmDgu5Kf/cs1rr9RIz9uIzTo1EO2Fjv47fi6NSsBf/j3rrO50Nmuqlz7ZZV//feuoxSox5gT9jih0lzg2NhncYMabS+2ftGEjmHYaMLA0G10zYrLNM4ICkWjs0khdQRDjxc5XTMxdAtNMzB0h0gGMXk8dNGETiYxjh80tzlchwOBaR9cd8+0Uz1GurcReg8ea+A1kVHYJ0lgOdk4GPsYAyc7WWBo+nxf0CSjgK1br7PywQ93ZSlzL4QQB+7A2+6euyfoFEJgJXJouomMHj9JgscZB+qqK3UenMEwRvJ9u3nvxjL+yiMIWsII78oiwUYZa/qOP5KWcB6K+OZ+oKQaWD4Thob+II+5u2CN5cg+M1i1PWp6VN+8tS/pACXVwFSxEIM5D7nMLKaRZKt8ALPnu5A+N8Pav/g5zfcX0W2TkW89S+GlJ/DWKhT/JC4JWxMFJn7/RTRDZ/EffAdvrYKechj55jOMfPMi3kaV6k/j8WSeOsLIN5+hfWOdjT98hbDaIXFshInf/7WBWachZ4aCPcmt+lv7LtE8CEkjT/seU1hN6EylnqAT1ii5d9rLLS3BbPopEmaWymslyq07cgDz793Jrgg0RhNzTCZPUw+2uPHGa/cdQ+hLlj9qsvzRDgberuSjn/d2Y4aB4sab+/eS+ouIlluk41dpulv4Ydz8kUvNMJo7jdB0JgpPkrKHWa9+iGPlGMmeBAHNzhbl5jyGbjM9fJF0YgxDszgx+QXWK+9Ta63gBQ22ateYGXmWtldhceuXh5aFiRfZwbZQe4GVKgyQFFB4rQd3+nqtKlHg9gVOTnYUoWkfq3J9euQYZqLXpFspRae+ydqVn+4raAK62k67XwcGQkkCtx5bt9zTXedkRtBN+5PAaY946AVOLWn37eS9+bUdVbwPG2GpTrjZGzgJ00A4B087HwqkJKj3t6bpjomRTez6NIXPnMAsDA4GGx8s71qCoA+aGCwhIRUocOwc+cxREBq1+gK6ZpHPHMEwHDpumWp9gVxmllRiDNerUm+ukE6N41g5FIpK7RZ+sPNus/HuPLVXriDdgADY+vZbpM5Mk3vhJKUfvIsKJeknZkidnmLhf/0TmpfiICMoNtj6d2+Qe/4E2WfnqL9xA+mHZD91HGHqbP3R63Sub3SfWycxN07y1ETf++vCxNBup7MPHxo6c9lP8WH5xz2ZJaUUDX+rr9kiaRZImgWu117Fj1pEO2T2FJKN9rU4A2Ee3A38zxU0DS3hoCUTCMdGs0yEacYNEJoGWixnopSK1bulRIUhyg9i2RTPQ7Y7yI4L9xGRHYQwcrm68u97Hqs0F6g0+zehxfo1ivV+gc2FzVcHnluqkNXyu6yWH4KvmtBw7jbn3Qc03cJJDfV3eCn1QJ866FqrBB24J0BJZMcxzAT+xyjkmMxNDJBUUDS2buG39zn3Cg3DSR1KN2HQqeO3670Gy0AyP41hpQjcx6PL/FcFj0C+u3/BiSqNR+ZzJjseUeueEpUYPK6PAzKM9ZruhbAMrNEMwtAeeK2ssSz5544P7M6LOj7VN28R1Pa54zENhDmglbWbvcqmpjHNFM3WOmHkIYQWe3N1igzlThCEHQrZY2xVrjKcO4FCUsgep95YwjRTFLJzbJY/2HFn7C6VekqZ/kaVoNLEHM5g5FNE9TbO7DB6ymbyP3qRsb/8/PZzha5jZJMYmQSaYyIMDXM0i3IDOvO9Xl+dW5uwQ0bOMdKczH8aQ9gU3Xk22zcRCCZSp8nbk0QqYKN9naq3RtocZip1FlNz8KIWy80PkCriSOYCS81LeFGTieQpNKHT8EvMZp5iOHGEJ4e/Sih9rtdeJVIhRzMXyVnjLDUvbWejptPnGU+cIGnmOZX/HKXOAsXOPLOZp1lufoAbNRhLHMfSE6w2Lz8U/SkrXcBIpGlv7ayM/bFCiHhjZNsIy0TPpDBGhzGGCxhDefRCHi2dRDNNMAyEocelEL0rcigE2x1kivgPpeKNQhS3iRN1A6kwRLY7RNUGUbVGWK4SbpUIKzWU56E8H+kH9/XJ/FWBEBqp/OSBrEfs9BB2eqifAxR4dOoP9t7zmiXcRgknM9ZnEpwZnaO0+M6+xnUYiHWa7g0IoV3bvw+cbpikCjMHHFkMr13FbQwwWHbSZMfm6NR33239CR5B4CS9rmXAXV9WbPXxiDgR3R1jDyL5eHTUEV8Lb7V/RyKEIDk3ipFJ3F/PSRPkn5sjOTc60JfOXalQe+vWvnWJ9ISFNqBkKL3YGLhcu0khN0cuM0Mkg27QVKLVKVLIzmFbWaSMaLU3yaanMY0kCknHqxKpkIRdQKDtqIujwqjnTlGhRIXyDgdM09BsEyUlrWtrfTyu5gdLeMslpBeiWTqaocflx3sI7/I+avIJPcNC/R1AcSRzkaZfImnmSZvD3Kq9QcoscCT9NO2ghh91WGtfJYhcZtJPMp48wUb7Bikzj96dWG09hSZ0NsLrLNTfxtFTfFT+s67/Yzyu5eb7WFkHS7vThrzavIwXtRhPnOBG7TW8qIWpOaTMArqIf8qWnsTRU13n9B0/0r6gmQ5Weihu/SYmlyYKkxhOisBt0imtYGWGsTPDKBXRLq5gJjNEXhslJWYyi98sY+fGMJ00frOCWzuEkr2moecycYA0Oow5Poo1PYExMYaeScVNDKIbFHWz34chpLjd1KHi4CoW6ZUo1ycslQnWNglWNwi3SgTFMmGxjOrsnWf4uMBOD5PIjtOu7qe7T5DMT+Fk+rNW7erqrjhOSkmq61fITZzu6QbTTYfC9Hmqa5eJBpCgHzqEFquL991TKs6Q7ROmnSEzMph+sVcEbpNmZZncxCnEXaU/IQTDR56htPTegb3h/iLhEAIngUDsuPBF1Ua8k7+L4CYssxtIPYLgydD7NIik6yObj4exoQojOktlZBj1yQikT09iT+TuGzhZw2mGv3AOIzVAfTyU1C8txkbB+4SettET/YFT1AlQUpJKjJKwC2iahd7V2JEqjA2DVUjHLZNKjHJk8rNomkGxuUImNcnY8BMIoVGu3byvSJ+RTSI0we2n6CkbPWEh/YCo5aLCiKDaQoWS8o8u0fpgeecPoyyitocwdYxsb0BqZBI7ZiEbQYlGUCSUPpOps6TMAjl7gpw9jqF9CoGOY2RwjDRe1CZnjePoGTLWCA1/pxJEXAqKVIhCEamg5zckVdhnKqy6gZVCdl/36EkddnYYoY3RKa+hWwkKxy/SKa8QenEwlT/6JDL08RolNF0nM3mSTnkVGfhkpk7S3LhFduYsbnWT/LGn2fro53s3GRUCYejouQzWiWPYJ45iTYxhjAyj5zMI49G0WG8vlLf/1rsFXctCz6ax547E5H7XI+wGTv7yGt61W/jLq8iO9yuTjbpNJM5PnKFTW98zd8qwEhSmznW7uO5AKUlj6ybhLu+B+sZ1/HYVJzNyZ2yaRnbsJPnJs5SW3n3oLgN9UBIpowEdfwJtgLfe7iAoTJ/HSY88+Km7gqK2doWxueexU72aisnCFCNHn2Xj+i8egpHxn08caIZJGFmGEkfQhMZS/RKWnkRDw43ulJ6C1WKfgKKeTyN0/ZH4nOkpBy3d64MUNdqEpceE1KrAW6/irVX7FL+NfJKRLz1Be76IHGBtIkyd4ZfOkjk/M5CCE9balP7syr6zTcLQsIYzA7v7wqaLiiTNziZuV7AwDDvd3ZcgigJWN98mDF3WNt9G1+NOIYUiDF2qjQU6boUg7HC/ADr99BEqP/kgNhTWBOnzs1gTeZqXFogaLihF+9oaYa3N0JeexFutEFbbgEKYBkbG2e6ck65P59YmmYvHyD5/kvKP30cFEVrCInPx2H3kFdRdfysQGho6ZXeJ9fYNIM40eFGTE7nP4EUt1lpXiFSAIe6eOOMvKdbEObxSsbpLlVETOgNvhkOADFzc2hZOLs4aRL5LY/UqicIUMopoF5dorFwlPTFHIjeOW71dphAxb0gzsNJDJIen4yywknGQs0tqirBM9KE81uwUyafOYc3NoqVTsW/lYyonIYRAJBys2SnMmUmc82dQL32GqFqnc/k67gdXCDaLsQfdY+77qOkmw0cvUtu4SquysqfX5sZPkp880/e416pQ37y5a780r1WmuPA2U+e+hHZX1sl00kyd+xJeq0KztHfZlYEQWjcIe/D8GbgNlIx6dZFEzH3axxuTGTnK6NxzA7359otWZYX65g1G557reVw3bCZOv0jQqVNe+eCx9K573LDvb8XUHGZzFzGEiWNkWa6/T8YaIe9McqNyh7zoXV9Guh5a4g6b35oZi33O/Idv4qcXsj2q5UopwmKVYPXBZMRHBXejRuv6Bs5Mb/1fCMHIl5+gs1Jh6weXeoInzTH5/9l77yA5zjTN75e+Ksu3N2g0Gt4Shha0QzcckrO7s7t3szrt3lyEdKeL08lESP8oZEJSXChCNi70x0kh3a3u4szu3czE7s7sGNqhHZAECcJ7oGHad3V3+Ur/ffojGw00uwG0AwnO8Ikgu1Gd9WVWVuaX7/e+z/s8rU9tp/sPH0FdhIMEUDp8Gefa3bkDt4NqGSR6FlrXQByUyVAQRR7RrX5Ht2SPwjBeRYaRSxjdbJEv167TdKZmg6bbQwQRejpBz995lsaFUVRLJ//4dhCC6bdOzq0sG2eGmX79OG3fPYDRmsG5NI4UEj2bxOzIMv3GCSqfXAQJ5UPnyR3cSuf3D2L1FAjKDRLr2kj0FG4rNJc2WrH1HEJGmKpNMyihotKS6CUULpEIMbQEQgqSeoZp9zqBcEkZBbywgUQgpSCpZ5EIclYX1Vm9s7hTTyGhpfFEc65Ut1TcGNvW80QiJG9144T3ZlFgpgtkujZhpLI406N49Rk0K0UUONitPZSvnkBPZhBhQLK1A1UziXyXTPcmpBAomo5XLdKcGsZvVgjdJqFz9xKNYplYmzeQ3LmVxM4tGN2dX6pJ6lpBUZQ4224acbasv5fMtx7DvzaCc/o87pmL+MNj920WSlEUUoUeurd/i+vHf75kTaJUoZeeHc/FGk63QIqIWnGQ+szSTYlFFDB1/Si5ri2zQpM3S692vof+/b/D9eO/oD51bcWSKJqRIN3aj2XnKY2eWRJx2qlOICJ/XjAHCtmOTZh2fln6TemWPvr2vkoy17Wm3nxShExe/oRc11bMWwj2iqKQSLfSt/cVNCPB1PVjiHBlUkGakcC087i14m909mpVliu6YjJYPsyWlicA8CMHU52f3QmnyrjnrpF6bPfcRWAO9KC35PDvdblM17A2r0Nvzc29JP0Q98wVROP+KNUBRHWXytFr5B/eiJ6en8rW0gnW/fHjZPeso3rsGkGpiZ63ye5dT+6B9ej5RVzFpcSbrDLz/jmiVZjwaknztr53/nQdsYj+1N0gZEildveJUgQhzQujTPz4I5IbO8kf3IqaNHGHpph58wTNCzd5FsINKP78CN54ifwT28k/vg1UhbDcpH76Os7Vmzwaf6LCyD97m7ZXDpB9cCMiiGicHWb8h4fo/vefQjT9eQvMSAZU/Am67K1YWoop9zrNsIwTVrE0m825g4Ck6k8wXD/NpDNIT2o7QdLFDWuEwieIXIrO1fh14eGFjdmAShIIj2n3Opvyj+GFNa5UPyepZ+hObSNndpI2WkjoWUbqZ/BFrJUWd9rN2ocIn6JzhS57C22J9fiRgxvWsTSbbnsbOasLXTHZmn+S0cZZ6sH0sr+zuXPhu1SGzqKoKoFTRYQBbnk87qgcPhef39o0odegPnkNvz5D6DYw0/lYlNZ3CJ0ake+hmUlE6N++rKKqaOkU1uZ+Uo8ewNywDi2buW8zSyuFallYWwawBvoIDz6Ee/EKjcNHCYZG4669L7vsdFcotKzbjaoZjF/4gEZplChc7DgVNDNBpm2A3p3PY+e7FwQBXrPM5OCn8XWwDLi1ImPn36d//+9i2TclEhRFId26nk2P/hHT148zM3QCr1FCRD5ChLccoxKXexUVVY3VszXdIpntJNPWT7p1PYlsB36zTLU4uKTAqT59jcBroJs352NFUUhmO+jZ8SyjZ9/Fd8q3/z4VFd2yyXVuoXvb06QKvbOdnWJ2rLVZKDRKw0xcOkTP9m+h6ta878RKtdC392WynZuZunqERmkUEXpxpeDWIGj23CmqjqpqaEaCRLqVTPsAmfaNRIHL4Kc/XhJv7euKFQdO8RcqZ0mvOgk9S0tyHU44X/1XOB71j06S3L0JLRNfVFouTerg7ljL6R6mp42OAunHH5hXagonZ2h8tjYaQ2sGCeXDl+l4+QHS23sWZJ2MnE3L41vJP7IpLrspCqqh3ba0JEJB6aOL1M+NrYpGpudtEn0tixyvxJuo3FOCffPSOJf++x8iXJ/q51co/vQzUJSbVjRfmICiukvpg7NUPrk4SwZWYsPMIFogMNq8OMbw//3GnIinCCJkGNG4MBZ/plvGnnGHKXmjs0w+9RYuUsRQ7STqLClbECGRjDcuUmxemXsNFAQR482LFJ349ThLdFPYcrh2ClU9FwcX0qceBAxWPp3jzsRcqDg7W/EmqPnFOUHOWHbgMlOzmmoCEXeBIbhWO45Sn/VknOVTrQaR7+DMzC/RODNj8/7t1eYHZpHfxJmZ39Hp12fuuB81myG5YzOpRw9gbepHsczZyfr+6IRdayiKAoaB0dWO3t6CvXcH7rlL1D/+HO/SVaT71TvYO7UiqmZg2XkUVafQs5N063oq4xeoTl7GdyqI0EdKiaobmMn8XHlOt9KLCkNODh6msYxs0xykpDRyBsvO07vrhS8EKypWqoXubU/TPvAgjdIozdIoXrM0RxxXNB1NM9FNG9POY6VbSKRb0a0UqqqjqBqKohA41dsdwQJ4jTLl0XMkts7vWlNUnY6Nj2KlCkxdO4pbnST0HYQIURUNVTfRrRTJXCctvbtIt/bPGQMLEVGfvo5mWKTy3axFCV5EAZOXPsa087RveHABUdywUrT27SXfvR2nNklzZgS3Pk3g1ZEiRFE0VM1AM5NYdgErXSCZ6UBPpOfOXbM08ht7r97Aqrzqpp3rdKd3krE62N76DE5Y4XrlCy2hUuKeuULjszNkntoft/3qGunHH8A9dxXnxOV7sqpS7QTZbz+KNdA9dxwijKi9f5RgfOWr7nuFoNSg+OYpUpu7Fm3/VzQVbYkWJ95YmclfnlhVtgkgtbEDbRG9q8gL8YvVeyspEQnErK2KFNFC24/FICTCXUL5V8520X3heSQWsXG5UQpbdHdEC4jtEkEoF573270+N84tK7q5bRe5LWJi+BdJ44uPHd1mjPsWmkZyxxbSTz1CYtsmlOTdneV/06BoGlo2g/3wPqytG3FOnqP+4WH8ayMLu4O/JEgpmbz0MVHg0rf3FXTTRlFVzGSW9oGHaOs/QBg4sZ+bFKi6hWHZi+oPSRnzAWeGT1Ec/HTFFjZShEwOHkZRdbq3PY1upeaV7RRNx0zmMJM5Cj07brxr9ufaX1NShExdO0KueyvJW+QSbhxLvnsH2Y5NeI1SrOItQhRVQ9MtjGQWM5GdV36WUlKfvs61oz+htW8vyWznoorrK0Hg1Rk98ytUVae1f/8XOgIVFFVBN5NkWvvJtN7a1Xfvzt/XDSu3XCFisnGZijeOXrGQUuJHjQWCfRBbrJR/8gFGRwuJHQPxF9Oep/VPXmbmh2/jnLq8dvYrmorR1UrupcdIP7kXZlP7MopoHDpB/cPjyxat+7JQOnSR7AN9tDy5bVGj3qUgcnzGf3KE5vVVcrgUyO7rX/Qe8adqeJNLX419gy8HCiqmHougBrexQLovoWkYPZ2kn3gYe/9utHz2ty5g+iIURUHP50g//hCJbZtofPw5jU+OEk7NfOnlu8Ct0qyMUSteRVE1enZ8C9Mu3AwOVBXDSi3JkkVEAaWR01w/8QsCd3VzSBS4jF/4EN+p0L39GZKZji9wjL6IZVxTswHectAsjTJ88g369rxEItO+oHKg6VZMFs/dfgwpJVJEVCcvM3TyNRozIyRSrUSBi7oGljc34DVmGDrxS7xmifaBhzGTuSXcc7/d9+StWBXHKWd1Mu1cB+IasKEmyVs9lL2FOh/h+DTT/+Y12n7wKtbWvnjF0tdJ+3/0e9QPnaT5+Xm8y8MrlwkwdMzedpI7B0g/8QDWxt6bpQ4haX5+ntKPfkVUun8VUoNSg5F/cwgjmyS7t3/ZJrqR4zP5y+NMvXNmxZ10N2C2ZUgvoqSNjDNa3tiXb1b5De6MlNnCjs4XkVJwofgeVe/+F7VTkwnsh/eRef4JzK6O+0aY9n6BomkYHW3kvvsCyd3bqb7xHs3jZ75UArlbm8KtFRGRz8Tlj/CdKj3bnyHdun7JqtZSSgKnSvHqZ4xf+HDNlKpF5DN19XMapRE6Nj5Cy7o9C9rtl4so9GnMDDM9dJxgGTwdKQUzQ8cRoUfPzufItG5YdhND6DeZvnaU0fPv4c/a0NRnhoh8Z028Am+F71QYPvkG9enrdGx8lGzHprky4UohwgC3PvUbTQyHVQROpmbTkdo8GzjFsPQUnektiwZOAP7VMab+5c8pfO8Z7APbYy2WTIrsC4+QenA7wfg0/tAE/tAkYbFEWKkjGy7yC5OEommotoWaSaG35jC6WzHXd2J0tqJ3FG7RiYp1kppHz1P+6QcIx0PN2PP5Eiu8RqK6c08yV871aa7/6Xus+9tPktvfj2Lqd72QpZAElSbF104w/tMjRI3VZ++yD6zHKKQWchOCkMblCYLK/UOu/wYx8sleWuz1hMLH0Ky7v+GrhKpgdHWSffEpknt3oqbsb4KmO0BRVcyBPgrf/x3M9b3UPviEaOYOZONVIPQdfCfuzJRS0iiN4N/g+0hJafQ0bq1IvmcHrX17SWRa54jCzJGYY49LIUIi36EycYnpa59Tm76+4o6t20PiVMYZOvk6M8MnyXdtI9u1lUSqBVXTUVQ9Jlffen3NZpSkFEgRIaL4gV+fvk596hqN8mjsn7cCA7zy+Hm8xgz53l209e3FSrWgaDfOz6wyPcDs/kUUEgUutamrTA+doDp5eU5kFuIAp1EenWfpsnbnUFIePUdjZoR06zpy3TvItm/EuIWztODcIecyY1IKZBQSuDXq09epjMdSFeEShT+liH30vkh+D/3VC5lKGeG71fnPMMmSNcPuBEXe4cZTFGXRPyb1PK32ejrszQzXTsy9njXbEVIwWP7k1kHQW3OoGRstl4rLddv7sR/agWrOF1aUUs6pessoin8Xcu73G673aBqKOqsNo6nzRC4XPOibLs7Zq4imi5ZKoqaSKLaFauirmqiL/89f4p65suL33xFKbKPS/uIeWh7fQmJdy6KGv1JKglKTxsVxim+epHLkyqKGwcuFlrLY8A+ep+353fF5vgX+VI2L/8tfUztxn1pu/BZjT/d36cnuIhQ+x0b+gunmGunZrDEUwyCxfRO5V57H7F+3qE7YN1gcUkoIQ5wzF6m+9g7elaE15z5pRnJeyUuEPtEiD2pF1dEtm1S+F7vQQyJVQDNtFBSiyCdwajjVceozQ/iN8qJj3AuomoGqWyQybdi5LqxUC2YyM9tFpiJFSBT6RIGL36zgNWZwalOxEW7oz/KuVh+QKqqOYaWw893Y+R6sVAHNSKBqOlKIeP9OBbc2RaM8gt+sxOdokWeyZiZRb9F0ikJv2d2ISzhiVN1AN5Iksh3Y2Q5MOx8HUTfOnYwQYUAUevhOFb9Zwa0V8RozRKE/e0xLP3eKoqIvkkkTkR9z5lb1cdRFs3QiCpakMC+lvG2AsKKMk6FZpIwClp6iNbn+xl5wwzrjjQvztlVTCVp+8DJmXyd6SxbFNG6bQVEUJQ6C1nAiVe0EqQe3r9l4N6AsEsisGST4E1VG/uwQpUMXyezqxd7QjtmRRUuayCAiqDm4IyUaF8epnRomXMQoeKVI9rWQ2dO3IGgCaFyaoHFx5f5L3+DeQFctMtbqTFi/DCimSfqJh8i8+DR6a+G3nsu0XNzowEvu2Y7ekqf8szdxTpxb09JdFDhES+mxECGBU6XsVCmP3T+dyiIKEFFA3atTn7r6lR2HFCG+U8F3KpTHzq1qrMh37oHz5BchEaGPH/r4ToXqxEKD6TXfoxT3zmD4Ho69osCp6k3gR02coMpI7SQwq6ksxQIbCMXQSWxZj17IrPpgf+sgJM3BSZpXiqgJHc2KjWqlkMggim1P1rpcqIDwI6bePDXPJucGyp8OItyvzoX8GyyObKILU7Pv60BEzaTIffsZUk88jJZeW77GbxsUVcVY103L3/p9qvlfUT/0GdL75r5cKsx0C/lN+0h3DcSaYlGAV52mcvUUtZELcyU61bDIrt9JtncrVrYNVJWgWaU6dJbKlZNzdkEtWx4iP/AAkyfepT4+OG9fqY5+OvY9R234AlNnfv2lf9ZvsPZYMcfJCxtMNa9gavMnwEj6+NE3/Jc1hZQIJ0A4915pHclssHYb89WvU3v7XaCrFimzlaSRRVdjMbhIhPhRk4Y/gxtU18wPTlfNW/aVABSEDPAjh6ZfohmUWenJVVDIJ3sxtOTdN/6KoLUWyP/Ot7EfemCBd+Q3WBkURUHLZcj/3kuoySS1dw8hmt/MvXeDoht0P/IKqfb11EYvzvouJjAzLaS7BqiNXpy7FY1UjrbtjxEFLs3pEaQISbb00PvId9HNJMXTHyJFRHN6hM79L5DbKzNXyAAAIABJREFUsJtGcQg5m7JTVI3Muq3Y7X0UT77/FX7qb7CWWAU5PMmmloNzvlvxT5XJ5mWGqsfX8hh/46DZKXJ7HsbI5mhcu0z90llYbhfCrEibXKEOCjBrlqojg/kBmWpaZHbspTF4nrC2dvYdumqxpf1pLC2FkBHjtXNM1hdPByf1LJvbn0JT4pKoRHKx+D7NoLTo9q2pAXqyu9AUHS+sc3HqfUKx+Arc0JJ0pLfQkd5MymzF0lNoqoGCipAhQeTihlVKzWFGq6epe1N3DaB01WRdfi/5RC8Aw5UTTDWuoCoabakNdGd3kbHaZ/dloQCRDAkjDyesUmoOMVo5Rd2f5k4BlIKKrlkk9Awps4WU2YJttlBI9s761IGm6mxqe4K+cP8dj3mkepJi/fIdt1kL6G0t5H/vJewDe74JmtYYiqKg2EkyLz6FYhlU33gf0Wje/Y2/xTASaRKFTuoTVxj5+KeIwANFwUhm4jvvlrnYr04zfOgvCZ06odsAJFa+gw3P/Qmp7o3MXDpC6NTxKkXq44NkerdinT+MW4o7WvVkhkzvNtyZsQVisd/g64tVCWBemP4gNitVQFMMOlNbF2pfCEFUrn1l4m33CtJfecCS2rgNPZujfOwTItdZUedGasNmVNOidu7E3Te+DfRMjtzuA0wf+tWCv6na6sjzt0PaaqfVXo+QgkC4tw2c0ol2erI3bXqklEzWL942cGpJ9tGd2Y6q6kw3rt5m7wpps5VNbY/Tlto4l2W6FZpioKkGCSNDxuqkLTXA1dKnjFfPzal3LwZV0ckneunKxnw6N6xS94r05vawLr+PhJ5e0DmiKya6amLpabJWJ4VkH5em3r8jqTuf7GF75wtYWgpN1VEVPV603DK2qmi02OtvO8YNlN0RigxyL9OIensL+d/7Dva+Xd8ETfcQWsom88xBEJLKG+8hndV3Jf2mInQbeOUiqfb1tG5/lProZbzaNEFzoa6UFBFedRojlcPOxN1xmmkhAh/NTM6Z8MoopDx4nPzAA6S6NuKWJ0EKEi1dJFt7GP3kZ4jApaXbpHO9haopSAGlCZ+xK1+f70pVoWdTEstWGTzZWMmj6zcCK7dcQeJF8zUuppqD9GR2zXstarhM/+vXfuMmTX9o+QRp1bTI7n6IzLZdgEJ25z5q504SuU2SPf0kuvtAShpXL+BNjpHsG0DVdMzWThRNozF4Dm9qgsy2PeT2PISiaSQ6e2gOXaFx5QJWexd2/2ZUw8QdG6J5fRAZhRiFNtKbdqBaFv70JPXLZ7Hau8jtfgi7bwDVMAlrVUqfH0Kz0+R2HUBNJObJQCi6TnLdRpI9fSAE9cFzeJPLW0EJGVH3irTa61EVlYSeQVctQvHF7gmFrNW5QEAum+hkrHpm4XlVNGwjjzKbcal5RcQid3TGamdH5wsUkuvmAg0hI5ygghvUkAgMLUHKaEHXLDRVJ5PoYFv7s4DKSGXpmdSM1cG6/H42tDyErppIKfHCBk2/TCQ8VNXANvJYemxHoSk6+WQPm9uewh2v0fAXtyUxtCRpsw1tDV3T7xW0XJb8917G3r8LRb//j/frDtVOkn72cYQfUPvVh99wnm4DEfqMf/4mbTsP0r7radp2PE5j/Arlq6eojZyfl8U30gXadjxGpmczimbOleCsXHucVbpl3eXMjNIYv0Jh4wOUB48R+Q75/t349TKNiatIIdj8QIqX/k4nrd0WmRad9348xb/8R/dn9+tiMBIqv/sPeli3Jcn/+P0zeM69j5xyOQXHkfiLXM7JpMIDD+icPhNSr8m57Z98yqRYFBz+5N7QW1Y8m2mKSbs9MPcAUmYfdu4XvOoII9zTg4uM8NsHEYY4Q4NYbe0gJfVLZwlqZUBB0TT8qQn0bI6WB59k8t2fY6/bgL1+M+Xjh9HtFIWHn2birZ/gFccJqmVkGMyOUZ0b40ZpLbf7IYTv488UKTz4OP5MEXe8OHcsQaWENzWBkStQv3QWMXtVCt/DnRyl9eBz1M6fJGrUQFFIb9xOetse6hfPxPod4fIzblIK6m4RKSWKomDpaSw9RejPD5wUFHKJ2CpHiHDWEkCNgynUBWUzS09jGelZU0xJzSsucEa3tDSb2h6fC5oiEVJyhhguH6fmFYmEj0SiKjpJI0tvbg8d6S3oqoWhJRloeYSGP03ZGV7SZ80le8kmutAUg4Y/w3D5BNONqwTCQchoVuU71kLrK+zD1GLNrFyii+7sTganP1pU+bvijnFi7K9RuDXDpNKb20NragMAkQi4OnOYmndn9fiaN8G9yjapyQS5V5+//4ImGWvQIAQyEvFPEfv7xf9xsx1cmf2fQpx9VZRZGZRZn0hVve889DQ7SfbbTyPqDeofHYEV3KdfKlQFNWXH3Ky7+JaqmRRqMoFwXEStEb+oKGj5DIphEFXrS/b1c0tjjB7+OdPnPibdtYn8wB56H/sdZi50MXniXaSIUDSdzr3PkVu/g6mzH1EdOocIPBRNp/fg7y1QKQ+dOpVrp+nc+yzpro04M2Okugaoj17Eq8b34okPK1w53WDjnjR/4z/vXf75+i1DMqnw6qsJLlwMuXghpFqVFAoKVkLBdSSqqvDwIybNpmRiQlAqCRxHEoawZYvOp4fjwCmbVbDtOAArl1c/5614RlMVjYzVPserkFJQ96eYbF5a9UH9xkJEeMUxgkoJhMQZmV1pKAp+aRqrrRMkGIVWVCP2iHPHh6lfOIWi63T3b0ZPpQnK04TVMsL3bo4BBNUKWjKFZqdREwn0TBZ/ehKkRLdTuKPX8aYmkWFAFAT4M0Wi7nXzxpBhgDc1TuTe5EkouoG9YTP1S2epnT8x+6xd/sUnETSCGSIZoCsmlp7C0tMLsiuGlsQ2Y3PhqjeBqaewjTwJI4ulp3DD+S2mcQCWBiCSAU2/hJx3fAo9uV20pTaiKCpCRkzUznNx6n2coLrgszhBmao7gR82WV94EE3VSZktrC8coOnP4Ed355DoqoFEp+FNc3biLWaa1xcEfG5YpeZOEsmALe1Po6ChKBptqU2MVE7hBAvV2b2wzkTtPLcudVVFo2D30Tr7byEFM82hJeg43ZugSUlYZJ57ktSj++csj74KyEggHAfRaCIaDqLRiH96HqLpIJououkgPQ8ZiVm9uNjwWUqBoqoomhbrxmkqaDqqZaDaSdRkEtVOoCQSqHYCLWWjpmxU20a1k1+pNpVmJ8m98hxRpYpz8tyXbtGyHKh2ktyrz1B7+2PC4h3MnzUNa0s/6SceJBibpPzj1wFQTB37oT0k9++g/t6nND9ZelZYBB7O9CjOzDilwWOsf+pvkuvfRfnqSbzyJIadxW7vw60UKZ7+MOZCEfOWNDM5l32ag5RUh8/TsvVhcv07MdIFFFWjMnR2TnPJbQjchk+2xSPw79/v5X6AokDvOpUnnjJZ16dhGgqHD/s88mj872xG4Yf/zqVQUHnqaYtUSuGN1z1OnAgoFgX5fLy4zGYV/vBvxI0zlqXwZ/+mSaWyunO/4sApEA6DpU9I6BkM1Zrjwxhq4puuumXCbGmn5eGncCfHYqn62dUtQFivxivi2VWxcpsSjWpatDz4BCKKCCrxBKQoCpHbpPT5ITJbd1N46CnciRHKxz5BBktP4yuqiqKbRM36qidhP3Jwgyppqw1TSy3oygRIW21zytdVdwJTT2MbeXTVIm21LQyctBTW7DiOX1kQ2NhGns7MtjmiedMvcXn6EE5we+J7KDyGysco2OvJJ7tRFIVWu5+M1cF08+qSPmsYeVwtfbZo0HQDEsF47Rzd2Z1kE3F5Mm22YmrJRQOnW9857/cFX4vkXgVGd4SmYe/bRebZg6jJxD3f3ZyAbxQrGIt6k2B8kmBkHH9sElGtIRpNoqaDqDcRzt0zG8uCqqImE3HQlEqi2jZaNo3R2Y7R04nR3YmWy8SBlKbNJrHucYZKUdAKeXIvP0dYqhAMLe7ksACaGmcHpUQG4dy9rhh6nF2TMs40C4li6EgZm4/LKLrpoqAoc7QMGUY3ua1q3IiCosRZviCMf5fQPHaOqN64eRxqbASPotwcO4pwPj+DalkY6zrnNpVeQP39T1EzqXkWVYqhx8d64xbQ9VjrSkrMTCuJfAduaXyW8A2aYaHMClPeOGYRBsgoQDOTGKk8fn0G3bJp3fYoVrZ1jgB+K4J6ifroJbLrd2DlOwmaNRrjKxdK1nQFTVdQ1JgKGwZiXh+RqoFhqoSBJAq/cL8rYFoqIPHd+X/TjXhcFBCRJAzkonwlVQPdUFHUeDtVVb60QFxKuHwp4tSJgA8+8Dl1KiSRAF1TKM1IHjxgYJrgepK//qnDrt0G/Rs0zpyZH9Cu79c4cMDgyJGArk6V9g6VSmV1Mj6r8qrbkHuQtNmGkMHc9Vlxx2jchsD7DRaHWWiNid7nT2LkW+b/cZGLVEoQUYhq3rjZI1QrgdXRTfH914mcJpntD8QbqypRs0H52CckOnspPPwU9QunCCo+MgxjKwDDQkTBbQn8MoqImnWs9i6c0WtzGaeVlOuCqEkzKJG22mJukhlzk24trWWtDrRZXlAcONXpymxFV03SVjtTjZsTkYJK0sihqXGGrhmUCOYF7gqtqX7SZttcSWWiduG2HKJb4YY1puqXyCW7UFAwNZuCvY6SM4yQd/7sUkrq3hQTtfN37cjzI4eyM0I2ET8QNNXA0jPA168LxxroI/edb6Fl0vd0P1JKpB8QVWuEUyX8wWu4F68QFqcQro/0faQf3PtJXojZjNb8YF0xDRTTRLFM9EIOa/MGElsG0NtaUXMZ1OTqPMHuBkVVMDesI/fSM5R+9DOiyp2FABXLJP3kgxh93RCGNI+fwz11Eb2tQPpbj6JmbGTTpfHRUaJKjdz3XiScKWO0FwinytTePYxoNEnu3kpi9xYUXcO/MkzjkxNIP8B+cBeJnZtRdI1gdJLqm79Gy6bJfucpjN5OZv7FXxI6Hqgqie0DJA/sQjENomKJ+vuf3vX4b4WaSpJ99Vnq739KOF5EK2TJvPjE7L+n0BMp2vc8g2FnEGF8jai6gZSCqbMf4dXiuSF065QHj9O683E2PPcnhG4dVTMImhUq189gZVoW3X/5ygkKm/eTyHcw9tnrc5mq5aKz3+KxV1rYdTCLndGpl0OOvVvm8OszzIzHwcG2hzL8/j/s5ZNfzvD2n8+XkGnrMfnb/00/M+M+f/6/DeE7AlWD/h0pHv+dVjbvS2EmVIrDHh//Yobj71dwajfnYTuj8dC3Czz6cgu5VoPisMenb5TQzeX5760GUoLnQd96jclJQTqtsH2Hzkcf+XgeoIBhKPRv0Ons1Bi6HpFMKvT2anR0qHR0qFQrkmvXIwYHQ86eDRgZXr324YoDJ0NNkDRynJ361QKS+DdYHoJqGRSFloefms0qaXdOFogIb2KU/L5HaX3ieZpXL+FOjBBUSuT3PUrkNtFMCwloyRS53QfQEjaqYcblOS++kYPKDFII2p76Nl5xjMrJz0j0rCc1sBWrrYvcA4/QvH6Z5tWL1C+eJv/gE7TnCsgwpH75HM7I1WU/mPzIpemX53hOtlFAU3TC2cBJVTTSVhuaYhBEDo3Z0pgQIaqikzZbURV9LnC5UUa7wW9q+iWC6GaXiq6a5JO96FocWAkZMdW8wlKyMUKGMdFcRGiqPstB6kZVtLsHTghKzvAXgrjb7EcEuMFNbqCiKBharPX0dRLO0vJZst/5Fvq9NOuVkqjp4F28gnvuEt7gNYKRiQWSGl81pB/EgVu9QTRdwrt0leqb76O3t2FtXE9i60YS2zahteTvWQClaBrJfbvxr41QffPOGkLJvdvRchnKP34No6udzIuPEwyPY23dgGLolP/dL1BMA+kHKJaFua4L9+xlGh8eIfe7z5PcuYlgdJLkvh3U3j6EcDwKf/gSwfgUwnGxH9pD9bX3CcanUBMWhBHRTIXqL96j8Le+CzdKm1ISjBYJxt8DIP+9FzE39OIcX7rytmg4iFqdxI6N1MeL6F3tMQeqFN9jzswYE8feIlnoQrNsQBK6DRqT13GmR292OUvJ1NmPcGbGSLb1oWoaXm2a+uhljFSOZGsv0SKean6jjFueRNF0qsMrUwxv6zX54/9qPb1bkhx7p0xpMqCtx+Slv9NJ33abH/4fw1SmAsauuEgpeejFAof+ehqnHs+jigLrt9nseTLLv/xH1wn9+DMN7E7xd/+nAZx6xOmPqviOYMOuFD/4b/v5+Z+O8ca/miDwJIap8NQftPH7/3EPFz6v88kvZ7AzGk//QRu9m5PUyref/xRDJ/3MAbSMPe/1cKZK/YOjyzagf/ddjwMPGvT2apw7F3LubEihoPL66y4z04J33vbo7FSZnhIcPeqTSiskEgqlkqCnN85AvfWGx5YtOrW6WBOR/VV01QmCyENXTUJx035ESoH4EsThv86onj0+73noTU0y9cEbaKk0UaNG9fRRwkaVyumjsyU6gQwDpj58g6Aar4ackatEzQZqIkFQKSE8j+mP38HItyB8n8rJIwjPRXgO9cvn0JMppIjwyzOIWf5SWK8y9eGb6JnczddqVZrXLuMMXwUpCJsNZBTijA0RfvA6ejoHUuDPTK1oNS9lRMO/yXNKmS1x4EQczFl6GtuMrTicsBoHTTLEDevYZp6kkcfSU3NlNlUxsK145RcJn4Y/My/Do6sWabPt5rkO63jB0gP9QLiEwkVT4wxK0sjP8fru/DljkvpSIJFEXwjENOU+IlQvAYppkH7qURLbNi3bEf5uuOHPFk6XaZ44g3vqPMHYBFH19qXjjj6Lx77bytt/NkGjEpHvMHj4pRbe+tcTXx3lJxKE45OE45M4x06hd7ST2LYJe/8ujO5OlMRCeYzVQjF00k8+gnflOt6lq7fdzuzvwdrST6GQBU1FTVio2TT+9VESOzeRfeUZnOPn8AaH0SyLqFzFvz5KNFPBHxpD72hFhhGJrf2xyXoUoeUzaIUseluBsDiNPzQWB0x3kkpQFPTWPImdm1HTNkZvJ8rJ88v+3O6ZS6S/9QiqncDa2Id37vJcl6GMAuqjl6iP3p2PK0VEfewy9bH5emehU8OZWrxRRE+ksHLt1IbOE9TvVG5fHJqu8NjLLWzYZfOjfzzCJ7+YwfcEVlJl+JLD7/79bi4fy/Puj4pUigFnPq7x5Pda2bIvzYkP43lRNxX2P5enOOxx6XgdEcXjvvjHnQSu4M/+5yEGT9QRIg7S/uS/7ufZP+rgyFtlxq+65NoNvvU32rl2rsmf/69DjF1x0XSF/c/m+cF/d2epE8U0yH3nccze+RZQ7sXrNA6dQIrlVSoGByMGB2/GFD/72fzr56OPvkA7KUl+9MP5C9ajRwOOHl27xdWKZ2chQizdZkfbs9S8qbkVeM2fYryx/Av9twlh9Qs3k4jwZ4owM/9BG1ZuKXlKiVe8WVOXYYhXnF/KCWuVRQUr/akJFmU0SUlQniYoT98yRpmwtvjNHpSmCUrTi/5tOWj6JcLZoDtp5uMyWxRzDRJ6lqSRB8AJKgSRg5ABTlDGNvPYZtzCfyNwihW548ApEO4CnSddNefGiz+yoC01gB91shTEY998mOmqubTACYF7Bw7Vwu0X8hO+Tvkmc2A9macfRTHNNR1XCkEwPolz/AyNw8cJJ6eWlF2ybI3eLcnZskKEiMBt3D8LOtF08a8O4Q+N0jh8FHv/buwHH8Bc33tHP8/lQlEU9M42Ms8cJJgo3uxGWwTO52eoHzo6e4AC4bggJOUfvoa1fYD0s4+ipmz8a6MxZ2k2QFaUG7wXiX9tlMpP3o7fKyXC80k9tId5fft3gJpKknnhcRqfnsC/NkrOMleUvQwmZxB1h8TOzegtWervHl72GCuBqpu0bH4QRVGpXDs1Z8myHKTzGlsOZKhOx6U534sXgp4j+PztMi/9oJPtj2T45LUZGpWIo++UePoP2tj1eJazn1YJPElrt8m2hzKc+rBCcShelObbDTbvSzE9HtDVb9HaHd+rmq4QhZJcq0H3QILxqy7tvRad6y0O/Wya4nD8/iiUXD5RZ+RSHFj9NmPlgZOMmGxcnie8p8CSOo6+trhhensj1ajEXAIpFiPnLhNfp6fkKtH0ZwiEiyXTGKpFwsjOBjwKydnOOSnlbODkEgkNJ6ggpcTS0ySMLDgjALPNCQmklASRR9OfHzgpijZXpgOwzQI7u7694mNXFAVNXdqkcTvl8t80KAmLzLcOouWyazamlBLpODQ+P0X9w0/xrw7dVUQ3mdF49JUW2nstKtMhQsTP3P6dNo+92kqtdHOl29lvsXl/Biup0tZjculYg5MflNnyYAY7o9HZnyCZ0jj6TpnLx+sYlsLeZ/IM7EkzPerx6Wsz1Msh/btS5NsMsq06XQNJjrxZYvBEfelZrSgiKlWovXMI58RZ7If3knn6UbSWAsCaBFCKqpJ8YAeJE2doHjmxaKnEu3SN5N4dqAkL4flouTSi4aC3F5BC4p4dxOzpRO9owb82ilbIYW5ajwwjzP4enBPnCcam4oxRWz7eJp9FFmfwr4+Q3LsNa2AdwVgR1U4QTpXjYEvVZmUe1DgY0zQUKy6raZkUZl837plZkdwb2yizUhBCxPOmps7KRNx8XXoe/vVRUgf34Z4dvOdq6pl120h3DWCk8mR6tlC5fmZBlmqpMBMq2RadylRA4M+/5n1XUJ0OybebGFa8KJi47nH5eJ1dB7P86t+aTFz3eODJHKqmcOpQdU5rKddmYCZUNu1J0TOwsHGjMuXP2ZNmW3UUTaFSDAiDm9eL2xA0quE3gdNK3xhKn7H6fEdsTTFIm623ecfSoSRM1KSFapmzJEsdxTBQNAUZRgg/jDkEQYh0PUTTi7s07iGsthRdz29DeCHj714kavp0Pr0Ze12eyQ8vU7t0Z82cOyHV30JmczuTH15GeKv8HDc0Z76IZdaV7yXcsIYXNmLC9qya90zzGpqik7E6UBWNMPJoeNNIBJEUNPwZhAzRVIOM2c6kchEhQ1JW25ymkRfWcMP5ZThdNRYodq8OCktdPd+NB3Xv8CV+14qCvX83iW2b1mxIGYT4Q6NU3/4Q59S5JatgP/pyC+u22hx5Y4YDz7dgJVSkhMnrHuc+rfL0H3bMLVDy7Sav/t1u3vhX41z4vM6T32tjatSjb5vNvmfzvPmvxrGSGi/8SScjlx12HcwysCfN8XfLbNqb4ts/6OLn/2yU3s1JnvjdVt7+s0lOvF+mMhWsrBQoJeHUDNW3PsC7eIXMs0+Q3LUVxV4b/0E1mSDz9GN4F68QlRcqZLunLqKmbLKvPgMSvMvXCcam0NtbsQ/sQjE0wukSzSOnARD1JkZnK4nN6wlGJ+PgpOlQf/cwyQM7sR/bRzRVovb2RwSjkzQ+Pkbq4H4UQ8cfGqP2q49J7tpKct921LRN9uWn8S5cpXnsDM6xs2SeP0g4U8E5eZ6o7qDls6SffghjfTeKYVD4o5epf3AEVIXUwf0YPR3oXW3oLTlq73yCqDUIxiZRTBNvcGhNzuGdoFs2dnsfiqozde5jZs5/sij/aSmIQvA9gZlQFwTOigqGpeA2onjBThxMffZWib/3fIENu1M49YidB7PMjPmc+/QmqT7wBULA4ddn+Ov/dwwRzb9QpYDqdDC7bZwM0E0F5ZZGOlUFTbt/dMu+KqwpkcLQEnSltlHxFrZp3g2KoWGu78Ic6MHq78LoaUdvy6NlU6iJ+elaKQSi4RJV6oSTJfyxIv7VcbzBEYLx6VW3Gxu5JPmdXXHEfW4C4Ud0PbsVuzdP8deDCDegsLeXtkc3MHNsGLdYx8gmyO3oQjU0KucnCOseuR1dc1mqmc+HsNflyQy0EjZ8SidHMTIWue2dJDoy8appOdejpmIWUlidOYzWFHo6iZY0UAw9bsu9ZSx/ssrMry8QNVbW3bHWkEjqXjG2BVHUuVKbphpznWWBcKl5N7tEal6RUHhx4JToQFMNRBSSseJuOYmY5RTNnwwURY1tgWbhh03q/vSi4pJLQRi5RGJptfKvLlT98iY2vbVA+vGH0FL23TdeAoTn0zh8jOrr7xBOLq8svHlfmk9+OcPZT2pEoeQ7/0EsourUI4pD3ryVM8D4NZfj75YpTQTseTJHe28sgTF4vMHJDyoIAQ++UKDQYXDguQJCSjbvS5Nt1Vm/I0Whw0TVYHTQ5eSHFXx3DWQOghDv0lX84THSTz1K9ttPo69RJs/sX4f94B5qb/96wd9kENL48AiND4/Me909fRH39HxbJK0lPxckfVF7yT03iHtuoeCxc/QsztH5C23n+LlFSd/19z+j/v5nC16v/HShNRRA+frC7lNF19Fb8gRDY8u+jlaC0uWjlC4fXZOxmtWQsUGXvU/naOkyGbl0s9zX3mvR0mly/tMaTj2+3qSAq6ebXD/b5MHnCzi1iHVbbN7+80mat3TJTY34VKYCWjpNfFcwPXr7jPjUiI/nRPRtsbFsFbcR7yvbalDoWNty/IqhQGt/iv4H24hCweVDk9SLX84zbtmBk63n6c3uYbJxib7sA/O86eIW8uU9kNR0ksS2flKP7cYa6EFvz9+1xq+oKlrGRsvYmOs6sOVWoqZLWCzjXRqmfugE3uAI0l1+qUQxNFoO9KGaGmHDo+2xAYq/HsSvuhhVF2+mgYgkQdUlaPi4k3WkkLQ93I+RTSDCiPbHNjB9ZIi2gwNMf3IVb7qBbpt0PrWJxvUS+T09RG6A3VdARoLIDTDb7t6+rSYMrPYsmQf6yO5eh9WVx8jZaGkLLTEbNKkLz1vt1DCVY9fum8AJoO4VQQoURSNpxhwkTTWxzZbZsps7TzKg4U0RRh6mliJttqIpOgFgGwVAQUqxKBk7EkEsZjibdWr4M5yf/BVeeHuux50gkfgrfO9vHDSNxK5tmAN9q+6ik1IiXZfaux9RfetDRG15nbqKArqp4js3+SBfXFF/Ec1qNCcfs8xTAAAgAElEQVRCKCJQZ1fS9XIQN1bFAuPohoJuqYwNOoxfcxm/BkffKVOZXZ03a9Fc19JaQboe9fc+IqrUyL/yXNwZtkrSvWKZ2Pt24xw/Qzi1CskYIRANJ9Y8ug+hplOkv/UIei5D49OTiMb9oSuoqHEZTtMUkmkt1mCyFFI5DSEg9AWBJ3GbgiNvl9j+SIbv/r0ufvnPJ6hMBRQ6DV79u13UyiHH3ivPC9Rnxn3OfFzlsVdbaFTyqBqc+vV8jqXbiPj1X03xvX/Yy3f/Xjfv/7hIaTIgkdboHohL00feKuE5gpkJnzMf19j/XJ4LR2uc+biKldQ4+GoLXQPWnBzCV4lEWmf/99bjOxHj5yqsaC2swL7fW8+ZN0bwm0sfYNmBky8cis3LCBmhKjoj9VNzfzM1m0JiaTLyiqFjbewl++IjJPduQbUTs/XrFUzAioKWSqLZiTiQOrCN5pGz1N75HO/q2LIMhlVdxcxaVC8W8csOnc8UYr7NSBnV0Ghcm0EGEc3RCl6xTu1SEeFH2OvymAUbd7KGO1FDURWCqkP1wiRB1SXRkSbVH2dWZBihaCpG2qJ0YgRFV8lnb5+SVxMGqc2dtDy9ncLDGzEKKVRTnyf4tpbQc0msztyC16WQuEPTqy8nwmxDQYSmaFhaCkNNYJuFWEwVaPjT8zzs3LCGG9awzRYMLUnSyCFkhKnHmQ4hQ+qLWIxEIiCI3LntVFXHCxsLrYG+wbKhZTOkHtmHulpCuJSIeoPqG+9R/+BwbL+x/CEoFwN6NiW5eqZBz6ZkLPB3x/cszk2UYv7LQsDYoIPnCM58XI1LGIaCd2OiXQOK46LH5wc0jxxH1OoU/vAVzL6eVQWoiqJgru8lsW0z9ZnPVly+jyo1Sj9+bcn2Jl82RKNJ/Z2P498d775RTm/rsXjxTzroWGdR6DRp77WwMxqFThO3EXH6UJV3f1RESjjzcY1f/H/jPPv9dv7T/3MTUSjRdIXaTMjP/+kY5z6dv7AIA8mJDyoc/G4rB19t4bM3S0yNzU8cSAmHfjaNndV58IUCe5/OoWkKQsTB2vH3Kxx9J24MapRDfvnPx/n9/6SH7/8X6/Bdie9EXDnd5NzhGm2z2dmVoHNrljAQ5DoTeI2QifNVQl9gJDTaN2dIZAyq4w4zQw2kjLNKgROR77FRdYXRU2USGYN1+wp07cxz+deT+E6IWwtQVIXWDSlyXUmcasDkxSqhJ1B1hcK6FPmeJFJIipfrBG5E3/4W9rzaS+hHeLWAa0dm8Jt3f74tO3AKhUfZHcVQE4zUTjHt3LR1MDV7Vn/mzlBMg/Qz+8m98jhGZ+uiWZIVQVFQdA29JUv2hUewNq2j/NMPaHx6ZsnlO+GFNEcrtBzoQwQR7mSdsH7nCUIEEeXTY2S3tBM2A5yxCpEXIoWcW5X5JYfy6bGYTB4KmsNlzIJN26Mb4pVtuHi0a3Xm6Pzuflqe3o7VkV27c3U7KGAPdLDh7z+HmphPAJSh4Or/9SaVY9dW/aTwwjpe2CBlWeiaScLIkDHbZjNDsfDlvH0Tt/e32P2zWk/tRCJAU00URcENawSLNCaEwscJKnOBk23k0DUL7nMLr68DElsH4of5KiE8n9o7h6i99/GqHsafv13ihT/upHdLEs1QaFRCDEvlkZdb2PlYlvXbbf7gP1vHmY+ryytmSjj82gwv/HEn3/8v+wgDwaWjdT5/685Zm45OlR/8wCaRmL+34eGIv/gLh+pSbB8igXv+MuWfvE7h+7+D0dG2quBJTSZI7ttF88SZO3bY3RFSIpsr4+98KZDyvsky3QrfE4xedilPxtmaT345/+8z4/7ctBqFkg//aoorJxts2GVjZ3QalZArpxuMDbqL5gKunG7wl/9khFyrwemPq7j1hc+Uejnir//pGMffL7Nuq42d0eIM05jP9XPNuc5TKeHi0Tr/4n+4xub9aTJ5ndKkz/nP6rT1mnSuTxAGK8s4PvkfbsZrhFQnHFr605x9a4xLH0yw+akONjzcSrMUkGm3OPaTIYqXajz0NzcghaQ+46EZKlNX6lgZnUKvTSKtk+lI0CwlGVZLdG7Osv97fdRnfNItFiMnS5x6bYTunXke/vc2UB13iEKJ34wojzZp35jGLpi09KXwGiHDJ8uwhD6CVViueFS9CRJ6do5DoijqXe0otGyK3HefJPOtA2jZhXYbawZFwRzoofUHr6B3FKi99SliCTe7FJLS8REaQyUURcEvOchQULtUpHG9hAjiCyts+Iz84jRB1Ylbdk+M0hwuo+gqYc0jbPqM/uIMYTO+SUQQMf72eYzZzFJQdZn6+CrV83GAEDoB0S2ZHMXQyO5dT+/fOkh6azeq+SXp+kjwxsuENZfMQPuCDGDrc7uonh5B+quLPCIZUPenSFktaIqJpWdIW22zKuKCiruQJxe/JlEUlYzVjhfW0WcVw+tecVHuUSBcKu4Y2URX3JqtJsgluuNS4W8UvvgQvrcBtmIa2A8+EGv2rAIyCKm99zG1dw6tOoNx6Vid0riPZas0qrG9Rq0UcvmYZGzQ5Vf/dhIRSarTAb4rmLjm0qzF1/Ev/nQUrynQjPi8RWGcRvrh/36dylRA6Et+8k9GyLToSAGV6YAwlHz+VqykvJhdRTaj8NJ3LAoFFUNXsFMKlqXw+RGfN95wlxY4AQiBc+Yi/PjnFP7odzHaFlesXioSWzdidHfi1b4xX/8yUSkGvPujpc87IoKhCw5DF5YWBIa+5Nc/uTufKwokV041uXLq7hHC5JDH5ND8+7IyFXD5+MrpCiKSjJ2pcOynQww80saul3qYGqyx/bkuLv+6SHGwxu6Xe9n8ZAel4SZ6QmPiQoVjfzWEFJLQF9QmXZoln5b+NCd/PszYmbgsueulHqpFl8u/LtKxKcP257sYOVVi+7NdDB8vcewn1+NERSQRoeTkL0bofaDAZ//uKs3y0qk9q1AOt/j/2Xvv6Miu/L7zc1+qXIVCjo1G5252IpvsZh6SwzQzGmqCrGCtNLKPrPWsJUv28dpea7S7Pmd3LR/77LFly2e1x14HjTyc0eQZDccTGIZNNlPnnIAGGjkUULlevPvHQ6NRqAJQBaCbPT788rCbBKreu/XqvXu/9xe+397EoYoIU8acWNZyRYmGSH7+aaJPPVDThCs9D2laeCXb9yyS0vdN0zVE0PD/XqkWSgjUZIzk554CKUn/4O2aIk+e5VIaL5f4d0sObmkRWfAk1uztG8+zXUoT5e9Z/HsAJ2fh5BZ9ObZLcaxysRe6SvMze+j+9ccwWjeuxbtWmJMZMmeHiO7pQujlmkXxfT0EWuOUhle3LFkJrmeTNadoi+1AVXRCesK3X0FQdHJVfeRy5hSudFCFTkhvmLda8e+jrDmFKyuvpetZpAo3aY/twtDCCCHoiO9hMnsF27uHd811QEoPT7pIJGJ+G3PLguZOIbCld92pI+l5FC9cJvvq0Q1pF/ccuaA5sxgzYxYzY5WTYil/+2ezE9VrNqZHbr8mPW2Tni5/XT7twjKCvzdvuvy930/T0KCQbFT47GeDPPvcGv37XJfimYtoLU0kP/uJBT+4tUAEDML378W88hFx+gh3H64jmRsr4loemfEielAlnAzQsjWG9KBrfxIhYPJKZt5hAqb7c9jFlWuQhOKn9YQiiLeGEAJGzs4BglCDwfVjkzgb0cDBOoiTqugE1DBj+Utl2k2Ou8yuURHEnnqA6MfuRwlUTuo+SbJxUhnM/hGs4Umc6Tm8fMm3LnA9f2EQArR5l/J4BL2tkUBvO0ZvB0o0VEGmhBAQMEi8+Aj2+AyF45fuqdb8pRCqQtPHdtH9649VrTO6K/AkmbPDtH6igNEYKwteaPEQ8QOb1k2cPOlQsGbxPF9iIBFsR1dCCCF8ElRFA8l2SxSsFPFgO4YWJhZoRRUarmdTsOaW7ZRLFYZIl8ZojmxBCEFDsIPuhoMMzZ2oep5qECgIoXyIEgPLQyKx3eJ8EbyKECqxQDOTuSt35oSqSmB7H2oivmadISklzuQMmR/9DHe2dqHQnyeYJpw7598vug6792g8+9w6DiglhfdOEdjaS/j+vWsvFheCwLbNKNEwXu6/Y929j3BPQlEFkUYDoQiCcR3PkZh5h5kbed5/eYCxi2m/pMWVaAF/4y5rWLOlhMxEiZFzc5z/b77On0Cg6gK76BJtCi4qdfFNjW8dV9HrUxxeM3FyPRtPumyKH8R08wsu5RlrktHs+YrXB3f3EX/uCGIJaZJS4mULlC4Pkn/nHKXLg3gFE8+y592sVxjEvOO2MHTUxhjhgzsIP7CTQF9nGTnzI09xGn7hcezxFPbNiRUO+iFCESQe7KPrVx/5UCJNi5G/MoY1nUNvjJa186shg9ieLqZ/fA5vnem6kpPBdPMEtTiJUCeGGp63KpksKwy/BcczyZrTxIPtBLUoSsgXzzPtHCVneRNQy81zc+4U0UALIT2Oqhj0NR5BUwOMZS74BK4KIVKEiq6GCKgREqEONMVgOH22Jv+5u42sNd91qIVRhEprdDtTuetkzEk2unRZjccIbu9bV3OCtB1yR9/DvD64+os/wgLcTJbsT95E72hD72hdE3EVQvjGw1t6KZ65uPobPsJH2EBoAYW+w824lkfPwUZuHJ9h9maewRMz3PdiF/G2EHpIZfT8HJmJOrICEq68McHu5zqQnsRzJYVZi5snZ7jx/jS7P96OUOYj0/05Jq9lKGVt8jMme57rZOZGjpunUjV1160j4mSgKQGG0qex3Nv5TrvKgqcmosSfO+xLDSx50K2hCdLfP0r++KX6Cw49uWCm6eUKpIcmyL99ltgzh4g/e7ishkoogsD2HqIP72VufOaOC2auBYHWOF2/9gihnpVFRKWUeCWb0ugs5ngae66AW7IItCZoemLnhozFLVjkr00Q2dFe9nOhCELdjRhtcUo31xd1Ktm+YGVIT8wLpwpcaZNfRmfJ9Sxy5hRSehhqBEMNIxDzgporu6dP5we4OXuCLU2PoKkBDC3ElsYjtES2MFscXjAHlkhUoaGrQYJajLCRJBpoJqjFmMr3M5q5sK7PfKcwWxym6KTRVT9qFwu2saf9ecYyFxYskQQKmmKgq0E0JUCqMETOql+4VWtqwOjtXleazh4ZI3/8TF0drx/BhzkwRPH0efSWRj+UtQYokTCBrb0Uz13+6Dv4CHcV9rx8QKQ5wM3TKa7+bAK76HL2+8NsPtxMY0+EUtbGKri4lseln44xO1wZGbXyDpd+MkZ28jZvuPH+NI7p0nlfA54jSQ3lfauYtyexCg5tO+N4jmT8it9V7Zge7798g82Hm0j2ROZTe3eQOHnSwcOlI7bLTxPM72qz1jTjuUWiZkIQ3NVLaN+2stCy9DzM6yPMfPmHmFeHNix95kzPMfedN7EnZkn+0tNozbfJmlAUIo/sI3fsLPbw5CpHustQBK0vHiC6o6Pqr6WUeEWbwuA0s+9cJXP2JvZsHrdg4ZkO0nFpeGjLhhEngNylUVo/caCizjjQliDQllg3cTKdHCU7iwzKBZ2lkpWlYFX3ypPIBYLjd8mJeWuWDKazsu6PJx2G5k7i4dGbPOQ3NQiFeLCNWKDVv5+lC0gECoqizafnFn34ezfDi+XkGZo9ya7WZ9CUAIpQaAh1EQ204LglPOmnuRWhoAgVCVyc+En9xElRMDb3+A73a4RXMsm99QHuXGWKbvdujf/rj+NcvOjwz/84SzQieO75IA8/YtCQEMzNSU6etPnxj0rcuOGuuOZHo4J9+zSe/FiAHTs1ohHBXFpy/pzNa6+aXLniYK5Qj64o0NGp8NhjAQ4f1unoUBEKjAy7vPmmxfvvWUxMeHefd3iS3LEThPbtxuiuPl+sBqGqGN0dqPFoVSXxuo5l6KjxCHpXC0ZvB0Z7M2pDFBH0o/5e0cTL5rFGpjD7R3Bns1Stpq8CaTk46Rws03W8+uAESiSImoxjbGonsKkdrbkBJRb268RcFy9fwplJY90cxxwYxZlJ+115d7qkQ1FQIiG0xjhGXydGTxtaYwI1GgRN8w2Rc0WcmTmswTHMgVHc2azf5HQnJRaET6y11iSBrd0ENrWhNTX436cn8YolnMlZzIFR/3ql0sjikgdphYdCepLJa1kG3i2fe8ycw+VXK5uCrr5Zfa22ii5XflaePfIcydCJFEMnytcmx/QYeHe64pwAU9ezTF1feeO9FGu3XPEsRrPnF+wubn2NS73qRNAg/OBulPDtidavb5gl9dUfY14Z2vCbQJoWuTdPoUaCJP/axxGh2+fWGuOED24nfY8Rp/DmZpqe3HXbD28xpMQcm2PiB6eY/sl8J1+VIne5TsX0pSjcmPI1p5Z09C3oPCliXZOLK20K9ixSugjhn8N0shTt5R3FC/YcJSdXpt9UsFI1KYE7nslg6gMK1iw9DQdpCHWiKUE/lSt0VKrv3l3PJm+lSBWHaq6J+jAwnr2ErgToabjfL7QXfoRJq1Io7nr2mvruhKoQ3N639miTlFiDwxTPX656D0djCocOGbS2KLz+qsEXfivMoQcNXNcvEg2FBM8+F+DFFwP88z/O8e67VtXpo61d4Xd+J8JnPhukoUHBdcGyJMGA4NmPB/hrvxziP/3HAl/7apFstvIAqgoPPmTwB38vwoMPGqgKlEz/dQ8fMfj0SyHefcfiT/4kx/EP7r4YoDOTIv/BafSu9jXXmemd7ajJxNqJkxAE+jqJPLSH0AO70FsbEZrqzwtLxySlnyFwvbrme7N/mOl//x3fB69OqIkIwd1biDy0h+CePpRIyK9xEUvGJ2+Nz8MrlChdHSL/7nmKZ67iZu6A2K0AtTFBeO9Wwg/uIbhjE0ooMH/dyl0f/LF54EncXIHShX7y712geGHgjtSniaBBaHcf0ccPEtq7dX5cSvm6tGhMzlyWwvGL5I6dxewf8QnuvLnzchg6mSI79fPdmLOuiNNsaWTV16mJCMEdm8ofbscl9845Shdv3Dnm7Hnkjp0ltH8boQPbb0edDJ3gzl4yP3rPLzq/ByA0hcbHdmK0xComQSklxRtT3PwvR5l7rx+51p3XGuBkiljTWYKdyfLxKgrB9gYUXcMz13cNR9PnyJlTCwS85GSwVqghKlizXJl6DUP107BSuqRLtdesSTwmc1dJl8ZoCHaSDPcQD7QSnNd3EvPpQtstUbLT5MwZsuYkWXOSnJlCLhPGdTyTwdnjTOauLZzHWiUKthgz+RucGf3+wv+nS6MLUdxa4XoWQ3MnmCuN0RLpIxHqmtet8ju5HNfE8UoU7DQ5c7qq5MNqUMIh9I7Wut93C9JxfL+01PLkGKC1TeUf/M9RNF3w7/40x5UrDoqA+/bqfO7zIQ4c1Pn9P4jwpT90uX69/DsJBOB3fifCb34hTKkk+cbXi7xzzCKXlySTCk89HeCppwx+/w+iOA78xZcLOIsy90LAvv06X/qjGHv2aFy94vC975Xo73eQErbv0PjsZ0I88YRBMBTlf/ujLJcu3eXUv+1gXhnAnU2jNTas6RBqQxytMYl1Y7jueViEAkQf3kf8+SMYm9pXL1QXAhR8YlXPeXS9fpKuKAR3bCL+wsP+4h8JrUwuxfwfqoKaiBI+tJvg7j6KZ66R+dE7lK7dXHvEaylUhfD+7cRfeNhfF4OBGsamggpaMk7k0QOE9m6jcOYa6R+8hTU4tmFrqNoQI/7iI8SeuB81WbkWVRuT3pIk/vzDhPZvJ/PDY2TfOOFnR1Youzn17TvvHXinccfFgQK9HSjRch8rN5Mn/865dXvKrQY3nSP/zjmCe/oW5A+EItBakmityXsmXWe0xInv76lo/QewpjI+aXr3+oZHlFaDZzqYE+kK4gQQaIujGOq6iVPemiFv1e4l5Uqb6fzAus4JfppwIneF6fwAqmKgKtp8Ebzve3erxd/17KoyB0vhSZfZ4k1mi2ubFOq9DiuNY644TKY0Pp+yU1HEraiwxJMennTmmzvqX+z1jjaUdRjPutk8hXOXVp3sDQOCQcE//d+zHD1qLqTUXnvNpL/f4Q//MMZDhw2efyHIf/j3eaxFG9xnngnwi78YxHXh3/1pnpe/UmBuTiKlv3l+7TWTL34xwhe+EOa3fivMqZMWp0/fvhaJhOC3/kaY++7TuHTR4X/5x2kuXnQWzvHaqybH37f50v8a49Ahg9/8Qpg//mdZMpm7m8u1xycwr91AfejA2orEVRW9sw1x5kJdNZ9KNEziE48Qf+4ISjRcdbOH6y1s8oSmzntx3nkPRREwiD52gMQnHkXvaF5TA4OYd6KIHL4Po7ed9Hd/Ru6dc8gVoii1QIkEiT7xAIlPPOqXkKxBzFgIgZqIEn10H0Z3K7Pf+CnFM9fWXbOrtTaS/KWPE3lod91peKEoGJ0tJD//DGo8QvaN43i5e6+BZiNxx4mT0d1aQQjMgVGc6ZV3nBsCT2IOjuNMpDB62hZ+rMbDaM0N9wxxCnYmiWxtqzqxTL92kbn37j5pAvAsB2umetREb4rWvXu8F+FKG9e1a6kH/LmCJx0sd+OjIHpnG2IdFiv22AT2cKUp61K4Lrz7nsXrr5u4i76bYhG+/70Sn34pxFNPGTz9TICv/2WRqSn/+YjGBM+/GKS5ReHomxbf+U6R2dnbhMbzYHLC4+WXizz+uEHfFo1P/kKQs2dzC2UZW7dqfOxjAYSAr361WEaqwJcZePtti+9+u8gf/P0oL34iwMsvFzlz+u5GsL1cgdLVAUL7dyPWWHNmdLX79TS1LryKIPaxB0h84jE/jbMI0vNwJmcpXR7EHpteiDqIUAC9rYng9h70zpZlyYx0XbxcEWcuh5vO4s7lMAdGcGtVOFcVYk8fIvlLH0eNVCf3vieihZvNI20Hoaoo0TBKJFgx/wrVJwSNf/0FlEiIzE/fQ65xoyiCBolPPlb1ui2Mbb5+yMsVkLaL0DXUWBgRqoxKCVUl0NdJ8299mtTXfkLu2Jk1R8WUeITkLz9L9MjeZed06Xq42Txe3v9OlUjQH5t6+/VqIkriU4+Dqqz5Ov284I4TJ7UxUfGgWMOT61aerhXuXA4nlSkjTkooiBrfGDf39UJoCqHeZtRw5WJkTmZIHb2MdD6crhfpuNjp6nl0LRZCaHfGK+8j3LvQWprWJb5oXhusKd1smpLz55wy0nT7d3DqpMUTTxhs364Rjwum5gWZuzpVtm/X8Dw4fdpmYrz6szMy7HLqtM2OnRp79ug0NytMTvqvvf8BnUhEkM1K3nt3+SjDW29Z/PbfkrS0quzZo3HurH3XC8WtwWG8fGHNxfpaSxNC02pOCge3byL2zIMLhd+34OaL5I6eIvvGCZyJVEWqRoQC6C1JIo/sI/b0g2iJRabmUuKkMqS++iPs0Wm8kolXspBF/++aLqqiEDmyl4ZPPV5BmqTn4WbymFeGKJy6gj065R/fdX1B5aCB1pIkvG8bob1b/cL2xYQgHqXhFz+Gmy2Qe+t0/V2ImkrsqQeJP3ukkmy6Hm4mR/HcdYqnr2JPzSJNC+l6CFVBBH3SGT64g9B9fX6Eb1FaVGtuIPm5p/HyRQonL9eftlME8acOEXloTwVpunXdCicvUzh5GXcmjWc7flxe11AbYgR39hK+fyd6exNCU1FCAeLPHfnQymCEIjAiOma2/LlVDQVFV7HzGzOuO06clEjQL3hbBHc26yuB3wX4DL48bCh0bV1dQRsJRdcIb67uP5U9P4w5dhcic8tAOh5uvnrbkRYJ3DGT4Y9wb0IEDNRkws93rQHSdbEGb9a08DgOTIwvP0fcvOl31IVCguZmdaHOKZkUtLQoFIuS4WG3KvECyOclY6P+MZqblTLi1NuroqowPu6SySw/1tFRl3xB0iqgr09DUe9+Z789NoGbzaE2Vkq91AI1HkUJh/Cyq9fjiWCAyKP7/UVy0bk8yyb70/eZ+97Plk3RyKKJNTSOPU+qkp9/pkxrT4kEURti5I6dXVPkJLCli4ZPP4naVC4aLKWkdHmQ9Pfe9DUClyFi5tWbFI5fJLCli8QLjxA6uANlkbuFGgvT8ItPYo9OYV4frn1gQhC6bwuJFx5GiZVv1qXjUjh7jfRfHcW8PuKnAqsQH/PqEIUPLhDc1UvixUcJ7u4rIzlaayMNn3kKezJVdxbF2NxJ7OlDFZsh6XqY/SPMfft1ihcGlh1b8ew1sm+eJP7cEWKPH0SEAiiRECwT8bvTCDUF2fP5HXzwZ6fLuqBb72smuSXBhW9c3ZDz3HHiJDS1op1dWvbdc6xelGtfgKrcM4u+0BQCLZVil9L1KNyYWtVg+E5Cer5eVDUIQ1uXjs9H+PmDEgmjRiprWmqFM5vGmaltIyAlrGRdl8/7NUtCQHjRemQEBMGgwHWhWFh+jpESCgU/JRgKijIj3mhUQVGgkJfLEi/wyV1h/hzxuFhTl+J6IU0Le2Tc19VaC1QVLZnAmVjdQ01rTlTKykiJ2T9C5rUPaqprkaZF7uhpQrv7CB3c4d9LQqAEA0Qe3EPh/QvY4/XV+inhILGnD2FsKi93kFJSujTIzJ//AGtgdJWB+Sm80oUB7IkUjZZN9LFFtWNCoLc3EX/hYWa+/Apejd12ajxC/LkjaG2NFWMrnLhE6uUfrd4x6PnF1oUTl7Gn5mj6tRduXzvmNQo3dxD/+GFSX/txpTTAMhABg9jjB9Cak0tkVyT26BQzX34F88rKArXSdrCHJ5n96o/BdYm/8MiaFO23vbAZp+TQvLuR0pzJtR/eoJQ2ad7VSLgpRKgxSGJTjBuvDzN1YZpYZ5TNT2/CCGuMHp9g/PQUzTsb2fnSVtoPtGBEdArTRU5/+QKbn+xm+6e2EG4M0rA5zuT5GcZOTNL9cAdXf9CP50hinRHaD7Yy+LNhrNzqUak7zh6kU6n+7bPluzTNqAosTSl5siYJ97sCVUFPVpoduwULayp79whmNUi5bJpQqMpd+wpXQjjQRLImOaMAACAASURBVDzUTizYhqHdQdPoRVAVg0igmUigacEr716AEAqRRddDVzc2Ha2EQyihNXqtAc50Cq9UWxuyECtrO+q68Hm7BHvRPOe6LEgXqKtsC291zrtueZDDtn1SpuliabC8Yoyadvs9Hxbs8bUbVgtVRW2owaVACIzOFvTWcoNhaTsUTl7Gmaxd082dy5I/frHC1NnY3IGxqb3uDVlgWw+RB3dXEDp7eJLZr/8E68bqNXVl45tJM/et1zGvDC44YoB/rcIP7CK0d0t12ZgqCN23hfD+7RWkybwyxOy3Xscer09mwR6eYPabr2LdGC0fm64RfXQ/gS1dNR/L6G4hdHBnRYrOs2zmvv8m5rXaG128Qon0D9+hdOlG2bhqRfejnfQ81sXIe+OEGoLs/sw2VEMluSXBvl/dhZkxuXlsjMJMkVBjiN2f205xpsjE2Wl2vrSVph1J5gbTjB2fIHMzy8VvXaX/p4O+ZtT5aaYvpZi6OMPFb11j9PgEdtGmdW8TTTsbEQq07m2moTeO59Y29jsecbot1nX7xlHiEYSqIO9CXFsJBlDD5ZO9dJxlOyRCMZUHXmhh8FyWkcv5unhLrFGndXMII6iQTdlM3ihirWIqKIRACVauEJ5l42TvAa2L5eYHz7sHBCEF+7o/TUOkG89zuTbxOgNTx+7oGRWh0dP4AJtbHkHi0T9xlOHUSSQfvvqyoUXY2/MSDeEuXM/i0uhPGE6d2LDjK6EgYh3EyZ1N19yZpCjQ0LA8a2lqUhACXE8yl7597XM5STot6epSaGpa/v2aBg1JBVWDbNYjl719jMlJD8/1z2Hoyy+QkaggGhUL7/mw9jjOdIqF8Fu9UBTUeKyG1wmMnraKSL2XL2L1j9Sn5yYl1uAYbjpfRsQVQ8fY1E7h5OXau8QUhehjB1AiS9JglkPu7dOULqytA9cemyL9o3dp7mhGjd+ux1IjIaKPH6R45vqqOkrC0Ig+cX9FGswrlMi8+j7WjVWiYNUgwbw+QubVD2j6tRcQi9Y2NR4h+vhBSpcHV093qiqB7Zsw2itdKorn+ymevlp33tmZmiX7xgmM3o5li/OXg2u5jB4fZ+zEJGbG4oHf3keoMYiiCOZupBk+NoZj+p+pdW8zHfe34pou4aYQkZYwbfuamTo/Q248j5mzmB1IL6xPhZkSxZkiSMlsvy+6KxTB5LkZuh5qJzOcJdmXYPz0FE6xtvvujhOnW/VMix84o7MFYWh3xfZETURQG8vz3l7RxM1Wv+ljjQaf/Qd9vPEXo0zeKOK5EkUTSA8cq/rkqOqCvU808sSvdtDaG0LVBKW8y/ClHG98ZYwbZzJ4y93Horq2iXTlXdVsqgpFVIhf3oJnOR9uNOxDgqrotCZ2EtAjSClpjm1lInMJy7kDQnn3GIShL8h61AspJW4mu6Iw3mIYhmDrVhUhKm8zIWDXLg1V9TvkZlO3J/iJcZehIZe+PpVt2zWiUUEuV3mfNjcrbNumISUMj7hMTN4+xvnzNqYlaW5W2NynMjhY/Tm87z6dcFjBcSSXL1UvZL8bcGbn1vwsCkWpIB1VXydExTwKfuptLR3SzmwWr0pKSWtJ+qHAGtcGo7OFQF9nRQTISaXJv7cOeyTp1++YN8b89OQiUhrc0o3R3Urp0o2Vx9bbUdaUdAv22DSFE5fXMTZJ4YOLxD/+EEZvR/nYdvaitzZij64chVQCOqE9WyrItlcyKZ5eo/CnlJSuDGGPTqFs66kvpS8lruU/g57t/63MZ4rMnIVr334+VV3BtTzSQ1nsgs3cYIa5G/UZhUtPMn0pxe7PbiO5pQEjZjB9qfao6R1P1dnjMxXsN7Clq8xH7o5BCPSuVvS2ch0iL1vAmVnmQgvQDYX2rWF2PZrk6d/o4qW/u5kX/lYPB55tItZYuXB074zw+X+0hT2PJYk0aAgBTV1BHvxkK7/8T7bSvSta5UTzkH4ErGIYivjQ67CEpqLFqkcY3IK1bokERWgYWvieSnfVC0l9Ssg/zxC6jrJGbzQ8v3uoVnZhGPDggwZd3ZWbii1bVB44pKMo8N57Vpl+0uSkxzvHLEolycNHDPbv1yvSbYoCDxzSuf8BnVxO8tZRi/wicnXmtM31aw66Di+9FCISqVwAGhoEn/xkkGhUcP68w5Urzod2G3iFYs2EtAKKWLY9vgxClLk/3IJ0vKoEaDXIklV13lPClbIAKyGwtQutKVHxntLFAZxUfYvpUnjZAqULAxUd4Eo4SPC+Lau+P7hrM0q0MvJSPHcdL78+1W93LusLSC+BlogS3L151fcrkRBGb3vFz51Uxhf8XOPN7EzOYo9M1e0ooegqyb4EekgjsSmGXXCwbnXGyYU/ACilTQrTReaGMtx8Z4zJc9Pkxn2i51guqq5iRI0F4oUEx3QxogZaUEWo/r2SGclh5Ww2PdZF+kaG4mztGZ47vjJbg+N4S1oT1cY4oQM77vSpUcJBIg/uKuugk1LizKSxJ1ZmlzsOJ/iN/2M7n/+HW3j2b3TzC7/by2/98U5+4fd6iSbLF48DH2+ipSfI2PUC3/wXA3z5j67ynX81wMRAgZ49UZ745Y5layVuGfYuhdBVlNDa9XI2AoqhYjRVD+Pbs/l1ySQIBM2xrezu+gRtid1rPs7dhuvZTKYvY9p5inaaycxl7BWUzv97gtC0NUsRSNdFWrUv7kII7tur87u/G6G3V53/GfT1qfze343S16cxPe3x05+Y5PPlOk3f/16Jixccejdr/E+/G+HwQ/pCLVIwCE8/HeCLX4ySTCqcPGnz05+UL/yTkx5f+2qRXE7y/AsBfvtvhWlvVxY25x2dCn/7ixGe+XiAYlHyzW8UGR39EKPDnlfXtS2DEIhAjR3Gd0PAso7aSaFr6N1tZZZagG9RdXVoQ1riS1cGK4+jqQS3dPuRseXGFjD81ObSCO18wfpGlDkUL92oIDgiaBDo6/Jre1eA3t6EsrSERUrcdA57tH57mwV4HtbNCaRd37UXAhq3NfDQFw+w7cU+Bl4dqpAUuIXMSI7R98fZ/dltHPm9+znwG3sIJf3PkhvPY+UsjvzuQXZ/bhsIP7qUuj5HpDXM4b9zkN7H/TowO28zfTlF0/YGRk9M1PWdbEiqTij+91RlA4GTSmMNjKLdf9t8VhgasY/dT+nyoJ/nvRM7NVUhcngPof3by34sLdtvr1xllxRN6pgFj9FrefJzDkZIobE9wJFPtzI3bvLj/ziMY/kD790XAwHv/9Uk7353AseSXHpnjrkJi1/+J1vZ+kCc1t4QEwNVFljXw0rlCfctGX7IwGi8O8XOy0ENGYQ2VebAwdeY8taRalXVAG2JnbTGd1BcxtT3XoQnHW6mTjCVvYaUHiU7U7c1ys8rhKqsWYoA16srNZ/LebzxusWzzwU4fMTwiYmE7m6Vrm6VYkHylb8ocvQtq6IUY3jY5V/+yxxf+qMYhw8b/Ot/08DIiEs2I0kmBV3dKvG4wvHjFv/q/84tiGfegm3D975XorVV5dd/I8Tf/mKET78UYnzMRUro6FTp7lYoleDlrxT5xteLLF0nmpsVHn7E99yLRAXxhMLDD/sboe4eld///SgTEx65nEcu56f6jh9f40LvybWTBCFqJsPVbDSEpqCEArjp2u2FAJRwoOp5vWKp5sYdJRxE72iqiDa5uSL2xOyGmPTa4yncXAF1kZSAn7aMoTUmli2KVxPRMoP5W3DS+Q0Tf7ZHp/CKZlk9kVAUtJYkajzimykvg1u6S2XwfA/ZpUX7dY9rfAZpu1BHOaRrugwfG2Pm2iyu6ZEbzyNdyY03hlF1pcwT2jVdrrwywMjxCbSgilNyKaT8e7M0Z/LB/3uGUDKIXXQWuMXs9Tne/bcnMSI6pTn/8wlV4Dke05dSZMfqS01uCHFqadfo2mxw8u3K8KNXNMkfv+TnibVbO0eB0dtO4688y8yfv+KH9jYSmkr4wHYaXnrS15FaBDeV8YXCVoFtevz0Pw/z+l+Mkp+z0QyFPY8l+aV/tIX9zzRx4kfTC0Qo2uDvKqaGSgtkynMll9+ZY/Bclq33x+naEalKnDzHw5ysNNkUukqwuxElqC8rCXCnYbTECfVUIU4SikMzeKW1EyddCZII194Bci/B9Szy5jp2ZT+vEEqFJlutkK5XNTWzHCwLvvH1Im8dNfns50Ls368TDgsKBcnFCzbf+U6Jl79SLIs2LZxLwrG3Lf7B30/za389zJEjOrt3awQC/vuHh12++50S//UvCly7Vj1SlE5L/vRPcwwOOXz2syF27NDo7fWJz2zK4913bV75QYnvfbdUdQzbtqn8438cpbv7thzLrTW0pUXhV3719mLnOPD1vyyumThJrz5SuhSLxR5XOoczOVv53mAAra2pbgkBrSXp6/0sgT01V7MVlxIKoDVV1l25c1m8/MZEgaVt40zNYnQ0l587HEJrXoE4xcKoicoSDXdmbt3EZGFsJQtnJl1RiK02RFET0RWJk9aUqPzePW9DSJ2TytRdnysBM2sxe708vWpmqkedXNMlc7PK55NQnClRnCkn+Z4jyS0iR+HmEJs/1k3zzkau/FU/Tp1rWc3ESTcEgVD1GGp3n8HOA6GqxAnXo3TxBubAKIFt3Yu0JxRC+7bR9BufYO47P/MFwNYbWhXC9wN6ZB+JTz1aqZ3heuTfPY8zUTkBLMXo1QJvf2Oc7Iw/Jqvoceb1FLseaeChT7USbzYWiJBQBJ5LWQEbQCHjMH2zxM4jDSRaq6fdpO1QHJpGSlk2ViEE0Z2d6I0RzNEPJyLT8GAfSpXicKdoUhpJrat4PRxoJKDX0M3zEe4hyLVLUEivLm9KIcCyJV/5SpG337bo7lEJBgWlkmR01GXwhlsR5VkMz4Pz5x3++J9l2bxZpaXV75ArlSSTkx43Bt0VdZ7AVyj/1jdLHHvbomeTH6UCyGQ8Bgfdhe67arhyxeGPvpQhFF79gknPj5KtC+vpUK4lBed6WINjSMdBaLfnBDUSIri9h+L567ULVyqKX+e6hFhIx8UeGq+ZBApDQ41WFra7+XXUfC2BdD3cdGU0QgnoVc+9MLagUZEKA9+rcaOafqTjVNWTUkKBVWVDlGi4oqD+llL4euHlCnXfj5e+eZX89N0reXAtl8xojqmLM6SuzdWd9aqZOD36XJRnfjFBIVf5pScaVS6dWr6wyh6fIfv6CfTOZl8a4BZ5Un3ypHe2UHj/AvkPLmKPz/hfXj2TrKGjNcYxNncQfXQ/wT2bKxyxpZSY126Se+v0qgRNSsnUUJHiks8qXcn4QJFgVMUILt15V0/YZGYshIBQpPqllo5H8cY0bt5Ei5bf7OG+FmK7uzDH0ne9ADnQliD58LaqC2VpZJbicO0dCNWQjPSgiDvndaerIQJ6DF0NIFDnDXstTCeH5RS4B7QUyiCEQkCLYmgRVMUn2Z50cNwSJTuL623MQrAuSOlPiDVEKCqgKHV5GwoBiuILWfb3u/T3r22xyeUk586tPRrjeTA66jE6Wt9CkEpJXnvtLn1ndaTbqqLGucUancIcHCe4dZHYpqYSPrSLwsnLmP3DNT1WemczkcP3VYzZGp7EujlR+1ynaZX1TcwXnm9Ux/a8+ORSCL36uW9BMfQyZfSFwxXNDXPNkG71wnwlYCACKzdxKKFAJWGW1T9rvfBMu26poZkrGcKhZhTFxfPufIbFzFgMH6tP32sxan7awlGFE0dznH6nMqrUtytAR88KhcyuR+7tM4T2bCby6P6ytVioCnprkvgnHyX6+AFK14exhyawx2dwUhncTAGvZILjID3pewsFdF+iPx5Fa2lA72gh0NeB0dteJpN/C1JK3Lksc987ijW8clpQuhKr6KEHlarlHD5hEguEQtXFimUf7rwwnrLClS6NzVEYmCK+r6fs54qu0vriftKnBrGXMdu9E1CCOq0v7ifU3ViRo5euR6F/klJdUTCBoUUIGw2EjAbCRpK2xG4ECiBJRnrY1vZU1XdK6TKd6yddGFnlHBJPegih0hLbSltiD4lwOwEtjqpouJ6N6eTJlaaYzFxmfO4inlz5ARUIGiLdNEW3LvuadGGEqWztMv4BPUZP4yEkHun8MNO5fgwtQmdyP03RPqLBFox54UrHMynZGeYKw4zNniNdGN1QvSiBQlvDbqKBFsBPQU5lrpIzqz8j0vP8upE18CahKGWRinsNwZYI7Y9uJnVujMz19W0K7jaEoiDW2u0INXfHOjNpCh9cwOhpW5hnhRAEejtoeOlJUl/98apt8GpjgsSnHiewpF3dsx0KJy7VlfITiqh6T0nX3ZD6Jv9gsjoJW20joCpVi8elvYFj85YZm6aumn71HT2WEic2hnBKr+6Oa10LsXnTxxgYfJVC8d4vg6h5JjvxVh7blKSmKtmyWZIUq+T5F0MWTWa//QZqY4Lgjp6KL1YIgZqIEnlgF/LgDmTJ8o0YLfv2g3BL5E31J2EloCOCAYShLd/CKiVevsjcd9+kcOrKqrsZy/SYHi7Re1+Mjm0Rrp/I+GqiAmJNBrsebkBR4P5nm5kaLJFoNQjFNRRF+KRqXs34FsJx30DTKi5/I1lTGTJnbhLd1Ymil1+XyM4OOj7/EKNfOXZXBDGFrtL46A5aXthflYQ62SKz71yry6S5q/EAm5oeQlcDaEoAVQ2gLNTKCBrC3TSEq1tGOK6F7RZXJU4SP2rT1/IwPU2HCGh+GlBKDym9ebXvAJFAI8lID7FgKwNTx1bUXxJCoSHczda2x5d9zdD0+/URJy3K1rbHkdJjPH2RgjXH9o6naY5tRRU6IJHS86U01BCGFiYWbKUp2sf1iZ8xnr7o/36dUBWDruQB+lofJajHsJwiw6kTmM4KBN1xfU22tUQ3VHV9UZF5KIaKUBXc4sbuSht2trDjf7if63+p/twRJ19vbY3ESUqkU+O1dFxyb50htHer75d2a8coBOEHdqEm4+TeOkXxfD9erugTbSkRqoISChLYvonYEwcJ7NhUNRuQff14/Qt3tWl/Sfv6+lHlWIJVU5xVf7vhmYPK44llT74Iyyifr0X1u/Ig1JUxWoAQ6HqYkGzE9Sxsu4AQCpoaxLLzgETXI3iejetaaFoITQsihIKUHraVx/UsdD2MpgZxPQfbzm3IfLkUNc9kE8O3b2hFheZ2DenB1JjD5KjN5OjqD589PEnqz18h+avPEdrTtyxjF4qCCAer5ojrgZQSdzZL+vtHyb52vKYcfDHrcPndOZ77m918/h9u4Z3vTJCetAiEFO57spGtDyRIjZnsfaqRTXtj6IagoTWAlLB5X4wzr85gFvwvKpzQ6NwRwbUlc5PLh+2l45E6epmmp3YR7Cz3DVIMjdYXD4DrMfbND7Bn75zQojA0Gh/bQfdvPo7eGKn68OWvTpA5PVTXcVWhoQgF17NxPRucHEE9saDfZLvFZQmM69nY7uqEUQhBc3QLiXAHitDJlsbJFicXiEBIbyAR7iQUaEBXQ3Q3PkDRSnMzdQIpq98XUkry5jQT6Uuoij7/r0FIT6Br6zWxFMSCbezqfI6m6BZczyJVHCFvTmO7JTTVIBJoIRHuRFMDhI1GtrQ+Ts6cIVscX9eZVcWgp+kQfS0Po6thLKfA4PR7DE2/j+MtX7gqbcdPc6/BIFtoKmKdxtpCFbQ9vIlAMszQDy4uCOVtBDIDKQb/6hIzZ9Z3bT8MCF1f+7X1ZM3eZuCrQ899902aGmIYXa23x6CpBLZ1E9jcgZsv4kykcPMF8PwOOr2tCSUWQehq2bQipcQemWT2G6/iTK1ee7p07DguLCGNvqTBBintLCNQjCdX1iTz/KiLWMJnq0Z61jw2UTWyJN3V6wlXtNJa97hYUy2krkdob7sfAEXRGB45hqLotLcd4PrAj3GcIpu6HyeTvUk6M0R358Moik402o7r2fQP/BjPc+jseGhhbZmeuURq9hobXZpR9xYwEBJ87FNxHn4myuBVk7/4N9PsOxLGLEkuHF+luEuC2T9C6suv0PCLTxJ+aI+fK95gfRApJXi+UeHsd35G4f2LNVs9WEWPE69MseNwA5v3xdi0J4pVclE1gaYrzI6bfPdf36C5J8RTv95JMKIyNVRk/HqBg882kxozOfdGCgQcerGF7YcSZGcshi+tnGorDEwx9+512j9zqOzBEkKgRQK0ffoB9KYY4996n8KNaT/ku1FQBEZjhNZPHKTlhX0YzbGq34lbsJj8b2dwC/XVbkykLzO3JGK0q/P5+SiTZDp7ncHp95Z5t6RkV3YdLoVAoTHaiyc9hlMnGE6dImdOL5AiVdFpjPSyte1J4qEONNWgI7mPifQlTKd694nEYypzjVRuEGWeNBlamC2tj9EaX58OmRCCSKCJSKCRkp3h+sSbTOf6Me0ctx7ygBalo2Evfa2PYWghQkaS9sSedREnRWhlpMl2CwxMHWN45uSKpAnAs+269VluQcxbewh97Y4Biq7S8fhm7II9H+3YOOJUGMlw+T99sGHHu5tQE7GaOuOqQsqa/QNvoXihn9mv/YSGzz2N0d26cG4hBOgaWkMMrWHlxg8pfdJjDoww+41XV1XhrnoM18UzrYoNtjD0uurpVoRQynQAF87tuHjm8s/C7U1GeQmLCBiraizVPDRVqUqYpe2s+oxJy76dwVk4oPDHt+6BVU+hrv42hXRmiOmZS2zqepREopd8fhIhNG5ZaCuKikAhFu1EUQ1u3PgpjY07iEU7yOcn6O15EsvMMDF1hnh8E+1tB8nmRrHtjQ041P3puvsMduwN8vr3M9z/aBgJBEMKW/cYqxOneVhDE8z8+Q8pXR0m9tQD6J3NVdNCa8GtKFPx3DXSP3wXa3C8ZrXiWxi5mudr/+c1nv6NLnr3RjGCKq4tSY2V+NnLY5x5bQbNULjy3hxNXUGGL+UIhFR+5Uvb+NTf6eWZ3/Tb7CMJP4V3+tUZxvtXI5WSyVdOE9+/ifDW1griogR1mj62i9juTlLHrjJ77BrmRBonXcAz17AQCVAjQYzmGLHdnTQ/u5fItjaUQHUi69kuM29e8qNNdYZzTSdbQU4c11+kJRLTyZEprr1QD/xJ25OSifQF+iffqkg5uZ7NdPY6uhZmZ8dzGFqIiJEkEmhcljjdGp/jWeBZQB7LyWM7G9P9IYTA9RyuTx5ldO5sRUjZdHIMp04SC7XRmdyHIlTioXYMLbKqxUu1b0hTAvQ0HaK35ch8pClP/+RbDKdOrVrvBSBLJp659lZqNRFDGMaKk3ou6/HB+9a855x/PdSgRqgtSqg1SuPeDtLXp0ne14ZnuUgpKU7mKE3dvh7BlghGPEBmYBYjFiDYEkE1NFzToTiZw87e/gzhjhjBpjAgfMf68SzmzJI6TgHRTUk826U0nSfcHkOPGni2R3Eyh5X+cD0ltebG1V+0DKTn4ebqXFQcl/zxi9iTsyQ++SjRI3trThVKKZGWgzMzR+H4JbKvfYA9MbOmuh9pOXjZAiTLTYqVcHDV4uhaIVSBEqvU05OWjZdbfh7wihZeoVThkKHGwhtH6lS1amefVzRXVXP38kX/mi/mcIqo22OuGpSAsabIlW3lyOZGcZwiJSuDYUQRFZFDv77YtguoqkEs1kUwmKRkpgFBLNaFpgWJRjtRFBXHKaEqOhtdbl43cQpHFbJpl5vXLQ4+4n9pqibqbrRx57JkfniM4rnrRI7cR/jAdozNHWsmUNLzcGYylM73k3v3HKVz/Wve2XouDJ7L8edfukJzV5Boo45ZcJkeLlGa77RzLJer76e5+v4t00B45c+GeP63u+nZHUVVBekpi+M/nObV/zJak+tycTjFyFffYfP/+AxGc/mOTQiB0FSCnUk6PvcQrS8eoHB9gnz/JKWRWexUrrrmEiAMlUBbHDVsoEWDGI1RAu0NhDc3E93VSaAjUeYsvhRSSgrXJxj7xvs4mXtXJdu0s4ykTi9bpyPno1t9LY9iaCEURSMSaCKVH7zLI72N2fwQU5mry+bhnfmC7c7kPr8QV4sQ0KKre+NJibcoBakqOpuaH6Kv5RFUxaBkZ+mffJPR2bN4srbnxCuWkMW1kwS1IeHvaFewm7h40eFzny2vMUpsb2bnFx4k0hUn3BEn0BQmuacNpK+Ddv1rpxn45rmF1/c8v4POp7Zy9k+O0vsLu2k9vAkjHqA0nef8//MOo69dX3htx5N99Dy/k0BjCD1icOHP3qX/G2fLzq/oKvt+71Fcy2X65Ci9n9xFsCWCdDwm37vJ1f96kuyNOtNMGwi9uXHt6R/XxZ1bfuOw/Ps8nMkU1s1JvPtt1FvztpS4+WJZ1EfaLl6+iDObwRmfwewfoXD22rw1x9qjhl7JxEmlMTaVW4eoieiq7fg1Q1XRmiu1oryiiZte/rp5uQJuOoe+xERXbYyjGDobkS9QDA2tKV7xcy9XwF3FgNhNZ/1U4iISJxQFLVl5vLrHFYusKaompYe8pe8hJWUFwwKEUFFVAxDkC1N+807DForFGaZnLgMS2ykyMXma6ZlL84eRVN9Grg91E6fpcYdwVOGR56K0dup8/DMJdt8f5KffXj2dUg328CRz4zPkjp7G6G4luLPX9x9qSfpF36rqF4PfqniT0r8Y84J6bqaAdXOC0qVBzIERnIkUXn5jdoCuLZm4UWTixupkQXpw+tUZRq/kaewMomqCbMpicrBIMVvjY+JJ5t69xlhrnM6/dgS9obpOyK30XXz/JmL7epCWg1u0WC6xHOppou/3XgD8h00NG6iRAIqqLveW259LSsyxOUa+cozi0L3b7SClpGjNkS6u7DhuOXkcz5x/oASaukET7BrgSY9UbnCVCJafqvSkiyLUhZTh6se+3darqUE2NT1Ib/NhVMWgaM1xbeINJtKXaiZNAF6xiLcO4qS3NqFEwrip+nTJsjdmufBn7xBIhnjgDz/O9MkR+r9+Btfy1cRLM5UkMtgSYccXDpEdmOXUv3gdz3IJM1CpnAAAIABJREFUt0XJDpYTnJs/vMLEsSGaH+hi528eWnYMQlFoPtiGW3K4+B/ew8qUaNzTxpbP7wMB5/7t2x9O5EkI9O6ONb9deh7uXP2eblpzA4lPP0n0sf2okdCCXUfu6GkKxy8C8rbKvOvh2Q6yYOLmCn60YwOKkL1CCXsiVaGBp8YjaI0xzGoO0XVCa4hV6k1JiZcr4Ewvf93cTB4nla4Ym5aMoSSiUKdgaDWoTQlfj2nJ2JzZzKpK7r7QqAssClQoClpr0i8cX0fnn9ac2LComu0UUVWNeLwHgUI43Exq9ir6fGG4ougEjCi6HmZ07H1mZ/tJNmzFNLP4jTaSbG6UD73GafymzV99ZY4nPxkjm3bp2xngje9nuXBiHZEIx8WZSOFMzlI8cw00FSUcRGuKoyaiqJGQ35EjhO95VbJxs3n/BpnNIW17vs1z46vn64Hn1E60lj2G6TD5g1MIXaXjcw+hx1cOnQohfHmGFULTasgg3Nu87O+Xg5QScyLNzf9ylLnjA/ea9NESSApmyi8+XwWet6jRYSWdiDsMxy1RsGZXlRjwOwNdECpCKIs6Eld+jysdNCVAb/NhepsfQlOCFKwUV8deYyp7tSwiVQu8QtEX8FuyGNQKJR5Db23CHh6ra0GzsybprEmwOYLnuJhzRdJXZ3BXSFGrAY3swCxX/vNx7Nxti4Wldh5WuoSVLhFuj+GtUlBrZ02GfnCJqeO+XlHm2gzB5gibPrWbgW+f/1CIk9bYgNa0jlSdZePUSWSVSIjEp58g/vShhZZ7L5Nn7luvkX3jBHKF2p+NhDRt3xfNtMpqfYSqEOjronDyyrpFlQNbuyv1mDwPa3RqRXVyr1DCujlJ+JBTlsYUqkpwSxfm5fVHuQPbeioskKTtYI9MIksr16Hao1P+tVlcHyZ8RXEtGceZWbtBstHZUncHrec5pLM3F0o4iqUUtlOkUJhmYvIMycQWLCvL5NQ5TDNLW+sBUqmrzKSuIBSVTd2PE4t2MTNzCZC0tu5Dei6p2do7nuvBmlaN4QGLL//JDEK5Pf9tSHn3Lc0M28Etmrjr+PJ+nuEWLCa+ewKvaPmRp6bohhfQrwYpJeboHEP/3xvMvndtY4vR7wD8Wqn6ajXW2PyxYXA9G6eGjkGo/znzpIuULp3J/fQ2PYSuhijZGa6MvTrvs7eG79P1/GiR48IapAWEEBibeyiculB33WG9cPImM6dGF0gT+Bpt60EpVfAjVvOHcYo2qXPjbPuVg0Q648xeqM8odCOg93RWiP3WA3c2XbcFSGjvFqKP7l9YHKWU5N87T/bNU3eNNN2COTCKM5tFbzduXwMhCO7ZgvLK27jrIU6aSnD35or6LWk7lGogPqUrg3j5Ynn5iRCE9m0j/eN3a1darwKhq4T2bKmYELyiSenqzVXf76Yyvl3LoiJ+IQRqMobR27Fm4iR0DWNTe93EyXFLDI8cW/j/2bn+hf+enrm0kHoDUBSdlubdlMw5HKdEIJhAVQ1c18T1LCanzjI5VZ5u32jUPfvtPBCkb1eQV16eWzDe23kgSHefseZ03b0KzRBEG3SMkIqi1WCd4Eqmh0u4zvpnTzdvMvGDU5jjaTp/+QjhbW0oK+lVbRCklHimQ+7SKCP/9W2y54brFjP7UCC5K4qzGwkp3bLo10ZCIGiN76A5tm0hHakIDUMLL5Uaqwv25DTSttesyRTYuhklYOAV7mytnGd7ZaRpIyAdD89atNhJcPLz6dCwv3BviA5OrdBUAlt7UcJrL+i1xybqswARgsjhveWdbI5L4fSVumQNNgrW0DjWjVH0tvI6L72zmdC+beSOnlrzsYNbuwlu66nQPHKm0zURJ/P6MPbIVEXdkNHbQXDXZkrnri/zzhrGtmcrRk9bxc+dsWnM68Orvt8zLYqXbhDY0lV23dRElNDerRTP99fcib4YRk8bRk/bijWz64Xn2YxPnqa1ZR/Jhj5c12Z65jK5/MQdO+dS1D37hSIKDY3qwrVWVGjv1mnpuHdVgdeCtr4QD36yhR2HG0i2BwiE1PIOhCoopB3+9d88w9zEBvkkWS6z71wjf32Sluf30fzUbkI96ygEXfWEktJIiulXLzD5wzN3Va18I7CRqtp3A3L+nzsBQ4/SmTyAIlQ8z0Yo2oKUgmnnmMquTdvEHp2YbwFf22KttzUT2LaZ4pmLa3p/PdhoDqMYKmpQg1ubcQF6zPC7fHLm3SVN+N10wR1b1i5FAFg3R2sXwGTeWLe5oWxhvCX/8qHglijn/u1lHWFKOEjs6UOULg/Wrw916/1PHar0O/U88u+dX9FAd+G1pkX26ClfLHRRsbTaECX+8Yewb06sWotUDWpDjPgzD6ImohVjyx07U5NtijRtSuf7cZ8+hLroWRaKQvihPeTePoN5bfXI1WIIXSN8/070jupNShuJoppjrOEm9vg0ztjKavV3AnWxnWdeivP0S3EaWzW6t/h5XyFAUQWvvPzhGNHeCXRuD/OZv9/H7seSaJrAsWVNXXGO5d2RiJA1lWHsL98l/UE/DQ9vo/HhbRhtcdSAAapY8zml9NXYPdPBms4y+/ZVUseuUbg+gVeHMvhHuPcgEHjSYzxzab4r7wBN0c2EjAa2tj2O6eTJrFJIXw3OdAovX4BkZadRLVDCIUL37aR44UrdqQrpekhXooUNFF3BvcsBjmBzhMS2ZopTefAkWsSg6UAnVrpEYTRzd9N0QhDY1IXe1b76a5eBVyxhT0zVp/I8P2csrnMTukb0sQO+TdZM2o9g3cV609KVIYpnrxE5vBcxHx0SQhDY2k3iE48y++3Xq5rhLgf/8+z3/fSWEER7eJLCiUu11U5JKJ3vp3RlkOCuzWUG9+ED27GePkTmR+/W5Q+nhIPEnnmQ0P5tC5/11tjM/hGKZ6/VTGLNoXHMy0OEDu4oL2BvShB//ggzEzO+3EMtEILAjk1En3zgrlgrSdtGa0wgVNUnTkL8/+y9d5Ac2Z3f+Xlpy1d3tXdoAA1vBsAMBuMtOTMkhzNLzpLcndUanaTVSScpFCsp4qTbkE53F6FQXEhap9U6SbtL3S7F5XDJ4ZDjvcfA24HrBtr76vJVad/9kY0GGm2r0cCA3PlGIBpdnZX5Kivz5ff9zPeLCBkIRQ1qnm0n+F3TgrpoK4iSS9sJlPZVFel6CFNHKErgUFJFF35Vn3D/WwUksPm2EO++GDBu35dMTXhMjPx0pUkWghFWuPOrjWx/oBa74nP2oxx9p/IUMi7+EhekY/mU8jeGcPi2S+HsMMWL44He044O4jvbCa+px6iLocVDqBFz1s00H6SUSMfDyZZxMkUqA2nypwbIHr6EPZFfmSbU57jlIKXPQPoIF8c/wHbLlOwMuhomGWkhHm5hQ9ODnB1+laJVnb2Ib9nY/UMYK+3kUhTMjeswWpux+5byH5wNz3LJX0xTv6eVti9upDiQRSiCwkA2IC5VQg1rhBtiqKZGbE0Nqq4SaY6T3FSP7/hYkyXs3JWHmh4z6XxqK1rUwJoqUbu1ieb71zL0bg+FwZtbj6lEwoT37ES5Do86Z2S86g5Hv2LjTmYxrxJPFEIQ2bcdvbWB8qkenKHxoB3e85mPTcppxW/puHjFMl6uiF8orThq5RdK5F7bj9nZgtZcN0MCFNMg/uhepOeTe/WjoAtuiaigmogSvXsnNU8/OEdYU1Ys8m8fwupdvuacO5kl98rH6E0p1NrElbGFQyS/fB8Ihfzbh/AySxBvAWptgvjDd5D80j2zRTmlxM+XyL32Cc7o8qNrXjpH4aMTmBs7ZulBCSGI7t2Gny+RfemDoN5pkbEJQ8fc2EHqF59Aa6hZ9vGvB36pgjt15Z5Xa5NE77oNAK9QpPjRUUJbN6A31yNMneIHRwjt2Ej5xDnUZAytsQ6nb5jwbZtBCLxMjuInx5e9mKuKOBXzPsc+LtJ7zqLnzM3PZ98MRBIaW+8Jvvz9z4/ywu/1UszcOmRC2i72WI6JN08x8eYpjPo4odYajPoEen0MPRFBS4RQQ0Zgb6AIfNdD2h5usYKbKWFPFbHHclSGM1gj2epqHD7HTwUkHkVrEtsNVozZ0hA9Y++zte0JQnqCutg61jfcz5nh13C8KuqNfA+ru5fYPQu37i8GIQR6SyPh3dtxhseqUiJ3Ky7d3zvOpl+9g21//y6kDDrdzvy3T1ZEnGo2NbD5b+8l3BBFj5sYNWHWfGULTfd24lkuPc+doP+VszPbF3qnSJ8YYeOzuwk3xvBsl9GP+rjwnSM4uZs7H5ob1xHavH7FafvA6mQYd6pKwiclxQOnCe+6JjWma5jrWjHXtS55XHwZCEhaNt5UHmd8Cqt7gPKRs9gr0XaSEut8H9mXP6L2W4+hRq8QHsU0SD5xN8aaJoofn6R88sK8BEqJRwhtWUvs7p2Ed29Cncfuq3T0PPn3jlYXKZWS0pGzGJ3NJJ+8f1ahuZqIUvP0g5hdbRQ/OkH5VE+gDXUNSVFrYoS3dxG7bxehretQrlEjl55PYf9JigdOVXfupKR46FOi+7YRuWPrbKuvsEn8i/vQmusovH2I0qke5DWRMWHqGB3NRG7fQuyenWiNQVpTej7SclAi12exVA2Mzlb09mbs3kGMtmYqiRjSdXGnskRu3470faTtYLQ1osSjeJk8emsjemsj9sAIekczyqkL+Itoc12NqmNqU+OBdsojTyVoaNUo5X1OfFKiv8f+rNUAVgW6oZBqDVHOe3zw3MgtRZrmgz2Rx56Y/rKFQOgKiqYhVDFd1DitfeVLpOshHW+m2FsRKmE1jtAEJTd7U2uErj6WWKp47HOsAiQT+Qt0j0bY0PQghhajqWYrlluge/Q9vGUohwPgS+z+QbxCETU2V1F5ORCqSuzu2ykfO4XdW0XUyZdMHBkkf2kKPRFEV33bo3KN0nffi2cY+eASpZHFJ8HshUlO/N4HKAuI9VUmi0hPIqZLiNyyQ9/LZxl8qxstrAfSCOnyLCXymwElHiN2/77rKgqXFQurp29FBcDl4+fJvfQRicf2oSSiVZUKCCGC8oKwGdRL1cQx1rYQ2b2JxCN7Kew/Qf6Ng1XXJUnHI//uYdSaGMkv3zsrIiN0jfDODYQ2dOBm8nhT+UBjyXIQmopaE0erSwbCmbHwnMJm6XpUzlxi6q/fDCJjVULaDrlXPkaNRYg9uGeWvIESMojcvoXQ5k68TAE3ncObygWpJkNHrU2gpRKoNbF5uyel41I8+CnZF95dUXG+LFXIPP8ueks9emvDNT6pOpHdmwltaMdN53DHM3j5IigKaiwcjC0ZQ0lEZzoHpeOSny7ITzyyt+rxLBcibAbzjwARDuEXS3jpLM7gGNaFvmnvxE6sMz0zgYHKp93Ev3APfqlM+dhZ9PZm3HQWZ2AU6+ylqr7bqolTbYPKN389BQhGBx1qGzS+8fdT/OjbU5w/uUoTyLSOk2Lqc0wRvVxpRRfvsiFAKIJSzqWYvbVJ0xxIibS9QBhwGTCUCB3RHdSabRxPv0LJvXl1aoFeh0QgMPXYktt/juuHLz0Gp46hqxHWN96Lqhi0p/ZQcfIMpI8sWwzTS2exL/YT3rllxWNRa5PEH7qH9Hd/VNXDW3qSykSRykRQsxKr09m0N0F+0mbobAFVE7R3mWiG4NK4XFSh2S3a5HuqSFWKgKjNsWS5mdA0onftIbRh7XVFm7xsjsrZlXV1+eUK+fcOoyajQU3LddqbCCECpfGmFDVPPoDZ2crkX7yEMzBW1X5kxSb74/fBlyQeuwslHrlSVyQEIhJCD5voLfNr2s1HAKXrUjpylvRfvVH1eK6Gly0w9f038S2bxKN3IsLmrLGpsQhKNIze1rDssfmWTeHD42R+8Dbu+MrnbqtnkPR3XiH1S19Cb6mfdSyhCNREDCUexeicm56fVZzuuBQPnCbz/DuENq4h8fAdK75GF4UArSGFCJsgQWuoxe4fRolF0ZvrcSensPtHcEcnUOtrqJzpxi8Frgf2xUH8Uhk/X8S+OIAaCaE31+OMTVYVrauaOLV0BG23/+N3JijmfRQ1KBrfdXd0ZcRp+qbRUgnMjR0Ya1vQ6pOokVDQ8qyqs8597vUD5N8+vPotM9NwHUlu3CJeZ2CYNyYSogoNKSWKCE6/K20g+F0VaqD+Kh0ux2xVoaMIBV/6eNJBoKCIwOzQx0VBw5cu/iKPCYGCKjSEUPClhycdKl6e3sIxQuq1xEXM2vbyA1URGgrTgnfTx1MIiK2CAgg86bL44ypA0UojkYFhY6iRRLiZXHmUOXHqzxwiMJgUgc2kquizJxahTH+P0/1x8nKn3K32OQJcrn0y9RgdqT3oWpi1DXdjuQXGcmcXtH65Gl6+gNXTS2jrhhUXggpFIbxrO5HuXoofH1pWgbKqCzRDQfrgWB6KKth6fx3hhE52zEIogpZNMTbcVUvvsSzSv+o9Epxy8B5VEwhV4DkS1/7pCpOba9uJP3LvdUWbAOy+IdyJ6urbEAItlSCyd2vQPdXaML9C9GJz8yIPUiEEaCrhHV2kvvUYE3/6At5UdSlYv1Qh85P3cScyJL9yX0CSVGUWSVkKM00z+RKFD46RfW0/7ioofXvZApkfvoM3lSfxpXvQUglQVjA2X+LlCuTfOEDujQMrs8y5Gr5P6dh5kFDzzCOYnS2zztlSY5PT+ovFj08w9f23cMfSuKkEfsVGCd+AdJ0Ep28Yp292rVn58KlZvxc/PDLnraWDV/SdZMWidOjUnG2Wg6pnPc+TqJogUaPi2JJQRCESU7Gs6icgJR4htGkN0bt3ENq0BiUWCRSwr/nSroZ66AzLEaJRoiGUeHTWfSodDzeTXzRHXc65nDuQ495nmth8Tw2jvWWW8SxZNjRhsC6+l4qXJ6qnAJ+e3CEkkjWxnYTVBJ50GSt3M2n1E9PraI9uRxMGtl9isHiGsBanPtSJroQoOlNEtCRT9jDDxTPzkieBQnNkIymzDYFCwU3Tmz+2AMERJI1GmiOb0IROxSsyVPwU2y/TFO6ixmxBESo5e5zB4mlaIpuI6ikECrpikrGG6S+eZKkvaKrYi+PtxdSiRMwUm1seYyB9JFDSln5gLyI0VFWnUBmjbN9sMVRBKrqGkJ5AVQ00xURTDTQ1TDLcNrNVTbSDTS2P4no2nm/h+g6eb1FxCmSK/VVZmtwsOF6J3vGPMdQwTcmthPQEXU0P4Lil5fn2+T6Vcz1E77sz8ElbCYRAiUVIfPEB3NEJrO5Liz5wVV1w22ONpFqD2pMTr49jRlW2PlBHbsJm4LRKrFZn+8P11K+JMDVYRvqSHY/UU9ceRjMVDr0wQqo9TMf2BI7lMXSmQPehzK3KcedArUmS+NIj12XqC0FkoHT4RHWLT00lvHMDNV+9H3NdW9CxNK1b5Zct3Ewev1BC2i543oKnVCgKqGqgSRcJBdGMiDn7Ia2pRPZsJnLiAvnX9lf/+coW+fePYl0aIv7g7YR3bkBrrF2WD6qc9u6zugfJvXWQytneVdWn8otlcq/tx+oeIPbQ7YS3rA3kHZahiyYdFzedpXKun/y7h7HO968o1TovXI/S0XO4ExkSX9xH+LYNwbiWkLrwKzbO0DiFj05QeP/oDInz8iXcicy8WlM/C6iaOA1eskmPufzqb9RTzPuYYUEx7/PDP6siJy0EZlcbicfvInLHlsDNepVDemZXO8mnHpjFeP18ifR3XsXuG1nwfZWixyc/HmPD3gQP/61WsuM2p96dwlkBMZwfgrCWwMOhO7cfgcDxK3TEdqKgcjb7PjGtjvbYNgpumvbodgrOJKPlbhpD62mPbafopBEIxssXaY5spL9wgvrwOhShzWujEdfraAyv52LuEEU3gyb0BaNCmjBojmxiyhpkyhpibXwPTeEu+osnmbKGGK/0ElKjdCXuYrx8EU0YRLVazmTeRaCwKXkvo+VubH/xdEauPMLw1EnW1N2BomikYp3URDtwvPI0cdLQFANPOpweePGmEydVaGxofpjaaMei28VDjcRDjXNez5dHOHTpu1jOda4GbxDKTpYLo+9i6jFSsbXEzHq6mh7EHnyZgrW0LordO4AzOIKWqlmx2J0QAqOtmeSTj5L+7gu4IwunQmqaQqRaQ3z83CBde2tp2RTj+OtjdB/MMHimwKWjwfXx6buTNG+ocPilMSJJjc331zF8tkBta4jG9VEURVApuBz44fCy72np+fS9eAYUsajNy42EEo+R/MqjhLZsuO650hkcxrrYt/w3CEH0jq2kfukJ9MYrpE36Eutcb6D50zOIMz4VtNYvVjw9nWFQoiG02gRGRxPRfdsJ7+iaRR6EphK/fzeFd4+sjBy4HvalYSb7XsTobCG8bT3G2haM1obAaDcSCqJlnh8Y9mbyOCOTQYv++T4q5/qWtC1ZKS4rj1vdAxjr2whtWYu5rhWjpR61Nh4YFKsK0vXwSxW8TAFneALr0hDWuT6snoGAoK42fB+7b4SJb/+E0MYOwju6MNe1ojXXo9XEgsJ2KfErNl46hz0wRuV8H+XjF3CGx2ctQLx0jsyP3kWrv9Jl56ZzyBvsGHCzUDVxymd8XviLKdZvCVHfrFHM+QxctJkYWd4XKUyd6D07qXnyPvS2hhumMOqMplFjYYy1LTMTjfR9wqe6sYfGF7y5FVVQSNt8+NwIX/mHa3j232yk/3SB3lN5MqM2juUtuFBzbZ8Tb6exy0tPyFOVQRw/6FJQhEZcr2es3IPjV8g74/jSJ6E3ElJj9BWO4fgVcs4YdaEOLKWI5RUpezkqXoGKVwgSSkLMu3qO6fWU3Cx5ZwKJjycXnhBURSdlthNREzSFN2AoYbJyFFXoJIwGEkYjuhIioiUR055pWXuEiptHBntHV0JLEifPt7k4/hGub9NSsx1Ti6IoGoYa4bI5oy9dHLeEt2DUJjDALVqT+NLDWaZ9ScXJUbLT+L6HvYDBrkRSsXMUtZWF6Mt2dt60ly9ditbkzDbLiUh50qVkp1EVnYqTX/B8SOlTsbMU1RCe7y5p51Ky05wfeYdNLQqGFkXXwrTW3kbP+AdLvlfaDqWjJwlt7prlE7YShDZvoOZrT5D54cu4o+PzXsN2xUNKSbLRJJIMUnNLwbV8immb7LjFeG+JobMFOrbHKWWdZemyXYb0JAOv3xjPq+VAScRJfulhovfcvqyoyWKQjkPp4HG83PKFF82NHdR8/WG0htqr9uMG9TU/egdneHL50SspkZaNZwUPX6tngNKJC9T/7a8S3btt1qZafRKjsxnrXBUk71r4EvviEHbvMEo4hBINo4SMgDQpgWeYdF2k5QR1MCUbf1oQVBNGUKqwjNKDlUC6XkCELvSjRMOBJ6upB+lvIcD3A/Jk2filSuCNdzOERl2PyqeXqJzvR42FUSLT41IVkEz7xdrThs2Veb97v1S5LtX260EoqtDeFUIogv7zZSrF1U/HV02cDFOwaWeIbbeHCUWCepwd+8Ic+bDE4fcWFxkTpk7i8btIPnnfHNXT1YY7lqZyvj8IFU7n4YWiELljK/l3jy4oiFbTZPAPf387da0hjLCCUASJB2rZem/NZe23BVHMOPQczWMvGdqVs7qY5Iyg3GUSKRBCmek8u9J1Jmbef1lxWkp/GerTQRH28iCpeHm68wcoOUHBoY9PQm+gJbKF7tx+POmwpebBmfF4vnNlDFIu+0i2W6Bn7H1Gs59SG+0gYtSgKiYSietZWE6OopVeVKjxRP+PZgjjck1rTw385Mp7FpgUfelycuCF6xIXnY8UFSoTfHT+v161zdJjLlTG2X/hzwnM3hd+j+0WZ85HcK0uve9MqZ+DPX95ZXGxwLjnQ+X0BZyhUcz1a5a1/YJQFSK7tiEUhcyPXg1MgK9BfsKm+2CGrjtrqRRdek/kkD6MXixRSF9ZCOTTNsolgfQkpazLsVfH6dyVQGkUDJ0pMDVioWnKkppstwrU2iSJxx8KuuiuNZutElJK7MERyifPLNsrUIRN4g/sCfzHrrpGKuf7yfzwbZzrrf2R4E1myb95kMiezbNSQ0rIxGhtuD7idBm+xC+WFzXmVYVOjdZAmiFUNFpCG8g4o+Td669vWnJs+dLyxSZvFlwPL1PAy/x0OUis3x7hn/zHtdiWz+/+xiW6T6z+ea2aOLWtNXj8GzWcPlRi4KIzwyTSY0tMtkIQvXsHNU89gJpYvI35auuCFZMrCeUT3cQf2D2rgNFobcBoqaOyAHFSNUGsVkcC1jIiR1fD8+Sy6yXkrP97TNlD1BqtFJxJYno9Uvpk7VGSRhMN4XX4ZY+U2U7JzWJ7ZUx1+a3gWXuMhtBaas1Wiu4UmjAouUFq43IRuDpd+O35DkVnipTZhu2V0RUT2y+jCAWQV0XCEosfdJmQ0qNQGaNQWVnHii/dqmtUlvuelex7aUi8qj31JL50btiYV/o5vXye4sFjGGtar0stOGhRVwnfthUlZJJ5/hWsi/1zulx6j+XoPTa7WPjC/tklAhO9ZSZ6rzwcB07nGTh9JV2am7gx6ZdVhxDozY0kn/picF6M6yNNEEQJy8dO44xOLPs9WjJGeNfG2d1TlkPp6NnrJ01XwRmbwsuX0K4ynUVVUWLVF8EntUY86RLTaii4U4TVBEUvQ8poBQRpO4j2N5hrph9fknG7j0ZzLU3mOqJ2knGrH03oNJprSemtpJ2hG0+gPsd1Q9UEHRvDpJp10iMO4gYp3VQ920UTCuNDDq88l8UqL3+2Nde3UvPU/SjxyLx/l46LlyviFcpIy8a3bPTmevTrUCK1+0bw8qVZdU4ipGNuXEPl7PyrmPSwxX/61WMrImy+L5ecmCU+JTczRzdnrNyNJnTWxffi+hb9hRM4foXB4mnaIttYH7+TsptloHiSiFYTRGV8h7KXw5ceZXf+1BBA0Z1isHSGpnAXitAoOBP0FU7SEO6kPtSJKnTWxHYxZQ0xWr7AQPEULZFNdCX24foWg6VPKTjXb/lOAAAgAElEQVRpsvYoa+O7KThpJiqX8KVDxbt6NSIpuplFUmuf42cKrkfl5Fns23didHVedwRZKArmpvWkfvnnyb3+HuVjp/ELy7fK+FmBCJmENq0n8cTDmOvXXJcX3dVwhkYp7j9SVdu13tqAmpzddetXLKzz1fmYLQXpefhlC64iTkIRKzKTNpQwCgopvRWBQkiNE1KjlL08nvSoN9qZtAeJa3UMVy5Q8Yp40iHvTJLQGhizevFk0K2cd9NI6VGjN1F0Mzcsbfc5VgdGSLDp9uq0xVaCZV+V8aRCqlEjHFVIplTueiTGQI8dRFmATNojO7lA3VA8QuJL98wR2ILpPG/PIKWj57HO92EPjgfGh55P/d99Gv2xfSv+cH6xjH1xCL3xSm5e6BpmVxuoyrwt0J4jGem5cc7tnnS4kJvbKeJJN+hGK56c9XrFK9Cd/2TOa5dRLAQtxb2FxfLJkonKJSYql2a9OlbuYazcM2frkpuhO/fJnNcv5g/NeW2kPLv240Lu40XG8Tl+1uCMjlM8cAy9vRkRmqu2XC2EomC0NZP6hacpbVhH/u0PsPuGbpj8yK0GrbGe+EN3E7379mmBv9V5AEjbofDeftwqLVa0uuTch5Dn4aarV2pfDEJR5tRvXVZ7rhZlL0eD2Ynll4lqNZS9/HRXcgVPuqiiAYGC41uUvNy0HAw40sKXLpZfQkXDlRYVLw8IwmqiinKHz/FZIRJX6do5f3BmNbFs4rRxZ4jHv5FEUQQ1dSpPfDNJMe/P1Ap88EqB916ap4NICEIbO4js3jTHMNEvVsi/EegyOWNTy867LxfSdrD6R4netf3KcBQlUGJNRJflcP05PsfnWARSUjx4jPDubYS3bly13Sohk+hdezDWdVDcf4TSwWOBPYj7MxjNVFXUZILwzs3E7r4DY03biiItC0H6PqWTZyifOFO1nYkw9RsjYngNLit3Xw3puLgrmKMvE6UJu496o4OCO4UQCnVGICOSdcZwZAVHWrPqQwMNOp/O8E5GrB5caU/XEwpcaS9aS6qoYIYVzLAS6IQJ8Fywyh7lgn9DeL8QYEaCY2p60BwkfYnrSOyKj1X2q3bzUHVBOKKgmwpqIE+H5wU+rJWSj+us/INcPkeGqaDqgst0wPfAda+M+Xrkfzq3hEnU3XiT4WUf4cQnZc4eX7jTxrHmP6HC1Ins3TonV+1PS73n3/gEv3RjLAuk4+GOTSF9Ocv8VomGUWvjnxOnz/E5VgF+vkD+rQ8xO9uvW5jxaghNRW9pJPnkF4ju3Ulx/1HKp8/hjI5DFU7mtyxUFb2xDnPjeqJ375kmTPqqphmklLgTafJvfYiXrT5K5FecudE+TUOrS+KOVSmguQCEoRHdtz3o2pp1bBtncGlpjGvh4dJTPIyPR86ZmEmv5d0JQOBLF4lksHx2VurNkRV6ikcC01fpMlzpnmnQKXnZ+TXyBNS1GGzdF2P7vhhrtoRJpjQ0Q1DIeFw4XuTTA4UF62VLeY9zR4qUC3P/rhmCjbui1DRo2Jak+3iRzHhw3YdjClvvjLHn4STrtoVJNRnopqBS8pkcdhi4UObUx3mOvJNbVleZqkHbhjDb74qx9c4YretDxGs1fE9SyHgMX6pw5mCRU/vzDJyv4LnLJ1DRhEpbV4jOLWHW7YjQts6kplEnFFWRPlRKHlOjDv3nK5w7UuDMwSITQ8urRQxFFRIpjWSdTk2Dxr1PpjDDwXVkhBR23pugoW3h+sBCxuP80SKVUnVsbdnEybEljl0921TjUUJb184uLvR8yscukH/zwA0jTcGBZOC8XbZmGz9GQnNWN9cDzRDs+kId7VtivPbfBijlqp/UI12NRNc3UewepTKYxre9n4n0RLSti8S6HYjp5UtxsIfshfnTijWbbifS3AlCQXou2e7jlIYv3twBf44VwTrbTfHgcWL37V21mhy4YsdhdLShtzQT2bcb68IlyifOYHVfCjR+fpruk5nP00p41zZCm7rQ25pWpfh7XrgehQ8PYp1f2X3kTkwFHb9XvaaETcI7u6ic7a3ekPdaqEGnc2Tv1jmRLXdkMtAHWgE83Fk/gTm1l/MRIY8rjRJX/30h3bste2N87X9tYuudMcyIStCvH+yipkGnfWOIR74xv8ULQN/ZMr/9Ty8ycGFuUCISU3nmHzWz6/4EubTDH//rfj55JUNto85X/nYDD329jmS9NuvZmqyDpg6TrXdG2XBblNOfFJYkTqGowoNfS/HIN+ro3BJB02d/D3XNQSTnzi/W0He2zFvPTfLODybnJXvX4o5HE9z7ZIqNuyI0tJuo2nyLAp3WdSG23x3nkZ9PcfLjAs//0QhnDxcXvLU1Q/DFX6xn0+4otU06dc0GtY06RkjMnI94rcaz/3xx0+nzx4r83j+7xEhvdTzkhse0jDVNc7ro/FKZ/FsHAw2IGwy/VMEvlGYTp5Axy9n7eqGbCtsfTLHzoRTv/c/hqomTUAWp+zbR9NTt+GWbykiG/PF+cicGsEYyOJkSvlV9rv9WgO86SM/BSKaItqwDKRckTr5jI32PUF0j4fo2rKnRz5Q46Tq0NqsMj3rYy2zGEgLq6xQsW5LLBXd9JCzoXKOiKIIL3Q7z6fmlahV0HcbGg7B+PCaIRASjYz8dliB+uUL+zffRWxsx16+dFeFdLQhNxVzThtHWTPSO23DGJrDO9VA5fxF3fAKvUEJWrFuLSAmBMA3UWBS1rpbQhrWYm9ajtzQGNUyqesMKWaXnUTp+muKHB1dMcJzhCbzJLMpV/m5C14jdvRPr0jDlI2eRK4n+CYFaGyd653aSX7lv3jRd4aPjN0bocRUgBGzcHeXXfrOdtdvCCAGlvMvwRYvhXgur5FNTr9HWFaKh3UDTgyiIlEHayyr72BWfiSEbZxm2P7qpkKzTqG3S+cXfaOHer6YwTIFdkVRKLp4TZFXMsEIoGugt9Z8vk5tc/PyFowpf+wfNPP636onEgwVPpeRRyHo4FR8EmCGFWI2GbgrWbgvzC7/RQqpZ56//y8iSpGzDrij3fKUGTQ9sj1zHp5T3qZQ8XEciRBAZik/v34yo7HkoQaJO48/+nwEuHJufPOmm4J6v1LLljrnd5VIyLd0CS7YLr3CquPHEqaNpTquyPTCG1buwevdqQjru3JtPUwMV1FWCogpCkekJcAVzoF4XJ7a1FT0RhkQYsylJclcnvuNR7B5l6K8+ZuqDz06A73pQHu2jPNpHuKmD9ke+tei2uYsnyV08SWLddloe+LmbNMKF0dSo8n/+qxr+73+f5VLf8ibwkCl49htRLvQ4vPhqsDCorVX41jNRHn7A5Nf/cZoLPXP39dUvhWltUfmdP8hTLkv27TW4Y4/J//tbq1uEeyPhDI2Se/VdUs+m0GqTN+w4QlVRk3HUZJzQxnUkXA9nbBz70gDO0CjuRBpnfBJ3Io0s3/jF2ZzxhUNodbVo9Sn0hjq05gbMtR3oLU3ze7vdAEgpsfsGyb74Jl5m5deQlylQPHia5JP3z9SoCiHQW+qp/9UnybXUUdx/Cmd8all+g8LQ0VvqMTd0EN27lfD29XPquaQvKZ3spnTozIrHfaORrNd4+tebWLc9jBCCqTGHH/zBCO/+ME0pfyU61bEpxDP/WzN3PlYz4336wQtpjr2fY2rMZWLIZnJk6VWZYSo0tJl86VcauO+rKTxXcuDdHJ8eKDDaZ1EqeGi6oL7VYM3mMJ2bwxx9J7foGkIzBI98s44nfrmBSFzF8yQXjhX5+OUMF44VyU64CAGpJp0te2Pc/3SKlnUm0YTG47/UwNSYw+vfmVg0E3XozSyPfrMeVfO4cKxE94kS/efKjA3YFHMuQghqG3W27ovxwFMp2jaEUNQgRfnoN+sYuFCeN7Ll2pIPXkhz7tBsjamt+2J03RZFVQWVoseB1zJMjS0cdJgYdihmqyfnN5w4aXXJOblr+9LwirolVgLpeshrCkqFqiD01ZvAFFVMh2lXBqM+TrhjbjhX0VXMxgR+6acz2vSzACEgHhc0NiiUy5JCUaLrAUHKFyRSQiIusGyJbUM0Kth/0GJo5MrkOTjk8d+/XaCzY/Y1ommQTCjBqsuYNvoEYjHB8IjPq29c6e6MRASqEmynKJAvSCqVYMWWiAtMU6CIoPQnm/M/sxrqyulz5N/+iORXHg26pG5CYbHQVIzWZoyWJqTv45fKeLkCfr6AlyvgTmXwprK46QxeJotfLCNtG+n7gRLztAAtMH+06moTViECY1ZFIAwjqJdMJtBSNaipGrTaGpREDDUeQ41HUaKRVU1dLhfe5BTZF9/EGZwrJloNpGVT+PhkYFmyvm1WdExNJaj52iNE79qBPTCOMzSOO5kNFLgdN4i26RpK2EStiaHV16A31KLV1wQ+aNN+d7OO5/tYF4fIPP8O3gJae581hIC1WyPsuCeOEALPlbz13CRvfX8S65pamf5zFf7qd4ZpaDPZuDsycw3tfyWDV8U9qqhw1+NJzIhKetThx/99lE9ezczUPF0N3RSkmgzyUwsfQAjYsDPC47/UQDim4HmSQ29k+d7vDdN3drY/6/AlizOHi5w/VuRX/1U7HZvChGMKjz1bz7nDxUUFJgcuVPjzfzdAIeMycL5CZtyZE/wc6bU4f7RI/9kyv/wv22nuDK6L2+6LU1OvUy7MTaM5luTVv5irR/bsv2hl3bYIqioo5T1e/v/GuXDsFhDArBZKLAzX2Kq46RxyMT+j1YTnzz2WoiBUlUhSI9Vqkh2zyacDgUFFE9Q0GlXN9/E6g0hyhREsRRBqqcFIzS9oWe6bpHB25ZOfUDWMRB16NIFQNXzPwSsXsfNT+HawGjdrGhGajlvOY9Y24ltlKulR9FgNRrIOt1Sgkh4J2h8AoajosRr0WBJFN5HSxysXsTLj+M4NrFn7DFBfp/C3vhUlFhOk0z5/9KcF1q7ReOILIf7D7+bI5SW/8Y8SvPdRhQ/3Wzz7zShffDjEH/9pnv6Bha9xVYVHHgjx8z8XIZvzaWpQOXPeQdPggftCfOuZCN09LkePB0Kl3/p6hG1bdHwJtUmFw8ds/uufF+hap/ErvxjF0AW37TQ4edrmt/9LnsGhz0ZvRtoOhQ8OoKWSxO7di9BXL7K7JIQIolHxgLjMECLPR/pe8NPzp60sXPxSGb9cwa9UgjnC8wMvLc9D+j5iep5AVYOfmopiGiiRaRsKTZueS5TAmFxRA5kTIW5Y+m058Aolsq+8TfnUuVWx6LAvDTH1o3dJ/cJjGK0NM68LIRAhA3NdG8aalulzGFjjBAQ0iMALRczMuYsZuEvXo3K+j6nvv4l1vu/WSrleBVUXbNgVIRwLnmsTQzbH3svNIU2XMXLJ4sSHOdZuC2OYgq13xmjqMBm6uPy5UghBc6dJbsrlB38wwnvPpxfscHMsyWjf4vs2wwr3P52ieW1gsDxwoczzfzxK75nyvOkrz5Gc3l/g1b8c51f+ZTtmWKF1XYjbH0nSd668YHNYpejz8UtTAVla5Ot0HcnR93Js3pvhK7/WiKZDtEajdX2I4Uu33jOlauJUW6/Svs7g7PEK9gIn62oIXZuTvvJL1s3x3AFQRPBv1gCCCfTRX2nj9i/Vc25/lud/+yLlvEeiTufZf7MB3Vy+5KhmKDSvC1fVaTAzPE0l3FE3Jyp3GbljfSuubxKKSu2WvaS2340erw0mdiROIcvEsfeY+vQASJ+aLXcQa11PZXKE5MZdOMUcE8feI7FuO7G2LuxcmsG3v09p5BIIhfjardTvehCzthFFCx6MTilPrvsE44ffxLNunA7WzYZpCvYfsPj4gM2/+KdxHrjXZHLSJx5XZlRp43FByBRUKvDfvl0gVaMQCi3+4EzEFZ5+MszLr5f55JDNv/7fkxi6wHHhJy+X0VS4684rwq2RiKCjXeM3/68Mqgr/9v9I8sMfl3jo/hATkz7/+Y/z/LN/nGB03PvMSNNl+Lk82ZfeQqurJbR982dHIi4TGEVBzDfV1dXOfe2nHH65QuGdjwKhy9UKO/qS0sFPAUnt1x8N7FeumVOFOk0gqZ4oB9I0ZQofHCf3+v6gk+4WJU0QqFO3rL1iTJ8edRZNt0kJA+cruLbEMCESV2jtClVFnCAgoCc/yvPJq5nrkgUAqG81uO2BBIoicB2fY+/muHi6tCS5OXOwyPDFCmu3RVBUwa4H4rz87TEca+E5x1/mdORYQaqwXPCI12qoKtQ23cSFVxWomjiFowqP/FyCR55OcPpwmfMnK4yPuJTy87NtOU/e+0YZ+84HxdBRrjEhlU5gnLh+T4KWrgiKInjpj1TKeQ8jrLJpXw3GdEvjsorHpueQYqb6iUroKmbL/PUgvu2SPzU47zlcDoyaBur3PEx5bIDBd36A71ioZohwfRt2bpIr8ViBUdNAtuck/a99h5b7n6b5ri8xdugNps4coOnOx0ms2x4QJ+njWWWKQ92MHXwDt5RDMUxS2++hbue9lEYukbt4akXjvRUxOuZx6ozD8KjHp2cdOjs0crnZk+RKeEEoBDUJhWMnHIaGPQ4ftWlqWDylc/ioxeCQi+eDbUtqkgq5vE9Hm87G9RqRiGB8/NZQNvbSGaZ+8DIp01xV9evPsTD8Upn8Ox+TffWdoEh+VXfuUzp4BmdgnNgDu4nu3YZam0AJm9U3Akg5nVK18DJ5Kmd7KXx4DOvSMHJJn8+VY1OXzp6dJt/94fK911I1Cs8+E+f3/3t25jVFCVrsL8Mqe0vacxVz3ozmoaoJYsnq7wfH9vnklQylwvXf45vviJKc1jsq5jxOf5LHXUbXfHrEZrTPZu22QGSyoc0k1WKQz6zOYjk74WBb0x6tQhCO3jyuUA2qJk5DvQ6//29HWdNlcttdER57Jkmp6DPQY3PqYJnJ8dm+V7JiB9El9SodpYgZRIFuQsOQEgvP7eqr2PiFMh88N0Eh7dBzNEc5P5v0TA1bnH5/Cruy9EVqRlR2PFS3QKvl4hCqgtkwv+9bZXAKe2LlhZ1CVVFUDc8q4eQmccvBhFEavnTNhuBVSuQunsItFyiPDRCubyV/6VPcSpHU1rvQE1dW58XBboqDPVz9RQtFJdbeRaiu5ZYhTmpNDeGNG5fXCOBL7KFBrN7ZVjyaKtCmBe00TeB5Et8PyJIQQeddJKxUTZ4uZzNUbaZsZkmUSnJWoFZR4NARm6e+HOabz0Q5edrhzXdufjH0QnD6h5j67o+o/eZXMTeuu6kLpr9p8MsV8u9+TO7lt25cQbzv4wyNM/W918m/eZDw9vWYG9rRm+pQa2IosQhKyAyyDMp0W5PnIx0X33KC1GihjDuVwx1LY/UMUbnQj5fO3ZQIUyQsuG27QaEYYWTM48gJi852jW2bDQpFn0PHLHRNcPsuE0MXHD1p4XnQ0qwG791mcPSkHZiDe1fGq2piyblfN6/MEdJfWPdwMUwMO4z229clEHkZ67ZH0I3gfqwUfYZ6lnfNlAsexau6xnVTUN9i0Pvp6hAn12bW51NuQHfuamBlNU4Sxocdzp8sU9cUZ+ueMMlalS27Q7z94zznrhLK9LL5oBX2qlSU1lCL0LUbX+ekKOiNKZTobCsIv1TGyxY4fGick++mcSr+rBsBoP9MkR/+1iUKU0unyZINBg1rwrRuXL7x7mUIRaDF5reqsEazeKWVm5La2QmyPSdIrNuBEa+lMHCBfP9Z7Owk0p39uaTv4lVKIH18p4JbLuB7DtJzkdJDUa5cKopuEKpvI9qyFj2aRGg6ejSJaoRvqciC0dxE7VefRIvHl9xWui7Zt9+ZQ5yamlTuvcvENGDXDoMXXioxPuGTTChs22wQjQrWdmpIAhKVTARpumhUIR4XFIsS0xAkEgrG9M9IRFAuS4ZGPB59MEQkbHH3nSbdF11UJUj9RaMKIVNQkxQUSwtPsjVJhXhMoaFeYe8eg0LB5413KreMwLbdN8jUX79I7TNf+Zw83SB4hSL5tz4k/9aH+Deji9CXuONT5N8+ROGDY6jJGEo8ghIOoZh60LUsRFDr5MugQcdxkBUbv1TBy5fwS/PX0txIKArEokGU9quPRygWfR5/JEJPr8OOrQaGHtx3DXUKE5M+X30iyutvl4hHFb7+ZJTJtBcsXDzJ+FUCjYmURjKlkR5Z+FnRvMaY0UeyLX9ZnXTXIpd2KRev/5mpG4KGViNQBgcSdRq/9psdM5GexSAISNdlKIogEl/6nhZK0BkYTag0d5o0rTGJ12pEEyqhiIJmKuhG0GEXr73xyt/Xi6pHmKhVueeLMbq2hRACTh0s8ZPvTFHI+uy4M8x9j8VnESdnJI10vVktp+a6FpSQiXcDw7IQRLZC29fNsXrxssVAUVyCtcCFWMq5Mz58S8HzJJWVXtBCIELzR0Ts8TxeeeXEybctxj55leJAN8mNu6jZdDupbXeR6/2UyePv4+SvOMtLKYMCWggE3OT8ApyqEaZh7xdIrNuOnZvEyozjFgKD4Yi/hhXpMdyiqFQkr75RRhHwy78Y5eARmw/3W9g2vPxamWeejjA04vLG22VGR302bdD5+Z8L09KskqpVSNUq/OVfFbljj8GjDwb3y68+G+XjTyx+/EqZv/yrIr/wjShtrSqHjlgMDnvU1ih8/amgEDwcFvzzf5Lgez8oMTAYfDeXI1WnzgST9NeeCvMnf1bg9BmHzjUqX38qwvkel56LtwhzAuxLA6S/9wI1Tz9BeOvGVbUT+RsNKfHyBXKvvUf+7Y+Q1s0vopWOizuRgYnqPPA+C0gJI2Mu731c4Z69IdZ16piGYP/hCp4XYk27hq4LTp2xudTv8uwzMeIxhY52jYpl8u3v5rBtiaLAuSNFvvgLPrqp0NxpsvmOKP3nK/PWHiVSGjvujc/UzU6OOPSfq57g2mVvWem0pWBGFMzIlSL9cFTljkdXJh8iBDMyC/NvAMk6jc13xLjjkSSb74gSTajohoKigqqKoENVBOQqGNKt/wypegZLplQaWnTefTHHuWsKxM+ftIhfk7u1LksPhK/UGWnNdZjrWihN3ViNGqO9idDWtbNfnA43L9jqKsF1fYoZB3+Zxd6+J7HKKyVOLFg865YqSOf6VhieVSZ38ST53k8J1beQWLed1La7EUIwuv+VqrvgQg2t1O24h2z3cUY+/MlM+i/aup5E59brGutqwysUsHp68GprUUIhlFAIEQot29ZiYtLn3/+n+a/R7/51ie/+9dw211Ofzl11vvRqhZdenTtRHj/lcPzU3AfO7/7hXCug46dm7/c//E6eupRCY4NKb79L34BLe5sapBNX0KRwQyElTt8QU//zefyvPkZ03+6bpmf0swopJe54muyLb1A6cAzp/PRLlqhmmNquPejR4CGevXSS0nj/qh6jpUnj134xRiQsOH7aor1V45tPxaitVXnrvTLhsODefSF275AMj3pksj6fnrN57kcFvvhQhNHxPBNpn/NHi1w4XmLL3hhmWOWJX25kcsTh8Fu5Wfdfsk7jqb/XxObbYyiKwPclH/1kimK++nnd97kim3EdCLztVjHyu8hU2rk5zNf+QRN7HkrOCGxehmP7FHMe5byHVQkEQY2QQltXaCaNeKuiauI03Gvz0v/MEIoImtqvREqyaY/MhDvH6Ncdn8LuHyV8lTKsEjaJP3w7lXN9+IUb04GlxMIkHt+HGr+mvqlsUz5+YcGcenq4wu/8nRPkJ51l1TcB+K4kO2qTm7DnpPyWfrNcsGvOt9xAa2aFEJqOUFR8x0L6HuWxAZxClmjLekJ1zahmqGripIVjCFWjPD6IWwmIg6KbhJvWoEWWTondTNjDI0z+4IcITQv+XW4pNwzCW7aSuP8+FNNceke3KKYyPj9+qcyvPBvIEeTyPt/9fpHB4YWvW72lmej2HQhDp3LxIuVz51fdXHshuBNpMj98CXcqQ/z+O1ES8c+0bf+nFdJxsXp6yf7kDSrnL9607+9GQ/o+bqWImWygtms3diGzqsTp7AWH3/7DDIYheP2dMkMjHs+9UKCpXsV2JQNDLooiuNTvoKmC4VGPUtnnD/8sx8Cgy0Tap1AM5veJIZsX/2yMVJNOY4dB63qTv/NvOnjomRID5ytY5aBDe8POCJ1bwpgRBdvy+eTVDB/+ZOqmpymvhhBiVk3mxLDNd/7jUPXPLsBzJRdPza+T1L4xxK/9Zjtb98Wmu/ckE4MWpz4p8OknBUb7LSpFH9fx8dwgc7Phtgj/y7/uoKbhZ4w4Jes0vvH3UrStM6hv0kiPudQ3a/zl70/y7ot5rMrsk++XLYqHzwZ+dZfrX4QgvKOLxGN3kX3xA+Qq24kokRDJL99LZPemOVIEztA4lbN9C7wzUCTtO7n8rgsIuire+c4Qh18ZX1ZN1NWQUi5YxySmNWFWWjgZ69hEfM1mrKkx3FIeIRTMVDNGTT2Z80fxrOrDxVZ2ArdcINl1G55Vxndswo3txNo3zpEhEJqOmaxHMUKE61tRjBB6vJZo2wak5+AU8zj5wChUNcMYibrp+qlWFM3ATDURbevCdx2c/BRuqUpTZs/Dy80fMVJj8aDG7qeXN+H78OOXy7z6ZgUhwPcljr2I0ocQRDZvJvmFRwMNIlWlcqE70C66SfAyOXIvvYkzNELi8Ycw17TdFJHMnwVIKfFLZYoHjpJ//X3c8clbum2/WviORebicSqZMWIt61d9/6WypKf3miagjM9U5urFqaT7mjR3/6A76ycELfYHXs9ghhW++ncb6dgYJtWsU9uU5PZHkiAlQggUFUCQGXf46KUpfvKnY6RHP9vooGP5uFdFxaySz+G3spRyK5sH5rsEwzGFx3+pni17oyhKMDcdeTvLc783zMCFCq4r5y1yb2gzZroPb2VUTZya23VUDZ77k0nu/kKMF/5HhrsejS0su+56VE72YPePYXQ2T7NdgYiESHzpbqTnkX/9QFDUeL3nSwjURITEl+4h8dg+lMjsomvpuBQ+OIhjW08AACAASURBVI5XWF0lUenDeF+F8b7qiYh0feyJPGxumfM3NWwgNAVpr+yCdkt59EiCeOcWFFUPSFq5QObsISZPfjwTbZKei+84gETVBNJ38d3g92CMLnL6Kremxhj9+CVqt+2jad8TSM+hPDHE+OG3qNm0B3mVHK4RT9F01xMBITJMVDNMpGkNbQ89g+855HpOMXbgNUASbmyn4fZH0SJxVCOEoukku3YR69iEdB0mjr1H5uyhFZ2H+bAaIe9bAb4f1GItB0oohNHaNhNlW6lF0PVC2g6lg8dxJ6dIfOF+wju2IELm59GnBXBZUNIdmyT3+nsUDx5Dlq4/Um/EUyTWbMVMNiCEgl3MkL10EiszNr2FIJRqItm5Az2SwCnnyfWeppwenml9irdtItrUycSnH+OWg4WNFonTfPtjTJ75hPLEAHqsltSGPeQGzhGpbyeUasGzimR6jlGZGmN2G7ZkqbYxRTOItW0g1tKFomqUJgfJXjqFV7m5SuO+F1iKrN0WpnV9CISgmPVgWjqwUgq86LpPlDj5UZ5PDxZWTE5WE8W8R6UYCJUKIdAMQSKlBWNfJTStMdn9YHImJTjcU+a5/zzMxdOLX7e6IW6Iz+Vqo/oqTQHFvE963MP3gtXtxKhL50aDj16f/y320DiFd45Q+60vIELGzApTS8aoefpBzM5m8u8cwe4bwcuVqjekVBW02jjm+jbij9xBaMtalPA12k2+T/lUD6UjZ+FmqZYvA9L1KA+k5/2bXhNBMXW8FRKn8tgAA29/D0UzZsQvpefORIouY/L4+0yd/gTfcdi0L8XQuXcoFXzcchGkZOi9568ar0Pm3BHyfWdRNAOJxLcreFaF0silKwXmgJ2bZOi956ePPRcBcQsmzdJIH4NvPbdg9OFmT4o/i1DjcYy2xd3CbxqkxL7YR/o7zxO98xKxB/ahNzfO8bX8Gw8p8XMFyqfOkn/7I+z+oVVJzYXrWmm7+2kkUJ4cQkqXUE0TxdBFLifvI41raN33ZdxKCSs7QaimkUT7ZkYOvUp+6DxIgtc6tjDVfWSGOKlGUKuUHzhPeWIALRShpmsP0Zb1uKUCbqVAtHk90aa1DHzwA6zs+LLHrWgGDTsfINm5ndLEENJzqNu0j0hdO8MHX8azVt9eYyHopuALv1DPAz+XQhHwyasZXviTUfKZwIPN9ySO5VMuBqa+t8pazbUlo70WnivRdEE4qtK+IcRwlYKcC0EIqG8xaGg3Zl47+l5uWYKf8dqgQP9WR9Wz1MSIy+BFm0rJJ5/1+PV/2YCU8NpfZxd+k+uRf/cw5uY1RO/aPmuRq8bCxO7bRXjXJqxzfVTO9+MMj+Om84FdyzUQuoYajwaO48koWkMNRnsjoS2dmF0dQTvsNZBS4qVzZH/yAe7Y1Jy/f5bwbY9S9yi+46Fc459nttSgRUy8/OKRrGSjwdpdSTRdcOl4jkLaYfO9tYQiKsMXioz35li/O0k4ruE6gk8/yHO1VaBXKaGGK2y5t4Zkg87g2Tya8OjYHefikRwt6yS5cQuzMUzH9jieK+k5lMWMurRviaHqccb7VHqPz06lSc+d1bm36HlwLOyfMbuWWw1abS16Q8PSG94sSPALRfJvf0TlbDex++4kum83anJ+XbO/aZCOi9V9ifx7+ykfO716/p5CUL/tXoSq0v/u965EmMSVuhLVjFC/7V48q0L/e8/hWSVUI0Tr3U/RcNuDlMb78ezlR9gVVcMp5Rj88Hl8xyJU28Sah5+ltmsPI4dfXfZ+QrVNpDbtZezY26TPHUD6Pok122i/72vkhy6QvXh82fu6XmzZG+PLv9ZAIqVz6XSJ7/7WEEM9Px1z2LmjRR78eh2aDpG4ypY7Yhx5O3fdiuQQXEaB8veVJ/3wJQu7skQkUYH2rjCh6MobR6Qvr8QvF2m8ul5UTZzGhx3ef8XFrkhe+V6W04fLVMqS3vOLXzB+sULmh++g1SUx17fNsRhRY2Eit28mfNsG/FIFv1RBiUXm7Cf+wG7CO7sQmoYSCkw2lWhoUX0YWbbI/v/svXd0Jfd95fmp+HJCjo0G0AGd2YmhmYMoUrQCvVSwZY+tkRzWxzOz67W9e3ZmZ+z17JyZtY/W47G9I1teWdmSJVkSxSSKOTXZ7BzQAY2cgZdTvUq//aPQQKMBNAF0pMx7DgPeq1evHlDvV7e+3/u99+k3KJ/qW9mHlcAfVEg0+GjaEKKq0YcekKmUXdKjBiPnSmQmKp4VwWrPNyEoDyUxhlME2+df1AKt1WhVISoTlyGlQOuWCDWtfkZOFzDLXgm2nPOSrW/5cB2vf3uELfdVc+DHE2y6q4rpQYORM/N1XI4tSI4YbLgjwdCpPOW8xLo9cfqP5Fi7PcrgyRz5pEUhZdG2PYpjuciKROOGED0HsnTtq2LifGn1tgwf4NpClvGtWXNzVnSEwBqdIPPk85SPnyZ89634N3Qgh0M3lS/Y9YAQAlExsSamKL75LqVj3TjpzFWNqFJ0P8GaFjIDJ+dXey5qkWnBKMHaFqaOv4Zjeu0VxzTIj/TQsPNB9Gg15emRZb+na5uUJgZn5QFGZhIzn8Jf1YCkqPNa/JdDsL4N17axilm0oDd951WZZPzxOnKyMq/qfa0gybBpT5h4rYYkQe+JEpnJ989k49lDRaZGTNq6AqiaxPa7orz1dJqe45ePXVkWLjEIBc/vSeLyu65v87Fpb2hVRtIXUCm5s6exokizeYJXG6vycYrGFQZ7TDJJh0yyTLxaoaZeZXTg8ieOOTBO6hvPkPjUQ/i72hZdFCVVQYmGFrh9zx7wTLL2ciCEwC2UyD67n/zLh2EF0SWSDGu3Rbjj8Xq23lNFMKahKMCMsZvrQDFr0f1Gmje+N07/sfyqS7HGWIbs0UH8rVXIF41pKyEf0W2tFM6OX/bY+w5nUVSZ5k0RygUbs+zSuTtOOW8Tr/eBBLkpk+HuAs1dYXzBhSeTYwmmBsoUUtaszEBWPA2MHlTQ/QodO4ME4ypVjX4y4walrM1kX5nRswXatkdQfTJ8QJxuSkiqiq997Y0+jMtCGBWMMz1U+ofwr2sneOsO/Os6UKpi/yyMM91KBXNghNKRk5SPnMROZ1a0Zi0Xih4AWcEuLe3YLasaqi+IVc7P28aplBDCRQ2EF33dUhDCnSVgMw/gVEpooRiyquEskzhpwSh6OE7jrR+5iGxJCMf0fr5OOjlZlvAF5VnH/5Z1ftZ0Beg/Vb6p2nJLIT1l8caTKZo6GtF0mdb1fh75F7V858/HmBpehnegBMGwgmW6C1zQXReySRvLdGdtBZo7/egBGaO4+PkcCMvc9dEE63aEruhPmJ6yZkmbPyTT1hXgxJv5Fat/3gsrJk5rN/jo2hFgsCc577H12/z8498srtWZhRAYZwZJfuNZEo/fR3D3Ri+A8xqc7EIInEyezJOvU3j5EG5pBcJtCbruiPPxf9NO27Ywwp2ZWDJcbNPxXE59Mol6H3c+0UBLV5gffrGP0/szq2Lrbtki9eppqvatR6+dG9GWJInEvvVMPX8CK720xqe6JUCiwYc/pBCKa8iyTaRao5i1KGY9MuuVYAXCEcs6xGLWQvfL3P2ZZqqb/QyfzBOt0ZE1iUrJwSy7uK6Y9Sy5Djd573/IMmoiga+lGbW6GjkUQtY0hO3glIrY00kqI8PYydTKdX6L4cL3SpbRamvR6+oWPC/JCkJe5h/vaq8+i0F4BKp84jRGTx++thYC2zcR2LEFtTp+zdaLG4ELom9RMTHO9lI+dgrj9HlvWu4awp0hHBcCuhc9NtfFdWwUVYOLagWSogLSbPLARaFLc/8nyTPbXQwJ+ZLHZFVDCHdFFSJhW5iFDBNHXsQqzJcBmMXcdZsQdWzBaG+FcsElEJZZf0uIz/9RK0PnDMoFZx5xEq43eZ1N2gyfMzh7uOj5ON1AcmVVBG89m2HbnVG23B5GkiXueDSBqsk8+/VJzh8v4VjzrxWSBJruGX527QnTviXAM1+dYuD0QsF3asLLtGvu9IY+dt4bZf8zaboPFOb9biQJwnGVhz5TzcOfrUXVZhznVzm1MnC6jFF2CYQVfAGZ2x9NcPLtAv2nSvOWr4t9NlcTYbMi4lTXpNHSrlPToLJhmzexpqiwZXeAbHr5J6zZN8r03/+EcM8QkXt2otVXXTU3YSEEwjCpnB8m89QbGCf7VqwNaN0U5qO/u5bmjSGGuosceX6ac+9mySctXFcgyxKxOp0Ne2PseLCalo0hPvqv11JIn2P49OpEzIXTo0y/eJLGJ26dZw4YbKuh5sEtjP/w4JIRNZP9JQppj2kX0xZCCLKTnqeU4wiKGYv93x/DtgSHfzqFbS48U/wRlUi1hi+oYBoOluHy8teHUX0yx16Yppi1GOrO4wsqCIGX7Se8ypxZdtj/gzHKufdPqfp6Qg4E8K1tI3zLTnxtbcihoGfCqaqzdhPCcRCmiVsqURkaIn/gAJWBQYSxAsIvy6ixKJLPjxqNotXXodXXo9XVoybiKLH57sDhPXsIdHUta6TdTmeY+uY3cUvXT3zrVaDOU+kfIv/a2/jXtxPYvhmtuQElEkbSl2dkelNhNuC2jJ3KYJzuoXz8NNbYJG6xeFVbckvBMYpYpRzB2lYUX3C+oHrmfLTLBYz0OMG6NjIDJxG2hSQrBKqbEK5DJTcNCFzLQPEFkbW5YZxATRPyJQMhsqrhT9R7RN11UP1htHACIzk6b1DlvVCaHiHeuRPXtihODs6du7P6rOvHRt75aYbNt4a5/dE4mi7T1hWkrWuhvORCh8K2BOWiw+h5g2e/PsXhV3NUStfhZmQJTA5V+M6fj/K5/6OFjq1BVF3mtg/H6doTYrjHYOisQSFjI8kQCCtUN+rUt+hEqzXCMQXLFLzyg8WLJRODJgdfzFK/ptaLeGnR+fwftfLaj1KcO1qiUnYIRhTatwTZdV+MtZu9luHhV3Os2RCgunEZ+aKLve+QyfE3ctz1sSpkWaJjS5Df+S9tHHwxy/CMs7svIBGKqsSqVdKTFq/+MEVxhdOOK2Ir67f62HVniIZWjdBMPo1wYWTAZP/PVuZ95KRyZH/8GuWj5wjv205g+zr0lrorIlCuYVLpHaH49kkKbx7Dza98kdf9Mrs+XEPbtjBn38ny7f+zh8n+hYx6vLfEmf0ZDjw9xWf/eD2du6LseLCaib4y1jIyfy6FcFwmnjxEaH09sZ1rZ1sTStBH/WO3UOqbInu4f9GF1Sg4GJckZqdG519wixmP1JRzi5fE4/U+1u2OMdxdIDvh6RDyyflEyDJcStnFX19coX/VPxdodXVE772H8M6dyP7FMwm9yo8MmoYSCqHV1hLo6qLw7kFyr7+GPb28CoQaj1H7K7+CVlODHAq9J6lQQiGU0PLyFSVVvWHtMlExscenKIxPUXjzIFpjHf6NnfjWtqK1NKLV1dz0MS6ebKCINTKOOTBMpXcQ41wfbuH6T4sK1yF1+m2abvsFGnY9RH6kB4SLGoxgpCcoTQ5ilXKkzh6kfueD1G6+k3JqDH+8jkTHDpJn3plNDDDSE8i6j+qu28gNxVB9Qao27p2tal2AJMuEmzdQXcxh5pNEmjeg6AEy/cdnt1H0AGowgi9eh6zq6OEE/qpGXMvALGRBuBQn+iknh6nbdg+qP4RVzKL4AqiBCPnhMxdZKVxbqLpE41of2aSFaQg0feltJUlCUb0wYF9AJl6j0bohwD/+xRgvfGd6aSufawzhelqnr/zJMJ/5vSY27gqh6TJV9TpV9Trb77z8kIaVWrq9Wim7PP/tKdq6Amy/K4IsS7RuCPDLf9C8+L5MlwPPZ/mHL47y0S/U8dBnalb1mUp5h2e/PkXL+gDtmwIoqsSaDQHWbFg4aAaencT+ZzIUuYbE6Y2fFkhN2XR0+XnlKc9YUAgwyp7z52pgDoyTHpsm/9oRfO1NM9NxLSjxCJKmeCLyS8rzXniki5hJ3nZSOYwzA5RP9WMOjGFPZ1fdVghEVTbeFse2BK98e2xR0nQxxs+XeOXbo3TcEmXD3jivfHtsVcQJwEwWGfrqa8i6SmRzy6yA3tcYp+VX78QpVSicGbsmd6XjPUXGez4Y+b9qkCT01laqHvsI/vb2eaJsIQQ4jucK77ogyd7fWlFmz3MlGCR6x+1otbWknnoKa3T0vd9S1/G1tv58C6odB2t4DGt4jGIwgFIVR6utQlvTgq+tBbWhFtnvQ9I8t/jr3doTrguO63mfWRZONo85PEalbxBrZBwnlcHO5G6423du6DSSohBr307djvsBgV3OU8lOz2whyA6eRFIUEh07iLZtQdgmydNvkzp3cLa9ZqTHGX/3pyTW7SRQ3YRdLpDpPepVUJ25mynHrFAYO0+wppl4x3aE6zB57GWKEwPeBpJEtLWLxPrdyJoPSVGJtm4kWNPsTeO9/RMco4hTKTF24DmqNuwhsW6nJyy3Lc9S4TolW4djCg9/tpZ7Hq+irlmnYgh6jhYZOmdQzNnzWj+SDJpPJppQaerwwm19AZlIQuWRf1HL6YMF+pfwNnJdQSFtk5rwKnKFjH1NJBHnjhT5m387yO2Pxtn3WILaZh3NJ6Oq0mwhT7hee9K2BKbhMnLeYP+zGYbPL10Rnxwy+dafjpBN1rHjrijBiIKqzezTWwKxTJdc0ubNp9K88N1pJodMTu4vsOv+GLYlMEor/MAC+k6W+f/+aIhP/FY9G/eE8fllFE1CludswhxHYJuCcsHBXYUgTbqcEaAkSQueVDVQNQnjMontq4YsIekask9DiUdQ6xKoiaiXvK1rzFiQ4poWbr6Ek8ljjaew0zmEaSFM+4qddGta/fzBt27BcVy++KvHmB5671ZJQ0eQ3//mDoyiw5999giZidUH8yJJhDc10frr9xDdOkeehONS7Jlg5Ftvkj0ygGt8UOG5EgS3b6fmk59ECXmldWHbZF9+hfTTT1+V/Wv19VT/D7+Iv6NjtlIjhMAtlzHHxrBGR7HSaYRRQfL5UBMJ9MZG9MYGlPCc8FY4DuVz50j+4J+wp6eXejsA1KoENZ/6FCxSGZIDAfT6+nkEzs5ksFOpZcX6OLkcye//ALd8bSKSrgjSTEVM05ACfrS6arSmBrT6WpSqGEooiOT3Iwd8SD4fsq6DqlwRoRLuDDmqmLhGBWF4k8BOvoA9lcQan8IaHcdOZRGW5WXJXQOh9xVBklE03yzRFo6DY1Xmiz5kGUXzz7bYLsQ3zd+N4pGdi7aRNR+u7Qm2AzXNtN71BJPHXiY3fAZZUb12pWXMO/dkVZ/X8puFENiV4ry1XVI0ZE1HkmRPJ2Vb80x7rxU0n8THvlDPY/+yjlBUZWKwwg/+epwjr+YwSg7CZYGGBwkUxRNT3/N4NR/7jXqCEQXTcPnufx3lx3+7eJVMkiAYVdB07zy1TEEp76xKk7Osz6ZLxGo0OrcFad8SoLpRJxBSEK7AKLmkpyzGByoMdJeZHDYpF5z3ti+YEZG3bwmwaU+Y+jYfgZCMY0MubTN8rsypdwpMDJmYZXf2OEIxTxJizHhgrRSS5FkidG4Lsm5HiNoWffZ9SwWH7LTFWH+F3hMlxnqNRe9jhBBLLhArrm8HQgpNbRp9pyuEozJ3PxqhXBK8+nTuysmU6+mTHMPEyRYxB8avbH+rgCR5JVXTEMvO7nEdgWO7KJp05Xe3QlDoHmHgSy/Q+mv3ENu9Fln1Km+hDQ20/6uHmX7xJFM/O0l5cPqGCgw/wOKQg0Fi992Lf+3aee0ta3KS3GuvUTp+Aie/MD5GjkQIdm0kevfd6M3NnsO+ohBYt47YffeSfvqZy2qM7HSGya99fVFdpb+tjZpPfxolMpcnWDp5kswLLyKWozFxBa5xE5Im8ATllo2wbCiVcZJpjO4e7zlVQYmEkcNhlEgIORxEDniBz3IwgOTTkXXd00upilehkuXZ6Vlc17u4WzauaXo3aIaBUzIQZQO3VMYpFHHzRZx8HrdYfv/EoFw66bYYXPc9TSWF6yzYZtHXCM8sd6nLoGubuPbybjqFY+E41//msa0rwJ0fTRCOqQgheO4bU7z6w+SyKkGlvMtz35zi9kfjtHUFUDSJ1vWLt5DAO42uxM3bH5S468MRinmXd14uvGdXyDIF06Mm06Mmbz+3MHx8VRBe++zk/gIn93vt3c7NPrbtDfL6c3mmxxcelGUKMlNXVj0UAnIpm8Ov5Dj8yuKxWwCyL4BAA1Z2Lq2YODWv1dh7b5jxIYv7Px6ltlHDsQS33hfm1adXmCV2E8KxBPmkSbzeR7xOJzX63oZm8QadUExj5GwRx74KtwMCSr2T9P7FczR8bBe1H9qKlvA0K3pNhIZP7CGxbwOF06Nk3u2jdH4Cp2zimjbCdhGuuOaLt2s5C+6gIzGZT38hTucmna/+tzSnj149M7hgWOLTn4+zYauPb38pw7F3Vx5vc10gywS3biW4bdtsdUe4LubICKmnnsLoOb9kG9nN5ykcPIQ5Nk71Jz6Or73dI0+qSnjXLir9/RQOHV66DS3EksTKNYwFMTPCsnBLxSWHJ+p21LHh8S5OffskqTNXZ9JLDahoIZ3y9OpF5oHaINt+fQfR1iiKT+X4V44wun8JTyHbwUlncdLZ+UujhNcalS+QpZmcjAslgtlBspnvkitmW6vCca9Lq00LaSg+BSN1k57rPzeQ0H0RVG1xEuM6JobhaazWbgpSv8arimWmbE6+nV9R+6yYdShkvBdIEotaw6wEsSqFT/1WFT/8+zRTY/PJhs8vc8dDYabGbQ6+VpydgL7RWNOp88DHo5w6VF6UOF2MQEjmU79ZxRs/zdNz8uqbi8Y6t1KeHsOYHF7R61ZMnHSfjCRBvFqhfYOP7305RXW9ysbtSzPn9xPKBZu+o3lu+3iQOx5vYOx8iXJ+6W9GMKay7xcbUDSJ84dyVFbak10CkiIjLJvkS6eQVZm6j9yCEvAUiLKu4m9O4G9OUHP/ZpxiBWM8gzmdx84bHoG6xm2B6RdOUjw7vyKYz7r841cy/NYfVBMMXV0hcakg+P5Xs3z+96pmBxNuRiiRCJHbbkUOzH0f3FKJ7IsvYpw99947cF3M4WHSz/2U2s/+MmrUE2hKPh+RO+6gfPbcksHFVxtaSCfaFkMNXD3hdfMdLTTubeLtP33LI/irQCVjcOb73dRtr2fzL21Bj64iqVkAtoOYEYXeHJeUOUiyRPuHOwnWBTnypUM33wGuAHa5SKbv6Mwk3s0HRdFYs+4BGltvXfT5bLqf7sPfxHWLRKtU1JlIkHzGXvFUnO6XCIbnBquuNLuutVNn694gT33rKlWIbjJU1ynsuSfEoddXr78NNXeiRasWfS68dhNm9j1slBbBilfETNImUavy+OeqGOm3GB2waG7XKd/AscqrCaPocOzlJJvvSrD70VqQ4O0fTTDWU8KxBUJ4dwqqLtG8IcRtH69nx4PVTA8ZHHspOdunXQm0eJDI9jXoiRBaVQgtHkSNBFCCOkpAR40F5lkUwEVW8oqEGg0QjgZgw8Kg4GuF4rnxBcQJPD+nS4td1XUKH/p4mNpGlf5zJq88U6S+WaVrm5+nv5fDsWHzTh+tazVefqZIx0adfQ8GUTWJA6+WOXagjG0v3Hc0LvPIL0Z4+nt5CjmXmnqFex4J8cozRZKTN0Z861vTit7UNK9lW+7pobwc0nQRKkNDlE6cIHLHHbPB2Fp9Pf516ygeOnS1D3tpCO8irgY1ZFVGuALHsHEvqaxKqoTq9ybvhONiGzbiola3rCuoukL9rkbUkIYv5kO4Atd2scoWF/dvZE1G8alIsoSwZ/Z1EclyLZdsbwbVp2KXr1wQrOgKik+ZFy4qHIFVtrzPIEtoARW74njffZ8Ksnccl77/hX0hgWu62BV7PumRQdFVFG2mHeh424iLqgGKT0EL69TvbKCcKuOL+b1qojXzu3qfkSirmGHy6Ms3+jAuC0mSl8zUvND7dl0wLxr8CceUFVWMJBk23xYhUa8jSRKO7TLSu7pqYn2zxl2PRth9V5A1nTpf+N/qKBdcyiWXn/1TltNH5vbr80vc+eEIXbf48fllzh03eP25PNnU3BqZqFW4/YEw67b6QcCpQ2X2v1CgmPc+b22TymO/FOfga0WOvzPX3v3ErydQVYnvfTk1+xk7NvrY93CE2kYVn19GVqBccnnjuTxvvzhDgCRo6dDZc683pZ9NOrz2bJ5zxw2E8I5n30MRbtkXZM06H5/5nWoemXZwHMHrz+V556XlE6nYxl0I28QxF1asVH9wVZZRKyZOI/0mT/9DhtoGlRPvlrEsMZtf9/MA4UL3mxle+84Y9/9qM3d8op7t91eTmaiQmTSxKy6a3zO/jNXpBGMq+aTFz74yzPlDuVV1yIKd9XT864eRZrRM3iThVdBL3SR47FNRXFfwsx8XuO2eIA8/HuHg6yW27PJxeL/G+LDFnjsDJKccmto0Hv5EhP0vl7AswaNPREhNO/SfW3h+FfMutY0q2/b4eeulEm3rdFrWahjlG3dlCXR1IWlzHiTCcSidOIm7Ej8mQBgG5dNnCO3YMWsZIPv9BLs2Ujp27LpNEEmKRP3OBtb9wnpCDWGEK+j/WR+9z/TgVLyFVwtptD3Yzpp716D4Vcy8yfCrgwy81I9dtpFUmZa7WllzbxsNuxtBgrv/430gIHM+zYmvHaOc9BZjf8JP52Prqd/diOpXqGQq9PzkLKNvjay6QnU5BOtDbPjERhLrq9ACKsG6EE7FYfL4JN3fOkFuKEeoLsgtv72boZcHCNQGabq1CT3qY/LoBCe+egyraIEEkeYInb+wgZrNNciqTG4ox7kfnSHZPadFrN5QTecvrCfSEvWIn2Ex8GI/fc/34hgOil9l7Yfaab6jhYbdjdhli8S6BAiYOjHJqW+cwCz8fKy1ml/BMlZ+g+OLaGh+heK0sez11h/TUFSZYqqyauIpXEiOmhSzDuG4SrxGO46K/wAAIABJREFUY88DMSaHTEqX6UoA+AIyG3aGePy3G4jVeJfdbNLi5P7VyVtcV5CasJket3FsOH/KIJt0MCtiHiEC2LI7SFWtyvlTFVRN4onfqCKaUPj+36UwK4J4tcLnfr+WxhaNU4fLSJLEhz8ZY806ne9+KUUx7xKKyOy4Pchwr8lx5ohT1y1+NG2OPK7p1PmXf1hLcsLmxLtltt0a4Lb7wzz5jQz9Z+eIS6JG5bFfjtN7ymB6zGbr3gDrt/r5qz+aYKjXRLiQTTmMD3nfrYFzFUb7LVwXku/R3rsUleQYud6TXmj9JXC23jYzVLAyrJg4OTacO25wbs5+gzNHf7568JWiwwtfG6GUt7n3l5uoWxMgVqvTdsl2ju0ycrbEi18b5t2nplYdkCipMmrk56PVeSkCQYn1W3T++j8lGRuykSX4+GdjvPNKieSkQ/sGnYrhUt+ksv+lEi1rNbp2+LBMgWkKWto11m3WFyVOjgOH95e55fYAR/aXWb/Fx8nDldm7pOsN2e9Hb2qaF/tgp9NY42Or0pxZ4+NYU1OzxEmSZbS6OtREAmtq+YnyVwJ/IkDzvlb6njvPwEv9NN3WwqbPbCE3lGXi4DiSLLH2Qx1sfGIT5588S34kT2Jdgs2f3YpwBb3Pnkc4LumzScpTJYJ1QRzD5vjfH0O4AqtoUsnNLKiyxMYnNtFyVys9T56jNFWicW8jO76wCyNtkDx19Vs96z+6gca9TZz42jHMgknXpzYTbghz5h9PkR/zLmqSIhNuCLPuoxvI9qXp+ck5FE3BrjizlTdfxMeO39iFP+Gn79nzWGWb9oc7uOW3dvHOn+0nP+S1V7WQTjlVZvLoJI5p03JnK1t/bQfZ/ixTxydxLYep45MUxwpEW6Oke9Kc/afTIKCSq3gVp58TtO+ro/eNCewV2rfUb4gSbwlx6tnhZb+2cUuCYFzn9POjONbq14eeYyX6u8tsuS2MrEg8+mt1hGIqh17yDBZzaRvHFiiqRCAkk6jTaOr007UnzO77YzS0eW1l23J57UdpRs6vTrczNWbz0pM5JBl27gvx0o9zS0aeqZrE1/58mvOnKvgCEp//gzrueCjMU9/OYFYcdtweZMvuIH/1RxMcfqOIEHDbA2F+83+v5eTB8jKqO3Nr26adAepbNP76jycY6bc4/naJjTsCJCctpkbnCE8gJHP4jSLf/ZJH3jbvCvC/frGRjTv8DPV6cW6vP5cnOWnz6KfjvPV8geMHVjeckj71jkeOFlmD8/3dOMbK9ZY3t2vcDYRRcHjtO2OcfitDxy0R2rZGiDf40H1eyG9yxKDvaI7+4wWSIwbuTSK8u9mgahKSBOZMnpE9EydVMQTnTlXo2KhTKbtUKjA2YtPSrjM1bvPuGyUMQ7D/5RKjg0tfLHq6TW69J8jGbT4amlWe/+GNG1BQq6tRLjGetKaTOMXVCaHtXA4nk0UIMefvFI2iVlVdN+IkhGDwpX7O/uA0ru2SOpOkYXcD1RtrmDg4TrgxzNqH2hl+fZDT3+tGOIKxA6PoER+dv7CewVcGscsW+eE8+eE8ZraCVbKYOj65oIIUbYnQdHsLPT8+y9kfnkE4gskj4zz4/zxM2/1rSZ1OXtWqkyRL1O2oZ+r4JEOvD4ErCNWH2PzLW7Erzrz2mazJuLbLia8fp5KdudhdlFpavaWWqg1VHPrLdxl+cwgEFMcL3PufHqD+lgbywzkQMHFknMmjE7OEqzheJN6ZIN6Z8H4njiDXn6U4XsQqWpQmi0wdn3zftecuxaYPN1O1JkwxVeHMC6PUrY+y84l2qtsjnH9tnPyEweZHmvFFdZK9OYaPpNj+sTW4jkBWJY7+0wBVa8O03FJNMOEjM1wkWOVj/b0N+CI6k2eznH9tnJ2fbMc2HLSAyslnhqhbF6VpexXhGj9T565cGzg9avL0309SVa/RuNZHJKHy4V+p4fZH4xQyDqbhRVHJioSqenl24ZhCKKoiK96kZiHj8NqPUzz3zalVjdqvFOdOGIz0ezeelbJgZMBkz70hLti97bg9yGBPhd7uuQpez0mDfMZhw7YAR95c5voleW1Bx/LWdwDL8qwM/AHZ82+a+bjpKZuT75ZnrwvjQyaODaHo1fegu+BM76tuxF9VP3usICFcl+Jwz4r3+QFxugxsUzDWU2K8t8T+H00iX6SBcF3PruBaeWr8vKCQc0lOOmzd5efdN8q0b9DJphzyOYezJyrs3hdg214/Pacq5DMuk2M2piFITjkM91lE4zK5zNJl8PS0w3C/xYMfDTPUZ90wbROAEo8h6fMthJ1sZsVtugsQpomdnTFznVnl5FBonqXAtYaZq5DuSc1e6M28iV1xZgXjwbog4eYIp759YlbT5FQckqeTNOxtItwcJtOTXnL/FyPWkcAX1QnWh+h4pHP2cUVXCDWEUYMa1lVsUwmEN2l40RDdxTqni+HaLume1BxpgnlkpmpjFZIqk1hfhS/uVRX0qA9Zkwk3hZE1Bdf0zs1wU4Rwcxg9pBOoDaL6VE839XOMQEwnO1piujdPpWAxfCRJsi/PsR8OUEqb6EGV5EABf1RnzZ5axruzhKr9vPLXp9j1yQ5qOqM0b0vQ++YksaYgkfoAri1I9RcIJHy07amh9/UJwjV+zrwwynRvHtWn0LqrmjMvjlG3PoYevPLfsRBw+NUctiX4yOfq2LgzhD8kU92gU90wtw3MzxsWQlDM2QyeLvP6k2neeDJFeYnA26uNbMqZN4jrOmKerCdWrVIquBgX6ZRtS5BLO8SrFRR1acmIdNG/ETDS77XWdu4LcuLdMht3+AkEZUb6zHl2CEbJpVSYez/P+UOwxNfvqkCLxAk0en0jSZbRwjEU3U8lOYZjrEx8fvN9WyUJlJnx4JlRbMmve+JoSfJceS0bt2J64/Bibkz4Wt2VCRccV+C832/7rjE23+LjnkdCrN+qEwhHaG3XeOHJAj/9YZ6PPBFl7z1BzIrLcz8oUCoIKoZNLuPS2q7x3A88j4+e7gpH3inz+K/EcF1Betrh+1/N0tCicd9HQmza4SNRo9DYqvL8jwrksy7nTlXY92CQZ753Y+0wlGBwnr4JwC2XPfPDVcItFRGOM2tSKKkqcjAwmyl2reHaMwLni3HR+8qqgqLJC0TSzsxrVkIItICK6lep3VJHbG189vH8aJ7U2eSyjDpXBBfG3x1jzX1ttD/SiZmvsOb+taTOJDHS89sCXltx6b+jFtTRQxr1uxqwjbnfxfSpKXJDORACSZHoeHQdax9qxypZGMkyik/xSOjNKGeUQNUVXMddUUVdkiVUv4JTcWa98M69PEbdhhgbH2giP1mmmKogXDEz+AB1G2M0bUmQHSuh+rxzvZiuYJUdLMNBUeXZYwLv9G/YFKemM0IxWfFeI4FtuhSmDBzTRdXlmRdc+OVene+LYwmOvpZjuKfMxt1hunaHaVnnp7pJJxjyXKptU1AxXPJpm6lhk5Feg94TJQa6y0yNmlf1hvu9PpXrioUbXXS+lQsOvoCMqkswow+VFfAHZIySO+/1F5NBSfIiyi7+LKcOl3nnpQKf/I1q7n7UQpLh6X/IcOLdS75Py3bMufDGy9n28ij0d1Po7579WQ3FqL7lrkUCqd8bNwdxUhSUWAi1KorWVIveWo/WUOW5hof8oMhzfzGBR5ZMC6dQwp7KYI0lMQfHsSZTOOnCFZnQqbpE47oQ2ckKuemVXfD0gEwornkGmmWHYsZelneGUzDId793pMbNBDu7sN/c32OS+obDk/+QByEwyoJy0aX7aIXx4RQ+v0S5KGYDoR0bvvPlDD6/NFspMkqCn/6wwDuvllFVr+RbyLkM9Zn8+FsOT33X23fF8PatKKBpEj2nTEYu09K7HpB0fZ7hpRDCM2W8ggu+a5rzHZMlCUn3ee7g1yu24zKnsFWysAoWger5Gj1fzMvlq2TnV9su97WsZCoY2Qqnv9fNxOGxec+55sIJtquBwVcHqN/dQMejnRhpg9TpJL3P9GCXFnmvyxy8kSpTmixx/CtHyfTNr7A5hoNrucQ7E6z/2AZP5P2tE9iGTWxNjHhHYvGdrj4k/qogsSbCHb+7naG3xzn+vfPLfl39lioe+uPbeOsvj3H+pWEQsPb2OsI1forpihcyLmDiTI5tH1tDz6vjGJkKml8hEPcxeSaDY7rkx701pjhtUJg2GD7q0HFHPSBI9hcopSvowQSO6TJ5NgsC8uPlWS+9StFm+EiS9fc0gASTZ7MLvMxWCyFgetQiOZbm0EtZfH4ZzedNkEkz5qmu48WUmBUX03Axjat/o2OZXufDfwV+UKePGjzyyRhNa3TOHve+r3WNGjWNGj/9fhaz4q3lCKiqU2fv2ZrW6tQ0qExf5B+l6xK1jSov/yTHyz/JYZneer/az+44YJqu9/kuao2vBpKqIatzHQFJUZA1HflyQYNL4MYSJ1nG19FEYMd6Al1t6O3NKOEViqQ3ef8Rros9naHSM0z5RC+lo+dwktkVH1KiwcfvfmkLL35tlBe+Ooy9jABGSYKm9SFu/3gdbdsi6H6ZXNLi3LtZDj4z9Z4mmvlTI5z6/W+t+FhvJBbziSoVBKXC4he3pVpouczC/ZiGYHJ0/n7KRUG5OP+xWELmoY9HWLte4/WfFskvsq/rCkmef0sGC+IpVgphL/R3uJmy6AqjedLn0zTd3szQq4M4FQdfzEf97gaKYwWKE/NL4GbRRA/ri7bdkmeSWAWTmi01jL49gl3yiLCsK8iqfE0qyomOBIqm8M6fvUWmd/VeOFPHJ9nw+EZiHXGmTkzizgiQVb86e7HWIzqKTyF9Lk15uoykSETXxgnWBhfuUAjMookv4puZvrs+U5QXQ4/oxNdESPWubB3V/Arx1gh6SJu91h3/8eCC7U78ZP5jr/xV9/znnxoC4MwLczeVQ4fmG7GOHp9PUo8/edE+BQwcmGbgwLXzjxICzwagcGPWnvEhi0LO4SOfjvPOywVcF/rOVEhOLP98OfBykbsfjfDEb1Tx+rN5hIAHPh6l/0yFY2+XvGnCCZuJEZs7PxwhNeVgVlx23xWiqladt1aruieKb+/yIclRXAeyaU/PNHTeXPE9ZDblMNxr8qHHY6iqhGMLhvvMJYXwl0NkbRfR9i2zP0uygl0pYeaXJyW4GDeGOKkKenMtkft2EdixHq02ccXp5t7EURVqbYLAjg1E7ttF4c1jFPefxMkWll2BkmQJf0ilfm2AurYAiQbPdsAoOIz1lJgeNhaE+Na0+vn0v+ukY2cUIcCxXFo2yWy8PU77jgjf/y+9JEcuQ57EzAXyA6wIRllw8pDB4bfKDM+Mqt5QuO5CkrOkN8zyMNuivgjiBgfEXgwjY3DmB93s+MJObvvDfRTG8kRbo0Saoxz+7wdnCcQFjL0zyvbP7WD37+6hOF6kOFlk6NVBrIKJkS5z5nvdbP7sVgLVQQqjeWRdIZDw0/fTXsbeGQUJ4u1xYh0JEh1xfAk/DbsbkSQoJ8tMHBmfJ+q+LCRwTAd/lZ89/9Nt2GXPnyo/nKP32fNk+5ZPpDLn05x/qofOR9eRWFdFebqEHtFR/Spnvn+a9LkUucEchdECnY+tI1ATQA/rRFujWGV7ASl0bZfRt0foemITO//H3Rhpg9xwjuHXB3FWMcK/GiTPZXj+379N6QPn8psW/WcrfP/LaR74eJQnfqOK6XGbH/xdiuSEjesKMkmHfMaZtyyVCi7T43Pr5dSYxd/8X5M8/ESMj3wmjiTB+e4Kz343w+RMNckyBf/4t0k+8esJHv1MjFLe5chbJTIpB7/fW59UDTZu96P7ZSqGQ3W9iqpKbL8tyD2PRvnL/zDBcJ9JuSRITtpYFxUlHAemJ2yKlxDQ6XGLf/jrJI99NsHjn0tQzLv85BuZFREnSVERrkN5cgS7VJh9XDgOViGDXVz50MB1J05SwEdo72Zij92J3lzr6Zmuol+RJEkoIT/y+lb01noCWzvJPvUGxtnBFYVsrt8bo21bhOomH5IiIVzIpyxe+dYIr31nfJ5D+I6HaujcFSM3bXLw2SlSIwaN60NsvbeKbfdWM36+xI//YuB9Pxlzs6FiCE4fu/o2/KuFa5rzSI0kSZ7mSVFW3VaTdJ3ZiHJm2n+meUXtv+WiPF1i7J1RKpm5C6dru4y9M0rmAqkQMHl4goN/cYA197URrA2RH87T/Z1Ti8a0DL8+hCRB7bZ6QvUhysnSHNkUMPjKAMXJIk23txCsCWKbDlMnp8j0eneFkiQR60jQdFszsiIxdWwSRVdo3NtEaarE1IlJnGXehESaI7Q92M7EoXEy59O4jkANqDTf3kKgJsiBL+7HKlrYZYvxg2PkBpdeYG3D5vR3T5HuSdGwu5FQfQizYDJ+cIziuLdYG6kyR//2MG0PriXSHKU0VeTYV45Su62O4lhh3v6EI+h7rhfXdKnaWEWwLkh+JHdd1xC74jB1ZuV34x8sc9cPlunlxL79UgFZ8UyCjZlJvULW5W//8yTCZR5JefXpPPtfKMyKs10Xzp+u8Hd/OoWmS0iAaYoF7bXe7gp/9R8mUDUJIbz1V5Y9PRRAVa3Kp3+7mme/m+HlJ/OeTEWCzk0+fuvf1dPe5WO4z+TdVwsce7s0e5wA+YzDn/zOyOyU3QU4Nhx5q0T3EQNFYeZ9V7b2RTq2UB4bQA2EMTPT2KUr18JeV+IkB/3EPrKP6IduRY6GrqnBoyRJSAEfwd1daPXVpL77PKUD3e/9whnUtHoOqrYlMIoOmi5R3eTjkd9cQz5l8c6Tk7OiuHW7osgKvPv0JE/+twHMsmeSOd5b4rHfaWPL3VW88u0xspM3n3GdpKhE120l0LCGwuBZCn2nb/QhvW/hFosLjCnlQABJVVddJVKCIc8Q9QJsG7dcWnYF9UqQOpsidXZ+HIFruRz+fw/Oe0y4gmT3tGf0+B6wSxZ9z/XS91zvos+7lsvUsUmmji2eGC9cwcDP+hj4Wd8yP8XSaLq9mXBTmJf/8IV503Ku7bLm3jZ8cT9W0cJIGRz78pH33J9t2Iy8OczIm0vnXmV607Mk8ALS5xaPfDBzFc796Mxl3zPSFKJtXwPxNREkJMqZCsmeDCOHpzDzc3flvqhO085aajfG8cd82BWb1Pkcg/vHKSXnV5TW3NFA275GFM1zih96e4LeVxbPAtQjGu13NVGzIe6R6mNX1zLiA7w3HId5E2oXIISnGb0UlinmESlvY08icTktkhBelZ8lDIY1XSJRo1IuesJ44XqkKppQCYXl2SlC2/J8rC7d91L+e67LFU0ghprWYqYniXZuId/XjV2ef5OymrX0uhEnSdeIPnoHscfuRA4sL1tKuK7XwrowPXfBz0aWvRbGMqpVkiShtdRS9UsPg+1QOtazrMqTawuO/CzJy98aJTtl4gvIbH+gmvs+28StH63j3IHsrHYpVuuJy4ZPF7Fm2LBluBx6dppt91bR0BmktSt0UxIn4ToUR/rQE3UE6lo+IE5XADudRhgGzOTLAajxGHIggFNZeWVM0nXUeMwTgs/AKZVw8jdnmLaKBkjYLDzPJSR0AsiSTEWUcXFQ0RAIHBbXY0hIqOjYmIiZOoaMjMtS318JH34kZCqUZl+zFFzLRQtoRNtiFMYKSBL44wEa9zRSni7Ntx64CVG3KcHdv7cTPaxhZE0kCbSgRuf9zbz8nw8ycdIjZIous+UTHWx4pG0mtsVGCyhseLiN9rubeOX/PjSvHWcbDo7pEG0K0bSzFrNkL0qcfBGNff9qB237GihNG1SKNs276shPlC7tLn+AfwbIphzefrHAR38lTtcOP0bZcyVvW+/j6P4S507cmJavmUuT2Ho7/qp6FD1AsKl9/nGfPYyVX5m+8foQJ1kmdPsWYo/ccVnSJBwHO5nDGpvGTmZxsgXcQhnXqCBsjzxJsozkU5EDfuRIEDURQa2JozXWIIcDixIpSZLQm2pJfPpDONkild4lktQvwtSgwY/+az+T/XPTY6M9JeraAnTtS5Bo8M0SJ0WTcV2olOf3ktMTFcbPl2jbGqG62b+CX9i1gaTqBJvW4quuQzg2hb7TWPkMdiGHXcqjBkLztg82rcVf14JTLlIYPINTLiGpGqHW9fji1TiVMsXh81i5NIo/SKh1HWowjDE1Snli6KbS4lwP2MkkTr6AWls7ex6qNbUooSBOZuXCYyUWQ4nF553TTi6HnV55++R6ICHXIaMy6Q4hLiE3PoI0K50YFJlyRjBx8EkBXBzKYnHiJKMQkMIURRYHGxmFarmBKXfx76+MREKuJyIn6LdPYXF54jPy1jDVm2rY+z/fhpk3kRQJLaiRH87R/d1TV9Uz6mpDVmWadtZSt7mKp/6X1xnc7+VGBhI+4msipPvn2oqO6dL/+ijjx5NMn0ljlmz0kMatv7mFjY+2Ub+tmr6LiNHo4SlGD09Rt6mKh//ktiWPoeO+FtrvaeL0T/o5+NVuKgWLRFuEB//9rTenvcIHuKYo5Fy++sUpbrkjxNoNOppPZrjf5NWn85w6VKJcvDGVyMypA4Ra16PHqnFtc9YQ8wJWUyG9LsRJa64l+vDtyItMzAnXxS2UMc4NUnr3NObgOE6mgFMoISrvIQCTZeSgDyUSQq2O4duwhuDujejNdUg+bQGJ0tfUE3lwL9Z4Ere0NPsVwOi5IsXM/Pd3bcHgqQK7H63FF7xE9CvEwoqfgMyUCRIEojfY+UFWiK7fRqi5g9JYvyc4vsxtYbC5nVjXLspjA+iJGqri1UwfeIlQSyfRzs0Uh3qQZAVZ1ZBUjVjXTpRACCubIr5lL5KiUhw6zz8nxYOwLCpDg/jWts0aVqrxGFpjI+boymNX9Pp6tNqauf27Ltb0NHZqFbqTxd5butjjZvWIS7XE5Br8UpCsm8JHgGqlERWNrDtNSeRpVNYSluMYbgkbi7AUp1puJOWOI1HyCI+UQEIiJ1Jk3Emq5AYCUpiykwdUauRmmpQO/FKYSWcQGZlqpQkZhYw7RU4kyYppfMJbZ3wEiMhVpNxxIlIckMiKuXZiabLEkS8dItIaRQtqXruiaFIYK2CkVhfvcL0gXEEpZeBYLk27askMFSinDcqZCuX0QsKY6s2h6DKqXyVQ5UOSJFK9WeyKQ6hm5Td1ik+h6ZYarLJD91N9VGbagun+PD0vDNG4veY99vABfh6Rz7q89mye15690UcyB6dSJtdzDMUfpDTWTyW5MJx+pbjmV3NJUwnt7sLX3rSAyAjbwTgzQPbZ/RjdfbilyspErzOkyy2UscamMc4MkH/lEOE7txN9YA9qXWKer44kywR3b6T49gnKxxa3WReuwK64l73cy7I0q9eVFw49zYNluEh40SM3Eoo/QKi1k9y54xT6z3jXy6WYtiwT6dxCeWyQzOlDqMEw9Xc9hq+6HklRkFQVu1TASI7jlIuo4SjhtV0UBs7imBUkWSHUuo7S2ABiFQGK72eUuk8TvvVWlKA3Yi4pCqEtWyif6sYtLT96RfL7CWzciBycG1UXlQrGuZ5VGWouJiiXfL55bcDVQEUjIdcz7Y4QlatRJIWEXItfClJy81QpDRTtHCl3AiSYckZmqkwFLCpokh+EREAKY4kKBZGhSm4kyzRFkSUkxZBRsDDJulPE5VrGnD5cHBrltahoWJhUyfUUnflj8yYVglKYouQRqLS7UDdlpA2M9Ptvaky4guEDkxz/7jnW3tVEx73NjB6ZYuD1McZPJOeRJ0mRqOqI0nlfC3WbvTBjSZEIxH34whqysvK1SQ+pBKv92GWb7PB8y4nMQP56SPA+wAdYETKnD4IQqMEIkqLimgZOZXU3SNecOMkhP8G9m+YLXPFIU+nwGVLffh5r9OrkbgnLxklmyT71JubAOFWf/TB6a/08wqaEg4T2bKJ8/PyiFQDbEuSmTZrWhwjFNIqZuTaCrEqs2RwGoLUrzJn9GaI1OrrfczpXFlmANL/3uZ1VBgBfLciKiqyo3uiluLzLuqSoqIEQxaIX9+GaJsK2UPxBikPnUQNhYl07CVcMUkffQJJktFAELRRB1jQq6UmMyZHrMvl1s8EcHcXo6yO0Zc4vxL9uHYH16ykePbrs/fhaWght2zo/9y6Vonz27KqOyy2VFzhva9XVyJqGs8pIGPB0SBISljBxhI0sKSiSioqGi0PamcTB9v4RDg4e6XNxcMVcK9cRNhYVLGECAgkJR9jzWn4ONgKBjYmMjCJpqJJG2S1SFNkF7UGBS8adplZuxhQVKmJ1mYE3K0pJg7f/5gT9b4zRels9a25roPP+FvpfH2P/fz9OcdK7KMRawjzwb/fii+icfqqfzEAOs2hTv6WKrb/Y+R7vsjgkWUKSPaPHSz3dPsjt/AA3IyRZJrF5L8HGtUiSjGNVyPUcpzB4ZsWykmtOnPQ1DWh1VfMeE66LcWaA1LeewxpbOLJ8xXAcysd6SCkytZ//GGp1bO45RUZvb0StiWFPLdSdGHmb84dy3PF4PR/7N2289M1R8kkL3Sez7b4qNt+VoFJ2uOczjTRtCOEPKyQafUhAfXsQVZdmTTM1v0x9exDXhVzyxlZeXNvCtSy0aNVMqVJCCHdRciNsCyufQY/XIA33ogaCyJqOXcwjXIfsmcMUBs9StWMfkY4t5M4dpZKeIt9/mvLYAJKsIlz7is0f349wSyUKb7+Dr6UFJRpFkiTkQIDYgw/glMsYPT2XJ5SyjN7QQOLDDyNflEnnWhaFd97xsutWAadUws5k0Krmvou+1lb0lhbK3cufNr0UFiZ5kaJRaUeWZLJukpLIIcsqQSmCg40rHFwcLCqzgu2EXE9MrvUeFwY2FrawcXExqSCjUqe0EpZi1CotjDv9ODiYwmCtsoVRp5ekO0q13ERwplqloFEntxCSY9TSwoTTT0nkqaWZnEhh8/NX/XRtwdjRaSZOpej+cR/rPtTKrV/YwmR3ihPf99y+G3fU0LC1mhf/4wEBsRiaAAAgAElEQVS6n+r3vLUkCNUGlszmey9YRZtypkK0OUy4Pkh+bI6UhuuD100cLssqiupDVnRkWUGaaQUIIRCug+ta2HYF17l59WoAkiQjKzqKoiHJ6kWfxbMQFTMZJa5wEK6N69g4jokQ//zW2NUi1rkNLZJg+vCruJaJHk0Q27ALK5/BmF5Zcsc1J06+9iYkfX5+lzBMcs/tvzakafZNBMbJXvKvHSH+sbtnW3aSJKEkomgN1YsSp3LB4d2npth4W5zdj9Sy/YFqynkHzSfjDysYBYeffnmI2tYAex6rQ/NJFNIWo2dL7H2sltFzRXoOehe3bfdWsWFvjGLGYuTsykIErzYco0y+v5to51b81XUIAfnzJ3DKJYItHYSaO5B1H7GunRSHzpM9fYSqnXdRvec+1ECY0sQQlfQk4baN+OuaEK6LrPsx0+c98Xj/GeJduwg2tiHJCrmeE1ell3xNoKrImrZQ3qMqSJrmWQqsttcgBOWzZygcOED0nntmY1j05maqP/EJcq++SunkyUUn4+RQiMCGDUTvuRtfa+vcLh2H8qluikePrdoPStg2ld5eAh0ds49JPh/xDz2EWypRGR6+/L5leUnCN+WOMMV8wfagc5qLMxJKIk/JmfvMKXfca995R0dezOm2Rh3vgj/knJ23D4B+5+TsY6Yo///svVeQXOeZpvn8x6d35T0K3hAgQVIkRYlUy3SrWz3T02amFbuxEWsuNzZirzZirvdi73YjNiZ2NybWzawbE93T0y1pJJGURCca0MJ7FMq79O74fy9OVQGFyipUFapAsIU3IpGoPCf/zHPymPf/vvd7P5pBbd06d4OrEER/6xhklR48XBpy967gTyKEKsiOpAjcgPp8i9ALaSy1qdytE7ghZtpY2y2KEnUxDoNwrRFzbjTF+O8NYiT1rT9oE3htn/kLRYa/0cvhPxjhwr+5idfySXRZjH9ncN/F4aaVIZUdIZkeJJHuw4rl0Y0EqmYAgjBw8bw2brtCszFPvTJDozZNq7nMg43iombPj5+AqKqBlSgQi3cRS0QPy8piWCk0PY6qGpGB7gphCn0X37dxnQaOXaXdXKLdXKLVWKTVXEaGe+MuL4RKofcEupHsuNx1apSXrxMGj2ciYsZyZAvjKErn1iiN2gz16vSG3/VBqPEUzelbtOenAIlbLRLvH3syW65oXdkNaTp3agH7xtR+fzTS8WhfuEnq28+uizqpcQs1u3mH+VtfVPmb/+EOP/gvhugbj2MlVWQI5TmHd//NHG//f3PEEioTF+t0DVtMnK8TBvAX/804/+SfHmThbhshoPdAjHSXwSc/W2L2jovQtah32W6hKNEYrrtzzbUM16ro9GQWGfiRi6qUBHaL2o3zAISeiwwDnNICpc/fw8h10XZs2oszUY+44kLUfFlRaU3dwl6eQwYBtRvnccpLaPEUoefsyo11r6Akk8SOHkGJxVBME8U0EUb0rBgG6BpaNodi3lfhqaokTj2D0d2N9H1C10O6DqHjrjw7SMfFnZ/HmZzcMmokXY/abz9A6+qO0m2qGtli9PaQ++MfkXj2WdzZGfxSmdC2EaaBls1hDAxgDA2i3RdpkmGIMz1N5a23CGqPsE+DINJfvfjiWiRMCIE5MkLXj/8S+/ZtvLk5gnY7Sr4ZBoploSQSqMkkfq1G5Re/2CFxe9hBup2DuNM6HaowOvwdInFkm0ZY+XsXbdIMlaM/HGXguW6ay23choce18iOpGiVbKbPLayRpMWrZUq3azz7Hx2l91QBGUrSAwlCX9JYXJ++jBcshl7sJdFlkR1JEctZDDzXzQv/2XHcls/ilRILF0vIUHL71zOMvNzHM39+iL4TBeyaS6LLwncCAm8/0vQCw0rT3X+aQs9xEqk+dD3eUWSqqjq6kSCe6CJTOEgw5NJuLFIu3mRx5jPazeUo4k50jgWPhQQIVFXHSnSR6zpMOjuCFS9gxjJoWueK8FUo6KBZGKSJJ3ui7y0lgW9jt8o0arMsL1ykVp7A9x61sEFS6D1Bz8CzaxG8++HYVS6e+z9o1uc6vHevIejqPcHYkR+ukOL1CHyHW1f+jnrl4XzCXpohffAZFN0gdB2MXDdGpoCRzqNoOnZ5Eb+xvYj+/mucUvENAlT75nQkBH8M8BfKeHPL64iTMPQtbRF8V/LlW0UmLtQZOpog02PQbgTM3miyPGnj2iHtms/7/3Y+6oTtRJ243zsQ49t/2c+xl7MIBexGwLUPK7z5v09jPHsGPZQ0P/x02w7maj4baVPsaF/pA70kX3mB6s/eImzuXK8hAx97YRp7Yb1BX3PyRsf1ndICTmlh3WtevYzXobdP6Lm05+7u+DvtB/TubvI/+hGKZUVkc8X7a7WScDPLCr2rgN5VWAuLrz7kaisVKWl8+inu3CzS2Tr0H1QqVH7+cwSS2IkTKHpU5anG41iHD2GNH1jpQxdG30nTYIVgrUIGAc7UNOWf/hR35uEWGg+DOztL/cOPyLz+WiQMFyKKhvX2ond1IT1vrfpuzS9NUUBRsCcmEIrytbKYCPCoyX2Man+F8N2AiffnUA2FzFCSZF+cwA6Y/GCO27+ZYen6vQhb8WaFt/7bjzn8gxFS/XG8ts+tX00z8+kSJ//sIPWFezdaK2sw+FwX8e4YiqqwdK2MDCR9p7tWCmcCFi+XkCE0l9u8999/zpE/HKP7aBY9rjHx3hxz55cJQ0lzqb1nNbWKopMtHGRo/DVSmSEU1di2gbIQAk0zSWaGSKT6yPccZ3bifZbnL+B7baQMCHznnk/gHkMIBTOWJZUZptB7gnRuFF1PoKgbK793Nq5A02Mk0haJVC+F3uOUl64zc/e3NKozu46iSRmyPH+BQu8JNG1jxaVhpMh3H3ksxEk3k2TyB1HUjZFRKSWtxiK10l22MwlTdAPVipEaO4YMQ4Qa0Z/E8KFovCufPjnEKeq1tf41f7mywWF5vxA02wS1B9JkqhI9tnqfLynNOls26A18GdnKA64d8ta/mGHqSoORkylUXVCctrn6QYXyvEN6xFoR527zRFEUkq++SPvCVdyJiE0LTUNJbiSiT7EeQlVQTCsiTrt5/wNWDff/YkLX2e5v6C0uUvz3f0u6WCT1jZdQU8l742taRJY2Qeg4NM+fp/b2O7hzO7cy6ATpONTeew/FNEi9/PK6/SNU9YlqHvwUW0MGkvnzy8yff7hbe+hL5s8XmT+/kUT+9n9cX7BQulXj1//dpxvW2wzVmSbn/tdLG17/xT/9YNtjPAyKotM38g2Gx1/HtNLsNg8ohECoOslUH+PH/ohYvMD0xHt4Tn2FOAUIsbe3RFUz6Rl4ju7+M6Szwx0JwKMiul6p6EaC7oFnSaQHmLz5Jsvzl3ZNnuqVaZq1OTL5AxsXChGRz8kPCfz9DYDE4gXSudGOBFPKgFplklZre02ca7cuUrt1cd1rihlD+h4y2Bkf2XfiJN2VBpb3bbe03cfSMgIAP0B6Dxw8QbijvnXbheeEXHq3zOX3yiA2plyNwT5yf/5HCMOg9cmX2FdvovV0kXjpOdRMmrDRpPH+OYJanfT3v038+dOYo0ME9QaN984hgwA1lST9g9dQknG8uUWa758DIPn6K/hLRcxDo4Qtm9rPf40wTZKvPI/W101QqdH88DP8xWX0/h4SLz8fjTGzQPOTLzFHBjDGR1EScbzpWYyRIVqfXcC+0jka9SRDuh7e8jKKufPc9cMQ1Os7OnaDapXKm2/Rvn6D5HPPYh08iJJIIHQ9IipCROMFAaHnEbbbuLOzND75FPv2bcLm3mrjwmaTyptv4UzPkHzheYy+PhTLikjcalROSghDZBAgfR/puvjF4uM7Z/cSK+ltoWn3PesoiThqKo6IxVBMA2EaCEOPNCWKAoqI7DpW94MX7QfpuIS2TdBoETZbSMeN9pG/sq88D/4eNOxOGd10xw8wWf0CXz5cWH0g8wJle4aKs7dRCFU1GRh7lcGxVzHMzpqb3UDTYwyMfhPNiHP3xpv4vk0Y+CjK3t4ShVBI50bJ5A/sa4uxe58niCd7OHD0j5BSsjx/kd146Xluk+WFS6Sywxv2iRCCWKKbbP4gxcXLe/TNN0IoKtn8QXQj0XG577UpLV55qLZpK6THT2Ivz2Ev7Syiv+/EKag3Iz3IfV3ihabyoOhz36AqCG19hCZ0vS0NMB8VUtJx05R0itqv3kfLZkh+60Wcu9OErTbtLy4R1OokXnme+ItnqP3sV9Tf+Qitt5vGbz/BvT2J9H2MkUHUQg73/XN4swtk/sH3ce9O480tYI6PgO9R++U7CFVFej7J119GmAbVn76FdWSc1HdeofrzX5N87SW8mQXs98+ReOk5Ei+eASLPraBUxjx4APvSNawj49hXb4KEWKaHRH5440atwG1XqS/e3rNKunh+ECvVjdeu0Vie3JHw0ZmaYu6f/bOtDbZ2CRkEOxZoR/5LN7Dv3EHL5TCHh9G7u1CTyaiPne8TNJqIlofpmVCuEvct4j3H140TBi7VuesE3qMdu2GrRfPzz2lduoTR348xMICWy64RKOn7K+SgiV8u4y8v4RVLjy1K/EhQFdRUEjWXRc2m0bJptK4CWj6Dms2g5jIoMeu+dk1iZVIntg5kyJV/Vp5X07nSdghqDYJKDb9SxS+WCYplgmp97bXVVPvXCZpiEteykcZlG5fpuJalruxtWlRRNHqHX2Bw7NVNb573I9L8OPi+jZQhilBQNQNVsxBsNPxVNYPegecIfIdmbY5wj8TV9yO6uV8l331kU7H1AxsRtSHynahqLgwAgaKuVA8qD0/vCSEwY1lGDv4e7eYSzfrOi3SkDKgWb9NuLpNI9W1Yrhtx8t1HqRRvEuxTxaKmxcj3HN10e5v1eeqVzftCriLeP4aezHRclhw5ilfbuaHwvhMnf7GMDIIVshRBScYQqrLBV2Y/oFgGIr4+ZRM22/iVx9/vy70ziTe/hD+/ROLls+hdBfxKFX24n1jhJHpfd0Q0iSwBCO/NdFfhzy/i3L5LUK7il6uomRTe3ALSD2hfvkFQirQNwjAwhgZovPsRQbGMff021onDGMODqLks1Z//hrBax7k1Qfz50/hLxbWLvmJZuHMLGGND0ViKQqb/KKNn/+Gm21aZvUqzOEWwB8TJSnUzevZPSBaGcRol7n72t1Tntm52ug5S7sokct/h+/hLS/hLnX3LUj0HOfCt/wR9fJNqllaFq7/657QfkTgB0T5yHJyJCZyJiUcf76uAAFQVxTDQ+nowDwxjDPWj5rNouQxqJo2wzP3Rraz+xzRRM2kYHgBWHNr9gKDRJKjUCCpVvMUi7t1p3LvTBNV6RED38Npnqgl6E0cIQ5+EnqPhFVlo3sCXLhmjj+74AYRQWGzdpurMIxB0xcbIW0O4YZuF5g1afgVDjTOYPImumPjSW7OOiGlpeuOHMdQ4FWeOYvsugfTJW0N0xcawgzq6arHXE+F0doThA69tGWmSMsT3bKqlO1TLd7BbpRXdUoiiqCiqgWmlSaaHyOQPYMVyCOWellAoGr2Dz9PKLKDuQxoNoFK8SaM6S7br8HoNo5RIGSJlgGvXaNbnaTUWabeK+F6LwF8hTiJKV6qaSSxRIJUdIZMbQ9NjwOaazXiql96hF7h7/Q2CYOfkvdVYoFaeIJboRlHWp/GFUMgUxoklumjUdlbKv10kM4PEEp0d6KWUFBevbGu7cidejDqUeBvX1VPZXWV+9504ObdnotYp96VN9P6uR68w2ybUbGqdMFxKSVCq7a8VwmZYCUXJle+BKki9/jIyCGl+/AWxE4fRertX1o0eD/6moe1GomKAUN6repAh4f0zW0E0w1q9QMtotizUlRnkqmt4eJ8gcrWZchCsfLhYeavEsxs0y7ORv4gSmWmqhoWqba9h804Qz/YTy/SiqDpmIk+yMLIz4vQ1ReA7tCrz6LEkiqIhFBVVN1H1rStuftcgDAM1l0Hv68Y6ehDz0BhqNoNimSvptq9OAyiEiKo2cxm0XAYph4gFAaHtErZt/PlF7Gu3cO5M4i+XCGr1R5YNaIrJSOoMU/XzLLUnGEo9g+3XcYIm/cnjFNtR0cZI+gy3yi0sLUM+Nsx88zpJvcBo5jlulN9nIHEcXbFYat+hP3EMVWjoisVA8gR20KDavsNA8gRSBth+naHkSRZat6MIh5qKojp7BE2PMzj+OobVOVIgpSQMPSrF28zefZ9GdWaNMG2EQNW+xDCTdPWdonfweWLxrqjv6YrAOp0bZb88FDy3ydL8BVK5ETTNQsoQz21it8rUKnepFG/SaiziezZh4K1EvjqTUKGoqKpFPNnNwOgr5HuOdRRwQxSxK/SeZGn2i6hcf4cIQ5/lhcsUek9imBur0GPxLtL5AzTrC3tu5yCEQr772Kb3F7tdolaa2OT3Xg+ntEjt9sWoivwB5E69TLiLSPq+Eyd3egl3ZpFY+p7IzDw4iJKw9jVdBoAQ6IM96L33GXAGIc7tWYLS7owEHwXG6BBaNoOaSiI0Fb9YRivkaF+4SlCrR6Rp9dwNQkLXQ81nEFP6fdVMW83q7i2Tjos3t4h5+ADe/BLG6BDS83CnZomdakRpuOu3McaG8IrlrdMJMqQ0eZ7awk1U3ULVLTQjRs/Bl8iPnH7U3bIBgX+vPUg0o9yDvmFCIZbqRtF02tWFPfUgMeJZdCuJ26ri2buPZLbKs9x8//+K9q9uoegW2f4j9B55FVX/6ptEf9VQUknMw2NYRw5iHRlH7+954gXtq4UAalJDTcbRu/PEnjlGaDs4E1M4125h35zAvTO1LrK8U7T9GsvtOzS9MnlrkJiewdSSpIxu/NBFEhDXcqSMHjJWPzEtTVdsFFXopIwe4lqWjNnHRPVTqu48mjDoTx5DV2N0xcco2zOYahxLTZI1B2goRXzpUWxPEMiAnvjuHMg32Wt0958hkx/bdMIgZcD81Dkmb/0Kz9l4Q3xgbQLfpu3bTN16m0rxFgeO/uGK7ui+lO0+orR0jb6hFzDMFKWla5SXr1Mr3cXzdqZhlGGAHzaplZu0GgsMtV9nYOTllejTRsTiedL5AzRqc7siN7XyXZr1+Y7ESSgKXb2nWJr9As/dWy2mFS9EYvoOmjMpZZRGbG0v+FG6+AGh73XUaNYnLhPYO69Q33fiFLZsGr+9gHlwGMWIPk4rZImdOUz9rU/2VXCqZhIkXzq5TiQc1Bo0P770WORV9yO0bbz5JVLfexUlHqd17kvCepPWl5eJnzmBeWScoFZfmy1Lz6P9+UXiL57BPDRG4/1PIu1Jo7lGKsJWi9BxIJSE9eaGmWvzo89Ifusb5P7xHxO2bRrvfUxQqdH47SckXj5L7ORR/HKF5oefYYwOgqKs6L/aSD9YZ3kgQx+vXcNrR15CQlFJ9+7lxfIeGsW7LE98Tqb/MI3iFJXpRxcg6laKsRf/FM1McOPdf4ld35s2PwiFvmOvke0/ytyVt1m6/fHux5IhvtPEd5qs0ljdShCGAU82Pdg/CENH68oTP3MS6+QR9L5ulFTyax+BUyyT2LFDWIfGSFZquNNztL64hH3tFkG1tmOBeRC6BCsaHUmIQEER4AYtau48gQwo27M0vRL52DAtr0LFibQvi63btPxa1IZCRmME0o/SXSiEoU/NWcQN21SceWy/RsroIZQhK4ocQrl32YNYvEDPwBlUtXO0IQwDFme/YOr229sgTQ9CUq9Mcfvqzzh08k9IZYYfy7HkOXVuXv5bANqNpV2lzh6E77WZvv0OmmbRN/Qiitr5dp7JjzM/+THBLqxEgsBhef4i2cLBjp5OiVQvqewwpcWrOx57cwjS2RFiie6OS32vRXn5BoG/vcBL6K1MSITAzHUT6x2hvTCFU1kidNqE7s4DOPtOnAgC2l9cxz57lNiZw1F41NRJfed57GuTeFMLDx9jN9BUkq+cJvbMvZu7DEKaH13GubM/Odmt0Pzgsyj1pUaVS6s+QO3zV7CvRU7J+P46AaN9/RbOxFS0/orpZeXf/3JtZlp/871IJxYElP/6Z0h3fRQlKFWi6jpNiyqD3Ih1R4LyxUhn5vtI18Mvr/i+SIl7ZxLpB9TeeOcrqaQK3DbT5/8DMxffiNom7IH4MJEfJJ4biLxTNrnA7AZmIkeyMIKV6kIzOs/6nmLnEIaOMTxA7PRx4s+fRstlIguHrzlhehBC01ALOWL5LNbxw3hzCzTPfYl9+Tre/OK203gbz1KJ7dcIpEfTK9P2a+iKiRu0abhFYlqKqjOHlCGq0PFDGydokjK6sf06SaOAEAp+6NAO6nihTcWeRlUMgtDDVBPoioWpJpBEOqu92SFRFVoqM7RJCbqkUZtl5s57uPbuswaN2gzTt9/m4Ik/WbE42F9IGdLYRbrsYfC9FrOTH5ItHCKe7Ew0EskeVM3cHVmTkmrpDq3mEolk74bFqmZR6DlJeenGnqXrNM0i23WoY5pOSkm7VaRaur3jca2uAQrPfgs9mQXAb9XIP/MKtVsXsZd3Vg26/8QJ8JcqVH7yPvpAN1pPLsqJj/WT//EPKP0/P4/0Rnt4gxaWQfKVZ8j86FWEFUWbpJQ4t6ap/eqTyA7hMWOtIunBDFEYItubMN5Qbkih3R/Ov18AvZkho3S9DYRqVRi8bo/fN8Ndbdq54X2PEWHgwR6m05KFkX3xUImluzETuT0f93cWioLe30Pymy8QO30crafr7x1ZehCrvmHCNDDHIoG7+9JztD45T/PjzwnK1YdcH+VKg+NonUh0LKk4CyT0AmPp55GEOEGTu9XPWWjdZCx9lqO51whlQN1dYqZxkYXmDUZSz5I1+1GEihfYOGGLheYNehOH6ImPE8qAqfp56u4Sbb/KwezLOEFzJfr06IJ3TTUo9B7f9FwNApel2S92VSm2DlJSXr5BZfkGPQPPfaW6uEeF3SxSXr6+KXFSNRMrlsN1dtd5wG6XqSzdIJ7o3hB1UhSVVHaYeLJnzwwxDSu1EuHqRJxDqsU7uM7OJRHJ4cM07l6PzIgVhdBzEYqKYu58wvtYiBOAfWWC8r99i/yPfx+1kEZoKvEzh1HTCWq/+BD72l2CSmP3gnFFoCTj6H0FUq8/R+KlkyiJSFQrpcSdnKf0r97Am1nc2w17iiceimaQyA+uNR/eKwihEMv0o1spHnvu9+8bFAU1lyH58lkSL59Fy+ei3oF/z0lTJwhNwxgeQO/tJv78MzTe/Yj2l1eiljsdDrOWV+N66R2cIEqtT9UvIAnxQ4fp+gUMNY4iVPzQjTyZAoc71XPoSgwBeKGNRFK2Z2j7VQQqgXSj62bYZql1m5qzgKrohDLACRqEMmCi9imGEieUPhKJHz56+ily2B7ZdLndKu7am+hBBL7D0vx5ct1H99Qj6nEjDH1qpQkGRl/pmE4TQsWwNm8x9tDxA5fS0jW6+k5hxrIblscSXWQLB2k1FrYl1n4YMvmDHTVVAIFvU1zYaLi6HcjAQ9E0FN1EyhAjXUA14/dSeTvAYyNOhCGNDy4Qth2yf/qdqPmvpmIdHsYc68e9O0/70m2cu/P4S2WCSp2g0Y4iKeEDJ4kSzc7UuIWSTqAVMugDXViHh7GOjaIk42sXXBlGYvDyv/ol9uU7j21zAZSEReq5cdz5Mu2bD2fjQtdInhkjtD2aV6b2xaTzdxHxTB9mshCR6D0cVzXiJLtGo1Yke+Rf9TigWyli2X7MRBZVM5FhiO+2sOtLtGuLhP7jjcgKyyT+7ElSr7+MMTb8xAu+HwfEagRqdAhjoI/26RM03v6A9pWbUUr/PkgC7OCe1scL7xVTBNKj7W9MaXmhjReuj3RLQtr+xqiERGIHdXjgEPdDZ0/I0v3I5A+gbVEIUV66vuvISSfUynexW8WvNXECid0u4znNjgQpajnzaDKCanmCRn0Ow0pvIGeqqlPoPc7S3PlH/m2EolLoPc5mYv1qaYJW854+VUXHVOKbjudLB1dGx3ntzhUKp79JrGcYoSjE+8ewF6dxKzvXuz4W4iQMPXLtNTSCRgvn5hTGcA9CjdJoQtcwDw1hjA8QthyCWpOwZUeuvK4f6XCCcKV/llhx/1URpoESM1GSMdRk/D5Tu3sI2w7u5DzGaD/W8QMoiRhK3ETo2iMZJFb+/du4d7YmQ1o6Rv6Hz1M/d2N7xMnQSJ09iFdu0Loxu5Yyux9qKkb+h2cp/uwTwubXz1jvQahGnJHn/hjd6nzhCn2X+avv0ihurw+eECpGIkss3Y2V6sZK95DIDa6l04SiMvbCn25qIikDn7uf/R1u60FTNIFuJbFSXVjpHmLpbmKZXpJdYyuLFboPfoPUFoL5pZsfUZ7Z3WxpL6BZSfLDz5AffgYr1Y1mxlFUPfI48V08u06rMsfirY+oL97Z0+hcRygKem8Xqe++Svy5U1FLmt/BCNPDIHSN2DPHMAb7aHzwKY23P4xsDL6OTu4PQTI9uKl7dxgGVEq39ySqsYrAd6lVJlfsCL6+CEMP329j0CFSI8QGH6Ydjx+4FBcuk82Pd9QepbIjxFM9j0yckukB4smejrQpDDzKS9fWicKTao5R8yQAmjAQsFLgIFCFyqx7gzkv0kN5tRLFL9/DKvSjGAZ+q4ldnCfcRTXr/veqi5lk/8G3Mcf60fsLUfpM1xDGxhy2UBTUZAw1eY8dy00uDtsN4Ssxk+Srp6PIriJWmpbe7xi8O9R/8xmw3Zzu9i5wYctm/v/+zYoGqbO+xxrtIfnsQUpvfAF8/YmTomqkew9iJQsdl/tui+LEF9seL5bt48CLf4aV7o78phQVxD1CLYRCumd80/cHnoOq/3LD65oZEbzMwLE1jyVxvxu+EMQyvcQyGwWUq6jN3+CxOeY/ADNZYPDU98kPn0bRjJUUdkgYBFHzYSOGZsaxUt0kCyPMXv41yxOfEe5TLyph6MROHSP9w+9gDPXDavuZp+gIIQRqPkv6B69hjAxSe2k+HqoAACAASURBVOMdnJsTO3axf5KhGQmseAE6pJsAnHYZp71zl+etIGW44nH0QF+wrxnCMNjCwTvqZfeoqBRvYrdKJNL9G5apqkFX7ykqy7fY9fVNKOS6DmMYnSdQreYStcrkOuJcD5a52v4AU0kwZBxhwbtLMygjUOkzDqz7JopuYnUNYua6EYqKke4i3jdK5dpnHRvXb4V9J06KZZD6vefRcrvLsT6qxiGq4tv7nmU7OceEoZH6xhFiB/sJ2w71z27hTC2vzRiFrpF+5Rixg5G1fevKFPVPbt4zugSM3izJZ8dJvXAIa7iL3h+/Ruh4hG2XyruXcGf2xtDT0OE7r8bIZVV++kaTRlNy6pjBc6cN3v3AZmJqb6MQgeewePNDzER+xR/KilJJmb5Ny2u3hAzx2vV14WTViGEmIsdgKSX2Fj5Oge91XiYlvtPCbZTWXhKKipXqQmhGZBL6EB8nr13nqyBNupVi8OT3KIw+h6JqBJ5Ndf4GjeIUvtNc245s/1FimR7MZJ6h078fzTInvthzczslHiP5rRdJfe/bUbXcU2wLq+m7+OnjaF05ar94m+a5Lzek7r6uMM00uhHf9Jrfai7vjafbOkicVokg8FDVfbhPPC7IcE8jcZ3gOnWKi1dJpPvodAPMFMYxYxmcdmVX45tminTuQMfCABkG1CtT69J0ACEhrrQxZZSuq/qLBETnQy1YpqANMr8ScUqOHiV98BTtpZl1LuK72W+PT+P0OwqhCFIvHiF2aAC/3MA4Nkj6m8eZ++e/oH0zskWQYYhfrBH0ZMi+fgqhqzS+uLOOOAldA01hNVIWtl1C2yWw3W1roZIJgRBgmQLbkZiGoGVLCjkVRcByKaDdliwVA55/1iQRV2g0AxaWfbLpGAN92p4Tp9B3mL/6zloER1FUYpk+Dr7yY4z4RiHiw9CuLXLnk79eR5wy/UcZPvOH6FYSGQbc/fzvaFc3t8FY9aq6H77bZubSm+tOaiOe4cBL/4R4phekZOnOJyze/HDTcX13ry/6D4dQNboOPE9+5AxCUbEbRWbO/5LK3DV8t7lG3hVVZ3niMwZPfJfs0El0K8XAie/SqszRKu+dfYfW20Xmh79H/OwzCGvvXed/V6D395L78z9C7+2i/uvfElS3V2WU0/rpMgZBCkwlzrI3zYJ7B4FgwDxERuvBlz6L7h3K/jxprYtufQRX2qTVAkVvhgV3gpSap988hCYM7LDBtHMNL7Tp0ofoMoYAwbI3zbI7Rb95EFPEMZQYqtBZ9qZYcDfqTU0rvaUWx26V8Lfp3bMT+L6Da9c2be/xFBHCwKO8fJ2ewWexYhsriU0rQ777OHOTH7KbCWI81UsqM9Rxmee1KS5e2VQ+4EkHFY0x8xStsI4iVLJqD0X/XvNeM9dN7eYFarcvPnKa+ylx2m8oAun5zP4v/wG/0sTozTL4X/6I3O8/u0acCEKalyaxp5aJHR7oOIwzvYwzswwSjIEcy3/7EX5lRRC6zWPg5ect4jHB889afHnJYXhQ441ft3j5BYu+Ho2JKY//968aFMshdvveoLW6pFbfv9mMDIM1cXUAaGZj130MZRhsID6+07zvRJG47Rpua6ezIhmNs+Hz/LXlgdvexbj7CytZoOfwKyiaQeDZzF35DcW7n2+YZYWBR6s0zfSFXxDL9mGlujGTBQpjZ/eGOAnQhwbI/dkfYh07HLX+eYpdQwiBkkqS/oPvoKRTVP/ujci24CHQFYO8PsjV5gcgJWOx09T9Igk1S1xNc7P1KQk1w7B1nGaziiYMCvogt1qfseDcASQKCj3GGHZYZ9a5hS4M/NAhpebpM8eZsC8gZciYdRo7aGCIGFm9l2utj9CFyaj1DCVvFk+uTwPrRmLTFhtSSjynvqeO/6sIAhfXaTwxxEkIBVUz0bSopVUkOdBWJAIKQkQTTCHUlb57CqaVjlJc+4xmfY5aaQJzILshMqiqJvnuIxQXLu7YLkBRdXKFw5s6oLebS9TKmxd32bLJbedLuvUR0lo3oQxY8CYo+jNo8RSKYRLYLWJ9I3jN2opbeHRP8BrVqDfsDrD/xEnKyPH6K/QE2hc8WOm3CWQgad+YxS9HJMddqNC+OUv8xAjC0JDufQx6peP65oOxtlxKuWNSX6qEDPYbxGOC4QGNej3Esu41iTxycH+aXD7FV4dM/1GMWJQOa1cXKE1e2DI03a4uUF+awEpFOoBU1yi6lcSzd+rQfB8UBfPACNk/+0PM8ZGnpGmPIIQAVSX50nMITaP60zfxF5Yf+r6GX6IZVPCliyttEmqGjNZNRuvhYNxEQcFQYlhKAhC0wzoVf5FgxYROoFAPivQaY4RSsuRNEhISVzPYYYuGXyYkoB3WSWmRdrHsL9AKqiiogEQX1gbipGrWpv5NYejj7XmaLoIM/W27UO8phEBRdDTNJJboIp7sIxbPY8ayaJqFohmoir6m1VwlSdHjvv8rq6/tv0bL99qUlq6T6z6KbqyvZhNCkMoOk0j175g46Xp8QxPkVUgpKS1eiVpxbQE3tKn5y9RFkZI/h4qGgkLm6HPE+0YQioqezmF19RO6Dqs30MWP3sAp7cyIe/9brtgu1Z99gGJ9jfPHHeDNb1NTJCVBrbXub7/WRmgqaszEdx+fPmF61udPf5TgynWXgwd05hYCThwxSMQFS8s+/X0apiEYH9Xp79M4MKpRrgQMD2gMDWgEgeTiVWVfo09PsYcQCpnew5GIXYY0S1P422hR0SxN0z3+AkIo6FYaK9W9e+IkBOahMXJ//iOM0cGvtdHgkwqh6yReOI1iGlT+3c8jx/EdDRBNnpbdGRbc2yv9xUPcsE1W6SOQASH3rlOSkCV3kmZQpdsY4XD8Be60v1xbem89ea/1pnTXv77hBimi6MomwnAZ+nvSpqQTwjAg2KciiE5QVB0rXiCZHiRbGCeVGUY3EijqSlRJPNn+ZeXl69it0gbiBKAbSXJdh6mWJ3bU8SGdG8Pq4BEFkbaqXLy15YRPw2DUPElSzRHgU/LnyWv96IrFwpVPqN74ctP3Bu2dX9v2nThJ26X2H3673x/z5EIQVQzd/5IW/S0fc0XMcingvY/aXLnmMTHlMzHpoaqC58+Y2Lbk8wstNB2SScH1Wy6ZlIquCwp5hZl5nyCMdFK13fexfYrHCCOWxkhEIfUwDLfUdd2P+1Odqm6ix3ZpnicE5sFRcn/xI4yRp6RpPyE0jdjp48ggpPxXPyUobl4llNLyxNUMUgYYwqIVVBEI8vogwUrfOV2YhA8aN61+FgqmEscJm8w5NzkS/waGiNEKa3QpwyTUDCEBcTXNsjdNXttYhbVhzJXoy2aEIQyDfUnTQSQODvbbeoMoBZdI9dMz8Cz57mNYicIj2wR8FfDcJqWlK6SyG/VIQgjyPceYnfwQe5tNeIVQyHcf3bQ3YbV066FjWUqkobtlf75mT+BJh5zSx6x9Y1vfYyd4qnHaZwhFYA4VEJqC9EMUy8AcyBNUmwS78GGSfgBSoujaJpe1zRGG8ItfReHuyZl7F4obt9dfkH7yi/Xdoj/61OGjT7/+1ge/azDimTXNiFBU+o9/h64DLzz0fZoRZ7VqRigayia6k4d+/tgQub/8hxjDA09J02OAUFXiZ44jfZ/K3/ycoNRZb+eGbQaMQ+iKybI3RSuo0Q7qGMLiUOwsAFV/ibZTJ5A+bthepwoQKOT1frJa79q6jaCELz0W3bsMWycQCIreNHW/SErNR02DiaJNTtgi3FCpKVC2PEbknld3Pp6xI5ixHH3DL9Ldfxorln9sqbX9gaS4eJX+kZc7OnybVpZc12HmJrdHnOKpPpKZwY4WBJ7Xprx886HVlKtRUnUlhakJnbiaXjO/3Gs8JU77DSGwxnrJff9Z7MklYuP9xI4OsvzvPlhnR6BlE2i5JErcRHV9jP4cQcPGLzfWVde582VkIMl8+yStS3eRocSZLRE2v4Ic/VM80VB1c81rSgglMu9M7UwAG0UCdk56tO4C2X/0wyjS9CTdIKREBiHSdQldF+l4Uc/HUEYFCWEYPcuVdJKi3DPWVZXIfNcwUEwj8qJTn6y0itB1Ei+eIaw3qP7kTcIOfTBbQZ3b7c/xpYckRK7QomnnKsKJKndXX6/6i9T8Je5PwYX4zDo3mXNuE1Gh1V55sODeYcmNzGpXx5hxrnPv1hZwo/Ux4YN97YTY1L8pGkxu6un3yLhPO7r3EKSyQ4wc+j7ZwsGVVNz2jpeo52BIELiEvkMY+iuPqJgmlP5aYY2i6iQzQ2i7nOTsFHarSHm1z98D26OoOoWe4yzOfr6NFKggkxtbIZPrx5FSYjeXqRRvPfT7OGETO2wyZBwlpeY5ZJ1FQeWusz+Gw0+J034ilAQNm9rH14kfHyb7ndMITaH63mVqH15bW80a7abnx6+hxAyMvhxGb5b+//wHhI7H/P/5Js70PebevjFL9Z2LZF45Rvrlo3gLFRb/zbs4T4nTUzwAoehrNyMpJW5759UjQeDiuzs7ttRsmsyPvod1+MBjJRVrN9YwjMiR5xFU6wTlCkG5il+qElRrhLaDdN3o4XhRZ4L7SVMoQYbRvlszzV0R4WoqwjAQK8RJsUyUdAotl0HLZVFzGdRMOlqurBAvIR7rfhCqSvKbL+AXy9Tf+XB9A28ZEhKs6JbWR1kiAvRg5EWuEav1r27W1Fd2GPeBCs5O71shCZtvlNhU//QkI5kZ5NCJPyGZGdzy+0spkWGA79u0Gou0GgvYrTKuU8P3bcLAJQxWiJMMVgjTvf/HEl0ceeYfo23S6Hev4XttykvXyXcfRTcS65YJIYin+khlR6gsb50m04042cJBVG2jBlrKkFr57rZ8oXw8pt1rZNQuyv4CISG1YJlWuHftee7HU+K0j3CXa8z8Tz8jaNoopo4aM5ChJKi3CO17NzB7apm5/+2NjmN4xfU/fGh7LP/kHJV3LiJUldDzCaqtju99it9tyMCLCACRuHb6y59TW7i543F8d/vHl7BMkq+9RPyFMwjtcRTtSghDwkaLoFbHL5XxZhdwp+bw5hYI2zbSi9o24ftrqe49gwBULSJUuobQNBTLROvtxhjsQx/sR+vKoaZTqOlk1IfvMZAoEY+R+t638Itl2heuRnl6oOTNUfGX1irknhzIh/R73EfiJNiXsU0rw9jh3yeZGdqUOEsp8dwG9eo0pcUr1MqTuG5jjShtN4UYpcwer7lupXSbZn2BTH7jBMk00+S6DlMrTRCGmx9rZixHJn+g47LAtykuXma72+VLl6J/zzpFwyCtdlELHl5pulM8HuIkIq2PDHdeQt8JqqnSc3aAMAhZ+nyO0Nu/Ki89oRN4IaG7ixx4EOKXIiV14Hjrq+vug3Q83LlSx2Wbre8tPWkXvqd40uB7NuH9olcZ7rvPVPzMCVKvfxOlQ0ulvYSUkrDWwJ2cwbk7jTsxhTs5ExlBPs4ebpIVQuYj7SgtEQDe3CLtL6I0gZKIYwwPYIwNY4wMYIwOoRVy+6r7EkKgdxfI/NF38RaW8OeXVr6bv9LLaz3iaQ2nHRB4e7fv+sZjDB6Jc/uLOuX5rSusVlNSUnaquANF0fbN2Tsq6d/bW6GiaPSPvESmML45aQpDquXbzEx8QHn5+o6q0J4EuHaVyvINUtlh1AdsJISikO8+xvzUx7SbmxEXQa7r0IaI1Srq1Rkate22NdsIU4nRo4/Sjgf4dvO+NPyj84XHQpwGT+f5zn91gpvvLvDRv7zxcPIkYOwb3Tz/43Gu/HKG6+8soOoqCPAaLqEfIkNJ7nCB0qVFQi9ETxqohkroh7h1B6EI9KSBoip4LY/ADTASBoqu4Lc9/PbGi4diqOiJ6ADwGi4IwcC3R2nO1qneKuE1PfRU9DmBG+C3fVRTJWj7CFWgaAq+7X8VXTWe4ik2wG2WCTw7isoIBSu9eR+9vYAxMkjqu99CSW7erfxRIMOQsG3jLxVpf3kF++oN/GKZoNZYi6g8iQibLeyrN7Gv3UJJxNHyWYwDw8SfPYUxPIASj3VsUL4XMIYHSf/eq5T/6qfITbz0dEvhhR8WmLjYYOGujdsKiKU0WMk0Nis+RkzBjKmEoaRdD9BMhcANCQOJGVdx7BDTUtAtBc8OaTcC7GZAYdCiuuQ+lDgBBL6DDH1EBy8nRVE3Ncd8VAihonVIFT0KEql+uvpOoW7SNioMA4oLl7h7401ajZ15CD1JWF68TO/wi8Ti+Q3LYoku0rlR2s0inW6KqmqQ7z7acdwwDCgtXn6ov1ZG7WHYONaxAlQTBq5skT12lurNCyQGx2nNTeAU57e1bVvhsRCnWMZg9MVuylMbnZc7QoKZ1Dn4ai++J/FTGbS4gRBQurLE3McztJeapIbTa2/pOt1LejSLmTG59q8vkhxI03O2n9CPolJOzWHke+PIMMStu0z8/OaGKFLvCwN0neyhXWoz/9E0QhH0vzKMvdRCi+nMfzxNz9l+UoNptJjOnZ9dp/+VYWbemyTZn8TMxZh9f3JfI2BPsRvcr9MQe9vKc+16IJ64JrWeXceuLRPP9iOEQrJrGEUz96Vxr5KIk/r+tzFGBvacAEgpCRtNnBsTND/9Euf6bYJma51252uBle1wG03c6Tla577EGB0ifvYU1vHDaN2FvSdPqkL87DPYNydoffLlhmicUKBnxOLkt/Pk+k0uv19h8kqD7/+nA1QWXAJP8vHPlhg6lmDsVIpMj8HHP1mifzxGZdGlOOvw3PcLXHynxKnXcgglauv0zr+ep1H2aJS3Hxn3vTZB4HU0wRSKFkUmhNjziKKiaqibOFbvBkLRyBTGiSW66dTTTUpJvXJ3j0nTV6MBc1plKsvXiY28vGGZoqgUek+yNHehYzQtlR0mFu9crBI5hU9urXsjiip50mb5vtYqq4gpSeJKBtWKE+seINYzFBlfPnD8uLXSE+gcvkv4ToDvBqT74qiGw8K5GaSUdJ/uZfHL9YxRKAIkOOU2Xad6MNImXc/0sHxhgaUvonUHvz1K5mCO8vUi6ZEMZtqkvbw+debVHZyqjVOxcWsObs2hdGmJ4uVFihcXEZoCIbRLbQa/1UPgBjhVm9RQGisXozFX//qQJsGaaFVCdDBt0w3964ZIKxD9LkJRELtpHtwBUob3vGWEWLngC56kkGNl9gq5oRMoqk4s00em7zDl6Yt7+yECrBNHiD93MtLw7BHkStcB+/INmh98in39NmFjm5OvJx1hSNhqY1+5gXNzAnN8hPjzp4mffQYlldgzAiWEQEknSb32Eu6dSfzl9ZIAGcLM9RZTVxp89ssiszdbWAkVJFx+v8LytE1UXChoVn1GTyWJp1Qqiy59B2JYSZV23SeZ0xk/k+bmZzX6xmOkuwzK8zsj6J7bIPDtjsaKQggMM42qGntuVqkoxp62K1FVY0X305nIBL7NwvRntBo7NCrdAkJRN3Vd308EgUtp6Rrd/c+i6daG5an0IIlUL/XK1PoFQiHXdWjF+mQ9IlH45IaGvp3QCCq0wzr1YKPUxVKSdGuCxmSVzJHniPUOY6Rz+O3115Clc289ec7hu8VqSxEjoaEYCvHeBDKUeG0foQhi3QnMjIVViBM4PoVTPSx/OU/gBBCC23CxCnFSIxmcio1TtaOU280Syw0Xp7oxBNiYayA0la5TPbhHCyycmyX0A+I9CRr5GJqlkT/ezcKns4ReAAgWzs0y/g+P0l5sUZt4svqUbQY9nyT7yhEyz4+jd6WQfkDr1gKlty/TvDaL9L5mM/mHwHeaa92whVBIFkZpFqd5VIIjA3/NLFIIQTw7gGYmtuXO/bhQW7hJY/kuqZ6D6FaS/uOv47VrNErTW+b6haKimQkCz3lohErNZUl/91UUc+9SKdIP8Gbnqb/9Ie3zlwlq9SeJj+4ppOdhX7uFc3ea1hcXSX//25iHDkSVeXtAoMSKEWn8hdPU3ny3Y6TO9yT5AZPashtF+d0QpxnJGeIZjcMvppm90cJpBkgJszdajJ9J0Tse461/MYsZU1mesVmYaDN9rUll0SVV0EnldZxWgBFTcNtbTyodu7alX08skUfTrD0nTppuYZjph6+4TaiaSSLZt+lyx65SXLrCXh7Qmmai6fuTIt8akkZ1hlr5LrnuIxuOV81IkO8+Tr2y/nprxbKks6MIsXGiFVXsXd2W5qsVVukU1QNwwhYL3gTulIe9PEfX2ddpztzGXl6vm/KbO6+8eyKJk1AF6b44RlzDt1sEdkByKI293GL+o2kUTSHeHcdruiQHUyx9uUD1ZgkrH2f+3Ax2pc3MO3fpe2mInrMDFC8tUrlRxEiaJAZStBaahP7GkzjRmyQzlqW10KB6O3LeXfhklu5n+0iPZiheWqJ6p0ysK87cB1N4LZeg7UMgaS008FtPvmBbiRsM/MffousPzqwTpyaPDZJ58SCT//MvqZ67tS76pOomimaiqHr00AxU3cRM3Mtra2aCVM9BfKdB6HuEwb1H4LY7hlyFoqLqMRRNvze2qhPL9KBo0exJCJV4bgDfba1UmqyM63sEvkP4kP5FAHZ9GadRwkr3IISg59DLtGtRTzZ5nxuxouoouonvtLYlIAx8h2ZpmuzQSRRFJdN/mK4Dz7N855N1DYGFUFENa+V7d/6+iqqj6tZ9+zh6jmf719yFhaKT7BpDNeNr+1iu7mPP6eis7LaqzF35DWYij5nMk+o+wNiLf87S7Y+pL97BaZUJfTf6LTQT3UphpgokcoPE84MsXHuP6ty1DeOuYaX0XR9+uDv0dhG22jQ/PU/9rffwZr+++o+dQtoO9uUbuJOzJF55ntTrr6D3FPZkbKFpJF95gfb5Kx336fnflDj4XIpmxWLqapPbX9Zx7egcaNcD7l5okOkxuHO+zvK0TbPqc+vzOqm8TnneRdUEF98t0z8ep1XzkaGkeziG70oSaZ1kVqfU3prwOHYV12lsKhCPJ3vR9DiO/fBmxtuHwIrnO0ZLdgvDTG45XquxhNehYfiuIRSseGGDQPtxwbGrlJaukcmPbdChqapOJn8AM5bFad9zs08k+0mkO6f1263itryb7qEzAdWFSUbtZsmfxG/WqN28gFst4rcevfXFvhAnVVcwk1qUQgOsdPSDapZKsmBtaWSmqIL+kzme/bNRhCJYvlXDrTkULy+ukRmA2z+5vu59U7/e2Dl58o31O3/2t5Nbfu/ipUWKl9aHT+uTVeqT907U+8fUUwZDf3CIMAgpX9/7ksf9QPLEENmXDnes6DF7MnT9/hkaF6fWXM1VI8bgqR+QLERNEhVFRajaWkRiFfFsH2Mv/KMoLRbeM2YLw4C7n/wNrcrshs9L9x5i4OT3Vrp+qwg1Gl9RjbUQrqLp9B75Jl0Hzq6Ntzp+ZfYq89fefSh5Cjyb0vQFEl0j6GaCWKaHA9/4C9rVhTWCo2omqm4RBC4T5/56XduRzSDDgOr8dfIjp4nnBlF1i4GT3yU7eByvVY2M6TQDVbcQisr81bepzF7dMI5QVLrGX6Br7Gy0HxR1bZ+oetTsE0AzYgyd/oOIgIU+MgjW9vPirY9Ynvi0g/5DUl24ycylNxk89QOMeJZ4boDhM3+I0yzjOy3C0EcIZU3roVtJNDNO6Lss3fxwy31gDPVF6SX90S/aUkqCcpXaG+/Q/Ogzwsbvps1G2GhS//Vv8eYXyf6DH9xzXn/E6JNayJF46Tkqf/tLCNZPDGZvtJi9cW9/X/3w3jUvDCSX3tsYTb/52b1zJPAlN87VuHHu3mvXP65y/ePtk5wwcGk1F8l2HUSIjbcm3UiQSPXSrM+zV9EaIRRSmY2tQx4FqmaxWRQEwHWq7GW0SVE00tmRPRtvNygvXWVg9GViie4NZCiR6iWZ6l8jToqqk86NdDTrlFJSXrqGv82mywoqmugs7I8rabJaD42CxK0WcatFgh160m2GfSFOfSezfPe/PkU8F22QEYs+5tC3++g/kWPzg0agGRHpMhI6rYrLlV9Mc/fT4kpq7MmC1/SYfS9y7+5UpfckInl0ADW5+WwocagPLRVbI06KohHPDpDqHttyXEXVMRO5jstUo7PwUrdSpHvGtxw3ajSbRLc2ahDsRmnNGfthKE1dwEp20X3wG2hmAitZiCJmq0Rj5WT32jWUHZQmN8uzzF15m4GT3yOW6UU3E6R7DgIr1hsCQCBDn+U7n2y6jWayQKq7s5/J2nqKghHPdFxWnb/OZvoqGfgsT3yG77ToO/YaidwgimYSz24eJQoDH7tRwnc2T50IQyd25iT6QO8j39SllPiLy1T/7k2an10A/+txPu0bfB/70jVK1TqZP/4+sVPH1npc7hZC14idOkbr0/O4kxsnMk8CauW79A290PEcFEIh132U5flLW3oD7QSabpHJje3JWKsQQtmKNxHucY9S00yTzo3u6Zg7RbtVolK8tSKIXw9Nj5POj1Eu3iQMXDQ9TrbrYMdrhu+3KS1tEeF+ADmtjzHzFK7cGM3UhcH/z957BUeWpul5z39seoeELZhCobxr3z3tpmdnpndmZ3spcpcbuytSITGoCDJC0pV0SV0pQgpF6EYRMhuhVVAUuZTI5XA4u9rpMTsz3dO+q7uqy1tUwQMJIL05eeyvi4NCFQoJFFyZIeftQFQDmfmf/5w85v2/7/3erxXUSB08TeXqFyQPnKAxdYP24sz2dq4DHglxahVtCtcqjLzUTXY4jmaEF3wkqRNJPnxlKqWktmBx5s/HmT67jN9++kgTAIHEbT796bn7oaXjCHXj6guhq6jJCKzo7323zeKtj6nMXd3xNu1G52hcszTN5Nm/2vG4VrVAsMVqCN+xmLvyC1qVObKDp4jnBtGjCRRFI/BdPKeN0yrTLM0+tAR2DWRAceo8jlWla/gZEvn9mIlcGCVa6bruWDXatUXa9c7HIQh8KrNXcK2dh5AbyxObVhtJ36M8c4lmeZbswDGSPQeIZfrRI0lU3Qx9dNw2jGC0ywAAIABJREFUTquGVSvQLM3QWJ6iVV5frXIXajZD/IVTu/cjkhJvqUj5+z/Cunh1XTTkP1gEEmdqlvL3fwRCEDt9bFcEVQiB3tdD5NghnNkCPOYm41tBozqDYzfQNqhyS2X3E0t006jtDfFLZUeIdCil3w2CYHNLmtAle++KSLr6TmBGMnsy1s4hWS5confwhXV+W0IIMrkxZrUPcXyHWLx7w2q6aukO7dbWPQ1VoVH2Fii4k+teiylJslofAkjuP0ok3490HTRz7bllLc7gb7JA7IRHQpzKM03e/1+uEE0bpPqiHPvuIM//3VEWrlW4+d7Cpqm6wAuoF9oUblQpTzXx7Kfv4n6cUNFQUXHYG0Fk4Hmbl/NKucamIfBdSlMXtrUNJRUn8Y1X0PNZ7Mk53InORMSqFrBqi0ROHkKJRWl9dn5b2+mE+GvPIaXEOneV2EuniD5zhPbFGzTeP4PvtilOfkV1/gaqGVvpG6UgZRCmAX0H37Xx3XC+SjpB6rffoP7zT/BLm6QcZEB98TZWa4ns7/020UM6jZ9/inX55sq4LsF9494PNZMi+farqMkE9WvjtM5cQrrroy3CNEh++1XMsWEa75/BOr8+5bcVOM0yhVufUJz8CtUINVV3q3/udqD33XY414eUfceeO4HWtfuHjrtcovKDd0OH69+QpnXwFpep/OBdEBA9cWRXlYtC14g9f5rmF+fxi09fMYvjNCkv3yQaz3fUv5iRFH1DL3Hn2rv4uzSM1PQYPQPPbkjSdgrPaW5aRh+JhX3Z9qL3XjIzQs/As0+kou5BNOsL1EoTZPOH1hH8aCJPLNGNY9fCnn0d5ut5NqXF67jb6FRQ8Rapsowt12vGXGkjhEL12gzpw89iZrpRVA0z17PmfU698nQQJyTYDQ+74VGZbSEUwfHvDFK4XuWjP7seOog/5PNPA4ShERvrRUvdrVaQ2PMV2lMP1zOpaMRFiprcOnvuhIySJyW6uOPvTbNCe75C4HioG4T9nWIDb5ctXMyxEbRMisoPf07QaiPtzW5wkqDe3NCcb7tQUwlkECBdj+ZHZxG6hppe28Hbc1pbaiMiVBUtn91y6xCvUWf5h39N5m99C9spYVUe7nobOXkQFJXyv34X6fkdSROAtB3qv/gUJRJBie/yRi/llo/BRlCScaKnjoG+u1uI32xR+9EvaH115amMgDwVkBJ3rkDl+z9CTSQwD+xOz2IM9WOODNF6ColT4DsUF6/Q038a3VyfnhdCJd97imppkuWFCw/1+dkIQqh0958ikz/Epnm1HcB1LVyn0dFWAUJjSDOao93anS42Es0yfPC3iCf7n4pG067dZLlwmVR2/7rec4qik8qOUqtMkcwMdaimk1jNZaqlsHH0VuHIjQmPIy0W3SkoShY/XUC6No3pm1gPpup2QGAfS1Vdq+JQK1ih7GOP2q48DhjdKYb+4TcxB0LtjgwkSz86x/y//HD1PQIFFZW7HcV9PBQUkiJLvzqC5TWRBHi4CAQq2pr3ipURfLyV8e79rqDSli1ceY94KKgr/4ZdzH281SaaKhoCZSUILFdeW3uw6+cncZZqRIbXruiklEjHo/zBVfydNgwWgsQ3XyH23HGUZJzM3/omra+u0r50i+jpw0gp0fu6UTNJ6r/8DG+pRPyV05iH9mNduk/sr6pEnztG5NAIQaNF68vLuHOLJN9+naDZQh/uJ2hatD49j7dcxhjqI/byaaTno2VTYRoi3Kk1F4UwDRJvvED76jju3GK4+v7as7hTcziTnUP/wtCJvXwaNRXDK5Rofn6BoNHCGOkn+twJhK7SvjxO+/rtsMw7WN/FPfnNrxG0bfShPoJGi+ZHZwksm+S3vkb0mVC7ophv0/z0PEHTwjwySvPjc8i2TfzNF3DnlnDGpzqO/aQQOTiK3ts5KrAlSBnaYHx+jta5S782pMnUUwx1v0i5PkGxfvuxbtudX6T20/fJ/f0/QI1Hd562U1Wiz54Ij/tTcj7dj2ZtjnLxFt39p9d5IQkhMCJJhsbewrFrVEt32O4DRQiFbP4Q+/a/if4ISvgD36FZnyeW6On4umEm6e4/zczt97fci24tBGYkzciht8nm11sAPDlIquUJWs0lEg9UzN0V4ccSPZiR9Lo5B0FAvTJFu1V8cNAtQUGlWx8ip/WjCRMpfYRQWXKnWHBvg5RUb54PPZz24Jx/LFajrZLN/KUyjeLeKNofF8y+NLFD/eiZ+MpPDDV6L8SooNCrDHFIe5bD2rMMqAdQ0ciIbkbUI2RFL4e0ZxhUDwEQFylG1RMc1p7lkPoMCZEhLlKMqafQCMfNiV5G1CPoGOSVAQ6qp+lW7lV99Cv7OaieZkw9zVHtBfYpBwBBjCQH1BMc1E7xjP4mB7XTmKyPTNizJeb/1cdYtxfxmzbSDwgcD3uhQuGHX1D8+aWd+zhJSevzi1hfXcO+OUntxx/Qvh7e2IyRfcRfOoU9PhUShGoDPB/r4g28pTJ6/72bTOT4AcyxIRofn8MrVkh++1VELErk5CGMkQFaZy4iVIX4a8+iZpIk3noZr1Slff0OancWNtBwSccFRSFyMvw+lHSSyOH9+PWNS4PVbAoEND+/iLF/H5GjB9B6u4i/8SLOnWmsizeIv/5cKJDeAObRAxj799H67AJC14i/+SIEAY2PztK+Mk77yjjVH72PMzmHkohhjg6uCoGN4X60XGdB+JOCMAzMwwdQEp17TG0FErDvTFH7+YcEre2FyZ8komaG3swx0vGHVWIJTD3Z0admN7Cu3KDx4WcE7u4itObIPrSuzsUcTxqu06Qw8wV2u7bhQiGe7OPAsXfI951YqWLbGnQzSc++5zlw7HeJxjvrbHYL33Oolu4QbNC0WFF0+oZeJN93cluFKBBW/2bzhzl08vc7EssnDauxSLV0pyMhNKNpMl0H0TuYjYYNfa/uOIIYUeL06MOrRpiL3iSedHDvE4279QpGKkd86BCJ4cOrP6q5/Qj+44k4lR0+/rPrBL9G0SahKsQPD6DoG9/4DCL0KPuY9m9Rl2VUdAJ8SrKACFR6lH1c9c6svr8tW8wEN3GkzaB6kB5lH1P+DRQUUiJHRS6RUrpoyxYeLovBNALIKPcqFTR04kqK695ZBAqH1GdYDGboUvpwcbjlXWBUPYEtLdqsT8VIP6D0q6u0bi8SP9KPnooRuD6tO4s0r83u2vwyqDfxGy1Uq41XqiJtB6FroCjY41PYtybX6FiChkXQstb0N4ucOITemyf+4kmEaaAP9aF3Z5FtG+viDZzxaYShE3/1OdRMCjWdoP3jX+EVK9hHN6nSkxLr4nXS73wDYRqYBwZxC8ub6pf8cg3r/DXcqXmcsSG0fBbp+5hjQ2FzV9dD7+3CGBnA3aBSSTou7Wu3cSZCoXXqe19HiUfxK3WCZguh6/jLldU5Pu1QMynMQ6O7EoVL26H+3sd4iztbYT4pNK0lbi98QL21eb+riJFif8+rTC59hmWXN33vdiDbNo1ffYZ5aBTzwMiOog1CCNR0CvPg/nVO4k8LKsVxlhcusm//Gx1fDyMY+zh44u9QLFxmuXCZemUaz11/zxNCJRLPkcoM09VzjEz+ENp9ZCsIPDynhWbEVz3TdgMpfWrlKazmEvHkeiNMIQTRWBf7j3wHM5pmae78Q72pVC1CKjtCV88xunqPY0bWLqY818Jz25jRzBONQEkZUCxcomfgGQzznkRCCIFuJOjqOdbR46rZKNCo7rzaTUXDCWyW3RliSpIldwY7aJPXBymutGOJDYySPfkKWjRxd7Ious7Mz/71U6JxegAykFRmf708WYSukji++arSwaYULLJPPUAlKFIM5telxu6HKaJ0K4MYmMRFkqas4eNRlUUySh7Lb2IQYUnObDpONSjSlk3kSsJPFwY+HhFiREmgPExMHkjaU8tb0mrtJfyW9fBmrEIgNBVnep725ZsAND85h1sohtGxu9EJKcNMhRr628i7aTLX3ZSce4UifqVO5G4U6Msrm05HOu49jVYgQQvn51frWFduIW0H68J13KVNHkBSrpJFeTcltRXSIUTYHuZpicSvQO/pwujfecNgKSXtKzfC9OavGVzfYr708GKJVKyPTGKImeUv93wOXrFE85OzGAN9iOjOjBtFxMQcHab15cXwmnnKIGXA/OQnJFIDm7YvMcwEfUMvkes5hmPXsK0Kjl0nCLyVxsBRzEgaw0ygm0k0LfKARCGgUhynWLjC8MFvriMkO0WzUWBp4SKRWG5dldldRKI5hg9+i57+Z6lXZ2jW53HsOv6KL52qmZiRNNF4N4lUP2Y0i2HE11mweK7F7MRHeK7FyKFv77nYfbto1uaplSfJ951c83fDTKAbsXXfZUi2ruB1KJ7ZKjxcfFyEUAhkwLBxFE2Y+PLeuR3rG6Z++3J4T5XQmr9D5tiLKFvUsN6Pp9I5fCvQlQiGEqXlVVc0PnvbI0zPJ4kMbu7aG+AzG4xTkUv0KSOMase56X2Fi9NxLkPKYRqywkxwiz51mBhJJJKaLJNR8qSVHD4eLbl5WbovvQdGF1SCIt3aIPvUMRqySjF4uDD5sUPy8K9IStyZAlpvF+7sItLzUSLGCnlZH7GUtkPgeuj93SAl+kA37vQmbtNS0r52m8RbL+HOLeIVtkAeH9imX66F2601cecWUeLRTc0ahaGjD/fTvjmBMdiH37QIWp1vEtL1EIaOkoyjJONo3Vm49hQxJ1XFGNu/K0+hoN6g8cmXT0XfOSEU4maeqJlFUw2CwMfxWrTsZWz3XuucZLSPRLQbgYJEUrcWaFhrzXIFCrFIFzEzR2/mBBE9RXfmKClnAADHa1FpTOEFNvFInnikm2pzBttde73rapRMYgjXa1NpdjDtlWB9dZnYi6eJHj24s/1WFPR9faiZFN7S0xn1s1pFJm78hLFjv0civW9D8iSEghlJYZhJEql9hPeJFRO1FR+18H1rryMpJc3aPFM3/wbPbeONvLpnxEkGHgvTZ0hnR8jmD28wb4GmRYinBoin+sM0lby/HTkr/URDPWunSJLntVmY+YLZiQ+JxHK4TuuJEyfPt1kuXCbXc3RNKlIIpeN32G6VqG2Q3tsq7KDFrH0DW1oU3An6jYP40mXBubc4k35onOw7baL5AYQadnRQjO0vPn4tiVNK72EkcRpDiXK58ksECmmjl4I1viqU3i3ih/pRY51XCnehoRMViZXIU4FB9eCqeNuTDjoGMZK42Lg4qELDky46OhnRjSPDh6clG7jSIaf0UQimCVb2QUFFQV0VoAcb7Jsg9LPQhY6GTpIMtrAoy8KmkatHBt8PI0ArkBKk560rNVfzGeKvPIN59ACKriGEoPnpV7TOXiH57VfJ/OF3kF6AOzVH44Mvw4qzuxGrQCJdj6DWpH3xBomvv4hfbYQrDquNls8Qe/EkkaMHwlQh0Pz0K7zFUkh2ohG8xRKBtckqR7ISwQqPofR98BTcuUWsSzdJfONlCCRBs0XtZx+jZpLEXj6FeWgEvTeHmk3T/OhcSOh6u8j+3e8gTIPG+2eQbXtlzADEvWPlLZVwC8ukv/cWQbOFtB2k46IP9hJ7+TSRo6MEw/2oqQSND7987O7aQlXCqq4dpgOklNi3p7DHJ594WlJVdAbzL9KXPY6pp1CEikTieA0mCp+siSzFI130Zk8QNdJEjQx3Ch+tI06qatCbOUouuZ94JI+mRujPnQx9fYCGtUijvYjn2ET0FIcHvsX08pdMLn7K/ew8HR/k2NDvMLX0RWfiBPjNJq0vLhA5NLpjewK9v+epJk4AtfIUd67/mNGjv7MiON7Eg271nBQPjdJKGdCsL3Dnxo+pV2cQiobntjZs97ITOO0qU7d+gWGmiCV6Nxw3/LvYdN/Wz1/ieRbzk58yO/EhnmuFrVycxqrdwRODlNQr0zRrCyuEd+O5SHlXUL677IckwJE2AT71oES9/flKIdY9NGfvIFQVp14mMXiQvtffwWvVcevbry59bMTJiIebcpqdy62NmEZ6IIYR1/DaPrWChVXpXMbeFz1EwbpDX3QMgYovHbrMQRbbd3YsLlsDRRA/MoAa3Zw4CRSySg8JkSbAZz6YXE2RNWWNqiyyXztGXZaZ9m8y70/Qqw6RVrqoyCWQ4QkV4FOTJXrFMJUgPIEMIgyooyRFFhWNg+pp5oIJ7FXdklzdDkCfMsy0f4uGrBIVcfrUYSyvgcXjbzhrXbxB++o40ln5/jyP+t98vIZMAfjVBs1PvqJ15hIQEpOg1kC6HrV3P0BNxABJ0LIhCKh8/6erkRrnzgzuwjJBvUnr8wu0r4WVE9L1QpLm+R3HRgiEruEtFnGm5jf1DfKrdcp/8ZPVyEjz0/MIIVYr+tqXbyE0Fem4BI2Q5DTeO0Pzgy9XSVdgtZGuS+vcVdzpeaTr49fufSd3x7yLoNEK9z0eDVuq+MFqtK3x/hmaH50Nx/a8zUnfI4KaTqHld94/TbZt2ldvrok2qakkSiwKuoZfqiCDADUdNl31K9WQGEhJYLVRkglku40wDNRkAum6eKXKw1PAD0AIhYHcMwz3vEylMcOt+few7AqqohOPdFFvrY1aLlavU6rfIRXfx9HB73Yc0/Ntppe/ZK54gZHer9GXPcG16R/TbIfXtJQ+7oq5arkxRdMukk8doFC5QtsJNS6qYtCVHEUCxdrNjXfAD7BvT+HOL2IM7qxPoJKIo3V3hST2vuOndWcJWm2CZgfdh6aiZdN4xfKafpaPDpJK6Ta3Lv+Q4bHfWvEA2l3zY993qZZuM3nzb2hUZ0IfN9+h3aqQyq7a/e8JauVJxq/8JcMHv0UqM4Ki7v6RK2VA26owO/EhhZkvVpse363mS2aGeNL5/bZVprR8g3iqr2P7nLvwXIvK8s3tmQ53QFRJ0q+PMelcxlupQk9rPcSUJDNO6ERuLc2G/lmBz9IXv0SNxPBtC7+9/cj3YyFOQhGc/N4QvUfTXP7RDFNnl9ekP2I5kxf++ACn3hki1RfFqjiMf1Tgs392i6Vb63uG6apJxZkjH7nrZ7I9tv4w6NkE0eH8Q9MRLjZTfmd7eA+XSX+tSWFJLlDy1otKVTRiIkkxWMAnzMk6tJnw17t11+Vaoem4fxEDE0NEaQY1WrKGgYlA4Ukp8WXbXrfljpER19tQmC2tNt4DxCC4j3BI10OupFKk63U083twbGEaxF4+jnlwBGdmAXd+cd1n1m4wIKjeS6PIVnt1v2SHuUvbwe/kWSUhsGy8pfUiYWm11x2rTvsO4NtP3ndH39eHEl3fY2qr8Gt1rMtrr5n4ay8SNFtEjh6k/suP8Gt1zJFBtHwOZ2aeoN1G68rROnuRxOsv0friPNGTR5Gui5rL0PzsHF5haVvziOgp+rInsJ0aN2Z/uiZdVrfWX6NB4OEEHo7X2mRxJnG9Fi4Cz7eRMsBxG+tScQCB9FgoX+Lo4HdJx/fRdmqAxNQT5NOHKNVuYzmbC4a9xSXaN++gD/TuSKgvhMDY10dLU5HOvX1KfudN2lduYZ1dr/9T00mS33mDyvd/inxcxF2Gpeo3Ln6f3sEX6N33ArFE97bv+VJK2q0ihdlzFGbOrBNkt5qLyMDftLPCtqe+oqFqW1UGhr9G98BpDDO1Y+LnuRbFxassTH9OrTy57lysVabp3ffinu7DTiADj9LiVXoHnt3QmV1KSdsqU17eZIGwRaho6IpJcF+6z5ceMSW1+nt83yh2ZRmvUcVvN/HtFrHeYRxFwWs+vDfp/XgsxCmeMxl7s5eRl7pZGq8z9eW9sJwWUXn274zw8t8fQzNU2nUXLaJy4neGMBM6P/nvz9NYWnuBNtwiQ/GTxLQU3ZFholqahlvam2gTYA5kMfsfj4V9QqTZpxwkwGMhWG8bvxU4OCwHcwyqYwgUfOlR8Cc7VtX9hwzpebgzhTBdN78U+i49BlTf/VVov/DvAfS+bsQuGvo6U7P45QcIgZQoERP71gRuYQm9twcRMVFiEbRchtZXlzCHB4kcOUDQaCKDAPPAMM7sAoppoMS378WjazGikRwLpUs43pPRWlUa01hOhe70EYq123h+m2xiBE01WK6N4z3EGVs6Ls74JMHLz6Lu4BgA6P29CE1bY0ArCHV5ajYdOt83WuAHCF1DaBqtLy6tfb+uhaasKylDadmPxGLCdRrMTnxIeekGma4xunqPE413o6r6ShcA9b4UskQGAYH0CDwnjIAsXqO0dJ1WY7Fjr7tmfQGrVUJ9wNXateu79k9rt5aZvPkzlgsX6eo9TjZ/BMNMoqjaageDe1Gi0K8t7GjghaTdrlMpjlNeukGjNovrdD5nG9UZrNbyGkG67zu7jujsBI3aHPXq7EqlXyciJ6kUb264L9uBj4eKTkLN0ArqCARpLY8t7z0D40OHCDwPrxHef4RQSIwcoTF98+kkTonuCLmRBO26y9zFtdVHXSMJnvn9/eimyvVfzHPx/5siOxTn5b93kLHXexl8Nse1n60t855rXqMnOkbbb5I0uqk5iyxYt/ZG36QIokNd6F3Jh793D9CUNcb9CwQEBOz0QS4pBFMsB3PcM9f0+bXxfnhc8APc2U2E448I3vz2oiFPLYRAy3etasZ2Ant8MtR13TcmgN9o4dfqCF3HGBpA+j5+vbnybwO/Xif2/ClqP36PoNnCmVvALSzhzi7gLjwkctgBilBRFR3Pbz+xy8R26yxWrrEv/xzxSJ56a4Hu9GEa7WWqrTm2MjF7aoagae2YOGn53PrvU1WJnjqMeWg/imnQ/OQc7cu30LpzJL/zOmouzfL//v8iW37oS/a1Z9GH+1GTcbTePPWffUTz43M7ms/DIAOfZn2eVqNAYeZLzFiWRKqfSLQLw4yHaTwEvu/guS3aVplmfYFWYxHfczYVIJeXrlMrTazLcskgwPd33/LK921q5Uka1Vlmbn9ALNFDLNFLJJpBN2KrbUgCP2z/5DgN7HYVq7mE1VzG9+xVvdxGaNYLnP/kT9fugwR/j5oibwcy8KiWbtPVe6wjcfI9m2Jh5z1Q70c7aFLzlxgxT+IGNorQkARMtC8iFJX44BiRrj4IfIxUGAFTDBMjkycYv7Tt7T0W4mQmdeJdEWoLLZbH14atD32jn1RvlOJEgw/+9CrL43X0iEo8Z/LqPzjM8Iv5dcTJkw6zrSvMtu4PJe9NTleNGsQO9qHsshP5ViGReOz+pN6rcZ5KiFDseTe8LSHUV2x3FdhpHCkfk1ZjE6zMCyEevN/d28cnPUdAiUZQ08kd+zcFjoszM79WT9OVRXoe0nEw9w8BYF29id6dw1su4i2XwfNp37yDX2vgLReRrkfriwvofT1hlHmb+iYIHySe18bUUyt9w3a0S1vAxvelQPoU67fpzRyjJ30YpCRm5ihUrq5qnh4Gb6mEX6mide9MEKzEoijJBP59KWkE+LUGtb/6JZHjY8S/9gz2nRncuUVqP/qAzB+8jUAgASWZwDyyn/rffIL0fVK/83XsmxPbnsd2IWWA51l4NYvmHjX8vTvmo0YQeAROg2qpsdJiZC8hH8s+bBWRaA7R4RqQUlKrTGHtUhR+FwE+M84NKt4SESUeisT9Io5sIxQV1Yyi6CFRUnQjlE/4HtUb57Er21/YPhbipBkKRlSlVXZw2/cYcyxnMPZGL0LA1Z/OUpkJQ3Zu22f+SgXPDegaWR/5OZj6Gjdr9ypRFKExEj/NZOP8tqM2wtBQo0b4EzeJjvaQPLVRLyiBnokTO7hzDxsIp+1WWrjFzW0HtgrF1DB6MyjG7smeU2zgVZqPbBWumBpmXxah33v4Si/AWazht9au6oSuEtmXI/3CAeLH9mHkk4DArTSxbheonZ+kdXsRv775jUKJ6ESGukg/N0r8yAB6VxKhCLx6m/ZsifrlKZrX53GWao+nyawi0FIx9FyC6FAX0f3dmH0ZtEwcNWqEgnPPx287eOUmdqGKNbFIe7oYfj9164k0w1Xi0V31yfOWiviVtSHxoG2DBC2XQXoe3uIy3lIR74Eokl8s4xfLa8baTTWY7daptubIJUdIxwepNmcJpAcINMUIWxYFO20iKwmkF5bJ6wka7Y0jYvXWApXmLJnEMIqiI4RgqXqDLV+Avo8zPYd5aHRnU1UVtGwad+Y++xLfx5mcI2i1cabmib38DGoijtfBQiMkvS56fzeB4xE0LaSzeVTkN3j0iHTFcGptAvfJNc2ORLNk8mPQIdoUaqCu4XYwLN0pJAH1oEg9WHtfkL5P9eZ5hKrRXp7DrqyQNSmR/s7O1cdWVSclBN7aL3HkpW6yQ3Gq8xYTny/h2fded1oeruVjJtZPMaImVlY8YQWELkziei5cuT/kfqMmIxjdKYx8CrMnjdmfwejLEBnIYvSmUU19w1JroQi6vnWKrm+d2vb+3w8ZSBb/3Rmm/+znuxrnLiLD3Yz+1+8QHd59C4GZ/+s9Cj/4fNcO4hshMpRn7J/8PmbPPb8Ur9Zi8n/7KeVf3QvbqnGT/Heeped3n8PoW++Gm3n5ID3/0UuUP75O4QdnsG53TsHpXQl63nmBrm+dQu9KrBsn9dx+ur/3HK2b8xT+6ksqn9wgsHbXdX0zREfypF4YI/nMCImjA2jJrRMRr27RvDZH7fwE1S/GaU893jJyJRpFie6COJXKBPZachw0mtR/8eEGn3h0cLwmM8tfcmTwOxwffofl2k3aTg1V0YnoKSrNGeZK5wHQ1AjxSB5djZCI9qCpBolID93pw/iBi2WXsZy1wv2GtUggffb3vkY8kieQPp7fXtEu3SMggfQpVC7TlXqHvuwJyo1JGtb20sne4kqxzQ6C7kJVVisY7/vrvfSdooII01WdEDRbuIUikeMHcWYWaLx/Br+2NwvC32BnEKpg7E+eZeIHl2nObi1y+SiQ7T68oTVCq7kU9hncI13yVlC5fnbPLFAeC3FyLA+77hLLmmimimv5RFI6B17vxUzq3PlwdMPDAAAgAElEQVR0kaWba79gRREoqlhzXONaluHEaTJmPyez3+YuS1KFTtUtbCgOF5pK9/eeI35kAKMrgZqKoSUjaMkoQlefoiaJvz5QFQOhKGHYOdidnkqJmmuIlBLR6fuDV+j+vRfR4htXcKlRg663jmPkU8z+s/doXp9bMw29K8m+//Qtcm8eRTE3FjQLRRA73M/QP/wmRleCxb8+S9DaQ/KkCIzuFLm3jpN9/QjRkW4UYwdutcko6ZfGSJ4eJvf1Y5Tev0rpg6u4xcZj8UQSERMlsouKumr9ngv7U4ByfZJr0+/Slz25IsqOEAQujfYybfdeZCxmZtnf8yoRI4Wq6EgZkIr1E4/kCAKf+dJFZopn19x/SvUJJhY+oi93kqHulwgCl2JjglJ9Yt08aq0FWnaJXGKUxco1gm0+TLxShR0zJ0VBST7QO0xTiRzejzM5R/T4wTAdWK0joiZKMobQdZRUYpVMKYkYSjKGPtAT+ov96gxe4en1hnoUGPm9YyAhMZIlcH1mfnqD+kSZzNFuMkd7QEpSozkWPpli6csZMofy9L05ihbRKZ6fo/DpFJljPaQPdqEnTMxMlPkP77B8bhahKPS/NUruZB9uzWbu/dvUxoukD+XJnerDdzwyB/Msfj5N5foSo39wir7XRoh2J3AbNne+f5H4vjQIWPhwAhlIBn5rDL/tUfhkZwVJD4NuJMh0HUJV198vgsCnXpmh1Xy82s9oT9gJxFqcJT44RmrsBFZhhtqtCwTu9u5Lj4U4NYs25ZkmmcE4R745wPS5IsMv5jn4Ri92w+XGL+exG/eFzAREUjpGVMVu3tPttLwqE/WzGEqEmebFlWekxJMullfbUBwudJX8b58mur97xYn1N0Rpt8ik9xOJZvE9G9dtUarc2nFVo9AUjN40iqkj/YCub54k/zvPbUqa7n1WJXlyiIE/eYPJ//UnOIshAddSUfr/+LWHkqbVcYRAz8To+4NXcIoNSr/YvmBwo/mlnttP7995mcSxfQhD2/X5p5g6sUP9RIbzJJ8ZYeHffErz6uxa0fUjgDB0hLGzijopJX6ltqYa63FDKCqRVHdYBl1bQsqAcmOSWms+rGxa6T4QSB/fvzfPurXIlem/XrH4WA8/cNad+37gMFM8x0L58qow1g/cDUS6YRVV3SpQa82z3UWIV17xsdqRJYGCGlsbRXQmZpGuS+K15wlsm8Z7Z8APiD5/HPPwKNJ1SX7ra9jX7xC0LNR4lMpf/ARpO8RffZbo6SPUf/bxtufytEOoKkZXD26tQtBeKw9IjXWhxQzG/9V58s8OMPJ7x7n6f3xGpDvB4NuHuPnPz1K6uIDbcDCzUfb/7RMsnpnGWmgw8s4xnLpNpCvGvt8a49o//QLVUBn+3lEaUxVSYznSh/JM/PAKqQM5Rn//JFf+9FPMrhiD3z7EzT8/y8RfXsFtudgVi+kfXyd1IMftf3OR5mwVt97GzMXof3M/xfPz+I5P9wuDTP1ob4TZnRBL9Ky0yll/r/O9NqWlq8iHCN33GvF9Y9iVJfRkk8yR57EWp4n176e9PEd7aXs6ucdCnGrzLaa+WKb3aJq3/qvjlKeadI0miHdFuPXBAnc+WasB0AyV3P5Qh1Ir3DtBJQGWX2eqcZGys42WIiumh7tpSvobrIXrNYnIDNFoF4aRoFy9vXPiJARGTwo1YaLnwtSantp6lZBQFZLPjpB98yiF73+G0BQyrx8h943jWyJN900ENRml53vPUb8wibu8u5SDYmrk3jpB/x+9htGX3lPCLoRAjRikXxzD6Ekz9y9+RfWzW4+UPCmGvvOKOt8naLZ2JOTeMwhBLDuAkcixeO1D/JXeWH7gbKpnCo0rt6/FkNLH9R8u1E1EekhEupkvX1rxc9rmdmyXoG2jdpA1PBSKQDzgy9X45WcAPFgk3vrsAq3P1vbpiz57FITAK1UQqoqIRpD1J99K51FAicTIvPwGtfNnsKYn1rwmfUnpwjy18SKe5XLiH3+NSD6OENCcrrJ8fg7fColC7mQfiq6y+Nk0bt0mfaSb7LEe2stNquNFylcX8SyX/rcOkNyfJf/8IMn9WYa/dxQtopEazRHrT4Zjz9VYPjeH17pHyJ1am8DxcaoWdik8b6vjRfZ96yDR3gSKruJZLo2pR+MLJxSNbPdhDDPR8fVWY4la+dFEujaD0DRk4BPrG8GzGlRvfIUWSz29LVc8O+DLf32HrtEkwy/mGXq+C98NmL9c5pP/8wbt+tpVmJnUGH6+CxlI5i+tNw0sO+vZYVrvpeYuPpkWI08YgeNiL1QQqhL+aEp4E9OU+/6mhjfJPXp4G3qSaCSH59s0mgu79tAyezMY3Wly3zhOZKhrNesQeH4oNhUCxdQ2jBgKTSX39WOUP7yGEtHpeecFtPi9C0JKiXQ8Ai9A0ZQNIz9CCKL7u8m8coilH53bcQpMiep0f/c5+v/oNdRkZNPjLlcq+6TnI70gbPughM2EH5ZKFoogOpJn+B+/zYyhUfrg2qMTjqta2CBzB5C+/1ibyQpVJzdyGiMW+rHVFm7RXJ6kVZ5DNUJSrkcSpPcdo3jnLJnB49j1Iq3y3lRoPQyaGsHQYhhagtG+13C8JoXylRWB+jYRBDuP5AmBMHeefrVvz6AP95P949+FIMBbKmOdv/bwDz5GCFVFjcXxmg3UWAICH79tocbiBFYLJRpDjUQJXBevXoPARxgmim4gFIFiRvCajbWZUCHQEikC11mNPt1NXUo/QEpQVkwoPcslsO9pRhVNCd9zt/G366MkzLBZub/S0FNKpC9RNAVVVyhfLrD4Wdh+x3c8mnN1Il0xfMvFtx84Zzr0BHUqFpVri3Q9M4AMAqo3lvCsR3M9RuNd5HtPdnxNBj7LCxfw3Mdf+deanyRz5HmEqlK+/PnK9yWR20zTwWMUh9fmW7z7353j0Ft9pPpjtEo24x8XKE+tr+DSIyq1eYsri7Pc+XRredCB+BHq1SKyw41Hej7VM7ewxh8uulQiOsnTw6ix9TcTKSXtmSLW+PZ9Y9aMg6Q5vt6deKdoTy5z+3/4AWo8gho3w38T5ur/a3f/FjPQ0jHSL449tJ3Mw2C7deqNOUwjRSoxSKW63sV2OzC6U6Se3U/29SMIVSFwPZo35mlcmcEpVBGqQvRAL+kXD2B08NgSQhAZyJF6fhSzL0NkMPTqkFLilhrUL05j3S7g1S20dIzkySESJ4Y6HgclapB6bj/lj67hVbYfaRC6Stc3Tz6UNAWej7NUw5pYwp6v4JYb+E0b6QcouoqWimF0JzH35Yjt79lwLCFEqOf6T97CrbSon594JFWRQlF2lA4CwA/CXoOPCUIIjFiG+uI4UkoS3cNYD5AioWjo0bBSU4skcNuPz6S0J3OE0d43MLQYLafMROHjjo7lW8JuidNuGjbXGtT+6r37vRsfi95uO9DSWbKvvEHp4/foevPb2EsFmjeukHnpdZo3rpA4fjo8t4WgceMKzRtXiI2MkTr9As7SAmoiRXP8GvbcdDigEJh9+8i89Br1i2dp3RlHKILMkR7mP5ggfTBP4Hi0Sy0SI5l1x6O12EDRVZIHcrTm66TGuiien0doguRojmh3AikletKgOVujcrNIajRLc65G4AVocQO/fd/3/cDhDlyfwPOJdMVoLdTDoqxAUvh0ipP/xWs0pissn5tbIWl7C6Fo9O57gUi0s4F0q7lIefnmnplVbwfN6Zt4rQbIALu8iFA16rcv36uy2wYea5PfZtHmq3/78BBdvdDmb/6nC8iAVY2TKnSG46coO3PkzREMdW1ePq7lNpRGSsdj7l98AMrDoy1Gd4qD/+QPOhInJNTO3mH2n//qoeM8DHtdtRbYHoHdwC1tcvMXYPRmiB/u3zVxkkGAomhU69O0WssdnXi3AzWi0/PO82iZGIHjsfyT8xR+eAZ7obLqYaREDRqXDtP/J29g9q+vtFNiBvlvn0LPJsKSfimx7iwx9+cfUL84hd9YqWYSUPzFJfp+/2Xy330ONbI2nSeEIHagFz2X3D5xUkSoafr9VzoSHSkl0g9ozxQp/fIy9YtT2PMVvFqrI9kRuorelSQ60k3u68dIvzyGGjPXjSuEwOhN0f9Hr+GWGrSn9sYf5cF928o11AkyCMIego8TQqCoRujALOXq74qqoaj6SnsNDdWIoEeStMXuFkTbQak+geu3URSNlrVMo71zoayUclfHdtcSBil3TdSj+8dIv/rG5m8KAoo/exe3tL1zW7oOgeOgxZNh38MgQEumw9Tt2GGc5UWqX31OpG+Q9HMvY8/PIFQFJRKhfuU8bq0SutsbEZASPddNpG+A5vUrtKbucHfntYTBsf/8ZbSozuzPb+HWbQI3wGuv/W7aS03mP7jD8O8cRSgCq9Bg+dws+Rf2If2A/X/7BEYqQvHcHM25GnbFIpKPcfwffY3AC6NFU+9eC8fuEDXyLJfC59MMffcIfa+PcvvfXqQ1V6O91KRdbIEQtOa2nxLeCtLZ/eR7TyCU9dQi8D2WF67QbpU6fHIHEKDHDaQfELjBuqr9TrCL9yQ+0nOxFmd2tOndEyehYAwM4C4W9iwU77sBVnXtQQikT9mZo+03iOtZ5lrXuf9qNWKbl0kH7a3NLbCcTez1w3TKnlZcPU7IkETuRTQiGs0Ri4Vl1ol4LwuFr3bhewMIgZ4LK3Uqn91k/i8+WacxCiyH0vtX0HMJ+v/49XXkTwhB/PDAajrPKdaZ/b/fp/rF+FoDSQnucp3CvztD7FA/yRND66ZjdKcwe1JYdwrbOl5mf5b+P3wNs3c9sZNShvvwwVUW//IL2jOlhxJo6fo4CxWcQoXmtVkal6bp/buvhOM/QGKEopA4MUj3d59l9p//au9tFeSK6ehO0r1PIBIhZUCy9wC+06a2cBNFM0j2HkCPJUn0jlKdvYbTqJA/8AIy8PDsx6fNaTtVzEw30UQX9dLlx7bdjngKimW0TJbkyWc2fU/geZQ/eh+2+dwNHIeg3cbo7sUtl0AIjO4e/FYTI99D8+ZVpOPglELyqiXTgMAtl3DLRaS/co0aEYSukzr9PHZhnubtG7DymvQDlr6YYenMDDIIcGqh7cby2VlKlxaQ991/Atdn/le3WT43h1AFXtNZ1SjVbpe484NLBK6PW7MJXB+n6nP7Ly6gxcP7nddykb6keGGeyvWlNWOHk4G5X46z/MUMKAKnYoEANarjWS7FC/P47b1fxERiXQwe+HpHCwIpJa3mEsvzFx7qfr5VRPMxXvlvv0F7uUX5VpHa7TJW0cKutnGqbXz70bXU2jVxErpG+tXXKb77149UwyAJqDhhKHu+dYOl9p01ryf03K77Cf0GW0fETFOu3ME0kmHJ6R7dfJ1ig+WfnN9QmC29gNJ7V+j6rZNhleQDuNvcUvoBpfcuU78wuaHrtltqUPn4RkfiJFQFczCH+EJBbmElE35IkH/7FPEj/etIzd05Lf34K+b/n4/wm9ts4SDBq7ZY/ul5vLrF0D/6dseUpaKpZL9+jMqnN6hfmNreNh6Gnbi1ryDU2T3WADeB51Cbu06rNLv6t4Ur7615z/L45491TvfDbpUJvN3fM4Wi7KoNzhMV7N+dgtXELiygmCZKJIJimHtWzBM4Dn7bwuwboHXnFnomh5HNY81MoGdyCD0kJEJRQFXvmSIG/jpSIhQV684ttGwX0ZEDtMavr14Tvu3RXl5Lvn3bW69BAgI3WBVu3w/pB9hlC/+BSJLXctcIwMP98gmczuQgcPwwukR47XW/OEjvqyP4lkvx3N7r+AwzyfDYb5HNH+4oJ5DSZ2n+As3G3klUvJZH4cwsqf0Zhr4xSuyPT+G3PRpzdRqzNeqTVWqTFUpXl1aJ7F5h93eyQOJVq6jRKIHVuu8htfkNVlEFqq4gFIHvBvjbcDgt2jOoYm16Zb51cxe93n6D7aLRLNCVO4SpJylVxvdkFSGlxBovhH5Mm8Cttqidn+hInO7CLlSpfH5r00ij9AJa4wV8y+mYujT7MqAqsEXiFD/cT/b1o+FnHtyWH1D+8BqFH5zZPml6YJzKZzeJDOYY+I/f6KhP0dMx8m+fpnF1dk9TwjIIBa1C3YEmRtkacVIMlfyrB2jcXqY1vb4wZKsIPJfyxHl8b2fHWtVMcgMnSeaGQQgqC9dpVGbI9R/HiGaQ0sf3bDQ9ysL4J2hGlPzQc+hmDNuqsTR5Btdu0j38PIncMEKoGGaCqas/pVmeJbfvJLmBE9SXJ6iXQvlC9/Dz6JEkRiSJoplUFq5RmruMGcvQPfwCeiSFGcvQqhVYuPUhTnsl3SLEzhsvSx65jcVW0LpzG+df/tOwqEUNmwlrqTSZV98kuv/A7gaXAb7VRE8fwS0to0ZjqMkU9uICQtWIHzyCdGwiA0N41TJOcRk909VxqMCxaU3ehslx0s+9gt+oYy/MMv5vLq4jNtvF0pkZShcXOhKt3UAGAfWJMm7NplWob3F8sWIovfG5IYSCpkeJpwbYN/IamfyhztuXklp5kqW5r3a4B53hNh2u/fkFtJiOkTTQEwbx/