{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz8lLSv6mVQo"
      },
      "source": [
        "# **🤖 Data Analysis & Visualization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpASMyIQMaAq"
      },
      "source": [
        "## **1.** 📦 Install required packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f48c8f8c",
        "outputId": "3686c11e-b211-43ea-ee64-9f7e854ecd5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
            "Requirement already satisfied: textblob in /usr/local/lib/python3.12/dist-packages (0.19.0)\n",
            "Collecting faker\n",
            "  Downloading faker-40.8.0-py3-none-any.whl.metadata (16 kB)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.0.0)\n",
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.12/dist-packages (3.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.61.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
            "Requirement already satisfied: nltk>=3.9 in /usr/local/lib/python3.12/dist-packages (from textblob) (3.9.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.24.3)\n",
            "Requirement already satisfied: huggingface-hub<2.0,>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (1.5.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n",
            "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.2)\n",
            "Requirement already satisfied: typer-slim in /usr/local/lib/python3.12/dist-packages (from transformers) (0.24.0)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from vaderSentiment) (2.32.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (2025.3.0)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (1.3.1)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.28.1)\n",
            "Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.24.1)\n",
            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (4.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (8.3.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (1.5.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2026.2.25)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (4.12.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (1.0.9)\n",
            "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (0.16.0)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (1.5.4)\n",
            "Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (13.9.4)\n",
            "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.0.4)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (4.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (2.19.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.1.2)\n",
            "Downloading faker-40.8.0-py3-none-any.whl (2.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faker\n",
            "Successfully installed faker-40.8.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas matplotlib seaborn numpy textblob faker transformers vaderSentiment\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZd99NpKkKyp"
      },
      "source": [
        "## **2.** ✅️ Load & inspect input datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JBLmm508Uq2"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "eBDXPQz18Xrs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IL8lZbMm8m3k"
      },
      "source": [
        "### *b. ✋🏻🛑⛔️ Create the df_reviews dataframe from the synthetic_book_reviews.csv file*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fdgjghfO8uuq"
      },
      "outputs": [],
      "source": [
        "df_reviews = pd.read_csv(\"synthetic_book_reviews.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-Dl37J0HLhU"
      },
      "source": [
        "### *c. ✋🏻🛑⛔️ Create the df_sales dataframe from the synthetic_sales_data.csv file*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "6XZs3P7fHgQe"
      },
      "outputs": [],
      "source": [
        "df_sales = pd.read_csv(\"synthetic_sales_data.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUI3SkmyrGQo"
      },
      "source": [
        "### *d. ✋🏻🛑⛔️ Visualize the first few lines of the two final datasets: df_reviews and df_sales*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "p8FdQFXErOqE",
        "outputId": "cb0b3b9a-66da-4327-ab2c-8593aaf97a71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  title sentiment_label  \\\n",
              "0  A Light in the Attic         neutral   \n",
              "1  A Light in the Attic         neutral   \n",
              "2  A Light in the Attic         neutral   \n",
              "3  A Light in the Attic         neutral   \n",
              "4  A Light in the Attic         neutral   \n",
              "\n",
              "                                         review_text rating  popularity_score  \n",
              "0            A competent effort, but lacked a spark.  Three                 3  \n",
              "1                The pacing felt a bit off at times.  Three                 3  \n",
              "2                   Standard fare with some promise.  Three                 3  \n",
              "3                               It was just... fine.  Three                 3  \n",
              "4  The concept was good, but the execution was la...  Three                 3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef08c8cd-d964-41f5-9e06-7e9ad48f4d63\" 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>title</th>\n",
              "      <th>sentiment_label</th>\n",
              "      <th>review_text</th>\n",
              "      <th>rating</th>\n",
              "      <th>popularity_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>A competent effort, but lacked a spark.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>The pacing felt a bit off at times.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>Standard fare with some promise.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>It was just... fine.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>The concept was good, but the execution was la...</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef08c8cd-d964-41f5-9e06-7e9ad48f4d63')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ef08c8cd-d964-41f5-9e06-7e9ad48f4d63 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-ef08c8cd-d964-41f5-9e06-7e9ad48f4d63');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_reviews",
              "summary": "{\n  \"name\": \"df_reviews\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"The Grownup\",\n          \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n          \"Ayumi's Violin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 135,\n        \"samples\": [\n          \"A fairly unremarkable book.\",\n          \"A vibrant and immersive experience. I felt like I was right there with the characters.\",\n          \"A frustrating and irritating read. I just wanted it to be over.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"One\",\n          \"Two\",\n          \"Four\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "df_reviews.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3oqGHsmrQzx"
      },
      "source": [
        "### *d. Run a quality check on the datasets*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VArQGPoKrfLm",
        "outputId": "6a8e4d40-ea1d-4049-9257-344da5ccbf34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔍 Quality Check Report for: df_reviews\n",
            "===================================\n",
            "\n",
            "📏 Shape: (10000, 5)\n",
            "\n",
            "🔠 Column Types:\n",
            "title               object\n",
            "sentiment_label     object\n",
            "review_text         object\n",
            "rating              object\n",
            "popularity_score     int64\n",
            "dtype: object\n",
            "\n",
            "❓ Missing Values:\n",
            "title               0\n",
            "sentiment_label     0\n",
            "review_text         0\n",
            "rating              0\n",
            "popularity_score    0\n",
            "dtype: int64\n",
            "\n",
            "📋 Duplicate Rows: 0\n",
            "\n",
            "📊 Summary Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    count unique  \\\n",
              "title               10000    999   \n",
              "sentiment_label     10000      3   \n",
              "review_text         10000    135   \n",
              "rating              10000      5   \n",
              "popularity_score  10000.0    NaN   \n",
              "\n",
              "                                                                top  freq  \\\n",
              "title                                        The Star-Touched Queen    20   \n",
              "sentiment_label                                            positive  4380   \n",
              "review_text       A captivating read that offered both escapism ...   112   \n",
              "rating                                                          One  2260   \n",
              "popularity_score                                                NaN   NaN   \n",
              "\n",
              "                   mean       std  min  25%  50%  75%  max  \n",
              "title               NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "sentiment_label     NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "review_text         NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "rating              NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "popularity_score  3.282  1.028874  1.0  3.0  3.0  4.0  5.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d327aeb-fc15-4f2c-91e2-8ef9e55f569e\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>10000</td>\n",
              "      <td>999</td>\n",
              "      <td>The Star-Touched Queen</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment_label</th>\n",
              "      <td>10000</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "      <td>4380</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>review_text</th>\n",
              "      <td>10000</td>\n",
              "      <td>135</td>\n",
              "      <td>A captivating read that offered both escapism ...</td>\n",
              "      <td>112</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rating</th>\n",
              "      <td>10000</td>\n",
              "      <td>5</td>\n",
              "      <td>One</td>\n",
              "      <td>2260</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popularity_score</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.282</td>\n",
              "      <td>1.028874</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d327aeb-fc15-4f2c-91e2-8ef9e55f569e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d327aeb-fc15-4f2c-91e2-8ef9e55f569e 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-8d327aeb-fc15-4f2c-91e2-8ef9e55f569e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"quality_check(df_sales, \\\"df_sales\\\")\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"10000\",\n        \"max\": \"10000\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"10000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 999,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"20\",\n        \"max\": \"4380\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"4380\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.282,\n        \"max\": 3.282,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0288740730458223,\n        \"max\": 1.0288740730458223,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0288740730458223\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 4.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "👀 Sample Rows:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                        title sentiment_label  \\\n",
              "6349  Critique of Pure Reason        negative   \n",
              "7391          The Lonely Ones        positive   \n",
              "2360   The Star-Touched Queen        positive   \n",
              "2294    This Is Where It Ends        positive   \n",
              "9195         The Selfish Gene        negative   \n",
              "\n",
              "                                            review_text rating  \\\n",
              "6349   Honestly, I couldn't finish it. It was that bad.    One   \n",
              "7391  I'm blown away by the creativity and skill. An...   Five   \n",
              "2360  I'm blown away by the creativity and skill. An...   Five   \n",
              "2294  Smart, witty, and utterly charming. A wonderfu...    Two   \n",
              "9195  A predictable and unoriginal story that brings...    One   \n",