iS5I3nSB3N0P9OHkTQ58z9+wNLZOTzbD9sr7UH7ql0TJxkEBLZN9u3vYk9PrSrU/WaT5qUL694fSen0Hc/QPZYi0R1BURUmPltk/MN7wm1VVzDiGoEvcRruukXuYPw4phoHQKz81/KqTDcv7U2j39/goahU7+C4DXQtitUubdo8c6uQjhcSmYcQC+n5NK/Ph9VnHVc3KyL+2w/XjXi1Fs5SraPrupFLIBRlS5k6JaKTffMYZu962wEpJe3pIos//GJP2uxI16f43hUyrx4mfqi/w2QE8aP7iB3so3l1dv3rO92u54HnwQ68nISqbOgBZeRiJEbzCFXQGF8m0p1AixtEepM0J0s45RaJA3n0VIT2Qp3WTJnEgTxmPo7XsGncKRLpTWFkwnR94/Yybq2N2975sQ4Cn2Z1jtrybSKJPD0jL2FbFVJdo5QWrtC17zTVpVtIzSSRG6JenKQ4cx7XadE7+jLZ/hMsjH/E4uQXLE2dI9k1Qn7wGdx2Ayl9SnOXUFSdWLLn3nGIpknlR5m89C6aEaPvwKvUlu+Q7TuOlAHTV37MviPfwqov4ty/b4qCMHeqWZRP1FtrdRaOjbu89npVojESx0/vyfjO4gLW1G28Rp32/CxqNIZXq9CohSX5iWOn8WoVKp9/hHQd3GoZa3aK+wMA0nOxpifwGjXcSgklEkXP5LAL81gLu7+unWobHoXRtwx1Ve2lraeio/E82fxBPNfC89oEgY9cMToWQkXTI0SiWVLZ/aRzo2h6NPRB63A/dtpVZu98QNva+UJoI4TaRdATJvG+BMmhNPGBJNGuGEhJfbqK9ALG3h7h1k8nGXi+l9kz8wTe7sjTnsTOg2YDZ34OoeurK5/VvPB9yI8lefUfHGbk5W6S3ZGwX5jt41reGuKU7Ivyjf/yOIEf8Mv/+Qr1wtrSxaZbwvHDMKQiVLLmABGts2fEb/BokEmPkk4OIZHEY73MF77cddQpcDysqS2IZAOJs1TFbzkdTTKl59OeWsZvru+t9SB8y8GrdhaAa6nYlu0bzL4M6RdGO0aApOtTOXOL1q29C1O7y3Uqn90kNtqzbpt3fbESJ4bC6N0eNQiWrrvzyjhVQ4lGVlax981HQN83j9BebuI1bCSgxgxkIIkOpDHzCeq3lsi/sp/WTIXU4V5m/vICiqGi6Cq93ziM17xEz9cPYi81UEwVIxNj8YPde1ppRoxs3zF0M0EknkMIBc9r024UV35KyECiamFaKZEbwohmiKX7aFXD71oGPpoZJdN7lKWps9grwlgZ+B0NAGvFCaxaYcX24VU0Ix6mkFZKzAPfJfAc7n+gKxETZafEKQgI2g+/Tp4MOtTV7xDO8iLOcij+d9oWzuI9kXD98lfUL6+NhtjzM9jza4XDgd2mdv7M6u/NG1f49xXxZB8Hjv4uCIHv2avESSJRFBVVM9G2INHwvDbz059TLt5ir0t99aTBsb/3DKnRDJFsFNVUaS01qY6XWfh8luZcneZCHen5HH77GSZ+NcPYd0YoXFrGs+6/9uS2idQepOp86l+dRTFM1Fg07ElntQictcLU9ECMb/83pxh5uRvfCagtWBgxDT26/kHjWT6J7ggDp7LcfH+Bqz9Zu2pettdqN4r2NKPJFxBCeSJljptCCPR8Hq9S2ZUGTInHEbqOX9m+aZmaTkMQ4Nf3rodUPNZDsXwD26kjZbAnqbrA8bDnt7Z/vuXi1VodiVNge1jTW2v5EDjehhEuNW6ygVn0WiiC+JEBIoOdw/tetUX5w+t7mhKRnk/zyixOsYHZm173utBUEkf6KSajGxLDbW/TcQkcl50UrwtFoKZTCENf03ZFMTSMrjjzP7uGW7VQIhp+y6V6ZR4kpI73ER/MkDzYTeCFqUI9FSF1pBcpJdF9GRRTQ/oB9ZuLoZ3IwW7EilfOTpHMDdMz8hKFic9ASqLJbkCEDw8ZrERYV8YXgv6Db+BYNZanz5HzT6JqIZFRNZOekRexagVqxYmHbtd322HzlBUxvRCC2vIdho69jR5JYrfKVBZvrvmMmkvv3CZCSgLraSVOv8GThqJoO2oPBRAEHkvzF5ib/ITA3/uoph7TGfnOQYQimHl/gsWzc7QWm7QWGtiV9mpKTo9quC2P5/6zk+TGMpz6k6Nrmh87DYdrPxzH30Av1gl7EnHSMxmSL72ClsqAAK9covb5Z3iVMDSnmSrP/+Eowy/mKU83Of+DSeYuljj5zhCn3hleN57ddJm/XGbouS72nc6tI05RNYUqVqYuBHE1gyZ2mON/xBC6Tua7v03lZ3+Du7C95p33I3r4EHpfP5V33932Z5MvvYjfalH/9LMdb389ArKZA7TbFXzfoVQZ33W6Tro+bmlr5C6wXfx6GzpkqqTrhzYGW9mm5+O3O1efKYbesbP3+vdppJ4bXRWmP4jWnUXaM3vfu8uaXsYt1TsTJyGIjvaipfaOOAVtG7mL6ISaTiIMYw1xCmyP9kKNnm8cwm86VC7NrZgDhiF4JDSny9RvLmEvN3Ar1v/P3psH15We6X2/s979XlzsIABiIQlu4N5s9q6WulsttZaZaMZLYmcytjMuZ3EqiaucpJJUxUnF5apkKrGT8pQ9jqccW55xRh6PpJHUUm/qvdncCS4giX3fLu5+z36+/HFAgGgAxMqlNXyqJDZwz/3Owb3nfN/7ve/zPg++7RFpSlHsm8UpBhOkHFKpPrUbSZEpD8/jb5PbpYViCN/FLM6SrO1A1ddWspckiVCkitz0bVy7QiRei20WkCSFqoYDKFqEqcGz9+WL3A+SJCMrGt6CaGAomqaSN1lsg6+pZks+dQT8Jq/48PSrnuDPBzzPYW6qh+E7b+M6OzP/fBF20ebm/3uZ1J5qUp1pGk41YRdtCkM5CsM58oNZCoNBp921P+ql8Vgd9d01mFlzGafaqTibbizbfledqhI/dRo3n6fc0xP4zLV3kDj5FNl33wKCbFPb03XYJZePfu8mve9O4DuCzufqVx3TtX3mR8ogQfXulSW41thhIkrg6C0Q+HhMGX34O8CzeRCQkJDDYZSqqkBTpGIEu0lVRY5GQZLwTROx4BwvLXSWgMAvV1ZotEiahhQK4Vcqgb5INDDeFLaNb3xhbARshcy7DvKFUaKRGoTwEGzP5PcuvLKFv1FipOfjr1E2Eq53fz2rZeOItbvmZAlJWX9BUpMRonsa1ny9dHNsx3W7AJxcGSdXWZPrpdclUatisMHs23oQFXNb2QklXYUc0vG/EBtPvXuLcH0CJAmnYDLzUR9uyQIJ7PkKdt7Aq9xCjem4hoM9X2b8p9eQFJli/yx2toJXtiiP5jAm85jTxW2XJ0u5CRI17ew+/A3M0hxGcSaQLLArCN/DtQ18z1koYzjMT96gbvdJXNvAMnK4toGihqhq2IceSdF+5FsAzAydwzJy1LWeIF7ThqqGaet+g9mRi3iOuUBmD0pUjlUGSSLdeJD5iWuY5XlCkRQN7acZv/VLbDMgxGj1tVuNm4JMdO7BaPo8wZ8/COFjWyWmx84zMfwptvXg7i234tD3726ihFXC6TChdITE7hTVB+poPNNCx7e68B2fy//XZ8xcnKQ4WSbWEKX3h3149jbL+Nu+ellGTaUofPwhXjkgn7nFAtWvfXPxkHhtiPTuGPnxCnc+mMJ37j+pCU9gFmwQEEmtrN33F8/fY7gpguBJuDwQueQdgBQJEz95ElQVfI/CR5/gZjLEThwn1N6OJEm4mQyFTz4FSSJx5gxqbQ2SJGEODFC6cHFhJIGkacRPnUJJJih89DFqOk386dNIioJwHMoXL2FPTRE7cZzw3r34lTJafQOVazurFVMsTVJaaC0VO8RFcIvGhiP/QFBx9WBEuN6S2OV64whxX4uSjbRER9rqUBOr+x0J38cYml2V87dteAI3Ww6ufxVulazKhOqSlL7IK9oi/IqxrcBJa6hFTsRhbrkQj1u0KBWXyqXePTpp3kKnkjG5nDVbGVkimsq6Sml4nlL/HNbszmRPrHKGkes/R5KVBS6ShO+7GKVZfN/FLGcCsqw0CgsWGdmpm8F/LpStfd9l6OqPF01+ATwvMASeGvgUBs8uJNUEvmtjlBaEHYXA9xyGr/00IOHGa5gduYBVyRKv3k28pm3pvlRVtJZV0q4bhPA8vOzmyv+SoqJWV6MmU8ihQOTVt23cYiHQPbJ2tvX7gUCSUKJRlHgSJRZH1vVgDvV9hOfiGwZusRBYsOzUs6soqMkUaiKJHIkE3YOSFJxzQTLBK5fwSsUtC5rKkYCwrsTiSLoefDeOg29UcHJZvFLxgeipCSGwzByF7AgzExfJZwbwvAevdyhrMnpCR0+GCKVCqBEV4Qt8O7CvUiMqsiYTqQ5j5kxu/Mn2gybYEY6Tj2+YRA8exui7DZJM9MBBvH+q8JMAACAASURBVOJSpKmGFPSoSiVr4W5SlGq1hTSh1VBxCzi+SX2kg6RWz6Rxm5Kz8+WQnYAky1hjY1Su3yDx7LNEuw9j3OwlcvAA+Xffw8sXqHr9NcJ7OpFUFaUqSe6tt5EUhfTrX8caHV0YRyF27ChqXS3Fjz7GNw1iJ76KNTyMces2sePHiJ08gX/2cyIHD1D8+BPc+SzVv/bdNctIW4fYcV9Az7A3Hn8J1swqeKazZlC16lBrTSQSG9KnCrfUrKmj45WsQH38AcX0bikoVa16lQuiosgS7IC9gm+aeIUiwve3pLEjR8Jou+qxh0Z3dPL2bZfMZ0M7Nt7iuJ7NFxVO/IUge5GzcU/57a5p8L1YSw5h1d9/oZTnezaO71HOjdO090WE8JAkhfxM32JXnVZfE5RAt6ij5pvWyoyTLBPdt5/Y/kMAWGMjFC6dB0Cvqydx4jSRjj1o6WqUcARkGd8ycXM5rMlxCpfPYwz0PXTB041A0jRCTc1E9+wjtKsFtao6CGRCoUUTWOG4eJVS8PeMj1K80YM1NrJ1DTNVI9zWQezAIUKNTQuBTQxJ1Rb86TyEbeFVyriFAk5mFmN4iPKtG4HEzwYghyNEuw4QP3AYvaEJNZlEDgXNGMK2cUtFnMwslb7bFK9eCgKo+6BcnGR04APC0WrCkSq0UBxF0VEUHVlWEcLHcy1su4hRzlAuTFLIjVDMj+G5D4czp6dCdP+NU8SbE0TqYoRSIey8RX44R7Z3jpG3+ylPlqhMF+n6VifX//g2e15r49aP+5dxnLaC7csRuC7F82dJPv0M0YOHFlSZZyl8/tniMZ7t45oeWkRFktfXUVA0mVRTFCQoZVZOME2RLgZLl0jq9TSE91BwZmmOHOCO89ljqeXkGwb2xCR+uYwzOUnk4AG0ujqEaeFMzwSqtZNT6E1NQbliZhYvnw/S6KUSWkNQBtJ3NaHvaiL/7ru42SySqhLu6EBvbCB68CBSSMedz6LEokiSHIxTKGBPTT2Wk9gX4ZvO5q5zjUN9cxMB2A4g3Jxek0DpWQ56Q+rBZJwANRG5b5lGTUaCRradOJkQuHPzCNdF0rfWxRXa00757CXYRGC7Hciyhq7GMO0H4wT/oCGEx9TgWfRQII3huQ6OVVrMaultLSixtflX68GdzayQI5AkmfCuVqrOPA9CUK6qptJ3m1BzKzVfex29vgGU5ebTSiSKEomiNzQSae8k99lHFC6cfaw69kK7Wki/9DUiu9uRozEkdaXRtyTLoGookQh6TR2Rtg5ih7rJf/4J+fNnN5lNk9Abm0g//xLRPftR4vFVNdAkWQZNQ4nF0WvrER17CLXsxpoYxV4vcJIkQo1NpF96hejeLuTIyk5gKRxGD4fRamqJtO8hduAQ2Q/epTLQD/7qz6FRnmO0/11kRUOWNWRZAUlGkuRgfCEWmoI8PM/Gc60dkaTZDGRVJlwdoTCcY+StfvKDWay8FYiFGs5icBRK6lS1J4k3RNl1qoGxs5PLbHCELzBz1qYmyR0hh9tTU2TffYdQczPC97EmxvHLS5oRlZxFfrJCsjFC7Z4kM7fuL1YRqwmx54UGhA+T11dqPyiyhi9c6sMdzFrDzBj9HKz6Cju3QuwwJHmpZfxuOtixg06YhUyQrGmL6VlJVRczHZKqBto5oVCQDp+dJbxvH9b4BMJ1cAt58u/+Ent8gUAvBFr9AndswbhyNRXrxxHC2Rk7GN/xdjwbthYkVUZNx9b8jEN1Sfb8t7/+UK5lNWy1I2YtuNOzwUK71cCpYzdKIo6XfRCCNSuRTrTTVHOEawP/jsdzclgfvmthrpKhksIhwvs6kSKrl4k3Amdi+v5lIUlCTVWROHaS5Oln0WtqEb6PV8jjFvII1w3KQ+maoDQky2jVNVR/7XUkRSH3yYeIHVBH3wlImkakrQM1udRMITwPt1jAKxXxHQdJUVATSdRUVaDIrqrotfXUvPKNoMFmIfO2EYR3t1P3xq8Rbl3eACWEQFgWnmkiPBdZ01CisWXBqDUxhptf5xmRJMKtbdS98WuEmlsXs8C+6+LmsnjlEgiBEo2hpquRF7ix0T1dqKk0sz/5Uyq3b645vO+7C93SxprHPEqYGYNP/od3AFDCKomWJLHGOJ7lUZ4sYuXNwHHB8siPFjn5O0eoPVjNqd85uqzj1sxZXPj9nhWegvfDDnjVSYTb2kg89QwQ+FjFjx6ncPZTrLGgxJQbrzB6McPR7+7mzF/dw8f/7BaFSWNZGUSSQNEVYrUhnv6re9l1JE1pzmTos5XGmxU3z77kM0iSwmj5GqoUwhOPx8O5GuRohMjefeD7hDs7sMcnsCeniHZ3E+nqwisW0ZqaKH76GZIsETtxgnB7WzARaRrW2Djhzg6cmVkK779P6qsvEz9xnNL5C1hDw8SOHgkeQFXFMy18w8A3TSJd+3Dm5tCbWzDyj7/mSNAJtQOL2wNw/V4LSjSEEtYfC7+v1SBpCltnDq+EMzkTdMXFY1t6v5JOET64j/InG1+AtgJZUlEUnepEO6oSIaQnggVLeLiuuSiUK0sqsqzheRayoqHIWpBZ8yx84SJJMpoSxfWMZc0nEjKqGsL3XbxtGlxvFVpDHaE9bVsu0wkhsMcm1y1ra9U1VL3wVZRIBHt2hsKFzzGGB/CMCngekqaj19aRPPU00T1dAd0gHCb19HOYoyMYg31bur6dhj01QaX/NrGD3TiZDMZgH+boMG4+h28a+J6HJMtB2atjD8mnn0NLB75rcihM6qlnqdy6iVdZX0gy1NRMzdffINTcAgSftW+ZWGOjlG/dwJqeRNj2otG0HIkSqm8gvLsdrbqGSt9tfOv+2To1VUXN115fDJqE61IZ7Kd46Rz27EzwfgFyKIRWV09yocQqaxpadQ3pl76Gm5vHntl6t/ejhqRI1HY3sPd7B0m2VyHrCsIVWDmD4V/0M/xWH57pcfNP7pBsjnPir3dz+8+WSw94to+3yeadHemqix05TvnqJcyxQDAs3N5B/NiJxcDJLrtc+dNhdh1Jc/AbLaTb4gx8PEP9viSSLFHdFufQN1up7UzQ8Ww9dXuT+K7P5T8ZYm5gZS12rHydKr2JspvD9EqElChTlce0q873MXpv4RVLxE6dws1kqFy7hl+pUPj0U2Ld3Ui6TqXnGvZYYBAphcJEu7sDqfxPP8MrFPDyBZAV/AVZgdixoyjJJKULF4mfOE7imWcQlkW55xr23FxwzNEjqOk0xq1b2DMzj3+5TogduUSxA27tG4Uc0ZFCj6cUBrDjAZ2bzePlCqg16S29Xw6HiBzqonLpGuIB6gdVJ/fQWNNNVXw3iqzR3fE9AMrmHEOTHy2W7mpSe2io7mYyc5Xa1D7ikSBbOzZzjunsdSKharo7vsfQ5IfM5Hq5e2PFIvXsa3mVicxlpuevrXoNqcYwifowvusz01dCj6mkmwN18/mRCtG0TiVnE68JUc7aqLpMYXqDn4miENrThtqwUvF+o/CLJdzZzLpedfLCBs6emWb2J3+KOTSw5Oe2AHtmCmtygppXXid+5FhAIE9VkXr6WazxUXz70RPGfdsm98mHVPpuYwz241XKQbZtlUnHnprAnp+j7pu/hlqVRlrQ4wu1tlG5df9NqByOkDrzPJG2jsUskJudJ/vJB5R6Li8GnF+E0XcL6dxnKNHo+sGZJJE8dYZI514kWcZ3HUo9V5h/9+eBkfEX/iZragJzZIja179D4sgxJFkmsruN5KkzZN55E2F/CY3rJag71sjxv30GgFzfPFbeRI1oJFtTHPyt48i6wsCPerEKNrOFeQbfG2Xqyuyj5zghScjhEObYaNAeT0AmjB3qXnbY1I0cH/zjm7z8tw/TfLSalmMLYoECDr3ewqFvtCwea5UdLv1giEt/PIhjrLzBbN9gxhxYOt4rY3kPz9l8MxCuS/7d94Ifzp1b9po9Moo9MrriPcaNGxg3lj+c5sDS3+tmMktjAoUPP1oxhjU0hDU0tI0rf/h43OO61SAp8gMg3j++EK6LNTxGaE/blt4vyTKhPW2E2lowex9cJqJkTDEyXUJRdDQlTN/Y2wgEnmdjO0udd7KkkozuQpYU5gsDzOZuoalhylbQaGI7ZSpWhtr0fjKFfjzfBiRSsV2E9SSlytpq8Me+04xVdsmNG8wNlQlFFVKNEWp2x0g1lqlpizF1q8D+r9Rz6/0ZwnGVnjcn1xzvXiiJGNFTR7dlhOtMzmy8ZOp5FC5+jtF/e/XXhcCZn2P+w3cJtbahVdcgyTLhlt2EdrVgDPVv+Tp3DEJgTYxhTYytf6jrUu69QWzfQZInT4OiIIfChBqa1g2cQs0txA4eXvxuPMMg++F75C+cvW+HnvA8hGfgm+uXxrTqGhJHjoEsB5nD6Snmf/kWzvwaDVJC4Oay5D55n0h7UK6UFJX44aMULn6OPb1zrgYPC1pMp+ONLjzL49I/+oz5G0uuE+GaCN1/4xQd39zHzMUJCoPBRunOTweDA6SlPLxY/L+NY/vkcM/DyWSoeuEljKHBgFjY0Yk1udIja+CTGfLjFfa/sou9X2kkXhNG0QOyme/7OBWPuYECPT8eZfjzWczi41t+e4IHgC9h5CQp8peGQ7Yj8HysviHEy89uedFW0iliz5zEHh3HLz8Y/oRp5zHtPI4TdDTmy+OsNTtqWpT56UHG5y6tILi6nkEm38fuhmeJhKooGTOoSpjqZCe50iimvXZ30uDZDK3HqkjUhVA0merW6EIWKoRjuRRnTGrbY/iuINUYYebOBpX9JYnoqaPorbs2+nGsgPB97JFx3NzGAie3kKPcu76kiT09ReVOL6kzzwNBOSnS0YkxMrhuZuuxg+dhDPYTP3IcRVGCEmR8HWsvSSJ++BhKJCDsC9+ndP0qxZ5LOydrIEnEDnWjptKLcgOlnssrvP5Wgz07TbnvFqkTpwP+WlWa8O72hx44hRqS1L2wl+m3b+LktzYHqFGN6sP19P3JDbK9c8teMzMGAz/u5cX/7XVijYnFwKnpZD1d3+4k0RjDNT3UiEphvMgnv3vhCzYs65x7S1d8LzyP4tnPiB8/QfzIcRA+5ugIpZ4rKw4VnmBuoMjc4C0+/1d9VLXEiNWEUDUF23DJT1QoThv4D5Gj8gRPsC2sI2HlGTbOXa2lR4BACHQHnychcCan8eZzqLXVWxpCkmWixw9RuXAVo6d3565ti3CcMiVzZs2uoGxxiKaaY9SmuigZM4T1BIlYE31j7+D5a5eg1JBMYcZi9/E0wxez1O9NIDxBOWPh2YL8tElzd4rBc/N0nK7hxtsbW7y0hlpiZ05uw9gX/HIFq29ow92N5tjook7ffSEElb5bpJ5+LigTyzJ6wy6UcGRD3KDHDU52flnAI2l60HSzRhCoxBOEm1sWLXC8conS9SuBMPEOQYnGiLS2L/rC+pZJue/Wht7rmyb25Dji2MlFHalwaxuF82cf6sbVtwPHAH8t8eENQJIlFE3GKdmrduo7FSfgp2lLG7w9r7Ux8tEEu55qYOj9MZLNcbSIGjgVbAI70nLjlUvkP/4w+CKFQLguanUN7lqtqAIcw2P2ToHZO6sfshqUVIpYdzflK1fwSl8QulMUogcO4BUKi7pH90JNp4l2d1M6f35Hb+LNQFJkZFXGW0cdWwmrCNff1k31BA8HQXp97e/JGJ5l8g8/xsk9GNuB9eDmK2sro28RXq6ANTCCUpPeMjFZCodJfPU57NGJR65c7Qv3vl6Lpl2gUBmnKt6KpkZJxVuxnRKlyv1JtUbRRQi49KMxsmMVet+bJtkQxncFhRkTq+JhlRzmxyrkJw0q+fV5JlIkTPylZ9CbG7ZFCvdyBcy+oQ2/x8nMbdhr08lk8G0bJRyIY2rV1cjh8JcycAo6AoNFVZKkdSmDek1dID4pScHnXCphjo7c/02bhBJPoFXXLn7/bj63ri7TvfBK5eD7iaiL17wdhOoT1L3UhV4bp9w3Q+azAfTqGOlTbUiKTKg+SfHWFHMf30HWVBpePUikOeBI5m9M4JVBTYSp+0oX0bYazMk8M+/2okQ0as50MvmTq/iOR7S9huShZmZ/2YtXsfFtDyNTIb2vhvEPh3FKS8+PpEiku2oCp4H80uZGkiVmrs2R7khi5S0GbmZ4+j89jhKSH75X3V0sPliyTPLpM8y/+dOdHB45FCLU2kqltxe+GDix0Lq/RvlADocJ7d5N+fLlVV9/GKg+VEdNdwO3//DqfY/b/x8cY/76DFNnVwaAkfoYbsVZdpM8waODb7v37UoSjocxMoc9/XDa7x8G/IqB2TdE5NhBpNBKk+WNQJIkQvs6Sbz8HPmfvffA1KbFPYveWkKn6+81BbO521S37qEq3hqU6YqjGPZKqZR7MdW7PCDMjhtkx5dv2iZuBHPm+PUN3B+SRPToQeLPPbWYbdgShMC81Y+/CY86t1RcQQhfC75t4xXyKOFAJkFNJINMzWMISQ062u4KYEqKGpSgZRlJltFr6zdlWaWmAjX1u3By8xsWsdwolGgMNVW19AvfJ9zavm4X3l1otcsbCuQFEdOtlBLlsEbDq4dwyxaz792i4dWDCMCeK9H0raOM/tE5indmaP7ucSoj85T6Z5h57xaRljQtv3ESJaLjZCvUf+0Aek2c6V/cIHW0hV3fOcbUmz1E22qI7amneHuK5MFdaInQ4nzrlGwmPhxm728cxi7bTJ8bx7NcZFUh2V5F1184zMzFSQrDSxpu2YE84XSIzJ0c+7/TiZG1EIKHl3GSIxESp06vKuiFJKM3NG516A1BCoeJHjyIPT6OOz9P/MQJlKoqnJkl+QJJ14kdPoyaTuMteLg9ShizZeZvrJRX+CL0ZAglvPKrkWSJ9m90MXt1irnLGyORPsGDhVe2AsXzNaBEQ8jaznsFPlIIgXVnADeTQ9+1tkfffSFJyCGd+ItPY49PUrnQ80A4MJ5noegpVCWM4259ASsbs5h2jobqbiJ6irGZ84gtmvZuFVrrLpJf/wpyNLKtcYRlU7m8cQumoJXe2vj86Xt49xCcJT20+jrxqCDJ6A0NRHZ3oNc3BAro8URgH6PrQTClqKAqSLKyqcyeHIksC2q9ws5nUyVNQ75nwxJubWPXX/lrWx9PkZE1Hd/bfCVGjepUn26nPJRBr44RbqoCSWL+3CCV0SzZS8PYmTJ1L+wjVJ+gdGcaJ1dBiWiLRtxySCV5sImJH12mdGcar2LR/tvPo8RClPqmSR1txpzKE2muYv7cIP5CZsizPYbe7CNSF2P/X+pm/1/sxi7ZqGEVLa4x+ekYvf/6KlZuKaC88+Ygnu2THy6gRVTijVF6//QOzib4TbCdwCkaI7KvC6N3FY6CvNwbq/FQFc/+9a4tnSc3Vua9/3P5Qy5HIkEbv6Zh9vUhPA9rcpJUZydaQwP2ZBBUxI4eJdzWRuXmTSL7929vl7YRyBINp3aR7EgjawqJ1hTDv+gj0zNF4zOttH6tk8JgjrkrAZdBCavsfnUPqb01ROtiuKZL3w+uIckSdcebqD+1CwSMvttPtneOfX+xm92v7aX2eBPmG10Mv3mHmYsTD/ZveoL7wrecJduTVUjiaiqKHH48d9vbgTM5gzUwjNZQu61FUUnEqfrWqwjTwrh+e8eDp0y+n3Sig/27v4lhzWPZRaazNzYdRHmexWzuFl2tr1OsTJIvr9+ZtWOQJPTWXVR97w20rQaqCxAL2SZnchPaPUJs6nsRC3SNu5A1bc1KwEOFLBNq3EXyqTNEO/eiJqsW/dzgHusl30f4PvgCIVxQtQ0roUmqttQ0IQSevfOSG/KC0OjOQQrW7K28U5HxbY/c1VHsuTLZ88PY2TKh2jhe2UIstP0L4a9pmB402CxRWIQvFiVliremafxGN7HOWmRNpTywnARuzFW49v9cZOLjEWq66wmlwjhlh9ydDHPXZzBmlpeHFVXGWvDB7fv5EJIsEUpufn7ecuAkLIvSlcuULl9c8Zokyyixpe6DeG2YA69srQPki8rhQgjix4+DJJF/771FrpMzM4NbXF7nDXd0ULl+ncrNm/i2TeqFF7Z0DRuFBETqYjQ938b13z/P2LsDOGUb3/GZ/HQUWVWoO7FkyFl9oI70/jp6v3+Z+pO7SO2pJnsnQ8d3ZJS4yo1/fpHqQ3W0v7Gf3J0MAz/qJdGSYuz9QWYuTixG7E/wCCHAnsohHHdVPSetKoaa2rolxmML36dysYfo8cMoWxTDvAu1qZ6qf+8bCM8PJAp2MHjKFgfpH3+HdKIDVQljiNyyTJFhZ5nN3lo3kBIIysYsrmcyl+/Df4iil1pzI1W//g3CB/Zse8EUFYPKhR780iYCR2kLC+u9WZrHoVtWkol1HaTmtW+i1zcGjgqShHBd7Fw2UNqulPFNE98y8R0H4dioyRTJU08j6VssSe+g+GwwoBTcA/d8vm6piJvLbvlzdnLZLTeveKaNMZlDQqLYN40S1vBtFz0dW+ibWf+aPMPGnM6TPNCEOZEj2laDb7o4uQq+4+GZDtWnOyj1z+AWVwaidsFi6tw40xcmgu8UAt7pKqc+9TeP8un/cQHXDNZORZc59h8e4tIfXMMubfyZ3nLg5JVLlK9dXXWSE75P8cKSZlE5Y3L7l2uXlmRZQg0rJOojJBvCqGGF/KTBjZ+NMdGz3EldicdREwnsiQn8dciKsq4vluh8wwh2EQ8akkS+b57s7bnFlCIQkL2/EOhIsrRoE+O7Pq7pIjwf3/HJXJ+gNBbwHppfbEeL6TglG98TeLaHtwl5+Cd4sDBG5vAdD3mVwElSZSK7ayleGdpxkvajhj08hjUwTOTIwS0TlSHgH2nNTaR/81vkfvRzjKu9OxY8+cJjLn+HufzqXSiF8gSF8kayttKC+rhPtji0I9e2/ikl9PYWqr7zGuGDe7cfNAmBNTyGeWtzBrySJC3ZQG3gfUEn09Kz4Dv2zrXibxGh5haqv/Y6ekNTsLh6HubEOMUr5zHHxnDz2SVBzHsQbusgcfQkbDBwEq4biBgrShDkbDHgWvsEAt91EEIsPnPmyBDzv3w7KKduBZ67ZYFSt2wz+94t6l7eT+poC77jMf3WDYTv49tLFlq+7S3nEd07XQiYeecmjd86Suff+grCE0y/dWNRpqB0Z4b6Vw4y/sNLq16DrMrImhxQIlaZhtyyg/AFakRBi6rocQ1ZkUGCWH2USHrzlkVbL9WFw4TbOjD676DV1WNPLNdtcjJLKbXp23l++vdW/6MheB4lWULVZdJtcU7+Zgctx2vwXZ/hc8tTc75pUjp3jujBg8SOHKF0+XLg5bYKvFIJraYGe2wMraZmh9Oba8M1nA11xGXvzNH6yh4O/fZJrLzJyC/6QAQTnFte4M3cnagWb4jHYPf2BMtgDM3ilS3U+OoPYPxwC7NvXka4v1qEfr9sUP78CuG9HUjb5N0EwVMj6b/wHZREgvKFq4jKo/fIkiSFsJ5EUyM01RxjvjCAYc2v/8btnlfXCB/eT9W3X0Xb1bAjHCG/YlA+e2lLXYxKJLpxArGiIkeXspC+ZT0wk+uNQNJDJI6dJNTUtBhsVPrvMPfmj7BnZ9c0uoXgvtxM0sg3KkGT1ELgeK8v3k7BNy18y0QJLzxzsoyTzSwKUD9U+IL8tXHKIxmUsIbwfJxc8Nwa4zncchCQjf3xeXzbRVKDbJkaDQVL2cIGqTycYeT7n6FEdHzLxV7oQpbUQCev2DuJnVnZlRlrStD53f00nWlBS+is9mVd/N2PKY/nOfZXDtJwtI4X/7szyySPxj6bxLUekuWKHI0S3X8Aa3SE1HMvMPuDf7Pmsb4jMHIbWzTykwaZwRLf/B+Pc+ovdzJ6KcPQ2XuEvRYENwuffkrqq1/Fzedxs1miBw8SbmtDTaeRVJXKjRuUr18n+cwzaPX1KJHIDjl171zqVZIktLiOMVfGqdjEdiUoDAelydU2dr7n4xou0foYSljFd/1fuSzGlxH2bAFjeJZQw+qTZGxvI6GmKoyB9RsDvlTwfazePsxb/USOH95W1gkWnoe6GtK/8QZ6axPFX36KM/FofbTCeopD7d9BVcJUzHlGZ87heg/WPkRJVxF//iniL51Brdq5hdfqG8K4ujXPSjVVhaSoGwqAlFAINZFY/NnN5x6p5YoSjQYeenIQfPq2Re6TDzYk+iiHw5uyLXIL+SCoiQbleS2dRo5Ed7SzzjPKuIX8YuCk19QiqxqPaiUQno+TrfDF+o9vLyU0nHzgTZs+1UbqaAtaPERlOINTWFiTBTg5YzHoAgg3Jqn76gEiTVVM/Ogy4gsVGzWi0vUXDrPn1w8wd22G7J3Mqt1xZtagMFrk4989z3P/9Sl6/nUvru0FBsCmi1W0H55yeMDpCJE48yx6XT2Jp59Z9rpfLlO+3rOlsUuzJrfemaDjmXr2vdy0GDi52Sy5d9/Fyweu3Pl33sF3XYRtYw0PY08EKXffNBGOgzk4iF8uBxoixSIC8NaLygX3qfdKq5Zj7oVbcXCKyztQQlVh2t/oovpQA6F0mOP/xbOMvNNPvDlJcTTP/M1gQd31fBtm1sAuWIulON/zMbMGwhP4tsfER0Psfm0v1QfrGHrzDvPXf8UW4y8hhO1SuDhI6qnOVbOaaipK+vkDGMOzD9WA+GHAKxQpf3aRUGcbSiqx/hs2ADkaIf7C0+i7mym+fxbj2s2Ak/MIuDK2U6R//L2AVG3nsZwHpDklgRQKEe7qJPHKC4Q625D1nWtm8QpFSh98hl/Z2uZRr28MqA8bCID0pmYkJVhahBA4mbkd2rRuDZKmo1UteSs6mQzO/Nx93rEErbZu8W/ZCOy5WbxScdHfTokniLR1bEh1faPwCgXsmWn0ukDLS0kk0RubcAuPueSJEFRGMviWi++4GBM5vMraCRWnaJG/MsrcR32YE7kVr2txnYanmxl5Z4Ab/+Iy5ryxqhDmXcqM7/hc/X4vxYki222I3TrH379P8gAAIABJREFUqVSk+PlnRPbsBVVFjS+fNN1tTHLCF5RmTRzDpXbP0rjCcZbJDThzSze/NbK6yNjdDrvNnNu31uBOSaAm1y5JCF8w8VHA1L83grXyJv1/epOBHy+ouy58Ns0vdZDvyzB1dgwtptP0zG7UkMqt71/BXwjeKtNlen7vLO5CIDV3ZYrsrTkkWXrCc3pMIDyf0vVRrMkc4eaVatqSrlL1zD5yZ+9Quf2rJyNhXLuFcf0W8eee2rExJVVF79hNdVMDxo0DlD+9iDUwtDlS8w7A8x1ypZ0VMPwipHCYUGcr0aeOET16EDke21FagXBcymcvYd4Z3HLwGWpsQkvXrC+0KCtE9+1fzNII18GaHN+wxtCDgCTLAUdrAb5lbihzJofDRNo6l713PXjFAub4KKFdLaAoKNEY8e5jGCND+DskAOpVypjDg8S6DiDpIWRNJ959HGNwAOE83nQAa6aINbMxsU6vbFG4sfZ8KSkyakRj9soUpbGNbWh8z0eLathlh+rOKhK7Ysz2zlOZ3RwtYOsCmL6POTSINT6GpOlk331ry0OtBkmRkBUZfRU9owcJ4XmrMvfvItSQQg5r+ObqwdWqrssiyEQtgwRzVyZpem431QfrQJYwswbZ23PLlcWFWOahI3yxcqwneOQwJ7LkLwwQaqxaYforSRKR3bXUv3GC0cks3n3ury8jhONQ/PAs4QN7UdKpbZfs7kKSJKRImOjJI4S7OjF7+6hc6AnEGyuPXpdtW5AkpJBOuKuTyLHDRA53oVZXrf++TUIIgT0+SfHDswhz6+UyORQmcewk5vjofTlBkd1tRNo6Fs/t5nIYw1sP2HYCwnPxLWuxfKbEYutL08gysf2HCbe2bzqILd3oIXHkeKAgLsvEDhzGHB2mcOHsCvL5liAEpd7rJE89HZDdZZlY1wGMQ90Ue67c9/tZgQ0S/h9H+E5QjVHDatD1uUq26YvY/+1OBt4ZQSs7HPutQwhfUL0vzdXv38TbBM9p+ya/jkPuw/e3O8wyhOIqrcdrUCMK5ezDrY0L28OeWT3lKUkSWk2CWFcTxavb3IUKmD43Rr5/gVTn+lg5E6f8eO8YnmB1+IbN/HvXSJ3sINRcvSJ4kBSZ9IsHsaZzTP/pefz7iGZuF5ISOKZvZCLZKTgj4xTf+5jUG68gRTbfpXI/BOWIONFTRwkf2Ic7l8G4ehPj+m28bA6vXNmw59ojhSIjR6Oo6RShfR1ET3SjNdYjxyIPTCDSL5Up/OID3On1DWDvC1kmfuQYTmaWwpULK4nIskxoVwvVX30NNZla6FxzqfTdxpragtacJAWfyT1BiwRLv9tE16VvWdgzU0TaOwHQqmuJduylkJ1fNZCR9BDRffup/tpr65v6rgJrfJRiz2VSp59FUhSUSITql19FDkco9VzCLRQWrFy+cF5FRQ6HUeIJlFgca2J0zRKnO58hf+4zal//FpIeQoknqPn6t5BDYcq913FLpdUDKEVBCUdQYjFCzbuRFJni1cuIR8hB2yqcosXYu4PsenY3871zFIZy+I63Ig70XX9xLozVR7FLDm0vNpMbynP7J4Oc+p0jKLr8cAMnAL98f+l+LaKQbFi/60ZSJKJVITqeqePY99pBwNjlzE5c4obhWQ7GyFzQUrrKTkNLx6h++TCV/mm88vZuNt/xqUx/+fybnmB1lG9NkvnldZr+0vNIq6iFKxGdxu89g6xrzPz4woIB785B0lViexuJ7msi/3kf1uT9LUF2EsJxKX18Dn13M9GTRx5IICDJMkoihpKIobe3knj1RezhMezBEazhcZzxKdz53CNvfV8GRUapSqE3N6LvbkZvbyXU3oKS3Bk+2P3g2w6lDz/H6Lm5rWZc37bxHRslGqPmtTcItbZh3LkV2Ik4Dko4QqhpF4ljpwg1Len12bMz5D//5L7fh6TrhJqaURMJJE1HUjVkTUPSdJRolFDDku4dskzq9LNE93QhHHtBa8kJ/nUdnNkZ7LmZFRkU36hQ6btFuGX3gq2KEgQyoRCVwb5FdW85HEGrrSPauZf40ROosTh2Zg45FFpBRbnv52Wa5D79EL22jkjnPiRZRk2mqHnldRLdR6n038GenQlKhr6HpGookShaVRq9voHQrhac7DzTP/jX9+WGFS9fQG9oJHniNLKmoVWlqfvWrxE/chxjqB9nfn6xRCprGnI4gpqqCs7R1IyaTFHsuUTp+tUvZa+2pEgI3yfekuTZ/+mrzF6dws5bK3hOgz+7Q74v6IQ18xZd3+qkpquKS39wHXcd39i18FDqYI0Hq3jt7x5d9zhJltCjKon6MLIqM3M7T98HG3MM3zF4PubIHM58Gb125cMiKTLp5/ZjDM6QeafnvuS2J/jzh8w714jvbyZ5qmPVwFuJhaj/7lNE2uqY+elFyr0TgWXLZgXoZAlZU5FCKnpNgvihZpLH2ol01KNEQ1TuTK4dOEkySiiCJMn4jom/yu73fpAUFSUUQfg+nrkU+PulCoW3PkRrrENr2bVjJbtVr0GSUGJRIoe6CB/Yi18q487n8bI5nOlZnLEp7Ikp/GIJ4XoBp+WuIbNYXRxv8xcBSHJQmlWUIFhUFJRkHK2pHr25Ca2pHqUqiZKuQknGH5okivA8zJ6bFD/4DGFtb47yLZPC+bPEDhxCb2gicfQEsf0H8SsVhOcGQU4svsgFCkp0WbIfvIuduX+mS00kqf7KK4Rbdi98hkFgs9rnJMkyyRNPLZ4D30d47oLRtkf+7CdkP3x3hRmxcF2K164Q6dhLpCMQEVWSKaq/9nWSuacX7WFkTV/I9gRSCtbkBPPvv01s/0GSJ05v6jNz5uaYe/PPqPnGt4m271m8P0K7WtCbmhGOjXACfSFJkRdtae4+M052fckL3zSYf+8t8H0SR08Gdi+qRrRzL5H2TnzbXsxsSYoSBKb3nIMFde4vK7R4iPZv7EONBmXXxtMtqx43c3FyMXC687Mh2l5opu8Xw+SGCkRrwkxemsGzNjf/PpTAKRTXaDy4fv1eCIHn+FSyNlM3c5z/fj+ZoZ3dlW8ExtAslYFptJr4qpO/kgjT9O8/j1YdJ/tRL+ZEdt3Si6SryCEVJaIjh3WcTHHbGatNQ5aQdfW+igqSKiOpSmCk+CV+qB4V7JkCk3/8KWo6RrSzYVUbFiWikzq9h9j+XZR6x8mf68MYnsPNV/DKFr7lBAs8wWZCUhUkTUUJa8hRHTUeRq9NEumoJ7qngXBrTeCJFwpECte7r2JNHVTvP41wHXKDPZTGbm/qbwxX1VN79EUUPcLIe3+Ef4+thD06Tv5n75H+i99BTSU31cq9VQQLYQIlEUe0NRNxvUCI0HHxDQM3m8ebz+HO5/ByBfxKBd9cWLgcF1x3cSFGiGDHKsSCSvOCSK0sB3OBqgTijpqGHNaRY1GUVAI1XYVSXYWarkKOhpE0DUlVQFUfaAC5FuzBUfI/eQdvfmU30mYhqSrmxBiVgT6qX36VcGsbcii8pCN0DwL7q3GyH74XdJKtk/2TZAUlFl/mNLGha5KkpWB1AXI4zFqTmzM3S+btn1H91a8T6ehE1nQkPYRev9LCxndsjKFBsr98G3NsGDkUInHs1CaDXoE1Oc7sD/8tqWdfINZ1AC1dsxi4SHpoVVFNIQTCcXDzuQ3xobxCnszbb2LPTJM88VTAedIC25fAZHn1srlv2zjZDNbk+H1Nyh9nmPMVPvy7v1hX1f5er7rsQI7CWDGQ8vEEpekKA28P49mPYeCUGSzy3j9cvx3T9wSO4ZKfqDB1I0d5/tHUXZ1smfznfSQOt6LEVt7ckiShVcVo/M0zVD3bRbl3HHNsHrdg4NtOYGCqKcghDTmio8bCyPEQaiyEmoigREOM/6sPKVwY2PpFSiDrGnIkmMCVsIYcXpjMw9rCz/riv3JYQw6pqMkIamLtsmnVmX3oNQk8w8G3HHzTxjcdfNPBMx18y8Y3HLwVr9kI+8v5AO4ohKB0fYyJf/UhrX/rVcKN6VUPkxQZLR0j/WwXVU/vxZkvYc3kcXNlvLKN7waqu5IiI+tqcB/FQ6jJKFo6jhIPrxqUbQSJ5n0YM6PM3zq3JTV9IzPB7JX3qT/xysplyvMxrvaiVqdJfesV5B3mO90X0oLBhaYiaSpECLI/DXXLDhNCgOfjWzbCshC2g/A98ETw70IAtWhvocggK0FmYMFgVQrrK6wvHhe4s/PkfvI29vjOZOvvZiqMgTtMZzPEDx0hurcLvaEJNRYPgnWjgj07gzE0QOn6FezJjfGa3FKR3CcfoCSS275Oa3wU4a0RbAiBOTLE7I9+QOzwUaJ79qHXNwZEcUVBOC5uqYA9PY0x1E/pRg/uQtan0nebuZ/+EBQFe2piU2RqZ36OzM//jFLPFaJ79hJu3o1WV4+SSAQBuAgyYp5Rxs3nsedmsMbHMIb6cdfrYFyAb1TIf/4Jlf7bRPfuJ9LWid7QgJpMIYfCwWbAcfCMCm4uizU7jT05gTk2gj09+UjFSbcD4QkqM+tQXSSWJwAEy7hMwhd49uYzBA8lcMqOlvn0Dzawq32MMhzZj3pJnuqk6pl9a+4YJUUhsruWcGsNwvGC//n+og8SihRMtoqybJETno8S3Z7xa3RvEy1/7WW06vjCOYIJXpJlpMXzLv89EuvufmP7mojta1okFwtvwfDSCyL0uz8L7+7vfIQvMAZnGP8X72NNbX+H+6WHEBQuDjD6T96m5T96mXBrzYpOu3shKTJ6XRK9bvuLx/2gxdPUHX2JRMs+PMsgUtfCzOX3sIvzpDq6iTftAaAwfIPCaC+KHiHddYpIdROeVSHbdwljbnyds4CwbYrvf4qSShD/yrM7qke0E7ibOVLUCMS2p3j+OEEIgTubIffDn2P29u9Yt5Qky4vZFjc7T+7TDyleuYgcDi+U5yTwXDzLwl/FtuR+8I0KxSsr/U4fFJzsPLlPPqB4+UJw/cpdKxk/yFBaRmDTdc+Gws1lyX364ZbPKVwXc2QQc3wEJRwJAm9NA2nJDDjIkNr4lhWIhW72uxMCZ26WfGZu8buRNQ1JVoJldaGs6TtOoHO4RTJ47dEGjv0np1EjGuWpEld+7xzF4Ucz56sRlfT+WuZ6plcVvlQjKq1f6yQ/mGX+xjabI7449o6Odj88RkHRRuAWDCb/6GNCDSkiHfX3DTiC1KsK+sP7OJWIRri1Br3mwZBM7w38NgLfcoLP4AmAwJsw/3k/br5C019+nuTJDmT1wXRObRROKcvEpz9i1zPfpjI7Rq7/CgifePM+Yg1tzF79AElVaTz1dcz5KeItXSh6mMnPf0qsqZPa7heY+PTHeNb6WkrCssn/7D2kcJjYmePI+vY2Ck+wPrz5HPkfvUXl/NUH22Lu+3il4vqaTo8rHtX1ex5euYS3TjPVtiAEvlHZUaXye6HFdKr21qDFdZSIihp+dHOangpz9G+d5sYfXGLq/Piy4EmNauz73kG6/tIRzv2DrQe9a+GhrHSyGpC+7YqL727ugZZVCUUNsiW+G3CgHhYqAzOM/bN3af7trwR8lUe88D3BlwxCUO6dYPSfvk3t149S/fJh9JrEhoPRzZ9OIFxvqWS8xjUJscDhWZDPjdQ0Ea1vo/5ECOH7qJEEerKaeFMHajSJFkshayHUUAQ1EttQ4ARBK3z+z95CkiWiTx1DDj0Jnh4EhBC4c1kKP32HyqWeL60uzxM8wWbgVhzKUyW6/+YpnIrN3LUZEBCpi7LvNw6z+9VORt7qZ753Z7NN8JACp+Zj1Xz9vznK2/97D8Ofb0zqHgmqdkXpfL4hIJZLkBurMHphjsmbOVzzIdRlPZ/C1WHc//vnNPz6aZIn2lFTsS1zS57gzyesiSyTf/QJ+XP91H79GPHuVvS6JLKyupv3ZiE8H7dgYE1mKfaMkPvsDsbwBp8zAEmiPDXEfO/ZoEtJ+LhmmXTXKQpD1yiOBor3vufiGpvbLXvZPLkf/hzhusSeOYkc2mG3+CfAmZwh/+O3qFy5sabh+RM8wa8a7KLF9X9+ke7/+BTdv/MUl/7RZwjX5+BfPUb9qV30//Amd/74Ok555wWjH05XXUyjYX8VtR0Jxq9mSTVFCMU1zKJDbryM76zcIVW3xXn17xyh49l6FO1uLRgKMwaf/LNbXPrB4Lb9ZjYEX1Dpm2Lk935B4lgbVc92kTzWFnCLNtla7Dsu9kwBc3QOe/ox9xV6gh2FbzqUro9Rvj1JrGsXiWNtJA63EN3bGBC9N0kyFp6PPVfEHJml0j9NuW+acu/4lrShzMwkkeomkCQco4QWTeK7LpXZMULJWjynB4SPrIW2RCb3cgVyP3oL37RJvHTm4RLGf5UhBPbEFLk/eTPQanqCJ/jzBAHFkTw9//Q8x//zMzz1d55DCIjWx7j5Ly8z+NM7G7IlC1eFMPPWpuhED5WUsv+VXex5sZF0SwxFl3Ftn6mbOc59v5+pm7nFGqUWVjjy7VY6nq3HLNiMXsxgGy71+1LUdiY481v7yAyVGP5851Nwa8ErW+Q+uU2xZwS9JkG4tYZoez2hpiq06jhKLByIHgqBcH0808ErVnDyFZy5ItZkDms6h1swcEsmXmFz3jhfRLlvir6/9wMk9eHowqwH33Q2RAw3xzL0/y//dlWBSCdbDtrDNwAnW2bkn7yFEgshKzKR2ghqKLidy+P5tf0Gv3jdlsPU//cpc7+4suI14Xi4+ZVdG0pIIVobpTJbwdtAJ6EW09BiGua8ie94lK6PUr41zlwqhpaOEWpOE22vQ6tNotckgr9JU4NuLs/HtxzcsoWbr2DPFbFncpjjWZz5El7RwC2am2opds0SnrNEDi1NDqJGE9R2v4Aky5jZaeZ6PiLff4X0/tM0nXkD4XuUxu/gVAqk954g3rwXPZGm6ZnvUBztpTB8I+hIWwN+sUT+p+/g5QskX3sJpSr5SNr0f1Xg2w7mrT7yP3kHe2jsUV/OI4OqR1G1CJ5r4Vh3Nw0SWjiBomg4dhnP+dWyOPqyQEJGVyII4WP7JhuJTCRkBJvbnJXGCvT8/nmO/2dnqD5YR8/vn2fgz24vmvuuhwO/tofrf3wbp7LxbO1DDZx2P1W30MAQcJUUXaZ6d5x0S4w3/9fLTPcGWRg9rtJ+ph5Jljj3/X7O/ss+ABL1EV7+24fo+moTh15vZqJnHsfYfsmu42ic/JzD/MTKTgM9ItPcFWPkRgnPEXhFE6NoYgzPkvvkdqAhIUkLXcl3FwIR0AzuCozdqw2zQ/ArNpW+hywOugPwTYdK//S2xxGuhzkaqMqHUiH2vniIhuMNpNpS9PyLHoq3pvA3YjniC6yp3Ka6AWv21/Di//wi7//37zN3ff2SWOtLrbS/0s75f3iewmhh4fp9nEwRJ1Ok0j9N7qNba9xLsPx+WriXtmGnMnf1w4DndHd0zyF75xK5/qvBaX0f4Xu4ZpnZng+Q5CDIvduqn71zkWz/ZSQkBGLx+PUgDJPS+5/hzedIfuNl9NbmB8b3+lWGV65QPnuJwlsf4GUenjr84wZJkknUtFPf9hRWJcvQ1R8DICsq1bu6qWnuZm70EjND5x7xlf75RDLUQF2sg6I1y1xlCE+sv5mtCjdRcuZxvJWJhWhDjPT+2lXfJzzBxCcjROqi1Byqw5hb4mHm7mSo2ZNak8he313LzT/p2+BfFeChBk6+5zP8+Ry335vAyNlEqnQOfbOF5qPVHPx6M/NDJRzTQ9Fkko0RhC8YPj+3KE6VGytz/o8GaDleQ31XimRjlMzg5jojInGFQ89XceHnS1YuT3+7ntvn8qsGTrEqlVOv1zDZX8Fz7lkcBAst+Ys/PsEjgl20uflvbjJ5bpLT/+Vp5AechZNkCTWsbpjrNtszizFrYGTWyDIKEWRbH9K9tGqQI/xF0c1l8P0V5bkggPK2dJ3CcahcuoabyZJ8/WWixw6BqjzJPm0AwvdxZ+cpvPU+lfNXA6PjxwoSshLIEwjfQwgPSQq0ru7ec/f+LEkykqwCAt93FzaW0kKgLpAkBSG84L2SjCwrwdgLvxPCJzt5A1nRSFTvXrwK33OYHT6HpkeRpI039MjqgoTLkkoAwvMDr7MtPpSBgK0c/Cvd7ckIqhJftAbZ3MD3XO9dn14h8L1g7C0Pq0jB/CkHmmjCF4tikZtBVEvTkjyCj4fnOwh8GmL7iGgpDKfATLmPkBqjNtqBIunMVQaRJIndqZOU7QwZY4SsuTyTWtPdwMn/6tlVzyc8H8/20aIqLV/poPGZ1sXXrv7jsxz85m4yd1bfZOgJfdNc04caOM3cyvPm379MYdIIpOZlmLyR47t//ynaTtdx/g8HcEwPSZJQdQXhC6zi8ih18nqW4nSFZFOURH0YVTi4jiBRrWGUPDLjJq4tiCQUapvDyIrE/KRFKesQr9bYdyrJme/Wk5u1sSs+/z977xkd2X2eef5urlyFQg4NNBoNdGZn5iBZFClKsoIlWbIszdge2+s5x2dnZ9ZfZr7tfpvZ8ex6bB9r17O2x7a8khVGoiiJQRJJMTXZOSNnFELlfPPdDxcNNBpodGSLlPj0wWmg6qaquvX/P//3fd7nnRmsIggQisn07AnjOJCZ0dGrDsGYRLJd49IbBaxlS3Y1IJJoU/2O9xGJSt4iN2/gOhBvVoi3qMiKiGN75FIG5dzdF6Z9gLXwXA89r1NJVbA3yWkLokCwMUiwMYggCugFnepidd2goEZVwm1hJFXCrtlUFirY9c3DuFpCI9oZpTxbxigaK4/Fu+MA6EUd9xpiIioi0a4oRsFAVET/ugSBeq5OLV1bc12iKhJpi6DGVMSrtHX1XJ1KqnJng/C9hudhTs2S/+dnMadmiDxyFLml6Z61I3m/wfM8vLqOPjRG6cVXMaZm33tNjQWBaONWmrr2I0oq9fIimZkzRBq2oIWTLIy9gec6NPccBg8ys2dp7j5IONEFeBQWh8mlLhKKtdK89Sh6JUM40UklO0V65jTRZDeNXfchSgpGvcjSxNuY9VvXiSoRlXhvA57rUVuqUE/XkEMKyR1NtD3YRXJXE4FkCDwwSjql8TyLJ1Nkzi2i5+s3TaBERSTSGaP5YDstB9sJtUZQwgp23aKaKrN0ap70uUUqM0WfmN30+wzB5jDJXc20Hukg3tuAFtNwHRc9Wyd7cYnFkykKozns2s3PPaIiEutJ0PZgF8372wg2hREEqGdqpM8uMP/2LKWJvE+kbkLnqNtlymYay9EpGgt4nodul7Fcg7bIDnL1adoiOykZi1StPJZTR0CgbpfI6TOUzfUynPxQhnN/devRw/xwllSzxsVvDW34/OHf33fL4+c9JU7TpzIUU7WVm89zfXPMubM5tj/WhqxdNXAKvpP4tdktx3QpztdpGYgTb9X4zB+0sDRdxzY94s0qr31rgelLVR7/UhuN7Rqu62HqLi/97RyJFpVdDyVo6tTY82gD5azF7HAVWRG470MNtPUGiDdrjJ8t8/NvLhBtUDj00Ua6d0eYPF+mZjk09wT46v/Wz8S5EqIoEGlQePbPpzBqLr/21XZsy6NrRxhFE/ne/zX1AXF6j0CQBNoOtbHny3sINPjiZLNiMvTdIaZenlq5J8OtYfZ+ZS8t+1tWBrTU8RSXvn4Jo7SxaZyW0Dj4hwdp6G/gnT99xydOAsR74uz58h5iPTHsus3r//vrFCdWB/tAIsDRf3OU8myZUHOIQDKAFtPQ8zon/+Ik6Ytp8EBSJXqf6qXvE304hkO4NUy0M0p+LM/w94YZXxq/Ka3Vew1OoUTphVcxxqeIPvEwwX07PxCOXwvPw5qdp/za29SOn8Wtvjv+PGtO6brUJ8fIvvziyjUYi5vLAmQlROvW+8mlLlDJzdDe/xgN7buoFlI0tO9C0SI4lkE0uZWlqePEW/oIRJqYHfwJshJky+6nqBZmEWWFSMMWiovDZGbOIAgCnmtTr6RJDb+K5zp07nqSSLKH3Ny5W35tyd0tfPjPnsG1HIa+cYHL/3iWHV/aS99ndhFoWG+G2nqog95P7mD+zRnO//UJSpM3TunLYYXejw/Q9+mdxHsb1kWmm/a20v1kH4WxHCPfvsjUC6Nr3KyvCwHa7u9i4It7aT3cgaSuj6a1P7yF7Z/bzdSLowx94wL1GzlrA5Im0/P0dnb81j5i3fE119uwwz/mtl/fwdA3zlNJlXF0B27g1et6Npaj+z+uQUCO0hTqpWrl0KQQoiAhiyqmU8Wwy1ftZ2E6dWx3/VhbmS1RmS3d8PWsgwAXU+XraphGX5i65Sr9e0KcPNfDsd0N9Uie62EbDpIqroTrb5QCMasWkir6IUUBRk6UOPdKjke/0EbfwRieC539IZ7982lqRZtP/vEWBo7EOfF8hrefW0INijz759OrBM6D+bE6P/5/ZujaEebXvtLBiR+nWZrSefUbC3z63/SsOb8gwMnnM8wO1/jcn2ylcyBMrWQTCEv84C9n2Lo3wq6HE8wN3/im/QD3BpGOCPf93n0Uxguc/tppHMuh+/Fudn9xN7VMjfS5NFJAYudv7iTRl+D4nx2nnq2T6E2w58t7MEsmF7++vm2QltDY99V9RLdEOfnnJ8kNLzfn9CBzKcNb//Et+n+9n65HNm5ACdDxYAcX/uEC6XNpgs1BDvz+Afo/0092KItruUQ6I+z5yh5Gnh1h+uVptLjGQ//+IRZOLTDx4sT7kjRdDWNkEmshQ/D8ZSKPP4ja3YGgqr/S6TvPcXDyRWqnL1A5dgortbBpI+jGdoUHP9ZAalzn9Ku3MbmsOblLfWKM+sTYTe+iBCKIkkIpM4Ft1qjkZog0dlNYGMZ1LALhJK7r4roWejVLS9NRYk3bUDS/R52kBFACMcDD0ouUs5M49pXJU0ALJoi3DqCoIcLxdiq56Tt6iYIsEt+eZM/vHmTbJ3cgqhL1TA09V8eqmihhlWBTCDWmIWkSXU/04LkuJ/7T65jl6/clVcIKO7+yn+0fC35DAAAgAElEQVSf3YUa03AtF6OoY+TqWDULJaISTAZR4wES25Ps+8MjqFGN0e9e2jSqLcoinU9sZe+/OkSsJ+GT2YKOnq9jFA0kVSKQDKIlAgSSQfp/YzeBZJCzf3mcenqTeUgU6Hmqj/v+6AiBBl8eY5YN6pkaZslACsgEG4OEWiPs+d1DLLwze9MRMsezlnVNHp7nIgoyihSkZhVxPJtsbZLm8HaaQ30sVceoWlnKRoa2yADZWmBdqu624bFCmuSARONAA/GeGFOvzmIbDpWl6rpswI1wT4iTVXcwyhaNW6NIsrjGxFIJSjR0RxCAeHuI/EyVSKPm54TFK0LZtRBl0Y9GuR61os38WB2j5lLJWSSaVBraVGolm8KiiWW4ZGYNkp3X949xbI+54SpGzaWctRBFULTrpw1y8wbZlIFecagWbAIRifmxGpIssvfxBpLtGosTdTbTy0ooJKVWRMH/CBzPIu8s4nD7PiwBIUxcaibvLGJ6m+sfImIDQSFMzlnE4Zc/KtZx1F+hjf5glOyQr2/TCzqdD3XS9XAX2ctZ4t1x2g61MfrcKAunFnz7i5kSyf4k3Y93M/L9EcyKP2h6rocckNn7lb007mrkzF+fYen80ppQvmu56DndH2g3iQQXJ4uM/WgM13IpThVZOLxA5/2dCJIAlh+ZCiQCpM+lqcxXqC5VKU2VUCPq+ytFtwnccoXq8bPow+OEj+4neHAvalfHr5xppue6OLkC+tAYlTdOYE7N3FSlaX7RYmHaIBTzoxCyItDVHyAUlcikTKpFh0hCIj1rEm9SEEWoV106t2mIkkBqXKdcuAMCvpwaWCG7y//bVo1qYY5IwxYcx6JeyWCb/thUWBxkceKdZb2Sh2PphBs6fIJ1Vc85SQnQvv1RsqkLpHMzdAw8cfvXyZXLE2g55KfRHMNm8tkRpl4cpTRVxHNcBFkksb2RgS/soePhLYiKROvRTjoe6Wby+Y2FxKIisvWZfrZ/xidNZslg6oVRxn84TG2h4h9XEkn0N9L/+d20P7iFQDLIzi/fR3mmxNxrk9cdJxp2NrHvDw8T3RLHNR2WTs0z8t1LZC8s4VoOCALBphBbP9ZP7ycHCCSDdD2+ldpilUv//cx103bxbQ3s+K19aIkAnuuRG8ww/M8XWDwx55fyiwKx7gS9H++n+8k+ej7ad/PazupqL1bDqTCeP7byt+NZ5PUUJWMJEHA9//Neqo6SqU3iee/OYrDnsS56nugi2hFh6VwG13EZ+GQfZ//+4nuvqq6S1cnPVOk+0sTuj3Ux/HIKq+4gByR2P91Fx94G5KDEo3+0k2RPhK5DjSjLCvhwU4DM+GooT5AEYm0hzJqDVXdwXXCWtSB+EZuHXnVQAyKK5lseBCPSSsrMc/GFdcJqYZLngb3sJXXlvt1sses43lWkyEMA8osmju2SaFFZGKsxfKKEu4mgThYU2pVthMQYISGKicHJ2kvUvdtvA5CQWtgffIKTtZfIONfvKSYg0C730qH0caL+IlX3l99TKtYTw6paVOZXfY7suk05VSbaGUUJKgQbgwQSAQoThdVopONRnC7S+Ugn4dbwCnFybZfep3rperiL0187zdK5pU3J0WYoTZVWVnGe62HXbARZYLllLY7p4LkeSkhBEH3xphJVqOfr94Q4hfp24Nk29enxd7+VR75I6aWfUzt7meDeAUKH96N2d/pNe7lxr8X3I644ubvlCrXTF6mdOo8xMYNn3Hw/MdcF23Qh6C/4OrYFOPShOHPjOn17Q5x8ucgDTyd44R8zHP1onNlRnWhCJpaUESWB5i6VYz8urIyDtwpLr2CbNeLN/ZRz00STPdRKC7i2SSk9zpY9T2PWS+TmL+K5NpX8LE3dB5DVEKZeQg1EqVkbpyEFQUCUVcx6EVkNEU50UC0sj2+CuCI4FwQRb9nc78pj/o+44pJ/NWRNxjEdRr97icv/cA67vpZcLJ1MoedqhFrDJHc2o0Y1Wg51MPPK5Ib+QNEtcfo/twctEcCuW4z+j8tc/oez60jL0skUldkS4p+ItD/kk6ftn93J0qkUVmV9NEtURPo/t5tYdwLXcVk8meLkn75JNbV2rrAqJhf/zo+m7/rKfuSgwtZn+km9Pk3m/PpKZkmT2Pr0dqJdvjVIZb7Mua8dZ/H42rkje3GJ0lQB1/Ho+8xOxJushL3WVmB9VZ237jEPF8e7fkTvTtF+qIUzf3eR3Z/rB1GgntYJNwVvubr3nhCn4lyNkVfmeehfDfDkn+zj6G/3oZdMAjGVRGcIPI8z35mk+3ATH/5f9iCpIkbVxnNc9n1yC9mJMvWiiSgLbL2/hcbeCMVUlUpGB67p1ebBwnidXQ+7HH66kXrFoXlLgFMv+lGGUsZCVkT2fShJbt5g5vJ1wpiCQEObStu2EKGYTGd/mPSMjngdth1JyGghiZ7dEdq3BeneE+Gnf5+ilNmY6RtenUHjHRRBpU89QEzauMzyA9wdXFklrSEayxUugij4VRXLP9eKxa/sc/VKS5RFEr0JynNl2o60Mff2HLWl29Of3CjVVklVmDs2x96v7iXRlyDUHEJWZaZfmca9By2IAm2dOIZBfXqCe1I/6oG9mKacyVI7eR5tey/hI/ehdHcgxWLvuabBtwvPdXGrNexMjvr5QepnL2Gls3j67TVgvRpNHSpLswanflbkqa80I8kC+bTNwQ/FUDWR3ILFvkdihKMS5YKNXncQJbjd4LNt1VmceJum7kPEWweoFufIpS7ieS56NYdRzeF5LrWir5UqZcaR1RBt2x5CECWqhTlq5SVc28Ko5rj6PnNsg9zcBdq2PYSplygujWAbVRQtSmvv/YQSHUiyxtb9n2Jp8jie59LcfYhgtBXPc1ADURbG38LS1y9Kc5eWGP/B0DrSdAWVuTKzr06R3NmMIAqEO6MEksF1pEWQBDoe7SHaHcfzPDIXlhj73uXrRnpqixXGnx2iaW8rWiJAw0ATzfvbSL2xPgXZuKeFlkPtABgFnaFvnF93/pX3SreZfnGM9vu7aNzXQrApRMcj3eSGMut8jcIdUVqPdCAqfiHW9EtjGxIs8EnZ2Pcu0/5gF9Et8Q23eT/AMV3UiIKkSKghmcBAA47lvDfF4Y7lcvrbk0RaAuz7ZLffQmUZ1azOqW9NcvIbY7TuiLPv0z0oAYnxNxaJtgY5/MVtRFuCLA4VUUISfQ+3EkqoXHphjvRYmXOv+JVtAPNjNWolm8yszk/+PsWBDyeJNiq8/PV5Fsb8SS03b/DGtxfZfjhGpEFmZrDK4LEC6RnfJK1Wsjnzcg6z5tB3KMbWfRFmB6vseiRB4EyZ1GiNcy/nMGr+TThysoRRdTjysSbGz5R563tLAHzqf+5m+6HYCmFbDw/T03E8+1ciVfaLRmW+QuOORoKNwRWNgiiLhFvD5Mfy2LqNUTQwyyaRjghLZ5dW9g23hv20cHaVGAmiwKmvncKu29z/7+5n5+d2cvZvzt6cyPMaeDcgI0bJYOnMEluf3EqoKYRRNDjxFyfIXr7evXX3IUeixA8cBUGkNjWKlU0jyAqhrX0oyWasfIbaxAhyJIbS2IIcjWEuzaM2t2EspjAWU8iRGKHefgRZQZ+dxEgvbB7BclycQonaibPUTp9H7WwnsKsfbftWtJ4uxHj0/ReB8jw8y8ZaTGOMTaJfHkEfmcSt3JkesnWLSv+BMMGwxPRQnUzK4OiTCR58pgFJguy8hW1W+NK/6+Dt5/Nk503GzlVpbFMp522mh+uY+p2QYo9qYW41EnTNc9MXn1/7iGuTnT1DdvbMmsdrpQVqlxau2dYhPX2S9PTJdUeeHfzphlczdf6HN75i12Pu9Wnq6esveFzToTieW1lgqREVJbSeuEuaTOdj3QiigK3bLJ1MUVvc/DPNnF9Ez9XQEgGUiErTvtb1xEnAj0otC9fzQxlyg5t7x5VnSxRGsyT3NCNKIs0H2pBUaR1xinTGiG9LAmAUdRZPzm3qtF1bqpI+u/C+Jk6TP5+l/5lemvc0okZVXMdl9PnJTauxN8I9q6qrF01e+6tBJt5aom1ngmBcpVYwmTuXI3U+j1GxmHg7zfylApIqUs+bRFuDdOxtoPtIE1sfbAZ8xjh7Ns+Z70xSK1i889xq2eLM5epKBCk9pfPS36XWXYdje1x6s8ClN1erI87+LLfye7Vg8/az6ZXHr37uCrKp1XNefK2ApAj0HYwRiEjEmhSiSYVwXCa/cOcrxysQEJGQEQXf48TxnE0Jl4SMtKyfcj0H+7rbCsgoy8dlmcit3kQiErKgrHv8yr6KoPriO+7ea71VXElfSQHJ/13xf8cEd9kDbP7EPL0f7WXL41swigau7dLxQAdaTGPx9CKO4VCcKpIdzNL9RDdLZ5ZWSFT70XbSF9Lo+bUOxI7hkL2c5dI/XWLvv9hLabbE+PPjK1EgURFXrkcQBWRNRtKkW/ZFUcMqbYfaKIwXGPruELZh4zkeclC+oU3C3UJwy1bKxTxSMETD/Y+RfukHRHbuJdi1FT01TbC7DykUwTUMIjv2YpeLhLftwEgvoCabcU2T2L5DOPUanm2RuP9R8m+9iplduvHJARwXc3oOcyaFdOwkcksT6pZOtP5etO4OxHDIT+fJ8nuGTHmeb1Tq2RaeYWJnCxhjkxgjE1iLaexMHs+8O2mJes3l7GtlRBGqZYdSzubYCwVCEZHLxy1qZQfTcPne/71AdsHCMj0uvFmmtUdDUQWqpfd3gcHtwKqalKdvbAfgGA6u5SBpMqIibditIdweIdgcBsCuW+SHb7yocSyH6mKV+LYkoiIS7oggadKaxZca1Yh2x1fOmR/K3tgR2/OoLlZxLRdREgm1hFGj2po0oCj7dglXKvOq8+VNCST40aziRB7P894z37FbxcKZJerZOpOvziKIApWFKoWp0i37VN1TO4J6wWTk1QUm3lxCkARcx3cQX1lwe6CXVif44nyNF//jOe77dDfNfTHwID1W4vwPZshO3r4W6G7DsTxOvpDh0FNNPPk7nZh1h1MvZa+fBrxFKGi0KN20yj2EhCguLiU3y7w1Ts5ZWJdLDolRmuUtJKQmRGQqbp4Za4ics8jVIXABgbjYSKO6j6jYAAjknUVmrCEqrm8WFpea6NcOs2RPM2leWHOeoBBhT+Aham6ZQeM47h0I228bAiQHkmx5bAuhphCR9gidD3WiRBSMgsHIsyMYRYPiRJHBbw0y8OkBmvc2gwuBhgBTL0+ROuYTbKticfmfL7P/9/bz8H94GKNoEEgGMAoGg98e3DBL5bkes2/MEumIsOs3d1FdrDJ/fB5REun/VD/h1jBNe5rWPL9wcoH5E/M3/xJlfxXb+VCnf+2eLzzPDGa4/M3LVG+wsr0bMObnKF04jSArtH3i8yiJBsJ9OymeeYf69ASB9iUS9z+GPjuFXSpQGblI/MADVIYukDj4AFpLG8HObkoXz+AaOnIkRqCr5+aJ0xV4Hk6xjFMsY4xPU3nrBFI4hNLVjra1C6W9FSkeQ4pFfDKlqffUH8qzHdxaHadaxS1VsDM5zNkUxsQM9lIWzzR9sfdd1oqVsjal7Nrv39zoWqJvmx4TF1eLRvSay9Tl95qJ5r2DUTQwijfRjuWKWz+rsqlrEe2Kr7R8EkSB6JYYgti9fsOrIGkSSlBZPq6AElKRA/Ia4qQ1BAg2hVaIihbXaHug64YTfbg9svK7qIioMZXqVUOOIIuE21a30XN1zPLmi1/Xdn1y5fuUvi+R7EuQGy1QmPQrTwVJIL4lSnGmfEvk6Z4SJ8DXL5g3qcvwIDdZ4ZU/u4Qo+5+Ua783q4gyswYv/s31Bdm3CwmZLeoOupWdZJwUaXsGCZkmuYtdgQe5rB8j66yNrHUq/RSdNLPWCIqg0S73MqAd5oL+BhV3NdImCxpd6gAFJ03BWiIoRGhTetGEIJeNtzG8GhWngOXpNEtdpIRRTG91oIlKDSSkFlLWOB6/uBWr53pYNYvidJHi11eF7o7lrLQWcS2XiRcnKIwXaNzZiCiLFCeLLJ1fWjNQ5UfyHP+z47QeaEWLadSzdRbPLlLPrE4w5bkyZ/76zIrQ3NZtRp4dQc/r/up1eVBxTAejaDD35hxzb67eG1cM5MyKyfD3h9Gz+hpSNn9intqS71QvyAJ9z/QRaAxw5q/PoOd0ECDaEWX3b+2mNFVi5LmRW14x3SqsYgHP9idmz/OQgmFERcUuF8FzcfQ6giAiKiqu5ZMD1zT8fQQBUdOQQmGUeAOeY1ObGsNM32HLIMfBqzvYdd3XCZ25CJKElIihNCWRGuJIiThyMuGTqUQUKRZdJVR3sGr2HBdX13HLVZxSGadYws4XcXIFnHwRO1fAzuZwK+++79IHuD24pn3XrDzUmIYgXyE3AQ7924dv+RiiIiJe08NT1mTk4GpqcPtv7Gb7b+y+tQMLAnJgbXpREIU1KUe7bt+UzMAx7JXo2/sRe7+0kzf+0zsrvk2iLLL78wOc+NpZzMrNS2beN6/+vUqY3m2ExThdyg6W7BlGzTPLNgMCGSfFLu0Btqp7KNSX1qTRyk6eYeMkNhYCIlW3yA7tKG1yL6Pm6ZXtPFwydopJ8wIuDiISFiY9yi4apXZS9hgWBkv2NNvU+2iUOpi3/RJTEYlWuYeqW6Topm+o07ldiJJAtFmlkjVxNqr48SA3nFv1T9oEru2SHcySHdw8jF5dqDL+/Ph1n6+lawx9Z60LrVE0GH1utUzZdV1Gf7B5/yO7bjP98noxaOZiZqUHnhyQ6Xy4k4WTC0z9bGpl5VueLbPtmW0EkgEESXjXiZN3TVWSa+g49RpqshmrmEeOxPBcB8eoI4UiV3Za3V6vY+YylC6exsrnEGUZ134XtH2Og5PNI5YLNDsqYl5EmFMQFBlBVRAUBUGWcASZQlnF8BQETUNU/W0QJb/CZrmPhee4eI4DloVrmHiGiVur49bqeJaNZ9t4luX/mNZNN6n+pYAgoCQbcS0Tp1x+dysu3wXcac/HqyFd1YLJ87zb0jpuVOghKtIao8srFba3Asdw1o3PggCiujr9+/f5jQMaru23oJGu7+6z9jwiNDTJRBM33/rmRjDqLgsztzZ2yAGJaEeEQEwluT2BrTsIgq9flQPyLd+67xvi9KuKuNSEhETWSV3lzeRRcfMUnDStSg8RMUHRXRUM5pz5FU2Th0vOmcfyDKJiAzLqijbK8WxyzjzucrTIxSFjz7JV2U1EakC0peXH5uhSdtAsbyFjz2FhEBKjxKVmluwp6m6FdwuhhMIn/20/L/7VOOnJX73Vu+d61NN1Wve3knsoh17QUUIKbYfbCLeEyQ5mN62sE6MhtJ5W5GQMQVX8lgk1HTtdwJxdwjNuj7x4rkv58lmiu/ajtXagxBJUhi6A664Sp6tgZJZQF1MkDj2EXSkhCCLFc8dxKreecpeSMUL7tiHIMrXzY9hL63tQdfaq/B//3IsW3DhNt5Sy+PP/MM3ZN69Jc24UhLqduVWWCB3YiWta6BdG7miClhoTaH1bMEancXJ3YB0iiWjbtiC3JKmduoRXvzNdoqgFaP/t30OfniD9o+/fNb3WPcNd7Lvums6K44FVNhn+9sUNrQU2Q22hglVdu497pVfeMqZ/MkZxLL+mSfeN4Og21fm1Y7Tn4fs/LcPv0XfjCKxwvVzldRAMiXzxj5t55ksNN73PjTB4usa///Ikzi1w02AyyI5f7yOxLc7+r+7Gc3wq6RgO4z+dfu+Kw38ZISIRV5ppVnsISBEcz6JgLbBkTGN5N5E7vwmoQgAHG3uDztKWpyMgoAhr6b+Fec12Ji4OkuALxq94Z3i4645regYeHgoqAiLgYHoGS/YUPcoewlKMgpOmWfZ7TGXtVeK1GRqUdrYEdyGyduXh4jJbv0zOSoEAalBCVgRc1zdOFfDz0GpQIhiTcWwPq+7geSDJ/uMIYNb9gUtSBWzdRQlKuLbvWC+rvp+XrPreXo7trW6vCIiSgCgKSIqAWXdvPpV8D+BYDpe+cYmdX9jJnt/e47eg8Dz0gs6pr51i8czixhO7AFpvB4lPPUZgeydSLIwgiX7fM9OiPjhN9u9/jLVw40hdefACuA54Lp5tkz/2KnapgJnL4NTrKLE4tckRjIU5xGAYM5vGrpQonTuOXSlSPP02diFP4eRbaK2dSIEATr2Gq9/ed0TtaCL5pSeRwgEW/+K7GxInQfCjlZK88SAvXW8BfJcmUkFTiT3zOE6pgn55HNzbj0QF9/YT/+yT5L/xQ2rHbr3NyMo1yTKho3sJHdmLMTKFfYfECfD1Y8IHPQbNirkSsbF1i6kXRilP37k/nmPYayb1xZMppp4fvWP/tivyhiuQAzKSKmNVN19Iiaq0YauXTfcRue738HYgSrd+rHKqwtt/cRrHdLj47eGViKBjuf77+0HE6d5AQKBF28pA5H4CYmSZZHi0atuIy62MVN/B8u58YHI8G3H537XwXce9ddVu0jUfq4SMgIiLu0ZILizX6q3ZVvC7m/tk6IopqEvWnqdT6adZ3oLu1miQ2qi7FQrO+maMGyEoRWnVepGEtbl2x7PJmrNgQWNnkId+s4tgXMasOZz+0QL5lE4opnD0Mx2oQQlLd3jzm7PkU3X2PdnKtsMJBFFg+nyRhZEK2x9IcuL7KT70u1uZOF1g7nKZAx9r5eLLafZ+pIVEq4ZZdzjzwiIzF0rseqyJ7n1xTN0h2Rnk1A8XGHsn997JOniQHcryzv/5DkrQN8D0XD8VYFbN66bopIYYDZ/7EOFDAzgVHX14BqdQBlFAioaxl/LYxZsTlVtXC7g9F31uauVPY34GY35m5W+nUsKp+MJLY3F++X9fg+c5NvWpzdOXH2A9rFSa+plB7MV7Zz/xAW4NlbnSymQsKhKRjuhdIU5GwUDP1FYq2aKdMURZvGNtlue41BZXo1BaIoASVf1GxteBIAkEEr404P0Iz/E49/XLOJZLIOF3J9Hzxm0tlj4gTrcJWdDoDu4lJF3taSGgCgE6ggMsmhM+IbhDlNwcoiATkRrIOKkV4iOjEhET2J5NzV3bmyoqNrDAxMrfYTGOLCiUnBzWVa6soiATlRKUrkrzxcQkAgI1t4JzVSSp6hbJ2CkapQ6qUpGIGGfSvHRXPai674ujhiSe+y8jyMpylEgR0UISS+NVzv1kiQ/9Tg/9DySZvVRm1+NN/Oz/ncSo2jz1r7chLEesIo0qkaRCICwRb9HQQhI7Hm3EqNr84E+n2X60gYe+0EV2ZoRgTKFrd5Rn//MIpSUd21rfWPoXDs+v+LNuQbyodjQS2rcNz7Qp/vgYhR+9iacvf/ayhKgqd5yq+QD3BsbIJMbI5C/6MjaBd098Ud/LqMyWqGdrhFrCKCGFxr2tzB+78/HfKOqUpgu0O1sQZIHGfa1IAfmOiZNruT7ZsxwkRSLcHiXYFNqU7EmqTKwn8b61IgCQgzL7vrydWKdvnF3P6wx+b5T8RPGW7uE7Ik5iwO8j5ervXm5ba2+g8SN7Wfz+CZzyrZXOCrJI4sEB7IpO+czkdbcTgyptn3uAzEvnMBfX3zhyLIhd1tckxANSmLC8sRGYLCjE5CZy5tx1RdNXfJZkVCQUBAQ0MYjjWnh4ywTHo+zmSNszdMh9GG6dgrOIKEi0yltJSM3MWMMYV/Wl8zyPJrmTspuj5GRRBI0edTcCAml7Bg93pZWHiESb3Ivh1qm5JQJimC3KDupehbyzwBr3XmzS9gxJqZ1OdQDX89ZV890pZi+V2P5AA0/9620M/jzD+KkCsiJSyhhMnilQyZpkp2vEWjSae4JUsibpiSqW4ZKerBFvDWCbLo2dIbKzddSgRGNXkMKiQd/hBKG4QueuKIom4TougbCEIEBquEJ6srqx+Pz9CEFAaogiaip2rkTt9NAqaQKwHVz7V8+3592EENT8aj1RxDNMPHfjdK8YDfvi9HwJIRhADAf9fWwbt1xdIy6X4lGE4HIa3vVwSuW1n+OV7RrjeLqJa1hI0RCCqoDr4tZ03Jp+YyGPKCLFIwiyhJ0vg31raUVBFAn17yCyczdyvAG7VKR8/jT18TG8qwoA5EQDkV17CWzpQYpE8RwHM7NE5cJZ9Jkpv2/MMiJ79xPesYvMiz8i2NNLZNc+pGgUp1KmdPJtamMjt3SN7zZs3Sb12hTJHU2IqkTbA53M/Gyc4vj6NPItwfVYODZLz0e3E2oJ09DfSOvRTmZenrhjYXtlrkRpskBDfyOBZJCWQ+1kLy5dV9iuNQRoPtB2S+fw8G0wTMNFkgRE6RfbNmng473oeYPxn06DC827kwx8Yhsn/9v5W/LEu33iJEDige14tkP+jaEbb3+bEAMKWkdyQ9OxG8IDu1LHqW1O7ARJROtoQNTWO8IKkkjbFx5i/ptv4lRWNRmKoC2n5zaGrzsSuB6N7VIGaJI7kQWFkBBDEhR2aQ9ieTqGV2fIOIHp1bE8gzHjLFvVPfSq+5CEA4CvW0pZY8xa11R3eXUW7Em61V1oQhARGdszmTGH1hGdipsn5yyyXTuAIgSQkKh7FSbNi5Tc9dqXvLNI3SvTJHUyaw2hu3dXrJ2eqvHD/zLC1oMJDn+6g2BCYeztPI7lreiO/DlAwHE8BGm1DYooCdSKFpIs0NYfZvpckda+COGEwsTpAl27opz7yRKDr/vpjhUPMcA2nE37Cr7vIAqIAQ0EwS+br30QWXo3IbckiX70EYL7BxBkGXspS/Wtsyv99a5G7BNPoLQkKT77MpEnjhDYO4AY0LCXshS+/YKvhwKQJSIfeZDQwV2I4SCIIvl/fJbaiYvXnFyi6X/6Iub4LPZSlvDDB5EaE+C46MOTlF98HXNq/vrkSRQJ7O4j8RtPYi1mKXz7RZxsYeNtr4PAlq1IkSh2oYCZXkRr76Tl018g88JzVC6c9QmRKBLq6yey/xB2Poe5tICoaoS27yDcv5OFb/4DxvyqZYccjRHo6cg9JicAACAASURBVKXh8V9Da2nDLpewC3kEVUNQ3nuNnz3bZfbnk3Q/1Ud8awPJnc3s+up+zv+3k1TnNi+AUOMagYYgpanChtNF+twiS6dSbP1YP2pMY9dX7sPI10mfXdi0mlYKyEQ6olQXKhu2fqnMlVk6NU+8twFRFul5ajuLJ+dJn17vMSepEj1P9RHtSax7bjNYhsc7PytTKTlEYhKhqEQgJKAFRQJBcfn/5b9DIlpARA0ICIJwKxr0m0asM8qpv7lAOeWnKauZGg8eaEG8RX5xY+IkCiQe7CeyqwvPdiidnqR8fpqmj95H46/txXNcIvf1UDo5TunkOKGBduKHehE0hdpQitLpSUID7YiqTPH4GHgesUO9IAqUTk4QO7iV6H09uLpJ/s1h9OkMUjRI44d3ozbFsAq1TVdMWmeS6H3diIqE2hynOpSi8M4oeB5NT+0n2N1E7rXLK9tLYY2GR3cS6EwihTWMxSLFd0YRFYn40T6antyHuVQk9/oQoirR8onDJB7YjhQJ4NZNFr59DCtXwfFsNovt+aLr6z9fdDNY1sYTmouzpvlhzSsxbJwkKiXRhCAeLnW3StUtrNE3FZ00l/Q3yTtLpO1ZQmIMAQHdrVJ2cysibg+PBXuSjDNHycmRtVMExQjgUXVLVN3Chtfu4lBzi1hiI1l7/q63imnaEkKUYPZSma7dMRJtgZXnrr4az/NIT9bY+Ugj3fti6FWbpp4gr//TDB07orRtj3DiB/M0doVIdgZJT9aYPFuk5744s5fKeJ6HrIhkpu4u8RORVqJ5nt+u8q4e/7oQBALbu9C2dyLFI0ixMNpWf2UoxUI0fuVp3Kv6n7l1k9JPT2BObeyjJARUAn2dBAa2ICfjIArY2SL64BT6xPyNU3yigNwQJbC7F21rG2IwgKebmPMZ6ufHsNIFuEHpsxgKENzTi7a9CykaxK3oGONz1C4sNxp+j+RTxUiI2McfJ3hgJ9XXTmHOzCMlooSO7kVuSWJfQ0IEWULpbCX+2SdxcgWKz/7Md5ePRnBKV2nOHIfqayfQzw8TPLCLyGOHfZXtRtcQDBB64D6suUWqb5/DKZRQt3YSeeIogiSS+/vv41avitZfee8EwSdNn30SJ1+i9OzLt0yaAARVpXT8GJXBi+C6aF3dtH7mN4kffYj62AhOtQKuS3XwEvrcDFbONwIVJInowSM0Pf3rBLq3riFOAFI4gppsIvvT5zHmZvFcBykUwt3E8kEU179N9qpc811FebrI8DcvsO8PjqA1BOj+yDa0RIDpn4yTvbhEPVPDc1wkTSKQDBHujJIcaKJhZxNGXufEf35jQ1dw13QY/tZFEv2NxHsbaBho4vCfPML0S2PMvz1LNVXGMR2/JUxcI9wWJb6tgcbdzQSbQpz6s2MUR9cvhB3dZuqFUToe3kKkK0akI8r+PzrK0DfPs3gyhWP4Jfuh1gjdH+2j71M7EETBdyRXbo5o2JbH6dernH7dv7cFEbSAiBYU0ALLZCl4FZEKiDz2iRiPfjzGu+GyWVms0vvhLUy/MYfnenQcbsMomTd0j78WNyROwe4mEg/0k37+DIIg4NQN8DxKZyYJbmvBypbJvzGEXdbxPA/XsChfmAFBoOmj92Fm/S9N/Egf1aEUTt0kfv92Cm8NE97ZQfzwNvJvDaMmI7R++gizf/sKycd2orU1kH99kMTDA4ibmG0pDWFaPnWExe++Q+n0BE1PH0Cfz1MfX6R0chyto4FAewOVC76ANX6kD60tQfrFs7R95n7cuoldrCEnwsjRAIXjoyQf3UX8fpv8zy+Tf2uI4LYWMj8+jV3WsUv+AKS7FQy3hioG112T49mU7eym3kYFZ4kCN++abGMup8+uj7pXoe74TLrimivu3xuhfFVEqeRm1uicrgdZUImKjZTc3Br7g7uFeKvGvidbUAMi+Xmds88v4tgu6ckqlu7f2OWsiet4LI1XOfGDefZ+pAVJFrjwszSpoQqSIpLs9NN48yMVIkmFwoLB+ZcWkeQ2Pvy7PdiWy+BrWdJTNcpZ0y8KusOBNSjG6I8cJSj6uXPDrXGp8hqm++47MwuSSOjQANHH9iPIEkjiSiNcQVUI7uldkwZxKnWqJy5vcCABpS1Jw2ceI3RgADG0tlrTrerUzo1S+MEbmDMb37uCphA+spPExx9CaW9CUGU/pSAI4DhYS3mKL7xD5c3zuBsRMAGUjiaSn32C4H3bEcMBf6J3XFzTon5pkvqZOyvvv5tQOloIHd5D9dg5ij961SeVkohTrqJ2t2+4jxSPUDtxgdLzr+OWqyD4FW9rfHQ8sNN57HQeuSV53dTf1TuUf/IW9fPDfrRpaAKpIU5gTx9iKLiGOHmOA6JI8MBOYh9/HHN2geIPXsHJ3l5ayZifo35Vqs1cmKc6OkT80P3IiQafOAFOtYLn2MjROKKq+qWNgoBrmkjh8PpXZFlUBy/6abxlsudUr1/QIAjwyMdi9OwMrHn8x/+UI7vw7vtruZbL1AujqFGN/i/sIdgUou3+Lhr3tGBVLV+X5HkIkui3hVIllJCCqEosvDO3KU3ID2U491fH2fN7h2jY4ROoXV/dT99nd/k+TbaLIPmtp0RVQg7KyAEFPVffNJqSH8ow/K2L7P39Q2ixAI17WzjS/Si1dBWjqCNrMoHGEIFkAKvsN/pt3t9Gw47ba0rvub5jvV4DrrOw7Nqm8sgzsXcl4jT03Dg7P72dI3+0H4DyXIXB74/efTsCp26CKBDZ0UHx1Dj6rD/hWoUqdlnHylcxUqtfOKesE+xuQklGUBujyNEA1eF5kh8SUVvi/jvnetSn0jQ/fQCtvYHofd0Ikkiwp5nAlkbCAx3kfn6JyiVfXNf6G/dveo3GXI7SqXGsbIX4/dvRWuLUxxYxc5U16TUAOR7CrugYcznMdAnXsHAtB7tYo/D2KNXLc6hNcQJbkuB5mOkybt3EWCiskCYAyzVI6cP0hY8gX1Up5noOC8Y4RWvjLtPvV4hIdMh9hMQoE+Z5DO/ueyqNHc8zdnz94P3CX66aUV58ebWKb/jNHMNvrl1JTZwsMHHSXzWPHMsxcsx/vla0efMb68Wal39+dwhgWI4tVw36X6maU1pXsfhuwXNcqscvY04v33OiQHBPL/Enj+KW6+S+8zLOVRV0nmWvbnsVlLYkTb/7CUL7tuHkK1ReP4eZyoDroXQ0ETrYT/TxA0jxCJm/eQ5r8ZrPSoDIQ3tp/K2PIsXD6EPT1C9O4JRriKEAgYEtBHf30vjbH0UMaRRfeBvPvKYiNBam8UtPEj6yC1c3qL59CWPSTzWpnc2EDvSjdbchRtYvWO45RAG5tREpFkG/OLIaiXNczNFpnOJ1OtgXK+gXR33SBL62+g6NM62ZRcyp1Eokz63p2IsZxMO7ffJ6FTzDIrC7j9jHHkO/NErhuy/hlm6/bY9bq+KaqyTYsy3sYsF3cY/4CwlEkWD3VqIHj6C1d/rbuQ6iqiGFQhtaGniWiZXP3nR0UQsKfPb3G9lxILTm8Xd+WronxAl8U9uhb16gkirT9+mdNO9vQ41qqNGNHSM916OeqZG9sIS7yWLAczxSb0yjZ2ts//weOh/tRosF1riKr9ne87CrFulzC5il60eIPddj4jlf7jHwhT1EOmNoiQBa4qpov+dRnS8z+PXzzP58ksNNodsmTr9oVBaqnP//Bom0hRFlwSe0lrti83KzuCFxMpeKLH7nbWIHe2n7/EMUj4+Sf23jvl1yLEjr5x5An8lSn0gT2d3lh/lLdWoj80R2deDqFvWZDE7VQNRkjMUiteF5PNejdHoSYy6HoIi4ywOqa1g39KxwqgbeFTMv12f010Pl4gztX3wY9Q8+glMzKZ7yq89cy8G5ogXx3GV/kutTXheH2foQtmfRqm0jKEawPIO0McWcPrymNcn7GXGpmS5lAE0IEhZj5J1FFuypG+/4KwQBkZjcvEKa7jk8D2M8hTG+rGETRV/j9CS4hknt9PB6knMNBE0l9pEjhPZuw84USf/3H1O/OLFCBoSASujsCI1fetInZU89QO7bL6+JGmlbO4h/7EGkaIjKsYvkv/3yCvG6IlhPfOJh4k8/QOKZhzAm5qlfuMqhXYDQ/n5CBwfwTIvCc29SfPEd3LJP0sVQgPDgFI2//RRSKLAm/fgLgSD6+iPPW5sKA9z6VWPSNfBM0xdt30W4dX1dkc5KBOuacUyKR4k++dCyt9ddIPei6E88V/4WhOXjer7/F6A0NNL0ic+A51F4/RXM9BKubaG1d9L09Cc3PKzncRORtlV09wdItm5MJK4gP5jmZ3/8Q8BvxluZXS0GEkS/QbTrrJUg5AYzvPq/voAgCji6RWmquLK9pGi4trlmH9d0mH15guzFJRr3NNNyoJ2G/jaUmIwog2cL6Lk6pekcucE0hZEclbkS3k2ki3KDGc7812NM/niY5gPtNO5qJtAYRAmruI6LWTSozpcpjufJXk5Tni6iZzZf5Np1m7HvD5K9uET7g1touq+VUFMIQRapZ2pkzi2QemOG/EgW13I4/9cnGfnuZRzdpjxT2vTY7zV0P9pJ/zO9vkB9+Yat53Xe/ovTd1ccLgZVrFyF9PNnaHhogNj+rcvEycUzLORoEEGTwXaRQhpaS5z0j077udzwMmv1PIonJ+j8l09gzOcpvjOKZznUZ7OE+tqojS3i1E2koIpd1jEzFYI9zdTGFglubd6UCPmHv3mm6Lkegiyip/I4ZR1RlXGN5aabG3X0sGw8/EiVUzfxbGdlO8vTma0PktJHVnycXM/G5b1joHincD0b2zMRgJy5wII9cdfMPX9ZIAoSCeXWqk3ea5CbE0Qe2I0gS5RfO0vt1PCa9J6nm1RPDqF2tdDw2ccJP7iH8pvnMcaWdSmyROhgP9qWFj8d9+NjmLNXeXx5Hk6uROnFtwnu6kHr7SD62H3UL11VHSSKhO/fhagq1IenKb18aoU0gR9FqR4fJLinl+jjB+7F27