              "\n",
              "      popularity_score  \n",
              "6349                 2  \n",
              "7391                 4  \n",
              "2360                 4  \n",
              "2294                 4  \n",
              "9195                 2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bfea5134-a37d-4c64-9011-6f5db6de7a05\" 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>title</th>\n",
              "      <th>sentiment_label</th>\n",
              "      <th>review_text</th>\n",
              "      <th>rating</th>\n",
              "      <th>popularity_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6349</th>\n",
              "      <td>Critique of Pure Reason</td>\n",
              "      <td>negative</td>\n",
              "      <td>Honestly, I couldn't finish it. It was that bad.</td>\n",
              "      <td>One</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7391</th>\n",
              "      <td>The Lonely Ones</td>\n",
              "      <td>positive</td>\n",
              "      <td>I'm blown away by the creativity and skill. An...</td>\n",
              "      <td>Five</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2360</th>\n",
              "      <td>The Star-Touched Queen</td>\n",
              "      <td>positive</td>\n",
              "      <td>I'm blown away by the creativity and skill. An...</td>\n",
              "      <td>Five</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2294</th>\n",
              "      <td>This Is Where It Ends</td>\n",
              "      <td>positive</td>\n",
              "      <td>Smart, witty, and utterly charming. A wonderfu...</td>\n",
              "      <td>Two</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9195</th>\n",
              "      <td>The Selfish Gene</td>\n",
              "      <td>negative</td>\n",
              "      <td>A predictable and unoriginal story that brings...</td>\n",
              "      <td>One</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfea5134-a37d-4c64-9011-6f5db6de7a05')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bfea5134-a37d-4c64-9011-6f5db6de7a05 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-bfea5134-a37d-4c64-9011-6f5db6de7a05');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"quality_check(df_sales, \\\"df_sales\\\")\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"The Lonely Ones\",\n          \"The Selfish Gene\",\n          \"The Star-Touched Queen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"positive\",\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"I'm blown away by the creativity and skill. An instant classic.\",\n          \"A predictable and unoriginal story that brings nothing new to the table.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"One\",\n          \"Five\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔍 Quality Check Report for: df_sales\n",
            "=================================\n",
            "\n",
            "📏 Shape: (18000, 4)\n",
            "\n",
            "🔠 Column Types:\n",
            "title              object\n",
            "month              object\n",
            "units_sold          int64\n",
            "sentiment_label    object\n",
            "dtype: object\n",
            "\n",
            "❓ Missing Values:\n",
            "title              0\n",
            "month              0\n",
            "units_sold         0\n",
            "sentiment_label    0\n",
            "dtype: int64\n",
            "\n",
            "📋 Duplicate Rows: 0\n",
            "\n",
            "📊 Summary Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   count unique                     top  freq        mean  \\\n",
              "title              18000    999  The Star-Touched Queen    36         NaN   \n",
              "month              18000     18                 2024-09  1000         NaN   \n",
              "units_sold       18000.0    NaN                     NaN   NaN  168.024167   \n",
              "sentiment_label    18000      3                positive  7884         NaN   \n",
              "\n",
              "                       std  min   25%    50%    75%    max  \n",
              "title                  NaN  NaN   NaN    NaN    NaN    NaN  \n",
              "month                  NaN  NaN   NaN    NaN    NaN    NaN  \n",
              "units_sold       98.656354  0.0  84.0  148.0  262.0  362.0  \n",
              "sentiment_label        NaN  NaN   NaN    NaN    NaN    NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-05c22ad4-7899-40ef-b2e3-9e8de90ac366\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>18000</td>\n",
              "      <td>999</td>\n",
              "      <td>The Star-Touched Queen</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>18000</td>\n",
              "      <td>18</td>\n",
              "      <td>2024-09</td>\n",
              "      <td>1000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>units_sold</th>\n",
              "      <td>18000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>168.024167</td>\n",
              "      <td>98.656354</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>362.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment_label</th>\n",
              "      <td>18000</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "      <td>7884</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05c22ad4-7899-40ef-b2e3-9e8de90ac366')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-05c22ad4-7899-40ef-b2e3-9e8de90ac366 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-05c22ad4-7899-40ef-b2e3-9e8de90ac366');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"quality_check(df_sales, \\\"df_sales\\\")\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"18000\",\n        \"max\": \"18000\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"18000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 999,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"The Star-Touched Queen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"36\",\n        \"max\": \"7884\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 168.02416666666667,\n        \"max\": 168.02416666666667,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          168.02416666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 98.65635350480927,\n        \"max\": 98.65635350480927,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          98.65635350480927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 84.0,\n        \"max\": 84.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 148.0,\n        \"max\": 148.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 262.0,\n        \"max\": 262.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          262.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 362.0,\n        \"max\": 362.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          362.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "👀 Sample Rows:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                  title    month  units_sold  \\\n",
              "1459   Princess Between Worlds (Wide-Awake Princess #5)  2024-10          91   \n",
              "17737                   Deep Under (Walker Security #1)  2025-04         305   \n",
              "2464                                  The Wedding Dress  2026-01           0   \n",
              "7577                                      Without Shame  2026-02         268   \n",
              "8663                     Future Shock (Future Shock #1)  2025-02         263   \n",
              "\n",
              "      sentiment_label  \n",
              "1459          neutral  \n",
              "17737        positive  \n",
              "2464         negative  \n",
              "7577         positive  \n",
              "8663         positive  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-092fdd04-baf6-4dff-b185-cddf2c84efd8\" 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>title</th>\n",
              "      <th>month</th>\n",
              "      <th>units_sold</th>\n",
              "      <th>sentiment_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>Princess Between Worlds (Wide-Awake Princess #5)</td>\n",
              "      <td>2024-10</td>\n",
              "      <td>91</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17737</th>\n",
              "      <td>Deep Under (Walker Security #1)</td>\n",
              "      <td>2025-04</td>\n",
              "      <td>305</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2464</th>\n",
              "      <td>The Wedding Dress</td>\n",
              "      <td>2026-01</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7577</th>\n",
              "      <td>Without Shame</td>\n",
              "      <td>2026-02</td>\n",
              "      <td>268</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8663</th>\n",
              "      <td>Future Shock (Future Shock #1)</td>\n",
              "      <td>2025-02</td>\n",
              "      <td>263</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-092fdd04-baf6-4dff-b185-cddf2c84efd8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-092fdd04-baf6-4dff-b185-cddf2c84efd8 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-092fdd04-baf6-4dff-b185-cddf2c84efd8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def quality_check(df, name=\"DataFrame\"):\n",
        "    print(f\"\\n🔍 Quality Check Report for: {name}\")\n",
        "    print(\"=\" * (25 + len(name)))\n",
        "\n",
        "    # Basic info\n",
        "    print(f\"\\n📏 Shape: {df.shape}\")\n",
        "    print(\"\\n🔠 Column Types:\")\n",
        "    print(df.dtypes)\n",
        "\n",
        "    # Missing values\n",
        "    print(\"\\n❓ Missing Values:\")\n",
        "    print(df.isnull().sum())\n",
        "\n",
        "    # Duplicates\n",
        "    duplicate_count = df.duplicated().sum()\n",
        "    print(f\"\\n📋 Duplicate Rows: {duplicate_count}\")\n",
        "\n",
        "    # Summary stats\n",
        "    print(\"\\n📊 Summary Statistics:\")\n",
        "    display(df.describe(include='all').transpose())\n",
        "\n",
        "    # Sample rows\n",
        "    print(\"\\n👀 Sample Rows:\")\n",
        "    display(df.sample(5))\n",
        "\n",
        "# Run checks\n",
        "quality_check(df_reviews, \"df_reviews\")\n",
        "quality_check(df_sales, \"df_sales\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTxUKDYINPxV"
      },
      "source": [
        "## **3.** 🎭 Perform sentiment analysis using VADER"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqhYU8rDxQRT"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "DNk5w8mNxSZ6"
      },
      "outputs": [],
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "# 🤖 Initialize VADER analyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P123TwSWxVAr"
      },
      "source": [
        "### *b. Create a function get_sentiment_label that will return the label negative, neutral, or positive based on the VADER analyzer's scoring of the text*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "89809e6f"
      },
      "outputs": [],
      "source": [
        "def get_sentiment_label(text):\n",
        "    score = analyzer.polarity_scores(text)[\"compound\"]\n",
        "    if score >= 0.05:\n",
        "        return \"positive\"\n",
        "    elif score <= -0.05:\n",
        "        return \"negative\"\n",
        "    else:\n",
        "        return \"neutral\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DS9eCZ95yQn3"
      },
      "source": [
        "### *c. ✋🏻🛑⛔️ Apply get_sentiment_label to df_reviews column named review_text to get sentiment_label column*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "SpXzFaDfyM7I"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cnPCFFnyXN6"
      },
      "source": [
        "### *d. ✋🏻🛑⛔️ View the first few lines of the resulting table df_reviews*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "ODGyfjBSyZEO"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qy3Hqm-FojvT"
      },
      "source": [
        "## **4.** 📊 Use the following data visualization code snippets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcjGSw2bzqtZ"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "p5LV2o1rzsiC"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.dates as mdates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvaBtswpGS__",
        "outputId": "2ed629e0-398b-4f4a-8507-abbc34668897"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Output folders:\n",
            " - /content/artifacts/py/figures\n",
            " - /content/artifacts/py/tables\n"
          ]
        }
      ],
      "source": [
        "# ----------------------------\n",
        "# Outputs (for Hugging Face app)\n",
        "# ----------------------------\n",
        "# In the notebook: you still SEE interactive tables/plots inline.\n",
        "# For the Space dashboard: we also SAVE the same outputs as files.\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "ART_DIR = Path(\"artifacts\")\n",
        "PY_FIG = ART_DIR / \"py\" / \"figures\"\n",
        "PY_TAB = ART_DIR / \"py\" / \"tables\"\n",
        "\n",
        "for p in [PY_FIG, PY_TAB]:\n",
        "    p.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "print(\"✅ Output folders:\")\n",
        "print(\" -\", PY_FIG.resolve())\n",
        "print(\" -\", PY_TAB.resolve())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9T1rkBe0AJU"
      },
      "source": [
        "### *b. Sample of 5 books for each popularity level for visualizations*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "sLdFmGqXqo_t"
      },
      "outputs": [],
      "source": [
        "sampled_titles = []\n",
        "for pop_score in sorted(df_reviews[\"popularity_score\"].dropna().unique()):\n",
        "    all_titles = df_reviews[df_reviews[\"popularity_score\"] == pop_score][\"title\"].unique()\n",
        "    sampled = random.sample(list(all_titles), min(5, len(all_titles)))\n",
        "    sampled_titles.extend(sampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xq7-C8m70mMH"
      },
      "source": [
        "### *c. Copy relevant sales, reviews, and book names*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "laDdMece0qrq"
      },
      "outputs": [],
      "source": [
        "sampled_sales = df_sales[df_sales[\"title\"].isin(sampled_titles)].copy()\n",
        "sampled_reviews = df_reviews[df_reviews[\"title\"].isin(sampled_titles)].copy()\n",
        "sampled_books = df_reviews[df_reviews[\"title\"].isin(sampled_titles)].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YtfkG_A0wTy"
      },
      "source": [
        "### *d. Plot sales trends over time for the sampled books*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1iTVzflW0Rkw",
        "outputId": "867cb842-d376-495a-b97c-034e58f6a897"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_379/3725203975.py:22: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
            "  plt.tight_layout()\n",
            "/tmp/ipykernel_379/3725203975.py:24: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
            "  plt.savefig(PY_FIG / 'sales_trends_sampled_titles.png', dpi=150)\n",
            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 🕒 Ensure datetime format\n",
        "df_sales[\"month\"] = pd.to_datetime(df_sales[\"month\"])\n",
        "# 🎨 Color mapping\n",
        "popularity_colors = {\n",
        "    1: \"darkred\", 2: \"orangered\", 3: \"gold\", 4: \"mediumseagreen\", 5: \"royalblue\"\n",
        "}\n",
        "\n",
        "# 📈 Plot 1: Sales trends\n",
        "plt.figure(figsize=(20, 8))\n",
        "for title in sampled_titles:\n",
        "    row = sampled_books[sampled_books[\"title\"] == title].iloc[0]\n",
        "    color = popularity_colors.get(row[\"popularity_score\"], \"gray\")\n",
        "    subset = sampled_sales[sampled_sales[\"title\"] == title]\n",
        "    plt.plot(subset[\"month\"], subset[\"units_sold\"], label=f\"{title} (Pop. {row['popularity_score']})\", color=color)\n",
        "\n",
        "plt.title(\"📈 Sales Trends Over Time (5 per Popularity Level)\")\n",
        "plt.xlabel(\"Month\")\n",
        "plt.ylabel(\"Units Sold\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize='small')\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
        "plt.savefig(PY_FIG / 'sales_trends_sampled_titles.png', dpi=150)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDpMkjDP1K6j"
      },
      "source": [
        "### *e. Plot sentiment_label distribution per book*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dn1Jgd5R1KLu",
        "outputId": "382c79a0-5d13-4c8c-8088-970c6acb9925"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_379/3244541824.py:28: UserWarning: Glyph 128172 (\\N{SPEECH BALLOON}) missing from font(s) DejaVu Sans.\n",
            "  plt.tight_layout()\n",
            "/tmp/ipykernel_379/3244541824.py:29: UserWarning: Glyph 128172 (\\N{SPEECH BALLOON}) missing from font(s) DejaVu Sans.\n",
            "  plt.savefig(PY_FIG / 'sentiment_distribution_sampled_titles.png', dpi=150)\n",
            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128172 (\\N{SPEECH BALLOON}) missing from font(s) DejaVu Sans.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1400 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABMMAAAVtCAYAAAD+iIkRAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdUFNffBvBnlt4RLKAiiIJg710BG2piicbesCaxR43l1QhoojGJiRpiNGrEFrtRo8YaVgWNHURUVCJiwVgQAZUiO+8fZieuFFlE4f54PufsUWan3J1ngN0v996RZFmWQUREREREREREVAyoCrsBRERERERERERE7wqLYUREREREREREVGywGEZERERERERERMUGi2FERERERERERFRssBhGRERERERERETFBothRERERERERERUbLAYRkRERERERERExQaLYUREREREREREVGywGEZERERERERERMUGi2FEREREgnJxcYGLi0thN0MoAQEBkCQJarX6nR87NjYWkiTBz89PZ7m3tzckSXrn7dEKDg6GJEkIDg4utDa8rKi1521Tq9WQJAkBAQGF3ZQCcefOHVhYWGDOnDmF3ZR3ij+P/ze8ixyL28+4gpDd7+6EhATY2Nhg8uTJ+doni2FEREQktMTExNc+UlJS8rSvJ0+eYM6cOahbty4sLS1hYmKC8uXLo0WLFpg2bRpiYmLe8qvR5efnB0mSEBsb+06P+67k9wOBdjvtQ6VSwdraGhUrVkSXLl3www8/ICEh4a20WZIkeHt7v5V9vy05FeGKq1evH0mSYGZmBnd3d4wZMwZ3794t7CYKbfr06TA3N8fYsWN1lmt/nuX0yCvt9fzqw8LCAjVr1kRgYGCef+ZTwcvp+8vDwwMTJkzAgwcPCruJRda7+P2iLSpt2LDhrR7nXbCzs8PYsWOxaNEi3LhxQ+/tDd9Cm4iIiIjemRIlSrx2HS8vr9f2BEpOTkbz5s1x/vx5VK5cGf3794e9vT0ePHiAkydP4quvvkKlSpVQqVKlAmr5mzt06FBhN6FQtW7dGs2bNwcApKSk4Pbt2zh69Ch27twJf39/LF26FD169NDZZvTo0ejduzcqVKjwzttbrlw5XLp0CTY2Nu/82Ln54IMP0LhxYzg6OhZ2UwC8u/a8fP08fPgQhw4dQlBQELZv346zZ8+iVKlSb/X4Wg0bNsSlS5dQsmTJd3K8t+nq1atYvXo1pk+fDktLy2zXGTduHGxtbd/4WJUqVUL//v0BALIs4/79+/jjjz8QEBCAvXv3IjQ0FAYGBm98HMqfl7+/7t+/j3379uH777/Htm3bcObMGdjb2xdyCwtPUfuZK7Lx48dj3rx5+OKLL7Bs2TK9tmUxjIiIiIQXHx8PBweHbJ/bsmULgoKCXruPBQsW4Pz58xg2bBh+/vnnLD0Vrl+/jrS0tAJpb0EpSoW5wtCmTRtMnTpVZ1lmZiZWrVqF0aNHo0+fPrCxsUG7du2U50uWLFloRQcjIyN4eHgUyrFzY2NjU6QKdO+qPa9ePxqNBp06dcKePXsQFBSEwMDAt94GADA3Ny+S10V+/Pzzz9BoNBgwYECO64wfP75AhqFVrlw5y9DStLQ0NGnSBH/99RcOHz6MVq1avfFxKH9e/f7KyMiAr68vQkJC8MMPP/zPDAvOj6L2M1dk9vb26NChA9avX4/58+fD2to6z9tymCQRERERgOPHjwMARo0ale2QnYoVK2b7gfXevXv49NNPUblyZZiYmKBkyZLo3r07Lly4kGVd7VwkKSkpGDduHMqWLQsTExPUrFkTW7ZsybLuqlWrlGNrh5u8PIQiu7lNXp5XY+XKlahRowbMzMxQsWJFLFq0CMCLXhTz589HlSpVYGpqCjc3N6xevTrb85Keno7vvvsOdevWhYWFBaysrNCiRQvs3Lkzy7raYVDXr1/HokWL4OHhARMTEzg7OyMwMBAajUZn3cGDBwMABg8enK/hUtkxMDDAkCFD8NNPPyEzMxMTJkyALMvZnp+XhYSEoEOHDkomZcqUQYsWLfDzzz8D+G9eJwA4fPiwTnu1wzxfHvb5+++/o1mzZrCyslIyet1wxdTUVEydOhUVKlSAqakpPD098cMPP+i0P7fX8GobtF9XrFgRALBq1Sqddmu3z224alhYGN577z3Y2dnB1NQUHh4e8Pf3x9OnT7Osq70+//nnHwwaNAglS5aEmZkZGjdurNccbTm1p6D2nxOVSqVkc+bMmSzP5/V7vXLlyrCyssr2HAFA586dIUkSrly5AiD3OcPyeswPPvgAKpUK9+/f11leu3ZtSJKEGTNm6CzXnmPtzxjgRa+uwYMHo2LFijAxMYGdnR1q1aqF8ePHZ7kGs6PRaLBq1SrUrl0bbm5ur13/bTAxMYGPjw8AZDsc78KFC+jZsydKly4NExMTVKxYEePHj8fDhw+z3Z++62fnu+++g0qlQuvWrZGcnAwAePz4MWbOnImqVavC0tIS1tbWqFy5MgYNGpSn4V4vXzOhoaHw9vaGlZUVbG1t0b17d1y7di3b7fLz+yoxMRGjR4+Gk5MTDA0N8z3PlZGRET766CMAwKlTp5TlDx48wPjx45XrrnTp0ujZs2e2bdL+jvn777/x9ddfw83NDaampqhYsSJmzZqFjIwMnfVz+9mmz1x9d+7cgb+/Pxo3bqxcCy4uLhg5ciTu3buXazvnz5+PqlWrwsTERPn58mq7Xvf7Zfny5ZAkCV9//XW27fvzzz8hSZJyfgvS+fPn0bt3bzg6OsLY2BjOzs4YM2aMzvfAjRs3oFKpciw+Z2RkoGTJknByctJ5H6DP+4vc9OzZE0+ePMHmzZv12o49w4iIiIgAZcjGlStXULt27TxtExMTA29vb9y6dQvt2rVD165dce/ePWzduhX79u3DoUOH0KhRI51tMjIy0K5dOzx69Ajdu3fH06dPsWHDBvTs2RN79+5VejGNHz8ewcHBiIiI0BlWlNceFQsWLIBarUaXLl3QqlUrbN26FePGjYO5uTnOnTuHrVu34v3330fr1q2xYcMGDBo0CC4uLmjZsqWyj7S0NLRv3x5qtRq1a9fG0KFDkZGRgd27dytzc40ePTrLsT/77DMcPnwY77//Pnx9fbF9+3YEBAQgPT0dX375JQCga9euSExMxI4dO9ClS5c8n/O8GjBgAPz9/REVFYULFy6gRo0aOa67e/dudOrUCba2tujSpQscHR1x//59REREYM2aNRgxYgRcXFzg7++PwMBAODs76xS0Xm375s2bsX//frz//vsYOXIkkpKS8tTmnj174ty5c+jevTsAYOvWrRg7dixiY2Mxf/58vc+Btm3jxo3DwoULUatWLXTt2lV57nXX0ubNm9GnTx+YmJigV69eKF26NPbv349Zs2Zh3759UKvVMDU11dkmMTERzZs3h42NDQYMGIB79+5h48aN8PX1xZkzZ1C9evV8vY53tX8tQ0Pdj0n6fK/3798fgYGB2L59O/r27auznwcPHmDv3r1o1KgR3N3dc22DPsf08fHB9u3boVarlaHBDx8+xPnz5wG8KPa+TPu1tnB0584dNGzYEE+ePMF7772HXr164cmTJ7h69SoWL16Mb7/9Nss5eVVkZCTu37+vXL852bVrF5KTk2FiYgJPT0+0bt0axsbGuW6TV+np6Uph4dXvy9DQUPj6+iI9PR0ffvghXFxccPz4cSxcuBC7du3CX3/9pdNrVN/1XyXLMqZMmYJvvvkGPXr0wNq1a2FsbAxZluHr64sTJ06gWbNmaN++PVQqFW7cuIGdO3diwIABcHZ2ztPr/euvvzB37ly0b98eY8aMQVRUFH777TccPXoUf/31F1xdXZV18/P7Ki0tDa1atUJKSgo6d+4MQ0NDlClTJk9ty4228HP//n00adJEaVvv3r1x/fp1bNmyBbt378a+ffuUoZYvGz9+PMLCwtCzZ09YWlri999/h7+/P86fP5/lD0sF4ciRI5g/fz5at26NRo0awcjICOfOncNPP/2Effv24ezZs9n29BozZgz++usvvPfee+jUqRNKly6d7f5f9/vFzc0NEydOxIoVK7KdLF47PHD48OEF84L/tXPnTvTs2RMqlQpdunSBk5MTLl68iKCgIOzbtw8nTpxAiRIl4OzsjJYtW+Lw4cO4desWypcvr7OfPXv24OHDh5gyZQpUqhf9sfL7/iI7TZo0AfBi6oihQ4fm/QXKRERERAIDIMfHx+f4/ObNm2UvL6/X7mfHjh0yANnKykqeOHGivG/fPvnBgwe5btO0aVPZwMBA3rt3r87y6Oho2crKSq5Ro4bOcmdnZxmA3KVLFzktLU1ZfvDgQRmA7Ovrq7P+oEGDZADy9evXsz2+s7Oz7OzsrLPM399fBiDb2dnJMTExyvK4uDjZ2NhYtrGxkd3d3eV79+4pz/31118yALlTp046+/q///s/GYD8+eefyxqNRlmelJQk169fXzY2NpZv376dpb0VK1aU79y5oyy/f/++bGtrK1tZWem87pUrV8oA5JUrV2b7+nKi3W7u3Lm5rjdgwAAZgLxixQplmfb8hISEKMu6desmA5DDw8Oz7OPVawBAjteTtl0qlUo+cOBAluevX78uA5AHDRqks9zLy0sGIFepUkVOTExUlicmJspVqlSRJUmST506letreLUNL5/TnI6b2zaPHz+WbWxsZBMTEzkiIkJZnpmZKffq1UsGIM+aNUtnPwBkAPLIkSPlzMxMZfny5ctlAPJHH32U7fHz0p63sf9Xr5/MzEy5Q4cOMgD5m2++0XlOn+/1q1evygDkDh06ZDn2Dz/8IAOQg4KClGUhISEyANnf3z/fxzx//rwMQP7kk0+UZVu3bpUByK1bt5aNjIzklJQU5TknJyfZ1dVV+XrRokUyAHnBggVZ2vzw4cMsy7Lz448/ygDkZcuWZfu89ufDqw9HR8csrzE32uu5UqVKsr+/v+zv7y/PnDlTHjlypFypUiXZ1NQ0S36ZmZlypUqVZABZjvXZZ5/JAOQhQ4bke31Z1v15nJGRIQ8cOFAGII8aNUrnetVm1bVr1yyvLTU1VU5OTn7tOdBeMwDkJUuW6Dy3ZMkSGYD8/vvv6yzP7+8rX19f+enTp69tk1ZO318ZGRlyq1atZAByYGCgLMuyPHjwYBmAPG3aNJ11d+/eLQOQK1eurHPutNdQqVKl5Js3byrL09LS5JYtW8oA5C1btmRpS3a/Y3L6vsvu9+o///yTbS6rVq2SAchffPGFznJtO8uXLy/fuHEjx3OU3c+4nH6/fPLJJzIAWa1W6yx/+PChbGJiIteuXTvb7V6l/f2xfv36XNd78OCBbG1tLZcrV06OjY3VeW79+vUyAHn06NHKMu3P4Xnz5mXZV/fu3WUA8oULF5Rl+r6/yO33nizLcokSJeQKFSrk+ppexWGSRERERHgxdGn+/PnKEEJfX1+ULFkSlStXxujRo3H16lWd9c+dO4djx45h0KBB8PX11XnO3d0dw4cPR2RkZLZDPb7//nudnhCtW7eGs7OzztCRNzVu3DidXgFOTk5o3rw5Hj9+jOnTp+tMDt6oUSO4uroiIiJCWabRaPDTTz+hUqVKCAwM1Bm+aGVlhZkzZyI9PR3btm3LcuzPP/9cZ2LgkiVLokuXLkhOTkZ0dHSBvcbXKVu2LIDsh0tlx8zMLMuy/Ezy3KVLF7Rp00bv7T7//HOd3gU2NjaYMWMGZFnWGc72LuzYsQOPHz/GkCFDULNmTWW5SqXC119/neNwKQsLC8ybN0/56z8ADBo0CIaGhgVyfRfk/g8ePIiAgAAEBARg7NixqF69Ov744w80bdoUn3zyibKevt/rlStXRpMmTXDgwIEsQ6jWrFkDIyMj9OrVK9e26XvM6tWro2TJkvjzzz+V9UJCQmBpaYnJkycjIyMDR48eBfCih9DNmzezvWtddt8DdnZ2ubZV69atWwCQY8+hli1bYtOmTYiLi8OzZ89w9epVzJo1C4mJiejcuTNOnz6dp+NoxcTEIDAwEIGBgZg1axYWL16MmJgYtGnTJsv3X1hYGGJiYtChQ4cs53PmzJmws7PDr7/+ivT09Hyt/7KnT5+iS5cuWL16NQIDAxEUFKRzvWpld65NTExyvPFAdrTXwsuGDx8ONzc37N69Wxk2+ya/r77++uts2/o6L39/jRkzBlWrVsWff/6JihUrYvTo0UhPT8f69ethb2+fZRhvx44d0bZtW1y7dg1hYWFZ9j1u3Did3kfGxsZKr+P8DuPMTenSpbPNZcCAAbC2tsbBgwez3e6zzz4rsJu1fPzxxwCA5cuX6yxfs2YN0tLSCrxX2OrVq5GUlIS5c+dm6anYu3dv1K1bV+eOlB9++CFMTU2xdu1anXUTExOxa9cu1K5dG9WqVQPwZu8vclKmTBncuXMnT0O6tThMkoiIiOhfEyZMwPDhw7F3714cO3YMp0+fxokTJ/Djjz9ixYoV2LhxIzp37gzgxfAUAPjnn3+ynXPk8uXLyr8vD92ytbVV5nB6Wfny5ZV5ywpCdsMOtQWqnJ47ceKE8nV0dDQePXqEsmXLZjuRuPZDlvZ1vqxevXpZlmk/uCQmJual+e9U7969sW3bNjRu3Bh9+/ZF69at0aJFi3xPtN+wYcN8bdeiRYscl507dy5f+8wv7fGyK5hUqFABrq6uuHLlCpKTk2FlZaU85+7unuVDo3ZoVUFkX5D7P3ToUJY7sjZr1gyHDh2CiYmJsiw/3+sDBgzA8ePHsX79eowbNw7Aizm5Tp48iU6dOr322tL3mNr51LZs2YL4+Hg4OjoiJCQELVq0QMuWLWFiYoKQkBC0b98+yxBJAOjUqROmTZuGUaNG4dChQ2jfvj28vLx0Cuqvo51DKKc7RQ4ZMkTn68qVK+Pzzz9HuXLlMHToUMyaNUuvuYJ8fX2xd+9eneOHhYVh3LhxaNasGf78809l2F9u17OlpSXq16+P/fv3Izo6GjVq1NB7fa1nz56hdevWOHnyJJYsWZLtHE6enp6oWbMm1q9fj1u3bqFr167w9vZG7dq1sy2a5aZZs2ZZtlGpVGjWrBmuXr2KiIgItGnTJt+/r0xNTXMdYp6bl7+/tHNsTZgwAdOmTYOdnR3Onz+P1NRU+Pj4wNzcPMv2Pj4+OHDgAMLDw7P8bMzuZ2WTJk1gaGj41n5Wbtu2DUuXLsXZs2fx6NEjZGZmKs/duXMn223y+7sgOzVr1kTjxo2xZcsW/PDDD8r32YoVK2Bubo5+/foV2LGA/34GnThxAjExMVmeT01NxYMHD/DgwQOULFkSNjY26Ny5MzZt2oSIiAjUqlULwIvh9mlpaTo31XiT9xc5sbOzw/Pnz5GYmJinu4wDLIYRERER6bCyskKPHj2UeXceP36M//u//8PixYsxdOhQ3L59G8bGxkhISADwYr6p3bt357i/J0+e6Hyd0x2kDA0NdSaWfVPZ3VFJO+dPTs89f/5c+Vr7+qKiohAVFZXjcV59fa879ssfIN427QeUl3vBZadHjx7Yvn07vvvuOyxZsgQ//vgjJEmCj48P5s+fr/d8ZvmdUye77bTLHj9+nK995pd2nrOcXoujoyOuXLmCpKQknWJYTnfyMjQ0LJDsC3L/c+fOxdSpU6HRaBAbG4uAgACsWbMGw4cP17mhRH6+13v16oXx48dj7dq1SjFszZo1AJDrnRbf5Jg+Pj7YsmULQkJC0LZtW0RFRcHPzw+mpqZo0qSJUgTLrhjm4uKCv/76CwEBAdizZw82bdoEAPDw8MCsWbOUn4e50fYeSk1Nfe26Lxs0aBBGjRqVbQ8gfdjb26Nz584wNzdH27ZtMWPGDBw4cABA3q7nl9fTd32t5ORknDt3Dvb29jrn92WGhob4888/ERAQgK1bt2LixIkAXvycGj16NKZPnw4DA4M8veac2vfqz438/r4qXbp0vm9qov3+ykl+z3FO2xgYGMDe3v6t/KycP38+Jk2ahFKlSqFdu3YoX768cr0vWLAgxztNF8T8ai