I5PA/P8MmKoK0VLPvtW64zbnncfY2W53Gz+WZBVai+cRpEgegTR4n+2oOUnn9tw4q9m4EcSyAGQyuGpqKqojY141kWdtEnGWpbO2pzC9kXf0T54jmu2D6rTS2Id0nsvWN/kEhs87nCLBksndy4GlgLJxAkmXphYd0+Gwmm1VCcpr6jVNJTFFNrU9+e61FbqFBbqpJ6fZrWnQ9Ty89Sy88RiLXiuS613AKu7d5yuyT/NcyTObeIqEgIoi+k9gBcD9fx8Gz3lnQ7rumQu5SmMJL1j7ns1eS5Hq7prOlOUJosUJq8dS3cewE9j3Yy9OwY6UvZla+g53q3RJrgJoiT1tZA44d2I2gyoiKvlPV7tkt1KEXT0/vp+p0PUTg2Qm1sET2Vo+1z92MVav4KaHlQNJeK2KUaYkClPutXNpWOjxPoSNLxlcdwbRcjlSP93CkKbw3R8qkjBHua8Dywi5swZs/vN7T6Jrh4rkegq5GGx3YSO9iLWzeQIgFyP79EZO8W6jNZBFEk0Jkk1N9G5sVzflTrykFcb2WlY1d0aqMLtH3+QaxchcXvHV9zPR4ujvfLQ5SuRdnNM2Qc/0VfxnsamhgkIt+9lgK/CFxxF3dNm9rZ0TWkaQWOS+3MKPGn7kdpiqN1t2JMpMD1kMIBAv1bQJYw59Kr0a9rYOcr1C+Mo/V1ovV2ICei2Dl/1SpFQqhdLQDog1O4lfX6MLdaxxhPEXlwz9178bcL18VO53FNC7W3y6+IWyYEcmsjYiSIk79zE8S7DbdWp3b8PE6lhhgKEnn0ME6hTOX1U7dsRQAeWnsHkV17KZ18B9eyCGztI9S/k/r0JHbZ/2w9ywIPpEjU71PoeagtrcQOHkEMBG5wjhtDDQhs2x24boudG+4fShBp2Ua96JOmQLyVaEsvgiCilzOU5ocJJtqItW7HdR1yU2cxKllq+dRKeX0w0YYgyejFNJGWrVTTU0SaetCijchqI1Z1EsGLoAU7qGancQyXUEM74aZuBEGkXlignJ4k3rEDLZxElBXy0+cxKhsbn7qWu2m7pdvBu3HM9wSuarxeXaphlO/MQumGxEmfzbL0w1N+ZMa0sXKrbqOVy7MYCwVEVcYq1nCqOvPffAslHsQ1bFzbwdWtZfGjhF3W0aczuMv2AHa5zuL/eAc5HkIQBeyq4es1hueZ+9tXEDQZp+qH469HnmqjCxjzBZyqv9pZ+M7buIYFrkfulYvkXx8E/E7YCALh/nYWvvUW+lyOYE8zrZ86AsDc376CXfLPUTwxTunclH8cD9LPnUJO+HnzK+f5AB/gCmJy87q2N+83qB1NIEs4udIaPdS1sBZzvr1BMobS1YwgSXiujRjQUNqSYDvYi7nranY808Kcz4LrIYYDyK0NK8RJbvT76QFY8zk/ursB7GwJVzc3tA+517BSS+gXR4k8chAnW8CcnkeKhQk/cggpErrtulNBVfw0oCQhxSIIkogUjyI1NfhC+WoNz7yzqlavplN+4f9n7z2D5EjP/M7f+6Yr313tLRoND8xgLMaQQ3LouTQriWuo3dWupJO5kwntRejiTNwHhS7iPtzFhSJOERcKSaGVdCvtiVpDLodLt0MOOeQ4YvwMvGugva0unz7f+5CNBhpd3Wi0bwx+CMygq6uysqqyMv/vY/7PK8hMiqavPU9YqWG/c+6eomEqCLGvXSG5/wCZh04CAi2diUfsvPZzIicWv+7EGLVL58iefIxE/z6U7yMME39uBnd840aR7d0G/Ycs5BrmrTXCd6tEkY+VaaE2O0wi04qRzFEcOUNz/0PUC+O07n+cuaF3CQOHyL/zOiAw03k0w8KrFUm39BLYVTLtA8xce4vOTAtCCHy7glIRZrqFenGKRFMnmpGgMn2NXPcRnMos2fb9VKaukes6RLjseR5wr2imZPAz+zBSOpqp8cw/eZyJd6fxKnHnu18LuPbT4XsSjHcVTsoLGppCQhx18maW5jjDir3UqFIK0sd6aTp1AGkalN5ZOnU+rLmL4miRSC3b7kpEbkDk3hJzwfytk747cUcrsKHhjhdo/dxJgnIdI5/BvjGDN12KBd7Nfaq7cJtOC+vurfqnBzzgDvJG187VN20SMp2I6zvqbtx1tQIqCBZri7RMCm5eqHQNmbTiyfB3WVwoJ055C11Hpm5FG2TSWkzLx8aNjU9kkePe1c5guwgLJUovvETTr36G/O98NU5JFitUf/E2WnN23TPFrOMHyH3h42i5DDKXQaZT5L76POlPPolyPEov/ATn7NWN73+xQuk7P0FLJWn++ueIyhXcy8NreqwKAkpvvY5z4zqR65A6dBQ910RQKVO7fIGgcCtSElbKzLzwLVKHj2K0tqHCEHd0GGd0mPTRE8u65ZzxUcRbbxAU19bp19Vv0ju4/pSfCgNCz0Ez4gVQFAW41QJubT62QTAshGbgVGdR4QpROaUQUkMIiWYkkLpJpCK8aoHAs+PkSOgvEUMq9HGrBbxaMa6tFYIoCjAzLRRGzhB4W9+Ve98jBIl8AitjUJ+xqc/Y6KaG3hrX97mWt6YBx7ez9Wd7FYuZ6pkR7NHCYrRpJ1B+yMwP3iPR14q09IV9mlsimh7wgHvBlEnSen5VM9Q9wWKd0RpOIAupCaWiW2U1t3sr3W0bQtyyaLktuqFur9MRN//TqPCw8c07hTc0RuE/fxe9owWh64SVGsHEDM75q3GTyW2Rs+pPf0n99If406vPnfNHJin/8JUV6zv9sekFm4aQwn96gchxUe5t57Ewon76A7zrYwSzsfhQvk/1Z29iv3s+nlt4866zRea/+X30jpYlt98NFfiU3nh18efS6ddWvX9Yq1J57+1lt1fef2fZbc6NIZwbQ8tub4RuCAaOWmSb1l/onsr3kus8hNB03GoBVEQUeLEQ9l3CwKVeGKXr2KcIA5fCjfex0i1kOw+gohCvXsSzy+S6DmEkskSBj1ebR4U+ncc/hWYmUSok3bqPbMcBlFJ49RJRFAIhSkWEvofUDKRmoltJsh2D+PUiXn33pXv3EqEbcuabFzZ1m9sgnBTuZBF3cncUkwWlOtXVaqYe8IB7IK01k5TZPT2/CSAs11BRhMwkGzpe30SYBnJhDEhUvmVOq4KQsFKPDTizqRUfjxBxZEnXUH5AdJtdSGS7i51gsVu2gAaFszJpwnomCWwhYaFEWFh6gfNHlvuu+WNr825rtL2GKHAvXW9wuyKYLhBM32bXESn88Wn88eX7EMwUCGaWmyTuBZJpybHHU/ccNbgduzTF+JmfxHZjgRcLcxFHomavvkkU+BRufIDUTUAR+g6h7+B+MBtHkgIPpSLGz/wEiGtto8Bj+tLrCKnFnZeBB1LGjyF+zM1CdBWFzF49TbK5i8CpYpcmSeV70K3MA+G0C9nb+YUHPGCHSWvNJLTl5n17DW90GuWH6E0Z9HwWf7yxv5XZ0xYLJ6XwRmcW03qR7eBPzGEOdGF0tiASZsMuLWEZmL3tcUqiZuPfdmEPZkuLUROjuzUWVw1Sclo+h7TiC9gDHpDOSQ4+tLECcxUFhF7jFFwUeA3vo8KA4I60XXhHau3mY2/dIVr2mNvva5em0K00UjOozY1il+8vP8D7hU0XTpowSMj04ugJiMdPOFFtyRiRWwgMYZGQaXJGGxm9hZTMYsgEmtBRKEIV4EU2dlihEhSoBHO4UW1xEO5mY4pEQ0fwtRIRYYcV1A7aEkgkhkySlBmyeisZPU9CZjCkhRQ6gtis01MOTlilEsxTCWZX+IxWx4lqBGqzU7ACUybi40JvJ6PnSWpZDGHddlz4uIvHxRyVoIC7Jftyc48EUujowkATOgmZod0aQIrlKQKJJK03o4frK2COj6Hyqu7zm4lzeZRgqoB1oIf0sw/hXB5ZZk4pTD22C0gn8SZmcW9MLqb4opqDfXaI1GOHMfs6SJ4YpP7upWWFxkZnntRjh1BRhH1heEkhelR3cK+Nx0aXDx+g/Jen8SeXRkFkNkXyaD/CMhatAB7w0ebA8QRN+e0xm91qIt+lNNbA1X8XIxPJ2LLL/ejUY226cMob3Tya+xzabW7avnI5X/kFk+7SwnBDJGi39tFpDZI3ujGEyd3n0yg85VLwxph2rzPrjW6qr5BE0pc8zqH0U+veRj0s8W7pR9TCnUlPZvVWOswB2sx+snrbmguX13uRPlP+GePuZdYrYiO1tBjZlEk6zAE6rQM0G50Lzux3Py7cyGbOH2PaHWLOG9uwgJJoWDKFpaVJyDRJmSWl50hpTaS0HJZMIZBLFgk3sWSaU01fWbfsscMyp4sv4G7yIOWVCIpVyj97h9buVrIfexh/bJbq6x8uChstmyLziUfIPvcIqIjKKx/iT9xWpxMpau9cJPXkUVKPHCL/1z5J5Lg4F4fjQm4pMXvayH/9eYzuNvzpeSq/eH+psAojqq+fIX3qGEZPG81fe475P/9FPO9NxaIp+6nHSD12eFvekwfsDR56KoVh7a7U7f1IsnsAZ3psaXG81MgdeQQVhpTOvrVzO7fNbLpwEtxcmd86kE0sEgszvOL7xE7L+1OP0Gb2ogvrHmpEBJZI0p04RKvZy4w7wnX7AyrBHJsTfRLL9n89W9gJDJGgO3GQ3sQxsnrrPb+G9e73Rst7bka5BJK80cVA6iQtRi+GvJcuGUFCS9OrHaHN6GXaG+ZG/QOq4frmb7WZ/fQljmFp6cUIpC7MNR+n8f3WfyRse7F5EFJ9/QxGTxvZTz1Gy29+hvSpY/hTBYgi9M4WEgd6EAmT2pvnKb/09rJ2+GC2RPG7r6I3Z0gc7qPj7/8V3OsT8ciVZAKrvwOjr51gvkzxhVfwbiw3FbQv3KDyygdkP/Uo2U89htnfgTc2CypCb8tj9rXhXBzBOtC9WGu11eg6aLpA0wVCsmRyu4oUUQRRpAgCCH216d6Wm43U4oJqTRNIbaFWf7HgX6Gi2I4qDBWhrxpaeu0W0jnJ4PEk2g4UnQgEQkgiFSLEglO6UkgZBw2iyN+SiLFuCDSd+POTxE7tdx6P4cLxGGzC8SgkeipN/vGPM/v6i4R2ndC149dqGDhTY0S3DawXhrkwk0+Pa7t8d/MNX3eYbTnchJCktByaMIhUSLu5j0PpU2T0lg0JlFgoHCKjN3Op+kvm/LG7P+g+JSmzDKYfp9s6hC6MPVSsrAiUj0DSlTjIwdSTpLUcYgPHhSlT9CaOkNGauVj9JcVg9eHIjcjoLXRYgxs6PvcaYalG4Y9fIpyvkPn4SazBbhLH9gGxLUlQKFF/9zKlH50mLDS2C7HPX2fmD75H05efIXG4n/STx2IH7Sgish2c8zco/fhN6u9eajiSJKrazH/rZ0S2S/rUMazBHqxDfSgvICxVqb52hvJLb9P5T34Ds7d9y94LwxS0dul09pkMHk/Qu9+ko8+gqUUnkRQYpiQIFK4dUSmGzE0HjA+5DF/xmLjhMTPuUS1Hu6YMyzAFrZ06bd0GvYMm+w4n6Og1yLfrJNMS04rPF66jqFdDZicCpkc9hi64jF93mZ3wKc6FO3r9kxJSWY1MkyTTpNHcqnPk0ST9B1df0Bx5JEkqvTmpPMeOuHHZxa5GWEaO1sx+xosf0p49hBfUQUBzqhcpdAq1GxRro2zGQZBMS9p7DNq7DQaOWvQMmLT3GjS36JhJiWEKwoXjsTwfMjcVH4/XL7lMj3rMTPhUS+tTwEauidanPk2qb5D2j3+RyPeYee1FQrtG00OnyA4eo3ThPbxCPCmg7ZnPgVJoyRRC0yiefQt7dG0dknuFbdPpKS2HLkyyRgtHMs+S1po2fHEXIo4ONRkdHM48jVf5+ULk6aOFJVMcTD9JT+Jww3qbm0QqxIuchQiPQJcmpkis+3NQKq4zCtX6B2gq4rl/HdYAR9JPk5CZTTou4jEoRzJPc6byM+rhvc1U2iuysyFK4V2bIHzhInnRTmQcYVL7AD9cXoMgkOTT+2jO9FNzbWbgQwAAIABJREFUZpkqnWf+O7+gevrcgrN3BhCEpSru9Yl49txqHkphhHNpGHd4ksSBHoy+DmTCRLk+/mwR98roqgabEBtcFr75Y2qnz2Hu60QmLSLbxRuexh0aQ/khxe+/jp7P4Y2urUvtTqIojqjciZRw+JEkT382y8lnUwweS5DKrP2iGwaKyRGPC+/ZvP1ylbdfrlItreyLtdUYpuDEqRSPfyLNiSdTDBxJkM5pa44SR6Fibjrgyoc2779W482fVZga8bdFQGk6tHUadPQbdO8z6ewz6ewz6Ogz6ejRyXcY6PrdX8g//N96Nm2fRq66/It/OsrlDx0cv4QmDVJmnoSRperO0pM/GUegEOSS3ZTrE0QbOD9mmzQe+Xiax56LP7+eARPDEms+RwaBYnLY4+J7Nh++UePNn1Upzt7b/vileWbfeAk9nWPype8Q2re+v8X330DqBtK8VZyvpzKxuHrlh6T3H6X5oadwp8eXDILe62yfcNKbaDY6OZB6fJloUioiUD6+cvEiGy+yCVWAJnQMmcCSKQxhrZoqyentDKYe5ULldTy1/iK1iIhp7wZ+xcUQFoa0MEQCQ8bPb0gLiY4UEkMmFupvdg5NGOxLPkyXdXCZaFJKERJQCeaYcoco+9N4kbtYtC6FRkKmyRvddFj7SWm5hsJLKYUX2dSjEnZYwQ6rOGEVO6rEt4dlNrKqymh5BlInl4kmpSJ85REoDy+ycSObSAVowsBcOC50Ya0YYRNC0Gx0sj/5CJdqbxKotX9xA+XjRrW7JNsEhjSX1PPdJFIRfmSvO1TvRvV7msG4BKWQY3XSL5eZqr5P3S0sqyNbvCsRNW+OXNRFS2Y/U6Xzcdv62CxqokJPy0mGZ++9dkE5Hva569jnrq/vJQQhzqURnEsjDX9ffeWDdW33JoGv8Oxb76+Q0DNg8sVv5PnYF7N09BoY5r1HGzVd0Dto0bPf5KlPZ7j0gc0P/r953n+thl3bvpxXKis5/kSKz/96M8efSNHSoaOtQWTcidQE7d0GbV06jz2X5ovfyPPqD8r87LtFpsf8mzN8N418u85zX86x/6hF76BFU4tGKqORykqSKYnUdteSZr42TFfzQ5Tq43h+FT+wCSMP2y/heKUVv3erIQRkmjSe+kyGz369mQPHE2Tz2rpc0XVd0HfAonfQ5JnPZ/nCNxx+/KdF3vpZlfmZYEsEsIpC7MlhQqeOPTlC9shJtFTmgXBaDwmZ4XD6KdJa85KLnBvVKXjjzHojFP1JnLC25GIjEFhamrzRRZd1kLzRhd6g9kUIQbs5wKw1woRzZQO5ZbXYoSUW9yD+/83n0YSOJdMcSp+i0xpc5/NsDk16B/3J4w0LwL3IZtg+y4h9Dl85Dd+TCnPMeqOMO5c5mH6CDmtgmRBQREy617hcO02kQtTCn80IQUs09qceXXJcKKVwozpz3iiz3iglfyoWErcfF0KSkGlazB66rIM0Gx0NBYwQcQpwxhthxrux5v2acoYo+tOryiZNmAymHqXDGlj2Oy+yOVv5OW60enRlJSIV4d+D0LudXLKHtuwBUlaeij2FKypEUYAUOk2pXiwjg+0VqdiTRCrE9SvU3QIJo2lxG0mzmfbcYbqaThCEPkHoMFO+vKOdoptJGCgcJ34tugEPP53md36/naOPp9A0NiXqmW3WeOKTGQ6fTPIXf1jgu39YoFLc2uiTENC93+Qrv5Pns3+tmWxeW1LDtP7tCpJpjcHjkr6DbTz5fJrv/mGB0y9VcOqbd/XtHTT5rX/cTq5FW5yRvJvLDtwgXlzZXpEgcpkqXaA1O0jaasXza9zrOVJIOPpYkr/yN1s59ZkMqbTckD/V4naFIJ2VnHgyxcGHkjz92Srf/oM5zr9TX6P4vcfXsVB0tvjZPahxWh9SyCWDUJVSVMICQ7X3mPWGV71I1MMS9bDEnDdKb+IYA6mTGHcUlAsEhrTosAaZcUc2odPu5mX6DrmhIFQBoQrxo51V0AJJb+JwQ+uEQPkM22e4bn94V4sBRUQ1LHCx+joCSYe1f0ltj0DSZvYz6V5l3l9e0LsRlh8XEaVglqH6e8x6IyvvuwqphUVqdpFZd4R9yYfoS55YVlAuEJgySae1n4I/vma7BU/ZeMHqkUtdmPhR4+MsIqQSzOFE1Ya/31oWRK1iQeLGoxzac4fIp/upe0Xy6X5m9TQz5ZW7ITVhLFT+qw0sRHYnccQpQjcET382w+/9D530HTDXPeusETeLyHN5ja//vVaspORb/26W0tzWiCdNh+NPpPjt32/noVOpdUXM7oYQAtMSnDiVpnvApPeAxQv/YY5aZXMEtZC3Cp93O5aRpSN3lLpXxPbiJhTHLzFWeG9d2zNMwdOfzfKNf9TGwYfWX0KxEje3l0gKnvlclq59Jn/2b2Z59UdlPGf173fkeagoxGrrwp2bInRi81thGAhNR0YRQjdQYYDQNFJ9B6iPXiM9cJigViaord2Rfi+wIwaYSimK/iQXa29Q8qfXfFJ2ohrX6+8jhcZg6lEEy9NKLUY3SS2LHyy9oFkyvZh6MWQSb5vavLeStNZE3uhedrtSijlvlFHn4j35MjlRjaH6e+SMNlJabvF2IQRJLUuHtZ+SP0PE+nP2qxGpiII/xqXqaSrB7JqPCzuqcK3+LlJo9CcfaljQ3WL0kpDpHbOI2E7KdixuU1YLU6XzuH4FTRp0NB2jWBul6sxg6inasgeZr94gaLAAsL0ic9UhMskOxuc3lhbbjQS+wnMUjz2X5m//T5307F97x+S9cjNa89XfzePUQ779BwWc+uZG7nRD8NRnMvzeP+2g/6C1LSmt5jadr//dVvJtOn/yr2eZGf9oja7ygjpTpfOEkb+ulNztJFKSL/xmM7/291pp69765h6pCfYftfi7/2sniZTkJ98q4rkrn28jz6F84X1yxx4j8j3mTv8ULZmm6fgTWK2doBRtVoL5D95ARREq9Gg99Twq8Cm+/wYquL+OjR0RTnZU4XLtLYr+vbuihgSM2OdoNXtpNjqX/V4XJs1GB+VgZvE2gaA7dYTr1XfRpUVP6gjXq+tbFewm8kb3CtEmjylnaF1pomo4z6wXR3BuRwpJq9nHiHaOerg1IwBqYZHL1dNLPru14iuXYfsseaObnNG27PeGTNBktH8khFMjBBpJM08QuiSM2BqkWB+9b1Jv90oQKNq6DX7nv++4q2jyvYhSIaQ8H+I5caecmZDk8hrNbTq6cfeLnBDE4un3Whm64PLGi5u3Atd0ePqzGf6b/7mT7oG1CUClFJ6rKM4GVEshnquQEqxk/LqaWu5eExWnfzS+9Ft5TEvwh/9imsL01iyqdiNKhXjB+lLxt6Np8Plfb+Z3fr+dXH5tl+QoVPExWQhw7IgwjOuZUllJc5tOOivvehwIIci3G/zuP+3AtSN+/r0Sq+mbyuUPqVz+cPHnsF5l5pUfNNi5iPrIVSqXz6zptexFtl04RSpk1L5A0b/3FvGbuFGdSffagrnj0qiTQJDVb104NWHQkRikLTGAqaWRCKr+3pzJdDsCQUbPr1Db5Kw7pRYqn4I3Tl/i+LLITVprwpKpLRFOkQoZsc9S3kBXpB1WmPauk9Hzy4rcNaGR1VuByxvc072JIqTuFpgqXWC+NrxQuadWXSkrFS26pcf3u3/Sdfk2g2/8ozYOHF/uIReFikopZPiyy7uvVLnyoUOpEODaEcGCLtB0SCQl+Q6dY4+lOPWZDH2DFlZy9Y6nphaNr/xOnssf2MxNbY7IOPZ4ir/1P95dNAWBojQXcPWsw3uvVhk671AtR3hOfOEVIr74WilBvl3n8MNJHv9khr6DFumsXDGNqeuCT361iXo14o/+5TS18vrF+LVzNv/7PxhZcyF73wGTv/6P2mnrXrlJ51/9s3FGrm6Oy7xrR4xe2zzHek2Hj30xx6/9/Vayzat0REeKWiVi9KrL+6/VuPyBTWEmwKlHBH7svSU0ME1BKiPp7Dc5+Uyak8+kaOsyVu3Ea2rR+O3fb6dSCnn75eqGfbtCp07kb2+ESUgNI5W7+x0XCJza8nE498C2C6dyMMe0e52I9Yc2FRFlfxo3rJLSm+74rVhIM8UXhlD5TDtDKGDOGY4rPjbQHrpb0IWJJdMN3Sc9VcdZZ1EyxCk7L7KXzWCTQiOlNTHvT7LZF9GiP82MO7KhCEhESNGfWtj3zJLfCSRJufYv1v1GGAXMli/T1XyclNmMEJKyPUHVmaEp2UNTqo+U1UJ77ghlewLXr+AGVSIV0tf6BHW3wFzl6tL0qa5j9vegfJ9gtoBy9k7XTNc+g84+Y1lKy6lHfPBGjb/843nOnK5j1+IL00oIAe+9UuNH/3Wez369mS/8RjMdfcaKIkNKwdHHUzz5fIYf/2lxwxeptm6D3/79dnrvEjWrlELefKnCi39S5MoZG9eJWGFk2uLrevcXNb73RwUe/2SGz329mYdOpTATjeumrKTgs19vYnbS5y/+sLBq2mc1auWIs2+uvYzCroa4zupv4uUPbS69v3nTJTYLIeDwySR//R+309GzcnrO9yLOnK7z4p8Uef/1GrVyGIulVd7iC+/avPajMh19Jp//tWY+9bUc7T1mQxsKIQTdAya//t+2MX7dY2xoY8Jw9pcvobbZNdVqaufQl/4+cPPKpJBaXKOpohBULK5AETg1xk5/l/LI+kfbbKtwilTEvDexKekSO6zgRnVSLBVOQgh0YaALY3HkRqh85txhNGkgkQszzvZ2jZMmDAxpNmyXd0N7QwIkVP5C6/5S4SQQWDK5IEk3j0iFzHmjm1JIXQ/L+MolwR3CScS+VRoGITuQb5cy7izZpu4S2ysxXviAILx5wVBMly/hBTWSVp4gtHH9KiDQNAvbm8f2ihhaYjFa5wV1bsyeJpvobHhS11uaaP3dX4vvOzaBNzSCe/UG3sQ0BMGu7qSRUnC7ObtSimop5Pt/NM/3/6jA7OTaFldKge8ppsd8/uzfznLjksPf+V9Wj/5kchpPPp/hjRcrlOfXv4C0koIv/1aeE0+mVuy8UkoxOxnwp/9mlp99p7RmT6mbr6s4G/LTb5c4/1adX/1bLXz5t1swE8ujF3EXoc5f/dutXHzX5uxbe/v8uh2kspKv/c0WBo8lGgoapRT1asRPvlXkO/9+jsmRtZ+3oghcWzFy2eWP/u9prnxo81v/pJ2BI42ndAghOPZ4ks//RjP/6V9Mb0jQ70Q9k18vM/7W94FYIOX6T2CkcpSGzxHYZZRS6Ik0Tf3H8OsV7MLGmpy2VTgFymXeH9+UugovclacRSaEhnabcAJBX+oEUmhEhNSDElP21Q3vw04ihWxYHA+sa1Dv7SgVEa6Qwrk1N27zLopeZFP0JzfpuKgTRI1fv4aGJg3CFX6/VRi5Fvq//LsUL7zD/IevxyugLcYP6xRqS+0XIhXEt91x+0z50gpbUVTsSSp247R64vAB9I42pGVi9HWTeuxholqdYLaAc2UI9+I1gtk5wmp910ejKsWQP/5Xs/zgv8yvu3Db9xRv/rRKMi357/5ZN5mmlVMvjzyTprXLWLdwEgIeOpXm87/RjLnKnLaZcZ9//39M8caLFXxv/d/ZyRGfb/4/s1RLEX/t77SSzjWuoWnt0vn632vl+kVn0zrt7keEhE98Ocep5zMrmpFWyxEv/Ic5vvMf5zaU/vRcxWt/WaZaDvkH/7ybvgONRxXphuDjX8rx5k+rnNtjwjd06xSuvA1AsqWb5sFHGT/9F9Rmbtw63wpJdeIqXY9/ETPdhF9bfwBnW+dJ+JFLaR2Fv40ICQhV0LDzKq7L0G77GRCCq5U3Gaq8s+dF013ZcEeGWMX4cXO7PdTCcN7NcnwPVEBE2Pi4EAK53TPgiD1NrJYO9FRmEz6bXYKmkXj4KMK45dciExZ6a57E0YM0f/XzdPz+36H9H/5N8r/xVTLPPYU50Iswd9YwthGeG/GXf1zckGi6SeArfvnjCq98v9TQmfwmuRadgycSK/7+bqSyki98o3mhA6vxfSrFkD/917MbFk23b+/bfzDHS39eXKz1uhMhBCefTfOxL+X2uP3+1tLZZ/C5X8+vKK6jSPHyd0p8+w82JppuEgbw3qs1/vzfz+F7jbcnhKBnwOSTX8mRSO3dD89INSF1C7daWLpIVRFerYjQNMxMy4aeY1sjTm5Ux4vW7+p9JxELs6Aa5W1v+7cijpQcyj2DHznUgzIzzt6enROqcMVaLUNYbCQqJIW2oiO6r7x1b7chCtyohrdh361bG1RqpeNi754MdhtGRytGV8eqdTVC1zH7ezH6elBPP0ZQKBFMz+JeG8a9MoQ/NYtyXZTv71jdeRQpzr9d54ff3LhoukmtEvHajyqc+nR21aLlAw8lEN9aX0bz2GMpTj6dXvH3ga/45U/K/Px75U0RTTdx6hHf+88FDp9McuzxZMPPP52RfOqrOT54vcb0WOPortHdRfLQIZAa9sWLmF2d+DMzmD29eBMTCMNAS6UwOjqI6nXqFy5g9ffhXL+BTCQwWltxru7NBbCmw6lPZzlwvLFXk1KKa+ccfvhfC5vqNq8UvPlShU98OcejH083fG6pCZ58PsMP/ss8w5d3d5R4JULfRU+kSLfvo+I7RGEsnoTUSLX2YSSzhN7GrjfbKpzsqLpDRnqKSfsylkzH4zsazOzaawTKxVM2SqllX4CETKMJfd0pO1MmMbVUw985YWVTP0OFwg431yTyfjNr3I2YA/3o+aY1RdCEEIhEArMngdHdQeLEYZTrEUzP4lwaovrKLwlmdqbTtVaO+PGfFZkc3rxOKYDz79YZv+HR2qWvKC77D1rrWt9oOnz8S9lVu7DmJn1++M3iljiVT9zwePFP5tl/1CLZYHiukILjT6Y4+niSmQkf1eDarxwXb3IKq78fq78PmUiApmHt20fkuijPIyiXiTyP9OOPYV+6hEwk0Vta0DIZCENUGLJDjjoboqlF57kvrRzVcW3Fyy+UtkS4FKYDXvtRmaOPJRt+dgDdAyZHHk0yetXdcPPCTuAUpyiPnKf7yV+hefBRvOo8oDDTTSRb+6hOXqM+N7qh59jWvIUX7lze1JRpmq1uIhWR0LM7th+bRagC6kGpYV2QKZNk9fWFIiWSnN7SMOIURN49D8u9O+q+MCNdGwqrpZPWxz9F53NfofXJ50l29MUFD3dgtXTS8uhzdDz3Fdqe+hzp/kOLYwxuku4/TMezX0LPLG2Q0FM52p/5IpmBo7dulBr5h58le+AEQjfJ7D9G+9NfoONjv0LziVNoyZWjF3ciTANzfy8iee+pJiEE0jTRshmsg/vJPPcUWvOdnbHbx+g1l7derm56HXu9EnH9orNq51pLh46xBg+oO+kdtDjyaGpV/6gPT9e4cmZrFohhAO+/XuPK2ZVX7amMxlPPxyND7kQYBsmjR9HzeWQygdANwnIFo62NsFzGaGtD+T6pY8cQpomWjL3qnKEhrL4+ZCqJNzm5q5sPVqP3QCxMGkebYHrM4/RLlVWPnfUSRXDxPXtVs1IpBY88m17XbMPdQOjWmXz/JabP/ByUIt2+j3THAEJqzJ5/lcl3/5LA3piP2rbK9fXO3tooAkGT2YEb1rG0FGk9T8HdmOLcDcz5E/RFzjLbAFMm6LQOUPbn7tnl25JpOq0Dy25XKErB9Lpnr62Gv0KR//1GsqOfVM8gQoujEFoqS8vJjzPx8p9THYpbY4XUyB06SeuTn0ZIjchzELpB8/EnqQ6dY+bNnxI6NUCQ6h6g5dHnqFw7S1C95a2lJ9O0PPIx5qWkOnwpHo0gJU2HHyFw6qR6D5DuGQRNR2o6fnme+sTwkqnnq6HlMlgDfRt2N1ZK4Vy8ij8xvaHtbOT53/hxhdoaO83uleHLLkGgVhQ4phXPs7tXP6ejjybp6l85Beh7Ea/+sEKwiSm6O5ke83n/1RpHHklirWBRcPJjGVo6dGqVpd9vFUVEnofe2oIKAoL5ecJaFb2tFefKVZJHjuBPTZE4eACjJY8/N4fyfcJaDWHoqGqFsLzZC7jt49TzWaxE42MiihSXPrAZv75158SRKy7TYz79hxp32AEcejhBMiPxC1vfyLIVhG6NuUu/pDj0PlKPvytRGMQpukYh0Htkm+0IduZDUMQdRUk9hymTuJucGtopyv40lWBumXASSLqsgxT9Sabd62tOXWno9CdPkGkQrYpUwIw7suk2DveLr9ZaSPXsZ/LV71E89zaokMzAUbo//XVaHn6W6vULoBRWWzcdH/8V3LkpJl/5C7ziHNK0yD/8DK2PfgK/VmXu3ZfXXROU3X+M8tUzjL/0LdzCFEI30FNZ/Mr8mrehNeUwerrWtwO3oWwH+8wFourmi/G1YNcizr5ZI9yi09LshE+0SoG41ATJBhGZ1UikBIdOJklmVn7c8GWXkSvulgZkohDef73Kl387j9XVeF+yzRpHH08tN58MQ2rvvHMrzbuwo97oGCiFOzwMSlF88ceLDxG6TmJwEKHruDeG92y0ybAEJ55MrlhuGQaKM6frW5oicx3FxA2PMIgHXDci06TRu9+kXNjDZS1KEXo2obf5r2FbhdPOjXdQTDvXaTF7Y2dsd2yH9mNzCQkYdS7QYvYscRCPZ8tlOJg6BQhm3WHCVSNPAlMm6E8cpzd5bJnrtlKKsj/HtDe0JfVDH5WapPr4dcpXPkQtONbWJ4ZxpscwmtvidJ2AdM8gRibPxM/+HG8+7kCNXJv5M78kO3iC7P6jlC6+s+6hmaHvMn/mDZyZ+DugwgDPvYcTixQYPV3IROOW5rWilCKYm8e5dG1D29kIo1dd5rdwREi9Gq16fZcSdPPeonaZJo3BBm7nN7lZWFwqbP1i5PoFl/nZgNauxldfwxQcezzFj/90hbbvO9+cmz/f+f+Ff0euS/3suT0dbeo7YNHSubLZZeAphi5svVnnzIS/ejQ0IekeMDn/zt4UTlI3SbX3Y2byiAalENWJq7iV9Xdy773KunUiEIzV43TI/TSfq+CNM+FcoSdxeJngyeotHM88x6zZz5R7HSesECh/0bVdomNIk7SWpydxiLzZg4a+zIrAjeoM22e3oL7po4VTmCLybqWrVeAT+i6mlAgZf7nNfAeBXcWvLL3YRK6NX5oj0dGHns6tLpxWuRb7pTn86gZG5kgNc3/fxm0VIoU7NExY2LnZgaPXvDUbQq4H31vd3VkI0O5xGG86q9E7uLJoDQMYuephV7f+HOfYEcOXXQ49vHxeJsQz2HoHTZJpueHuMBUEeGN7f8G775BJOrtytLAwE1Ce2/rMTKUYrhoNNUxBa+fusw5ZC5qZpOuxz5PrP07o2RjJHGHgosIQI5XDqxRwSjMPhNPdEAg6EoMMVd/Z6V3ZdHzlcL3+PkktR4vRtURdCyFIaGn6ksfpThxedFsPVYAANGGS1DIkZLqhKofYe2vYPsuUu7ftG3YDKvAbrLKX/iikjHPwDa64KooQt4msFZFyRWEThcGGxiEITWL2dq/78TdRYYD9wfkdS7koFbt9b2a797LniO722sQ96898u05Ty8rddLVKyPSYty1vq1JxvcxKCCHI5TXae4w929q+2XQPmCRWSc/OTfl4K/gsbSauvXo0VNcFTa0rH2e7mURzJ037TjB74ZeUxy7Q/dgXqE4NUZ8doWngYaRm4Myvf1YufESEkwIC5dOeGMSL6viRSz3YuZXuZlMN57lUfZ3DmWfIG10NB/9qQiej58mQX9M2lVI4UYXr9Q8Zcy7ec5H5A9ZBFOGV5tCS6WVdbkLTMbLNhK5NYFdZ9KsSIAxzyX2NbH5hLtPmo+Wb0Zo23pUaTM/ijW5s7MFG8F3F/GywZfVNW4EQ0NG7cpoHwK5GFLYw/Xg7SsHk6OqWJ6msRmun/kA4AWZC0NZtoK/SrXbwRJJ//u8GCIKtVb65vEYitbKAk1o8xHovopkJQt+leP0DvGoB36kSuDa16Rv4doW+Z/4qqbZ+KuMrTUy4Ox8J4QSKQHkktSxJLYsdlu8r4QRQCmY4V/k5/cmH6LIOkJCZdXU9KRUPRi4GU9yon2HW29jg3QesHRWF1MevETk2zceewJufJaiXEZpO7tBJzHw7lSsfLqbxwnotbrftGcSeGkEFPno6R+7QSTRzYzVIK2F2tSNM8+53vAvOpSGi2s7VT7iuolbeQ6oJQMQWBqvh2NGG5t/dEyr2i1Jq5cxtIinI5T8il5m7kEhJss3aquflTJPG4Ucapz63EyHuvf5ut6CiMPZdWHifQ9fGysYNT5Hvoojn1m2Ej8wRPW1fI6llccLafdvFVQ/LXK29jVIR+1OPLqlVUivGZdXinyDymPcnmfFuMOeNbcrQ3QfcG/b0OLPvvEzLo8/Rk87hl+eRZoJU9z7cwhSFM29ws+WmNjGEW5gmf/JjWK2dhK6NkWuBMCTyt2Yen9begjA3dtqIXA9vaBjl7ZwNReCpLU3TbQWCeFTLaviuwq5tnyC0axGBH2GYjaMTVkKSadqbkYvNJpGUZHJ7I/0lhEBKgZCb0r2/rfh2FaWihXqmOez5CdpPPIdXKyI1AyvTQujvIefwnUPQkzpKxmhh2h4iqWUZrZ/b6Z3adAyRWKhnOrQomm4KoqI/RagCDGkhkSgUoQrwlIMTVqkGBSrBHG5kEyj3I9Ppth2owKc+OYxfnl9S06NUhFecXahrUgv39Sh8+AZecZbsoZOYLR1EvkvhzC8pX/kQv3SroNGbn2Hy5RdoOv4kidZOpJmgPjZE9fp5Wl1n4fkWnwynMAmIBcfl9aG3tSD0jZ02grkC/uT0jraUh4Ha1FEk24KA5CrpFYhHrWzW6Ji1EAYKx1YYKwQhdUNg7dGUz2ZjWGLV9NhuQ4jNHue+PXiVOWbOvbpQ0gDViSvkeo/Q8fDzoKLFeqeN8JEQTgIwZIJ5dyKewya3Jo2xk+jC4mD6CfqTJxZrnOI6pSpD9ffnZN+bAAAgAElEQVQYdy4RrHMEywM2hl+Z58a3/+2y21XgM3P6xQa3e1SGzlEZuou4Vwp7egR7evlJYOzFby69axgw+fIL97bjdyAsE70ph9A2sGpWimB6bsdGrNwkUopwi+tItgLTWj19EkVqSxynV34+CPwIaHxMSI1VHc53CzJlofwA5a+8qDA686DAn17d8yxxqBd/pkhYWupPpmliT7wXK5E62kP6WC9CCmoXx6mdGyV36gDu+Dzu+DxaLkn24X2U3xvCbMuhZZPoTSmsrmYq7w1hD01jtGTJPTGITJrUzo9RvzoJd22iuDeiwGP+2ruLPwdOjbHT3yXZ2osKQ+zCOFGwsWj33pG/G0ABblijLbGPtsQ+avdZfZNA0p88Tm/i6JLCcE/ZXK69yah94YFoesCGkcnkusas3I4KQvzxSaL6DvvDqL3poSjvMgZDqVg8bRtKEa0SwBRCxKM7drFeEIZO5tRREgd7kal4US3TCWQ6gZZLgRQIQ0PoGipYeLFSIDNJtKY0worb9mXKQmtKk3p4ED2fWfY8UmPPjjExu5pp/cIjBKU63kwZocXSIff0Yay+VgCMphT5T51ASydIHuqi6xsfR0sYeNNlIj9Ez6Vo+/JjyIRBWHVo/9oTJAfat37nhST0PaoTV6lNX9+waIKPQMRJLqyEpuyrlP0ZIhViB/eXH1FOb6M3cQRd3IqXK6WYcK4w7V5f9G16wPYhkMv8sPY6MpVApjZWuBq5Lt7oxlqBP8rcLUomRDxrbNsSLEIgV/GiUkrFfkG7WKQa7U2knzhMUKggEgb1D4bIf+VZgmIVlKLy2lm0bJLsJx/BuThCUChjdreSeeoYoAjmq9TevUzus0+gvACzp5Xa+1eWP9Ea3oPyfECpEK7BymLrmZvyl+yyMHSEJqlfHMebWf0aKjSJN1Om9MvLhHUPUKQOdZM+0U/x1QuoSKHnUqSP92IPbcHIJSFINHeSbO5CT8YiNnCqOMUp7PmpDRdu3ffCKW00owkTXZrMOjd2enc2HYGk1ewlpTUt6dYIlMucN0rwEZkDd69ICYhblkk3f0Yt1l5zs6M/CuPf3bRPUmrhcagVRakm9A3PctttyERi447hnr9js+nuB3z3LsJJim2NakjJqu31cSpv50XAanjjcziXx7Avj+JcGgUZR8jqZ4bwJ+Kawqju4A5NxJEWTZI42IPZ24Y3NovZ14Y/V0YA899/g9bffL7h84Th3d+Ld35R5Yf/ZR7X2fmK7HIhXNQX3lSRwovvkzt1iOyj+5n/xTnKb15d+gAhELeJ6KBUI7S9xdCuTBhoKRMtZSKkpPzONeqXN38RJYSkaeBhOk4+j5VrXxy5opsJ3EqBmXOvMn/t3bj7bp3c98JJEybNZjdpvSn2vQG8yKbiz+7wnm0OujDI6C3LXMP9yMOPHninNCLfafDxX21F0wVTN1yuvl/lub/aRuBFCCn4xbdnae0xOf5UFs0QnHujjG5IHnu+CbsWUZ7zefnPZlBhhB81Fqaa0EjI7H3lti4sY8NWBGG1RjC39rl4D7gNxV07AXVDkEgKNmIOfy9ouiCRWlk4hb7CdXa3cII4hay35tCa0oSVOoQRkX3r/CkzSfSmDCqKkEmLoFzDm5rHGZogrNSJ6i7iof0Y3S2IZOPFxVoaEmqViCtnnW1xfr8XhK5RvzqFfWOG3BMHyH/iOOU3r6JcHz2TQOgSq7cFtNuqf+7IhYdVB2+ySOnNq7ijBaSlEzmbX0KSaO6k85HPUJ2+zsir3yL0Y+GkGQnyBx+n/cRzOKUZ6jPrD6Tc98Kp4s+hS4uc0UZCi0N291PHmEBiiOVfVENaWFqaB76Vy9n/UJritM/bP57n83+jk2opIJGSfOs/TPCZb3Sw/6EUhx7L4NZCUHDo0Qzz0z7zMz6vvjDHF/5GB5kmnXIhxI1qKBUtc14XSFrMbgr+3h8TcRNhmghzY2MYgpk5VPDgoFwPCijOrv7emZYgmdHYri9+Mi1XtCKAeKDsVo612SxqH14jdWIAs7cN+/ww9qURlHNrUWS0N4MmEFKiN2dwLo0hDR2joxmiCPfGFPaFYVIn9uNeGSMoLh9c7TkR9erq70Ump93zGJ7twOrO0/Ts4YXXn6I+NAVA9dwo+U8cw+xswmxvIqqvnOFwJ+apnh+j7cuPExTrCCmY/dF7+LPrm7u5Eka6CSE1Zs7+Aq+ytAklcKqkOwawsi0PhNNqhMpjzrmBE1ao+uufTbNbUUQN03G6NBlIPhxH14I5QhU8MLJcwHcizDaDTLOOEPGqWDcFmWYdwxT4ToRbD3HtiJkxj7lxl5Zuk1pJoCJF4KuFug6FHVbwlYspltb+CCRt5j7G7UvUo/sj6iR0HWFs0Ipgdn5vVmXvBlQ8y2w1rKQk17xNXkFrMOR0nYhKcfcLJ398jtL4retD/cOlI6bcoQncoaVO99U3Ly75uf7+HamrO7DrEdW7vBf5Nn1Xdt55s2UqH9xASyWIHA9nOM7YlN+5hj9XRUtbtHyQwY2qHOYUwZWIyQtXEUrQYvXRmtiHFBq1V0tUOq7Qlj9AVPcIK3FUryt5iFAFzDjXN7yvYeAR+g5CLj82hdSIfJfQ31g25r4XTkktixT6fXuyDpRPLSgSqQh5+5w6BHmji0dyn6PoT1IOZnCiGmEUrBpxUwujPEICgsjDUzZ+5NxXUbrr5+q091k8/Sst3DhfZ27SQzckz365hXol5Mb5OvPTPg99LEfPYILSrEdxxsd3IwJfMXXDwV+YJ1UL56mHZQyZWFIMLoQgq7dyIP04V2pv3x9motoa5uTdhWBufu856u0SlILpMY8wVCtGJVJpSb59e07rAujsWz11a1cjincRex8VnHpEcS4kitRCAf9y2nsNzMTuE05R3aN+YXzZ7coNqF+Kb0+2ncTzioza5+l0D9EStVFVE7hhncn6FaSQDGQepXR5gnl5kY7kATRfIoRBi9XPWG393opC6piZ5oWdDQmcOi2Hn6R04wzhwmB1zbDI9R0l8BzswvLXci/c/8JJbyKtN2NpGUZrZwAIVYAfrc85VCDRhI4lU+iicdpCCoklU3iRQ6j8Le1qU0TM+WP0RIdJaU1L91VIklqGpHaIbg6tYVsq7oJRIYHy8CIbO6pQDQrMeWMU/an7okOvWgz46R/PLP7c1mNSmPB48Y+mCRZqEGrlkImhxsfI6R/eqtGphxVK/gw5vQ1xR52ZJjR6EkcwZIIx+yLz/gS+WmmlI9CFgSmTmDKJUopSMM1uakcSUt6qkF8nUaWym17SnmN+JqQ0F9DS0fjck8pK2nuMbXEuFBJ6B1cXTtVyyPT4AysUgDCAqREPz1Er1oU1t2rk23Sm7zIDcDcSRB5zzihlfwZdWvSmjqFLg0gF5K1uLC1NUsuhS5OKP0t38shCYEMjXAgArBczm2fgk7+18JNCs5I09R+j5cDjBE6cNtWtNHoqhz03hpFqwq+t//nue+FUD4pk9FaajHai1LHF2ybtpe2ipkzSZvSjSQNdmOjCWPwbd+UZaBgL3VISSSyOGmHJNA/nPk2k4uhOpEJCFRAon0B5hMonUD6h8gii+N92WGHen1iXMCn6UwzbZxlMPbbiPq0FgYit9oVExyChpcnRRru5j97EMUrBDMP2Wea9iftq6O/8jM/L35pZV/ePImLcuUSb1U/6DuEKIIVGh7mfZqMLL6pjh9WFCF5cF6WhYwgLXZpIoSPR0ITGvD/Jh+Wf7i6hKsTKQ8nWgFKKsFK7b6O/20GtEjJyxV1ROOmGoO+gRTItt7zA2DAFA0dX7rKMQsX0mL8napy2i+ErLvVquKKDuG4Ijj6W5OJ7O+xztk5uloPEjVgCS8swkHmUgjvKrHODrNGGQOBFNkV/irzVi1IRZX+GcAMd4KFrMz/07t3vePP+3sbe3/teODlhlZHaB9SDIrPu8MKty0/cGa2Fk7lPsxantru1mUuhkdJyC8+0Fg8TRdGf4v3yj3Gi5UWFd390xHD9LEHkM5h6dJk1wUaRQsPSUnTIAZqNDq7XP2DEPk+wYvRkbxH6ivLc+oVgJZhlxD7HodQpdLn8giaEwBJJTJEgo7WsuJ3bPzMj3IXu9lEU/12vc3gQoLy9t5LeTVSLIZc/dDj5bLphukcIwaGHEzS3aFsunPYfTawo4AB8T3HxvfoDnXwbQxccSoWQfHtjuxJNFzz8dJrv/efCtjrAbwaaMGgyO6gF82TNNtywBihMmaDoTcaZGu3Wwn7WucGxpk9QC+Yp1Mc2VA4SOFVmzr6yCa9ibXxEnMMVBXcUUAji6NKdCOLUlhDirn/vhZtRnNX/yg2s5AW6MGk2Oklrzcu6uzaLm6/DkikOpB5jX/IE2gqpyo8a0ULUacy5QBB5Kw5U3szjaidQkdrQeAQVhLdMsh6wLjxXcfWcs2oUp/+QRfd+a0vduoWAh06lyDatLKJdO+LCOzsfOdlN363SXMi1cyuXiQgB+49aDBzZmEP/zqBI63kO5z5GRm9l0r6ME1So+HMcyJ6iK3kYO6gQqfjYdcPqgrgS2OE2+WdsEvd9xCkeQSIwZAKJRJMmzWYnIwv1TnsZTRi0GD30JY/RpHcsDPDVtvxEYcgEA8lHqIZFpt2huz/gI4AX2VypvYUXOfQmj5KUmS0TsTtGFKKiELHO04YKwxVF5QPWzqX364xf98g2N/6um5bkmc9l+fCN2pYNMm7p1Hnk42ms5MrnmqvnHCZHt9aAN1yDFt9tg3XffrnC87/ahN5g3SmEoL3b4NnPZxm54u6pQdSB8pmoX6YeFolUuOAjqBiqvI0uTSIVxpbBUQAINGEQKJ95d5xQ7a3w2n0vnEyZQhM6Xakj+JGLJnQMuTETv92AKZPsSz5Mf/L4sromhSKIPKrBPPWojB+5i/VWKyEQSKEtvD8JEjJNWmtGF2bjk7NM0J84TsEbe+BOvoCvXK7V32Xen6TLOkCb1U9Sy97T6JVIRbhRjUowt+s6GZUXoPwArHWmEXfRyn8vMzXic/7tOgcfSmCYjdJ18MQn0/QdtBg6v74mmNWQEo4/keLY46kVF2lhqDj90yq18tZGGH13YaTLKmS3y55hjVx412bkqsvgscZRJTMh+eTXcrzxkwrXzm7+57eVhMpbiCLdIlAeQXjrGiGQtFr9tCcGCJRPwdt7XnebLpzm/UneLH4X0SALuNl+Nldqb3GjfmbZZSkkwFn48JywihCSkjdJ0ZuM01pm97JtlYIZ3ih8e1P3714IlI8brS2sbckUh9NP05U4uKSzTymFF9mMu5eZcodwwiqB8m8TTaudYMRCOk6ioaEJA0um6LQG6U4cxpJLT5I32+3zRhcz3vAq242ZcYc5HXx3mYhQKOxNdte+UH0NXVjLjov4Pa5v6nPdiSKi4I9RDqYZts+S0fPk9DZSWhOWTKJLE4FcaBoI8JWHF9Vxwhp2VKYelHGjOl5k7zrfrcjzUK4HmfS6Hi90fcN2BpuNREMK7a7iX6IhEIS7oClCKfjFD8o895UcHT2NF4HtPQa/8lt5/t//a4r6Jtc6tXbqfPE3m8nkGgsSpRTDl1zOnq5t+biVajnEu4sz+cBhi1fF7ulJKEwHvPqDMn0HzBXNQ/sOWHzt91r4j//nFOX5vVFcf6X8xjLR1AhFRDUo4Ncd7LBCtMeiTbAFwilQHqVg5u533ATqYQlYPTcqFtq8y95MfOJTAXPu8gt9oDyKwdQW7enmIZD0JU/QnTi4pMZIKUUtLHK5+iYz3vA6ut4W4hvq/2fvvaPsOM8zz99X6dbNfTsndDcaORIgCYKZFkVSlCVZtmV7NJatdTi2d9dzdnb2zPGOfc7s7Jyds3t2xl6vZ2d27NkZWXKQ5CArJ0oiKSaQIJFzA+hudA63bw6Vv/2jGg00ugF0I4Piw9MEUF1V96tw63vqfZ/3eX18XJAW9aBMyctSD8psiD+yxKHcUKKk9VZmnVGup4B3pUXRt1CiEYLaZa0MohGCwFnYXBgaenMKd7aIdG/sgVH1b7zM9FbBky4VP0fFzzFtDy0QxsuJo1z0f7kCcnt3IW2HwL7x6KLQ1LAP2D2ElNZCTE0xYQ9cc72IEkcTGmU/d8317hQunLF56/tlPvlrjctGfTRd8MRHU5w9VufVrxdvGYExIoLnfynD9kfiVw0gurbk7R+UuHD29hePlAs+lXKYAr5a9GvLQzEMU2DX743vlutI3vp+iT3PJti4M7rsuBVF8ORHU8yMOnz9C7l7rgXLcqh5K9cphfqm+9fb7t56/bsNiGopuuJb6Uvupi+5m7XJ3TSbvXd7WDeMlNZER2QdyhWc15MOw7WjzDhDt9QqIMBnyhqk4i2dMBShEFWTYdRLCISuIgwNoasgQGhK+G8tvM2UmElq7yaEoYX5BFUhuWcDemNyoceR0C+6U88TDW1+n4Z2UcEffk5k/nPmIVQNRTPuyXSQJCDAJ8DHx1v4CfAWlofRpXvjwX41BLU6sn7jqQOhKIgbrci7TVCEQlprocfcRrPeDUCr0Ueb0U+PuQ1DRIkocZr0TjQlfHGIqxl6zG30mNtpNfoAaNbX0GNuY330IZJq020ft1ULeP1bRcYGly9GEEKQadb41G81sX1PjFsht9Mjgqc+lubFT2euadI4ccHh1W8UFzzRbic8VzJ1wcG/xjtW3yaTbXvit1Usv1qMnrN57ZvFaxKiWELhE7/WxM98tpHENUT4twKaLmhq09j5WJxY8n1PC24a73uNU90rMVE9TXd8G+O1k2giQmOk+24P64ZwsY1HTE0teUspejPMOMO3RRcTRuNmyCyT4tSFiSo0tJ4MiQfXoaZiBFWLwitHSTy4HjUZxa9alN46SeKBdaSf2YmajFJ66xRaY5L00zuIdDdTPngOa3CK5EPrMToayX3nXYgZNL7wINIPUBNRci8dRG+IE9vag5ZJ4GZL5L53AGm7tD74YRId/Uy+/W1qM9dPHX6A1SOo1vCrN5HqFAIltrSi9W5CIHClTdYZpTOygaKXJaFmKHs5pp0hXGkjZKhfM5Ww12VUiWMoUSbtc3QY6yiIKRr0NoruDKaeuGOav3PHLb7z1zl+5Z+1Eksoy0YuutdF+M0/aOOL/36WQ29UsWo3FrlINqg89bEUn/7dFhpbly+lh7DFynf+OsfE8J3TPZ45XOdDP9uApi0/pnSjyid/rZHZcWeeaN6xoV0VQQCvfqPI5gdjPPFi6qrWEulGjU/9TjPtPQbf+MIc44MOjn2LooemoKFJo6PX4MGnEux6Ik6l6PPHvzdBrXzvR7juJt73xCnAxw5qSCQZoxNV6PdiUGJF0JUIab0FRSx9+5hzJvCC2xMal4TaqeWgCCUUlidMhKbiTubwSjW0TJLY5m4qR4aIbeqiesSkcvg80f525r7xDgDuXAl7aIr8Dw/hzoRh3tqpUbRMAhQFoQiUmEn2q2+RenQzRmsDStQAKXGmcjhTBaQd+gLp0QRGqhGxXKnKB7glCOp1gnLlmmmR60FtSIdRwXth9mK+kEI6WPPPCFWoSAJqQXFBDycJXx6M+e9dIAPqfhnLrxLgIwidj001zqx74Ya82G4Eniv50VcKbNoV5ZlPLDVfhTDl0781yj/5N5384O8LvPK1AiNn7RWffk2HjTtjPPcLDTz1sRSxxNUjH74nefsHZV7/dumOuk6cOlSjMOfRHlte76Wogt1PJvjsP2/jy/9hlvP3iOC6MOfzlT/L0t0foW+Tuey8JATEkyrP/UID2x6O8cb3Shz4cYXzJ+pYtdV/h5INKl1rDbr7I6zdarJhu0n/VhMzFhLvs8fq9+38eCfxvidOISRTtQESejOedCjb2bs9oBuCJgyianLJciklNb9IcBfExIEMQnWO60MgsafyOONzKHETv2Lhl+uU3jqNm6+gaCqoClpDHK9cBz9ASomWiuMVa0jXQ02YKFEDNW4ibZegUkf6AdL1EEIgHS/8nNEs9tidv46ZjQ+DDMifPXjddfVkhqYte8md2r+kS/d9iUDizs4hXRdh3FhlqtqQCnVO16mEulMIpIchTHqj23BkHTuo4wbOgtcMQFJtoknvRhUadb+MROJLF+arV9X5alQFlUY9ihNYd0xnVy0H/N2fZmntMti8KzrffHopGpo1PvnrjTz8UwlOHqhx6PUKw2dsrFrYfzFM9wkUJUzbxFMq67aaPPR0gs0PxWjt0q8a0YHQJfzEuzX+5v+dpXSHm/rOTXu892qFn/5M5qo94FRNsPfDSdp7DPa/XObdl8vMTri4jgxJnpRhRHS+q5CiChQ1tDJIN2pkmjWa2jSKOZ+3f1i+4cjdIkgYPGXxl380w2/+QRudfctXMENIgLv6I/z8bzXzzMfTTI06DJ60uHDWJjvpUsqHTcllAKoGuqkQTygkG1QaW3VaOnXae3Sa2nTSjSrpRi2MUt4hzeHCOVXCVj2KEp5fRbl4zgVCDdeLp9RrZlV1Q9DSqeO6ksAP/eVCb975P5dZdqvbY/6EEKdQSF73S4sEufcbFNRl++NdepDfHggUospSwgbhm3iggpGOIQyN6IYuzN5Wiq+foHpsiEhXE36ljjw1QuAHWMPTpJ/aRuG1E/jFKtXjw8S29QASe3yO6PpOFE0lur6D2skRrLEs0vNxZopIx8XoaEREDWKb1xDpaib/yhGk5d6RK6roBun+HdjFLKyAOJmZNhrW76Y4eAzKd2CAdwDuxHTo/n2DxEnLpO8pHVrRm6Xs5UAIpAzCFjr2wKKXkIqf53z9IALwLyNUkoAx+wwxNYUnPap+gYTagCFM7kzMKcSFAZu/+KNpfv332tmww7wqeTIiCn2bIqxZH+HZn01TLQdkp1yKWQ/bkiAgGgubBDe1aZhxFcMIJ7hrRRilDNOGf/0nM4wM2Hf88VqvBLz+7SIPPZ2go/fq96WqCdZujrBmncHHPtNIdsolN+1Rr/r4PmiaQI8IonGFWFIhmdaIxsXChK+ogv0vlzn4RgXrFhXn+h68+2qZeErhM/+0lfaea3+vdF3Q3mPQtkZn+yNxPDe0YwiCy4K44lJ3JGWBpAhU7eLy2/v9i0QF67dHicYVdENgmApmTCEaU4jGwx/zsr9H4wpm/NLvkw3aNfVofZtM/u3frqVeDbBqwdI/a/N/VsK/27UAqx7g2BLXkeRnXEbO2TflzP4TQ5yAFet/liuZXx3EfI/NW/sECe/3pcK90IPp9l1KXTFI661Llkspsf0qgfBQopHQ58d2CWyXwHIov3t28fo+FF4+smhZ9egw1aPDC//O/2BxvyE3G1oV1E5cQDF1zLXt4ef4QVjhdbEaLxzQwptj+PS4bNnVsGjd+fWXXLfwKWRm2omkGnHKuUVl9fLKzxACoWrE2/tQ1LAE/+L6Eq7u2Hd5L7hrjVso4Z6WHCu3/tXqCriT00jbvmFLAq21GaEoyHukB59EhhYDl53qK/sDSgL8q5zXAJ+aX6LozqAKjaI3S8mfu51DXgIZwPF3anz+303zm7/fxrqt5lUjCUIINA00TSUaV2luv/HUtpQSKWHolMUX/nCakwfuXnuVs0frvPzVAj//281Er2F4KYRANwS6Eaat1m5e3eeo14i63SgCH17/dgnPlXzmf2ylq8+4biRICIGmh9HBew3N7Tr/3b/uoLVLx4goaPqtJWuhkH3l9234KA1Jk2NL3n25zJ/960kqN+Ex9hNFnFYCnQgb2EGMBAoqLjZnOEJthSEDBYVeNpGmiePsx+PWiSQDGSwrPBVCkFQbyTJyy5vCChTaI+uWbWDrSZuyN4fvOJTfHUCNmyAlfs0mqN96cWhguRTfPIkai4SfU7UXNE4XoceStO3+MMk1G1H0CPXcJPkz71GdGkZe/oohBJF0Cw3rd5HoXIcaieJVS5RGTlIYPI5XCwmbUFTS/TtJr91OtKkTI9WIFk+T6Fq/sKvyhVNMH/wRgWtjJBtp3vEU0aYOzOZOVMOk58OfIfDDcfpWlan936c6dclxXdENEl0baFi7g0hjGwJBLTtG4ewhqtMXFo07km6m/ZGPUpk4T3nkNJkNu0l0b0Q1TJxKnuzxN6mMLSastxJ+oYQ7lUVrunrPvWtBbUihNqTwZu4subidCPDJe5N3dQxSwvF3qvzHfznJp/9JC7ueiGNEbm8rH9eRHHmryl/+8QzDpyyCu8iFbUvy7b/K0d5j8PQn0uh3mVAIw0CJRhGKinRDGw/FMPCrFdREksCxUcwoQgujK16xyFvfL5OdcvlH/30LO/bGiUTvz+o2VROkMirx5L1RQXsxyhYxBRFzPhV4kynK9wFxEsRI4OLgcvPiaB+XSS5gEqWdHmIiiSJXcwMLIpjESKzKMXol8KRD3S+T1JZOWq2RPibtc/PeVrcGAoW2SB+9sR1oV7itSymp+xXy7hQAQc1e5M90uxBULYLq8uJO1YjSsutD+HaN+twEQtVJdq0n2bWBsde+Qmnk1EIUJ9rSTdcTP4uRaKA6OYSVnyKSaqb9kZ8m2rKGibe+iW/XAIEMPKzcJDLw0WJJ7MIs5ZFTC59r5aaQ87OGROLVy1SnLFQzhpJqojJ+FrcaXpfAdXBrlxl+CkHTtsdpe/DD2IUs9WzoohtvX0uqZzMT+75FYfAYF2cloWpE0s0omk6yeyNaNIFdzIbuDtEEqnZ7XfGl52GfHSK6beMNba+YEfSOtvcVcbpXEAQwcKTOf/iDCT7+3zTy3KcaVvVmvlJIKZmb9nj160W+8fk55qbvDQPDYs7nC384jZTwxItJovG7N3FHOruIb92GMzOLm51Fei5mTy/Ft94g+dAerKHzxHfuxsvn0DIZKocPYo+NcvK9On/yLyZ48dMZPvSzDbT36FfVbd1qOHbA5AUH2/qgou56uO+Jk4FBL5uYYYw5pm56fwEBeWYRKCRIE2N5bc/dgBNYlLwszcYalCuMWRJaI32xnQxWD950VY9AQaCnASgAACAASURBVBcGHeYGemM7ltU3SQKyzij14N4Q70hAMUycco6pd76LXZpDKCqpnk10P/OLNG17jOrUML5dQ41EadnxJJF0ExNvfYvShRMEroMWTdD24HOk+3dQmRwkf/pdZOBRHDxGkWOkercQ71hLbfoC0wd+uOw43HKemUMvI1QtjE6ZMeZOvr1AiK5ErLWHpq2PUp0aZvKd72IXZgCIt/fR/fQv0PLAM1SnhnEri8XGsbZe8gPvMfnOdxZImWrG5snebYQfYA+NENg2yg20XhGRCEZ3B/UjJ2/D4D4AQG7W4+/+U5bTB2s89wsZtj8SI5FWQwHuDc7BUkp8H6oln6NvV/nB3xU4+V6NevXemmTnpjw+/2+nuTBg8eKnM6GoXb+FkbdVpCK9QpHKkYNI1yXS1b2Qhl9I29sWlSOHiG7YiBqLL1Sb5mY8/v7Pshx+q8pPfTLNQ08nyLRotzSCeDHN6liSSsnn9MEa775S4eSBGuU7LO6/H3HfE6cIUTI0M8fdDZVfjtuV5r9IVjoi65d4OSkodJmbMJU4o/VTFL3ZeQuBleu6dGFiqgnSWgttZj8NWuu8fcNSzVfZyzFunbmVh3dTEEDgWpRGTmEXQ+d66QdUJoeoTg4Sb1+Lnkjj2zX0eJpUzxYqk0OURk7h26HVglstUjh/mHT/DhId/RQHjxE4t7F0WQiSPZtRDZP82UNYcxMLv6rNjFIeG6Bx8x7MxvbFxEkIfLtG9tibi6r1Avf2R/wA3OlZnOExzE3rVr2t0DWM3m6UeIzgZjyhPsA1Ua8GvPtqhVMH62x/JMbjL6ZYt9WktUtfKD1fCYJAUisHTI87nDtm8eb3SgwcqVMp+bdbTnfDyM96fOsvchzdV+WZT6TZvjdOZ69BLKmsOnojpcRzoVzwyGc9hs9YKzb2DBwHgnDdwLZR4wkiPb2oqVD2ELguUgZhqPCKYTm25OSBGoMnLb73xRyPvpBi+5447T36PIlafRpPSonnQSnnkZ/1mBxxOHO4ztF9VaZGHOq14K6mW+8n3KfESZAgRQPNpGnCFDE6ZC9pQsdeD5dpRqlxydJdQaGBZlJkUNFwsJhjZsXapavBJEYT7ZhEsbHIM8vtLCspujPMOhfoiW5DcJlzthCoaLRG+mjQ28i705S9OWp+cb5nnbPQnRqU+RJqHUMxiSgxTCVJXE2T0DJE1dSSiNblsPwqQ7VDtzQteCsQuDZupbhkmVPKkejagBFvwJqbRI+nUSMxjGSG9odfQAaXUg2aGUfRDfRYClU3bitxUjQDI5lB0SOk+7YRa+la+J0QCrHmLoRQMBINizeUEq9WuWsWB36xjHV2iMj6vlU7gQsh0Dta0dpacAYv3KYRhv3AvvCH01cV89YqwW03aZyddPnLP5pBjyw/hnolIDt5+6phZRC2JNn3Upn3Xq3QsyFC/1aT3o0R1vRHaOnSSTdpmDEFwxD4fiierVcCcrMe2UmX0XM2I2dthk5bjA3eXCXSnYRjS84dtzh/wqKzz2DDzmh43OsitM4fdzypokfCijnfC8XDVk1SKXqUcj65GY+ZSZfZifBn8oLD9KgTViBeB24uR1CvL6TwvUKe+vAgWjJF9eRx3Hweef4s0nGwJ8aRnre0GESGDvGDp2wGT82SyuRYu8VkzboIXWsNOnoNGts0Ghq1sIItIlA1gQxCj696LaBW9inmfHIzLtlJj6kRh6lRh4lhh+mx0I7hViOf9fjSv5/FjN95jZZAEE930dSxDccuMTX0NhKJECqZ1o2kGnsZOHIeuz56U59zXxIngSBOihQZTKIoKBhEFkqIXRyUy0iFhs5attBMO958mwsVjXZ6ucAZZpm4oQq4NE2sZzsGJg42IGmiHe02nlZJwIXaMWJqaj5lt3TiMpQobZE+Wow1+NLDlx6S0G+Jeb8WIcR8NZ6KIjRUVMR1+jJc9Is6VzvAjH3htriU3zyufPjI8K0OwtpcQsE3gL4g8l68jVPO4VQKyOA2H58QCKEiFJVoSzdmY9uSVazcFL6zNJIU3M0ZzPexjp8h/sgutJamVacPtKZGjJ5OnOHRq1cY3iSKOZ9vfP7uemcVsj7f+ssVjkGAmTGJpCI4ZYf6XBgF1aIaekynnqsvubWFIjAzJnbJJnCvfR5dR3L+REgkDFMQSyhEogpGRKCqAkURSBmWtXtuSKBsK8C2QEtGqM+5BB4omkKkIRKO5x6NOF0OKWF8yGF8yEE3BNGEgnnxuPV5XyEhFnx/fF/iOZcqsKx6gHsDTt1+qYhfuvQSJz2P+sDiCL1fDrWO7uzMivZZyvsceavK0X1VDDO0TTAi88eiXfRICq+jDMD3w2vpOgGOJbHrAbYtb7tdRDnv890v5W9wa0HX+qcYP//GDVcIa3qVhlZo6tjGmffmCA9YYJgWrWt0NL3xpt3X70viJAmYZpRpxmimnQwtjHCWGcaXncw76KGNbkY5zzhD+LhEiLKBHfSzDYs6JVb3kNXQ6WE9URHntDzMHJMoqHTQwzq2497CarorUQ/KnKnsw497tEb6UK9iRRCSIhWd1WtRroQvPQruNIO1g8w5E9xrXlgSEKqOZi4ukxeajhZNggzw6mEE0qtXkIFH4dxhJvd/l8Bd7lpdx8ZgxbiG/43n4lkVvHqF0Vf/lurk0FVWvPdmKHt4FPvcMFpTBlYbdVIVYru2UTt4jKB0/zb6vJVQVIWW7S1s+PgGSqMl3vt/3gOgeWszPc/0cPA/HcSrLybLRsJgx2d3cOarZygOrzz661gSx/JhBRW4ya4ku//b3Rz4jweoTlWJNkfZ9svbOPSfD+FWrh0tU3SFpk1NzB6/M03frwfXkbg5n9I9YoVxo5AS7LrErq/sGt5NGGaKZKYHTY9i1wuUcsMEvouiaCQauokmWpDSp5gdREpJY9sWmjsfIPA9fN9hbuIYZrwZGfjUylMIoZBuXk+lMIpQNFKZHjQjhmOVKM4NEvgunlvHqmYXon0hJI5Vwq4X0HQTACFUUo29WLUcdj2UQiQzPbhOFat67eKV+5I4XYJcIErysr9fDoMIjbRhYzHOIB7hl92ixiiD7OIJMrRQobAq520DkzRN5OQ0BWYW/GBmmaSFLqLcmM/NSlH1i5yp7KPiF+g2NxNRoghWrl1YCaQMj6nmFZm0zzFlD1L3y9xrpAlCeqJGTGJtvZRGTs+n2ARGspF4ex9OKbeQ2nIqBWrTIyQ6+4mkm7HmJuejUmLec0lF+j5SLp6oAj8Mp2tmPFxn4Yt50URpMQLPRdEjKIZ52TqX1pWBT3VyiIb+naR6NmPNTeI7Vvh7IRBKGAUMid09ds6DgOr+w0S3b0ZNJVa3rRBE+tYQ6e2mfuz07RnfXYYaUZGBRPoSLarh1T2EGkZ6pZRoEQ0U8B0f3/IJvIDR10bRYzqZ9ZlLOxKXfoQi0EwNzwrvS6EKRl4fWYhOIUAzNZAhaQn8ICRbMowUaaa2UIbtOR6+dfVJV4tqKLqCal4q3VY0BRlIRl4dWbStUAVaNNx34IWfqWgKqTUpNv7cRspjZQIvwKk4C8cgNIH0JV7dQwbhOZJSoupquNwKl188JkVXQIJbc5G+XDgeBHh1j8C7914ubha7H9T51c/GiFyjofLl+PGrNl/7hzrOLXpnb2xU+OyvxdiwcWU0YXbW50/+uEo+d+laaHoUI5LED1yaOnegqAa56VNk2jbR2LaVcmEUgYqianiORRC4oeeSXcH3Qw1YpnUjvmtTq8wgFI22nj3zREeim0kC36WxfSuKZjA3cWwVRyhJZNYQTbYxfWE/QlFo632EyaF9193yPidO14dOhAgmNaoLpOkibGq42MRJoqCuijhFMFFR5/d7aYL1cLGo3VLipBoxQOI7dbRIgsBzCHwHK6gyVD1M1h6lw1xHRu8gqibQhXnDBErKAFfa2EGdqlcg64yQcycW+nLdq5CA9D0Snetoe/A5qlNDKJpO48aH0aIJpo6/gT9v9+vVK8wee4OOR16k++lPURw8hlPJo6g6eqIBs6GV3MCBRZYDAE5pDrdaJN7ZT/POp7DmplA0DbdWpj47tugNRwYB9dkxGjftoWXHk2hmnMBzEIpKbWZ0wSeqPHqG0sgpGjc9jJ5IU50YxHdtNDOOmWkHYHL/d/GtO+lFvTI4w6PUjpwk8cSeVfuiCDNCbM8D1E+dA+8+Ec6sAmufX4tdtKlOV9n+q9s58l+OkF6bRotq2EWb7se7USMqtdkaA18doDZ7baG8oim0PNxCx54OTv1NeF9u/cdbadnewr7/Yx9O2UGP6+z5p3uozdSItcTwHZ+Brw9QGinR+6Femrc2E22KEm2OcuYrZxh6afkIZ6IzwZZf2oIe13EqDooWvpClelNs/fRWIqkIc2fmCLwAoQm6H++mc28nqq5Smaxw5h/OkFyTZPOnNtO6s5UHf/dB6nN1jnzuCLGmGOs/sZ5oUxTpSwZfGmT22Gw47mw4bhlIzn/7PNmTWTIbMvS/0E8kHcF3fU79zSkqUxV6P9RL645WhCqYPTbL0A+G8O179/l0I2jvUHnhxQjxFWqFsrMB3/z6/IvXLUA0Jtj7mMGjj67M4mR42OM//2mNy5N0dr1AuTCKqhrEEq3EUx2U5oZoaNlIbvo0uakTgJh/cZUUs+dpbN/K3OTxSxKLq8CuFynnR1E1g2iimUS6a1XEScqA4uw52nofQdUjROPN+J5NrXT9QrP3PXES867Py12E0CM6QCzjxn39/SqAmBdbX77P5SNfN4NEQxcSSWnmHA1tG6mVpqgVwgqsAI+iN02xMkNMTZLSmklqTcTUBkwlTkSJoimRhV5al8YY4EsPTzp4gYMd1LCDKjW/TM0vUvHyVP3iPePwvBJYuWmmD/yQpm2P0bTtURTVwC5lmT74Q/IDBy8RGxlQGj5O4Lk0bd5D844n0aIJpO/PezANLZT4Xw6nlGP26Gu07HyG9odfQNF0fLtO9vibYdTq8tCwDCgOn8RsbCe9djupvu0gA6y5CUZ//JUF4hS4NlPvfh+3UiTVt5V03zaEquM7Fk5pjuL5o4uNO+8hBLU61XcOYm5eh97StKpthaJgblqHuWEt1qnbZ9h5t+CUHWItMQI/rJiKtcVIdCSoTFTIDeTIn8uDhN2/s5vGjY3XJU6tO1vpeaaHga8OUM+GEaYTf32CR/7ZIyjavHZPCFJrUuTP5jn996fZ9KlNdO3tInADOh/p5MSXTqBoClt+cQvTh6ev+ll9z/bh1lyOfu4ovR/uJdOfQUpJ4XyB439xnF2/s2uBKBtxgzVPrmH8nXEm3pnAiBu4VZfs8Syn/FMoqsJb//tbC3O5VbA4/+3z1HN1+p7ro/dDvcwenyXRmaA8XubQnx5i7Qtr6Xmmh8pkhQ2fCFOXx/7iGIqh4JQcmjY30bKthZNfPkngBTz0uw+RP5dn7vQH3mD3Gpo6thNPd2LXCxhmErtWQFFUFFXHsQrXJUfL4WJQoKljK4mGNdi1PEYkiecu34j+WqiWJgkCj3iqnUTDGkpzwysa0/uGOF3NbNLFwZU2JjEUlEVRJR0DAxOL6qob5DpYBAREiM5Hq8JJU0VD59aZEEZTbTSt2YWqm2Q6tgJQnltcjaQLk43JvSS0cPJShELdLzNQfQcnsFCEgjJP9C6mii6SJykDAvx5EbmLJz1u5o1FFTp9sQdojvSsaP1A+pypvEXJvTkdxOyhVxCajp2foTY7hh5LIhQF37ZwKvkl5EMGAeWRU9RnR9GiSRRND8+F5+LVKwvRqcuRXrsDTY8y+srfoBgRhFCQgYdbLRN4S/UeXq1E9tibqJpJ7uwBAtcKCVE5f8V6ZWYOv0J+4ACqGQvTc75H4Fh49QqBdyn2bpfmGHn5y0h55wmtGg3NFP36pWO1h0aovPku6Y9+aNW+Tmo6ReLpvbgTU/jFe8MP7FahPFEmsyGDW3PJncnR0NeAqqvYRZtUd4qOPR0YSYPM+gzj+5b3+LqIeGucjT+3keEfDDN35hrkQICVt5g5OoOVtyiPlWlY2wASAi8g3hZHKAKn4lwztZVck2TszTHsks30oWm6H+++6rpu1WXywCQ9T/WQaEswtm8MOXvp+XHlS2QkHWHNU2uItcZItCfwXR+BwCk7TB+exspblEZKpPvSoVg+HWH6G9PYxUsFEsnuJG2724ikIgR+gJEyiDZFr3kO70cMnvf44l/VaW5RSKcVGhoEyaSCEYF4XJDJKKhX6Ut4K1AuBXznWxYXhj0a0grpBkEqrRCNCiKGoKlJuU4aUZBp3cjM6EGKc0N0rX8aRagE0ifwHSLRDJXCxEJrLCnD+UggUFQD37dBSgLfRdUMFKESiTagahGEEDS0bmRu4gSFmQE6+59Aj6w+yyNlQDF7nqaOnQS+w9zk8RVtd98TpwCfAJ8YySXECEKCk2eWLvpppZsZxgnw0dDpoAcfjwLZVaehbOpUKNJIK0nSFMkhEKRpJEXmlkWd6uUZpgf3AYJ6aTokO1eQAEUoJLRGMkb7wrJA+thB7Y5bBggEMS29aCzXgi9ddHHzRPPy0nyvVlqI6FwPXr2yIBq/HvRoEtWILkvErrp/q0p5bGC+5cvVxbTS98JjuI7FgPRcrNxd8CwT0PzYWqQXMPPauUvLXY/KG/sxN6wlum3T6napKES3b6a+7TTVtw/etgq7u4HKRAUzY+KUHWaOzLD2hbVUZ6pIKdnyS1s4/53zzJ2eY9dv77pmQ1MI9Ur583matzYzsX8CK3cVi4x5guTZ4b0pZagRqufrlEZL9D3bR3GkyPnvnscqXN1mQwZyIaJ0vZR/4AUM/3CY7IksvR/qZfdv7+bdP3mX6lQ1lOrN/3fxebjxkxvxLI+jf36Ujj0ddD/RDQICP7h6qu3KhEAA04emOfaFY7h1FyTvuzQdwMAZj3/3f5YXWlgKAaoqiMcFH3o2wu/9fpLGxttHnEolyRf/qoaqsmgMEVOwdq3Gv/xXSXY/eK1nt8Sq5Ug3ryMSaySWbKNemcV3LfIzAzR1bMeIpkFKCrPnqJWncJ0qjl2ms/9xrGqOuamTVApjtPXupb1vL3okASK8t61qjlTTWgwzSSzdjlMvIRSVZKaHdNNazFgTLV0PUClN4lplkpkeUo19RKJpmjp3UM5dwLFKVApjtPfupTg3iGOtbN6474mTRY2izNFBLyYxfDx8fCYZpkYFiWSSERKkWcsWmmjDm6+qS5JmjEEKXHqLS5EhQRoNgxSN8wSrlzoVPDyyTOLh4uEyzhAb2ckGHqBMft4WwcTBQrtVUScpqRWnMKIpjGgSu1YMm7zeg5VWFyFlQCB9Lj42L+JmheuxBp2OjQnO78/T0G6SbDYYPb6yG3210GIp0n3bMVMt+K7F3Om3AdDjDbQ98CyKHqE0eorKxDkUPUJD3w6iTZ3Y5RyFwcN49QpmQxtNm/cSeC5CHFug0lo0Sbp3G2ZDK4Hnkj39Nm6lQLytl2T3ZoSA4vAJatkxlov+CVXQ9EgfDTu7cIt1Zl4/R32iROdHtlC5kKN0ZpqG7Z2YbUkKR8dpebwf3/aIrclQOj1N9u0hAscjub6F5sf6UTSFuf0XKJ6aRPqS5KY2Wh7rR43pVM5nmX5lgPbnNtHx/BYCPyCzq5u5/RfIvh1qZIJShdJLr6F3tKJmGlZ1nZWIQepDj+OOTeKMXDvycj/BrboIBEbSoDhSRDVUhBA4pVAz5FQcEp0JGjc2MvnuJEIIFENBjagLwmffCclAebzM6b87zYZPbqD/xX5O/c2psMmrqaFooYD74v6Xg6IpRFIR9LhOrDlG595O6tn6VdOD+XN5Wne2khvI0bm3MxRmw4JYXFGVUPBuhYL3dE8au2QzdXCKlh0toWgbQn2UoZBem8bKWVhFCy2qUc/V0aIabbvaFtKMME/0LoNdDjViXY924ZQcVEPFylkUhgp07O0g3ZumcKFArClGYagA92ZG+4YhJbhL3rUk1apkatrH929/wYjvhz+Xo16XjBs+tdr1P39y6G2SmTVIGTA68KOQRMuAwswAjlXGjDciAx/XCTWcMvAZP/86iXQnvu+AlJTzI0gpiUTTlAuj5KZO4Vglpi/sJ9GwBiFgbODV0GZmvke7Vc0xOfz2ZQ3Yw59Sbjgc2GX3WhB41KtZauXphZ6i18N9T5zq1BjgCB30EieFRFKlvCjyZFPnLMdopYsGmokRwcHmLMeYYzrsjj6PFBmaaJ9PbUFVlkiQnt93QIE5PFwkkiwTeLi0s4YYyflKvfMYRGiibYn+6UYRTbbQ2LU99FEqTWGVZqiXV+b9cacRSJ9pexA7qKErEXQRQVciqMJAFSqmmsBQVh9W1wyFjo0JNjzWhF31ae6NgeC2ECdFj9Cy9YnwCz54JNQyzbtyR5s6mDlyJozAbH6M+twE6d7tmJk2CkNHibX20rz1CaYPv4xTyVEcOUnLticRiga4KJpB85bHEKpGYegoiqYTuBbRxg4a+h+gNHYGIRSatz3B5Hvfw60Wloyv4YFuMg90M/3aWRK9jfT8/G7Of24fdr5G54tbUTSFtmc3MvPaORRdpePFbUx89zi5g6N0fmQLTqGGk6vS8ZGt5A6OIj2fzo9vxylZBLZLz6d2kd03RH2yGFZKuT65AyMk1rVgZyvMvHYOr7w4YmGdHaL00mukP/4cSjy2KvKkd3eQevGnyP/tN/ELN3Y9VV0Qy0SoZK1F7xRGXEPVBPXi0geiqivEMgblWWuBnxoxlVSrSa3gUivcXHnS3Ok5VFPFKTnMDcwRuAGVyQojPx5h/cfXU5upMfLjEay8hdlssuHjG8isy6BGVHb9zi7OffscbsWlMlHBLtqc+coZNv38JjLrMpgZkzVPrUGLamz6uU3MHp9l7M0xiiNFfMsn2RLBt1yq01UaNzSiRTWO/cUxfNun7/k+uh7v4uzXl9eWXXjlApt+bhM7fm0HpeES04enkYGk79k+2h9uRzM1dv7GTibfnWT64DQtO1to3NBI4AWMvDISRpuA2myNibcn2PrprZTGSpz84kmGXhqi/8V+dvzqDvLn80RSEQigNFJaqBa0Szbl8TJO2eHs18/S93wfO399J17d4/TfnyZ/Ls/gdwfpfqqbvhf6KA2XKI7cW2a8HyCEYxWZm1x6bYLAo1IYpVJYakRpVbNY1eyiZeX8Ba5QOOB71ry4fDFKueWLHvIzi320hFCIJVtJZnoJfJdybuR6h7OA+544gaRMgTJLJ5jLYVNnlHOMcu6a640xyBiDK/rkgIAc0+RYKrScYuUX4XrQzRTVwiTRZHOY31Xv3csW4DNrX2DWXqzDEigYSpSNib10xTaver96VKW1P0HTmijr9mZwrYAzb2Svv+ENQI+liaSbmTrwElZh8bWtzYxQHh8g8Bwy6x4kkmoi0bGO3MB+qtPDuLUSHQ//NJFkI1ZhGrdSQF72yqZFE5iZNqYP/4j6ZS1WYmt3Em3swHdsZOBjNrQSbexYljg17enFbE/R9FAPatQgsa6ZaEeK/JExUhtaWfcbjzP96gD5Q6NEO9LY2Qq5A6PUxvIk17eQ2tiGna2Q2tiKX3MIXJ9YVwOpDS0IVcHOVpl+dWBRsMvJ1/DKFk6+Rn18me+a51F58z3UhjTJZ59AGCtvLitUleiOLXhzBYrf/iHSWn3rmFgmwgOf6Gb/l4ZwapfOdzSlo5vqssQp3miw8+Pd7PvLQXwnZFtmUmf7R7uYOV/m5Es3lxI9+81LxOTM3196aJ//znnOf+f8kvWP/vnRZfdzUfTs2z5H/suRheUT70wsWffI/xf+/rHP9nPuzRlGXhuj79k+Ai+gMFhA1VUURbmmjNHKWRz5r0eWLB/8/iCD31/6bLz82C6Hb/sMfG2Aga8NLCzLnsySPbn0e3v5cWVPZMmeCNcp1Uoc/dzS8zLxzsSyx/8BPsBKIYSCmWhB0QymR97Fc1feAurenYE/wAKceoHGzu3EM51UcqOUZq5N/u5FSALcwMaTN9Ziol50OfCNCc69M8fs0O3tcSYUZd5FealuwrNrC1UXUkqEqiMUZUEcLoMACK7aikSIcNIKroh/K6qOWy1Rnx0hCHyq00NY+WWaVguBGtGpjRcong5J3ey+QeqTRaQb4JYtjEwMp3DJaVr6QeiJIyFwPNSIhmJoOEWL0sAMvu1RODFJbSRP8+P9+NZVrtF1IvPStim//AZqMk58726EtvLHixIxSD71CEG1RvlHryPd1eddkq0me36pD0UTHPraKKqusOXD7cxdqJIdqhBtMNjx0U7MhI6iC868Ok26Pcref9yHUASHvz5Kadpi6kwxjJgJWPNABilh+kyJ9U+0MHasQHlmcbSt/7Fm0u1RInGNCwdylGcsNv1UG2ZSY/psmcG3s/Q+3ETXtjSu5XP02+M0r02w5oEMvhMwuH+Oji0pGjpiOHUP35PU8g7JlgiqoUAAZ9+cQY+orNkVejydfWOGVFuU7u0NSCmZOVdm+L05tr7QSTSl09Sb4NybMyBZKOvf8z/sIQgC6nP1D0jHTxiEpiM0nWCZopdrbqcbCEUlsFdfsXavIwg8cpNLI1YrwQfE6T5AvTTDjPMuxnQKp17CtX8y3ZZlIPHsgHhGp3VdgpnzFar5W9/ry3fqEPhEM214tSJC1S9Vtl2hwwg8B6ecI9rchZWfItLQggwC3PryVWK+axP4LtHGdtxKHqFqBJ6DXc5hNnZgFWZwqkXUSBRvuYeclFSH54i0JimdnkJ6Aaqp49UcEv3NpDa3M/KVQzQ+1EN5IEznGo1xYl1pvKpNrKuB4skp7NkKfs2hNl6gPl5AS5i4pTq10RzpLVuJNCdwSxaKoeFVbGQg8W0PPWmiRDSkFyD9palov1Ci+J2XEYZObNd2hL7yR4yImqSeexLpOlTefG/VkSfpS459d5ytz3eQWRNj7EiB3GiNaDqMfpkJjVRblMmTBRo6YtTyDr4bDVfuGgAAIABJREFUcPRb4+z8eBcNnTGqucvSczIk7H17mqjlHVJtJtUfLx1TsjmCDCSHvz6KZwf0P9pMU1+cydMleh5sIjtcZcMTrbz5+XO4lo9uqvQ+1MSZV6fIdMXofbARI6ZSzlpE4hq+E9CxOUV2uEq80aAyZ9O9M0OmK0ZupEqqPUrX9gY0Q6VWcBg9kmfzs+3Uiy7J5gj7vzTM07+9IVTyApWpCkc/fzQ0l5SSwL2GEPsDvC8R611PrHstc/t+RLBMC6erIdG/CaOxjbl9P7yNo7v/8AFxuucRPvxcq4xrlUk0rglFpvWfvJx+PGOw8YkmXDugsStKLKVx4uVb387BrZUpDB0l3bud1JotBL7L7Ik35ktmLyNOMkAGAfnzh2je+jixR38GhKAweATPqpLu20mqexNmYwftD71Aefwc5YkBCoNHaFi7k2TnBmTgM3viDapTQ5gNrbQ+8KFQLFktMnv89UVWBBcx88Y5ej61mw2/9QSBG1AeyjL1w9O0PbuJwtExpl4ZQKgKHR/Zwszr5xACWh5fR+szG5BuQOHYOG7JIr62iZ6f30Xg+rgli9F/OEzxxBTprR2s+43H8C2P2liB8W8eJXB8Sqem6PzYNtb/1hPMvjVI/uDyjTK92TkKX/0eQteJ7tyCUFbmkyaEQE0lSX/0WRTTpPTym8j6ypssV3M2VtnFqfqo6nzU0AvCvowCfDeMvPmu5NTLk0gpqeZs6vPbKLqy4NAt5p07StN17IoXRpuOFgi85cNupWkLq+IhxLxLt6HiWT6Db8/i2T5CBafu4zkBelRFCPDsgMCTKJog8CRWyYVAEsxXtdkVF1UXOHWPZNxEiyiousLMuTK50Sot/Qncuo9VCd20VWPeXdv2F9sNSPAt/5pO4R/gfQoR3tOKqqHoxpLlLIinIVxxXps4v1yoGoquL97uHi5MulP4gDjd42heswtF0xfSJPHGbmaG9v9EEieAhnaT8pzDyJEiiaZb55e1CDKgNHqaWnYcRTcWiEx+8DAIZcGKYGL/t/CsKtL3mTr4A9RIlMB1cOulsCXJ9BBWYQpx/LWwfY1TQ3oe5fEB6rkJFD0CQYBTLSB9j+ypfWjRRBgad+xlvaEAnFyN4S+/h9EQA8AtWwSOz9jXjuCWLALbY/L7J9HiEdSojjVbYeL7J/EqNm7Zwi2GabyJ754g0hhH6Ap+3cWt2BBIRv/hMEZjHKEKvGqogQIonJigPllEiWg4hWuH/L1sjvw/fAeCAHP7ZpRVaJ7UVJLU80+jNqQovfQaXjZ33b6BvhtQnrUJAkmt4ODUPNo3peh9uAndUMmP1/AsHyOq0bYxSXN/nIEfz1CeDY+5WrBx6z5d2xtYs7MRiaQ4aTF5qkit4JDuiJIdXj7SWyu42JV5C4AAxo7lSbWaNPUlmLtQoVZwGH53jsd/tR+76nH0O+OMHs6z7SOdoXv2O1kauqLUSy6BHxKn0oxFvTTfHqrs4lkBk6eL9OxqxEzpzA1XFiJmvispz9nkLlRpXZfk0V9Zi2sHeHeAKAkB8YQgnRLE4gq6BoiwEsu2JbWqpFQKsFbOf6+JaBRSaYV4XGAYAlWBQIZ96Go1SbEYVp3dDsRigkRCEIsJdEOgaSHPCCR4LliWpFoNKJflMtVwdx5mezcNux5DBkHY/NdzQVGJ9awjsWE7ihHBnpmgeOIA0nVJbd1NtKs37G4wOkjxxIFLO1MUkuu3YjS3Uzi0D3j/pe5Wgw+I0z0OLRKnWhhfcKXWzMS8juYnD7WCy9TZCnOjdaoFl9oygt9bhZAsLRZB+1d4N13uLu7Vy3hXpOeWW3Zp38tUmrg2jruyMLpXtvHKi9e1Zy9N7H7dxa+7RDvTyEDi5KpY04vHEtge9WUqXvy6u6wAXHoB1szKjSq9qVlyX/4G6Z8uE3/8YRRj5URXiUVJPPkIemc7pZd+TP34aVgmNXgRtbzD4a+HEbDTr1zShk2dvlSlt+HpVkrTdYpTdVJtJlbZXdjm1A8vbTN+7NKxt21I0rElzdjRPE5ted3V2dcXV7hWZm32f3l40bIzr05z5tVLhQbD780x/N4lG5TJUyt7ERo9fKm0aObcpWtx+GvhcVz5uTcCRYEdO3Ue2XuJ7L69z+H4MW8Rf21sVHjqaYPHnzDYvEWnq0shmVJQFKjVJLm5gLExn4EzHkePuryzz2Fq6saeXQ0ZwSOPGDyy12Dbdo01PRqZjCASEXguFIsB4+M+J0967H/b4Z23HWZmgpvu022asH6DxrbtOlu2avT3a3R1qzQ0hORN00LLgEpFMjvjMzLic+qkx4H3HA4fcqlU7k6PSaFqpHfswZoapTJ4muZHP4zQdPRkitS2hygdP4BTyNLy9EdxSwWc3CyJtZvIvvMKXqmA0PXQV00CQpBYt4VY30by772OX69CavXdNq6H9naF516IEI1eqsidnAj4/vesmyKiQsD69Ro/9eyl54+UoU/Waz++scrZD4jTPY7syCE851KfMqdeWCiN/0mDa/kMHyrQ3BfHcwImB95fbtO3A/ZclQtfehcnf3sF9VeDXyhS/PaPCOoWiacfRYlFV2xVIFQVc8NatEyaSl831X0H8ebyS41lVojRQzlqOQcpJRMni5Rmrh8GqZddzr81y9xw5ScmQ6Gq8NjjBv/z7ycXlv3f/1eZgTMetg26Dg8+ZPBrvx7jkUcNGhuXTqKplCCVUuhbq/HEkxHGx3z+1b8sMTW1umdXxIRHHzX45V+JsftBnaYmBeWKvoiqCq2mSmubyq7dOh/7mMmxYy5f/mKdV1+xqK3y1hcCmlsUHnvM4PmPmGzapNHZpRCLiWXvXVUF0xQ0Nyts2arz4eck01NR3nvX4XP/tcrxY94d93YVqoqWSJE/9BZeqUBtbJDYmn7UaIJ4zzoUXSdwXYyGJvR0A7WR89i5GTK7H6M2fI7qhfmKUAFGUytGcxv5997Ayd+eSmYIjTU/8ckoe/bo882w4fw5j+Ehj+PHb9yky4jAL/6jKL/1O5ecxW1L8m/+tzJwjxMnVejzDWijqEKbd/8MK63soIoT1G95j7drQaBgqnEMJYYmdAQqYe2Xhxc4OIGFE9RvSWNbVehElDiGEkEVOoqYF2nizR9/DSdYvjnj5aQJwKmvzOcm9JBavD9VaJhKAl0xw2MWgkAGeNLFCWpYfvWWeU/dDsQyBg/9TCeVvMOabSliDTqD7+avv+E8dGESUWPowkQV2vyXM8CXLk5gYQc1PHmLWovPQ6AQU9OYahyBgicd6n4ZO6ix3PU2lCgxNYUmIguViHW/hCtvjCwHtkf57K3Xga0GfrFM8Xuv4s3lST3/DFpr06p8ntSmDKnnnyG6fTPVfQeoHz8Tpu9WORtZZY/x49e2LbkSpSmL0tQtyjPdx2hpUdF1ge9Lnns+wv/0z5Os7VdX1PJDCPADyfDw6ia/dFrwmV+N8cufidHesbL2IkIIGjKCJ5402LhJY8tWjT//XI3c3MrvFcOAf/H7SZ7/SIRYTKBc1LytEJom6OpWaWs36V+n8b/+LyUOH3LvrjG+lPPRI3Bys+T2/xivFkaoA9sisC1y+1/DbO8muXknkdYOsm++BAgUVcMtF4i0dFAfHw5TfrcB42M+b7xm88BOnYgZ3jfda1SeeCrC6dPeDfcCb2pUePKpxdHuqSmft9688QDETROniBKjO7oFTQn7VNW8IpPW2YUJSEGjObKGlkgvKb2ZqJpEE/8/e+8dXFl233d+zs0v4T085Ax0zt2TOT2JnKAhRzSTaMmU1rK2ZEurVF5ZteuyV7K9LmvlrXKVvQqrlSxbkpUpiaQkcoZDDifn6emZzhmN3MjhxZvP/nHRD/0a6EZooKdHxLeqG6iLG86999xzvucXvj8DEITSxwlLFP1ZZrwrjNqXKAdrFzTUhEmLtY24lgbADx0GS6dw5cLAJxCk9UYazR7SehNxrQZdWCgiIk4RiShjB3kK/gzT7ghT7hDBGtLodWFSb3aQNdpJatmIOComCipy/lpOUKIUzDLrjTFmX6YUrG5gvxGkXCg2LBDUGe00Wj3UaPVYamq+HQqBDPCkTcmfY9YbZczuJedPsV4VttcTZkzFKQe8+1fDtO+toXHrymoTxdQaGs1uMnozCS2DpSbQhIFAEBLihw7lsEDBm2HaHWbC7ccLVz5Z1uqt1JsdKELFC20GSifxpYsmDNpiu2iJbSep1qIIFTe0yXkTDJXPMOH0VxHVWr2Fzvg+ao0WDCVGKEOcsMiMe4XB8inmvAnuxPeyEsiyTeG1dwnm8qR/8AmMrvaVW56EQBg6Zlc7RmsT8XsPUv7wFOUTZ/DGJqIgk01sKOobohppBw8Z/NL/nmLLlmql8jCUlMsS1yWqZWZWVwq4dNFnZHjli1Bdh3/yE3F+6mcSJBLVFq0wlExOhIyNh9hlSTwuaGpSqatXKgRHUaJt//SnojHi1/9LYcXuHs+D4eGAVGppd1SxEDI6GpLPh3geJJOC1laVmnS1RUrTBHv3afzzX0zyy/8qx+Dg7QvOl0GAX8gRb+8hdGxibd2gCIJigcCx0WoyuDOTaMkUoW2jWjEUK449OgiKIHPwE1EgOBJ7bJiZD96k/vBTJLbsonD+xIa02ffhxRccfvhH4rR3RHIuliV45FGDv/l6ec1u3nvuM2htq5aHee1Vl/GxtTPZWyZOumLRHt9LTE0CkPMmmfVGyfvTJLVaeuKHqDc7MZTFJnpFGGiKQULLUGe202xtpb94gjGnd21ERTFoiW0ja7QB4ARFJt1BXC+aBE0lTkd8L63WDiw1OU+WqmEIFUOxSGq11BntxLU0OW9iVe1RhU6D2UlnbB9JvQ5dmIvuXVx7Lb2WerOTVmsHw+WzXLEv4oTFpU++QoSESCmJq2m64gdosnowlaiA7LXQhIKGTkxNUWu00GxtY6h8lqHy6VWRh9sBu+hjWAoP/+NOdFPl4rs3r+tmKBat1k7aYruJqamKlelaqCioqoapJkhrjTRZPbR5u+gvHWfKHSSQyy9z0noDXfEDaIqOHRSYdofJ+9P0JA7RFT+AquiV0jOWmsBU4qT0LFpB50o5Mok3mF1sTz5AUqutvCNFqGhKhphaQ1pv5Ez+DabdYT6u5AkpKZ84gz8xRerxh4nfvR81GV/VKYSuY23rxuhoJfnoA7h9Q5Q+PIU7OExYLBGW7ZvGQm1ibWhsVNh/QOcX/0WSnh6VIICJiYB33nZ58w2Xixd8SkVJGIJuQHOzyt59Gvfdb7Btm8arL7srJi6GAV/+hzF+/CfixOPRdyOlJJeTvP6ay99+o0xvb0C5FBIEoGpR/badOzW+/MMx7rvPIDZ/nGUJvvJjcYaHA/7qq+UVWS3CEL79nM2XvhyjqUlhdiZkZCTkvXdd3n3HZWgooFCQ+F50v5oeBY3f/4DBD305xu49GroeXV9RBJ940OCJp0z+5I9Kty1oXAY+cyfeI3PXg5iNrQTlIuFsCS8/y8zRN0jt3E9q+z6CconpI68ihEJ6373o6Vqk75M7eQQZ+AR2Gb+Yx8/PMfPhW6R3H8KZuALcfOxdK86ejeLD2tqtylh99z0Gu/ZojI25q45ZM0345KdMksmFcX96OuS1V51bSiJYd1edqcSx1CQChd01j5DRm4Dl65SpQqNGa2B3zcNYxQT9peMrmrRuBiEUTCVacVhKkp2pB2m0elC4cV2n685Ayc/hhivPIDAUi674ATrj+9CWIExLX0WgCo2klmVH6hNkjGYuFt6j4K+9c4YyIKam6E4cotHsjq6zTFsUoRJX02xL3kNCTXO+8A5u+NHExiyF0qzHO381TG1rVBJjbuzGptaEmmFb8r5VvW8hBBoGWaOVlJ6lr3isYj1aKVShk9BqsdQkHfG9aMrigGghBJaSojt+kLw3RShDtiXvJ6lll2zn1SLOWxN3U/Cn76h3smqEEm9kjJmv/h3u4Aipxw+jNzWsWLLgKhTTQDTUoTXUEb9nP8FcHrdvEOfyIN7YBMH0LP7sHGGhuGmRWgd0dmr8Lz+T4MBBHdeF773g8Af/rcixYx6etzjp8fQpn5dedDDNIrt26wwPBSsKTRMCDh7S+ac/lSBbp8y70yVXroT85q8X+Ntv2Dec8M6f83njdYef+bkkP/KVWMVilM0KfuJ/jnPqpMeJ4yubU/ouB/yPPyjR3qHy4gsO7x9xKRTkTSfuc2d9Xn/N4Vf+bQ2PPGpUXIuGIfjMMxbf+HqZ2Znb1xft0SFGv/1XCxtk9F956DLl4T7m9QgqL++qa+7abcXesxTnF33O2DDj4yPR35rWPzgcovDFb/6dzaefsTAjJxaWJfjMZyzefN3FXWUkxc5dGvv2a1wdXqSUnDrpceyYd0uJA+tOnHTFJK03korVkzGaondAiBd68ySkhCREFxYxLYWhxCoTmxACXZh0xQ/ghjbD5TO3FPckUDGVOJow2JK8hyZrK4qI9F0C6RPKgED689YkgSb0eauEioKCLx1y3viK45w0YdCTuJuO2J7KhCnnCwwG825JOyjgSQcFDUuNSKYmDISIBgmBSqMZTfbn8m9RDFYew1PdFp2uxAEazM6FWKbQxQ4KOGERX3powiCm1mAq8Yo1RgiBik5LbDtuWOZi4b11ifNaDyTrDDoPpDn90gR1nTF67slw6d3FzyeuptmRepAGs7NiVYxiykIC6VLyczjz/TB6Bqn5Z6BXiIshYnQnDiKR9JdOEK6QxKtCI2u0ogsLTRiU/BxFfwZFqKT0OnRhVZ5zQsvSYHahKyYprQ5JSMnPU/RnUIVOjd4w3zei/VN6PVmjhVF7cbmOjxuk61J4/V3cwRFqnniI2P7dKJa5qnNUSKaqomUzaNkMsYN7CUslglyBIF8gnMvjz8wS5AqE+UL0s2wjPQ/p+0g/QPr+snIHfx8hHRfprGwmytYJ7s8aBAF842tlfus3isu6nqQE24YPP1i5mSWRFHzxSzG6uhcWO8Wi5L/91yJ//ZflZSfO6WnJ//f/FmhuVvj0MxaaFn0727Zr/MDTFhcvFCivYB1cKkn+9I9LBEFU1HYlkBJ6LwX8zm8X2bVbo7l5waPR1aXS1KQyO3ObKxHfqF9XCt8usy36w/LnW0ecOulx6qTHXXcbCBGR6XvvN+joVLl0ceVzkarCXXcZdHYueBpsG95922Vi/Nas0utOnIRQ6IzvRxNRKmspyDFmX2LM6cUOCoQyCloWQkEXFo1WN+2x3cTV9MKkpcToiu+n4E8z6y1RdmKFUIRCTE3RHttDi7UNRSh4oc2cN8GkM8isN4odFCrkTBUacS1NRm+at5QJ5ryVFdNVUGmP7aYttqvKyhBIj2l3hOHyOXL+RGS9kFGUnhAKcbWGJquHJnMrMTWJEAqKUKg3Owikx9n8G/OBxKtDxmhGIBAouGGZcbuPEfs8RX+WUAZcjRTUFZNao4WO2B7SemPFTaQKjdbYTua8ccacldXu22jolkq2PYZQIJ42SDdZi/bRhElP4q5KzBFEpKkYzDJqX2LcuRwFwcurH2Bk7csYzbTHdpExWip9VxcWXfH9lIM8Y/alFZF4gUKTuQWJZMy5TG/hfeygiECQNdvYmXqQmBplK6lCpdXagaHGCKTPiH2O/uIJ3LCEIlQazC62Je/HUhPz92aQNdoYtXv52LrrrkUQ4F4eYPYbBaTrkXzovls+pVAV1FQSNRWFDsgwhCCM5DyCMKobGMqFqumVieDvwfNcJfKvvEPuuRdXtG80NkveP+Lxe7+7PGlaK3bu0PjU42ZV5txbb7j83d/YK7Y2TE1J/vzPynzisEF9fTQGqKrgqadN/uLPywytsO1rlRI4c9rjrTddvvilhWLmVkzQ1aVy7uxtJk4fQ0xOhnz3Ow779usYRtQPmpoUHn/CpPdSacXcLZ1ReOQxA9Nc6EuTEwEvvujccqD++hMnBIYSTWgFf5pz+beZdPoXTzoSXMr0FWfJeZPsSj1IUqurrK6TWpa22E4K/vSaM50UlIqrRlOi1X9f6Rij5Yu4cullRymYY9IZqFgiSsHK9FXSRiMd8b2VewfwQ5eB0kn6SseWdvdJcMMSc944k84gu1IPVdw1ilBpsrYw543TXzq+asubKqJX6wRlLhWPMFw+s6Tr0wtsSuU5ct4Eu1IPkTXaKgTWVOK0x3cz541jhx99mRe37GMlNR798S70mLooo04gaLa20mxtqdy/lJI5b4zzhbeZca8s+Rw9CaP2RWbcK2xN3kNbbFfFAmepSbrjB5h1R1f0DIQQaMKg4E9zqXCkyt06Zl8irTfSkzhU2ZbUs0gpmXD6uFR4fyG2TcIV+yJZo40Wa/t8n4gy9HRh4sk7K/5sOQhdR0nGUZMJlFQCJZmIfk8mUFMJ9LbmjbmuooCiIDaVVxZBiS9eeNwM+bzka39d5tKljbNAP/6kSe018gblsuTZZ22mVpEVB3D6lMeF836FOAG0tans2autmDitFcWi5MRxr4o4qUo0kW9ieXgevPWmw5e+bLF9e7SIjccFDz1s8jdftxlfobWovV3l3vuMqozIYx96XLp46+R1Q0YTKSWetOktHGXSGbjppC+RTLtDXCq+z+7UI5hqFCwqhKDR7GGwdIacvzKrzyIIQULNAFDyc5zNv86UM7Qi15MvXfL+FMLQUUyDsFC6oZlSE+a81aymsi2QPsPls1wufrBsKrkkZMod4lzhLfbXfApz3sKgCJX2+G4m3cE1xTsF0meofJrh0hkCbt5Z8v4UFwvvsScdI6lmKwS2Vm8hYzTPu4c+2lV5adbjyDdGyDRb2AWfyf5qS1xMrYkyPEXk8pFSUgrmOJd/ixlvlOXa74RFegtHiatp6s2OyvYavYFGs5uB8skVtVNKybjdtyhDVCIZd/roiu+vSkzwpLNkQkAgPXLeBI1mz4IVTDEx1Bie/xESJxEVDUVTEaqKuPrTMFBSCdR0CrUmhZquqfyuxGMIQ0foGkLXo3+GhtC0Vcc3beL2Q0rJQH/AKy87G+atyWQEBw/pXKuT2tfnc/7s6nWQbBtOnfR58PCC+9cwBAcO6Hzn2xurg+f7MDkREoayYjlTFEgkVqFp8H2Os2d83n/Po6dbQ9OjuWjfPo0DB3W+98LK+uBjnzRIpRaeuedJvv2cs+o4qaWwYcuwOW+CUbt3RbpAEsmE00+TtZUWdVtlu6FYNFid5AprI06CqMJ5KAMGSieYcPpXfQ6ju4PYwd3M/e13bxgPkNAyNJo9VRlrRX+WgdLJVenvzMy79LoTBysTa0ytodnaxqXCe6u2OtlBgRH73LKkqXJ9b5Rxu494Io063zWiDMEuJp2Bddc4Wi1kCLNXbGavLEUaBFmjjbTecE2AteSKfYEZ78qKr2GHRYbLZ8karZV3IFBosrYwVD6zItIdEpDzJ5e08DlBkXKQJ6FlKtv80GHGXdolXQrmCPGBiDhpwkAXq4sFWm+omTTJxz4RucRqUvM/kyiJOKhRrB7X/2P55IRN3LkIQ7h0yWdsjSnhK0F3j0ZzS3Uix0B/sKY0dM+TDA1Vf6uaBj1bbo/l0XUlQUAlKBkRxdxsYmVwHPjO8zY/8LRFti7qD9k6hUc/afL2W+6ybtRkUvDYJ80qa9OZMz4nT9xaUPhVbFAvkkw6A/MD/soQSJ8xu5dmc+s1H44ga7TSi7ilIPGcP8W4fXlhg6IgNBWEiGIe/PkPTBEITZvfHkZLB0VE+wKI6HfpB1XWp3qjfV6bKkLkehmgtEpNqkD68wRyS2ViVYRK1mhlWE1SDlanlD3jjuAEq4mPkow7l2mP7UZVo64RWZ2a0RUTP/hoidPNoKDSaHZXkVc3tKvf+4ogyXmTlIIcSa0WiJ5BTK0hqdWS85dXzvXmxVOXwtUkgQQLxMkJyzeUn/BCZz4uMIIi1Iob8oa4WqF2g6SutWwtmWee2JBzb+LORBiyLi6Om6G1VaW2duH7lRJyuZAglMRXaa1RFfC96jlDiMiqpWmsWUwRIjKkqFGdumvWBfOfXSQ8Ypri+zHfYF1x7EOPs2c9Dj8ULRSFEDz2mMEf/r5CYZkg8bvu0enoXCDhvi9583WX0dH1cdNuCHEKCZnzxpbf8ToU/GnKQa4iYImIZASiWKO1CWNKKZmw+7CvTkyKgrlzC/FDexCGgT85TfH19wgKJWIHdmHt2Y7QNLyRMQqvvVs5j1BVjB3tWHt3UPjeGwRz+fkmCmqN1ur7lz5T7uCaVLjz/hQFf6YSLC+I3I0JtXZVxCmK7RlftR5Wyc9R8GcqLlOItLoSambVxO12IqamSGrZqm05f2Lhva8CnrQp+jMV4gSRiyyp1a2QOLn44dIkUxLih9XvJEpQWLqvRFarhRFYoCC4uWtLT2dRYzHsK0PLtnUTm1gJwjDSv9lI1NUrVXo7AE9/2uLe+4zVRwkISF5HtoQQ6LrAtAT+KgK/FQVSNYKGBpXGRoWOTpXWVpX6eoVUjSAeExhmVDfPNCOXYKZWQV95XetNLIFcTvLt52zuf8BA0+aDxJtVHn3U5NLFGxsEDAMeecSsKgU0NhbyxusOzjp5aTeEODlBCWcV2kdX4YU2xWC2QpzEvERAXM2smTh50iHvTy1MTFLiXRknNzYJUpL+/FPonW0wOEL87n0U3z6Ke3kIYZnIa9TKjK2dxO/eR+G19yqkCcBSI2J3rXm5FOSwg7UFU/vSpeBPUW92VNxlhhojoWXmydjKPvirpT1Wa6kL8cn7U9SZbZVtilBJaLVMuoOrOtftRI1et0gzKe9N3ZDA3AyB9BZlMkYlg1I3OGLx8TfSIIsU3atXPTfTCbueUEW2pIW+Zja3YWQbUK045cFe/GIBs6kVPxcp0Bt1jSAl7swkVnM7Xm4WNRbHamojcMqUBy9j1DWip2sRikp5ZABvA+tRbeLjCSm5JcHA5SAEpFKiIhx5dVtNjULNOhaUVZTVucw6OlUefdTgvvt0waz5AAAgAElEQVQNDhyMrBhXJ/FNbCx8H9592+Nyb8D2HfNzoQFPPGXyl18t39Bd196hct/9euU9haHk/DmfD46un/roxhCnsDSf8r46BNLDCaotBIrQKsHSa4EblqsnQSHQshmsPdtQ4nH01ibEyXMExTL2hT7i9x9Cq8tSPnW+IpynZmpIPvoA5fdP4vZXr+IjRerqpUU5zOOvQfn8Kor+HKEMKi4ZgSChpVGEtmILkhfaa4pJCmWIfZ1lSSBWTBo+KsTVTJULSxLpNkV9Z3UD/lJWHTEv3SBW4DYOI8WoG/79+uNv9k7lMldLbt2FOzWB0dBEeaSf0HNRdB2zsQX7yiBC04h3bSM4+T6JbbuYO36E1K4DOONXsFq7kL5HrL0HLzcLQUC8axu53Ezkwt7EJq7BRnYJVY3cW3dKGFxtreCZz1p88Usxdu7USN6g/IqUkXr41X8yjKYNRYmEGzdxaxgYCHjtVYeeLeqCJtc2jXvv03n5pcXzm6LA7j06O3ctzMmODS+/dGtK4ddjQ4iTL501uakCGcwXu12AIlQMZe3BsAsCl/PnS8RIPX6Y0vsncPuH50X3BHgexTeP4F4eIH7PftKffYLZrz0HRKnU3ug4ekcLyqlzhPkFcmco8UWlW9ygvGLBxKXghIuL7ZpKAgWVgJURJ/8mVo+bQRIusrZEEhOxGxxxZ8BU4yjXkZ3u+EE64/tWfS5BRNirtgmBIjQEyiKL0fWQMkSuIr7oVgRGQ9dFz2QpD/TiTU8iPRcvn0OviWKovLlZZBiS2n0IdyqyshrZBmQQID0X6QeEro07EQWnx9q6otFnkzht4jbjetIUhpKZGcnMzPq5CIeHAsJlunZdncLP/nyCH/lKjHi8uv5cEEhmpkPGxkKmp0MKeUk+H1IqRbX6bBtsW7J9h8aX/+GdPWZ+HFAuS1560eGZH7Robonm2foGhcc+afLO294icdJ4XPDYJ40q0jo6FhUPXk9sCHEKpD+vmL06SMLKsVc7q4KyyKKzqnPKoIrECEVBmAbBzFykH9PRQvn0BYRpoNbVEszmKB8/S82nH0PM58X6E1PkX3iD5GMPEL//EIUX36wEh2vCWDRhB9KrCuhdLXzpLXp+lfItK3ysoQzW3Ibr30EkEnnnOuwjl65RNfIKBJqiczUbbb2uAytZRd5IgXedoShRWr+ioqXSqMkUmkiT3LITNRbHnZ7AGR3Gn5uhZv89TL78LGG5QGmwF0XTkWGIX5hDyFaQIRJxU8InlA2LN9/E9znCEFxPVo07QQDf/Y7NX/zZ6sM+boRiUd5UCTweF/xPPx7nH30lXglIj3RSJRfO+zz3nM2pEz6DAwHjEwH5nFwy0Pyzn7M2idM64ehRj/PnfRqbFBRFoKqCBx8yaO9QuXC++uE3Ninc/8CCdpOUkjdecxm7hYK+S2HDsurWfuT1ywGxbDDsci25tjVBqUz52GmSn3qQYGYO+8RZwmIJoWvE9u5Ab21CBgGlI8cJc3lkKoE/PUtYKFJ48U2Sn3oQvb0Fb3AEYJ40VU+mIcGaLG6V46W/yEGjCKUqtmU5yGWdPDe7foBEVl1PEXeu1o5AQWF1z2cjcZtoE3omi/R9nKkrGPWNGLX1lAcvM3v0TRCCwC4jA59i3wXssWGaWny6Hsvy9rc+QLHiUYp0UKTRusQVOUduysObna6K7buKZK3GXU/WcfQ7U6vIld3EJlaGMITCPAm5GlStqlDIy1WVbLkVCAH33qfzI1+JVYoEQ2RBeu5Zm9/97SKX+3zcFRgv7hSX498HlIqS55+zeeABA3Nes3XrVo177tXpveRXGccffsSkvn5hrpqbk7zyskOptL4j8gYRp1vpNRs8QXs+xdePUHz9yKI/5b/72qJtbt8Qbl8U1xS4HnPf+E7V3+WS06SYj4VZG6IYm1vHWs9xNZvvY4XrmitlSCnI44XrJxQZCVreOTnGfm4Ob2YKLZkiKBWwrwwSug7hdSO79Fx8z0XvTJLMaCRrQho6fPpPFWjaFscyXHzbQ1UCOneqZBqzCEVw8WgO1w7ZfncNqTqduhYTRf2Y9YtNfGwwNRVSLEgytVdFIwXZrEIsJlZcL+5WEI8LnnjSpKVlIfQi8CWvvuLwa7+aZ3JiZYthRQHL3PxO1hOvv+YyMLAQJK5pUeHkZ79pk8tFfSOREDz6qEEstmApPPZhVPduvaUhNoQ4KUJdlVtpAVFZiWt9yhK5pkDz24XIulR9o1fLdax1jo2CnKs/vGAJK9TNED3HtZHQq0WXr7/+nYqoj4RVZv6QkMHyKSbsvnW7jifdO6bgMYD0PQoXTq18fynJNBrc/WQdfScLhAEEnqR7b4LRy2XkpOSep+voPZanttFg66EUdimkZWuM0ctlOnatPUljE5tYDsPDATMzIZlrtJza2lUaGhUG+jf+u0skBIfuqs7MLZYkf/s35RWTJogsZenMJnFaT0xMBHzvBZut2xIVNfaduzR279F55+0oSHzPXo2t27XK321b8s5b6++mgw0iTpow1mSxUFCqhCQhct191IrVN4Mb2ouIXRT3pK5Ysft66Iq5iPR4YXlVAccqGgqrl6qNsseufwcSL9zYMgW3Akk430eiwsUQ9SU/dCkGsx9p2+4kCCFo2RqnXAiYHXcJA8nsuEtxbqGflvM+A6eLuOWQZFZDtxSmRhwGzhTZcvDOzqzcxMcbly/7XLkS0N2zsHDbskWlu1tlcCDYcEFJ0xS0tlWPu8Wi5NiHqxvHTUuwZetmbcT1RLkcWZ3+wedjtLVF81omo/DJT5m8966LUODue3VaWxfmvImJgJdeuvWCvkthQ/xihoitadJWhYapxKu2RZl26xccuN5wgsIihXRLSdxSMLWlJhcFnNthcVXWDk0x19QGgYKpJK/bKu/odwBgB0XCa+LKhFAwlfgtxcf9fcTZt2e5fCzPocezxGs09jyUoedAir0PZUhldYIgCtCVUiIDmL7i0r0vyT1P16Hpm89yExuHmWnJsQ+9qlpi9Q0qDz9sYMU23oKjaizSaCoWJPYq3YSZtMJdd9+5yTQfV5w84XH8mEc4LxNkGIJDd+k0NilkMgr33mtgGOKa/X0uXtgYT8mG0GJTTURihKtkeppiYKnVk3YgvTtasboUzOGFDpay4CaKaTXoiokdrk0EM6HWVkkchDKk6M8SrMJlaSjWmiQEFKEsKLdfc/2SP7fqc91OFP1pAulVaTml9Do0YeDJj7Ag7h2EkYslpkYcSjmfVLaIUw64+H6OwbNFPCckP+3xxl+PU8z5FGZ8FFXg2gHTVxyEAM8JKc556LXLX2sTm1gLnv+2zec+H6Otfb5OpIDPfi7G66+7vPaqu6FWJ88Fx66+gGmxKgVwVYVPP2PR2blpcVpvzM1JvveCzeGHDNLpSPNry1aVPXt0RkcD7r5n4UX5vuS5b9m3VFrnZtiQJaQqtEXlL1YCU0lUHSeljNwt/p3rbvGlx5w3UbXNUpIk1nD/ED2DlF5XZSlxwiJFf47VBE0JFFJadtWWP1UYpLWGqm0hAXl/alXnud2Y8yYWuRPTemNV6Zjvd7h2RI4CXzI77hF4kpkxl9HeMlPDDm45JDcVbbeLAaWcj+9KpoYdJocc5ia8ZTVwNrGJW8H5cwHPfssmCBbGuqZmhX/+i0n2H9AWiuauAkJA/RLlXK6H60lGr4uHSaUUdu1eGQlSFPjEgwY/+mMxYptKBBuCV192q+Ld6usV9u3XuOtuvarO4fnzPh9+uHHZmBtCnMR8cd7VHpPRm9CvE7ss+NN3vMVg0hmokh8QQtBodi5fjHUJZPQmEmqmYr2SUlLwp1cdqyOEoNZoWVSGZDmk9YZFSu0lf+6GRWjvFLhhmVlvtCqA3lQS1BudH2GrNrGJTawG5bLk618rc+zDBZeMoggOHNT5D/9Xms9/waK2VmAYS6f8CxFZiGIxqM0KDh7S+NmfT/Cf/nOafftvbjoqlyQnj1dr6CWTgi9+KUZDo3JDiQEhooy8J58y+de/nKKrO1qsrkXLcCVQFJYkkGK++PBGSyEIERVRvv46gqW3rydmZkJe/J5TebaKItizR+fRx8xr9L8kr77sMDmxcau8DbMnZo1WklqWgj+9ov1NJUGzta1qmyRkwhnYiOatK+a8cXLeBBmjubIta7SRNdqYcPpXfB5DidMa21HlYgukx6QziBveuKjhjVCj15PRmxl3Lq9of1UYNFvbq2KjpJRMuUN4a6j5djsREjBq99JkbqmQRYGgLb6TaXdoRcV5N7EyhLaNfb73o27GJm4R/uTKxubbjfPnfH7rN4r88r9JVQLFNU2wd5/Gr/y7Gr78wx7vvO1y5rRPLhfiedGEremCdEbQ0aHS3a2xd59OS6tCKqWgqvAnf3TzMbRUkrz2qsMzn43IGUSutyeeMglC+OpflBns9ymVJFKCbghqaqLrPf6EyVNPWzQ2KoQhjAwHpFIKtdnVs4hYDJqbVXRdoOmg6wJ9/qdlQSKhcOgufVFJl23bNH7wsxazM5GSuetGulieJ/E88D1JqSS5ciW8qctTCGhuUUjEBdo119b1iLDG44KWVpXGpmpvRjKp8PRnLEZGfErFSEXd96NrX9uG0dFwzfISvg8vv+zwlR+L0dgYXX/Xbo1kcoFJjo6GvPmGi72B9pYNIU5CCGJqDV3xA1zIv427jMVIRaM9tnuRe6/ozzHjXdmIJq4rnLDEiH2epJatTNqmEqc7foiyn6MQzCx7DoGgzdpBvdlZJQVQ8GcYs9c2SWnCpCu+j7w/Na9BdLPrKzRbW6gz2qrELu2wyJQ7tCgA/k7EnDfKhDNAs7U10qISgqRay/bUA5zLv7ViEn8tBAJLTeEEqwvOv13oeHoHqa4MhYFZhl/pJShv/HvyRsYY//X/vuHXWS06nt5OqquWwsAsI6/04q/HsxBgZeNk9zWRaKtBi+mEfoibcygOzTF9agy/dHsEGtcbd2o9wiCAF190SNUI/rd/mapkUSmKoLZWcPghk8MPmUgpcZwoLknVoqy4qJ7Z4nO67vITdRjCe++6vPiCw+e+YKHr0RiSSERWp0ceNTh31mdmOiSU8wSiRaW7RyWRWBgzTxx3+a3fKPIjX4nxxJPWqu9/z16dX/43KWJxQcwSxGLRPysWqWbfyKLz4GGDBw8b8/cicd3IgmeXr5aDkZw76/Ov/uUczk2SpC1L8C9+KcWevRpWbOH6sdhVS9/SDairV/g/fiXKvJUyIk12WVK2F9pQtiW/+u/zHHlv7d/M5V6fN193+fwXYwgBXd0LNCYMJefPehzbQDcdbKDFSREqLdZ2QhkwUDpBOcgvMfEITCVOa2wnHfG9Va6tQHoM22exgzvbRQSRZWzUvkRGb6HZ2lrRUKo1mtlV8zC9haPM+eNLFnMVKFhqghZrO13xA1UxSV7oMFA6gb1GN1nkrmtlV+oheovvU/Cnl9Rj0oRBo9nN1sS9VdauUAaMO5eZ88bXdP3rWnNDbSkhlPkacLeWN+qGNv2l4yS1WpJadp48KdQZHeyrMRkonWTaHcGT9pLPQaCgCBVVaOjCokavp97sJKFlODb73WXJ50eB9ie20ny4i7G3Bhh7Z/C2ECekRLp3ngWy9XAHLQ93M/pWP6OvXbr1NgpoeqCDXf/kHhLtafSEgVAFMgTph+T6p3nnXz+PN3vnPYuPPSQ8+02bmemQf/bTCe66SyeRrB47hIgsMMsV0w1DSbEoV0Sepqclv/WbBRoaFT7xYHWWVn29Sv3DS8eMSinxXHj/qMd//k95PjjqcdfdOo8+KtGNlVudhIB0RlmkJ7VaKMo1z+aaZA4hmNc5uvGzUNVII2nP3rVnBkYu08hKlaqp/ls6E7k91+rJzM1JXn7J4bFPmtRmq/tEuSx5+WWnIoq5UdgQ4lTyc5hqDE3R6YjvodZoZsy5TN6bxAsdQkI0oRFTa2gwu6k3O6pEF0MZMukMMVq+uGxB1TsFbljmcvEoMTVJrdECROSxzmgnmall3Oljxh3FCYsE0o80qxSDpJalwewiozdFiuHzz8APPQZLpxl3+litkqaUEjssRARAsWg0u0hpWUadXnLeOE5YJpQBqlAxlSR1RhuNVg+6MK+zdk0zWDq1JOFbCjE1hTlf9FhBi37OExFVaGjCpEarrzomsnRtI6lmKwWZQwJCGRDIoPK7HeQXFR9eCrPeKJeLH7Ij9UAlQ1MRCmm9iT01dRT8aWa8KxT9mfmagCFCKPPFpGPElBQJLU1Kq0NTTBSUyvvaxPcX4o0pdv/kfdTubsSeLDH6Zj/lqRKKqmDUmBSv5O98a5OAVHctoeNTHC1AeOco3y8Hz4PXXnU5d87nC1+I8eQPmOzarZFIiBtaPa5Cysg9NDYacOaMz2uvOpw6ubJFxeXegH/7yzn+2U8n+NTjJk3NSkVUcSmEYeT++u7zNn/w+yUG+iPNqePHPPIFSXYN7rpN3BhSwptvuly86HPvfXpVXxgbC3ntlY1fyGwIcRp3etEVixZrB4pQqdEbSOn1eKGDXyFOOrpiLQqgvhoMfbn4wZrT+T8q5P1pLhbeY0fqwQpBEEJgqUk64/totXbiSZtQ+oh54hSRlYVJWUpJKH1G7PP0l46tSfwzkD6DpVMIFLoTB9EVk7iWpkc7hB96+NImmCdOurAWBZBLKXFlmcvFD1bs3lJQaY/tpdnaMk+c1ApxWkqJvHKcUGmP7YbYfPkaKQkJCaUfFSomIlD9xeMMlk+zEhI5al9CFRo9ibuIqamK204TOhmjiYzRFD1nggXiRBTVuKRw68dnrtnEOqL+7laSHRkCJ+D8n3xI/7Nn8YvR96iaGoqu4BXvbGuTkbbY/3OHmT03wbk/OkpgL08eggBe/J7D+FhQte2Do7efJEoJY6Mhv/dfi3z72zb7D+gcPKizfYdGW5tKplZgGALfg7ItmZ0NGRkOGBwIOHfOp6/X5+LFgOnp1Vmz+/oC/uOv5fn28x6HH4mz74BOZ6dKKgkqPrYdMjMd0t8fcOK4x9H3I/dQsbgwWLz7jssv/6s5YjGBH8DpU8s/Pymj/X7pf92YTPK5uWUsb0Lghjr/5f8pk0ltjLdnPUqgTIyHvPWmy4GDOuZ8PpmUkjde3xil8OuxIcQplJLewgdIKWmytkRK4kLMawvd2OcbyoCcN8GFwrvMeWMb0bQNhmTaHeZ07hW2Ju4la7RWKaFrio7Gjc2fVy1Fw+WzDJZOrcjCshQ8aTPpDFZcS53xfRhKDCEiVXCdG5uBpZQUgxkuFz9kzF5ZUDkAQmCpCRJaZk1thiieCCFQURYRakOxuLmBeQEhPsPls5SDHD2Ju8gYTYvEQIUQqGjLFvQLZRhZ6G7RjbiJjx9SXRm0mE5+YIbJD4YrpAkgcHyCO1dMv4LM9noyO+uxJ4uIm1hNrkUYRgHa58/dOXGNQQD9fQED/QEvvuAQiwtMIxKsFEpEOMIwiqtxHYntgF2Wt6Qanc9Jjk5uYyT/KM+fjRMbMvBHh5h5/ls40zNRDI8tKRXlkjFD09OS555dfScZvRLytb/+aDLJjaZm6j/3Rd4/c4q5F15jQ2S31wmOUz0b5POSV15ybktdww0hTrpi4IRFzubfJOdP0GbtIqXXVwUdXw8nKDJm9zJQOrmiYOo7FRLJnDfOydzLtFo7aLa2UKM3VAlaLnWMF0ZkZ7h8hhn3yi1N1HZQpOBPExJwufghBX+ajvheao2Wm0okeKHDpDPAYOkU097Imq9/JyAkYNIdJO9P02ptp8HqpkZrQFNW5rf3Q6/i1ptyhnA+BrF2m1hfaPEopsnLu/jlO9wldwNk9zahJ24tXuZOgpRR9tt6V7u/EZyREcZeepOJeJzaTz6BN5tkfFDiT308QkhWC8Wy0Jua0UaGN17X4BaQzggeetioEic99qHHmdPrX9B3KWwIcVKFjkDgSYfB0mmmnCHSeiO1RitJrRZdsRAoBNKlFOSZ88aYdUfJ+1O3VJfOCUqczb2Bdo0WlB+6H8mk54Yl+kvHGXcuV2QBklotppJAFRohIV5oUw7yzHkT5IMpHGHjKz66VUMYevje0vXpFEVH0y2EUAGJDALO5t+oWFW80K4E4of4jDm95LwJavQGao0WUlodphpHoBJKDzsskfMnyYVTlGQeT3UwlBp83yYMVvY+QhnQWzjKcPlsZZtAoBlxkBLPW5v17CrKQW5VRY6vwgmL9JWOM+r0UmM2UR/vwggNTKyoLI2ioxlxAnzcsEzRnWW2PETem8QOClGpm2UKHI/al5jzJiruSC90cMOlV4y+dLlYeI+B0smqe7vZfR+b/U6FeIcyWFIQVs7HrghNQU8YqGYk6BJ6IX7JXdZFY9bGUAwVL+dUSIJqaWhxHUWbv7YfENh+9PcbvQoBWkxHi+kITQEZHeeXvBW5iYSmoMV0VFNFqNFCSwaS0PXxSh7Sv7UVsJ400JMRkfCKHl7eqfqbokfXVQwVI20hhEDRFWINCeQ1ooxSSpzZMqGz9ASqmipqTEfVVZi39Eg/xL/6/FYQa6ToavT8DbViLZJBSOgG+LZP6C6+tlAFqqmhmhpG2iK7twnFiM4Tb0ouyjT0yx5uzv574Y6O79mLlqoh985b63ZOf3qKwnQk/ltz7/3rdt7bhdjWbRgtreTefgvpL0/+nZFhRv/g9whyucjMd4figQcMtl9X0PetN26Pmw42TI5A4aoPRBJSDGYpBnOM2r3zVqerTFbOV7YPbjmjCiIrw52k1yMJKQVzlIIc43bffPaYoPr+Q4Sikm3cTUf7/VjxLFKGFPMjXDr3HHapWrFb02K0dNxPQ/MBdCNBGPrMTJ6n/9L38NwbE8RymKfsFJhw+itZbAttkCRSzbRtfYjOdDtCqIShR//FF5gYPb7iuy0GMxSvsRYaZg1btzyCJODs8b/koxqdJSHlIE9Neiu1ex7l1NE/JD87CAjiyUa2dD6DZdWh6TGU4hijx17HcVdeYsYOCyuOx5OEq5JFCKTPrDe67H5hEGKkLVof7aH54W5SXRlUQ6M8UWDi/WGGX7rE3MWpqsn/Wuz/hcM03tvOqd99h4HnzpHeVk/rYz003NNGrCGJDCX2VJHpk2Oc/+MPcGYW1y7U4jp1B1poeaSb2l2NWHUxZAjl8QJTx68w8uplZs9NEHqLv3XV1KjpqSW7v5n6Ay0kOzMYmYi4uHmHfP8ME+8PM/pGH6XRtcU+xhoS7P7J+2h+qAtnuszZ//E+I69cRvohqqWx56fup6Yni1kbw0hb6MloAZbZ0cCD//EzVYKGfsnj6K+9zPiRoaprGGmL2l0N1B1ooXZ3A4nWNFoikjBwpsvMXZzkyhv9TLxf7fqrgoBUZ4bmh7ppuLuNZHsNWsKAUOLMyyDMnp1g/P0hpk+NVT4roQjq9jfT/tR2Ul0Zkm1pzNrIRd/ySA+N97UvutTg8xc4+TvvEHxMLWpXITSdxK49oOmwjsTpYw1VJbZtO0ZLG7n33mElijLSdXEG72ztxFRK8KnHTerq5xdWUnLlSsCLLzq3jettDHFacqskxP84JXUsgqoaJGvayM0NIsPV+P+jQGRuUGsulWyme/sP4NhzDFx6kTAMkNJfkgjV1m+nreshcrMDjAxEA4TrFgj8lViGlm6HEAqt3YdJZ7cyOvwe5eIkimpQzC8/Yd8MQghUTScM7gCTrwChqKi6SUhYsWwWSmNcuvAtTCtDe/cjCFVbk2Xro4ZQBNt/9BBtn9yCYqgEjo+iKVjZBmp3NtBwTxsnfvMtpo4trYumxXXMbAwrG6f+UCv7f/5BarZkoRJZJog3JdFiOhe/uphM60mDrV/eT8/n92DWxZGhJLB9hCqw6hrJ7mmk5ZEezv73Iwy9eHERgYu3ptj3cw9Sd6AlSlUOJYHjIyUk22oiIvGJTrK7Gzn5229jT63OgqmnTHb++N10fnoHbs6h92snufJaX8WCJRRBvKUGo8ZCBhJnuoxQFVRDJfQC7OlSFeHzbY/AXTwGNNzTxr6f/QTxptR8oeSQwPZRLQ2rLk5mZz3Nh7u48Ccf0vv1UwTO4nNkdjSw72c/Qf3BFlAEoRcQOAGqpWJkYtR0Z2h+sBOzLsb06fGFvG5FkOzMULcvEuL1Sh5a0kTRVQLbozxZWmTpcvN2dV64EDd20UhZva+iLGy7/rgbxcZcu9/151u0r1I9mdxof0VBy2bRm5rxZ2cWy2rf7jid65/FcvcJi9t8o2NWem6hoKVSGG3t0UJZVZHXXuP6Z7KWNq/kna+ljyyDbds1HvuUWbE2SQnvH/G4cP72xeRtViJcBZLpdtq7Hub8qa/hueuV8SeIJxqxYrX0nv0Wk+OnbrynopKqiVaNA5depJBfnzgkTY+TqmlnZuoCg5dfJfDXJzDRcXKcP/WN+Q/wziQjYeCSnxuiXJqmsfkgZnxtNQY/ajTc1YZf9uh/9iwjL1/GmSmhmBoth7vo+cJeanc1susn7uH9X30Re/LGpKN2dyMtD3eDEJz7w6NMnRjFK3lY2RjZvU24c06Vewsi91DnM7vY8WN34dsevV87ycjLl7GnSyiqoGZLHT2f30PdoRZ2/+S92DMlJo4MV53DniqR75sh9EPG3x1k5uwEznQJKSHRkqL7c7tpfrCL1sd6mD4zzuWvn6q4J5dDrCHBjn8ckabyRJGzv3+EoRcvVbm6/LLHB//3yxX3IMDen36Azqd3kLs8zYnffIvS6DXFxiU4s4utbrneaYpDOaZPjDF+ZIjcpSm8koeiKWR2NbD1h/aR2VHPtq8cZOLoMLPnqy3ketKg5wt7qD/USnF4jgt/foyZ0+MEjo9QFeLNSbL7msnsqGfs7cEqIiT9kKHvXWLsnUEgEu7c/wuHqT/Ywvi7Q5z5/SOLYrWCslchb2qqhuSBg8S27UBNJBC6jlBUpAyRrkPp/HlmXng+amddPdmnP0O59xKlc2dJHrqL+JfbBkYAACAASURBVLYdKJaFNztD7q03KF+8sNBHDINYzxYS+w5iNDdH2VtXRigc+wC7vw/pLbRLiceJ79hFfPtO9MZGhKYRFIvY/ZcpfPgB3sS8rpyiEN+xi+TBQxjNLegNjRiNTbT/wi9WzuUMDjD1/LOExdsQriEEel09yYOHsHq2osbjBIU8pXNnKZ4+iT+zOHZXy9QS27mL+PYdaLVZkJKgkMfu7yP/3jsEhWieEYZJfH4/vbEJxTQJy2XswX4Kxz7EvTJSISdW9xZS99yL0dQcPetQ0vpTP1shQu7oFaa/8238mcjyLUyTzCOPEd+5G6GqCFUjf/QIs6+9vCS5EbqO1b2F5KG7MBoaQVHwJicofPgB5d6LVfpp9Z/7EjIMyL/3Dol9B4ht3YqiG3gzMxRPHqd4+hTSXXkAfTot+NEfi9HcvPCd5uYk3/ha+bby403itArUZLowrTTLpmKtAkIoaHoMIRRs++YpqIrQ0HQLGQbL7rsaaJqFUDQ8t7hupAkAKXGdO0808u8j9KRB/zfPcPK33ya8Jg4of3kar+iy56fuJ7uvidZHt9D79ZM35LEtD3czfWqMI//+e+R6q12KV17rW/KYVFctW7+0D6EpXPrqcS78xfEqUpK7PEO+b5p7fuUJ0lvr6P7sbmbPTuAVFgZYL+dw+vfewy97i2J3CgOz5PtnMDMx6g62ULevicHvXsDLLT3gylBW3GpWfZzdP3kfnZ/ZSWksz4nffIvRN/oWuywliwjlVY2m0A2wJ4uUx5ZfLOX7Z3jv/3whIlXXXSLXO409WeK+f/cksfoEmV0Ni4iTFjfI7mkCJCOv9NL3t2eqz983w9jbgyiaUqnlVtXmoltxAUo/rJAiv+xRHi/cWHdKUck8/iSpg4conjlN+dJF1GSS5KG7UVSF3MnjlM4utEVoGlpdPXFNj4hWMok3Fd2LmkwijGsC0oWg5r4HqP3Uk3izM7gjw0gpsTq7iO/YydTzz1E49kElpsZobqH28Sfx5+ZwhocgDNEbGsk89jhmWwfjX/1TwlL0rmQY4E6ME7ouWjqNn8tROHFs4XnMTFeRso2E0dpGwxd/CDVVg917CXd0BK0mTe3jTxLbtoPJb/4N/tTC+9YbGqn77OeIbd2GMzyMOxpZg7WaNGZ7J4UPP1jYt7aW2id/gLBYxBsfI3Rd9Lo60ocfJrZlG2N/9sf487FYMgzwpqcIbRuttpbQcSiePon0o74Q5HKEzsI4L32f8sULBPk8RnMLNfc9gJpKLWl5FJpG6u77qH3yKfzZ2cr7MZpbaPihH2b2lRfJvft2hTxptRn0ugasjk5Cx8EZGUHRdayeLcR37ESoCvn3j6zo+SYSgn/0o3Ge/oy1oPkYSp571ub4sdvrav6+Jk66kaK+aQ+pdAeaZhEGLuXSFNOT58jPDQMSVTWpb95PTbqDTN02DCPJzv1fJpx31RXzowz3vY7vR6tPTY9TW7eNdKYbw6qJJAbKU0yNnSafG6oEe1uxLK2dD2KYSRKpSDBzy85n8H0bZMj05AVGh94FIJXpjKwhZopUphPdSLBr/w9HOkeBx9jwUWamzq/q3oVQaW6/l2RNG5aVwTCT1DXuwZq3uLhOnoFL38N1olV2LF5P59bHGRl4izD0qG/aTzzRgJQhs1MXGRv5ADnvAkzWtNHS8QC6HkdRVPK5EfovfnfJdiiKRirdQX3TXkwzTRB65OcGmRw7dQ3pErR03I9p1TI69C71TXtJ1rQhhEIhf4WJK8ewy9dO8oJ4ooH65v0kkk34vs3s1EUUda1KuIKOnkcxzBqG+l7DuY60Zht20dC0j9GRo8xNfzQ13PySx9BLl6pIE0DohYy9M0jHU9up3dNI9irpyN+YdPT+9clFpOlmaD7chdWQIN83w5U3+pcMWs4PzjH5wQjpbXWkt9aRaEsze26iah937sakvTRWoDA0R93+ZozaGKqp4bH0PQROADIiTbt+4l7an9hGeaLAqd95Z2nStJ6QLBn/dRW5S1O4s2XMtIVVn+B6jQ0ZhvhlDzHvdjOzMdxZe5F17fr3fKvQ6+uJ79hJ+eIFpr71d4SlIqgqMvBJH34Yd/QKztDgouPMri4KR48w/fyz+LkoLlCNxwlKCyTUbG+n5oEHcUZHmH7uW7jjYyAlZnsH9V/4ITKPfhK773Jl4neGhxj7iz8lLBbw8wWQIXq2juwzn8Xq6MRobsHuvQRhSPn8Ocrnz2F2dBLbug139AqzL76wrs9mJRC6TvrwI2i1Waa/9XcUT58idGyUWIzUXfeSefSTpB/4BFPffhbCEKFp1HziMFZHF3NvvE7+yDv4czkQoMZioKr4cwtxlt7UFON/8aeEdpkgn0f6Plo6Te1Tnya57wBme0f0/KTE6e/D6e9Dr28gtnUbfj7P7CsvIW9UZyUIsPsuY/ddxurqIXXXPTe8T6O1jfTDj+COjzP5N1+LyLIELZOh7jM/SPrhR3GvjFC+dLFyjJ7NUjw5xPQL38GbnEBoGok9+6h75h+Q2H+wQpwUJSJHqhpx6KvePU2D9naVL/5QjM993iKZvOqik1w47/PXf1Umn7+9Ho3vW+IkFI2e7U+Rzm6hVBgn8G1UzaS2bjsIQSE3gpQyiotRdQLfIQw9pAxwyrME89lmrpO7JrBdUJPppKPnMTyvhO+VQCjUNeymrnEv50/+FbnZhaK/MgxwnQKGGQ0yjp3DcwtIGeJ7xco5QRCGHo6TJ+bbaFoMuzxDGPqEoU8QrN1K5HtlHKEgwwDfL2OXognT84rIcGEC1DSL2rpt+F6JWLweIRTC0EPVEpixWoQQFZe455Uo5IYj8tK4F6GoLJohiFyPjS130db9EJ5TwHUL6HqMts7DpGt7uHT2mxXylEg2Ud+0n1i8Fl2P47oFVM2krfNBUjWtXDz7TVw7Gmjiyf+/vTMJkiO97vsv19rX3hu9AI3GjsFgMKs0G4f7DDdRCksOhSVFSGFHWOHwwQf74KuPjnDYsn2Q6XBIYVmiKNKkSA1JDYecfeMAg8EO9L4vVV175Z75+ZCNAhq9oIEBZoZm/S4Auqu+ykxUZr587//+r5sDR76BHk3TrC+jKBq9A49uOe5ldwhcp8ngyHNUy9PYVrW1L7Ks0dV7gkSyB9eu77zMfaS5VNtW92Ms1WjMV8kd7SY5kEHPRLYNnIzlOuUrux+xoyY0MqMdKBEF3/HJHOgk3pfa8rV6OgKBIJKLEcnFtnwNUiiwjmRjaAkdWVeQVRlJkYnk4ghA0ZQdPYl8y0XPRDnwTx9k8AujGCt1Ln3rl/c/aLp5N1SZ6LrIXIlpKHrYradnQs0RgKJvtihx6w4r786SOdBJz+NDnPp3n2HupTEqVwsYy/UthfX3AiWZRFJU3HI5DJoAfB+3sIqk6SiJBLfOyZCQCAyD6ltvtso+AN7NN2hJIrb/IHIiQeONV8MMxTr24gLGtatkn3wavbfvRsbEtnEWNoru3WIBe3aG6OAwSnLr79cniZrLERsZwZqawrh6hcAKg+fAMKh/cJrEsWNER0bR8h24xQJaZyexvftwiwWqb76OX7sRJHlbBDjCc3EWN5a3vUoFa2qSxJGwm/C+oyhEh/ai5TuovPoL3NUbXoteaY3aL9+l7+hxYqMHsWZnWpm+wLJofHi29XrhOFhzM7ilImo6g6SqCM8jl5P5V/86wf5RtWXUqWvQ1R3OA8xmZXT9xqy+clnwV//b5OwHH39jw69t4BSJpEnn9lEujjF59UWCwEWSZKKxPEL4rcyQ5xoszr59o6SWG2Z6/Ge4zlY3SUGtMsvElR/SbKy2AqeunuMcPPY7ZPP7qVfnEcLHMktMjf0ESVLoH3qCXMd+FqZf30K3JKhXZqhXZlAUnRHlqygdEabH/xHP3f7J9nYI4bM09y4AsXgH2fx+qqVJJq++uOP7OrqPMjf5CoWVC3iu2SozBjeJ5W2zzNLcu0SiWeLJnm3XSib72DP8JPXKPFNjP8V16siyRu+ehxna/1m6ex9kfuaN1us1PYEIAq5e+C62VUFRIuw7+GW6+k6QSvWzth449fSfIpboZGbiZVYWThMEPunsIAeO/fZdH69KeQrHqtLZfZRy8Rr+uvthItVDMtVHrTqL0fzkOjrtikngbt18ELgBdsVE+CK8kUe2P+3NQvOOhuNqyUirc6vjeA8dx7f//76OrCtbBg3RrgR7PjNC54N9xPtSRLIx1Ji2HjwpSOrtR21AaF8w8lvHGHr+EASC+ZfHWXxt6uMZNyJB7nA3/c/uIzPaSbwniZaKoETVMODT5J2DPttj5h+uomdiDD9/kL6n9tL18B6qY0WK55ZZeWuGtQsrCP/eBlBepYxwHfSubpRUGr9eA0UhMjCEsO1Qa3OLWFggCBqNDUHTrUiahprLI+sR4gcPh5qY1i9lInsGQJbRchuNc+VEgujgEFpHJ3I0iqRqRAaHQnf/W4XUnwK0XAeyHsErreGbGx9gAqOJV60SHdqLmsngFgso6QxyPIE1PbkhaNoJORYjMjCI1tmFEosjaSp6b3+YqtmluelHQVJVtI6OUJ+2vLnJxCuVCGwbrbMrDIbWAye/UW9lI68jXA/huBCNgawAHqoKQ8MKTz8T2bT2rbiu4O++Y/L975mfiGvCr23g5LpNHKtKOjtER/dRatVZXLuBaXy0m5/nGtSrC6hajFi8A0mS8X0X122iR5JIktwqaf0q0qgtsLp0tpVx8+7Sn0mSZNK5YaLxPAszb6LpcTQ9DoBhFBFCkMoOoC7GWgGi5xqsLJ5ulcp836a8NkbvwCOhXxSgqFHS2WGMZoG11UutgK5RW6JUuEJyvSx6p9hWleLKBbr7TxFP9lCvziJJMpncCJqepLh8kU9SAB+4wY5i6cDxEUIga/IGAfTmdfwNbfe3Q9YU5PUgyFpr0piv3jar49s+9s1lufU2+qN/8ijZI10IT2CuNihfWQ0DuYaDZ7n0PzNC7kj39guv0/PEEGpcQ/gBsqbQ89ggy2/PhqXB+/hfJOsKw88fYvT3ThDvTeHU7DCDd62AUzJxDQdJkdn3jaNE8/Ft1zFXG1z+1nssvzXD0JcPkj/eQ+ZgJ7mj3Qx+4QBr55aY+v5FShdX7lkGyiuXqb33Ltmnn6Xnn/1ReHOPJ9A6O6mfPY05tfUUgeu6mW2RJCQl7OqK9O1B6+za9BJnaYnAXP8+yDKJo8fJPP0ssqbhmwaBYSBcF1mP7Cpw/kS43uEVBFt3owXBevEgPPckKQx2hLe7e0Fs9AC5z30BORojME18o4lwHOTIx3xMbt7PrQiC9cD2xjYJ30fsFN3cweZ7Xmg98J2/Mfmrv/r4S3TX+bUNnHzPZnr8JQb2Ps3+Qy/gOA3Ka2OsrV6hXp3d0njy9kjEk110950ikxsONTVCoCgaeiR9k3fSry5Gs7Ahu3S3yLJKNJYPs2iHX9h0sw4vBhKyfCMz4fsOprHx6db3bEBqleE0PYGqRjCNIo59Q8wbZvnu3pFeBB5rhSt0dB+lq/cB6tU5ND1BR/cRjMYyjdrC7Re5j9yufKXo4bzAwA12Dmzu8DokvKB18149vcC5//TGBtH3tu+7KciL5mIc+eNH6DzVj7FU5/L/ep/lN2dw6/aG70VyMEvu8OYb761EslFW3p2ldGGF4a8eIX+sl2P/4nE++I+vYizev3Jqx4leDv7hKWJdCYofLHL1L89QurCM7964mUbycQY+u3/HwAnAbTisvjdH4fQ8qb15ep8YpPPUntCn6fOj5I/1cP6/vs3iq/dIUycExpWLJI4eQ7guwraxq1Vq772DOT6GcO/OmFi4Ln6jgd9sUPzB9zDGt9Firt+I1XSG3Be+hCRJFH/4A8zJ8dbvss8+R+apZ+5qO+43Xq2G8D2UVApJjyBuEl9LqoacTBK4Lv56GdQ3DcS6oF3StB0F7JIeIfe5L6JmMqz95B8wLl9umVmmHn4UvW/nh8F7Flb5fqjDIuwGvLV0KMdiSNEoXr26QeaxWxxHMDnhMzISlu0iUQlNA9eBWj1gYd7n4kWPF39k8d67zifqz/mxBk6SJJNP7aMjNYLrGSyVzmO523ddRbQUHen9LK6d/cifHdUzdGcOs1j6EG9dE1SrzDB2aY1kqp9sx346uo6Q7zzE/MwbrCx8AHdoyqlqMfYd+DKJVB+Ls2/TqM3j+Q7RSIaRw1/9yPvwaeDuAsot1mE91e+7TF59cV03tBHXaeLeXI4UguB2J2TrRitt0mR81EtIo75Eo7ZAOjtMLN5BJJohmepjevylUNS/JdL6mBuBfxsH8o+Cnosh61ufzrImo2djSIqEU7Xw7XunCXCbDk7VQghBrCOBmtBxtul2247UcI7soS4QgtmfXmP2J9c2l9VkCSWq7qokUblW5OKfv0djropdNjn2L5+g65E9HPz9k1z47+9s31n2EZB1hY4Heon3JLEKTcb+z4cUzixsCkRlRQ73Y5cIX1CbWKM2VWLupTF6Hh/i0B+dIt4bWjSsnVvaUYx+JySOPYAcjVL48Y+wZ2du/4bdEARYs9MkHzxJ7NBhrPm5UP+zrvyVFAVkOezCEgIlkUBNpjAnx7EX50OVsCShpFLoff3resnNiCDMaijxBCgq+Ovn2qZrwP3BWyviLC8RHd6L3tsbmkgGAcgy0ZERtM4unIUF3GJY0XCLBdzCKvqeAeKHj2JcuXQjeyev+y65bnhMYlHUdBq3WMSenW0FsXI8QWTPAJK69fdJBAHC85Dj8TB7d107dZfHRHge9vwcfrNB4thxzIkxhO0AAknTSBx7YF1oPr3BkmA3RPQ0nfnHeOu1NC+/9BqaXkGPSKgKeB40m4LCqs/iYnDHs+h0PcmevseI6Gmm517Fsj76SLePNXDS1QS9uWMUq+PUzRVcf+cTXkJC3mG22p2gKlEyiT0slzf6JLlOg/LaNaqVKVYWTnPg2Dfp6T9FtTS5IUMhWl5E2+ss4oku0tlBVpfOsTj3TmtciZSRUO66o+v/T0TgYRml0BbVtSgXd+oK3H3A47pNPM9C0+PokSSOFQbmkqQQjd79AOLr21xYucDoka+TzY8QT/ZgW1Wq5elty69JNU9PdATDr1Kwpj/SSKGdSPSliHUmMBY3P4jE+9IkBzIANOardxzY7ITbsKmOF+l7cpj0/jzpffnQ7+gOrm3RzjiyKiMCqI6vbalFSvSmiPfsThTs1KywE80LWHhlkmhHgoN/8BADnz+AudJg4u8u3PPZc7ImE+1I3HA6ny1veQwy+/Oosbu4FgQCc7XJ7E+uEetJcvgPTxHvSRLrTm4bOImbTAxldWdtFYAST6DEE8RG9iNHoiDC8q+wrbC93by7AM0cH8O4fJHk8ROoqRTm+BiBbSMnEuhd3UiaRuknL4ZamEYdt1IhOjRM8oEHcVZXUBKJUB/V3bNtadCv1XCLBaLDe8k++xnshXkkRQlb4Gemb19S3AJJ01CzOWRdB0VB0iPIskykfw9KNIbww7Z/4TgEtk31rTfo/Po36Xj+q9TPvI9XqaB3dpJ65DGE61J77+2WZ1FgGNR++S4dnV10fOVrRIeGsZcWkJBQMhnUTJbq66/irhXxDRO3VELv7yd58iT23BySrhM/cIjI8F6CbYKUwDRwlpdIHH+AzDPPYU2OgyQhPC8Ub68HUnI8jprJIilK6Psky6jpNNGBIQLXQdg2brkEQYA9O039zPukTj5Mxwtfxxy7AoEguncfiQdP0jj/YZglvMPAzHGbLK9cIhZ5jolpmaZx766TrmtQKF5i39BzaGoMi1+hwCmbGKQ7e5h0vI8g8FCVCEul80T1LMloFwJBOtZLtTlPuTlLMtpNV+YAtrvROyWmZ+nMHECWVEr1KermCrnkILKkkoh2IkkKa7VxGlYBRdbpyhwkpmdxfXND2j8SzaBH0hjN1bBjzndxXQPfd1DVyBYdWALPs1EUnUSqd71N/xaxpAgACVnR1kerhGNHuvseQtOT9+Go/uoiREC1Mo1prNE/9AS2VVnvZAzQtDixZBeOXd80cuZ2+J5NrTJDT//DdHUfZ2n+PYLAI5HqJd916CNvd60yi9ksku86TDzZTWVtAqNZ2PK1USXFQOIoAI67hC88OiNDgIQmRyhY06iyTk7vRxBQshfwhU8+0o8mRam5q7sez6LGNAY+P0r5yuoGOwBZk+l5fJD0vjy+6VK6sLJtR91dIWDpjRmGXzhMciDDvt86SmO2QmN+a8GrrCuoUXVD8OYZbnhuShDrTGz5nj2f3U96JH/Heg7PcJn6+0tEu+Ls/eoR9v+TE1hrBnP/OHZPW/qFLzbM+ItkYxhLG8uCkXyMoRcOo8a3H7qrRBRkXd3eLmLdjVyI8DN32gff9PBMDyEE8f40WiqyfRlVknDXisjxGB3P38iOC9/Hq1Uxrlym/PJLoWj8DhGOQ/nln+FVaySOHSP/wteQNY3AtnHLaxgXL7RKT369QeXlfyTz1DPkv/Q8kqzgNxo0Lp2n/POfkfvMZ7fe13qd2puvI2s6uWc/i7S+fuPc2dA36i4CJ62rm47nv4rW1Y2sa8ixcO5m12//LsKxCVyX1W//NfbcDAiBOXaN4g+/T/rx36Tjyy8gR2P4RhNrZprau+9gTk5sWN+4egWEIPXwYyRPPEjmN59CiICg0cScGG/pgoTrUHnlZbKf+SzZp58L9800aV69TOXll8g+85kttz8wTWrvvIUcj5N+5DGyTz2DcB2aly7hrKy0MlDxQ0dC/ZQeQY7oyLpO/OhxoiOjCNfBXlyg8N2/JTAMAsui8uorBJZF8sGHSJ16GKSww69x9gNqb78Rzrm7Q4TwwwkYt8hANDVOZ8dhdD1FrT5PpRo+pKpqlHzuAIlYJ55vs7J6Dj9wyGX3k4h3EwQuxbUrmFYJIYL1te/dw9JHDpwMr8YH5RdbQ0gBnMDCExtPfNMuU27Mkox2UW7MUDdXEcInrufY2/MbLK6do24u4/ph2t9ya5h2he7sYRbWQiOwiJZmoOsRmlYBNzAY6nqUqZW3yCYGySWHWSidRVfiDHU/ztX5n9KdPUw2MUCxNk5HegRFvrG70ViO4dEvIMsqnmchEc6BU7QoS3PvYpmbDSZLhcvkOkbZf/ir2GYZIQSN2gLz06/jeRZms0i5eI1850GiJ7N4noWqxfFcg1p5+qMe6l8JFDVCV88DxBKd6JEUiWQPAsHo0a/jezZGs8Dq0llE4GPUl5kZ/xlDI89x8PjvhOabAiRZJvA95qZevePACWB5/v1w9t7ep+joOUbgO0iSQrO+TDx5Q1wsSUroCZXqR9MTJDMDqGrYqefYdRyrxtLCewT+jRPO9ywKy+cYOfQCQeBTKlzZdhCyG1iYXo0An6YXBhK5yB7K9gJVN2z574mO4gQGUSVJPrIHL3BJqZ0YfoXu6D6sZmNXWSq3YbPn2X0gBPM/n8Baa6LoKr2/Ocz+3zmOElUpnFlg6fWpey6Qrk+XmPjOOY7+88fpfnSQU//+ORZfmaR8pYDXdFCiKrHuJOmRPNmDnSy9Mb3B2LE2VcJcbZIazjL8lUPUZys0ZisIIdDTEQY+P8rgFw8SOD5B9M6DHadqcfUvzhDtSND/9F4O/eEprJLByrvz96yM49selasF3IZNrCvJ/t99gPFvn8Mum0iSRHIow8g3j5E/3otnOOjp6JbrpPbmOf6nT1AdK7J2bpnGfLVlYqkldbofHWDv144AUBkr0lzYviPLs1wq14r0PDFE7nAXo793gtkfX8WtOyBLqDENz3AwCyapR58geeIklVd+gbO8SOB6oZY5EiF++AjJB0/iLC22Bui6pTUK3/mbXWtZ/GaD6huv0Tj3AXIsHpaifI/AsvEb9RulncCneeUS1sI8SiKBrKrIgYxVXEZ4Hu7qCl59K52ahDU9zep3v42STCHJMsL38ZsNAstCkhU0LY7vu7s2+XXXipR/8mNQ5G3HMLmFGy35wvcxrlzGnp8P7R1UFeG5+LU6vmmgKpHwvnhd7hAEGFevYM3NoiZTSJoOiFAX1mzg3+R2bk5O4BRWURKhbYTwXLxqBeG6oQHoNtlAe2Ge4ve/G2qvFBURBATNJn6jjqxHyZ54DGN5htW//ettj4NwHALrxjELjCbV11+lef5D5GhoKxLYdrg9t2S/1n7090iKgru28TruNxsUf/h9JFXZtqwnyxr9fY8iywqN5gpdHUdQZJVydYo9fY+hqjGq1WlkWUWIoKUhbjQWScS7Geh/nLHJn3A/OkI+cuAU4O1qsK7tNWiYKzieQd1YoWmvv0cCx2tSqF3D9W50aLmeQcMq0ClGWz9LRjtJx3qxnCoSElE9TSbRD0DNXKJQuYosq+RTe9G1FLnkEIXqVQrVcXzfYaj7xnTrRn2Jpbl3Sab3oGoxQODa81RKk1TLM1uWXiqlKcYv/4B812FULU7gOzTqyy2xtOeZTF77MR1dR0ikehBCUCpepVwcI9cxCohNGiFBgNFYYXn+fdzbdKgJEVCrzOB71u21PneA59kUV85Tr85v+xrXbVJYPtfyt9oOSZLR9CSalkCIgHIxHLsgSyqypqLrJhJSqHESAWurlzEaBfJdB4nFO8MnX6dBvTJP9aZgs1aZxffdTUGKY9dZWTyDeZMVgGkUGbv4fTq6jxJPdIXdd8Wx9TKeibNuJSFJEpqeQNPDLEe1NAFICMKsF4hNgn4hAhr1RRyniecaVMpbdxsB+MLFDpoEwsP2w8xpIFzq7hp20ESTIsTVNFoQwRU2bmATVVIk1Cy+cDH9XQqZA8HUDy6hJSMMffkgw185jGe5yGqY3ZFkicpYkat/eQazcO9HTwhfMPPja8iawr5vHCV/rIf8kW4C//qMqvBYS4qMZ7qtkSDXMQtNJr97gSN//AiZ0U4e/w9fxFozEH5AJB+W8RZfmaQ2XebQHzx0V9torRlc/p+/JNaVIHe4iyN/8ih2ydzk3H3XE9zDKwAADXpJREFUCCicWWT+5xMMfekgA58bpefRQeyyuV7Gi+M2Xab+70WinQn2fePolsvImkzucDedJ/sZ+e3jBF446w5AiYWWBkJA4fQC4397rvW77bZp4efjdD3UT9cjexj55nGGXziMZ4bfDSWiMPF355n4hzmSJx/Cq9aovPJzhH/TmpKEcBxiowdQ0pkbS7vuli3pOx4i3wvHjmwxemQDQYBfreBXK2h6ko6BU6z4K3iei7Oy9fzMaKIDWVYxqsv41c3BpBZJ0Dv4KJZRYnXhgy1W2GJ7bZuIrVMvz2x4eNr5TQK/XtuUmVPUKD2Dj1BYONsyFb7++qDZxLndSBgh8Gu1LbM5N/spbYVfr+NvEWwKEeA163jlEl7zzpomhOe19Fo70RqPs2mj/O1/t46mxkkl+xif+imWVUFCIpsdwXYaJBN9zMy9SqO5zHWPwHD6RplYNE8gfBLx7vV7zacwcLoXeL7dEmzvhCxr6yW08FAsls5TM5aIZtLYbmgcKUSAQCBLCoqs4fo2IHB9i+CmoMX3bArL5ygsbx5Yuj2Canl6ww39Vhy7ztK64/fNrCye2WZJQXltnPLa+Na/v4kg8FhZOL3bjd01rtO4rX+TZZaZuPKj267luSZzU6/cwacLTKPAwszW5a7rr1ldOgtLm5sEjOYqYxe/t+nntlVhcfatTT8fv/z3rb8HgdcalHwnaFoCXY+zsnj6tk+vQgQEN524gQhahqmecCjasyTUXOj46zew/SYROXyKcwPrttkm3/YwC01KF1dYO79MbXKN3if3ktobmpLWpssUTi8w//L4JqfuW9fxjPW5ZXeRhfGaDhPfvUD58iq9T+4lf7yHeG8KNaaFI0vWDOozZYofLrHyzsbp64HjM/vTazg1i8EvHCBzsJNINorXdKmOFVl+a4b5l8dR4zp7v3YkDBa22Ebf9vFMF9/e2lKhPl3m0rfe44E//Q2SA1kO/P5Jzv/ZW9sahwaOj2s44bDhXXhA2SWDq39xGmOpTt9Te0MvqnwMp2az9OYMC7+YoHB6gb6n9zHwhdEtrQQac1Uu/vm7dJ7oazmHKxEVgnDwcH22QuH9eZbfmtm2HLphvfkq5//b2ww9f5DOk/3Eu5MouoLbdKhPN6hNlRGej/AD1FwOvb8ft7CKCIIwS9PTQ/KhU0iKirO4/YPVfUMCLZKkZ/ARhPApLp5HkhVyXQdAkqmuTRJ4Nr2Dj6LpccqFaxSXzpPMDpDKDhH4DmvLF3GsGvXK3PpD8tZkOvaTSPfiew5rK5eIJTrZs+9Jqpl+KoUxLKNMR99xVC2GUVvCaBSIJTqplqZIpvsRwsf3bDKdo0hIVIrjeK5BR+9xVC2KHk1vP0B5C+RIlPShB4l09ODbJvVr57GLy6jJNKmDJ9DSWazleRqTlxG+T+rAcXyzSax/GEnVqF74JbE9+7BW5rELS+Hom0MP4lRLeLUyuZO/gZJIYczfePiT9QjJkSPEegfDB/Wr57BW5tHzXaQOPICiR2jOTWLMjm8MsO8DihKWtMOpHALPt1Fkdf3nomWJcz2jlEkP0d15nHojlHy0hkTfh96AT0XgBOxq51zPwHbrrNWmMJ0KiqyFBy/DFhdSgeM1ies5atIisUgO+a6do9u0CdH0JF29J/A8h1Lh6m1fX7TnCAP98Ps537yIux4MCQRr1hw1eRWQcIWNED5zhomMTLCLLrxz//lNZE3BLpv4lsfU9y8x//IEynqmKXB93LpzWzH0+f/yFpf+xy/xLQ+ncndO9IHjUzy7RPlqAS0eun5LsgQi9IfyLA/PcLa0Q/CaDgu/mGD1/XnUmIYkS4hA4Nsebt0mcAMkxeTNf/MjhBdglTYHO+f/7E0ufes9fMvdco6dCASFM4u89W9fRNZVhBds9JO6hat/eYaJ710gsD2s0u6E0cZyg7Fvf8jMi1dQdBWkcDyKZ7i4TQcCweIrk+Hw5Lq96brnVC2mfnCJuZfGUCMqkqa07rXCD/AsH69p796/SUB1rMil+SpaUm8ZjwpfhN+NhkPgCervv0fmyafp/t3fD2+Ivg+K0hITV998DXNi4jYfdn+QZZXq2gTp/F4Smf6w6cNu4DkGua6DFBbOYjRWUNUo1bVJVC1GR88xGtUFkpl+Eul+qmu33/ZEug/PaWI0C6GsoL6CbVYpr15rTVSwmmuoWoxc92FqpRmS/XswGiukckPU1ibJ9xwNpQGyQrZzFMsso6gRyoUxeocevaP9jg/uJ9o7QOXce8iaTuA6SJpO5tgjCN+jPn6RzOGT4aSLySvEB/eDCKheOYuEhGc0kPUIieEDOKVVJFUnuf8oxbdfwjcN6uOX6HrySyjRGF69ArJMcvQYieEDVC+eAeHjGw3URIrciScwl+ewjAaZ448S2Cbm4j3qvNwG1zMIAo94rItGc5lYNIfrGuFUCyGIRnNYdhVZVggCn1SyH8+3KBQvkU4PcD9N2z49gdNNyJJKZ3o/udQwiWgXw91PUGnM0bAK1IxlhroexfMtAuEzX9w+A1OoXmNPxyni0Q40JbaefWrT5k6R6Og+QjSWI5Hqo6PzEAuzb2FuIwq/GV9sDFjcW7R/AT52sDEIcIPddy/dOpg28IK7ak/fLutyN/imh38H7uPXEb7AqVjbBm7CF5sE1zezm30QXoC5urtypV0xw2G9d0hg+1j29tvimS7eDtki4QW4NXvbWXx3g2+6+DsEz/X338NemCc6OIScTCLJCsJ1WoN23WLhY2nr3wrHrmObFVy7iaJoyIqO6xRwnSayoiIQeG74nXGdJnokjR5NEbHTWEZ5Y2lsBwqLH5LJ7yPfcxTHrOLYdYLAxbUbBL5DMjNAtnMU26yg6QmCwKVRmaez7wFcu47jNNEiCUQQxTYrmOtBlu/ZOHYdf7flvnUCy0SJxon27KE5dQW3VkFNpEmOHMFaXUDWI6jpLHH2YsxNICkKjakxzPlprgcNxtwk+VNPIms60d4hfLOJUwqvW269QuDe+I7JqkZ8z14a4xcxZsdaP4/1DxMf3I8IfALXQcvkiPUN3dPAKZMeIp8dJRHvor/3YcqVKSrVKYqlq/T3PYLnmiiKzuLy+xhmkbXSVXq7HySf3Y8gYHH5NM3mKr09JxkaeBJJklsNWunUAPncKMlED309DxGNZCmVxz6SWPxjDZxst8HUyhuY7o2LRs1YwnJunvcWljeadgnXN1mtXEMQYLt1PN9ivniGRDSPIuu4vonr2yyVL7RKdH7gMbH0GqZTxbDLOK6BqkRxvFBjcjsLhDZttiKdHSbfdZDAd5mfeYPl+V/eEyPQNm0+LThLizhLt458+mQRQuC7JkIE+L6D79vYVpVMxwgg0ajO47lmOAUiP0xX/4MUls5TWrlMLNGJtD5nNJHuI9s5iqRoWGaFemXuhkh7nWznKJFoBoRolXrN5hq9w49RWrmMEAGKGkFR9VbTitkskOs5RL0yh2s3KK1cIZ0fRtUTBLVlDKtKz8Aj9Aw8TOC7d+SDZy7NIk4LEkP76XzyS1Q+fAe3XgURYK0s4DVrmEuzePUqwfXxJqbBzZkWZ22FwHOJdO8hPrBvQ1luMxKSrBA4GwN2SVEIHAtzeT78c3EGp3Jvx0vZdo1SZSLsmiPAcZr4651xhlFEUXQcp45plQFBYe0SjeYKmhYjCDxcp0nFqeO4DRQlgm1XWV79ECECbLtOuTJJtTqLIMB1jQ2ynbtB2lnoK31yMyTatPkUISsasqS2TDt/lcfmtGnzq4QsqwSBhyQpXC97y3LohSUCb13PIqHIWuv8lCQZWVZBkvA9B0kKbWIg1DZu1Q0oK9r6SKygJQaXZDUsBfmhZYai6i1PP0lSSOeGiSU7WZk/je9aIEnrWlyJwA+3LbSnCYPA8GFrd7dVJRpvlfjzDz0Z6mHPvk33Z75Cc+oa9cnLyJresnLo+ew3qF46gzG7US+bOvQgiaH9IARr77+GWwmDPiWepPdzv0XxnZexC0tIqkbH488hPI/y2XV9aCBQ01m6nvwipfdfw1pdRIlE8S3zvmucPmmEENsK0j6Vpbo2bT5tBL5LwMc/hbtNm193rmd2b35Y2WQBIm4WC9PKUN34t1gfz7TD52xRShOBt8Fb6OY1VE1DCJ/S6pUwaFrfjlu3bTu7ktsR6x8ise9wWHaSZSoXT+PbFrUrH5I+fJL48GioP7t0Oiy/bTMnz1qZJ3/qKZoz1/CbYeUlPrif1Ogxol395B95BmN2gvr4BRpjF8g99CQ9z30N4XnUrp7DXJ6jMXmF7InHQwsJz6X0/uuhLurXlHbGqU2bNm3atPmUIetR1EQSJJnAtfEa9bC8KMuo8RSyHkEEPl4jnJOnJlL4trV5rqAko6WzBI6Nb4b6PiUaR4klQJbDYM+x1y0JBEosgRINzT69Zo3AsZEUFTWRQlI1hO+FJcN7aInzaWSnjFM7cGrTpk2bNm3atLmJnQKndn9+mzZt2rRp06bNLtkx49SmTZs2bdq0adPmBu2MU5s2bdq0adOmzS5pB05t2rRp06ZNmza7pB04tWnTpk2bNm3a7JJ24NSmTZs2bdq0abNL2oFTmzZt2rRp06bNLmkHTm3atGnTpk2bNrvk/wF+BBmzONyE4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "kata yang paling sering muncul sampai jarang muncul ditandai dengan ukuran kata itu senidri kata semakin besar maka kata tersebut semakin sering muncul begitu juga sebaliknya\n",
        "\n",
        "\n",
        "Wordcloud diatas kata yang paling sering muncul adalah hotel, room, day, time, dan seterusnya secara berurutan"
      ],
      "metadata": {
        "id": "1nhuZXveSSTk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.countplot(tripAdvisor_reviews[\"Rating\"])\n",
        "plt.title(\"Ratings of Hotels\")"
      ],
      "metadata": {
        "id": "VGS_ZxJ0nGzl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "b7de03a2-2c16-4d99-c76d-d15becca8ad7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ratings of Hotels')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari grafik diatas paling banyak reviewes memberikan rating 5, kemudian rating 4 dan seterusnya. Dapat disimpulkan juga untuk kasus multiclass classification data adalah imbalanced"
      ],
      "metadata": {
        "id": "gdBiF4lETNCD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of Characters in Reviews\n",
        "fig, axs = plt.subplots(3,2, figsize=(10, 15))\n",
        "\n",
        "#Score 1\n",
        "review_len = tripAdvisor_reviews.loc[tripAdvisor_reviews['Rating']==1]['Review'].str.len()\n",
        "axs[0,0].hist(review_len, color='blue', range=(0,1500))\n",
        "axs[0,0].set_title('Score is 1')\n",
        "\n",
        "#Score 2\n",
        "review_len = tripAdvisor_reviews.loc[tripAdvisor_reviews['Rating']==2]['Review'].str.len()\n",
        "axs[0,1].hist(review_len, color='red',range=(0,1500))\n",
        "axs[0,1].set_title('Score is 2')\n",
        "\n",
        "#Score 3\n",
        "review_len = tripAdvisor_reviews.loc[tripAdvisor_reviews['Rating']==3]['Review'].str.len()\n",
        "axs[1,0].hist(review_len, color='green', range=(0,1500))\n",
        "axs[1,0].set_title('Score is 3')\n",
        "\n",
        "#Score 4\n",
        "review_len = tripAdvisor_reviews.loc[tripAdvisor_reviews['Rating']==4]['Review'].str.len()\n",
        "axs[1,1].hist(review_len, color='orange', range=(0,1500))\n",
        "axs[1,1].set_title('Score is 4')\n",
        "\n",
        "\n",
        "#Score 5\n",
        "review_len = tripAdvisor_reviews.loc[tripAdvisor_reviews['Rating']==5]['Review'].str.len()\n",
        "axs[2,0].hist(review_len, color='purple', range=(0,1500))\n",
        "axs[2,0].set_title('Score is 5')\n",
        "\n",
        "#All Scores\n",
        "review_len = tripAdvisor_reviews['Review'].str.len()\n",
        "axs[2,1].hist(review_len, color='black', range=(0,1500))\n",
        "axs[2,1].set_title('Length of All Reviews')\n",
        "\n",
        "\n",
        "fig.suptitle('Number of characters in Reviews')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "GnwZILIBwXqo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "outputId": "ac9a8f26-489e-4c22-a405-43a9d72a0b72"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jumlah karakter pada setiap label memiliki distribusi yang sama"
      ],
      "metadata": {
        "id": "eSmsZLJGbndW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita juga bisa melihat sentiment reviewer antara positive (jika rating lebih dari sama dengan 4) dan negative (jika rating kurang dari sama dengan 3)"
      ],
      "metadata": {
        "id": "5hGlMNvMTyYG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 5 Stars to Binary Value\n",
        "tripAdvisor_reviews['Good_reviews'] = tripAdvisor_reviews.Rating\n",
        "tripAdvisor_reviews.Good_reviews[tripAdvisor_reviews.Rating <= 3] = 0\n",
        "tripAdvisor_reviews.Good_reviews[tripAdvisor_reviews.Rating >= 4] = 1"
      ],
      "metadata": {
        "id": "D2dqoQ4IxNm3"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = tripAdvisor_reviews.groupby(['Good_reviews']).count()\n",
        "a = a.reset_index()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "\n",
        "sns.barplot(x='Good_reviews', y='Review', data=a)\n",
        "plt.xlabel(\"Good Reviews\")\n",
        "plt.ylabel('Number of Reviews')\n",
        "plt.title('Distribution of Good Reviews')"
      ],
      "metadata": {
        "id": "-NhJYHKRxoze",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "60cd6e48-f540-4173-82a5-b95d1624fdb4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of Good Reviews')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari barplot tersebut dapat disimpulkan bahwa reviewer lebih banyak memiliki sentiment positive daripada negative"
      ],
      "metadata": {
        "id": "wjpmWLy7UNO1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preprocessing"
      ],
      "metadata": {
        "id": "J8dfbEyLNfpE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Stopwords\n",
        "\n",
        "Kita akan menghapus stopword yang ada pada data"
      ],
      "metadata": {
        "id": "noeeD0VZVCU3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words = set(stopwords.words(\"english\"))"
      ],
      "metadata": {
        "id": "OkNbD8AtZGu0"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Stopwords\n",
        "tripAdvisor_reviews['stopwords'] = tripAdvisor_reviews['Review'].apply(lambda x: len([x for x in x.split() if x in stop_words]))\n",
        "tripAdvisor_reviews[['Review','stopwords']].head()"
      ],
      "metadata": {
        "id": "Br4KHU6y1gxr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f695827f-1fd0-4d4b-ad1c-0a7f75ef010d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <td>ok nothing special charge diamond member hilto...</td>\n",
              "      <td>16</td>\n",
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              "      <th>2</th>\n",
              "      <td>nice rooms not  experience hotel monaco seattl...</td>\n",
              "      <td>15</td>\n",
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              "      <th>3</th>\n",
              "      <td>unique great stay wonderful time hotel monaco ...</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>great stay great stay went seahawk game awesom...</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-36fbdd91-8d79-48e4-85aa-53fdd75ec88c');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                              Review  stopwords\n",
              "0  nice hotel expensive parking got good deal sta...          6\n",
              "1  ok nothing special charge diamond member hilto...         16\n",
              "2  nice rooms not  experience hotel monaco seattl...         15\n",
              "3  unique great stay wonderful time hotel monaco ...          5\n",
              "4  great stay great stay went seahawk game awesom...         17"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews['Review'] = tripAdvisor_reviews['Review'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop_words))\n",
        "tripAdvisor_reviews['Review'].sample(10)"
      ],
      "metadata": {
        "id": "B8uNwcMi15nJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fb5f765-b3be-4575-dd16-e00d4584160b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2945     best beach stayed bavaro princess suites march...\n",
              "16742    fantastic hotel quieter area barcelona stayed ...\n",
              "19652    contiki far away kuta hey got bali resort spen...\n",
              "15628    flowers service birthday cake beijing month bi...\n",
              "2428     hix want booked night stay hix house read revi...\n",
              "8851     absolutely fabulous absolutely beautiful room ...\n",
              "3091     beautiful resort trip report melia caribe roya...\n",
              "16950    incredible wife went boston wedding anniversar...\n",
              "7050     secrets average simply ok time nothing sorry n...\n",
              "4461     wonderful staff planning trip paris friends lo...\n",
              "Name: Review, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lemmatization"
      ],
      "metadata": {
        "id": "wXzHtfIrVeMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import WhitespaceTokenizer\n",