/76KOPMHjwYKxduxajR4/GiRMnEBkZiUGDBhX43Sm118yPP/6Y63pPnjxRivsDBgzApk2bsHbtWqUYtmbNGhgYGOjMn/gm7y9y8uzZMwDItrCaEw6TJCIiIsqFjY0NgoKC4OzsjAcPHiAyMhLAfx/KtXf7y+kxaNCgwmx+vmlfX/fu3XN9fStXrizklmZPo9HgyJEjAIAGDRq8dv0uXbrg8OHDePToEf744w8MGzYMarUa7du317vHUX4/PP7zzz85Lnv5g462J8jLxUutgvogqM0/uzYBwN27d3XWE5lKpYKrqytWrVqFli1bYs2aNdi+fbvyfH6+1+3s7NCxY0ecPn1aGRq8du1a2NjYoFOnTq9tU36OqS2+hISEKHfX1C7z8fHB2bNn8fjxY6jVari5uaFcuXI6x6xevTq2bNmChIQEHD9+HDNnzsTdu3fRq1evPBWqtEVn7QfdvDIwMICtra1eH3xzo+0N9vKwWX2v5/xe/6VLl8aOHTuQnJwMb2/vHIeF29vb44cffsDt27eVCcnt7Ozg7++f4x0Ds5NT+179uZHf31dvenff3LzJz5jstsnMzMTDhw8L/Gfl8+fPMXv2bDg6OuLChQtYt24d5s2bh4CAAPj7+2c7VFaroM9fr169YGtrqwyV1P5b0EMkgf/Oe2RkZK7XzMtDKNu3b49SpUph/fr1yh8ZQkND0aZNGzg4OGTZd0G+v0hISICVlZVOr97XYTGMiIiI6DUkSYKFhYXOMu0HroIc2vgqbe+Ad9mbSsvT0xPW1tY4ffp0ltvVF5S3+frWrFmDGzduoEaNGso8JXlhZWWF9u3b4+eff4afnx/++ecfneGjKpXqreWhndMpu2V16tRRlmmHgNy+fTvL+tkNEcrPedYeT1tUednNmzcRExMDV1dXnV5hopMkCQsXLoQkSZg2bZrSUzO/3+vaHmBr165FWFgYrl+/rsyr8zr5OaanpyccHBzw559/IiQkBCVKlFBybNWqFTIzM7F8+XLcuXMn2+F/WkZGRmjcuDECAwOxaNEiyLKMXbt2vfb42uF0+s4LGBcXh7t37+b5Trmv8+jRIwDQ6Wmb2/X85MkTnD59GmZmZqhSpUq+1n+Zr68vdu7cicTERPj4+OR6PiRJgqenJ0aNGqX0YtNnqGhYWFiWHsUajQbHjh2DJElK75x38ftKXx4eHjA1NcWpU6fw9OnTLM9rz312PXOz+1l5/PhxPH/+/I1+VmbnwYMHePz4MZo0aZLlbpCnT59WeiQVhNf9fjEzM8PAgQMRERGBkJAQbNy4EZ6enmjWrFmBtUErP9eMoaEhevfujdu3byMkJATr1q2DLMvo37+/znoF/f7iyZMnuHXrlt5DelkMIyIiIgKwdOnSHCfg3r59Oy5dugRbW1tlPpWGDRuiUaNGWL9+PTZu3JhlG41Gg8OHD79Rm7QTV9+8efON9pMfhoaG+OSTT3Djxg1MmjQp2zesFy5cyDJBuD7exuvLzMzEypUr8cknn8DAwADffffda/86f+TIkWw/gGhf28vFCzs7O2Wi8II2e/Zsnd4Kjx8/xhdffAFJknR6bGh7uq1evVrng/Dx48exbt26LPstUaIEJEnS6zx36dIFNjY2WLlypc4wFlmWMWXKFDx//hx+fn76vDwh1K5dG127dsXly5eVc5nf7/X33nsPJUqUwLp165Rhl3kZIvkmx/T29sbff/+NLVu2wMvLS+kZ07BhQ5ibm2PevHkAkGUI35kzZ7IdjqbtgZOXAl6LFi2gUql0isdad+/ezbYgkZiYqFxHLw+jehPfffcdgBcT9ms1a9YMlSpVwh9//JFlsvMvvvgCDx8+RJ8+fZQbm+i7/qvatm2L33//HYmJifD29taZ+yg2NhaxsbFZttHnXGtduXIFy5Yt01m2bNkyXLlyBe+9957SW+9d/L7Sl7GxMfr06YMHDx5g7ty5Os/t3bsX+/btQ+XKlbMt9CxcuFDn53B6ejqmT58OADo/l+rVqwdJkrBhwwad4btXr17FwoUL89TO0qVLw8zMDGfPntUp2j169AhjxozJ0z7yKi+/X7Tz0PXv3x/JyclvpVcYAAwePBhWVlaYPn16tkMZnz59qswr9jLtz7g1a9ZgzZo1sLCwwAcffKCzTkG/vzhz5gwyMzPh5eWVp/WVdui1NhEREdH/qD/++AMff/yx8ua7bNmyePLkCc6dO4ejR49CpVJh8eLFOl3w169fDx8fH/Tu3RsLFixA3bp1YWZmhri4OBw/fhz379/Xe/6cl7Vq1QrffvstRowYge7du8PCwgLOzs55/kD9pgIDA3H27FksWrQIu3fvRsuWLVG6dGncvn0bkZGRiIiIwPHjx7P8tTyvmjRpAjMzMyxYsACPHj1SPri9emexnBw8eFA5v0+fPsWtW7dw5MgR3L59G3Z2dlizZk2e7uo4duxY3LlzB82bN4eLiwskSUJoaChOnjyJxo0bo3nz5sq6rVq1wqZNm9C1a1fUqVMHBgYG6Ny5s84dF/PL3d0d1atXR/fu3QEAW7duxa1btzBhwgTUr19fWa9x48bKBOFNmjRBy5YtcePGDezYsQOdOnXCb7/9prNfS0tLNGjQAEeOHMGAAQPg5uYGlUqFAQMGZLlLmJa1tTWWLVuGPn36oFGjRujVqxdKlSqFgwcP4syZM2jYsCE+++yzN37NRZG/vz+2b9+OWbNmoU+fPjA0NMzX97qJiQl69uyJpUuXYuXKlXB2dtYp0LxOfo7p4+ODDRs24P79+zoFL2NjYzRr1kzpffRqz7A1a9Zg6dKlaNmyJSpVqgRra2tcvHgRe/bsgZ2dHQYPHvza9pYoUQJeXl4IDQ1FamqqTlHn8uXLaNu2LZo2bQo3NzeUKlUKN2/exN69e/Hw4UO0atUKkydPzvO5AYBr167pTAafkJCAsLAwnD17FiVKlFAKf8CLHjfBwcHw9fVFx44d0aNHDzg7O+P48eNQq9WoVKkSvvrqq3yvn53WrVtj165d6NSpE3x8fPDnn3/C09MT4eHh6NatGxo2bIiqVavCwcEBt2/fxvbt26FSqfDpp5/m+Rz4+vpi7Nix2LNnD6pVq4aoqCj8/vvvKFmyZJZiz9v+fZUf8+bNw+HDh/HFF1/g2LFjaNSoEWJjY7F582aYm5tj5cqV2d5UoHHjxqhVqxZ69eoFCwsL/P7774iOjka3bt2Un5/AizsK9+nTB7/++ivq1auH9u3b4969e/jtt9/Qvn17bN269bVtVKlUGDlyJObPn49atWqhU6dOSEpKwh9//AFnZ2flrsUFIS+/X6pWrYoWLVrg6NGjMDExwcCBA/N1rJ9++knnBhQvGzZsGJo3b47169ejR48eqFWrFtq3bw8PDw+kpaUhNjYWhw8fRtOmTbPso0GDBqhSpQp+/fVXZGRkYMCAAVl61gMF+/5C+3Ota9eu+p0EmYiIiEhgAOT4+Pgcn9+8ebPs5eX12v1cvnxZ/vrrr+W2bdvKFStWlE1NTWVTU1O5UqVK8qBBg+TTp09nu11CQoI8Y8YMuXr16rKZmZlsaWkpu7m5yX379pW3bdums66zs7Ps7Oyc7X68vLzk7N6aff3117Kbm5tsZGQkA9B5Ldntz9/fXwYgh4SEZNnXoEGDZADy9evX83z858+fy0uXLpWbNWsmW1tbyyYmJnKFChXk9u3byz/99JOckpKSp/3n1K7du3fLDRo0kM3MzGQA2bbhVStXrlTWBSBLkiRbWlrKLi4ucqdOneQffvhBTkhIyHbb7NqxYcMGuWfPnnKlSpVkc3Nz2cbGRq5Vq5Y8b948OTk5WWf7+Ph4uWfPnnLJkiVllUolA5BXrlyp0y7t16+6fv26DEAeNGiQznLtuX/27Jk8efJk2cnJSTY2NparVKkiL1q0SNZoNFn29eDBA3ngwIGynZ2dbGZmJjdu3Fjet29fjm2Ijo6WO3bsKNva2sqSJOmcg9zafeTIEblDhw6yra2tbGxsLLu7u8uff/65Tu5ar16fL8vt2n9VTu0p6P3PnTs3x3W6d+8uA5BXrFihLNPne10rNDRUuU6nTZuW7TohISEyANnf3z/Lc/oe88qVK8rxIiMjdZ6bM2eODECuUqVKlu3++usv+aOPPpKrV68u29raymZmZrKbm5s8evRo+caNGzmep1dt3LhRBiBv3LhRZ3lcXJw8bNgwuVatWrK9vb1saGgo29rayi1btpSXLFkiP3/+PM/H0H4fvfowMTGRK1WqJH/yySc5tvn8+fPyhx9+KJcsWVI2MjKSnZ2d5XHjxsn3799/4/VzugZDQkJkCwsLuUyZMnJUVJR88+ZNeerUqXLjxo3l0qVLy8bGxnKFChXkbt26ycePH8/TOXj5mjl69Kjs5eUlW1hYyNbW1vIHH3wgX716NdvtCur3VW7y8v31svv378tjx46VnZ2dZSMjI7lkyZLyhx9+mOX6leX/fsfExMTIX331lVy5cmXZ2NhYdnZ2lgMCAuS0tLQs2zx9+lQeO3asXKZMGdnExESuWbOmvG7duhy/77J73enp6fKXX34pu7m5Kb8DJ06cKCcnJ2e7fm6/C18+R6/+jMvt98vLli9fLgOQe/fune3+c6P9HZjb4+VjXr58WR46dKjs7OwsGxsbyyVKlJBr1Kghjx07Vj558mS2x/jiiy+Ufe3bty/Htujz/iK39zYVK1aUa9eurfe5kGRZlvUrnxEREREVHZIkIT4+Xmdy1pdt2bIFQUFB2c79QkREBScjIwNVqlRBpUqVlN4aVPDUajV8fHzg7++v0zvuf52fnx9WrVqF69evF9gccyIaPXo0fvzxRxw6dAitWrUq7OYUqoMHD6Jt27ZYtWqV3r3kOGcYERERERERvTEjIyPMnTsXBw8exLFjxwq7OUT/c+7fv49Vq1ahSpUqWeb+K44CAwNRu3btLJP05wXnDCMiIiLhOTo65vq8vpOqEhFR/vTq1QtxcXF4+PBhYTeF6H/G7t27cfbsWWzZsgUpKSkICAh47c1h/tclJCSgdevW6NSpU7Zzy70Oi2FEREQktEePHr12HUNDvuUhInpX/ldvrkBUWDZv3oxVq1ahbNmymDNnDnr37l3YTSp0dnZ2bzRMmHOGERERERERERFRscE5w4iIiIiIiIiIqNjgmAEiIiKiPNJoNLhz5w6srKyK/VwdRERERDmRZRnJyckoW7Zsvub0ettYDCMiIiLKozt37sDJyamwm0FEREQkhJs3b6J8+fKF3YwsWAwjIiIiyiMrKysAQGxsLEqUKFHIrSF9ZWZmIioqCtWqVYOBgUFhN4f0xPzExwzFxvzE9q7zS0pKgpOTk/LeqahhMYyIiIgoj7RDI62trWFtbV3IrSF9ZWZmwtLSEtbW1vwgJyDmJz5mKDbmJ7bCyq+oTivBu0kSERER5VFSUhJsbGyQmJgIGxubwm4O6UmWZWg0GqhUqiL75pxyxvzExwzFxvzE9q7z075nevz4cZH8A2LRm8WMiIiIiOgtSU9PL+wm0BtgfuJjhmJjfmJjfv9hMYyIiIhITxqNprCbQPmg0WgQHR3N/ATF/MTHDMXG/MTG/HSxGEZERERERERERMUGi2FERERERERERFRssBhGRERERMUG74AmNuYnPmYoNuYnNub3H95NkoiIiCiPivqdkYiIiIiKgqL+nok9w4iIiIj0xL8likmWZSQlJTE/QTE/8TFDsTE/sTE/XSyGEREREemJd2ISk0ajwd9//838BMX8xMcMxcb8xMb8dLEYRkRERERERERExYZhYTeAiIiISDTdptyCZJhc2M0gPRmoNPDySMFny28iU8O/CYuG+YmPGYqN+YnNQKXBN8MKuxVFB69gIiIiIn1xug0xycCTNCPmJyrmJz5mKDbmJzYZMDU1LexWFBnsGUZERESkp0xZxTdRAsqUVTgR41jYzaB8Yn7iY4ZiY35iy5RV8PDwKOxmFBnsGUZERESkJ0nin8VFJEkyytqmMD9BMT/xMUOxMT+xSZKMhw8fcgL9f7EYRkRERKQnFT8ICEklyfAom8D8BMX8xMcMxcb8xKaSZNy8eROyzPwAFsOIiIiIiIiIiKgYYTGMiIiIiIiIiIiKDRbDiIiIiPTFEQZikoGEFFPmJyrmJz5mKDbmJzYZsLKyKuxWFBm8ERIRERGRnng3STFlyiqEx5Uu7GZQPjE/8TFDsTE/sWXKKlSq5FLYzSgy2DOMiP6n6DshpFqthouLy9tpzP8wb29vjB8//o33c+jQIXh6eiIzM/ON9xUbGwtJkhAeHg7gRbaSJCExMfGN911c+Pn5oWvXrnpt4+LiggULFryV9rxNDx48QOnSpXHr1q18bc87aYlJkmRULPWY+QmK+YmPGYqN+YlNkmTcvXuXd5P8F4thRFSgJEnK9REQEPBWj//zzz8X+D7zUyAoKiRJwvbt25WvMzIy0KdPH5QrVw4XLlwovIb9a/LkyZgxYwYMDAwAAMHBwdleN8uXLy/klubu7t27GDNmDFxdXWFiYgInJyd06tQJhw4deqvHzWshKqf1AgICULt2beXrhQsXIjg4uMDalxf379+HsbExnjx5goyMDFhYWCAuLk5nHW9v7yzXxMcff5zrfv38/LJs0759e+X5kiVLYuDAgfD3989Xu3knLTGp/v0gx/zExPzExwzFxvzEpvq3GMa7Sb7AHv5EVKDi4+OV/2/cuBEzZ85EdHS0sszS0vKtHTsiIgLz5s3DRx999NaOIbKnT5+ie/fuuHr1KkJDQ1GxYsVCbU9oaChiYmLQvXt3neXW1tY61wwA2NjYvMum6SU2NhbNmjWDra0tvvnmG9SoUQMZGRnYt28fRo0ahcuXL2e7XUZGBoyMjN5xa3NXGOf5+PHjqFWrFiwsLHDixAnY2dmhQoUKWdYbPnw4Zs2apXxtbm7+2n23b98eK1euVL42MTHReX7w4MGoV68evvnmG9jZ2b3BqyAiIiIikbBnGBEVKAcHB+VhY2MDSZLg4OAAMzMzlCtXTikMaDQa2NnZoXHjxsq2a9euhZOTk/J1ZGQkWrVqBTMzM9jb22PEiBFISUnJ8dhbtmzB9evXcfbs2bf3ArNx+PBhNGzYECYmJnB0dMTUqVPx/Plz5fnk5GT069cPFhYWcHR0xPfff59lmOGaNWtQv359WFlZwcHBAX379sW9e/eU57VD/g4dOoT69evD3NwcTZs2zVI0ykliYiLatm2LO3fu6BTCHj58qPQUMzc3R40aNbB+/XqdbZ88eYKBAwfC0tISjo6OmD9/fpb9p6WlYdKkSShXrhwsLCzQqFEjqNXqXNu0YcMGtG3bFqampjrLtdfMyw8zMzPs3bsXzZs3h62tLezt7fH+++8jJiYmT69fKzQ0FC1atICZmRmcnJwwduxYPHnyRHnexcUFc+bMwZAhQ2BlZYUKFSq8trfhyJEjIUkSTp48ie7du8Pd3R3VqlXDhAkT8Ndff+m8rp9++gmdO3eGhYUFvvzySwDAjh07ULduXZiamsLV1RWBgYHK9SPLMgICAlChQgWYmJigbNmyGDt2LIAXvaVu3LiBTz/9VOn59KZe7QWZl2sXeFFo1eecvezYsWNo1qwZgBf5aP//KnNzc51rwtra+rX7NjEx0dmmRIkSOs9Xq1YNZcuWxW+//ZbjPtLS0pCUlKTzICIiIiKxsRhGRO+EjY0NateurRRIIiMjIUkSzp07pxS4Dh8+DC8vLwAvCjC+vr4oUaIETp06hc2bN+PgwYMYPXq0ss/w8HCEhoYqj82bNwMAlixZorP85s2bb+113b59Gx07dkSDBg0QERGBn376CStWrMAXX3yhrDNhwgSEhYVh586dOHDgAI4ePZqlYJeRkYHZs2cjIiIC27dvR2xsLPz8/LIcb/r06Zg/fz5Onz4NQ0NDDBky5LVtvHv3rnJeDx8+DAcHB+W51NRU1KtXD7t378aFCxcwYsQIDBgwACdPnlTW+eyzz3D48GHs2LED+/fvh1qtztL+0aNH4/jx49iwYQPOnz+PHj16oH379rh69WqO7Tp69Cjq16//2vZrPXnyBBMmTMDp06dx6NAhqFQqfPDBB3me9yAmJgbt27dH9+7dcf78eWzcuBGhoaE61xQAzJ8/H/Xr18e5c+cwcuRIfPLJJzkWHRMSErB3716MGjUKFhYWWZ63tbXV+TogIAAffPABIiMjMWTIEBw9ehQDBw7EuHHjcPHiRSxduhTBwcFKoWzr1q34/vvvsXTpUly9ehXbt29HjRo1AADbtm1D+fLlMWvWLMTHx+v0yiwoebl2Af3OGQDExcXB1tYWtra2+O6777B06VLY2tri//7v/7B9+3bY2tpi5MiROtusW7cOJUuWRPXq1TFt2jQ8ffr0te1Xq9UoXbo0qlSpgk8++QQPHz7Msk7Dhg1x9OjRHPcxd+5c2NjYKA9twV6W37z4SO+eLEu4k2jB/ATF/MTHDMXG/MQmyxLs7OwK5A+o/ws4TJKI3hlvb2+o1WpMmjQJarUabdu2xeXLlxEaGor27dtDrVZj8uTJAIBff/0VqampWL16tVJkCAoKQqdOnTBv3jyUKVMGpqam8PPzQ0REhM5xli1bhmXLlgF4MQwqKCjorb2mxYsXw8nJCUFBQZAkCR4eHrhz5w6mTJmCmTNn4smTJ1i1ahV+/fVXtG7dGgCwcuVKlC1bVmc/Lxe1XF1dsWjRIjRo0AApKSk6Q0u//PJLpbA1depUvPfee0hNTc3Su+pl48aNg6urKw4cOJBlaFm5cuUwadIk5esxY8Zg37592LRpExo2bIiUlBSsWLECa9euVdq/atUqlC9fXtkmLi4OK1euRFxcnPK6Jk2ahL1792LlypWYM2dOtu26ceNGlvMAAI8fP9Z5zZaWlrh7926W4ZS//PILSpUqhYsXL6J69eo5vn6tuXPnol+/fkqvJjc3NyxatAheXl746aeflHPYsWNHpRAzZcoUfP/99wgJCUGVKlWy7PPatWuQZRkeHh6vPT4A9O3bF4MHD1a+HjJkCKZOnYpBgwYBeJH97NmzMXnyZPj7+yMuLg4ODg5o06YNjIyMUKFCBTRs2BAAYGdnBwMDA6U34etMmTIFM2bM0FmWnp6OqlWrZrt+cnJynq5dQL9zBgBly5ZFeHg4kpKSUL9+fZw4cQIWFhaoXbs2du/ejQoVKuhcA3379oWzszPKli2L8+fPY8qUKYiOjsa2bdtyfL3t27dHt27dULFiRcTExOD//u//0KFDBxw/flyZo07blnPnzuW4n2nTpmHChAnK10lJSXBycoJGlvgXRQFpZAmX79gXdjMon5if+Jih2Jif2DSylO1UFMUVi2FE9M54eXlhxYoVyMzMxOHDh9GuXTs4ODhArVajZs2auHbtGry9vQEAly5dUuYR0mrWrBk0Gg2io6NRpkwZeHh44MSJE5g4cSJ+/PFHnWNZWVlhyZIl6Nu371t9TZcuXUKTJk10/sLSrFkzpKSk4NatW3j06BEyMjKUAgbwopfcq0WCM2fOICAgABEREXj06JHS2ykuLk6nWFGzZk3l/46OjgCAe/fu5fqL7f3338f27duxdOlSfPrppzrPZWZmYs6cOdi0aRNu376N9PR0pKWlKUWzmJgYpKeno1GjRso2dnZ2Ou2PjIxEZmYm3N3ddfadlpYGe/uc3zA9e/Ys2yKelZWVTu8jlepFyeHq1auYOXMmTpw4gQcPHuico7wUwyIiInD+/HmsW7dOWSbLMjQaDa5fvw5PT08AuudYO2Tz5SGrL9N3AtJXe8JFREQgLCxM6QkGvMgkNTUVT58+RY8ePbBgwQK4urqiffv26NixIzp16gRDQ/1/fX/22WdZehsuWrQIR44cyXb9v//+O0/XLqDfOQMAQ0NDuLi4YNOmTWjQoAFq1qyJsLAwlClTBi1btsyy/ogRI5T/16hRA46OjmjdujViYmJQqVKlbI/Ru3dvnW1q1qyJSpUqQa1WK8U9ADAzM8u1l5mJiUmWucYATqAvKpUkw90xAVfi7aBhzwbhMD/xMUOxMT+xqSQZcXFxKF++vPL+ujhjMYyI3pmWLVsiOTkZZ8+exZEjRzBnzhw4ODjgq6++Qq1atVC2bFm4ubnptU8TExMEBQXh6NGjOH/+vLJ8zJgxb70QVlC0Q0J9fX2xbt06lCpVCnFxcfD19UV6errOui9PuK4twL1umOCAAQPQuXNnDBkyBLIs6/Ry+eabb7Bw4UIsWLAANWrUgIWFBcaPH5/luLlJSUmBgYEBzpw5o9PjBsj9hgklS5bEo0ePsixXqVSoXLlyluWdOnWCs7Mzli1bhrJly0Kj0aB69ep5bmtKSgo++ugjZc6tl71cTHx1UntJknI8x25ubpAkKcdJ8l/16lDKlJQUBAYGolu3blnWNTU1hZOTE6Kjo3Hw4EEcOHAAI0eOxDfffIPDhw/rPfl+yZIls5zXgpo0Xp9zBryYq+vGjRvIyMiARqOBpaUlnj9/jufPn8PS0hLOzs6IiorKcXttcfbatWs5FsNe5erqipIlS+LatWs6xbCEhASUKlUqT/t4GW8rLyZJklHW9gmu3i0B8IOccJif+Jih2Jif2CRJRkLCI5QrV66wm1IksBxIRO+Mra0tatasiaCgIBgZGcHDwwMtW7bEuXPnsGvXLmX4HwB4enoiIiJCZ3LzsLAwqFSqLD1Tbty4gfPnz0OSJOW5Xbt2vZPX5OnpiePHj+v0EAoLC4OVlRXKly8PV1dXGBkZ4dSpU8rzjx8/xpUrV5SvL1++jIcPH+Krr75CixYt4OHhkWuvmvwYNGgQgoODMXnyZHz77bc6be3SpQv69++PWrVqwdXVVadtlSpVgpGREU6cOKEse/Tokc46derUQWZmJu7du4fKlSvrPHIbvlenTh1cvHgxT+1/+PAhoqOjMWPGDLRu3Rqenp7ZFtJyU7duXVy8eDFLGytXrgxjY2O99qVlZ2cHX19f/PjjjzrXqlZiYuJr2xQdHZ1tm7R/sTMzM0OnTp2waNEiqNVqHD9+HJGRkQAAY2NjZGZm5qvtr5OXaze/9uzZg/DwcDg4OGDt2rUIDw9H9erVsWDBAoSHh2PPnj25bh8eHg7gv96ReXHr1i08fPgwyzYXLlxAnTp19H4NRERERCQuFsOI6J3y9vbGunXrlMKXnZ0dPD09sXHjRp1iWL9+/WBqaopBgwbhwoULCAkJwZgxYzBgwACUKVNGZ59bt25FmTJlsHfvXkRFRWHmzJmIiorS+06DuXn8+DHCw8N1Hjdv3sTIkSNx8+ZNjBkzBpcvX8aOHTvg7++PCRMmQKVSwcrKCoMGDcJnn32GkJAQREVFYejQoVCpVErPrgoVKsDY2Bg//PAD/v77b+zcuROzZ88usLZrDRgwAKtWrcLUqVPxzTffAHjRs+nAgQM4duwYLl26hI8++gj//POPso2lpSWGDh2Kzz77DH/++ScuXLgAPz8/na7V7u7u6NevHwYOHIht27bh+vXrOHnyJObOnYvdu3fn2B5fX1+Ehobmqe0lSpSAvb09fv75Z1y7dg1//vmnTg+3vJgyZQqOHTuG0aNHIzw8HFevXsWOHTuyTKCvrx9//BGZmZlo2LAhtm7diqtXr+LSpUtYtGgRmjRpkuu2M2fOxOrVqxEYGIioqChcunQJGzZsUOb2Cg4OxooVK3DhwgX8/fffWLt2LczMzODs7Azgxd0vjxw5gtu3b+PBgwdv9DpelZdrN7+cnZ1haWmJf/75B126dIGTkxOioqLQvXt3VK5cWXl9wIuhurNnz8aZM2cQGxuLnTt3YuDAgWjZsqXO8EwPDw/lrpApKSn47LPP8NdffyE2NhaHDh1Cly5dULlyZfj6+irbPH36FGfOnEG7du3e6PUQERERkVhYDCOid8rLywuZmZnK3GDAiwLZq8vMzc2xb98+JCQkoEGDBvjwww/RunXrbCfDT0lJQUREBNq1awcDAwMEBgbizz//1OnR8qbUajXq1Kmj8wgMDES5cuWwZ88enDx5ErVq1cLHH3+MoUOH6kxU/t1336FJkyZ4//330aZNGzRr1gyenp7KfFmlSpVCcHAwNm/ejKpVq+Krr77S6b1VkPr164c1a9Zg2rRpmDdvHmbMmIG6devC19cX3t7ecHBwQNeuXXW2+eabb9CiRQt06tQJbdq0QfPmzVGvXj2ddVauXImBAwdi4sSJqFKlCrp27YpTp07lOpdZv379EBUVletdB7VUKhU2bNiAM2fOoHr16vj000+Vgl5e1axZE4cPH8aVK1fQokUL1KlTBzNnzsx2Qnh9uLq64uzZs/Dx8cHEiRNRvXp1tG3bFocOHcJPP/2U67a+vr7YtWsX9u/fjwYNGqBx48b4/vvvlWKQra0tli1bhmbNmqFmzZo4ePAgfv/9d2UutlmzZiE2NhaVKlXK11C/13ndtfsm1Go1GjRoAFNTU5w8eRLly5fPtqeXsbExDh48iHbt2sHDwwMTJ05E9+7d8fvvv+usFx0djcePHwMADAwMcP78eXTu3Bnu7u4YOnQo6tWrh6NHj+rM/7Vjxw5UqFABLVq00Lv9nCtFTBpZwvX7NsxPUMxPfMxQbMxPbBr5xbyuvJvkC5Ks7+y/RET/Q9RqNfz8/BAbG/vOjvnkyROUK1cO8+fPx9ChQ9/ZcYuizz77DElJSVi6dGlhN4Xy4H/t2m3cuDHGjh2r1/yCSUlJsLGxQYuhF2BobPUWW0dERERUsP5c/O7uJql9z/T48WNYW1u/s+PmFXuGERG