        "\n",
        "w_tokenizer = WhitespaceTokenizer()\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def lemmatize_text(text):\n",
        "    return [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(text)]\n",
        "\n",
        "tripAdvisor_reviews['Review']= tripAdvisor_reviews.Review.apply(lemmatize_text)"
      ],
      "metadata": {
        "id": "iGbC2BGf_ak3"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews['Review'].head()"
      ],
      "metadata": {
        "id": "lOObtvrMmDbl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0798405-0527-4319-c712-ea422ad7911d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [nice, hotel, expensive, parking, got, good, d...\n",
              "1    [ok, nothing, special, charge, diamond, member...\n",
              "2    [nice, room, experience, hotel, monaco, seattl...\n",
              "3    [unique, great, stay, wonderful, time, hotel, ...\n",
              "4    [great, stay, great, stay, went, seahawk, game...\n",
              "Name: Review, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lemmatize_output(text):\n",
        "  return ' '.join([lemmatizer.lemmatize(w) for w in text])\n",
        "\n",
        "tripAdvisor_reviews['Review']= tripAdvisor_reviews.Review.apply(lemmatize_output)\n",
        "tripAdvisor_reviews['Review'].head()"
      ],
      "metadata": {
        "id": "SNBYnR4PnkI7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "672377d0-1e48-44dd-9ce6-91b237c9bd00"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    nice hotel expensive parking got good deal sta...\n",
              "1    ok nothing special charge diamond member hilto...\n",
              "2    nice room experience hotel monaco seattle good...\n",
              "3    unique great stay wonderful time hotel monaco ...\n",
              "4    great stay great stay went seahawk game awesom...\n",
              "Name: Review, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews = tripAdvisor_reviews[['Review',\t'Rating']]\n",
        "tripAdvisor_reviews.head()"
      ],
      "metadata": {
        "id": "zFbUFLnM8vqK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c22b604e-a90c-40f5-abfa-313ea6b06f73"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Review</th>\n",
              "      <th>Rating</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>4</td>\n",
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              "      <td>2</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>unique great stay wonderful time hotel monaco ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>great stay great stay went seahawk game awesom...</td>\n",
              "      <td>5</td>\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-98030870-ca8d-4d74-8c3f-b30e1fda0965 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-98030870-ca8d-4d74-8c3f-b30e1fda0965');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                              Review  Rating\n",
              "0  nice hotel expensive parking got good deal sta...       4\n",
              "1  ok nothing special charge diamond member hilto...       2\n",
              "2  nice room experience hotel monaco seattle good...       3\n",
              "3  unique great stay wonderful time hotel monaco ...       5\n",
              "4  great stay great stay went seahawk game awesom...       5"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menghitung jumlah kata yang terdapat pada dataset"
      ],
      "metadata": {
        "id": "YE8Je4NzWFwz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "results = Counter()\n",
        "tripAdvisor_reviews['Review'].str.lower().str.split().apply(results.update)\n",
        "print(len(results))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSBUcFMnHm57",
        "outputId": "bf757f4c-28e1-4a1d-b80f-85a0ae66c3d2"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terdapat 45706 kata yang ada pada data, selanjutnya kita dari data ini kita akan tentukan jumlah vocab yang akan digunakan untuk pendefinisian model"
      ],
      "metadata": {
        "id": "c0H-njizWLNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews['totalwords'] = [len(x.split()) for x in tripAdvisor_reviews['Review'].tolist()]"
      ],
      "metadata": {
        "id": "VsEud4YtJHZt"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "i6UPLhEeJVTg",
        "outputId": "c0ebf758-c5e3-43ee-8123-7154aa46d15e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-af05e573-ac9b-4378-8abf-8f50444ac313\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>totalwords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20491.000000</td>\n",
              "      <td>20491.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.952223</td>\n",
              "      <td>97.720609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.233030</td>\n",
              "      <td>93.551179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>46.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>116.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1855.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af05e573-ac9b-4378-8abf-8f50444ac313')\"\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-af05e573-ac9b-4378-8abf-8f50444ac313 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-af05e573-ac9b-4378-8abf-8f50444ac313');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             Rating    totalwords\n",
              "count  20491.000000  20491.000000\n",
              "mean       3.952223     97.720609\n",
              "std        1.233030     93.551179\n",
              "min        1.000000      6.000000\n",
              "25%        3.000000     46.000000\n",
              "50%        4.000000     72.000000\n",
              "75%        5.000000    116.000000\n",
              "max        5.000000   1855.000000"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Totalkata pada setiap baris sangat berbeda signifikan dengan minimum jumlah kata 6 dan maksimum 1855 sedangkan rata-ratanya 97"
      ],
      "metadata": {
        "id": "sczlFdCAkaqX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tripAdvisor_reviews.to_csv('./data.csv', index=False)"
      ],
      "metadata": {
        "id": "vYiziV5B88IN"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('./data.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "id": "jzdwLBIe9xx4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "99e270a0-014c-40a5-f511-de9800d96f48"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Rating</th>\n",
              "      <th>totalwords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>2</th>\n",
              "      <td>nice room experience hotel monaco seattle good...</td>\n",
              "      <td>3</td>\n",
              "      <td>191</td>\n",
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              "      <td>unique great stay wonderful time hotel monaco ...</td>\n",
              "      <td>5</td>\n",
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              "      <th>4</th>\n",
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              "      <td>5</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ],
            "text/plain": [
              "                                              Review  Rating  totalwords\n",
              "0  nice hotel expensive parking got good deal sta...       4          81\n",
              "1  ok nothing special charge diamond member hilto...       2         229\n",
              "2  nice room experience hotel monaco seattle good...       3         191\n",
              "3  unique great stay wonderful time hotel monaco ...       5          84\n",
              "4  great stay great stay went seahawk game awesom...       5         172"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.copy()\n",
        "# reviews = df.iloc[:, :-1]\n",
        "reviews = df['Review']\n",
        "ratings = df['Rating']"
      ],
      "metadata": {
        "id": "8DE5kLXaMZWO"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "training_reviews, testing_reviews, training_ratings, testing_ratings = train_test_split(reviews, ratings, test_size=0.1, random_state=42, stratify=ratings.values)\n",
        "training_reviews, val_reviews, training_ratings, val_ratings = train_test_split(training_reviews, training_ratings, test_size=0.1, random_state=42, stratify=training_ratings.values)"
      ],
      "metadata": {
        "id": "5wYXnmEuMI0P"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BP9YJcSpduon",
        "outputId": "06e4183e-9e1c-49c1-8062-4c7d8873dd3d"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18890    4\n",
              "18831    3\n",
              "3956     4\n",
              "5998     5\n",
              "3104     5\n",
              "        ..\n",
              "2497     5\n",
              "16623    4\n",
              "13739    5\n",
              "5930     4\n",
              "17600    2\n",
              "Name: Rating, Length: 1845, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()"
      ],
      "metadata": {
        "id": "DeuXzosPcxu7"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_ratings = le.fit_transform(training_ratings)\n",
        "val_ratings = le.transform(val_ratings)"
      ],
      "metadata": {
        "id": "tXBQhAhhc_Fo"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWYFUuHbDUOW",
        "outputId": "73ffbb9d-fecd-49e9-b4d8-29b2d36d75b3"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 3, ..., 4, 3, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_reviews.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoxBF-4RFuFn",
        "outputId": "063845dc-406c-4540-ebec-cacfa91f9c39"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16596,)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_reviews"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdI8ptOG0b1u",
        "outputId": "5614b621-8886-433c-d0cd-203c62fa9e31"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9368     good location adult stayed night triple room l...\n",
              "8986     maybe individual tale woe stayed april point p...\n",
              "14002    great european hotel overall wife great stay h...\n",
              "16501    true gem planning bali trip listened indonesia...\n",
              "14550    great location average hotel great location cl...\n",
              "                               ...                        \n",
              "1111     perfect stay wonderful service received eduard...\n",
              "541      wow marriott follower wife visited dallas frie...\n",
              "12019    good better stayed time going regent hotel pro...\n",
              "6913     nightmare holiday favourite holiday partner we...\n",
              "12875    nice ambience good location tiny shower return...\n",
              "Name: Review, Length: 16596, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Modelling Text Classification\n",
        "\n",
        "Model yang akan kita gunakan terdiri dari Baseline model, Improvement model, dan Fine Tuning model.\n",
        "\n",
        "Setiap model terdiri dari Pendefenisian model dan Pelatihan model"
      ],
      "metadata": {
        "id": "gAsdHT7vivcC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 64\n",
        "max_length = 100\n",
        "trunc_type = 'post'\n",
        "padding_type = 'post'\n",
        "oov_tok = '<OOV>'"
      ],
      "metadata": {
        "id": "r7HL2szjnfAL"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_reviews)"
      ],
      "metadata": {
        "id": "bu-M9XU7nohr"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_index = tokenizer.word_index\n",
        "dict(list(word_index.items())[0:10])"
      ],
      "metadata": {
        "id": "cAYO-aBDnvc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3abaf49e-2aba-45c4-89b2-ba9454dd2b85"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'<OOV>': 1,\n",
              " 'day': 9,\n",
              " 'good': 5,\n",
              " 'great': 4,\n",
              " 'hotel': 2,\n",
              " 'night': 8,\n",
              " 'room': 3,\n",
              " 'staff': 6,\n",
              " 'stay': 7,\n",
              " 'time': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sequences = tokenizer.texts_to_sequences(training_reviews)\n",
        "print(train_sequences[10])"
      ],
      "metadata": {
        "id": "ykQIY-TZnxvM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef23b18e-ddd2-426d-e18d-1496ddd7faf4"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[39, 12, 679, 25, 343, 2, 189, 8, 1423, 1664, 127, 16, 8, 117, 1, 180, 8, 40, 275, 281, 1, 16, 163, 3, 87, 1, 1232, 1, 1797, 7024, 1324, 785, 81, 1861, 177, 2770, 780, 3275, 1080, 48, 35, 24, 52, 51, 6, 270, 38, 14, 1138, 1, 4, 20, 150, 5463, 774, 545, 116, 345, 137, 13, 165, 197, 304, 37, 102, 114, 1210, 304, 252, 11, 188]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_padded = pad_sequences(train_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "print(len(train_sequences[0]))\n",
        "print(len(train_padded[0]))\n",
        "\n",
        "print(len(train_sequences[1]))\n",
        "print(len(train_padded[1]))\n",
        "\n",
        "print(len(train_sequences[10]))\n",
        "print(len(train_padded[10]))"
      ],
      "metadata": {
        "id": "fFu8PIq7oBR3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6e62aa3-ad9e-4f07-8a21-5d0161afe28f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "87\n",
            "100\n",
            "122\n",
            "100\n",
            "71\n",
            "100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_sequences = tokenizer.texts_to_sequences(val_reviews)\n",
        "validation_padded = pad_sequences(validation_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "print(len(validation_sequences))\n",
        "print(validation_padded.shape)"
      ],
      "metadata": {
        "id": "3Zm-ebtpoOsU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99a38f87-49b7-438c-9b57-4a9aa6a6d7a0"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1845\n",
            "(1845, 100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_label_seq = np.array(training_ratings)\n",
        "validation_label_seq = np.array(val_ratings)"
      ],
      "metadata": {
        "id": "7X1DLabKAjrC"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(training_reviews)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1HYWYNtfBBl",
        "outputId": "35e23fd5-03f6-44b3-bdee-2066b3d15b8c"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16596,)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Baseline Model"
      ],
      "metadata": {
        "id": "sTSF8V9-NfmA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Pendefinisian Model\n",
        "\n",
        "Baseline model kita ambil dari artikel berikut [ini](https://towardsdatascience.com/multi-class-text-classification-with-lstm-using-tensorflow-2-0-d88627c10a35) namun kita buat model nya dengan menggunakan ***Functional API***"
      ],
      "metadata": {
        "id": "LoM0tspNjxqk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "def baseline_model():\n",
        "  input_ = keras.layers.Input(1)\n",
        "  embedding = keras.layers.Embedding(vocab_size, embedding_dim)(input_)\n",
        "  bidirectional = keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim))(embedding)\n",
        "  hidden1 = keras.layers.Dense(embedding_dim, activation='relu')(bidirectional)\n",
        "  output = keras.layers.Dense(5, activation='softmax')(hidden1)\n",
        "  model = keras.models.Model(inputs=[input_], outputs=[output])\n",
        "\n",
        "  model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "liolPalfZtpZ"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "baseline_model = baseline_model()\n",
        "baseline_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrwy0RUIfzON",
        "outputId": "169cd1be-bd9d-46c3-d93a-fd7c74f1aaaa"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 1)]               0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, 1, 64)             640000    \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 128)              66048     \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 714,629\n",
            "Trainable params: 714,629\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(baseline_model, \"baseline_model.png\", show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "9Sh5OA0ygZU4",
        "outputId": "726628ee-f774-4348-d2e7-b47fe4b989ee"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Pelatihan Model"
      ],
      "metadata": {
        "id": "IrPxGc45ke3J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 10\n",
        "history = baseline_model.fit(train_padded, training_label_seq, epochs=num_epochs, validation_data=(validation_padded, validation_label_seq), batch_size=128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sI0CpKuegYb8",
        "outputId": "00c5b488-c70a-4b8d-8f68-8f1098d1f963"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name='input_1'), name='input_1', description=\"created by layer 'input_1'\"), but it was called on an input with incompatible shape (None, 100).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name='input_1'), name='input_1', description=\"created by layer 'input_1'\"), but it was called on an input with incompatible shape (None, 100).\n",
            "130/130 [==============================] - ETA: 0s - loss: 1.1520 - accuracy: 0.4968WARNING:tensorflow:Model was constructed with shape (None, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name='input_1'), name='input_1', description=\"created by layer 'input_1'\"), but it was called on an input with incompatible shape (None, 100).\n",
            "130/130 [==============================] - 16s 64ms/step - loss: 1.1520 - accuracy: 0.4968 - val_loss: 0.9144 - val_accuracy: 0.5973\n",
            "Epoch 2/10\n",
            "130/130 [==============================] - 7s 54ms/step - loss: 0.7964 - accuracy: 0.6443 - val_loss: 0.8507 - val_accuracy: 0.6228\n",
            "Epoch 3/10\n",
            "130/130 [==============================] - 7s 53ms/step - loss: 0.6761 - accuracy: 0.7085 - val_loss: 0.8808 - val_accuracy: 0.6206\n",
            "Epoch 4/10\n",
            "130/130 [==============================] - 7s 52ms/step - loss: 0.6019 - accuracy: 0.7489 - val_loss: 0.9370 - val_accuracy: 0.6238\n",
            "Epoch 5/10\n",
            "130/130 [==============================] - 7s 53ms/step - loss: 0.5303 - accuracy: 0.7877 - val_loss: 1.0110 - val_accuracy: 0.6168\n",
            "Epoch 6/10\n",
            "130/130 [==============================] - 6s 50ms/step - loss: 0.4713 - accuracy: 0.8156 - val_loss: 1.0925 - val_accuracy: 0.6016\n",
            "Epoch 7/10\n",
            "130/130 [==============================] - 6s 50ms/step - loss: 0.4113 - accuracy: 0.8432 - val_loss: 1.1995 - val_accuracy: 0.5935\n",
            "Epoch 8/10\n",
            "130/130 [==============================] - 6s 49ms/step - loss: 0.3567 - accuracy: 0.8673 - val_loss: 1.3702 - val_accuracy: 0.5870\n",
            "Epoch 9/10\n",
            "130/130 [==============================] - 6s 48ms/step - loss: 0.2880 - accuracy: 0.8974 - val_loss: 1.4990 - val_accuracy: 0.5832\n",
            "Epoch 10/10\n",
            "130/130 [==============================] - 6s 47ms/step - loss: 0.2247 - accuracy: 0.9247 - val_loss: 1.6523 - val_accuracy: 0.5637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dokumen [Embedding layer](http://man.hubwiz.com/docset/TensorFlow.docset/Contents/Resources/Documents/api_docs/python/tf/keras/layers/Embedding.html)"
      ],
      "metadata": {
        "id": "YiKOLPR-3pzm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluasi Model"
      ],
      "metadata": {
        "id": "UHfXACPcSKiG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "metadata": {
        "id": "fiUj4fM1qrEK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "bd51cce9-4838-4f85-e4b9-c3c1b037ae66"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jy8s763YOh4PgYLmopIa39kdYWNjJC9OEqOF2u9mdVcya3Tms3XOMzIIKAgMsjO0TzS8uTGbywG7E+PBQz22ZTwRBcHAwycnJDW7nCz3/LUn2h/C2mg7ftXuMY/7WokqCAiyM6xvDvRefw2UDuxHVKcTsMv2eTwSBEKLtcLnc7DxaZHzz332M7GIbwYEWLugbw5yJKVw6IJ4uHeXDvy2RIBBC/GROl5u0zCLW7D7G2j3HyC2xExIYwEX9YvjtZMXE1HgiO8hh2bZKgkAI0SxOl5uthwpYuzuHdXtzyCu1ExoUwHgVy7TB3ZnQP04GemsnJAiEEI1W7XSxJaOQNXuO8dHeHPLLqggLDmBC/zimDurOJf3j5FTPdkj+x4QQZ+Vwuth8sIC1u4+xfm8ORRUOOoYEMqF/HNMGd2e8iqVjiHyUtGfyvyeE+BGH082G/cdZs/sYH+3LpbjSQXhoEBNTjW/+41UsYcEyxIOvkCAQQgDGqZ7bM4tYvt3Kh99mUe44RERoEJcOiGfa4O5ckBIjH/4+SoJACD+XfaKSlTusLE+zcriggo4hgYzr2ZFbLkxlXN9oGdzND0gQCOGHbA4n6/fmsDzNypcH8nG7YXRyFLMnpDB1UDeOZPxAav84s8sUrUSCQAg/4Xa72Xn0BMvTrHzwbTaltmoSunTggQkpXDcskZ7R3h/XX7RNEgRC+LjcEhsrd2SxPO0oB/PKCQsOYNqg7swYkciY5GgCZFYvvydBIIQPslc7+WTfcZalHeXz7/NwuWFk76788qI+TBvcXS70EqeQIBDCR9SM7rk8zcp/d2VTXOmge2QY943vy3XDE0mO6WR2iaKNkiAQop3LK7WzamcWy9Os6NxSQoMCmDywGzOGJ3J+3xiZ0F00SIJAiHaoqtrFZ/uPszztKBt0Hk6XmyFJXfjrtYO44tweMsCbaBIJAiHakb3ZtYd+CsuriIsI5a4L+zBjeAJ94yLMLk+0UxIEQrRxheVVJw/97DtWQkhgAJcOiGfGiEQu7BtDUGCA2SWKdk6CQIg2yOF0sUnnsSztKJ/tP47D6ebcxEgev3ogV53XQyZ2ES1KgkCINkTnlLI87Sjv78wmv8xOTHgIt4/rzXXDE+nfrbPZ5QkfJUEghMmKKxys/i6bZduP8p21mKAACxNT45g5PImLVSzBcuhHeJkEgRAmcLrcfHUgn2VpVtbvzaGq2kX/bhE8csUArhnSg+jwULNLFH5EgkCIVpRZUM7yNCsr0qxkF9uI7BDMTSOTmDkiiYE9OmOxyDn/ovVJEAjhZRVV1azZncOy7UfZeqgQiwUuSonlj5enMik1Xsb4F6aTIBDCC9xuN2mZRSzbbuXD77Ipr3LSO7ojv5usmD4sge6RHcwuUYiTvBoESqkpwAtAILBQaz3vtMd7Am8CXTzbzNVar/FmTUJ4U26JjRU7rCzfbiUjv5yOIYFcPrg7M0ckMbJ3Vzn0I9okrwWBUioQmA9cCliBbUqp1VrrfXU2+xPwntZ6gVJqALAG6O2tmoTwBnu1k0/Tj7Ns+1E2eUb6HNU7invGn8Plg7vTKVQa3qJt8+Zv6CjggNY6A0AptQS4GqgbBG6g5uToSCDbi/UI0aL2ZhezbLuVVbuyOFHhoFvnMO4dfw4zhifJSJ+iXfFmECQAR+vctwKjT9vmUeAjpdT9QCdgUkMvarfbSU9Pb1ZBNput2c/1RbI/TtWY/VFic7LhUBkfHSglo7CKoAAY17MTl/WNZkj3DgQGuLHlHSE9r5WK9hL53TiVr+8Ps9usNwFvaK3/rpQaC7yllBqktXad6QmhoaGkpqY2683S09Ob/VxfJPvjVGfaH06Xm89/yGP5disf78ulyuliUEJnHr86xWeHe5DfjVP5wv5IS0s742PeDIIsIKnO/UTPurp+AUwB0Fp/rZQKA2KA416sS4hGycgrY1malZU7rOSW2OnaMZhbxvRk5vAkBvSQ4R6E7/BmEGwDUpRSyRgBcCNw82nbHAEmAm8opVKBMKCdN6pFe1Zmr+Z/32WzbLuV7ZlFBFhgvIrjsasSmdA/npAgGe5B+B6vBYHWulopNRtYj3Fq6Gta671KqceB7Vrr1cBvgH8rpR7E6Di+XWvt9lZNQtTH7Xaz7XAR//ryOF+9k0mlw0mf2E7Mndqf6UMTiOscZnaJQniVV/sIPNcErDlt3Z/rLO8DzvdmDUKcTVpmEc+s28/WQ4V0CLZwzdBEZgxPYljPLnLOv/AbZncWC2GK73NL+dt6zcf7cokJD+GxqwZybng5Q88daHZpQrQ6CQLhV44WVvCPT35g5U4r4SFB/PayftxxfjKdQoN8+vRAIc5GgkD4hfwyO/M3HGDxliNggbsu7MO9F59D106+d+qnEE0lQSB8WqnNwb+/OMSiLzKodDi5fkQScyalyKBvQtQhQSB8ks3h5O0tmczfcICiCgfTBnfj/12q6BsXbnZpQrQ5EgTCp1Q7XazcmcU/Pv6e7GIbF6bE8LvJinMTu5hdmhBtlgSB8Alut5v1e3N59iPNgeNlnJcYyd9mnsf5fWPMLk2INk+CQLR7mw/k8/R6zbdHT3BObCdevXUYkwd2k+sAhGgkCQLRbu22FvPM+v188UM+PSLDeOa6c5k+LIGgQBkGQoimkCAQ7c7BvDKe++h7/rf7GF07BvOny1O5dUwvmftXiGaSIBDtxrHiSl789Afe224lNCiAByamcNeFyUSEBZtdmhDtmgSBaPOKyqtYsOkgb2w+jNvtZtaYXsye0JeY8FCzSxPCJ0gQiDaroqqa1748xD83ZVBWVc21QxN4cFI/kqI6ml2aED5FgkC0OVXVLpZsO8KLnx4gv8zOpQPi+e1lCtUtwuzShPBJEgSizXC53Kz+Npu/f6w5WljJqOQo/jlrOMN7dTW7NCF8WqOCQCm1ElgErD3bfMJCNIfb7WaDPs4z6zT7c0oZ0L0zb9wxiIv7xcq1AEK0gsa2CF4B7gBeVEotA17XWmvvlSX8xfbDhTy9bj/bDhfRK7ojL940lCsGdycgQAJAiNbSqCDQWn8CfKKUigRu8iwfBf4NvK21dnixRuGDDuaV8fTa/Xy0L5fYiFCeuGYQN4xMIlguBhOi1TW6j0ApFQ3cCswCdgKLgQuAnwHjvVGc8D35ZXb+8cn3vPvNUcKCAvjtZf34+QXJdAyR7iohzNLYPoL3AQW8BVyptT7meWipUmq7t4oTvqOyysnCLzJ4ddNBbNUubh7VkzmTUuRaACHagMZ+DXtRa72hvge01iNasB7hY5wuNyvSrPz9Y01uiZ3LBsTz0NT+nBMr8wII0VY0NggGKKV2aq1PACilugI3aa1f8V5poj1zu91s+j6PeWv3sz+nlCFJXXjppmGMSo4yuzQhxGka2zN3V00IAGiti4C7vFOSaO/2Zhcza9E33P76NiqqnLx881Dev2+chIAQbVRjWwSBSimL1toNoJQKBGTWb3GK7BOVPPuR5v2dWUR2COaRKwZw65iehAbJqKBCtGWNDYJ1GB3D//Tcv9uzTghKbA4WbDzIa18ewg388sI+3HdJXyI7yKigQrQHjQ2ChzA+/O/13P8YWOiVikS7UVXt4p2tmbz42QEKy6u4dmgCv7msH4ldZVA4IdqTxl5Q5gIWeG7Cz7ndbtbuyeGZdfs5XFDBuHOi+eO0VAYlRJpdmhCiGRp7HUEK8BQwAAirWa+17uOlukQblZZZyF//l86OIyfoFx/O67ePZLySMYGEaM8ae2jodeAvwPPAJRjjDslYAH7kUH45z6zbz9o9OcRFhDJv+mBmDE+U+YGF8AGNDYIOWutPPWcOZQKPKqXSgD97sTbRBhSWV/Hipz/w9pZMQoICeHBSP+66SIaEEMKXNPav2a6UCgB+UErNBrIAuTTUh9kcTl776hALNhykwuHkhpFJ/HpSCnERYQ0/WQjRrjQ2COYAHYEHgP/DODz0M28VJczjcrlZuTOLv3+kOVZsY1JqHHOn9qdvnMwOJoSvajAIPBeP3aC1/i1QhtE/IHzQFz/k8eSa/aQfK+HcxEiev2EIY/pEm12WEMLLGgwCrbVTKXVBaxQjzJF+rISn1u7n8+/zSOzagRduHMKV5/aQyWGE8BONPTS0Uym1GlgGlNes1Fqv9EpVolXkl1fzu2XfsnyHlc5hwfzp8lRmje0lQ0II4WcaGwRhQAEwoc46NyBB0A653W5e2XiQFz85ihsLd16QzK8u6UuXjjJ8lBD+qLFXFku/gI+oqnbx0IrveH9nFuf36sS8G0aRFCVDQgjhzxp7ZfHrGC2AU2itf97A86YALwCBwEKt9bzTHq+5QA2Ms5LitNZdGlOTaLoSm4N73kpj88ECfntZPyZ0c0gICCEafWjowzrLYcC1QPbZnuA522g+cClgBbYppVZrrffVbKO1frDO9vcDQxtZj2ii7BOV3PH6Ng7mlfHc9ecxfVgi6enpZpclhGgDGntoaEXd+0qpd4EvG3jaKOCA1jrD85wlwNXAvjNsfxPGMBaihe3LLuHnb2yj3F7Nmz8fxfl9Y8wuSQjRhjR3nIAUIK6BbRKAo3XuW4HR9W2olOoFJAOfNfTGdru92d9kbTab330L3pFdwRMbc+kYHMDTk7sT5cgjPT0P8M/9cTayP2rJvjiVr++PxvYRlHJqH0EOxhwFLeVGYLnW2tnQhqGhoaSmpjbrTdLT05v93PZo2faj/OXTQ/SNC+f1O0bSPbLDKY/72/5oiOyPWrIvTuUL+yMtLe2MjzX20FBzxhfIApLq3E/0rKvPjcCvmvEeoh5ut5sXPz3A8598z/l9o1lw63A6h8lsYUKI+jW2RXAt8JnWuthzvwswXmu96ixP2wakKKWSMQLgRuDmel67P9AV+LqJtYt6OJwu/vT+HpZuP8r0YQnMm34uIUEyVLQQ4swa+wnxl5oQANBan6CBjl2tdTUwG1gPpAPvaa33KqUeV0pdVWfTG4ElWusfnZ4qmqbMXs2db25n6fajPDChL3+feZ6EgBCiQY3tLK7v06Qx4xStAdactu7Pp91/tJE1iLM4XmLjjje2sT+nlHnTB3PjqJ5mlySEaCcaGwTblVLPYVwXAMbx/DP3PIhW9UNuKbe/vo2iiioW/mwEl6iGTugSQohajT1ucD9QBSwFlgA2pHO3Tfj6YAHTF2ymyunivbvHSggIIZqssWcNlQNzvVyLaKL/7srid8u+o2d0R964YySJXWW4CCFE0zWqRaCU+thzplDN/a5KqfXeK0ucjdvtZsHGg8xZsouhPbuw4p5xEgJCiGZr7KGhGM+ZQgBorYto+Mpi4QXVTheP/HcPT6/bz5Xn9eA/vxhFZEe5RkAI0XyNDQKXUurkaShKqd7UMxqp8K6KqmrueTuNt7cc4Z6Lz+GFG4bIJDJCiJ+ssWcNPQx8qZTaBFiAC4Ffeq0q8SN5pXbufHMbu7OK+b+rBzJrbG+zSxJC+IhGtQi01uuAEYAG3gV+A1R6sS5RR0ZeGdMXfIXOLeWfs0ZICAghWlRjh5i4E5iDMV7QLmAMxpAQE872PPHTbT9cyJ3/2U6gxcKSX45lSJLM2yOEaFmN7SOYA4wEMrXWl2BMIHPi7E8RP9Xa3ce4eeFWunYMYeV94yQEhBBe0dggsGmtbQBKqVCt9X5Aea8ssejLQ9z3zg4G9ejMinvH0Su6k9klCSF8VGM7i62e6whWAR8rpYqATO+V5b9cLjdP/C+d1746xJSB3fjHjUMIC5Yzg4QQ3tPYK4uv9Sw+qpTaAEQC67xWlZ+yOZw8uHQXa/fk8PPzk3n48lQCAyxmlyWE8HFNnqpSa73JG4X4u8LyKu76z3Z2HCnikSsG8IsLks0uSQjhJ5o7Z7FoQZkF5dz++jayTlTyys3DmDq4u9klCSH8iASByXYdPcEv3tiG0+3mnTtHM6J3lNklCSH8jASBiT7el8v97+4gNiKUN+4YxTmx4WaXJITwQxIEJnnr68P8ZfVeBidEsvBnI4mNCDW7JCGEn5IgaGUul5un1+/nn5symJQax4s3DaVjiPw3CCHMI59ArawmBG4d05NHrxxIUKBMLi+EMJcEQSvKKbbx+peHmT4sgf+7ehAWi1wjIIQwn3wdbUWvbjqIy+3mwRj8tTYAABMTSURBVEn9JASEEG2GBEEryS2x8c43R5g+LIGkKJlWUgjRdkgQtJJXNx3E6XIz+5IUs0sRQohTSBC0guMlNt7ZeoRrhybQM1paA0KItkWCoBX88/MMql1uZl/S1+xShBDiRyQIvCyv1M7irZlcMySB3jEyp4AQou2RIPCyf31+kKpqF7MnSGtACNE2yXUEXpRfZuetLUZrILlua8DlggAfzmCnA6rKoKoCHBW1y1Xl4Cg/ddlRCSHhENENwuMgvBtExENYF5BTbIVoFRIEXvSvzzNqWwN5GnYvhz0roPAgBARDUKhxC/T8DAqDoBDjZ6DnZ1Cdx06ua8I2J1//x8+zVFdC2XHPh7Lnw7nmVvf+WZfr+cB3OX76zgsMhfB4IxTCPbfTwyI8HjrFQaD8GgvxU8hfkJfkl9n55Os0/pH4HX1WPAU5uwEL9L4ABl0HrmqotoPTDtU2Y7nm5vT8rCjwrLOBs8qzneen0/6Ta+zflI0DQyGkIwR3gpBOtcvh3YzlkE6ex05fDofgjqcte14juCMEdwB7CZTmQlmdW2lO7XLBQcj8CiqL6inMAp1izh4WNetDpI9GiPpIELS0sjzYt4rKz//DZ4HfQR6QMBwmPwUDr4XOLTTpjNtdTzhUnRoqNYFyeoB41uXmFxKf0LvOB/PpH+R1PvC9+a07LNK4xfY7+3bVdqMFc0pYHIeynNogydtv/HRV//j5IeE/DovwOON+RDcCbSHe+fcJ0cZJELQEWzGkfwh7lkPGJnA7sbkT+TD2F1xx82yI6tPy72mx1B4SaqbC9HTiU1NbsCgvCwqFLknG7WxcLqP1UJZTf1iU5cKx76DsE6gqPfm0fgAbk6DHUEgYBj2GQY8hRkgJ4cMkCJrLUQnfrzc+/L//yPim3aUnnD+HRSXDeWKbhY+vvwiiIsyu1P8EBECnaOMWP/Ds21aVG8FQbCV310fEV2dB9g5IX127TXRKbTAkDINug41DWkL4CAmCpnA6IGOj0em7/3/Gt8lOcTDiDhg0AxJHUFTh4LmnP+OKc+PpGych0OaFdDJabFF9KLTF1raQKgqNQMjaafzM2ATfLTUeCwiCuFRPMAw3wiE2VTqtRbslv7kNcbngyNfGN/+9q6CyEEIjYeDVMHgm9L4QAgJPbr7wywwqHE4ekOsG2reOUdB3knGrUZINWTs8AbED9q2CHW8ajwV1gO7n1rYaegwzAsaXTxMWPkOCoD5uNxzbZXzz3/s+lGQZf+hqKgyeYXw41HNsvqi8ijc3ZzJtcHdS4qU14HM69zBuqVcY991uKMyA7J21AZH2BmxdYDweGmn0MdQ9rNQ5Qa6PEG2OV4NAKTUFeAEIBBZqrefVs831wKOAG/hWa32zN2s6q/wfPOf6L4eCA8YhgL6TYNJjRgiEnn1y+UVfHqLMXs0DE2SEUb9gsUD0OcZt8AxjnbPaOHOpptWQvQM2v1R7FlN4/KmthoRhRutDCBN5LQiUUoHAfOBSwApsU0qt1lrvq7NNCvAH4HytdZFSKs5b9ZxRsdW4yGv3csj5jpPn+o+7H1KvavQf6YmKKt7YfJhpg7uhuklrwG8FBkG3QcZt2G3GOocNcveceljp+3UY332ALr1qg6H7ecZJB517/KQzwoRoCm+2CEYBB7TWGQBKqSXA1cC+OtvcBczXWhcBaK2Pe7GeWuX5xiGfPSuM4/9g/BFOftJzrn+PJr/kazWtgYnSGhCnCQ6DxBHGrYatBI59WxsMWWnG72RdnWI9h6MSPLceEJlYZ52EhWgZ3gyCBOBonftWYPRp2/QDUEp9hXH46FGt9bqzvajdbic9Pb3p1bicdDzwIWWbfk2n3G1Y3E7snZMpHvRLSnpeiiPCc256VrFxa4JSu5OFXxzh/J4dcRdlkV6U1fT6TGCz2Zq3L31U6++PGIi6zLgNhkBbEaHFBwiuyCWo4jjBFccJrjxOUI4m+NCXBFaV/OgVqkO74ugYR3WHOBwd46nuGIejQyzVHeNPrncHNv1COfndOJWv7w+zO4uDgBRgPJAIfK6UGqy1PnGmJ4SGhpLanIug9q2GnU9BZE84/wEYNIPQ+IHEWSz81ONRz3/8PRUONw9fM5zUHp1/4qu1nvT09ObtSx/VNvbHuDM/VFVunLlUkgXFWVCSTVCJlaCSbOO+dTfY6vnT6RgDkXVaFTUtjEjP/YgeRquljraxL9oOX9gfaWlpZ3zMm0GQBdS9BDTRs64uK7BVa+0ADimlvscIhm0tXo2ayoFp79F35GUtetZGcaWD1746xGUD4hnQjkJAtEMhnSAmxbidST1hQYnV+FmUCZmbzxwWdQ49RduCoHKgcZ1Mp1gIjzWWTwsM4Ru8GQTbgBSlVDJGANwInH5G0CrgJuB1pVQMxqGiDK9UExiMI6Jni5+698ZXhym1Sd+AaCN+alicOAKZm4mznYDd9Tw3tLMxyF+nOE84xNZZrgkNz8/QCDlVtp3wWhBorauVUrOB9RjH/1/TWu9VSj0ObNdar/Y8dplSah/gBH6ntS7wVk0trcTmYNGXGVw6IJ5BCTIejWgnGhEW+3fvpH9StDGIYvlxKM8zxmyq+zP/Bzj8lXGRZX2CwjxBURMONQESd9r6OOjQVS6+M5FX+wi01muANaet+3OdZTfw/zy3dueNrw5TYqtmjrQGhI9xB4UZp7F26dnwxk6HMWR62XEjNMryjKA4uXzcaH1k7zLWu50/fg1LYD0tjVjj9O2QCOMantAIYwTZ0Ig6y+HGzzpX94umM7uzuN0qtTlY9OUhJqXGSWtA+LfA4JNDeTeoZmTYk0HhaV2c3uLI/8H42dh5N4I71YZCaLhxCOvkcp0AORkk4Z6AiajzPM/9wOCftj/aIQmCZnpz82GKKx3MmdjAGPpCiFp1R4ZtaGokt9uYQ8NeZgzwaC/1LJd5lks9yzXrSuoslxkXi1bVeU61rXE1BobWCRAjKLoFdwP3tcbYYj54JbgEQTOU2atZ+OUhJvSPY3CitAaE8AqLxRjuO7gDEPvTX8/pODU87KWnBkW960rBVkLnzPVw8H3AYowf1Wc89LkEkkb7xJlUEgTN8Obmw5yocEjfgBDtSWCw8W2+Gd/ov9+7m9SICmMY+oyNxvhRXz5vdIj3HOsJhvHQ7dx22ektQdBE5fZqFn6RwXgVy3lJXcwuRwjRGgKCoOdo4zb+IaOlkLm5Nhg++YuxXYco6HNxbTB07W1SwU0jQdBE//k6kyJpDQjh30IjoN9k4wbGlKgZmzzBsKF23KiuvY1DSH3GQ/JFbbZ/QYKgCcrt1fz7iwwu7hfL0J5dzS5HCNFWRHSD824wbm63cdZTxobaGQ3TXgcsxuiy53iCIWlMm+lfkCBogre3ZFJYXsWcSdIaEEKcgcUCsf2M2+i7jTkqsnfAwQ319C+Mqe14NrF/QYKgkSqqqvnX5xlcmBLDMGkNCCEaKzAIkkYZt/EPGWcknexf2ACfPAo8avQvJF9U278QldxqJUoQNNLbWzIpKK/i19IaEEL8FKHh0O8y4wZQmguHPP0LBzcYc2GDp39hvKd/4WKv9i9IEDRCZZWTf32ewQV9Yxjeq2129ggh2qmIeDj3euN2sn9ho3Hbs9KYBxsL9BgKMxZBVJ8WL0GCoBEWb80kv0z6BoQQXnZK/8Iva/sXMjYaM9q56hmnqQVIEDSgssrJq5syGHdONCN7S2tACNGK6vYveFH7uwSulb3zzRHyy+xy3YAQwmdJEJyFzeHk1U0HGdsnmtF9os0uRwghvEKC4Cze/eYIeaV26RsQQvg0CYIzsDmcLNh4kNHJUYyR1oAQwodJEJzBkm+OcFxaA0IIPyBBUA+bw8mCTQcZ1TuKsdIaEEL4OAmCery3/Si5JUZrwGKxmF2OEEJ4lQTBaezVRt/AiF5dGXeOtAaEEL5PguA07223cqzYJq0BIYTfkCCow17tZMGGAwzr2YUL+saYXY4QQrQKCYI6lqdZyS628etJ/aQ1IITwGxIEHlXVLl7ZcJChPbtwYYq0BoQQ/kOCwGN5mpWsE5XMmSh9A0II/yJBgNEamL/hAOcldeHifrFmlyOEEK1KggBYucNoDfxaWgNCCD/k90HgcLp4ecMBzk2MZLyS1oAQwv/4fRC8vyMLa5H0DQgh/JdfB0FNa2BwQiQT+seZXY4QQpjCr4Ng1c4sjhRWSGtACOHX/DYIqj2tgUEJnZmYKq0BIYT/8tsgWLUrm8yCCh6YIK0BIYR/88sgqHa6ePmzHxjQvTOXDog3uxwhhDCVXwbB6m+zOVxQwQPSNyCEEP4XBE6Xm5c/O0D/bhFcJq0BIYQgyJsvrpSaArwABAILtdbzTnv8duBvQJZn1cta64XerOmDb7PJyC9nwS3DCAiQ1oAQQngtCJRSgcB84FLACmxTSq3WWu87bdOlWuvZ3qqjLqfLzYuf/YCKj2DywG6t8ZZCCNHmefPQ0CjggNY6Q2tdBSwBrvbi+zXoi8PlZOSV88DEFGkNCCGEhzcPDSUAR+vctwKj69nuOqXURcD3wINa66P1bHOS3W4nPT29ycU4XW4Wf1tIry7B9A4qIj39RJNfw9fYbLZm7UtfJfujluyLU/n6/vBqH0EjfAC8q7W2K6XuBt4EJpztCaGhoaSmpjb5jT7bn4u15BAv3zyUgQN6NK9aH5Oent6sfemrZH/Ukn1xKl/YH2lpaWd8zJtBkAUk1bmfSG2nMABa64I6dxcCz3irmOSYcG4d0pVpg7p76y2EEKJd8mYfwTYgRSmVrJQKAW4EVtfdQClV91P5KsBrba/kmE7ccl5X6RsQQojTeK1FoLWuVkrNBtZjnD76mtZ6r1LqcWC71no18IBS6iqgGigEbvdWPUIIIern1T4CrfUaYM1p6/5cZ/kPwB+8WYMQQoiz87sri4UQQpxKgkAIIfycBIEQQvg5CQIhhPBzEgRCCOHnJAiEEMLPWdxut9k1NElaWloekGl2HUII0c70Gj58eGx9D7S7IBBCCNGy5NCQEEL4OQkCIYTwcxIEQgjh5yQIhBDCz0kQCCGEn5MgEEIIP2f2VJWtRik1BXgBY26EhVrreSaXZAqlVBLwHyAecAP/0lq/YG5V5lNKBQLbgSyt9RVm12MmpVQXjBkDB2H8jvxca/21uVWZQyn1IHAnxn7YDdyhtbaZW1XL84sWgeePfD4wFRgA3KSUGmBuVaapBn6jtR4AjAF+5cf7oq45eHGGvHbmBWCd1ro/cB5+ul+UUgnAA8AIrfUgjC+RN5pblXf4RRAAo4ADWusMrXUVsAS42uSaTKG1Pqa13uFZLsX4I08wtypzKaUSgcsxvgX7NaVUJHARsAhAa12ltT5hblWmCgI6KKWCgI5Atsn1eIW/BEECcLTOfSt+/uEHoJTqDQwFtppcitn+AfwecJldSBuQDOQBryuldiqlFiqlOpldlBm01lnAs8AR4BhQrLX+yNyqvMNfgkCcRikVDqwAfq21LjG7HrMopa4Ajmut08yupY0IAoYBC7TWQ4FyYK65JZlDKdUV48hBMtAD6KSUutXcqrzDX4IgC0iqcz/Rs84vKaWCMUJgsdZ6pdn1mOx84Cql1GGMQ4YTlFJvm1qRuayAVWtd00pcjhEM/mgScEhrnae1dgArgXEm1+QV/hIE24AUpVSyUioEo8Nntck1mUIpZcE4/puutX7O7HrMprX+g9Y6UWvdG+P34jOttU9+62sMrXUOcFQppTyrJgL7TCzJTEeAMUqpjp6/m4n4aMe5XwSB1roamA2sx/iPfE9rvdfcqkxzPjAL45vvLs9tmtlFiTblfmCxUuo7YAjwpMn1mMLTKloO7MA4dTQA+JepRXmJDEMthBB+zi9aBEIIIc5MgkAIIfycBIEQQvg5CQIhhPBzEgRCCOHn/Gb0USEaopRyYpwmWGNJS41S6xnO40PP4GVCtCkSBELUqtRaDzG7CCFamwSBEA3wDD/xHsYw5pXAzVrrA55v+a8BMRgDtd2htT6ilIoHXgX6eF7iXoxRKwOVUv/GGKYgC7haa12plHoAuAdjiPB9WmufHOpYtF3SRyBErQ51rrbepZS6oc5jxVrrwcDLGKOVArwEvKm1PhdYDLzoWf8isElrfR7GOD01V7GnAPO11gOBE8B1nvVzgaGe17nHW/84Ic5EWgRC1DrboaF36/x83rM8FpjuWX4LeMazPAG4DUBr7QSKPSNZHtJa7/Jskwb09ix/hzGkwypgVQv8O4RoEmkRCNE47jMsN4W9zrKT2i9il2PMoDcM2OaZBEWIViNBIETj3FDnZ838vZupnbrwFuALz/KnGP0CKKUCPbN+1UspFQAkaa03AA8BkUB4y5YuxNnJNw8hanVQSu2qc3+d1rpmUpauntE47cBNnnX3Y8zk9Ts8ncWe9XOAfymlfoHxzf9ejBmu6hMIvO0JCwvwop9PDSlMIKOPCtEAz1lDI7TW+SaXIoRXyKEhIYTwc9IiEEIIPyctAiGE8HMSBEII4eckCIQQws9JEAghhJ+TIBBCCD/3/wGNY/+YjAfzIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari Baseline Model yang kita gunakan terlihat bahwa model tidak cukup baik dalam belajar. Hal ini dapat ditunjukkan Loss belum konvergen dan Val Loss yang semakin naik, dari hasil accuracy dapat disimpulkan bahwa model overfit artinya untuk kasus training memiliki akurasi sangat baik ditandai dengan tiap epoch akurasi mengalami peningkatan namun untuk data validasi akurasi mengalami penurunan. Selanjutnya kita akan lakukan improvement model dengan memperbaiki arsitektur.\n",
        "\n",
        "Result:\n",
        "\n",
        "loss: 0.2247 - accuracy: 0.9247 - val_loss: 1.6523 - val_accuracy: 0.5637"
      ],
      "metadata": {
        "id": "AoRcAk_qMcpr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model Improvement"
      ],
      "metadata": {
        "id": "8nOWIsJ6rWZK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling overfitting\n",
        "Now, we can try to do something about the overfitting. There are different options to do that.\n",
        "\n",
        "1. Reduce the network’s capacity by removing layers or reducing the number of elements in the hidden layers\n",
        "2. Apply regularization, which comes down to adding a cost to the loss function for large weights\n",
        "3. Use Dropout layers, which will randomly remove certain features by setting them to zero\n",
        "\n",
        "\n",
        "[link text](https://towardsdatascience.com/handling-overfitting-in-deep-learning-models-c760ee047c6e)"
      ],
      "metadata": {
        "id": "woefVVH9O0ec"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Pendefinisian Model\n",
        "\n",
        "Kita lakukan Improvement model dengan menggunakan ***Sequential API***"
      ],
      "metadata": {
        "id": "aDLB50jkhS__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def model_rnn():\n",
        "  model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length, mask_zero=True),\n",
        "                             tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(256, \n",
        "                                                       return_sequences=True)),\n",
        "                             tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128)),\n",
        "                             tf.keras.layers.Dense(128,kernel_initializer=\"he_normal\", kernel_regularizer = tf.keras.regularizers.l2(0.1),\n",
        "                                                   bias_regularizer = tf.keras.regularizers.l2(0.01),\n",
        "                                                   activation='LeakyReLU'),\n",
        "                             tf.keras.layers.Dropout(0.5),\n",
        "                             tf.keras.layers.BatchNormalization(),\n",
        "                             tf.keras.layers.Dense(64, kernel_initializer=\"he_normal\", kernel_regularizer = tf.keras.regularizers.l2(0.1),\n",
        "                                                   bias_regularizer  = tf.keras.regularizers.l2(0.01),\n",
        "                                                   activation='elu'),\n",
        "                             tf.keras.layers.Dropout(0.5),\n",
        "                             tf.keras.layers.BatchNormalization(),\n",
        "                             tf.keras.layers.Dense(32, kernel_initializer=\"he_normal\", kernel_regularizer = tf.keras.regularizers.l2(0.1),\n",
        "                                                   activation='relu'),\n",
        "                             tf.keras.layers.Dense(5, activation='softmax')\n",
        "                             ])\n",
        "  \n",
        "  \n",
        "  opt = tf.keras.optimizers.Nadam(lr=0.0001, clipvalue=1.0)\n",
        "  model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
        "  \n",
        "  return model"
      ],
      "metadata": {
        "id": "g_dbntIimmx0"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Penjelasan Model:\n",
        "1. Embedding Layer: It requires that the input data be integer encoded, so that each word is represented by a unique integer\n",
        "2. Bidirectional LSTM: The process of making any neural network o have the sequence information in both directions backwards (future to past) or forward(past to future)\n",
        "3. Jumlah Node pada hidden layer pertama adalah 128, kemudian Hidden layer kedua 64, dan Hidden layer ke tiga 32.\n",
        "4. Weight Inizialition digunakan untuk mengatur weight pada Neural Network ke small random values. Karena kita menggunakan fungsi aktivasinya adalah grup dari Relu maka kita gunakan Weight Inizialition adalah He\n",
        "5. Batch Normalization digunakan untuk scaling dan pembatasan input pada setiap batch\n",
        "6. Drop Out=0.5 artinya sebanyak 50 persen node pada hidden layer diaknonatifkan secara random yang digunakan untuk mengurangi overfit\n",
        "7. Output 5 hidden layer karena multiclass dan menggunakan fungsi aktifasi softmax\n",
        "8. Regularizers: Allow you to apply penalties on layer parameters or layer activity during optimization.\n",
        "9. Optimizer menggunakan Nadam\n",
        "10. loss nya kita gunakan sparse_categorical_crossentropy karena multiclass\n",
        "11. metrics nya kita gunakan accuracy sebagai initial dari model berjalan dengan baik\n"
      ],
      "metadata": {
        "id": "XPcvFwA2f6NP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn = model_rnn()\n",
        "model_rnn.summary()"
      ],
      "metadata": {
        "id": "KDm1tx5YoM9o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c4151b9-2220-4571-9f81-0af878012732"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 100, 64)           640000    \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 100, 512)         657408    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 256)              656384    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 128)              512       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,997,957\n",
            "Trainable params: 1,997,573\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model_rnn, \"model_rnn.png\", show_shapes=True)"
      ],
      "metadata": {
        "id": "uWaf9NUModlf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "891d1662-6fb8-4eb2-92c6-9712e3fe0e5d"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Penjelasan Model:\n"
      ],
      "metadata": {
        "id": "PJCIQxtYlYp1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://medium.com/analytics-vidhya/author-multi-class-text-classification-using-bidirectional-lstm-keras-c9a533a1cc4a"
      ],
      "metadata": {
        "id": "tFcK2V8j7ODF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Pelatihan Model"
      ],
      "metadata": {
        "id": "ae94m__AmPxg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=1, verbose=1)\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n",
        "                              patience=5, min_lr=0.001)\n",
        "checkpoint_filepath = './checkpoint_model'\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    save_weights_only=True,\n",
        "    monitor='val_accuracy',\n",
        "    mode='max',\n",
        "    save_best_only=True)\n",
        "\n",
        "num_epochs = 30\n",
        "history = model_rnn.fit(train_padded, training_label_seq, epochs=num_epochs, validation_data=(validation_padded, validation_label_seq), batch_size=100, callbacks=[callback, reduce_lr, model_checkpoint])"
      ],
      "metadata": {
        "id": "XxruPv1JRCyP",
        "outputId": "386bd4af-c7d6-4b4f-82c3-890af14b49de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "166/166 [==============================] - 56s 208ms/step - loss: 41.4904 - accuracy: 0.3697 - val_loss: 36.8555 - val_accuracy: 0.4612 - lr: 1.0000e-04\n",
            "Epoch 2/30\n",
            "166/166 [==============================] - 28s 169ms/step - loss: 32.7008 - accuracy: 0.4455 - val_loss: 29.1402 - val_accuracy: 0.5187 - lr: 1.0000e-04\n",
            "Epoch 3/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 25.7538 - accuracy: 0.5091 - val_loss: 22.9019 - val_accuracy: 0.5415 - lr: 1.0000e-04\n",
            "Epoch 4/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 20.3183 - accuracy: 0.5459 - val_loss: 18.0609 - val_accuracy: 0.5810 - lr: 1.0000e-04\n",
            "Epoch 5/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 16.0462 - accuracy: 0.5830 - val_loss: 14.2884 - val_accuracy: 0.5924 - lr: 1.0000e-04\n",
            "Epoch 6/30\n",
            "166/166 [==============================] - 28s 167ms/step - loss: 12.7011 - accuracy: 0.6085 - val_loss: 11.3581 - val_accuracy: 0.5837 - lr: 1.0000e-04\n",
            "Epoch 7/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 10.0695 - accuracy: 0.6358 - val_loss: 9.0342 - val_accuracy: 0.5967 - lr: 1.0000e-04\n",
            "Epoch 8/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 8.0084 - accuracy: 0.6564 - val_loss: 7.2015 - val_accuracy: 0.6168 - lr: 1.0000e-04\n",
            "Epoch 9/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 6.3820 - accuracy: 0.6787 - val_loss: 5.8046 - val_accuracy: 0.6282 - lr: 1.0000e-04\n",
            "Epoch 10/30\n",