9ZefOncP69esRExODs2fPol+/fgCALl26FHLLCt/06dPh7Oz82psAUOH4X752Hzx4gG7duqFPnz752t5A4jUrIgNJg9oV7jE/QTE/8TFDsTE/sRlIGsTExLy1+WZFw7tJEhG9A99++y2io6NhbGysDNcqWbJkYTer0Nna2uL//u//CrsZlIv/1Wu3ZMmSmDx5cv53wBEGYpIAO8vUF/lxbIR4mJ/4mKHYmJ/YJCA5ObmwW1FksBhGRMWai4sLxo8f/1aPUadOHZw5c+atHoPobeC1S0RERET/izhMkoiKtXdRDCMiIiIiIqKig8UwIiIiIj3xTlpi0sgSLt+xY36CYn7iY4ZiY35i08gSnJyceDfJf3GYJBEREZGefp9foUjeGYmIiIiIXo89w4iIiIj0xDsxiSkzMxOXL19mfoJifuJjhmJjfmJjfrpYDCMiIiKiYiM1NbWwm0BvgPmJjxmKjfmJjfn9h8UwIiIiIiIiIiIqNlgMIyIiIiIiIiKiYoPFMCIiIiI9qVR8CyUilUoFV1dX5ico5ic+Zig25ic25qeLd5MkIiIi0hNvSy4mSZJ4F1CBMT/xMUOxMT+xMT9dLAkSERER6Yl3YhJTZmYmIiMjmZ+gmJ/4mKHYmJ/YmJ8uFsOIiIiIqNjghwCxMT/xMUOxMT+xMb//sBhGRERERERERETFBothRERERERERERUbEiyLMuF3QgiIiIiESQlJcHGxgaJiYmwsbEp7OaQnmRZRmpqKkxNTXkTBAExP/ExQ7ExP7G96/y075keP35cJCfuZ88wIiIiIio2jI2NC7sJ9AaYn/iYodiYn9iY339YDCMiIiLSk0ajKewmUD5oNBpERkYyP0ExP/ExQ7ExP7ExP10shhERERERERERUbHBYhgRERERERERERUbLIYREREREREREVGxwbtJEhEREeUR7yYpNlmWodFooFKpeCc0ATE/8TFDsTE/sb3r/Hg3SSIiIiKiIiI9Pb2wm0BvgPmJjxmKjfmJjfn9h8UwIiIiIj3xTkxi0mg0iI6OZn6CYn7iY4ZiY35iY366WAwjIiIiIiIiIqJig8UwIiIiIiIiIiIqNlgMIyIiIqJiw8DAoLCbQG+A+YmPGYqN+YmN+f2Hd5MkIiIiyqOifmckIiIioqKgqL9nYs8wIiIiIj3xb4likmUZSUlJzE9QzE98zFBszE9szE8Xi2FEREREeuKdmMSk0Wjw999/Mz9BMT/xMUOxMT+xMT9dLIYREREREREREVGxYVjYDSAiIiISzaVLl4rk/BeUO41Gg0ePHiEqKgoqFf8mLBrmJz5mKDbmJzb2CNPFK5iIiIhIT5IkFXYTKB8kSYKBgQHzExTzEx8zFBvzE5skSTA1NS3sZhQZ7BlGREREpCd+EBCTJEmws7Mr7GZQPjE/8TFDsTE/sUmSBA8Pj8JuRpHBnmFEREREeuKdmMQkyzKePXvG/ATF/MTHDMXG/MQmyzIePnzI4ZL/YjGMiIiISE/8ICAmWZaRkpLC/ATF/MTHDMXG/MQmyzJu3rzJ/P7FYhgRERERERERERUbLIYREREREREREVGxwWIYERERkZ44gb6YJEmCkZER8xMU8xMfMxQb8xObJEmwsrIq7GYUGbybJBEREZGe+EFATJIkwdbWtrCbQfnE/MTHDMXG/MQmSRIqVapU2M0oMtgzjIiIqIDoOyGpWq2Gi4vLGx1TkiRs375d+fry5cto3LgxTE1NUbt27Tfa9+uEhYWhRo0aMDIyQteuXd/qsfLCz89Ppx2yLGPEiBGws7ODJEkIDw8vsGNx8lkxybKMJ0+eMD9BMT/xMUOxMT+xybKMu3fv8m6S/2IxjIiI/mdIkpTrIyAg4K0e/+effy7wfb5a4HlVfHw8OnTooHzt7+8PCwsLREdH49ChQ298fF9fXxgYGODUqVNZnpswYQJq166N69evIzg4GAEBAW+1AKdWqyFJEhITE7N9fuHChQgODla+3rt3L4KDg7Fr1y7Ex8ejevXqBdYWfhAQkyzLePr0KfMTFPMTHzMUG/MTm7YYxvxeYDGMiIj+Z8THxyuPBQsWwNraWmfZpEmT3tqxIyIiMG/evLe2/5w4ODjAxMRE+TomJgbNmzeHs7Mz7O3t32jfcXFxOHbsGEaPHo1ffvkly/MxMTFo1aoVypcvX6DDJtLT0/O1nY2NjU47YmJi4OjoiKZNm8LBwQGGhpwdgoiIiIhYDCMiov8hDg4OysPGxgaSJMHBwQFmZmYoV64cLl++DADQaDSws7ND48aNlW3Xrl0LJycn5evIyEi0atUKZmZmsLe3x4gRI5CSkpLjsbds2YLr16/j7Nmzb+8FZuPlYZKSJOHMmTOYNWuWTk+4mzdvomfPnrC1tYWdnR26dOmC2NjY1+575cqVeP/99/HJJ59g/fr1ePbsGQAgNjYWkiTh4cOHGDJkCCRJQnBwMAIDAxEREaH0xNP20kpMTMSwYcNQqlQpWFtbo1WrVoiIiFCOo+1Rtnz5clSsWBGmpqb5Ohcv96Lz8/PDmDFjEBcXB0mSlOGoGo0Gc+fORcWKFWFmZoZatWphy5YtOe4zLS0NSUlJOg8iIiIiEhuLYURE9D/PxsYGtWvXhlqtBvCi0CVJEs6dO6cUuA4fPgwvLy8AwJMnT+Dr64sSJUrg1KlT2Lx5Mw4ePIjRo0cr+wwPD0doaKjy2Lx5MwBgyZIlOstv3rz5zl5nfHw8qlWrhokTJyo94TIyMuDr6wsrKyscPXoUYWFhsLS0RPv27XPtgSXLMlauXIn+/fvDw8MDlStXVopGTk5OiI+Ph7W1NRYsWID4+Hj06tULEydORLVq1ZSeeL169QIA9OjRA/fu3cMff/yBM2fOoG7dumjdujUSEhKU4127dg1bt27Ftm3bCmRur4ULF2LWrFkoX7484uPjlWGec+fOxerVq7FkyRJERUXh008/Rf/+/XH48OFs9zN37lzY2NgoD23BlBPoi0mSJJiamjI/QTE/8TFDsTE/sUmSpMyjSiyGERFRMeHt7a0Uw9RqNdq2bQtPT0+EhoYqy7TFsF9//RWpqalYvXo1qlevjlatWiEoKAhr1qzBP//8AwAwNTXF6NGj0aJFC7Ro0QLR0dEAgGXLlinLfvnllzceqqgP7VBAS0tLODg4wNLSEhs3boRGo8Hy5ctRo0YNeHp6YuXKlYiLi1POR3YOHjyIp0+fwtfXFwDQv39/rFixAgBgYGAABwcHSJIEGxsbpfedpaUlDA0Nld55ZmZmCA0NxcmTJ7F582bUr18fbm5u+Pbbb2Fra6vTIys9PR2rV69GnTp1ULNmzTc+FzY2NrCyslLaWqpUKaSlpWHOnDn45Zdf4OvrC1dXV/j5+aF///5YunRptvuZNm0aHj9+rDy0xU2+kRST9rbyzE9MzE98zFBszE9skiShQoUKUKlYBgJYDCMiomLCy8sLoaGhyMzMxOHDh+Ht7a0UyO7cuYNr167B29sbAHDp0iXUqlULFhYWyvbNmjWDRqNRil4eHh44ceIERo0aleVYVlZWWLduHX755ReYm5u/k9eXk4iICFy7dg1WVlawtLSEpaUl7OzskJqaipiYmBy3++WXX9CrVy9lnq0+ffogLCws121yOn5KSgrs7e2V41taWuL69es6+3J2dkapUqXy9yLz6Nq1a3j69Cnatm2r05bVq1fn+LpMTExgbW2t8wA4gb6oZFlGcnIy8xMU8xMfMxQb8xObLMuIi4vj3ST/xZlkiYioWGjZsiWSk5Nx9uxZHDlyBHPmzIGDgwO++uor1KpVC2XLloWbm5te+zQxMUFQUBCOHj2K8+fPK8vHjBmDvn37FvRLyJeUlBTUq1cP69aty/JcTsWnhIQE/Pbbb8jIyMBPP/2kLM/MzMQvv/yCL7/8Uq/jOzo6ZtsL7eXJ7l8uPL4t2iGxu3fvRrly5XSee/kmBHnBDwJikmUZqampsLCwYM8GATE/8TFDsTE/scmyjISEhCzvgYorFsOIiKhYsLW1Rc2aNREUFAQjIyN4eHigdOnS6NWrF3bt2qUMkQQAT09PBAcH48mTJ0qRJiwsDCqVClWqVNHZ740bN3D+/HlIkgR3d3dER0dj165dehWM3qa6deti48aNKF26tNKr6XXWrVuH8uXLKxPza+3fvx/z58/HrFmzYGBgkGU7Y2NjZGZmZjn+3bt3YWhoqExiX1iqVq0KExMTxMXF6eRNRERERMULh0kSEVGx4e3tjXXr1imFEDs7O3h6emLjxo06xZF+/frB1NQUgwYNwoULFxASEoIxY8ZgwIABKFOmjM4+t27dijJlymDv3r2IiorCzJkzERUVpfdwwtw8fvwY4eHhOo+8Tszfr18/lCxZEl26dMHRo0dx/fp1qNVqjB07Frdu3cp2mxUrVuDDDz9E9erVdR5Dhw7FgwcPsHfv3my3c3FxwfXr1xEeHo4HDx4gLS0Nbdq0QZMmTdC1a1fs378fsbGxOHbsGKZPn47Tp0/n63xERkbqnIuX70yZGysrK0yaNAmffvopVq1ahZiYGJw9exY//PADVq1ala+2EBEREZF4WAwjIqJiw8vLC5mZmcrcYMCLAtmry8zNzbFv3z4kJCSgQYMG+PDDD9G6dWsEBQVl2WdKSgoiIiLQrl07GBgYIDAwEH/++ady98KCoFarUadOHZ1HYGBgnrY1NzfHkSNHUKFCBXTr1g2enp4YOnQoUlNTs+0pdubMGURERKB79+5ZnrOxsUHr1q2VifRf1b17d7Rv3x4+Pj4oVaoU1q9fD0mSsGfPHrRs2RKDBw+Gu7s7evfujRs3bmQpLOZVy5Ytdc5FvXr18rzt7Nmz8fnnn2Pu3Lnw9PRE+/btsXv3blSsWFGvNnB4iJgkSYK5uTnzExTzEx8zFBvzE5skScoNkAiQZE56QUREVCjUajX8/PwQGxtb2E2hPEpKSoKNjQ2OHz8OS0vLwm4OERERUZ5Vr179nR1L+57p8ePHeZ6q411izzAiIiIiPfFviWKSZRmJiYnMT1DMT3zMUGzMT2yyLCMmJibL/K7FFYthRERERHriBwExybKMjIwM5ico5ic+Zig25ic2WZaRnJxc2M0oMlgMIyIiKiQuLi4YP358YTeDiIiIiKhYYTGMiIiokLAYRkRERET07rEYRkRERKQn3olJTJIkwdLSkvkJivmJjxmKjfmJTZIkODk5Mb9/GRZ2A4iIiIhEU61atSJ5ZyQiIiIiej32DCMiIiLSE+/EJKbMzExcvnyZ+QmK+YmPGYqN+YmN+eliMYyIiIiIio3U1NTCbgK9AeYnPmYoNuYnNub3HxbDiIiIiIiIiIio2GAxjIiIiIiIiIiIig0Ww4iIiIj0pFLxLZSIVCoVXF1dmZ+gmJ/4mKHYmJ/YmJ8u3k2SiIiISE+8LbmYJEniXUAFxvzExwzFxvzExvx0sSRIREREpCfeiUlMmZmZiIyMZH6CYn7iY4ZiY35iY366WAwjIiIiomKDHwLExvzExwzFxvzExvz+w2IYEREREREREREVGyyGERERERERERFRsSHJsiwXdiOIiIiIRJCUlAQbGxskJibCxsamsJtDepJlGampqTA1NeVNEATE/MTHDMXG/MT2rvPTvmd6/PhxkZy4nz3DiIiIiKjYMDY2Luwm0BtgfuJjhmJjfmJjfv9hMYyIiIhITxqNprCbQPmg0WgQGRnJ/ATF/MTHDMXG/MTG/HSxGEZERERERERERMUGi2FERERERERERFRssBhGRERERERERETFBu8mSURERJRHvJuk2GRZhkajgUql4p3QBMT8xMcMxcb8xPau8+PdJImIiIiIioj09PTCbgK9AeYnPmYoNuYnNub3HxbDiIiIiPTEOzGJSaPRIDo6mvkJivmJjxmKjfmJjfnpYjGMiIiIiIiIiIiKDRbDiIiIiIiIiIio2GAxjIiIiIiKDQMDg8JuAr0B5ic+Zig25ic25vcf3k2SiIiIKI+K+p2RiIiIiIqCov6eiT3DiIiIiPTEvyWKSZZlJCUlMT9BMT/xMUOxMT+xMT9dLIYRERER6Yl3YhKTRqPB33//zfwExfzExwzFxvzExvx0GRZ2A4iIiIhEc+nSpSLZ5Z9yp9Fo8OjRI0RFRUGl4t+ERcP8xMcMxcb8xMYimC5ewUREREREREREVGywGEZERESkJ0mSCrsJlA+SJMHAwID5CYr5iY8Zio35iU2SJJiamhZ2M4oMDpMkIiIi0hM/CIhJkiTY2dkVdjMon5if+Jih2Jif2CRJgoeHR2E3o8hgzzAiIiIiPfFOTGKSZRnPnj1jfoJifuJjhmJjfmKTZRkPHz7k3GH/YjGMiIiISE/8ICAmWZaRkpLC/ATF/MTHDMXG/MQmyzJu3rzJ/P7FYhgRERERERERERUbLIYREREREREREVGxwWIYERERkZ44gb6YJEmCkZER8xMU8xMfMxQb8xObJEmwsrIq7GYUGSyGERG9A/qOzVer1XBxcSmQY0uShO3bt+d5fRcXFyxYsCDb7WNjYyFJEsLDwwukbTnx9vbG+PHj3+ox3qZXz2FRU9TbJwJ+EBCTJEmwtbVlfoJifuJjhmJjfmKTJAmVKlWCgYFBYTelSGAxjIiKBUmScn0EBAS81eP//PPPb2W/d+/exZgxY+Dq6goTExM4OTmhU6dOOHTokLJOfHw8OnTokOd9njp1CiNGjMj2OScnJ8THx6N69epv3HbgRdFPkiQkJibqLN+2bRtmz55dIMcoigICAlC7dm3laz8/P3Tt2vWN96m9ng0NDVGyZEm0bNkSCxYsQFpams66uWVcGPQt2OZVTtdXQeDks2KSZRlPnjxhfoJifuJjhmJjfmKTZRl3797l3ST/ZVjYDSAiehfi4+OV/2/cuBEzZ85EdHS0sszS0vKtHTsiIgLz5s3DRx99VKD7jY2NRbNmzWBra4tvvvkGNWrUQEZGBvbt24dRo0bh8uXLAAAHBwe99luqVKkcnzMwMNB7f/lhZ2f31o/xv6hatWo4ePAgNBoNHj58CLVajS+++AJr1qyBWq1WusbnljHlDT8IiEmWZTx9+hRmZmbs2SAg5ic+Zig25ic2bTGM7wNfYM8wIioWHBwclIeNjQ0kSYKDgwPMzMxQrlw5pXCk0WhgZ2eHxo0bK9uuXbsWTk5OyteRkZFo1aoVzMzMYG9vjxEjRiAlJSXHY2/ZsgXXr1/H2bNnC/Q1jRw5EpIk4eTJk+jevTvc3d1RrVo1TJgwAX/99Zey3su9bpo2bYopU6bo7Of+/fswMjLCkSNHAOQ+hO7VYZJ+fn7Z9rRTq9UAgDVr1qB+/fqwsrKCg4MD+vbti3v37in78vHxAQCUKFECkiTBz88PQNZhko8ePcLAgQNRokQJmJubo0OHDrh69aryfHBwMGxtbbFv3z54enrC0tIS7du31ymCviozMxNDhw5FxYoVYWZmhipVqmDhwoU662h7bH377bdwdHSEvb09Ro0ahYyMDGWde/fuoVOnTjAzM0PFihWxbt26HI+ZnYCAAKxatQo7duzQOX/p6ekYPXo0HB0dYWpqCmdnZ8ydOzfXfRkaGsLBwQFly5ZFjRo1MGbMGBw+fBgXLlzAvHnzlPVezliWZQQEBKBChQowMTFB2bJlMXbsWGXdtLQ0TJkyBU5OTjAxMUHlypWxYsUK5fnDhw+jYcOGMDExgaOjI6ZOnYrnz58rz3t7e2Ps2LGYPHky7Ozs4ODgoNMTUzsc+IMPPoAkSTrDg3fs2IG6devC1NQUrq6uCAwM1Nm3JElYvnw5PvjgA5ibm8PNzQ07d+4EkPv1tWXLFtSoUUP5Hm7Tpg2ePHmS7TlNS0tDUlKSzoOIiIiIxMZiGBEVazY2Nqhdu7ZSvImMjIQkSTh37pxS4Dp8+DC8vLwAAE+ePIGvry9KlCiBU6dOYfPmzTh48CBGjx6t7DM8PByhoaHKY/PmzQCAJUuW6Cy/efNmvtudkJCAvXv3YtSoUbCwsMjyvK2tbbbb9evXDxs2bNDp1bJx40aULVsWLVq00LsdCxcuRHx8vPIYN24cSpcuDQ8PDwBARkYGZs+ejYiICGzfvh2xsbFKQcLJyQlbt24FAERHRyM+Pj5LMUrLz88Pp0+fxs6dO3H8+HHIsoyOHTvqFKWePn2Kb7/9FmvWrMGRI0cQFxeHSZMm5dh2jUaD8uXLY/Pmzbh48SJmzpyJ//u//8OmTZt01gsJCUFMTAxCQkKwatUqBAcHIzg4WKdtN2/eREhICLZs2YLFixcrBb+8mDRpEnr27KkU7+Lj49G0aVMsWrQIO3fuxKZNmxAdHY1169blax45Dw8PdOjQAdu2bcv2+a1bt+L777/H0qVLcfXqVWzfvh01atRQnh84cCDWr1+PRYsW4dKlS1i6dKnSk/L27dvo2LEjGjRogIiICPz0009YsWIFvvjiC51jrFq1ChYWFjhx4gS+/vprzJo1CwcOHADwYsgmAKxcuRLx8fHK10ePHsXAgQMxbtw4XLx4EUuXLkVwcDC+/PJLnX0HBgaiZ8+eOH/+PDp27Ih+/fohISEhx+srPj4effr0wZAhQ3Dp0iWo1Wp069Ytx55ec+fOhY2NjfJ4uTBORERERGLiMEkiKva8vb2hVqsxadIkqNVqtG3bFpcvX0ZoaCjat28PtVqNyZMnAwB+/fVXpKamYvXq1UoRKigoCJ06dcK8efNQpkwZmJqaws/PDxERETrHWbZsGZYtWwYAGDx4MIKCgvLd5mvXrkGWZaXolFc9e/bE+PHjERoaqhS/fv31V/Tp0ydf3d21BQLgxTxfS5cuxcGDB5WhlEOGDFHWdXV1xaJFi9CgQQOkpKTA0tJSGQ5ZunTpHAt4V69exc6dOxEWFoamTZsCANatWwcnJyds374dPXr0APCi8LZkyRJUqlQJADB69GjMmjUrx7YbGRkhMDBQ+bpixYo4fvw4Nm3ahJ49eyrLS5QogaCgIBgYGMDDwwPvvfceDh06hOHDh+PKlSv4448/cPLkSTRo0AAAsGLFCnh6eub5HFpaWsLMzAxpaWk6Q1Dj4uLg5uaG5s2bQ5IkODs753mfr/Lw8MD+/fuzfS4uLg4ODg5o06YNjIyMUKFCBTRs2BAAcOXKFWzatAkHDhxAmzZtALzIUWvx4sVwcnJCUFAQJEmCh4cH7ty5gylTpmDmzJlQqV78za1mzZrw9/cHALi5uSEoKAiHDh1C27Ztla76tra2Oq8/MDAQU6dOxaBBg5Tjzp49G5MnT1b2BbwoRvbp0wcAMGfOHCxatAgnT55E+/bts72+YmJi8Pz5c3Tr1k05py8X/141bdo0TJgwQfk6KSkJTk5OHB4iKEmSYGpqyvwExfzExwzFxvzEJkkS7OzsmN+/2DOMiIo9Ly8vhIaGIjMzE4cPH4a3t7dSILtz5w6uXbsGb29vAMClS5dQq1Ytnd5YzZo1g0ajUeYg8/DwwIkTJzBq1Kgsx7KyssK6devwyy+/wNzcPN9tzu98RaVKlUK7du2UoXzXr1/H8ePH0a9fv3y3BQDOnTuHAQMGICgoCM2aNVOWnzlzBp06dUKFChVgZWWl9LCLi4vL874vXboEQ0NDNGrUSFlmb2+PKlWq4NKlS8oyc3NzpRAGAI6Ojq/tofXjjz+iXr16KFWqFCwtLfHzzz9naVu1atV07rrz8n61batXr57yvIeHR46FPX34+fkhPDwcVapUwdixY3MsZuWFLMs5vvHp0aMHnj17BldXVwwfPhy//fabMhQxPDwcBgYGSm6vunTpEpo0aaKz72bNmiElJQW3bt1SltWsWVNnu7xkExERgVmzZsHS0lJ5DB8+HPHx8Xj69Gm2+7awsIC1tXWu+65VqxZat26NGjVqoEePHli2bBkePXqU4/omJiawtrbWeQC8m6SotLeVZ35iYn7iY4ZiY35ikyQJFSpUUP5YWdzxLBBRsdeyZUskJyfj7NmzOHLkiE4x7PDhwyhbtizc3Nz02qeJiQmCgoKyFAHGjBmDvn37vnGb3dzcIEmSMteZPvr164ctW7YgIyMDv/76K2rUqJFrz5jXuXv3Ljp37oxhw4Zh6NChynLtkFJra2usW7cOp06dwm+//QYASE9Pz/fxcmJkZKTztSRJuRYNN2zYgEmTJmHo0KHYv38/wsPDMXjw4Cxty26/7+IuPHXr1sX169cxe/ZsPHv2DD179sSHH36Yr31dunQJFStWzPY5JycnREdHY/HixTAzM8PIkSPRsmVLZGRkwMzM7E1egiI/5zAlJQWBgYEIDw9XHpGRkbh69SpMTU3zvW8DAwMcOHAAf/zxB6pWrYoffvgBVapUwfXr1/V6TZxAX0yyLCM5OZn5CYr5iY8Zio35iU2WZcTFxfFukv9iMYyIij1bW1vUrFkTQUFBMDIygoeHB1q2bIlz585h165dOr1iPD09ERERoTPZdlhYGFQqFapUqaKz3xs3buD8+fOQJEl5bteuXQXSZjs7O/j6+uLHH3/MduLvxMTEHLft0qULUlNTsXfvXvz6669v1CssNTUVXbp0gYeHB7777jud5y5fvoyHDx/iq6++QosWLeDh4ZGlx46xsTGAF5PZ58TT0xPPnz/HiRMnlGUPHz5EdHQ0qlatmu+2a4ddjhw5EnXq1EHlypURExOj1z48PDzw/PlznDlzRlkWHR2d6/nPjrGxcbbnwNraGr169cKyZcuwceNGbN26FQkJCXrt+/Lly9i7dy+6d++e4zpmZmbo1KkTFi1aBLVajePHjyMyMhI1atSARqPB4cOHs93O09NTmcNNKywsDFZWVihfvnye22hkZJTl9detWxfR0dGoXLlylkde/6KZ0/UlSRKaNWuGwMBAnDt3DsbGxkqhNq/4QUBMsiwjNTWV+QmK+YmPGYqN+YlNlmUkJCQwv3+xGEZEhBfzhq1bt04pfNnZ2cHT0xMbN27UKYb169cPpqamGDRoEC5cuICQkBCMGTMGAwYMQJkyZXT2uXXrVpQpUwZ79+5FVFQUZs6ciaioKL0LLjn58ccfkZmZiYYNG2Lr1q24evUqLl26hEWLFqFJkyY5bmdhYYGuXbvi888/x6VLl5T5lvLjo48+ws2bN7Fo0SLcv38fd+/exd27d5Geno4KFSrA2NgYP/zwA/7++2/s3LkTs2fP1tne2dkZkiRh165duH//frZ35XRzc0OXLl0wfPhwhIaGIiIiAv3790e5cuXQpUuXfLfdzc0Np0+fxr59+3DlyhV8/vnnyuTteVWlShW0b98eH330EU6cOIEzZ85g2LBheveocnFxwfnz5xEdHY0HDx4gIyMD3333HdavX4/Lly/jypUr2Lx5MxwcHHIdgvn8+XPcvXsXd+7cQWRkJH744Qd4eXmhdu3a+Oyzz7LdJjg4GCtWrMCFCxfw999/Y+3atTAzM4OzszNcXFwwaNAgDBkyBNu3b8f169ehVquVmwyMHDkSN2/exJgxY3D58mXs2LED/v7+mDBhgl5d8F1cXHDo0CHcvXtXGbI4c+ZMrF69GoGBgYiKisKlS5ewYcMGzJgxI8/7ze76OnHiBObMmYPTp08jLi4O27Ztw/379/Wa542IiIiIxMZiGBERXswblpmZqcwNBrwokL26zNzcHPv27UNCQgIaNGiADz/8EK1bt852MvyUlBRERESgXbt2MDAwQGBgIP7880+9Cy45cXV1xdmzZ+Hj44OJEyeievXqaNu2LQ4dOoSffvop12379euHiIgItGjRAhUqVMh3Gw4fPoz4+HhUrVoVjo6OyuPYsWMoVaoUgoODsXnzZlStWhVfffUVvv32W53ty5Urp0yUXqZMGZ27cr5s5cqVqFevHt5//300adIEsixjz549WYbI6eOjjz5Ct27d0KtXLzRq1AgPHz7EyJEj9d7PypUrUbZsWXh5eaFbt24YMWIESpcurdc+hg8fjipVqqB+/fooVaqU0rvq66+/Rv369dGgQQPExsZiz549uRaZoqKi4OjoiAoVKsDb2xubNm3CtGnTcPToUeUOkK+ytbXFsmXL0KxZM9SsWRMHDx7E77//Dnt7ewDATz/9hA8//BAjR46Eh4cHhg8frvRGLFeuHPbs2YOTJ0+iVq1a+PjjjzF06FC9ClYAMH/+fBw4cABOTk6oU6cOAMDX1xe7du3C/v370aBBAzRu3Bjff/+9XjcSyO76sra2xpEjR9CxY0e4u7tjxowZmD9/Pjp06KBXm4mIiIhIXJLMPnJEREWOWq2Gn58fYmNjC7spRPSSpKQk2NjY4NixY7Cysirs5pCeZFnG06dPYW5uzgmgBcT8xMcMxcb8xCbLMkqVKoXSpUu/k0n0te+ZHj9+rNyAqCgxLOwGEBEREYmGHwLEJEmSzt2ASSzMT3zMUGzMT2ySJMHBwaGwm1FkcJgkERERkZ7YsV5MsiwjMTGR+QmK+YmPGYqN+YlNlmXExMTkeuOq4oTFMCKiIsjFxQXjx48v7GYQUQ74QUBMsiwjIyOD+QmK+YmPGYqN+YlNlmUkJycXdjOKDBbDiIiKIBbDiIiIiIiI3g4Ww4iIiIiIiIiIqNjgBPpEREREeqpatSpsbW0LuxmkJ41Gg0ePHqFEiRLv5E5aVLCYn/iYodiYn9i0+fEmQC+wGEZERESkJ34IEJNKpYK9vX1hN4PyifmJjxmKjfmJjfnp4js5IiIiIj3xTkxiyszMxOXLl5mfoJif+Jih2Jif2JifLhbDiIiIiKjYSE1NLewm0BtgfuJjhmJjfmJjfv9hMYyIiIiIiIiIiIoNFsOIiIiIiIiIiKjYYDGMiIiISE+cQF9MKpUKrq6uzE9QzE98zFBszE9szE8X7yZJREREpCfellxMkiTB2tq6sJtB+cT8xMcMxcb8xMb8dLEkSERERKQn3olJTJmZmYiMjGR+gmJ+4mOGYmN+YmN+ulgMIyIiIqJigx8CxMb8xMcMxcb8xMb8/sNiGBERERERERERFRsshhERERERERERUbEhybIsF3YjiIiIiESQlJQEGxsbJCYmwsbGprCbQ3qSZRmpqakwNTXlTRAExPzExwzFxvzE9q7z075nevz4cZGcuJ89w4iIiIio2DA2Ni7sJtAbYH7iY4ZiY35iY37/YTGMiIiISE8ajaawm0D5oNFoEBkZyfwExfzExwzFxvzExvx0sRhGRERERERERETFBothRERERERERERUbLAYRkRERERERERExQbvJklERESUR7ybpNhkWYZGo4FKpeKd0ATE/MTHDMXG/MT2rvPj3SSJiIiIiIqI9PT0wm4CvQHmJz5mKDbmJzbm9x8Ww4iIiIj0xDsxiUmj0SA6Opr5CYr5iY8Zio35iY356WIxjIiIiIiIiIiIig0Ww4iIiIiIiIiIqNhgMYyIiIiIig0DA4PCbgK9AeYnPmYoNuYnNub3H95NkoiIiCiPivqdkYiIiIiKgqL+nok9w4iIiIj0xL8likmWZSQlJTE/QTE/8TFDsTE/sTE/XYaF3QAiIiIi0XT9LA6SoU1hN4P0ZKDSwMvjFg5fLo9MDf8mLBrmJz5mKDbmJzYDlQbfDEtEjRo1OFwS7BlGRERERERERETFCIthRERERERERERUbLAYRkRERKQvTrchJhl4kmbE/ETF/MTHDMXG/MQmA6ampoXdiiKDc4YRERER6SlTVvFNlIAyZRVOxDgWdjMon5if+Jih2Jif2DJlFTw8PAq7GUUGe4YRERER6UmS+GdxEUmSjLK2KcxPUMxPfMxQbMxPbJIk4+HDh9BoNIXdlCKBxTAiIiIiPan4QUBIKkmGR9kE5ico5ic+Zig25ic2lSTj5s2bkGXmB7AYRkRERERERERExQiLYUREREREREREVGywGEZERK+lVqshSRISExMLuykAkK/u3X5+fggICCj4xuSTn58funbtKty+3xYXFxcsWLAgz+vHxsZCkiSEh4e/tTbliiMMxCQDCSmmzE9UzE98zFBszE9sMmBlZVXYrSgyWAwjIirmJEnK9VGUCkhaYWFhiIqKKtB9Nm7cGB9//LHOsiVLlkCSJAQHB+ss9/PzQ4sWLfK038Io2ixcuDBLm9+GnIpu+Smenjp1CiNGjCi4xgEIDg6Gra1tge5TK1PmWygRZcoqhMeVZn6CYn7iY4ZiY35iy5RVqFSpEgwMDAq7KUUCr2IiomIuPj5eeSxYsADW1tY6yyZNmpSv/aanpxdwS/+zdetWbN26tUD36ePjA7VarbMsJCQETk5OWZar1Wq0atWqQI9fEDIzM6HRaGBjY/PWikBvS6lSpWBubl7Yzcgz3klLTJIko2Kpx8xPUMxPfMxQbMxPbJIk4+7du7yb5L9YDCMiKuYcHByUh42NDSRJ0llmaWmprHvmzBnUr18f5ubmaNq0KaKjo5XnAgICULt2bSxfvhwVK1aEqakpACAxMRHDhg1DqVKlYG1tjVatWiEiIkKnDTt27EDdunVhamoKV1dXBAYG4vnz5zm2edu2bdiyZUuBngcfHx9ER0fj7t27yrLDhw9j6tSpOsWw69ev48aNG/Dx8QHwomfd9u3bdfZla2ur9MyqWLEiAKBOnTqQJAne3t4663777bdwdHSEvb09Ro0ahYyMDOW5tLQ0TJo0CeXKlYOFhQUaNWqk0xZtz6edO3eiatWqMDExQVxcXJYeW97e3hg7diwmT54MOzs7ODg4ZOnxd/nyZTRv3hympqaoWrUqDh48mO1ry6/Q0FC0aNECZmZmcHJywtixY/HkyRPl+VeHSea1PX///Td8fHxgbm6OWrVq4fjx4wBeFCwHDx6Mx48fZ+nluHjxYri5ucHU1BRlypTBhx9+mGO709LSkJSUpPMAeDdJUan+/SDH/MTE/MTHDMXG/MSm+rcYxrtJvsBiGBER5dn06dMxf/58nD59GoaGhhgyZIjO89euXcPWrVuxbds2ZVhgjx49cO/ePfzxxx84c+YM6tati9atWyMhIQEAcPToUQwcOBDjxo3DxYsXsXTpUgQHB+PLL78EADx58gShoaHKY/Xq1YiLi0NkZCR+++03ZXlYWBjS0tLy/dqaNWsGIyMjhISEAAAuXryIZ8+eYejQoXj48CGuX78O4EVvMVNTUzRp0iRP+z158iQA4ODBg4iPj8e2bduU50JCQhATE4OQkBCsWrUKwcHBOsMbR48ejePHj2PDhg04f/48evTogfbt2+Pq1avKOk+fPsW8efOwfPlyREVFoXTp0tm2Y9WqVbCwsMCJEyfw9ddfY9asWThw4ACAFz3KunbtCnNzc5w4cQI///wzpk+fnveT9xoxMTFo3749unfvjvPnz2Pjxo0IDQ3F6NGjs11fn/ZMnz4dkyZNQnh4ONzd3dGnTx88f/4cTZs2zdLTcdKkSTh9+jTGjh2LWbNmITo6Gnv37kXLli1zbPvcuXNhY2OjPJycnArknBARERFR4TEs7AYQEZE4vvzyS3h5eQEApk6divfeew+pqalKL7D09HSsXr0apUqVAvCiN9DJkydx7949mJiYAHjRE2r79u3YsmULRowYgcDAQEydOhWDBg0CALi6umL27NmYPHky/P39YWFhgejoaIwdOxZPnz7VaU+3bt0AANbW1vj555/RrFmzfL82CwsLNGzYEGq1Gn369IFarUbz5s1hYmKCpk2bQq1Wo2LFilCr1WjSpInyel5Hey7s7e3h4OCg81yJEiUQFBQEAwMDeHh44L333sOhQ4cwfPhwxMXFYeXKlYiLi0PZsmUBAJMmTcLevXuxcuVKzJkzBwCQkZGBxYsXo1atWrm2o2bNmvD39wcAuLm5ISgoCIcOHULbtm1x4MABxMTEQK1WK2388ssv0bZt29e+vl27dun0HgReFLNeNnfuXPTr1w/jx49Xjr9o0SJ4eXnhp59+Uq4fLX3aM2nSJLz33nsAgMDAQFSrVg3Xrl2Dh4eHTk9Hrbi4OFhYWOD999+HlZUVnJ2dUadOnRxf37Rp0zBhwgTl66SkJBbEiIiIiATHnmFERJRnNWvWVP7v6OgIALh3756yzNnZWSn+AEBERARSUlJgb28PS0tL5XH9+nXExMQo68yaNUvn+eHDhyM+Pl4pfg0dOhSnT5/WOb5WgwYNcO7cOfTq1euNX5+3t7cyDFGtVitDGr28vHSWa4dIvqlq1arpTGLq6OionM/IyEhkZmbC3d1d59wcPnxYOXcAYGxsnO15edWr67x8rOjoaDg5OekUjRo2bJin1+Dj44Pw8HCdx/Lly3XWiYiIQHBwsM7r8PX1hUajUXrcvUyf9rzumnxV27Zt4ezsDFdXVwwYMADr1q3LUmR9mYmJCaytrXUeACDLUo7bUNElyxLuJFowP0ExP/ExQ7ExP7HJsgQ7OztIEvMD2DOMiIj0YGRkpPxf+4v05Uk4LSwsdNZPSUmBo6NjlgnoASgTvKekpCAwMFDp5fWyl3sMeXp6Ijg4GHXr1tVZZ926dXB1ddX7tWTHx8cHX375JW7fvg21Wq3cPMDLywtLly5FTEwMbt68qTN5viRJWeZeeHner9y8fD61+9Kez5SUFBgYGODMmTNZ7vrzck8sMzOzPL2pye1Yb8LCwgKVK1fWWXbr1i2dr1NSUvDRRx9h7NixWbavUKHCGx3/ddfkq6ysrHD27Fmo1Wrs378fM2fOREBAAE6dOqXXTQc0ssS/KApII0u4fMe+sJtB+cT8xMcMxcb8xKaRpTd+3/W/hMUwIiJ6a+rWrYu7d+/C0NAQLi4uOa4THR2dpaCSnZ07dwIAypQpg/T0dDx69Ai7du3Cp59+WiDtbdq0KYyNjbF48WKkpqaiXr16AF70Prt//z5++eUXZTilVqlSpRAfH698ffXqVZ2eRsbGxgCyDh18nTp16iAzMxP37t1DixYt3uRlvVaVKlVw8+ZN/PPPPyhTpgwA4NSpUwW2/7p16+LixYt5yrgg22NsbJzteTc0NESbNm3Qpk0b+Pv7w9bWFn/++We2BdmccPJgMakkGe6OCbgSbwcNezYIh/mJjxmKjfmJTSXJiIuLQ/ny5aFS8U96PANERPTWtGnTBk2aNEHXrl2xf/9+xMbG4tixY5g+fTpOnz4NAJg5cyZWr16NwMBAREVF4dKlS9iwYQNmzJiRZX9bt26Fr68vIiIiEBERgebNm+tMSP+mzMzM0LhxY/zwww9o1qyZ0iPL2NhYZ/nLvZFatWqFoKAgnDt3DqdPn8bHH3+s83zp0qVhZmaGvXv34p9//sHjx4/z1BZ3d3f069cPAwcOxLZt23D9+nWcPHkSc+fOxe7duwvsNQMvhg5WqlQJgwYNwvnz5xEWFqac/4LoSj9lyhQcO3YMo0ePRnh4OK5evYodO3bkOIF+QbXHxcUFKSkpOHToEB48eICnT59i165dWLRoEcLDw3Hjxg2sXr0aGo0GVapU0es18bbyYpIkGWVtnzA/QTE/8TFDsTE/sUmSjISEBN5N8l8shhER0VsjSRL27NmDli1bYvDgwXB3d0fv3r1x48YNpcePr68vdu3ahf3796NBgwZo3Lgxvv/+ezg7O+vs68aNGxgwYAD++OMPlClTBk5OTlCr1WjTpg0ePHhQYG328fFBcnKyMl+YlpeXF5KTk7PMFzZ//nw4OTmhRYsW6Nu3LyZNmgRzc3PleUNDQyxatAhLly5F2bJl0aVLlzy3ZeXKlRg4cCAmTpyIKlWqoGvXrjh16lSBd3E3MDDA9u3bkZKSggYNGmDYsGHK3Rtfndw+P2rWrInDhw/jypUraNGiBerUqYOZM2cqNwZ4W+1p2rQpPv74Y/Tq1QulSpXC119/DVtbW2zbtg2tWrWCp6cnlixZgvXr16NatWpv/DqJiIiISAySzLIgEREVA35+fnBxcUFAQEBhN0UIYWFhaN68Oa5du4ZKlSoVdnOKTHuSkpJgY2MD7+HnIRnaFFo7KH8MVBp4edzC4cvlkanh34RFw/zExwzFxvzEZqDS4JthiahRo0aW+WjfBu17psePHys3ICpKOGcYERER4bfffoOlpSXc3Nxw7do1jBs3Ds2aNSu0wlNRa8+rNLKEt/82kgqaRpZw/b4N57oRFPMTHzMUG/MTm0aW4ODgwLtJ/ovFMCIiIkJycjKmTJmCuLg4lCxZEm3atMH8+fPZnhzwtvJikv/9IEdiYn7iY4ZiY35ik/8thtELHCZJRETFwvbt22Fra5tlLjAifSjDJIedh2TEDwSiMZA0qOH0AJE3SyJT5hAf0TA/8TFDsTE/sRlIGiyZkAkXFxcOkwR7hhERUTHRtWvXwm4C/S9hxzAxSYCdZeqL/PjnYPEwP/ExQ7ExP7FJL3re0wss5xIRERERERERUbHBYZJEREREeaTt8p+QkIASJUoUdnNIT5mZmYiMjHxnd9KigsX8xMcMxcb8xPau8yvqwyTZM4yIiIhIT7wTk5gkSYKTkxPzExTzEx8zFBvzExvz08U5w4iIiIj0pFLx74kiUqlUsLe3L+xmUD4xP/ExQ7ExP7ExP118J0dERESkp8zMzMJuAuVDZmYmLl++zPwExfzExwzFxvzExvx0sRhGRERERMVGampqYTeB3gDzEx8zFBvzExvz+w+LYUREREREREREVGywGEZERERERERERMUGi2FEREREeuIE+mJSqVRwdXVlfoJifuJjhmJjfmJjfrp4N0kiIiIiPfG25GKSJAnW1taF3QzKJ+YnPmYoNuYnNuaniyVBIiIiIj3xTkxiyszMRGRkJPMTFPMTHzMUG/MTG/PTxWIYERERERUb/BAgNuYnPmYoNuYnNub3HxbDiIiIiIiIiIio2GAxjIiIiIiIiIiIig1JlmW5sBtBREREJIKkpCTY2NggMTERNjY2hd0c0pMsy0hNTYWpqSlvgiAg5ic+Zig25ie2d52f9j3T48ePi+TE/ewZRkRERETFhrGxcWE3gd4A8xMfMxQb8xMb8/sPi2FEREREetJoNIXdBMoHjUaDyMhI5ico5ic+Zig25ic25qeLxTAiIiIiIiIiIio2WAwjIiIiIiIiIqJig8UwIiIiIiIiIiIqNng3SSIiIqI84t0kxSbLMjQaDVQqFe+EJiDmJz5mKDbmJ7Z3nR/vJklEREREVESkp6cXdhPoDTA/8TFDsTE/sTG//7AYRkRERKQn3olJTBqNBtHR0cxPUMxPfMxQbMxPbMxPF4thRERERERERERUbLAYRkRERERERERExQaLYURERERUbBgYGBR2E+gNMD/xMUOxMT+xMb//8G6SRERERHlU1O+MRERERFQUFPX3TIaF3QAiIiIi0bw/IQ6GxkXvjR29jgw7i1QkPDEF8PZvK08FjfmJjxmKjfmJTcb2r0rAysoKksT8OEySiIiISE8GKnasF5GBSkZt5/vMT1DMT3zMUGzMT2wGKhl///037yb5LxbDiIiIiIiIiIio2GAxjIiIiIiIiIiIig0Ww4iIiIj0xREiYpKBJ2lGzE9UzE98zFBszE9sMmBqalrYrSgyOIE+ERERkZ4yZRXfRAkoU1bhRIxjYTeD8on5iY8Zio35iS1TVsHDw6Owm1FksGcYERERkZ4kiX8WF5EkyShrm8L8BMX8xMcMxcb8xCZJMh4+fMgJ9P/FYhgRERGRnlT8ICAklSTDo2wC8xMU8xMfMxQb8xObSpJx8+ZNyDLzA1gMIyIiIiIiIiKiYoTFMCLKllqthiRJSExMLOymAEC+/oLh5+eHgICANzqun58funbt+kb7KEwBAQGoXbt2ruu8+hq9vb0xfvz4t9quoubQoUPw9PREZmZmnreRJAnbt29/e40qAl5+jbGxsZAkCeHh4YXapoK0ZMkSdOrUqbCbQURERETvGIthRMWQJEm5Pt60gPQ2hIWFISoqqsD2Fxwc/NrzEBsbW2DHe1nv3r3Rvn17nWV79+7N9twHBASgQoUKb6UdOdm2bRtmz5791o/j7e2tnGtTU1NUrVoVixcvLrD9u7i4YMGCBXlad/LkyZgxYwYMDAx02pXdw9vbu8DamJ3MzEx8//33qFGjBkxNTVGiRAl06NABYWFhOusFBwfrtCUgIACSJGW5tgDgm2++eSdtf5c2b96Mpk2bAgCOHTsGV1dXnecjIiLQp08fODk5wczMDJ6enli4cKHOOkOGDMHZs2dx9OhR/RvAEQZikoGEFFPmJyrmJz5mKDbmJzYZsLKyKuxWFBkshhEVQ/Hx8cpjwYIFsLa21lk2adKkfO03PT29gFv6n61bt2Lr1q0Ftr9evXrpvOYmTZpg+PDhOsucnJwK7Hgv8/HxQVhYGJ4/f64sCwkJgZOTE9Rqtc66ISEh8PHxeSvtyImdnd07+0WpPecXL15Ez549MWrUKKxfv/6dHFsrNDQUMTEx6N69O4AXxUDtNXDy5EkAwMGDB5Vl27Zte2ttkWUZvXv3xqxZszBu3DhcunQJarUaTk5O8Pb2fm1PNEdHR4SEhODWrVs6y3/55Zd3XlR9244fP45mzZoBAI4ePar8X+vMmTMoXbo01q5di6ioKEyfPh3Tpk1DUFCQso6xsTH69u2LRYsW6X38TJlvoUSUKasQHlea+QmK+YmPGYqN+YktU1ahUqVKMDAwKOymFAm8iomKIQcHB+VhY2MDSZJ0lllaWirrnjlzBvXr14e5uTmaNm2K6Oho5TntELzly5ejYsWKMDU1BQAkJiZi2LBhKFWqFKytrdGqVStERETotGHHjh2oW7cuTE1N4erqisDAQJ3i0Ku2bduGLVu2FNg5MDMz03nNxsbGMDc311n28i+Kb7/9Fo6OjrC3t8eoUaOQkZGhPJeWloZJkyahXLlysLCwQKNGjbIUtV7m4+ODlJQUnD59WlmmVqsxdepUnDhxAqmpqQCA1NRUnDhxQimG5eW8fvXVVyhTpgysrKwwdOhQZV9amZmZmDBhAmxtbWFvb4/JkydnGYL66jBJFxcXzJkzB0OGDIGVlRUqVKiAn3/+WWebY8eOoXbt2jA1NUX9+vWxffv2PA2p055zV1dXBAQEwM3NDTt37gQATJkyBe7u7jA3N4erqys+//xznfMOAL///jsaNGgAU1NTlCxZEh988IHyGm7cuIFPP/1U6dGVkw0bNqBt27bK9WtnZ6dcA6VKlQIA2NvbK8vs7OyUbR88eIAPPvgA5ubmOm3XunDhAjp06ABLS0uUKVMGAwYMwIMHD3Jsy6ZNm7BlyxasXr0aw4YNQ8WKFVGrVi38/PPP6Ny5M4YNG4YnT57kuH3p0qXRrl07rFq1Sll27NgxPHjwAO+9957OuqdOnULbtm1RsmRJ2NjYwMvLC2fPns1x39nJ7fWtXr0a9vb2SEtL09mma9euGDBgAIAXvbd8fHxgZWUFa2tr1KtXT+f7IjfHjh1TCmChoaFZimFDhgzBwoUL4eXlBVdXV/Tv3x+DBw/OUszs1KkTdu7ciWfPnmV7nLS0NCQlJek8AN5NUlSSJKNiqcfMT1DMT3zMUGzMT2ySJOPu3bu8m+S/WAwjolxNnz4d8+fPx+nTp2FoaIghQ4boPH/t2jVs3boV27ZtUwofPXr0wL179/DHH3/gzJkzqFu3Llq3bo2EhAQAL3pxDBw4EOPGjcPFixexdOlSBAcH48svvwQAPHnyBKGhocpj9erViIuLQ2RkJH777TdleVhYWJYP2m9DSEgIYmJiEBISglWrViE4OBjBwcHK86NHj8bx48exYcMGnD9/Hj169ED79u1x9erVbPfn7u6OsmXLIiQkBACQnJyMs2fPokePHnBxccHx48cBvPiwn5aWphTDXndeN23ahICAAMyZMwenT5+Go6NjlmGH8+fPR3BwMH755ReEhoYiISEBv/3222vPwfz581G/fn2cO3cOI0eOxCeffKIURpOSktCpUyfUqFEDZ8+exezZszFlyhT9TvK/zMzMlB6GVlZWCA4OxsWLF7Fw4UIsW7YM33//vbLu7t278cEHH6Bjx444d+4cDh06hIYNGwJ4UTwtX748Zs2apfToysnRo0dRv379fLU3MDAQPXv2xPnz59GxY0f069dPySMxMRGtWrVCnTp1cPr0aezduxf//PMPevbsmeP+fv31V7i7u2c7j9XEiRPx8OFDHDhwINc2DRkyROf6/OWXX9CvXz8YGxvrrJecnIxBgwYhNDQUf/31F9zc3NCxY0ckJyfn6bW/7vX16NEDmZmZOgXCe/fuYffu3crPkX79+qF8+fI4deoUzpw5g6lTp8LIyCjHY/7666+wtbWFra0tTp48iQEDBsDW1hZ79uzBpEmTYGtri19//TXH7R8/fqxTzASA+vXr4/nz5zhx4kS228ydOxc2NjbKQ9tjlHfSEpPq3w9yzE9MzE98zFBszE9sqn+LYbyb5AsshhFRrr788kt4eXmhatWqmDp1Ko4dO6bT2yg9PR2rV69GnTp1ULNmTYSGhuLkyZPYvHkz6tevDzc3N3z77bewtbVVenYFBgZi6tSpGDRoEFxdXdG2bVvMnj0bS5cuBQBYWFggOjoavr6+aNGiBQYNGqQcr1u3bmjRogXee+893Lp1CyYmJm/9HJQoUQJBQUHw8PDA+++/j/feew+HDh0CAMTFxWHlypXYvHkzWrRogUqVKmHSpElo3rw5Vq5cmeM+fXx8lN5jR48ehbu7O0qVKoWWLVsqy9VqNSpWrAhnZ+c8ndcFCxZg6NChGDp0KKpUqYIvvvgCVatW1TnuggULMG3aNHTr1g2enp5YsmQJbGxsXnsOOnbsiJEjR6Jy5cqYMmUKSpYsqRTzfv31V0iShGXLlqFq1aro0KEDPvvsM73OcWZmJtauXYvz58+jVatWAIAZM2agadOmcHFxQadOnTBp0iRs2rRJ2ebLL79E7969ERgYCE9PT9SqVQvTpk0D8KJ3l4GBAaysrJQeXTm5ceMGypYtq1d7tfz8/NCnTx9UrlwZc+bMQUpKijK0MigoCHXq1MGcOXPg4eGBOnXq4JdffkFISAiuXLmS7f6uXLkCT0/PbJ/TLtdu6+fnl20PxPfffx9JSUk4cuQInjx5gk2bNmUpYgNAq1at0L9/f3h4eMDT0xM///wznj59isOHD+fptb/u9ZmZmaFv37463wdr165FhQoVlLnL4uLi0KZNG3h4eMDNzQ09evRArVq1cjxm586dER4ejm+//RZVq1ZFZGQkVq9ejTJlyuDChQsIDw9H586ds9322LFj2LhxI0aMGKGz3NzcHDY2Nrhx40a2202bNg2PHz9WHjdv3szT+SEiIiKiosuwsBtAREVbzZo1lf87OjoCeNG7Qzv/kLOzszKUDHgx7CklJQX29vY6+3n27BliYmKUdcLCwpSeYMCLYkhqaiqePn0Kc3NzDB06FE2bNkXv3r1x/vx5nX01aNAAGzZsyDJh9ttSrVo1nSGTjo6OiIyMBABERkYiMzMT7u7uOtukpaVlOQcv0w5FzMjIgFqtVooDXl5eSlFQrVYrvcLycl4vXbqEjz/+WOf5Jk2aKEWrx48fIz4+Ho0aNVKeNzQ0RP369V/7F6KXrwPtsNp79+4BAKKjo1GzZk1lmCEApYfW6yxevBjLly9Heno6DAwM8Omnn+KTTz4BAGzcuBGLFi1CTEwMUlJS8Pz5c1hbWyvbhoeHY/jw4Xk6Tm6ePXum03Z9vHxeLCwsYG1trZyXiIgIhISE6Aw71oqJiclyzWi96V/rjIyM0L9/f6xcuRJ///033N3dddqp9c8//2DGjBlQq9W4d+8eMjMz8fTpU8TFxeXpOHl5fcOHD0eDBg1w+/ZtlCtXDsHBwfDz81OGrU6YMAHDhg3DmjVr0KZNG/To0QOVKlXK8ZiWlpawtLTE2bNn0aVLF7i4uGDdunXo2LEjXFxcctzuwoUL6NKlC/z9/dGuXbssz5uZmeHp06fZbmtiYvJOiu5ERERE9O6wGEZEuXp5yJL2A+zL48wtLCx01k9JSYGjo2O2PVZsbW2VdQIDA9GtW7cs67xclPD09ERwcDDq1q2rs866deveWSEMQJZhW5IkKecgJSUFBgYGOHPmTJbJKLMrEmj5+PjgyZMnOHXqFEJCQpSeVF5eXhgyZAgSEhJw4sQJfPTRR8pxXnde36bczsGb6NevH6ZPnw4zMzM4OjpCpXrRYfn48ePo168fAgMD4evrCxsbG2zYsAHz589XtjUzM3vj4wNAyZIl8ejRo3xt+7pro1OnTpg3b16W7bSF5Ve5u7vj0qVL2T6nXZ5TEe1lQ4YMQaNGjXDhwoVse4UBwKBBg/Dw4UMsXLgQzs7OMDExQZMmTfJ8I4y8vL46deqgVq1aWL16Ndq1a4eoqCjs3r1bWS8gIAB9+/bF7t278ccff8Df3x8bNmxQ5n57WVxcnNLTMTU1FYaGhli4cCHS0tKgUqmwYcMG9O/fH0uWLNHZ7uLFi2jdujVGjBiBGTNmZPtaEhISdIr6eSHLEnKeiY6KKlmWcCfRArLM9ETE/MTHDMXG/MQmyxLs7OxynUu3OGExjIgKVN26dXH37l0YGhrm2FOjbt26iI6ORuXKlV+7P+18Q2XKlEF6ejoePXqEXbt24dNPPy3IZudbnTp1kJmZiXv37qFFixZ53q5SpUpwcnLCzp07ER4eDi8vLwBAuXLlUK5cOcyfPx/p6elKz7C8nFdPT0+cOHECAwcOVJb99ddfyv9tbGzg6OiIEydOoGXLlgCA58+fK/OP5VeVKlWwdu1apKWlKT1oTp06ladtbWxssr0Ojh07BmdnZ0yfPl1Z9uowtpo1a+LQoUMYPHhwtvs2NjZGZmbma9tQp04dXLx4MU/t1UfdunWxdetWuLi4wNAwb79ue/fujb59++L333/PMm/Y/PnzYW9vj7Zt2752P9WqVUO1atVw/vx59O3bN9t1wsLCsHjxYnTs2BEAcPPmzVwn939VXl/fsGHDsGDBAty+fRtt2rTJcpdWd3d3uLu749NPP0WfPn2wcuXKbIthZcuWRXh4OO7evYs2bdogPDwcmZmZqF27No4ePQo7OzudnoMAEBUVhVatWmHQoEE6PVFfFhMTg9TUVNSpUyfPrx0ANLLEuSYEpJElXL6Tc69dKtqYn/iYodiYn9g0svQ/d3fxN8H3cURUoNq0aYMmTZqga9eu2L9/P2JjY3Hs2DFMnz5duUvczJkzsXr1agQGBiIqKgqXLl3Chg0bsu21sXXrVvj6+iIiIgIRERFo3rx5lrvBFSZ3d3f069cPAwcOxLZt23D9+nWcPHkSc+fO1ekBkx0fHx8sXrwYlStXRpkyZZTlXl5e+OGHH5SJ9oG8nddx48bhl19+wcqVK3HlyhX4+/sjKipK55jjxo3DV199he3bt+Py5csYOXIkEhMT3+gc9O3bFxqNBiNGjMClS5ewb98+fPvttwCQ7788ubm5IS4uDhs2bEBMTAwWLVqUZaJ/f39/rF+/Hv7+/rh06RIiIyN1eim5uLjgyJEjuH37dq5FHl9fX4SGhuarnbkZNWoUEhIS0KdPH5w6dQoxMTHYt28fBg8enGORrnfv3vjggw8waNAgrFixArGxsTh//jw++ugj7Ny5E8uXL8/SGzMnf/75J+