            "166/166 [==============================] - 29s 174ms/step - loss: 5.1136 - accuracy: 0.6976 - val_loss: 4.7145 - val_accuracy: 0.6374 - lr: 1.0000e-04\n",
            "Epoch 11/30\n",
            "166/166 [==============================] - 28s 167ms/step - loss: 4.1145 - accuracy: 0.7154 - val_loss: 3.8706 - val_accuracy: 0.6125 - lr: 1.0000e-04\n",
            "Epoch 12/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 3.3318 - accuracy: 0.7346 - val_loss: 3.2198 - val_accuracy: 0.6184 - lr: 1.0000e-04\n",
            "Epoch 13/30\n",
            "166/166 [==============================] - 30s 181ms/step - loss: 2.7150 - accuracy: 0.7557 - val_loss: 2.7440 - val_accuracy: 0.6141 - lr: 1.0000e-04\n",
            "Epoch 14/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 2.2295 - accuracy: 0.7715 - val_loss: 2.3621 - val_accuracy: 0.6070 - lr: 1.0000e-04\n",
            "Epoch 15/30\n",
            "166/166 [==============================] - 29s 174ms/step - loss: 1.8471 - accuracy: 0.7937 - val_loss: 2.0678 - val_accuracy: 0.5984 - lr: 1.0000e-04\n",
            "Epoch 16/30\n",
            "166/166 [==============================] - 28s 167ms/step - loss: 1.5372 - accuracy: 0.8115 - val_loss: 1.8834 - val_accuracy: 0.5832 - lr: 1.0000e-04\n",
            "Epoch 17/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 1.2973 - accuracy: 0.8281 - val_loss: 1.7191 - val_accuracy: 0.5989 - lr: 1.0000e-04\n",
            "Epoch 18/30\n",
            "166/166 [==============================] - 29s 173ms/step - loss: 1.0979 - accuracy: 0.8467 - val_loss: 1.6385 - val_accuracy: 0.5919 - lr: 1.0000e-04\n",
            "Epoch 19/30\n",
            "166/166 [==============================] - 28s 171ms/step - loss: 0.9354 - accuracy: 0.8629 - val_loss: 1.5395 - val_accuracy: 0.5892 - lr: 1.0000e-04\n",
            "Epoch 20/30\n",
            "166/166 [==============================] - 28s 168ms/step - loss: 0.8052 - accuracy: 0.8811 - val_loss: 1.4894 - val_accuracy: 0.5783 - lr: 1.0000e-04\n",
            "Epoch 21/30\n",
            "166/166 [==============================] - 28s 169ms/step - loss: 0.7008 - accuracy: 0.8951 - val_loss: 1.4585 - val_accuracy: 0.5751 - lr: 1.0000e-04\n",
            "Epoch 22/30\n",
            "166/166 [==============================] - 30s 180ms/step - loss: 0.6047 - accuracy: 0.9124 - val_loss: 1.4831 - val_accuracy: 0.5745 - lr: 1.0000e-04\n",
            "Epoch 00022: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita tambahkan fungsi callback yang terdiri dari:\n",
        "1. Early Stopping digunakan untuk memberhentikan training model ketika pada val_loss yang harusnya konvergen atau menurun namun mengalami kenaikan pada setiap epoch \n",
        "2. ReduceLROnPlateau untuk mengatur learning rate mencapai loss yang optimum\n",
        "3. model_checkpoint menyimpan model dengan val_accuracy yang terbaik\n"
      ],
      "metadata": {
        "id": "oYeMNe6Bkkpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oe4hclUvrw7S",
        "outputId": "e056f42b-ef71-461e-9d20-f2a46899c3dd"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.tensorflow.org/guide/keras/train_and_evaluate"
      ],
      "metadata": {
        "id": "Sinw_DwufJc9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluasi Model"
      ],
      "metadata": {
        "id": "N1C4SOk2R7UW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.plot(epochs, acc, 'red', label='Accuracy')\n",
        "plt.plot(epochs, val_acc, 'blue', label='ValidationAccuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Training and validation loss')\n",
        "plt.plot(epochs, loss, 'red', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'blue', label='Validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BfxOWr6uSNwb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "d6473ac5-cd14-4bcb-80f3-cde8253fd5ac"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil Improvement Model menunjukkan untuk Loss dan Val_Loss hasilnya konvergen pada epoch ke-22 artinya hingga epoch ke-22 model dapat belajar dengan baik. Nilai Accuracy semakin tinggi berbanding terbalik dengan Validation Accuracy setelah epoch ke-10 nilainya semakin menurun.\n",
        "Hal-hal yang perlu diperhatikan disini adalah data yang kita gunakan adalah imbalanced dan jumlah kata pada setiap input data berbeda sangat signifikan itu bisa ditunjukkan dari perhitungan totalword dengan nilai minimum kata pada setiap baris adala 6 sedangkan maximum nya adalah 1855 dan rata-ratanya kata pada setiap baris adalah 97.\n",
        "\n",
        "Result\n",
        "\n",
        "loss: 0.6047 - accuracy: 0.9124 - val_loss: 1.4831 - val_accuracy: 0.5745"
      ],
      "metadata": {
        "id": "E00j-sRDONeF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Fine Tuning"
      ],
      "metadata": {
        "id": "KYxy8rkzrjxh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Pendefinisian Model\n",
        "\n",
        "Kita lakukan Improvement model yang kedua dengan Fine Tuning [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert) dengan menggunakan **PIPELINE**"
      ],
      "metadata": {
        "id": "_HIEVN2wlixZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CH_jc9FpEJor",
        "outputId": "70f14067-0a56-48b6-89d7-570b0aaa7071"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git\n",
            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-ie8bkx_e\n",
            "  Running command git clone -q https://github.com/huggingface/transformers.git /tmp/pip-req-build-ie8bkx_e\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (4.62.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (1.19.5)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (4.10.1)\n",
            "Collecting tokenizers!=0.11.3,>=0.10.1\n",
            "  Downloading tokenizers-0.11.4-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.8 MB 44.3 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 47.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (2.23.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 5.1 MB/s \n",
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            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.17.0.dev0-py3-none-any.whl size=3608283 sha256=cb8cc6ccb5b8e54ab2c8cba46e52e248816729e72f9df41070caefd5eb5b9b85\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3vtykys8/wheels/90/a5/44/6bcd83827c8a60628c5ad602f429cd5076bcce5f2a90054947\n",
            "Successfully built transformers\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.11.4 transformers-4.17.0.dev0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import Library\n",
        "from transformers import DistilBertTokenizer\n",
        "from transformers import TFDistilBertForSequenceClassification\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import json\n",
        "import gc"
      ],
      "metadata": {
        "id": "ODqI-DoWFfDR"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merubah dataframe ke dalam bentuk list\n",
        "training_reviews = training_reviews.to_list()\n",
        "val_reviews = val_reviews.to_list()"
      ],
      "metadata": {
        "id": "taCm0D2VGU1l"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "train_encodings = tokenizer(training_reviews, truncation=True, padding=True)\n",
        "val_encodings = tokenizer(val_reviews, truncation=True, padding=True)"
      ],
      "metadata": {
        "id": "FlLFgw4qFldE"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    training_ratings\n",
        "))\n",
        "val_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(val_encodings),\n",
        "    val_ratings\n",
        "))"
      ],
      "metadata": {
        "id": "9TGx8h_6G21h"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=5)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n",
        "model.compile(optimizer=optimizer, loss=model.compute_loss, metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "knnTESjzH0ek",
        "outputId": "bf6c4796-b1f4-4120-bf2b-997a66a6074d"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['activation_13', 'vocab_projector', 'vocab_transform', 'vocab_layer_norm']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_101', 'classifier', 'pre_classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16,\n",
        "          validation_data=val_dataset.shuffle(1000).batch(16))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCgUcNDMIDeg",
        "outputId": "27ac238c-b5b6-47b8-98f9-4deb88058173"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "1038/1038 [==============================] - 1808s 2s/step - loss: 0.9000 - accuracy: 0.6044 - val_loss: 0.9109 - val_accuracy: 0.6125\n",
            "Epoch 2/3\n",
            "1038/1038 [==============================] - 1792s 2s/step - loss: 0.7128 - accuracy: 0.6905 - val_loss: 0.7930 - val_accuracy: 0.6553\n",
            "Epoch 3/3\n",
            "1038/1038 [==============================] - 1796s 2s/step - loss: 0.5679 - accuracy: 0.7644 - val_loss: 0.9129 - val_accuracy: 0.6098\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCgMB9oFhDfY",
        "outputId": "244bc415-130f-4b72-c7c5-c46dbfeb2688"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.plot(epochs, acc, 'red', label='Accuracy')\n",
        "plt.plot(epochs, val_acc, 'blue', label='ValidationAccuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Training and validation loss')\n",
        "plt.plot(epochs, loss, 'red', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'blue', label='Validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "QuBMNULId_1L",
        "outputId": "0a29d869-927b-43f1-f5c3-cb679afc44bd"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5bsqWLRtuOYZR9Fi/Hlq3hi5d4KSTYMkSjRAsAEYeAjD0InIEGAjMA+KBqSKy1jk33DnXwa9pD+ADEfH57ZsC3A984Zz7EYgC3sjLCzCgSZMmLFq0iP79+4dbimEULVKjaerVOz6a5oorwq3sOALy0YtILBCbbt3QdMvDMtl3AVA/h/oMwzAKHgXYTZMRFmxtGIYRDOvXw913H5v0tGRJgevBp8dSIBiGYQRCejfNCy8USDdNRliP3jAMIytS3TT33gtbthR4N01GWI8+QPr06cNXX3113LpJkybxxBNPZNr+xx9/BODmm2/mn3/++VebcePG8eabWUeafv7552zcuDFt+YUXXmDp0qXByj+OO+64g+7du+fqGIZRJPCPpilfvsBF0wSKGfoAadeuHbGxx41HExsbG1Dc+htvvEG5cuVydN70hv6ee+6hSZMmOToW6KzbtWvXsm/fPrZs2ZL9DjnkyJEjITu2YYScQuymyQgz9AHSsmVLvvzySw4dOgRowfIdO3bw2Wefcd1119G2bVtefPHFDPdt2rQpe7wpz6+88gotW7akZ8+ebNq0Ka3N1KlT6dKlCx06dOCuu+7i4MGDfP/99yxcuJBRo0bRsWNHfv/9d4YMGcLcuXMBWLZsGZ06daJ9+/Y8/PDDadqaNm3Kiy++SOfOnWnfvv1xRVLmz5/PNddcQ9u2bY/LqZ9R+mOA119/nfbt29OhQwdGjx4NHP+2snv3bpo2bQrAjBkzuO222+jbty/9+/cnMTGRfv36penwT508c+bMtOM+8MAD7N+/n6ZNm3L48GGAfy0bRr7g88GMGZpC+NlnoUcPTSF8990hSyGcHxQ65dlkKebo0RIhyVJcvnx56tevz5IlS2jevDmxsbG0bt2aW2+9lfLly5OSkkL//v35+eefqVWrVobH+Omnn4iNjWXmzJmkpKTQuXNn6tatC0CLFi3S3Cljxoxh2rRp9OnTh6ZNm3L11VfTqlWr446VnJzMkCFDmDRpEjVq1ODBBx/k/fffT4ulr1ChAh9//DHvvfceEydO5JlnngE0A+edd95JpUqVuPvuu+nXrx9AhumPFy9ezMKFC5k6dSqlSpVi79692d7LdevWMWvWLMqXL8+RI0cYP348ZcqUYffu3Vx//fU0a9aMjRs38sorrzBlyhQqVqzI3r17KVOmDI0bN2bx4sU0b96cuXPncu2111K8ePFsz2kYecL69ZpCeP58qF8f3n8fLr883KryBOvRB0Hbtm3T3DepKYvnzJlD586d6dSpExs2bMiyxODKlSvTctGUKVMmrScMsGHDBnr16kX79u359NNP2bBhQ5ZaNm3aRPXq1alRowYAnTt3ZuXKlWnbr732WgDOO+88tm7VjBU7d+5k8+bNNGzYkBo1alCsWLEs0x8vW7aM6667Li3nTPny5bO9R5dddllaO5/Px/PPP0/79u0ZMGAA27dvZ+fOnSxfvpxWrVpRsWLF447btWtXpk+fDsCsWbO47rrrsj2fYeSaxER45BFNIbx8ubpp4uIixshDIezRZ5OlmIMHD4UsSVezZs147rnnWLt2LUlJSZx00klMnDiRadOmcdJJJzFkyJBs0wlnxpAhQ3j55ZepVasWM2bM4LvvvsuV1tSe8AknnJBWmnDOnDn8/fffNGvWDFD3yJw5c7jjjjuCOnZ0dHRaIZVUd1Eq/vf+008/Zffu3cyYMYPixYvTtGnTLO9Pw4YNefLJJ/n2229JSUnh3HPPDUqXYQRF+miavn1h5MhCN9AaCNajD4ITTzyRxo0b88gjj9C2bVsSExMpVaoUZcuWZefOnSxZsiTL/S+++GI+//xzkpKS2L9/P4sWLUrblpiYSJUqVTh8+DCffvrpcedMTEz817Fq1KjB1q1b2bx5MwCffPIJF198cZbnnz17NhMmTGDhwoUsXLiQ6dOnM2/ePMqUKUPVqlXTfOiHDh3i4MGDNGnShBkzZqSlC0513VSrVo2ffvoJIG28ICP27dtHpUqVKF68OMuXL097s7jkkkuYO3du2riFv0uoU6dO3HfffXTs2DHLazGM3FDit9+gVSuNpqlQAb76Ct5+OyKNPJihD5p27drx888/07ZtW2rVqkWdOnVo3bo19913HxdeeGGW+9atW5c2bdrQsWNHbr755uOKltxzzz1069aNnj17ctZZZ6Wtb9OmDW+++SadOnVKGyAFda8899xz3HPPPbRv356oqCh69uyZ6bkTEhLYunUrDRo0SFt32mmnUaZMGVavXs2oUaOYPHky7du3p0ePHuzcuZMrr7ySpk2b0qVLFzp27MhEb3DkxhtvZMqUKXTq1CnNWGdE+/bt+emnn2jfvj2ffPJJ2nXVrFmT2267jT59+tChQwdGjBhx3D7//PPPv8YkDCNP8Nw0Z3XooG6aF1+MODdNhvh8vgL1s27dOl96MlqXGQcOHAi4bX5iugJjzpw5vvvvvz9LXcE8D3lNOM+dFaYrG44e9fmmTfP5TjvN5wPfno4dfb4//wy3qn+Rm/u1cuXKlb5M7Gqh89EbkctTTz3FkiVLeP3118MtxYgkMoim2VapEuVPOSXcyvINM/RGgcG/TGNBLG9oFDISE+GZZ7TSU6lS6qa5/XaNhy+A5Q1DSaEx9D6fj6ioqHDLMMKMz+fLvpFRtEmd9DRokEbT9Oun0TRFqAefnoAMvXOuFfACEA1MEJER6baPAa7xFksDJ4tIeb/t5dDKUjNFZGCwIlOLZFeqVMmMfRHG5/Oxa9cuSpYsGW4pRkHF301z/vkwZQpcdlm4VYWdbA29cy4aGA+0QGvArnDOzRKRtJKAIjLIr/1daDFwf54Cso49zILq1auTkJDAX3/9lW3bw4cPF8jZlKYrODLTVbJkSapXrx4GRUaBxt9NU7r08W4aI6AefSNgo1+B7w+AjmRe+7UnkJbS0TnXEDgFmAtclBORxYsXT5sBmh1Fsup8LjBdRqHG3DQBEYihrwb4pzlMABpn1NA5dwZQA1joLZ8A/A/oDTQPRFBycjLxuRgoSUpKytX+ocJ0BYfpCo6iqKvEpk2c8swzlFm6lCTn+PPddzl44YWwe7f+hElXbgiVrrx+r+kBTPOKggPcAcSKSIJzLqADxMTE5KonV1B7gqYrOExXcBQpXRm4aUrefjtnBuGmicT7FRcXl+m2QO7MVuA0v+Xq3rqM6AHc6bd8KXCFc+4OoAxQwjm3X0SGBHBewzCMY5ibJscEYuhXADWdczVQA98D6JW+kXOuFlABWJa6TkRu8NveH7jIjLxhGEEjotE0CxZYNE0OyDbXjYgcAQYC84B4YKqIrHXODXfOdfBr2gP4QEQs0NkwjLwhMREeflgrPX33HYwbBytXmpEPkoCcWiISC8SmWzc03fKwbI4xCZgUlDrDMIomPh9Mnw6DB5ubJg+w7JWGYRQsRKBlS+jWDSpWhK+/hkmTzMjnAjP0hmEUDMxNEzJs2phhGOEl1U0zaBAkJED//jBihPXg8xDr0RuGET5+/hmuvVbdNJUqqZvmrbfMyOcxZugNw8h/Ut009evDihXmpgkx5roxDCP/MDdNWLAevWEY+YO/m6ZyZfjmG3PT5BNm6A3DCC3798OQIce7aVasgCZNwq2syGCuG8MwQoPPR9m5c+H554+5aUaOhJNPDreyIocZesMw8p6ff4a77qL6559Dgwbw4YfWgw8j5roxDCPvSOem+fPRR81NUwAwQ28YRu7x+eCjj6B2bXXP3HADrF/PnhtusHJ+BQAz9IZh5I7UaJru3Y+PpjFffIHBDL1hGDkjfTTNSy/ppCdz0xQ47J3KMIzg8Plg2jRNIWzRNIUC69EbhhE45qYplATUo3fOtQJeAKKBCSIyIt32McA13mJp4GQRKe+cawC8ApQDUoBnROTDvBJvGEY+sX8/PP20xsSXLq1umttug+jocCszAiDbHr1zLhoYD7QG6gA9nXN1/NuIyCARaSAiDYBxwAxv0wGgr4jUBVoBY51z5fPyAgzDCCHpo2l694b16+HOO83IFyICcd00AjaKyK8icgj4AOiYRfuewBQAEVkvIhu8v/8AdgBVcifZMIx84eefoUWLY26apUth4kRz0xRCAnHdVAO2+C0nAI0zauicOwOoASzMYFsjoATwS1YnS05OJj4+PgBZGZOUlJSr/UOF6QoO0xUceakrKjGRyq+9RqVJkzhaujR/PfYYe66/XnvwQZ6jKNyvvCRUuvI66qYHME1EUvxXOudOBd4B+onI0awOEBMTQ+3atXMsID4+Plf7hwrTFRymKzjyRFf6aJoBA4geMYKqJ59M1XDqCgGRqCsuLi7TbYG4brYCp/ktV/fWZUQPPLdNKs65csBs4FERWR7A+QzDyG/MTRPRBNKjXwHUdM7VQA18D6BX+kbOuVpABWCZ37oSwMfAZBGZlieKDcPIO/bvh6eegjFj4MQTYfx4uPVWG2iNMLLt0YvIEWAgMA+IB6aKyFrn3HDnXAe/pj2AD0TE57euO3Al0N85t8r7aZCH+g3DyAk+H0ydCrVqwahR0KcPiMAdd5iRj0AC8tGLSCwQm27d0HTLwzLY713g3VzoMwwjr/n5Zxg4EL74QlMIf/QRXHppuFUZIcRmxhpGUWH/fnjoIc1NExenbpqVK83IFwEs141hRDqpk54GD4atW+HGG+G552ygtQhhPXrDiGTi4zWa5vrr1bAvXQpvvmlGvohhht4wIpGM3DQrVpibpohirhvDiCQyctOMGAFVLPNIUcZ69IYRKWTmpjEjX+QxQ28YhZ39+6nyv/+Zm8bIFHPdGEZhxc9NU9ncNEYWWI/eMAoj8fHQvHmam+a39983N42RKWboDaMwsX8/PPigumm+/x5efhlWrOBgA8ssYmSOuW4MozBg0TRGLrAevWEUdNK5aSyaxggWM/SGUVDZty9DN41F0xjBYq4bwyhopKYQHjwY/vgDbrpJc9NYD97IIdajN4yCRKqbpkcPqFoVli2DCRPMyBu5wgy9YRQEMnLTfPcdXHJJuJUZEUBArhvnXCvgBSAamCAiI9JtHwNc4y2WBk4WkfLetn7AY962p0Xk7bwQbhgRgblpjHwgW0PvnIsGxgMtgARghXNuloisS20jIoP82t8FXOD9XRF4ArgI8AFx3r578vQqDKMwsm4d3HUXLFwIF14I06dbD94ICYG4bhoBG0XkVxE5BHwAdMyifU9givd3S2CBiOz2jPsCoFVuBBtGoWffPnjgATj/fPjhB3PTGCEnENdNNWCL33IC0Dijhs65M4AawMIs9q2W1cmSk5OJj48PQFbGJCUl5Wr/UGG6giMidfl8lJ07l1NGjqT4jh3s7dKFHYMGkVKxIqxfHz5dIcR0BUeodOV1eGUPYJqIpOT0ADExMdSuXTvHAuLj43O1f6gwXcERcbrSu2k++YTyl1xC+XDrCjGmKzhyoysuLi7TbYG4brYCp/ktV/fWZUQPjrltgt3XMCIPc9MYBYBAevQrgJrOuRqoke4B9ErfyDlXC6gALPNbPQ941jlXwVu+Fng4V4oNozDg88GHH8J991k0jRF2su3Ri8gRYCBqtOOBqSKy1jk33DnXwa9pD+ADEfH57bsbeAr9slgBDPfWGUbksm4dNGsGPXvapCejQBCQj15EYoHYdOuGplselsm+E4GJOdRnGIWHfftg+HAYOxbKloVXXoGbb4bo6HArM4o4luvGMHJLejfNf/+rbprKlcOtzDAAS4FgGLkjIzfNG2+YkTcKFGboDSMn+EfTrFqlbhqLpjEKKOa6MYxg8Pnggw/MTWMUKszQG0agrFvH6TfeCN9+Cw0bwowZ0DjDSeKGUaAw141hZMfRozB6NFxwASXj49VN8+23ZuSNQoP16A0jK37/Hfr1gy+/hE6d+OX++zn3ssvCrcowgsJ69IaRET4fvPeeFgJZuRImToQZMzQBmWEUMszQG0Z69uzRcMneveG882D1ahgwAKKiwq3MMHKEGXrD8OeLL6BePS0C8swzsHgxnHVWuFUZRq4wQ28YAElJMGiQFuYuWxaWL4dHHrH0BUZEYIOxhrF6NdxwA6xdCwMHwsiRULp0uFUZRp5hPXqj6JKSAqNGwcUXw+7dMHcujBtnRqLqyoMAACAASURBVN6IOKxHbxRNNm+Gvn1hyRLo0gVeew0qVQq3KsMICdajN4oWPh+8846GTf7wA0yaBB99ZEbeiGgC6tE751oBLwDRwAQRGZFBm+7AMMAHrBaRXt76UUBb9EtlAXCPf3ESw8g3du+G225Tw3755TB5MtSoEW5VhhFysu3RO+eigfFAa6AO0NM5Vyddm5poicDLRKQucK+3vglwGVAfOA+4GLgqLy/AMAJiwQINm5w5U5OQffmlGXmjyBCI66YRsFFEfhWRQ8AHQMd0bW4GxovIHgAR2eGt9wElgRJADFAc2J4Xwg0jIA4ehHvugWuvhZNO0hw1Q4ZY2KRRpAjE0FcDtvgtJ3jr/DkXONc5941zbrnn6kFElgGLgG3ezzwRic+9bMMIgB9+gIsughdfhLvvhrg4uOCCcKsyjHwnr6JuigE1gauB6sAS51w9oDJQ21sHsMA5d4WIfJXZgZKTk4mPz/l3QVJSUq72DxWmKzhypSslhUpvvkmVl17iSMWKbJswgcQmTeC338KrK4SYruAoaroCMfRbgdP8lqt76/xJAL4VkcPAJufceo4Z/uUish/AOTcHuBTI1NDHxMRQu3btgC8gPfHx8bnaP1SYruDIsa5NmzRs8uuvoVs3ir/6KqfnYSKyiLtfIcZ0BUdudMXFxWW6LRDXzQqgpnOuhnOuBNADmJWuzUzUqOOcq4y6cn4Ffgeucs4Vc84VRwdiC97XqFH48fng7be1tN+aNRpR8+GHYNkmDSN7Qy8iR4CBwDzUSE8VkbXOueHOuQ5es3nALufcOtQn/4CI7AKmAb8APwKr0bDLT0NwHUZRZtcu6NYN+vdXH/yaNdCnj2WbNAyPgHz0IhILxKZbN9Tvbx8w2Pvxb5MC3Jp7mYaRCXPnwo03ws6dms5g8GCLqDGMdNjMWKNwcuCAJiBr3VrdM999Bw88YEbeMDLADL1R+IiL0+Lc48fDvfdqBagGDcKtyjAKLGbojcJDSooWA7nkEti3T2e7jhkDJUuGW5lhFGgse6VROPj1Vx1gXboUrr8eXn7ZImoMI0CsR28UbHw+eOstDZtcu1YLdn/wgRl5wwgCM/RGweWvvzRX/I03aiqDNWugV69wqzKMQocZeqNAcuLixZptcvZsGD1ai3affnq4ZRlGocR89EbB4sABuP9+Tn/lFTjvPJg/X4uEGIaRY8zQGwWHFSugd29Yv55d/fpR6dVXLaLGMPIAM/RG+DlyRIuBDB8Op54KX3zBjlNPpZIZecPIE8xHb4SXX36BK6+EoUOhe3cdcG3aNNyqDCOiMENvhAefDyZM0LDJ+HiYMkVDJ8uXD7cyw4g4zNAb+c+OHdCpE9x8MzRurL34Hj3CrcowIhYz9Eb+Mnu2hk3OmwfPP69pDE47Lfv9DMPIMWbojfwhMRFuuw3atYOqVTUR2aBBcII9goYRauxTZoSe777TgiCvv66phL/7TmPkDcPIFwIKr3TOtQJeAKKBCSIyIoM23YFhgA+tJNXLW386MAGtO+sD2ojIb3kh3ijgHDmi2SafegqqVYNFi+Cqq8KtyjCKHNn26J1z0cB4oDVQB+jpnKuTrk1N4GHgMhGpC9zrt3ky8H8iUhtoBOzII+1GQWbDBrjsMhg2DHr21AFXM/KGERYC6dE3AjaKyK8AzrkPgI7AOr82NwPjRWQPgIjs8NrWAYqJyAJv/f481G4URHw+eOMN9b/HxGiB7u7dw63KMIo0gRj6asAWv+UEoHG6NucCOOe+Qd07w0Rkrrd+r3NuBlAD+BwY4tWSzZDk5GTi4+MDv4J0JCUl5Wr/UFEUdEXv3MmpQ4dS9ssv2X/ppWx79lmOnHKKxsmHUVdeYrqCw3QFR6h05VUKhGJATeBqoDqwxDlXz1t/BXAB8DvwIdAfeDOzA8XExFC7du0cC4mPj8/V/qEi4nV9+incdBP88w+MHUuZu+6iZi4iaiL+fuUxpis4IlFXXFxcptsC+SRuRQdSU6nurfMnAZglIodFZBOwHjX8CcAqEflVRI4AM4ELg9BuFHT274dbboEOHXTANS4O7rnHwiYNowARyKdxBVDTOVfDOVcC6AHMStdmJtqbxzlXGXXZ/OrtW945V8Vr15TjfftGYWbZMi3KPWECPPQQfPst1K0bblWGYaQjW0Pv9cQHAvOAeGCqiKx1zg13znXwms0Ddjnn1gGLgAdEZJfni78f+MI59yMQBbwRigsx8pHDhzUJ2eWXawjll1/CiBFQokS4lRmGkQEB+ehFJBaITbduqN/fPmCw95N+3wWAVY6IFNav15zxK1ZA377w4otw0knhVhVSUlJg8WL4+GOIiqrM4MFw5pnhVmUYgWP56I3A8Pngtddg8GAoVQo++gi6dg23qpDh8+kE3ilTYOpU2LZNL/vgwSqMG6dTAvr21VtQrly41RpG1tiImZE9f/4J7dvD7bfDFVfAjz9GrJH/6Sd49FE45xy45BJ45RX9PXUq7NwJCxZs4Omn4Y8/NMioalV9wZk/X3v+hlEQMUNvZM3MmZpt8osv1E0zZw785z/hVpWnbNoEzz6rl1mvng43nHMOvPWWZlSeMQO6dYPSpaFatSM8+iiI6Fh0v36akLNlS61d/tBDsM7CDYwChhl6I2P27dMua+fOmkb4++/hrrsiJmxy2zZ44QXtrZ91lvbiy5WDl17SbfPmQf/+mQ8/REUd6/Fv26aerAsvhP/9TwOPLr4Yxo3TtwDDCDeR8ak18palSzVsctIkePhhWL4cCuDkkmDZs0cjQZs1g+rV4d57ITkZRo6E336Db76BO++Ek08O7rglS6on69NP1aUzZoy6ce6+W0vgduqkA7mHDoXksgwjW8zQG8c4fBgee0z98EePaqjJs88W6rDJxEQdUO3QAU45RYtabdmil7luHfzwAzz4IJxxRt6c7+ST9Qvk++81j9s99+j0guuuU6M/cKAGLPl8eXM+wwgEM/SG8vPPcOmlmla4b19YvVrj5Ashhw7BrFmaNPPkk6FXr2Oep5Ur1b/+5JOhf0mpVw9Gj9YvlthYuPZaePNNaNRI3TsjRkBCQmg1GAaYoTd8Phg/Xh3Mv/0G06frKGQhixlMSdHx4v/+V3vuHTtqlcK+ffXF5Pff1X/esKH61/OTYsWgdWt9s9i2TeuvVKyoXrHTT4cWLeDdd/XtwzBCgcXRF2W2beO0W2+Fr7+GVq1g4kT1LxQSfD51i6TGuv/5J5Qpo+PHPXtC8+ZQvHi4VR5P+fLqPrr5ZvjlF5g8WX/69FHtXbtqJM+VV0bMuLdRALBHqagyYwbUq0fplSu1Rx8bW2iM/I8/wiOPwNlnq7fptdf099SpGg45ebL2oAuakU/P2WerC+mXX/Sto3t3faG65hqNBHr8ca3fYhi5xQx9UeOff2DAAOjSBc48k03TpsEdd+S/PyNIfv1Vhw/OOw/q14dRo+DcczUwaPv2Y7HupUqFW2nwnHCC9uDffFPfSt57D2rV0nHwc8/VQl2vvQZ794ZbqVFYMUNflPj6azj/fO3yPvYYLFvGobPOCreqTNm2DcaOhcaNtff72GNQoYK+gPzxB8ydq26OSEq1U7q0Dh7PnavjCiNHqoG/7Tadhdu9u07QOnIk3EqNwoQZ+qLAoUPq67jqKu0+fvWVFuwugL6N3bu1EuGAAadTrZpWJDx0SA3e5s0q/Y47go91L4xUq6ahnz/9pNFCt9wCCxdCu3Y6D2DwYA2OMozsMEMf6cTHqwP7uefUZbNqFTRpEm5Vx7F/P7z/vqbTqVpVDdr27cUYOlTlp8a6n356uJWGh6gojRZ68UV9k5k5U/+FL72k89p0bltFtm8Pt1KjoGKGPlI5elTn4F94ofoAPv5Yp4WWLRtuZYDOSP3kE+jRQ8Mhb7hBv4PuuUeLVM2e/SvDhqmv2jhGiRIaOjpjhrq2XnpJa7CPGnUK1apB27Y6KJ2UFG6lRkHCwisjkT/+0N77/PnQpo2O8lWtGm5VpKTAokUaDjljhvqeK1XSWPeePXV+VmpIYQGs21zgqFRJUzbceSd89tkvfPPN2bzzDlx/vY5bXH+9jmFcemmBH2s3QkxAht451wp4AYgGJojIiAzadAeGAT5gtYj08ttWDi0hOFNEBuaBbiMzpk2DW2+Fgwc149att4b1U+7zaaqc1Fj37dv1paJzZ+3NF8RY98LI2Wcfol07ePpp/TJ9+22dhPX665qJs29fjdW3gilFk2xdN865aGA80BqoA/R0ztVJ16Ym8DBwmYjUBe5Nd5ingCV5otjImL//1u5bt24aorJqlYZqhMHI+3ya5+XhhzUevEkTNTiXXabfQ9u3qyEqDLHuhY3oaP3yfOcdDdV86y0duB06FGrU0Bj9t97S5KRG0SEQH30jYKOI/Coih4APgI7p2twMjBeRPQAisiN1g3OuIXAKMD9vJBv/YskSDZt89139RH/zjQZg5zO//KI9yvPOUzn/93/qY3/7bTXu06dr+H5hjHUvjJQtq6mWFy3S7BZPPQVbt8KNN+q4SO/emibCCqZEPlG+bNLoOee6Aq1E5L/ech+gsb8Lxjk3E1gPXIa6d4aJyFzn3AnAQqA30By4KDvXzapVq3wxMTE5vqCkpCRKliyZ4/1DRSh0RR06ROVx46g0cSKHq1dn66hRJJ1/fr7q2rGjGHPmlCM2thw//qgWvGHDA7Rt+zfXXruPihVzZkWK0v8xLwhUl88Hq1eXYubMk5g7txz//BPNKaccpn37v+nY8W/OPjtvcykX9vuV3+RG14EDB+IaNmx4UUbb8mowthhQE7gaqA4scc7VQw18rIgkOOcCOlBMTAy1c5FWMD4+Plf7h4o817V2rbpqVq+Gm2+mxPPPU6NMmXzRtWuX9s6nTNGp+z4fXHCBzla9/no4/fTSQGkg5ykVisz/MY8IRledOjr4nZSkOfTffrs4b71VmQkTKnPxxccGxytVyl9d+Ukk6oqLi8t0WyCum63AaX7L1b11/iQAs0TksIhsQnv3NYFLgYHOud+A0UBf59y/BnKNIDh6VEsjNWyo0TWffKIO8BwY+WDYv1+n5rdrpwE8t96q4X1PPKEZjr//Hh54oOjGuhdGSpbUIZ3PPlOXzvPP6+S0u+7StEfXXacx+1YwpfATSI9+BVDTOVcDNfA9gF7p2swEegJvOecqA+cCv4rIDakNnHP9UdfNkLwQXiTZulWdrp9/rhZ3wgR1toaI5GQtETtlivb8Dh7UqoKDBmmPr0EDC9uLFE45Rf+vgwbpS+LkyfrF/vHH2rPv2VNfIMOR5tnIPdn26EXkCDAQmAfEA1NFZK1zbrhzroPXbB6wyzm3DlgEPCAiu0IlukgydapWsli6VDNczZoVEiN/5IgO0KUO2HXurNPu+/fXMd/fflMXzQUX2Ac+Ujn/fM3dn5CgeXWaNdO0FBdfrAPtI0dqn8MoPATkoxeRWCA23bqhfn/7gMHeT2bHmARMyonIIs3evfou/e67mt3rnXegZs08PYXPB8uWHYt137FDIzauu05j3Zs1szDIokixYjrfrk0brbf70UcaQTVkiKZOat5c/fmdO2syNqPgYjNjCzJffqmfpD/+gGHD4NFH9dOXB6TGur/0UhUWLNCEYTExmm+mZ0/9cBfAoAQjTFSooDmIbrkFNm48VjCld28dHurWTV07V1xhBVMKIvYvKYgkJ+vIZtOmam2/+UZHPfPAyG/cqPHUdeuqj/2ttypRu7b21Hbs0F7bddeZkTcy55xzYPhwrRHw5Zdq5D/6CK6+WufqDR2qz5lRcDBDX9D48UetHj16tHaffvhBXTa5IDWi4uKL1eszdChUqQIvvwyLF29gzhx9cShkZWKNMHPCCZr5euJEnYX77rv6fD39tP6+7DKYOrW8FUwpAJihLygcParW+KKL9FPz6afw6qtw4ok5OtyuXTpme/XVGilz3316itGjYcsWjX+//XZyPKHJMPw58UTNQDp/viZLHTFC/frDhp1K1ao61hMbawVTwoX56AsCW7ZoWMvChdChg4Y45KCyxr59GlY/ZYp+4I4cAefU69OzZ1iyIhhFkOrV4aGHtIbAtGmbWLKkBlOmwIcfHktJ3a+floQ08gfr0YebKVP0if/2WzXwM2cGZeSTkjTWuXt3/RD16aPen0GDdBJTfLwaejPyRn4TFQXnnZfEuHEaT/Dxx5oyedw4DeG84AIYMwYrmJIPmKEPF3v2aHHQXr0089eqVfDf/wYUnH7kiPbYBwxQ437ddTooNmCAloW1WHejoFGiBHTqpMb+jz/U2BcrpuUQq1XT+X8ffWQFU0KFuW7CwcKF+u66bZuGwAwZkm1EzdGjx2LdP/pII2TKldMY5p49NdY9jyIvDSOkVK4MAwfqz7p1Gqb5zjs6Oat8+WMFUy65xDoqeYX16POTpCQdFW3WTGeYLFsGjz2WqYX2+bSj/9BDmkv88su1WNSVV2pSse3bYdIkaNnSjLxROKlTRwduf/9d31LbtlXD36SJji89/bTO8TByhxn6fCJGRMMmn39ew12+/17jHTNgwwaNU65TR90vzz+vce+TJ1usuxGZREdDixYaovnnnxqy+Z//wOOPa1Wsa67RTo0VTMkZZuhDjRfTeGb37mqlZ8/WAPZ0YZMJCZpf5KKLdOB02DAdk33lFfXwxMbqQGsBqe1tGCGjXDkdb/ryS52UNXy4BqYNGKCZU/v00bx+VjAlcMzQh5Lff1c3zQMPkHjllRoO06ZN2uadOzVU/qqrNL3v/ffr+tGjddfFi7UaYOXKYdJvGGGmRg3t1W/YoIEGvXvrFJMWLbSn//DDmibbyBoz9KHA59Mcr/Xrw8qVMHEiCS++CFWqsG+fDjy1aaM5v2+/XTv6w4bB+vXa/L77NBbZMAwlKkpn2r72mr7hfvihfrz+7/+gdm2dPD5+vE4UNP6NGfq8Zs8eDYPp3Vtzuq5eTVLPASz4vBzduqk7pm9fLRA1eLBmOFi3TtMS5HFSSsOISEqV0nkjs2cfc3kmJWkUT2rBlE8+sYIp/lisRl7yxRcaF7Z9O0eGP8sXDR9kyvBoPv4Y/vmnOiefDDfdpN8Dl15qWf4MI7dUraodpsGDNULNv2BK5crHCqZceGHRDtU0U5MXHDwIgwZxtHkLvo6+ijs7b+U/4x6mVVs18tddB2+88Ttbt8JLL+krqBl5w8hbGjTQCLWEBPXjX3ONunouukhr9owapZO1iiIB9eidc62AF4BoYIKI/Kvuq3OuOzAM8AGrRaSXc64B8ApQDkgBnhGRD/NIe4HA98MqVnV5iimbGvNBmZ1s+b0iJXccy+veurWGQcbHJ1qsu2HkA8WL60zbdu3Ukzp1qqbhfughHbxt3hyaNy/HGWcUnYIp2fYrnXPRwHigNVAH6Omcq5OuTU3gYeAyEakL3OttOgD09da1AsY658rnof6wsT4+hSdbfE3tC0tx4abpjIm+n3pXVuSdd3RwdepUnbVqse6GET4qVNBC9kuXgohWxvr5Z3jwwWpUraqu1CVLNAo6kgnEgdAI2Cgiv4rIIeADoGO6NjcD40VkD4CI7PB+rxeRDd7ffwA7gCp5JT6/2bJFQx8b1kvG1Ynmyc+bULXyEV4dvZ8/t5/A7Nk6Bmux7oZR8Dj3XM04smkTvPXWZrp00eidq67SYipPPAG//BJulaEhyufzZdnAOdcVaCUi//WW+wCNRWSgX5uZwHrgMtS9M0xE5qY7TiPgbaCuiGT6/blq1SpfTExMDi8HkpKSKJmH3ejdu6OZP78ss2efRFycvudddEIc1xf7iGsGVaN032YBjfLkta68wnQFh+kKjoKu68CBKD7/vCyffHISy5efiM8XxYUXHqBjx79p1eofypbN365+bu7XgQMH4ho2bHhRRtvyymtcDKgJXA1UB5Y45+qJyF4A59ypwDtAv6yMPEBMTAy1a9fOsZD4+Phc7Q/wzz+aLXjKFFiwQGfg1T43heF1P6TH2seo2aSqDu/XqJGvukKB6QoO0xUchUFXw4bqv9+yRSN23n67NE88UZrnnjuVjh01aqdFi/zJJ5Wb+xUXF5fptkBcN1uB0/yWq3vr/EkAZonIYRHZhPbuawI458oBs4FHRWR5ELrzlYMHNVFY166a+rdfP83lfv/9sOqVZazddzqPr+9Dzedu0rnZQRh5wzAKPqedpolk163T8hA33aQdvTZtdNv99+vk9sJIIIZ+BVDTOVfDOVcC6AHMStdmJtqbxzlXGTgX+NVr/zEwWUSm5ZnqPOLwYZg7V436Kaeokf/qK00L/803sGndQUYcuJvzb29CVPmT9L8/ZIhmYDIMIyKJitL8gy+9pOGY06frzNsXXtDZuBdcAGPHatBFYSFbQy8iR4CBwDwgHpgqImudc8Odcx28ZvOAXc65dcAi4AER2QV0B64E+jvnVnk/DUJyJQFy9Kga8zvu0Ox4rVvrLLquXTVN6tatWhShScnvibqooS7cfTfExel/2DCMIkNMjM6DmTlTjf6LL2o/b9AgLZjSoQNMmwbJyeFWmjUBeZ1EJBaITbduqN/fPmCw9+Pf5l3g3dzLzB0+n6YaSK1buWWLTqP2j3VPG/9NSYHnRukQfJUqav1btAirfsMwwk+VKnDXXfqzdq0O0737rk7OqlDhWMGUxo0L3izciJ6fKaLJwmrV0gGXsWP11evdd/W168MPtbxZmpHftAmuvlqDbTt1UoecGXnDMNJRty6MHKlZZufO1c7i229rapNateCZZ3RbQSHiDP22bcX4v//T3Ba1amku6//8R6dC//knfPaZVqEvU8ZvJ59Pqxqcfz6sWaNf1R9+CBUrhusyDMMoBERHa4W3995T+zJhgubfeewxTaPctKl+AezfH16dEWPoU1K0E96sWU0efFBDoZ5/Xt00ixbBLbdApUoZ7LhzpzroBwxQH/yaNVrZoKC9exmGUaApV04jdRYv1olXw4ZpGcT+/TXYo29fzXsYjlm4EWPoTzgBzjgD7r57Bxs2wHffHRswyZS5czXb0aef6nvYwoV6EMMwjFxw1lmaenzjRg3+uOEGDfpo3lx7+o88oq7l/CJiDH1UlIY/3XbbLs45J5vGBw5o8urWrbWb/9138OCDFjZpGEaeEhUFl18Or7+urp0pU7RMxciR6lpu3Fgri+7eHVodEWPoAyYuTkdmx4+He+/Vkk4NwhrxaRhGEaBUKejRQ+s/JyRodawDB+DOO7VgSpcuEB+f8/QvWVF0DH1Kig6FX3KJlpJfsADGjLH0koZh5Dunnqozbdesge+/13k9X30F778fmgCQopEh/ddfdYB16VINdn35ZYuoMQwj7ERFaQzIBRdoSUSRbUDeZ3KP7B69zwcTJ2rY5Nq1GkA/ZYoZecMwChyhrDoXuYb+r7907vJNN2ktsTVrdOjbwiYNwyhiRKahj43VsMnYWB3x+OILOP30cKsyDMMIC5Hloz9wgKrDh8MHH2gM0/z5mvPAMAyjCBM5hj4lBS6/nAo//ACDB2uEjUXUGIZhRJDr5oQToFMnNr/1lg5fm5E3DMMAIqlHHxUFQ4dyID4+3EoMwzAKFAEZeudcK+AFtPD3BBEZkUGb7sAwwAesFpFe3vp+wGNes6dF5O080G0YhmEESLauG+dcNDAeaA3UAXo65+qka1MTeBi4TETqAvd66ysCTwCNgUbAE865Cnl6BYZhGEaWBOKjbwRsFJFfReQQ8AHQMV2bm4HxIrIHQERSqym2BBaIyG5v2wKgVd5INwzDMAIhENdNNWCL33IC2kP351wA59w3qHtnmIjMzWTfrBIHk5ycTHwu/OxJSUm52j9UmK7gMF3BYbqCo6jpyqvB2GJATeBqoDqwxDlXLycHiomJoXbt2jkWEh8fn6v9Q4XpCg7TFRymKzgiUVdcXFym2wJx3WwFTvNbru6t8ycBmCUih0VkE7AeNfyB7GsYhmGEkEB69CuAms65GqiR7gH0StdmJtATeMs5Vxl15fwK/AI86zcAey06aGsYhmHkE9n26EXkCDAQmAfEA1NFZK1zbrhzroPXbB6wyzm3DlgEPCAiu0RkN/AU+mWxAhjurTMMwzDyiSifzxduDccRFxf3F7A53DoMwzAKGWc0bNiwSkYbCpyhNwzDMPKWyMl1YxiGYWSIGXrDMIwIxwy9YRhGhGOG3jAMI8IxQ28YhhHhmKE3DMOIcApN4RHn3ESgHbBDRM7LYHsUmjO/DXAA6C8i33vbQpYTPwBdNwAPAVHAPuB2EVntbfvNW5cCHBGRi/JR19XAJ8Amb9UMERnubcu2/kAIdT0A3OAtFgNqA1VEZHeI79dpwGTgFLSmwusi8kK6Nvn6jAWoKVzPVyDariafn7EAdeX7M+acKwksAWK8c04TkSfStYnxtDcEdgHXi8hv3raHgZs8XXeLyLxgzl+YevSTyDrFcWs0v05N4BbgFciXnPjZ6doEXCUi9dBZwq+n236NiDTIyw9hgLoAvvLO3cDvA5ht/YFQ6hKR/0vVhKbLWJxuNnWo7tcR4D4RqQNcAtyZwXXn9zMWiKZwPV+BaIP8f8ay1RWmZywZaCoi5wMNgFbOuUvStbkJ2CMi5wBjgJEAnv4eQF30s/Oydw8DptAYehFZAmSVPqEjMFlEfCKyHCjvnDuVEOfEz06XiCxNzdMPLEcTu4WcAO5XZgRSfyC/dPUEpuTVubNCRLal9s5FZB+a7iN9Su18fcYC0RTG5yuQ+5UZIXvGcqArX54x75nZ7y0W937Sz1btCKS+CU4DmnlvkR2BD0Qk2UsauRG9hwFTaAx9AGSW+z7onPgh5CZgjt+yD5jvnItzzt0SBj2XOudWO+fmOOfqeusKxP1yzpVGjeV0v9X5cr+cc2cCFwDfptsUtmcsC03+hOX5ykZb2J6x7O5Zfj9jzrlo59wqYAfaMcj0+fJyjP0NVCIP7lckGfoCjXPuGvSDciTquwAAAmVJREFU+JDf6stF5EL0FfZO59yV+Sjpe+AM71VyHJqBtCDRHvgm3St1yO+Xc64M+sG/V0T+yevj54RANIXr+cpGW9iesQD/j/n6jIlIiucuqg40cs79a4wqVESSoc8s933Yc+I75+oDE4COIrIrdb2IbPV+7wA+JsjXsdwgIv+kvkqKSCxQ3EsxHfb75dGDdK/Uob5fzrniqHF4T0RmZNAk35+xADSF7fnKTlu4nrFA7plHvj9j3rH3oll+07v30u6Lc64YcBI6KJvr+xVJhn4W0Nc5F+UNcvwtItvQFMrXOucqeANk13rr8gXn3OnADKCPiKz3W3+ic65s6t+erp/yUVdVz/+Hc64R+izswq/+gHOuBPphmJVfujw9JwFXoREbqetCer+8e/EmEC8iz2fSLF+fsUA0hev5ClBbvj9jAf4f8/0Zc85Vcc6V9/4uBbQAfk7XbBbQz/u7K7BQRHze+h7OuRindUFqAt8Fc/7CFF45BS1VWNk5l4BGORQHEJFXgVg07G0jGvo2wNu22zmXmhMf8jgnfgC6hqJ+tpedc3AsZOsU4GNvXTHgfdE6u/mlqytwu3PuCHAQ6OE9VEecc6n1B6KBiSKyNh91AXQG5otIot+uIb1fwGVAH+BHz48K8Ahwup+2/H7GAtEUlucrQG3heMYC0QX5/4ydCrztRcucgNb1+Mw5NxxYKSKz0C+od5xzG9GAhR6e5rXOuanAOjSq6E4RSQnm5Jam2DAMI8KJJNeNYRiGkQFm6A3DMCIcM/SGYRgRjhl6wzCMCMcMvWEYRoRjht4wDCPCMUNvGIYR4fw/bxOrm84T9G8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil model fine tuning dengan 3 Epoch val loss mengalami peningkatan dari 0.79 ke 0.91 sedangkan val_accuracy mengalami penurunan dari 0.5 ke 0.61. Hasil ini menunjukkan bahwa kesimpulan smenetara model kurang dapat belajar dengan baik namun karena hanya 3 epoch kita perlu melakukan eksplorasi lebih lanjut contohnya dengan menambah jumlah epoch dan lain sebagainya"
      ],
      "metadata": {
        "id": "aXgMYpBUiAJu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://towardsdatascience.com/build-better-pipelines-with-tensorflow-dataset-328932b16d56"
      ],
      "metadata": {
        "id": "vex3jgEeLmFp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluasi Model"
      ],
      "metadata": {
        "id": "p1XiR-qTNfgx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Saving"
      ],
      "metadata": {
        "id": "9phB1BN8Nfd1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_PATH = \"./tf_model\"\n",
        "model_rnn.save(MODEL_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWx1TkCc0gDC",
        "outputId": "e3f9c5c8-1e3a-4ac5-d969-eca4b4ca5d28"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as leaky_re_lu_layer_call_fn, leaky_re_lu_layer_call_and_return_conditional_losses, lstm_cell_4_layer_call_fn, lstm_cell_4_layer_call_and_return_conditional_losses, lstm_cell_5_layer_call_fn while saving (showing 5 of 25). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: ./tf_model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: ./tf_model/assets\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f98e3fbe290> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f98e4655890> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f98e462cd10> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f98e469ac90> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# zip hasil model\n",
        "!zip -r /content/file.zip /content/tf_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncutddgQw0bd",
        "outputId": "163d81ed-cf2d-4183-c770-3afa34a655d8"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/tf_model/ (stored 0%)\n",
            "  adding: content/tf_model/assets/ (stored 0%)\n",
            "  adding: content/tf_model/keras_metadata.pb (deflated 93%)\n",
            "  adding: content/tf_model/saved_model.pb (deflated 90%)\n",
            "  adding: content/tf_model/variables/ (stored 0%)\n",
            "  adding: content/tf_model/variables/variables.index (deflated 71%)\n",
            "  adding: content/tf_model/variables/variables.data-00000-of-00001 (deflated 7%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_final = tf.keras.models.load_model(MODEL_PATH)"
      ],
      "metadata": {
        "id": "Ibsp-1Bh0lo_"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Inference"
      ],
      "metadata": {
        "id": "DqXkNprFOVZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testing_reviews = testing_reviews.to_list()"
      ],
      "metadata": {
        "id": "sTbGZQhL32qK"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testing_reviews[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8Tzt39X2-xj",
        "outputId": "ce5994a5-57c9-462a-8bd5-fc90d818cf58"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['great stay return night day stay overall excellent break want relax lay pool beach place want party try resort exchange privilege property casino disco party foodaverage propertynice cleanroomnice floor vault ceiling nice touch rain staffvery helpful',\n",
              " 'fine came good hotel pretty period theme nicely situated edge chinatown walking distance little italy east village staff attentive stay pleasant continental breakfast enjoyable overall atmosphere positive bad travelled business booked hotel guest separate room based somewhat affordable room rate night told cancellation reservation hour advance notified stay ended early charged room rental remaining night consequently business concluded early forced pay roughty cost stay experience large ny hotel host business traveller manager nice point refused compromise unlikely stay hotel recommend traveler fact given personal attention manager owner based review probably invited hotel',\n",
              " 'deluxe accomodations heart amsterdam good price great location wife stayed famed marshmallow bed disappointed stayed night relaxing end tour london paris came train paris hotel short walk central station street tiny street damrak ton shopping dining amsterdam checked worker redoing road outside desk advised room overlooking street asked construction assured able hear double window room nice quite polite exactly right sent fruit basket later make sure comfortable room practically suite high ceiling large window overlooking street outside bed king promised comfort marshmallow goodness shower tropical rain spout directly overhead like showering hot waterfall bathroom kit included nice french soap lotion shampoo maybe enjoyed extra smaller room london paris loved sofitel definitely stay make tradition magical bed',\n",
              " 'avearge rating got day stay suite described previous post check issue reached hotel tried early check come went exploring mean time came told come got room bit hassel choose queen bed high floor view king bed low floor view city choose comfortable compact clean location good shopping eating close major site guest service attentative called arrange early morning pick airport amd got pleasant voice hard believe time query site resturants advised handled',\n",
              " 'excellent stayed night vacation march tower deluxe room night arriving regretted booking hotel harbour view event pleased stayed westin room luxurious classy like rest hotel worth price business hotel location ideal central main tourist attraction upmarket steak house plus restaurant basement westin dined occasion hotel bar served great cocktail strongly recommended']"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_sequences = tokenizer.texts_to_sequences(testing_reviews)\n",
        "print(test_sequences[10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtYi5SkS3ySG",
        "outputId": "38105bf9-0b38-49c4-f85a-d6437f21270c"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[259, 16, 8, 164, 300, 452, 87, 764, 160, 432, 190, 1068, 6427, 3, 985, 7777, 41, 174, 1040, 4252, 253, 17, 1220, 1126, 1224, 6, 27, 38]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_padded = pad_sequences(test_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "print(len(test_sequences))\n",
        "print(test_padded.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwEGN9K74klO",
        "outputId": "4875b256-534f-47b1-9b68-867a2e83f357"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2050\n",
            "(2050, 100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_padded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEHxzbiJGLD5",
        "outputId": "e9bfcc81-d9ce-44cc-88a4-37691ea1aaed"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   4,    7,  143, ...,    0,    0,    0],\n",
              "       [ 178,  186,    5, ...,    0,    0,    0],\n",
              "       [ 741, 1477,  641, ...,  283, 1149, 2070],\n",
              "       ...,\n",
              "       [  87,    2,   91, ...,    0,    0,    0],\n",
              "       [  29,  206, 1352, ...,    0,    0,    0],\n",
              "       [ 355,    5, 2403, ...,  490,   15,  811]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_ratings = model_final.predict(test_padded)"
      ],
      "metadata": {
        "id": "HNTXXw9A1RHd"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wX9QE4JVFzRz",
        "outputId": "0b2ae2d7-11d0-4a13-c487-6d9a289e24cb"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00782151, 0.00832124, 0.01390384, 0.32962468, 0.6403287 ],\n",
              "       [0.07041969, 0.6401142 , 0.24529307, 0.03168322, 0.0124899 ],\n",
              "       [0.01492968, 0.05282479, 0.40625596, 0.47439966, 0.05158992],\n",
              "       ...,\n",
              "       [0.00485297, 0.01169604, 0.10216092, 0.84751904, 0.03377101],\n",
              "       [0.00654033, 0.00720003, 0.00830512, 0.05762197, 0.9203325 ],\n",
              "       [0.00199593, 0.04282769, 0.91053385, 0.04236681, 0.00227576]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_ratings_list = []\n",
        "for rate in predict_ratings:\n",
        "  #print(rate)\n",
        "  predict_ratings_list.append(np.argmax(rate)+1) # ditambah 1 karena pada data train dilakukan encoding [0, 1, 2, 3, 4]"
      ],
      "metadata": {
        "id": "ovLBSAM77JVf"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#make dataframe for comparing the orignal and predict values\n",
        "data_test_predict = {'orignal': testing_ratings, 'predicted':predict_ratings_list}\n",
        "df_check = pd.DataFrame(data_test_predict)\n",
        "df_check.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "DdxtLjGmBJNh",
        "outputId": "6243b0f8-3124-4ce4-98f1-4eb1b37de444"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7efd1d41-6020-4a1e-8847-ac585e1ab58b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>orignal</th>\n",
              "      <th>predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>16778</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "      <th>19314</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>9073</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18377</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <th>12241</th>\n",
              "      <td>5</td>\n",
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              "      <th>17081</th>\n",
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              "      <td>5</td>\n",
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              "      <th>7694</th>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-7efd1d41-6020-4a1e-8847-ac585e1ab58b');\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ],
            "text/plain": [
              "       orignal  predicted\n",
              "16778        5          5\n",
              "19314        2          2\n",
              "9073         5          4\n",
              "18377        3          3\n",
              "12241        5          5\n",
              "12719        5          5\n",
              "1769         5          5\n",
              "17081        3          5\n",
              "6874         5          3\n",
              "7694         5          5"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluasi Metrics "
      ],
      "metadata": {
        "id": "UxGNfSseWfBV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# checking for performance metrices\n",
        "#importing classification_report and confusion metrics\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "#print classification_report\n",
        "print(classification_report(testing_ratings,predict_ratings_list))\n",
        "# ploting the confusion metrix plot\n",
        "conf_mat = tf.math.confusion_matrix(labels=testing_ratings,predictions=predict_ratings_list)\n",
        "plt.figure(figsize = (17,7))\n",
        "sns.heatmap(conf_mat, annot=True,fmt='d')\n",
        "plt.xlabel('Predicted_number')\n",
        "plt.ylabel('True_number')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "0mlfS82-CAGc",
        "outputId": "b175e89a-a99a-43b6-f7de-1f88f1fa600b"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.62      0.59      0.61       142\n",
            "           2       0.35      0.31      0.33       179\n",
            "           3       0.35      0.36      0.35       219\n",
            "           4       0.47      0.46      0.46       604\n",
            "           5       0.70      0.73      0.72       906\n",
            "\n",
            "    accuracy                           0.56      2050\n",
            "   macro avg       0.50      0.49      0.49      2050\n",
            "weighted avg       0.56      0.56      0.56      2050\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(132.0, 0.5, 'True_number')"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Untuk kasus multiclass saat ini kita gunakan metrics f1-score, dapat dilihat f1-score dapat diurutkan dari yang paling tinggi ke yang paling rendah adalah Rating 5, Rating 1, Rating 4, Rating 3, dan Rating 2.\n",
        "\n",
        "Rating 5 memiliki jumlah data paling tinggi sedangkan Rating 1 memiliki jumlah data yang paling rendah. "
      ],
      "metadata": {
        "id": "sEwripceWqaY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pengambilan Kesimpulan"
      ],
      "metadata": {
        "id": "Aj6UeuCJOYpf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Data terdiri dari 5 class yaitu terdiri dari rating 1 sampai rating 5, hasil dari eksplorasi data menunjukkan bahwa data imbalance dan jumlah kata pada setiap data sangat berbeda signifikan dengan nilai minimum 6 kata, maximum 1855 sedangakan rata-rata pada setiap kata 97 hal ini sangat berpegaruh pada accuracy pada model.\n",
        "2. Hasil Improvement model menunjukkan model dapat belajar lebih baik daripada baseline model, namun untuk val accuracy semakin menurun ini dimungkingkan karena data nya imblanced dan totalword yang berbeda cukup signifikan.\n",
        "3. Dari ketiga model yaitu baseline model, LSTM model dan Fine Tuning model, Model yang akan kita gunakan adalah LSTM Model alasannya loss model konvergen.\n",
        "4. Model cukup baik belajar data yang yang memiliki Rating 5 dan Rating 1 meskipun data imbalanced artinya model dapat mengenali dengan baik kata-kata yang menunjukkan bahwa reviewer akan memberikan Rating 5 dan sebaliknya kata-kata yang sangat tidak baik yang menunjukkan bahwa reviewer akan meberikan Rating 1, sedangkan untuk Rating 4, Rating 3 dan Rating 2 model masih belum baik mengenali kata-kata pada masing-masing rating tersebut.\n",
        "\n",
        "  Contoh Review dengan Rating 5\n",
        "\n",
        "  - unique great stay wonderful time hotel\n",
        "  - great stay great stay went seahawk game awesome\n",
        "\n",
        "  Contoh Review dengan Rating 1\n",
        "\n",
        "  - horrible customer service hotel stay\n",
        "  - bad choice booked hotel hot wire\n",
        "\n"
      ],
      "metadata": {
        "id": "FxwZ0hRKTiZC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zIOMlGPdjOQO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}