Lj43PsOejm5oY1a9bg0qVLOHHiBPr166dXb7u8vr6+ffvi1q1bWLZsmU4vtWfPnmH06NFQq9W4ceMGwsLCcOrUqRznTDM0NETlypVx69YtNGrUCB4eHnj48CFcXV3RsGFDVK5cGaVLl1bWv3DhAnx8fNCuXTtMmDABd+/exd27d3H//n2d/R49ehSurq65Ds/MDicPFpNKkuFR9iHzExTzEx8zFBvzE5tKkhEXF8e7Sf6LxTAiKlCSJGHPnj1o2bIlBg8eDHd3d/Tu3Rs3btxQCj6+vr7YtWsX9u/fjwYNGqBx48b4/vvv4ezsrLOvGzduYMCAAfjjjz9QpkwZODk5Qa1Wo02bNnr1YHnbVq5ciYEDB2LixImoUqUKunbtilOnTr32Ly8+Pj5ITk5W5gvT8vLyQnJystIrDMjbee3Vqxc+//xzTJ48GfXq1cONGzeU+be0Jk6ciAEDBmDQoEFo0qQJrKyssu2Fow9ra2v8/vvvCA8PR+3atTF9+nTMnDkTAPI9F1fnzp3x6aefYvTo0ahduzaOHTuGzz//XGcdb29vbN68GTt37kTt2rXRqlUrZfJ6AJg1axZiY2NRqVKlXIfA/T97dx4e0/m+Afw+k32PJEhCIiIhCUEi1B61BaWUWlPE2lqrRfG1xlqttFqqlBLaULX7qapaQoTasyAiYomWlFoSQRJmzu8PcmpkkYnE5G3uz3XNdTFz5pznzD3VmWfe877BwcE4e/assjpmcXF2dkZ0dDTUajXatm0LX19fjBkzBra2tsrloC+SJAk///wz/ve//+HLL79EjRo10KxZM1y9ehWRkZHo0qVLoY9vYWFR4CW033//Pe7evQt/f3/07dsXo0eP1momFdf52djYoFu3brC0tNSq38DAALdv30a/fv1QvXp19OjRA+3bt0doaGiBx42MjFRGNh44cED584s2btyIW7du4ccff4STk5Nyq1+/vtZ269atK9Lcc1xWXkySJMPZ9gHzExTzEx8zFBvzE5skybhz5w5Xk3xGkvlKENF/VEhICNzc3DBjxgx9l1LmREREYMCAAUhLSyu2ub1K0vjx45Genq4sXkDFq1WrVqhZsya+/vprfZeiJecyygsXLhRqVVUASE9Ph42NDVoMiYNkWLjnUOlhoNIg0OtPHDhfGWoNfxMWDfMTHzMUG/MTm4FKg88H34Ovr2+uuY5LQs5nprS0tFxTWZQGnDOMiIhe2Zo1a+Du7o5KlSohNjYWEyZMQI8ePYRohAHA5MmTsWTJEmg0mnxHbZHu7t69i8jISERGRmLJkiX6LieXGzduYM2aNYVuhBERERHRfwObYURE9MpSU1Mxbdo0pKamwsnJCd27d893wvLSyNbWFv/73//0XcZ/jp+fH+7evYv58+ejRo0a+i4nl9atWxf5uRpZQsn/pkrFTSNLuHzLBhquhCYk5ic+Zig25ic2jSzB0dGRq0k+w8skieg/a+vWrbC1tc01JxcRUVHlDPlvNugMDI2t9F0OERERUaHtW/L6VpMs7ZdJ8loQIvrP6tKlCxthRFQiDCSuxCQiA0mDuq43mZ+gmJ/4mKHYmJ/YDCQNkpOT813VvKxhM4yIiIhIV7zCQEwSYGeZyfxExfzExwzFxvzEJgH379/XdxWlBi+TJCIiIiqknCH/d+7cQbly5fRdDulIrVYjPj7+ta2kRcWL+YmPGYqN+YntdefHyySJiIiIiIiIiIhKCTbDiIiIiHTElZjEJEkSXFxcmJ+gmJ/4mKHYmJ/YmJ82Q30XQERERCQalYq/J4pIpVLB3t5e32VQETE/8TFDsTE/sTE/bfwkR0RERKQjrsQkJrVajfPnzzM/QTE/8TFDsTE/sTE/bWyGEREREVGZkZmZqe8S6BUwP/ExQ7ExP7Exv3+xGUZERERERERERGUGm2FERERERERERFRmsBlGREREpCNOoC8mlUoFd3d35ico5ic+Zig25ic25qeNq0kSERER6YjLkotJkiRYW1vruwwqIuYnPmYoNuYnNuanjS1BIiIiIh1xJSYxqdVqxMfHMz9BMT/xMUOxMT+xMT9tbIYRERERUZnBLwFiY37iY4ZiY35iY37/YjOMiIiIiIiIiIjKDDbDiIiIiIiIiIiozJBkWZb1XQQRERGRCNLT02FjY4N79+7BxsZG3+WQjmRZRmZmJkxNTbkIgoCYn/iYodiYn9hed345n5nS0tJK5cT9HBlGRERERGWGsbGxvkugV8D8xMcMxcb8xMb8/sVmGBEREZGONBqNvkugItBoNIiPj2d+gmJ+4mOGYmN+YmN+2tgMIyIiIiIiIiKiMoPNMCIiIiIiIiIiKjPYDCMiIiIiIiIiojKDq0kSERERFRJXkxSbLMvQaDRQqVRcCU1AzE98zFBszE9srzs/riZJRERERFRKZGdn67sEegXMT3zMUGzMT2zM719shhERERHpiCsxiUmj0SAxMZH5CYr5iY8Zio35iY35aWMzjIiIiIiIiIiIygw2w4iIiIiIiIiIqMxgM4yIiIiIygwDAwN9l0CvgPmJjxmKjfmJjfn9i6tJEhERERVSzspIR44cgaWlpb7LISIiIiq0WrVqvbZjcTVJIiIiov8Y/pYoJlmWkZ2dzfwExfzExwzFxvzEJssy0tPTmd8zbIYRERER6YgfJMUkyzLS0tKYn6CYn/iYodiYn9hkWcalS5e4muQzbIYREREREREREVGZwWYYERERERERERGVGWyGEREREelIkiR9l0BFIEkSDAwMmJ+gmJ/4mKHYmJ/YJEmCqampvssoNQz1XQARERGRaPhFQEySJMHOzk7fZVARMT/xMUOxMT+xSZIELy8vfZdRanBkGBEREZGOOHmwmGRZxqNHj5ifoJif+Jih2Jif2GRZxu3btzmB/jNshhERERHpiF8ExCTLMjIyMpifoJif+Jih2Jif2GRZxrVr15jfM2yGEZHwIiMjIUkS7t27p+9SABTtS3JISAhmzJjxSscNCQlBly5dXmkfr5ObmxsWLlz42o4nyzKGDh0KOzs7SJKEmJiYEjtWaXtPvg4v5ilJErZu3ar8/fz582jYsCFMTU1Rt25dXLlypcRzICIiIiLKC5thRFSqSZJU4O1VG0glITo6GmfPni22/YWHh7/0dbhy5UqxHe9Fbm5uynEsLCzg7++PDRs2lNjxSsquXbsQHh6OHTt24MaNG6hVq1aubXKaWDk3MzMz1KxZE999951Ox2rcuDFu3LgBGxub4iq/WGRkZMDIyAg//fST1v29evXK833k5uaGqVOnFsuxp0+fDgsLCyQmJmLv3r3Fsk8iIiIioqJgM4yISrUbN24ot4ULF8La2lrrvnHjxhVpv9nZ2cVc6b82bdqETZs2Fdv+evbsqXXOjRo1wpAhQ7Tuc3FxKbbj5WXmzJm4ceMGTp8+jfr166Nnz544fPhwkfZVkq99QZKTk+Hk5ITGjRvD0dERhob5ryGTmJiIGzdu4Ny5c3j//fcxbNgwnRo4xsbGcHR0LHWTrFtaWiIgIACRkZFa90dGRsLFxUXr/suXL+Pq1ato2bJlsRw7OTkZTZs2RZUqVWBvb18s+yxuurw3S1u2VDiSJMHIyIj5CYr5iY8Zio35iU2SJFhZWem7jFKDzTAiKtUcHR2Vm42NDSRJ0rrP0tJS2fbkyZMICAiAubk5GjdujMTEROWxGTNmoG7dulixYgWqVq2qLCt87949DB48GOXLl4e1tTVatmyJ2NhYrRq2bdsGf39/mJqawt3dHaGhoXjy5Em+NW/evBkbN24sttfAzMxM65yNjY1hbm6udZ+BgYGy/YIFC+Dk5AR7e3uMGDECjx8/Vh7LysrCuHHjUKlSJVhYWOCNN97I1RjJi5WVFRwdHVG9enV88803MDMzw//93/8BAK5du4YePXrA1tYWdnZ26Ny5s9YIo5zLN+fMmQNnZ2fUqFEjz2OsWLECtra2StNp48aN8PX1hZmZGezt7dG6dWs8ePAg3xoPHDiABg0awMTEBE5OTpg4caKSU0hICEaNGoWUlBRIkgQ3N7cCz7dChQpwdHRE1apVMXr0aFStWhWnTp1SHtdoNJg3bx6qVq0KMzMz1KlTRyvzFy+TDA8Ph62tLX777Td4e3vD0tIS7dq1w40bN5TnPHnyBKNHj4atrS3s7e0xYcIE9O/fv8BLX2/fvo3evXujUqVKMDc3h6+vL9atW1fgub355ptamSckJCAzMxPDhg3Tuj8yMhImJiZo1KgRkpOT0blzZ1SsWBGWlpaoX78+9uzZU+BxnidJEk6ePImZM2cWOKKzoAx37NgBW1tbqNVqAEBMTAwkScLEiROV5w8ePBjvvfee8vdDhw6hWbNmMDMzg4uLC0aPHq31HnJzc8OsWbPQr18/WFtbY+jQoblqysrKQnp6utYt55xIPJIkwdbWlvkJivmJjxmKjfmJTZIkVKtWTet7Q1nGZhgR/WdMnjwZYWFhOHHiBAwNDTFw4ECtxy9evIhNmzZh8+bNyjxF3bt3x82bN/Hrr7/i5MmT8Pf3R6tWrXDnzh0AQFRUFPr164cPP/wQ586dw7JlyxAeHo45c+YAAB48eIBDhw4ptzVr1iAlJQXx8fHYsmWLcn90dDSysrJK/DXYv38/kpOTsX//fqxevRrh4eEIDw9XHh85ciSOHDmCn376CXFxcejevTvatWuHpKSkQh/D0NAQRkZGyM7OxuPHjxEUFAQrKytERUUhOjpaafQ8P8pm7969SExMxO+//44dO3bk2udnn32GiRMnYvfu3WjVqhVu3LiB3r17Y+DAgUhISEBkZCS6du2a73xsf/31Fzp06ID69esjNjYW3377Lb7//nvMnj0bAPDVV19h5syZqFy5Mm7cuIHjx48X6lxlWcauXbuQkpKCN954Q7l/3rx5WLNmDZYuXYqzZ8/io48+wnvvvYcDBw7ku6+HDx9iwYIF+OGHH3Dw4EGkpKRojWycP38+IiIisGrVKkRHRyM9PV1rzq28ZGZmol69evjll19w5swZDB06FH379sWxY8fyfc6bb76pjHwDnr5nmjZtipYtW2o1w/bv349GjRrB1NQUGRkZ6NChA/bu3YvTp0+jXbt26NSpE1JSUl7yCj5148YN1KxZE2PHjs13ROfLMmzWrBnu37+P06dPA3jaOHNwcNCq+cCBA2jRogWApyPR2rVrh27duiEuLg7r16/HoUOHMHLkSK3jLliwAHXq1MHp06fzvCR03rx5sLGxUW45ozA5+ayYZFnGgwcPmJ+gmJ/4mKHYmJ/YZFlGamoqV5N8Jv9rRIiIBDNnzhwEBgYCACZOnIi33noLmZmZyiiw7OxsrFmzBuXLlwfwdNTIsWPHcPPmTZiYmAB4+sV469at2LhxI4YOHYrQ0FBMnDgR/fv3BwC4u7tj1qxZ+OSTT7TmQBo9ejQePnyoVU/Xrl0BANbW1vjuu+/QpEmTEn8NypUrh8WLF8PAwABeXl546623sHfvXgwZMgQpKSlYtWoVUlJS4OzsDAAYN24cdu3ahVWrVmHu3Lkv3X92djbCwsKQlpaGli1bYv369dBoNFixYoXyK+GqVatga2uLyMhItG3bFgBgYWGBFStWwNjYONc+J0yYgB9++AEHDhxAzZo1ATxtnjx58gRdu3ZFlSpVAAC+vr751rVkyRK4uLhg8eLFkCQJXl5euH79OiZMmIBp06bBxsYGVlZWMDAwgKOj40vPs3LlygCejgrSaDSYOXMmmjdvrtw3d+5c7NmzB40aNQLw9H1x6NAhLFu2THkPvujx48dYunQpqlWrBuBpY3LmzJnK44sWLcKkSZPwzjvvAAAWL16MnTt3FlhnpUqVtBpLo0aNwm+//Yaff/4ZDRo0yPM5TZo0gbGxMSIjI9G7d29ERkYiMDAQ9erVwz///IPLly+jatWqOHDgAAYNGgQAqFOnDurUqaPsY9asWdiyZQu2b9+eq7mUl5zLUi0tLZXX/59//tHapjAZ1q1bF5GRkcqlnh999BFCQ0ORkZGBtLQ0XLx4UXn9582bh+DgYIwZMwYA4Onpia+//hqBgYH49ttvlX8XWrZsibFjx+Zb+6RJk/Dxxx8rf09PT4eLiwu/CAhKlmU8fPgQZmZmHNkgIOYnPmYoNuYntpxmWM53obKOzTAi+s+oXbu28mcnJycAwM2bN+Hq6goAqFKlitY//rGxscjIyMg1f9GjR4+QnJysbBMdHa2MBAMAtVqNzMxMPHz4EObm5hg0aBAaN26MXr16IS4uTmtf9evXx08//QR3d/fiPdl81KxZU2vos5OTE+Lj4wEA8fHxUKvVqF69utZzsrKyXjqH04QJEzBlyhRkZmbC0tISn376Kd566y2MHz8eFy9ezDX/QGZmpvIaAk8bWXk1wsLCwvDgwQOcOHFC6zWqU6cOWrVqBV9fXwQFBaFt27Z49913Ua5cuTzrS0hIQKNGjbQ+mDVp0gQZGRn4888/lfdAYUVFRcHKygpZWVk4duwYRo4cCTs7OwwbNgwXL17Ew4cP0aZNG63nZGdnw8/PL999mpubK40w4Gk2N2/eBACkpaXh77//1mpgGRgYoF69egX+eqdWqzF37lz8/PPP+Ouvv5CdnY2srCyYm5sXWEf9+vWVZtiBAwcwfvx4GBoaonHjxoiMjIQsy0hJScGbb74J4OnE+zNmzMAvv/yiNCofPXpU6JFhhVGYDAMDAxEZGYmxY8ciKioK8+bNw88//4xDhw7hzp07cHZ2hqenJ4Cn/+3GxcUhIiJC2Z8sy9BoNLh8+TK8vb0BAAEBAQXWZWJiojTLiYiIiOi/gc0wIvrPMDIyUv6c84X6+UaChYWF1vYZGRlwcnLKc84sW1tbZZvQ0FBllNfzckaWAIC3tzfCw8Ph7++vtU1ERMRra4QB2q8B8PR1yHkNMjIyYGBggJMnT+aaK+D5udfyMn78eISEhMDS0hIVK1ZUXt+MjAzUq1dPq+GQ4/nG44uvfY5mzZrhl19+wc8//6w195OBgQF+//13HD58GLt378aiRYswefJkHD16FFWrVi2w1uJQtWpV5T1Qs2ZNHD16FHPmzMGwYcOQkZEBAPjll19QqVIlrecV1DTJK5tXHV30+eef46uvvsLChQvh6+sLCwsLjBkz5qUTwb/55ptYv349zp49i0ePHinv28DAQOzfvx8ajQbm5ubKpaHjxo3D77//jgULFsDDwwNmZmZ49913X/tiCC1atMDKlSsRGxsLIyMjeHl5oUWLFoiMjMTdu3e1RuVlZGTg/fffx+jRo3Pt5/nmaH7vTSIiIiL672IzjIjKLH9/f6SmpsLQ0DDfCdX9/f2RmJgIDw+Pl+5v+/btAICKFSsiOzsbd+/exY4dO/DRRx8VZ9lF5ufnB7VajZs3b6JZs2Y6PdfBwSHP18Df3x/r169HhQoVYG1trXNNDRo0wMiRI9GuXTsYGhpqXfInSRKaNGmCJk2aYNq0aahSpQq2bNmidclaDm9vb2zatAmyLCuNuujoaFhZWSmXPL4KAwMDPHr0CADg4+MDExMTpKSk5HtJpK5sbGxQsWJFHD9+XLkcU61W49SpU6hbt26+z4uOjkbnzp2VSeM1Gg0uXLgAHx+fAo/35ptvYvbs2Vi7di2aNm2qNEebN2+O7777DrIsK5dT5hwnJCREuYQzIyNDa5GE4lCYDHPmDfvyyy+V175Fixb49NNPcffuXa3LHf39/XHu3LlC/bdbFLw8REySJMHU1JT5CYr5iY8Zio35iU2SJNjZ2TG/ZziBPhGVWa1bt0ajRo3QpUsX7N69G1euXMHhw4cxefJknDhxAgAwbdo0rFmzBqGhoTh79iwSEhLw008/YcqUKbn2t2nTJgQFBSE2NhaxsbFo2rQpNm/e/LpPK1/Vq1dHcHAw+vXrh82bN+Py5cs4duwY5s2bh19++aVI+wwODoaDgwM6d+6MqKgoXL58GZGRkRg9ejT+/PPPQu2jcePG2LlzJ0JDQ7Fw4UIAwNGjRzF37lycOHECKSkp2Lx5M27duqVc2vai4cOH49q1axg1ahTOnz+Pbdu2Yfr06fj444+hUun+v7qbN28iNTUVV69exYYNG/DDDz+gc+fOAJ6urDlu3Dh89NFHWL16NZKTk3Hq1CksWrQIq1ev1vlYOUaNGoV58+Zh27ZtSExMxIcffoi7d+8W+IHF09NTGUGXkJCA999/H3///fdLj9W4cWOYmJhg0aJFWg29Bg0a4ObNm9i2bZtyiWTOcXIWnoiNjUWfPn2KffLVwmRYrlw51K5dGxEREcpE+c2bN8epU6dw4cIFrXOZMGECDh8+jJEjRyImJgZJSUnYtm1boeY4Kwx+kBRTzrLyzE9MzE98zFBszE9skiTB1dW1SJ+N/4s4MoyIyixJkrBz505MnjwZAwYMwK1bt+Do6IjmzZujYsWKAICgoCDs2LEDM2fOxPz585VLswYPHqy1r6tXr6Jv374YN26c8gEhMjISs2fPxj///AMHB4fXfn55WbVqFWbPno2xY8fir7/+goODAxo2bIiOHTsWaX/m5uY4ePAgJkyYgK5du+L+/fuoVKkSWrVqpdNIsaZNm+KXX35Bhw4dYGBggNatW+PgwYNYuHAh0tPTUaVKFYSFhaF9+/Z5Pr9SpUrYuXMnxo8fjzp16sDOzg6DBg3Ks2lZGDVq1ADwdOVMFxcXvP/++5gxY4by+KxZs1C+fHnMmzcPly5dgq2tLfz9/fG///2vSMcDnjZvUlNT0a9fPxgYGGDo0KEICgoqcPnrKVOm4NKlSwgKCoK5uTmGDh2KLl26IC0trcBjmZqaomHDhlqrLwJPL/Ns2LAhIiMjtZphX3zxBQYOHIjGjRvDwcEBEyZMQHp6epHPNS+FzTAwMBAxMTFK3XZ2dvDx8cHff/+t5AY8nUPwwIEDmDx5Mpo1awZZllGtWjX07NmzWOrlBPpikmUZGRkZsLS05Jc5ATE/8TFDsTE/seXMCVu5cmU2xABIMj/NERHpXUhICNzc3LQaLlS2aTQaeHt7o0ePHpg1a5a+y6Fn0tPTYWNjg+jo6CJdGkz6pdFocPv2bdjb2/OLgICYn/iYodiYn9g0Gg00Gg18fX0L/LG1uOR8ZkpLSyuVn5k4MoyIiKgUuHr1Knbv3o3AwEBkZWVh8eLFuHz5Mvr06aPv0oiIiIiI/lPYziUiIioFVCoVwsPDUb9+fTRp0gTx8fHYs2dPvvOkERERERFR0XBkGBFRKdClSxfY2trquwzSIxcXF0RHR+u7DCokzpUiJkmSYG5uzvwExfzExwzFxvzEJkkSHB0dmd8zbIYREZUCXbp00XcJRKQDfpAUkyRJsLCw0HcZVETMT3zMUGzMT2w5zTB6ipdJEhEREemI6w+JSZZl3Lt3j/kJivmJjxmKjfmJTZZlJCcnQ61W67uUUoEjw4iIiIh05OXlhXLlyum7DNKRWq1GfHw8fHx8XstKWlS8mJ/4mKHYmJ/YcvKjpzgyjIiIiIiIiIiIygw2w4iIiIiIiIiIqMxgM4yIiIhIR5xAX0ySJMHFxYX5CYr5iY8Zio35iY35aeOcYUREREQ6Uqn4e6KIVCoV7O3t9V0GFRHzEx8zFBvzExvz08ZPckREREQ64kpMYlKr1Th//jzzExTzEx8zFBvzExvz08ZmGBERERGVGZmZmfougV4B8xMfMxQb8xMb8/sXm2FERERERERERFRmsBlGRERERERERERlBpthRERERDriBPpiUqlUcHd3Z36CYn7iY4ZiY35iY37auJokERERkY64LLmYJEmCtbW1vsugImJ+4mOGYmN+YmN+2tgSJCIiItIRV2ISk1qtRnx8PPMTFPMTHzMUG/MTG/PTxmYYEREREZUZ/BIgNuYnPmYoNuYnNub3LzbDiIiIiIiIiIiozGAzjIiIiIiIiIiIygxJlmVZ30UQERERiSA9PR02Nja4d+8ebGxs9F0O6UiWZWRmZsLU1JSLIAiI+YmPGYqN+YntdeeX85kpLS2tVE7cz5FhRERERFRmGBsb67sEegXMT3zMUGzMT2zM719shhERERHpSKPR6LsEKgKNRoP4+HjmJyjmJz5mKDbmJzbmp43NMCIiIiIiIiIiKjPYDCMiIiIiIiIiojKDzTAiIiIiIiIiIiozuJokERERUSFxNUmxybIMjUYDlUrFldAExPzExwzFxvzE9rrz42qSRERERESlRHZ2tr5LoFfA/MTHDMXG/MTG/P7FZhgRERGRjrgSk5g0Gg0SExOZn6CYn/iYodiYn9iYnzY2w4iIiIiIiIiIqMxgM4yIiIiIiIiIiMoMQ30XQERERCSaK16dcEfDyYNFozE0QMa7zXFp4xionqj1XQ7piPmJjxmKjfmJTWNoAIPdi/RdRqnB1SSJiIiICilnZaTTdg1hpeJvikRERCSOareiXtuxuJokERER0X+MzEFhQpIlINPZnvkJivmJjxmKjfmJTZaeNqg4HuopNsOIiIiIdGVgoO8KqAhkAwPcbuUPmfkJifmJjxmKjfmJTTYwwKVLl7ia5DNshhERERERERERUZnBZhgREREREREREZUZbIYRERERUZlhmPZA3yXQK2B+4mOGYmN+YjM1NdV3CaUGV5MkIiIiKiSuJklERESi4mqS/+LIMCIiIiIdySoupSUiWSXhgUcl5ico5ic+Zig25ic2WSXh9u3bnED/GTbDiIgoT5GRkZAkCffu3dN3KQBQpGWgQ0JCMGPGjFc6bkhICLp06fJK+/gvuHLlCiRJQkxMjL5LUcyYMQN169Z96XZubm5YuHBh8R5cxY9QIpJVKtxr5AOZ+QmJ+YmPGYqN+YlNVqlw7dq1In2m/i/iu5iIqAySJKnA26s2kEpCdHQ0zp49W2z7Cw8Pf+nrcOXKlWI73ovc3NyU45iZmcHNzQ09evTAvn37SuyYr8LFxQU3btxArVq1XsvxNm3ahJYtW6JcuXIwMzNDjRo1MHDgQJw+fVrZZty4cdi7d+9rqYeIiIiI/jvYDCMiKoNu3Lih3BYuXAhra2ut+8aNG1ek/WZnZxdzpf/atGkTNm3aVGz769mzp9Y5N2rUCEOGDNG6z8XFpdiOl5eZM2fixo0bSExMxJo1a2Bra4vWrVtjzpw5+T5HlmU8efKkROvKi4GBARwdHWFoWPLzZE2YMAE9e/ZE3bp1sX37diQmJmLt2rVwd3fHpEmTlO0sLS1hb2+f735K8v1IREREROJiM4yIqAxydHRUbjY2NpAkSes+S0tLZduTJ08iICAA5ubmaNy4MRITE5XHci5TW7FiBapWraqsUHPv3j0MHjwY5cuXh7W1NVq2bInY2FitGrZt2wZ/f3+YmprC3d0doaGhBTZ5Nm/ejI0bNxbba2BmZqZ1zsbGxjA3N9e6z8DAQNl+wYIFcHJygr29PUaMGIHHjx8rj2VlZWHcuHGoVKkSLCws8MYbbyAyMvKlNVhZWcHR0RGurq5o3rw5vvvuO0ydOhXTpk1TXuecy1V//fVX1KtXDyYmJjh06BCysrIwevRoVKhQAaampmjatCmOHz+u7DsgIAALFixQ/t6lSxcYGRkhIyMDAPDnn39CkiRcvHgRwNORanPnzsXAgQNhZWUFV1dXfPfdd8rzX7xMMqeuvXv35vv+AIDZs2ejQoUKsLKywuDBgzFx4sQCL238448/8Nlnn+GLL77AF198gWbNmsHV1RX16tXDlClT8OuvvyrbvniZZM4lrXPmzIGzszNq1KiRa/+yLGPGjBlwdXWFiYkJnJ2dMXr06HzrycrKQnp6utaNxGZy/ba+S6BXwPzExwzFxvzEZmVlpe8SSg02w4iIqECTJ09GWFgYTpw4AUNDQwwcOFDr8YsXL2LTpk3YvHmz0ijp3r07bt68iV9//RUnT56Ev78/WrVqhTt37gAAoqKi0K9fP3z44Yc4d+4cli1bhvDwcGVE1IMHD3Do0CHltmbNGqSkpCA+Ph5btmxR7o+OjkZWVlaJvwb79+9HcnIy9u/fj9WrVyM8PBzh4eHK4yNHjsSRI0fw008/IS4uDt27d0e7du2QlJSk87E+/PBDyLKMbdu2ad0/ceJEfPrpp0hISEDt2rXxySefYNOmTVi9ejVOnToFDw8PBAUFKa9xYGCg0pCTZRlRUVGwtbXFoUOHAAAHDhxApUqV4OHhoRwjLCwMAQEBOH36NIYPH45hw4blam69qKD3R0REBObMmYP58+fj5MmTcHV1xbffflvg/tatWwdLS0sMHz48z8clqeBJe/fu3YvExET8/vvv2LFjR67HN23ahC+//BLLli1DUlIStm7dCl9f33z3N2/ePNjY2Ci3nNGC0hN1gXVQ6aR6oobD3lNQMT8hMT/xMUOxMT+xqZ6oUa1aNa0fe8syNsOIiKhAc+bMQWBgIHx8fDBx4kQcPnwYmZmZyuPZ2dlYs2YN/Pz8ULt2bRw6dAjHjh3Dhg0bEBAQAE9PTyxYsAC2trbKyK7Q0FBMnDgR/fv3h7u7O9q0aYNZs2Zh2bJlAAALCwskJiYiKCgIzZo1Q//+/ZXjde3aFc2aNcNbb72FP//8EyYmJiX+GpQrVw6LFy+Gl5cXOnbsiLfeekuZqyolJQWrVq3Chg0b0KxZM1SrVg3jxo1D06ZNsWrVKp2PZWdnhwoVKuSar2zmzJlo06YNqlWrBhMTE3z77bf4/PPP0b59e/j4+GD58uUwMzPD999/DwBo0aIFDh06BLVajbi4OBgbGyM4OFhpkEVGRiIwMFDrGB06dMDw4cPh4eGBCRMmwMHBAfv37y+w3oLeH4sWLcKgQYMwYMAAVK9eHdOmTSuw8QQAFy5cgLu7u9blmF988QUsLS2VW1paWr7Pt7CwwIoVK1CzZk3UrFkz1+MpKSlwdHRE69at4erqigYNGmDIkCH57m/SpElIS0tTbteuXQPA1SRFJaskpNdxZ36CYn7iY4ZiY35ik1USUlNTuZrkM2yGERFRgWrXrq382cnJCQBw8+ZN5b4qVaqgfPnyyt9jY2ORkZEBe3t7rQbG5cuXkZycrGwzc+ZMrcdz5ut6+PAhAGDQoEE4ceKE1vFz1K9fH6dPn0bPnj1L5JxfVLNmTa1f0ZycnJTXID4+Hmq1GtWrV9c6nwMHDijnqytZlnONgAoICFD+nJycjMePH6NJkybKfUZGRmjQoAESEhIAAM2aNcP9+/dx+vRpHDhwAIGBgWjRooXSDDtw4ABatGihdYznX+ucS2efzzovBb0/EhMT0aBBA63tX/x7YQwcOBAxMTFYtmwZHjx4UOAqSL6+vjA2Ns738e7du+PRo0dwd3fHkCFDsGXLlgIvzzUxMYG1tbXWDQBXkxSUrFLhfu1qXAlNUMxPfMxQbMxPbLJKhdTUVK4m+UzJz4JLRERCMzIyUv6c06B5/hclCwsLre0zMjLg5OSU55xZtra2yjahoaHo2rVrrm1y5h0DAG9vb4SHh8Pf319rm4iICLi7u+t8LkX1/GsAPH0dcl6DjIwMGBgY4OTJk7mGnT8/91ph3b59G7du3ULVqlW17n/xdX4ZW1tb1KlTB5GRkThy5AjatGmD5s2bo2fPnrhw4QKSkpJyjQwr6Dzz87L3h648PT1x6NAhPH78WNm3ra0tbG1t8eeff770+S97nVxcXJCYmIg9e/bg999/x/Dhw/H555/jwIEDuc6fiIiIiP6b2NIlIqJi5e/vj9TUVBgaGsLDw0Pr5uDgoGyTmJiY63EPDw+oXvi1cfv27QCAihUroly5cgCQ51xQ+uLn5we1Wo2bN2/mOhdHR0ed9/fVV19BpVKhS5cu+W5TrVo1GBsbIzo6Wrnv8ePHOH78OHx8fJT7AgMDsX//fhw8eBAtWrSAnZ0dvL29MWfOHDg5OaF69eo616eLGjVqaE3qDyDX31/Uu3dvZGRkYMmSJSVWl5mZGTp16oSvv/5aaRbGx8eX2PGIiIiIqHThyDAiIipWrVu3RqNGjdClSxd89tlnqF69Oq5fv45ffvkF77zzDgICAjBt2jR07NgRrq6uePfdd6FSqRAbG4szZ85g9uzZWvvbtGkTgoKCsHr1amRnZ6NPnz7YvHkzPvroIz2dobbq1asjODgY/fr1Q1hYGPz8/HDr1i3s3bsXtWvXxltvvZXvc+/fv4/U1FQ8fvwYly9fxo8//ogVK1Zg3rx5WhPbv8jCwgLDhg3D+PHjYWdnB1dXV3z22Wd4+PAhBg0apGzXokULLFq0COXLl4eXl5dy3+LFi9G9e/fiexHyMWrUKAwZMgQBAQFo3Lgx1q9fj7i4uAJH9TVq1Ahjx47F2LFjcfXqVXTt2hUuLi64ceMGvv/+e0iSlKthqovw8HCo1Wq88cYbMDc3x48//ggzMzNUqVJFtx3JMgDOmSIaSZZhfvEvSLxEREjMT3zMUGzMT2ySLMPOzu6lixGVFRwZRkRExUqSJOzcuRPNmzdXJk7v1asXrl69iooVKwIAgoKCsGPHDuzevRv169dHw4YN8eWXX+ZqSFy9ehV9+/bFr7/+iooVK8LFxQWRkZFo3bo1/vnnH32cXp5WrVqFfv36YezYsahRowa6dOmC48ePw9XVtcDnTZs2DU5OTvDw8EDfvn2RlpaGvXv3YsKECS895qeffopu3bqhb9++8Pf3x8WLF/Hbb78po+eAp/OGaTQarcshW7RoAbVanWu+sJIQHByMSZMmYdy4cfD398fly5cREhKidSlsXhYsWIC1a9fi9OnT6NixIzw9PdG9e3doNBocOXLk33m7isDW1hbLly9HkyZNULt2bezZswf/93//B3t7e532I6k5+ayIJLUG5Y6cY36CYn7iY4ZiY35ik9QauLq6vtKPiv8lkszZ04iI6D8qJCQEbm5umDFjhr5LoWfatGkDR0dH/PDDD/oupUjS09NhY2ODU+Ubw1rmh0nRyAYq3GvgBdtj5/llTkDMT3zMUGzMT2yygQrGx35A5cqVX0tDLOczU1pa2iv9kFlS+CmOiIiISsTDhw/xxRdf4OzZszh//jymT5+OPXv2oH///vou7dXxEgMhyZKEhx6VIDM/ITE/8TFDsTE/scmShDt37nA1yWc4ZxgRERGViJxLZufMmYPMzEzUqFEDmzZtQuvWrfVdGhERERGVYWyGERHRf1aXLl1ga2ur7zLKLDMzM+zZs0ffZRARERERaWEzjIiI/rO6dOmi7xLov0qjAWCg7ypIR5JGA6u4ZEgaznUjIuYnPmYoNuYnNkmjgaOjI1eTfIbNMCIiIiIdSRqZM68KSNLIsI69pO8yqIiYn/iYodiYn9gkjQxHR0d9l1FqsBlGREREpKMqF3eiXLly+i6DdKRWq3HlyhW4ubnBwIAj+0TD/MTHDMXG/MSmVquRnJzM/J7hb5pEREREVGbcv39f3yXQK2B+4mOGYmN+YmN+/2IzjIiIiIiIiIiIygw2w4iIiIiIiIiIqMxgM4yIiIhIR1yJSUySJMHFxYX5CYr5iY8Zio35iY35aeME+kREREQ6Uqn4e6KIVCoV7O3t9V0GFRHzEx8zFBvzExvz08ZPckREREQ6UqvV+i6BikCtVuP8+fPMT1DMT3zMUGzMT2zMTxubYURERERUZmRmZuq7BHoFzE98zFBszE9szO9fbIYREREREREREVGZwWYYERERERERERGVGWyGEREREemIE+iLSaVSwd3dnfkJivmJjxmKjfmJjflp42qSRERERDrisuRikiQJ1tbW+i6Dioj5iY8Zio35iY35aWNLkIiIiEhHXIlJTGq1GvHx8cxPUMxPfMxQbMxPbMxPG5thRERERFRm8EuA2Jif+Jih2Jif2Jjfv9gMIyIiIiIiIiKiMoPNMCIiIiIiIiIiKjMkWZZlfRdBREREJIL09HTY2Njg3r17sLGx0Xc5pCNZlpGZmQlTU1MugiAg5ic+Zig25ie2151fzmemtLS0UjlxP0eGEREREVGZYWxsrO8S6BUwP/ExQ7ExP7Exv3+xGUZERESkI41Go+8SqAg0Gg3i4+OZn6CYn/iYodiYn9iYnzY2w4iIiIiIiIiIqMxgM4yIiIiIiIiIiMoMNsOIiIiIiIiIiKjM4GqSRERERIXE1STFJssyNBoNVCoVV0ITEPMTHzMUG/MT2+vOj6tJEhERERGVEtnZ2fougV4B8xMfMxQb8xMb8/sXm2FEREREOuJKTGLSaDRITExkfoJifuJjhmJjfmJjftrYDCMiIiIiIiIiojLDUN8FEBEREYmm64Q/IRne13cZpCMDlQaBXhkYv+Ia1Br+Jiwa5ic+Zig25ic2A5UGnw/WdxWlB9/BRERERFRmPOEXOKExP/ExQ7ExP7EZGBjou4RSg6tJEhERERVSzspIzQadgaGxlb7LISIiIiq0fUtcX9uxuJokERER0X8Of0sUkww7i0dgfqJifuJjhmJjfmKTkZ6eDo6HeorNMCIiIiIdGaj4QVJEBioZdavcYn6CYn7iY4ZiY35iM1DJuHTpEleTfIbNMCIiIiIiIiIiKjPYDCMiIiIiIiIiojKDzTAiIiIiXfEKETHJwIMsI+YnKuYnPmYoNuYnNhkwNTXVdxWlhqG+CyAiIiISjVpW8UOUgNSyCkeTnfRdBhUR8xMfMxQb8xObWlbBy8tL32WUGhwZRkRC0HXVk8jISLi5ub3SMSMjIyFJEu7du5fvNjNmzEDdunVf6TikH82bN8fatWtf2/EkScLWrVvzfbww77fXISQkBF26dHmlfZSWc3mZXr16ISwsrEjPlST+LC4iSZLhbJvB/ATF/MTHDMXG/MQmSTJu377NCfSfYTOMiArl2rVrGDhwIJydnWFsbIwqVargww8/xO3bt1/L8b/77rti3Z8kSQXeZsyYUaj9jBs3Dnv37n2lWnbs2IHAwEBYWVnB3Nwc9evXR3h4+Cvts7BmzJgBSZLQrl27XI99/vnnkCQJLVq0eC21vKrw8HDY2toWatvt27fj77//Rq9evZT73NzclPwtLCzg7++PDRs2lFC1uTVu3Bg3btyAjY1NiR7nZY2qr7766pXffy+eiy7ZFNZbb72l/LswdOhQzJw5U+vxs2fPolu3bkquCxcuzLWPKVOmYM6cOUhLS9P5+Cp+ERCSSpLh5XyH+QmK+YmPGYqN+YlNJcm4du2azoMM/qvYDCOil7p06RICAgKQlJSEdevW4eLFi1i6dCn27t2LRo0a4c6dOyV6/NjYWMyfP79Y93njxg3ltnDhQlhbW2vdN27cuELtx9LSEvb29kWuY9GiRejcuTOaNGmCo0ePIi4uDr169cIHH3xQ6BpelZOTE/bv348///xT6/6VK1fC1dX1tdTwun399dcYMGAAVCrt/w3OnDkTN27cwOnTp1G/fn307NkThw8ffi01GRsbw9HREZIkvZbj5cfGxuaVG1clfS6yLOOPP/5AkyZNAABRUVHKn3M8fPgQ7u7u+PTTT+Ho6JjnfmrVqoVq1arhxx9/LJE6iYiIiKh0YjOMiF5qxIgRMDY2xu7duxEYGAhXV1e0b98ee/bswV9//YXJkycr27q5uWHu3LkYOHAgrKys4OrqmmtU17Vr19CjRw/Y2trCzs4OnTt3xpUrV/I9/saNG3H58mWcOnWq2M7J0dFRudnY2ECSJK37LC0tlW1PnjyJgIAAmJubo3HjxkhMTFQee/EyycjISDRo0AAWFhawtbVFkyZNcPXq1TxruHbtGsaOHYsxY8Zg7ty58PHxgYeHB8aOHYvPP/8cYWFhOHr0qLJfSZKwd+/efGsBgG3btsHf3x+mpqZwd3dHaGgonjx5UuBrUaFCBbRt2xarV69W7jt8+DD++ecfvPXWW1rbajQazJw5E5UrV4aJiQnq1q2LXbt2KY9fuXIFkiTh559/RrNmzWBmZob69evjwoULOH78OAICAmBpaYn27dvj1q1bWvtesWIFvL29YWpqCi8vLyxZsiTXfjdv3ow333wT5ubmqFOnDo4cOaK8PgMGDEBaWtpLR/fdunUL+/btQ6dOnXI9ZmVlBUdHR1SvXh3ffPMNzMzM8H//938A8r7M0dbWVhlFlZ2djZEjR8LJyQmmpqaoUqUK5s2bp7X9P//8g3feeQfm5ubw9PTE9u3blcdeHLGVM5rqt99+g7e3NywtLdGuXTvcuHFDec6TJ08wevRo2Nrawt7eHhMmTED//v1f6TLHFy+TbNGiBUaNGoUxY8agXLlyqFixIpYvX44HDx5gwIABsLKygoeHB3799dc8z6WgbJYsWQJPT0+YmpqiYsWKePfddwtVY2JiImRZho+PD/755x9cvHgRb7zxhtY29evXx+eff45evXrBxMQk33116tQJP/30U76PZ2VlIT09XetGRERERGJjM4yICnTnzh389ttvGD58OMzMzLQec3R0RHBwMNavX6813DYsLAwBAQE4ffo0hg8fjmHDhilNm8ePHyMoKAhWVlaIiopCdHS08iU/OzsbABATE4NDhw4pt5xL1ZYuXap1/7Vr117LazB58mSEhYXhxIkTMDQ0xMCBA/Pc7smTJ+jSpQsCAwMRFxeHI0eOYOjQofmOjtm4cSMeP36c5wiw999/H5aWlli3bl2ha4mKikK/fv3w4Ycf4ty5c1i2bBnCw8MxZ86cl57jwIEDtS6NW7lyJYKDg2FsbKy13VdffYWwsDAsWLAAcXFxCAoKwttvv42kpCSt7aZPn44pU6bg1KlTMDQ0RJ8+ffDJJ5/gq6++QlRUFC5evIhp06Yp20dERGDatGmYM2cOEhISMHfuXEydOlWrQZdz/uPGjUNMTAyqV6+O3r1748mTJ2jcuHGuEX75jaw7dOgQzM3N4e3tXeBrYmhoCCMjI+V9+TJff/01tm/fjp9//hmJiYmIiIjINW9daGgoevTogbi4OHTo0AHBwcEFjqx8+PAhFixYgB9++AEHDx5ESkqK1nnNnz8fERERWLVqFaKjo5Genl7gvGRFtXr1ajg4OODYsWMYNWoUhg0bhu7du6Nx48Y4deoU2rZti759++Lhw4e5nptfNidOnMDo0aMxc+ZMJCYmYteuXWjevHmBdXTs2BG2trYICAhAWloaypUrh6pVq0KtVqNy5cpFGtHWoEEDHDt2DFlZWXk+Pm/ePNjY2Cg3FxeXpw/wCgMxycCdDFPmJyrmJz5mKDbmJzb56Q+/9BSbYURUoKSkJMiynG/jwNvbG3fv3tUa5dOhQwcMHz4cHh4emDBhAhwcHLB//34AwPr166HRaLBixQr4+vrC29sbq1atQkpKCiIjIwE8XfJ35MiRaNasGZo1a6Y00pYvX67ct3Llyle6PFEXc+bMQWBgIHx8fDBx4kQcPnwYmZmZubZLT09HWloaOnbsiGrVqsHb2xv9+/fP91LDCxcuwMbGBk5OuVflMTY2hru7Oy5cuFDoWkJDQzFx4kT0798f7u7uaNOmDWbNmoVly5a99Bw7duyI9PR0HDx4EA8ePMDPP/+cZ9NvwYIFmDBhAnr16oUaNWpg/vz5qFu3bq75mMaNG4egoCB4e3vjww8/xMmTJzF16lQ0adIEfn5+GDRokPKeAJ42z8LCwtC1a1dUrVoVXbt2xUcffZSr9nHjxuGtt95C9erVERoaiqtXr+LixYswNjbONcLv+dF9z7t69SoqVqyY6xLJ52VnZ2PevHlIS0tDy5YtX/r6AUBKSgo8PT3RtGlTVKlSBU2bNkXv3r21tgkJCUHv3r3h4eGBuXPnIiMjA8eOHct3n48fP8bSpUsREBAAf39/jBw5UmuOukWLFmHSpEl455134OXlhcWLFxf73FwAUKdOHUyZMgWenp6YNGkSTE1N4eDggCFDhsDT0xPTpk3D7du3ERcXl+u5+WWTkpICCwsLdOzYEVWqVIGfnx9Gjx5dYB0rVqxATEwMmjZtiqlTpyImJgbvvPMORo4ciZiYGMTExOh8bs7OzsjOzkZqamqej0+aNAlpaWnKLacJr5b5EUpEalmFmJQKzE9QzE98zFBszE9salmFatWqwcDAQN+llAp8FxNRoegy0WLt2rWVP+d8Ab558yaAp/N/Xbx4EVZWVrC0tISlpSXs7OyQmZmJ5ORkAICXlxeOHj2KESNG5Nq3lZUVIiIisHLlSpibm7/iWel+PjmNq5zzeZ6dnR1CQkIQFBSETp064auvvtK6pK2ka4mNjcXMmTOV19XS0hJDhgzBjRs38hyx8zwjIyO89957WLVqFTZs2IDq1atrHQt42uy7fv16rrmZmjRpgoSEhHzrrFixIgDA19dX676cuh88eIDk5GQMGjRIq/bZs2cr74nCnH9hPXr0CKampnk+NmHCBFhaWsLc3Bzz58/Hp59+mutS0fyEhIQgJiYGNWrUwOjRo7F79+5c2zxfv4WFBaytrQus39zcHNWqVVP+7uTkpGyflpaGv//+Gw0aNFAeNzAwQL169QpVry6er9vAwAD29va58gR0y6JNmzaoUqUK3N3d0bdvX0RERLz0fero6IhKlSrhjz/+QHBwMNzc3HDkyBH06NEDbm5uRVpBNmfEa37HNjExgbW1tdYN4GqSopIkGVXLpzE/QTE/8TFDsTE/sUmSjNTUVK4m+QybYURUIA8PD0iSlKvZkSMhIQHlypVD+fLllfuMjIy0tpEkSflHNyMjA/Xq1VNGceTcLly4gD59+ijPMTExweLFi3M1ZEaNGqW13evw/PnkXPKY3/9EVq1ahSNHjqBx48ZYv349qlevjj/++CPPbatXr460tDRcv34912PZ2dlITk5G9erVC11LRkYGQkNDtV7X+Ph4JCUl5dv8ed7AgQOxYcMGfPPNN/leClpYedX54n3P1w08Hfn3fO1nzpzJ9drpkkV+HBwccPfu3TwfGz9+PGJiYvDnn3/i7t27mDBhgtbxXmwKP378WPmzv78/Ll++jFmzZuHRo0fo0aNHrjmwCvpvIy95ba+PFYDyquNVs7CyssKpU6ewbt06ODk5Ydq0aahTp06+q1zOnTsXlpaWsLW1RVpaGvz8/GBpaYmLFy8iKCgIlpaWiIqK0vncci5Tff7fsMLgSlpiUj37Isf8xMT8xMcMxcb8xKZ61gzjapJPsRlGRAWyt7dHmzZtsGTJEjx69EjrsdTUVERERKBnz56FXjXO398fSUlJqFChAjw8PLRuNjY2WttevXoVcXFxkCQJNWrUAADs2LGjeE6sBPn5+WHSpEk4fPgwatWqhbVr1+a5Xbdu3WBkZISwsLBcjy1duhQPHjzIdZldQfz9/ZGYmJjrdfXw8CjwksAcNWvWRM2aNXHmzJk8G47W1tZwdnZGdHS01v3R0dHw8fEpdJ0vqlixIpydnXHp0qVcdVetWrXQ+zE2NoZarX7pdn5+fkhNTc2zIebg4AAPD488V0IsX7681ki/pKSkXKOJrK2t0bNnTyxfvhzr16/Hpk2bSmy1VRsbG1SsWBHHjx9X7lOr1cW60ERxyS8bQ0NDtG7dGp999hni4uJw5coV7Nu3L899fPDBB4iJicH777+P7t27IyYmBpMnT0aLFi0QGxuLmJgYBAQE6FzbmTNnULlyZTg4OOj8XCIiIiISk6G+CyCi0m/x4sVo3LgxgoKCMHv2bFStWhVnz57F+PHjUalSpUJN0J4jODgYn3/+OTp37qysSnj16lVs3rwZn3zyCSpXrqxsu2nTJlSsWBFr1qxBq1atMHPmTMyZMwfJyclal46VFpcvX8Z3332Ht99+G87OzkhMTERSUhL69euX5/aurq747LPPMHbsWJiamqJv374wMjLCtm3b8L///Q9jx47NtUJeQaZNm4aOHTvC1dUV7777LlQqFWJjY3HmzBnMnj27UPvYt28fHj9+nO+8U+PHj8f06dNRrVo11K1bF6tWrUJMTAwiIiIKXWdeQkNDMXr0aNjY2KBdu3bIysrCiRMncPfuXXz88ceF2oebmxsyMjKwd+9e1KlTB+bm5nleSuvn5wcHBwdER0ejY8eOha6xZcuWWLx4MRo1agS1Wo0JEyZojY764osv4OTkBD8/P6hUKmzYsAGOjo4lModXjlGjRmHevHnw8PCAl5cXFi1ahLt37xaqOR0fH681iaokSahTp06J1JlXNvv27cOlS5fQvHlzlCtXDjt37oRGo1Ea3y+ys7ODnZ0dzp07h549e8LDwwNJSUlo27YtPDw8cm2fnZ2Nc+fOKX/+66+/EBMTA0tLS63to6Ki0LZt2xI5byIiIiIqndgMI6KX8vT0xIkTJzB9+nT06NEDd+7cgaOjI7p06YLp06fDzs6u0PsyNzfHwYMHMWHCBHTt2hX3799HpUqV0KpVK2UunhwZGRmIjY1V5iMKDQ1Fq1atcPz48VLZDDM3N8f58+exevVq3L59G05OThgxYgTef//9fJ8zZswYuLu7Y8GCBfjqq6+gVqtRs2ZNfPvttxgwYIBOxw8KCsKOHTswc+ZMzJ8/H0ZGRvDy8sLgwYMLvQ8LC4sCHx89ejTS0tIwduxY3Lx5Ez4+Pti+fTs8PT11qvVFgwcPhrm5OT7//HOMHz8eFhYW8PX1xZgxYwq9j8aNG+ODDz5Az549cfv2bUyfPh0zZszItZ2BgQEGDBiAiIgInZphYWFhGDBgAJo1awZnZ2d89dVXOHnypPK4lZUVPvvsMyQlJcHAwAD169fHzp07CzUqr6gmTJiA1NRU9OvXDwYGBhg6dCiCgoIKNTHqiys3GhgY4MmTJyVSZ17ZtG7dGps3b8aMGTOQmZkJT09PrFu3DjVr1sx3P0+ePEF0dDS++eYbAMCBAwcwaNCgPLe9fv06/Pz8lL8vWLAACxYsQGBgoLJYR2ZmJrZu3Ypdu3bpfE6yLKFw42GpNJFlCdfvWUCWmZ6ImJ/4mKHYmJ/YZFmCnZ1doa/o+a+TZF4wSkT/QZGRkQgJCcGVK1f0XQqVQqmpqahZsyZOnTqFKlWq6LucYqPRaODt7Y0ePXpg1qxZ+i6n1Pv222+xZcuWPBc7yE96ejpsbGzQbNAZGBpzeXIiIiISx74lea9yXxJyPjOlpaXlGvRQGnDOMCIiKnMcHR3x/fffIyUlRd+lvJKrV69i+fLluHDhAuLj4zFs2DBcvnz5tS8yISojIyMsWrSoSM/l5MFiUkkyvJxvMz9BMT/xMUOxMT+xqSQZKSkpXE3yGTbDiIioTOrSpQuaNWum7zJeiUqlQnh4OOrXr48mTZogPj4ee/bsgbe3t75LE8LgwYPznaPsZbisvJgkSYaz7QPmJyjmJz5mKDbmJzZJknHnzh2uJvkM5wwjov8kNzc3neabIhKRi4tLrtU9iYiIiIioYBwZRkT/SWyGERERERERUV7YDCMiIiLSkYYraQlJI0u4fMuG+QmK+YmPGYqN+YlNI0twdHTkapLP8DJJIiIiIh39X5hrqVwZiYiIiIhejiPDiIiIiHSkVqv1XQIVgVqtRnJyMvMTFPMTHzMUG/MTG/PTxmYYEREREZUZ9+/f13cJ9AqYn/iYodiYn9iY37/YDCMiIiIiIiIiojKDzTAiIiIiIiIiIioz2AwjIiIi0hFXYhKTJElwcXFhfoJifuJjhmJjfmJjftq4miQRERGRjlQq/p4oIpVKBXt7e32XQUXE/MTHDMXG/MTG/LTxkxwRERGRjrgSk5jUajXOnz/P/ATF/MTHDMXG/MTG/LSxGUZEREREZUZmZqa+S6BXwPzExwzFxvzExvz+xWYYERERERERERGVGWyGERERERERERFRmcFmGBEREZGOOIG+mFQqFdzd3ZmfoJif+Jih2Jif2JifNq4mSURERKQjLksuJkmSYG1tre8yqIiYn/iYodiYn9iYnza2BImIiIh0xJWYxKRWqxEfH8/8BMX8xMcMxcb8xMb8tLEZRkRERERlBr8EiI35iY8Zio35iY35/YvNMCIiIiIiIiIiKjPYDCMiIiIiIiIiojJDkmVZ1ncRRERERCJIT0+HjY0N7t27BxsbG32XQzqSZRmZmZkwNTXlIggCYn7iY4ZiY35ie9355XxmSktLK5UT93NkGBERERGVGcbGxvougV4B8xMfMxQb8xMb8/sXm2FEREREOtJoNPougYpAo9EgPj6e+QmK+YmPGYqN+YmN+WljM4yIiIiIiIiIiMoMNsOIiIiIiIiIiKjMYDOMiIiIiIiIiIjKDK4mSURERFRIXE1SbLIsQ6PRQKVScSU0ATE/8TFDsTE/sb3u/LiaJBERERFRKZGdna3vEugVMD/xMUOxMT+xMb9/sRlGREREpCOuxCQmjUaDxMRE5ico5ic+Zig25ic25qfNUN8FEBEREYkmISGhVA75p4JpNBrcvXsXZ8+ehUrF34RFw/zExwzFxvzExiaYNr6DiYiIiIiIiIiozGAzjIiIiIjKDE76LDbmJz5mKDbmJzYDAwN9l1Bq8DJJIiIiIh3x8hAxqVQqODg46LsMKiLmJz5mKDbmJzaVSoVatWrpu4xSg5/kiIiIiHQky7K+S6AikGUZ2dnZzE9QzE98zFBszE9ssiwjPT2d+T3DZhgRERGRjvhBUkyyLCMtLY35CYr5iY8Zio35iU2WZVy6dIkT6T/DZhgREREREREREZUZbIYREREREREREVGZwWYYERERkY64mpaYJEmCgYEB8xMU8xMfMxQb8xObJEkwNTXVdxmlBpthRET0n6TrfBaRkZFwc3N7pWNGRkZCkiTcu3fvlfZT2oWEhKBLly76LkOv+EVATJIkwc7OjvkJivmJjxmKjfmJTZIkeHl5wcDAQN+llApshhERUYm4du0aBg4cCGdnZxgbG6NKlSr48MMPcfv27ddy/O+++65Y9ydJUoG3GTNmFOvxnjdjxgzlOIaGhnBwcEDz5s2xcOFCZGVlldhxKX+cPFhMsizj0aNHzE9QzE98zFBszE9ssizj9u3bnED/GTbDiIio2F26dAkBAQFISkrCunXrcPHiRSxduhR79+5Fo0aNcOfOnRI9fmxsLObPn1+s+7xx44ZyW7hwIaytrbXuGzduXLEe70U1a9bEjRs3kJKSgv3796N79+6YN28eGjdujPv375fosf8rsrOzi21f/CIgJlmWkZGRwfwExfzExwzFxvzEJssyrl27xvyeYTOMiIiK3YgRI2BsbIzdu3cjMDAQrq6uaN++Pfbs2YO//voLkydPVrZ1c3PD3LlzMXDgQFhZWcHV1TXXqK5r166hR48esLW1hZ2dHTp37owrV67ke/yNGzfi8uXLOHXqVLGdk6Ojo3KzsbGBJEla91laWirbnjx5EgEBATA3N0fjxo2RmJiota9t27bB398fpqamcHd3R2hoKJ48eVLg8Q0NDeHo6AhnZ2f4+vpi1KhROHDgAM6cOaPV+Lt79y769euHcuXKwdzcHO3bt0dSUhIAID09HWZmZvj111+19r1lyxZYWVnh4cOHAHR/vY8fP47y5csrddy7dw+DBw9G+fLlYW1tjZYtWyI2Nlan10CSJHz77bdo3749zMzM4O7ujo0bN2rt42V15lzOOWfOHDg7O6NGjRoAgCVLlsDT0xOmpqaoWLEi3n333XzPLSsrC+np6Vo3IiIiIhIbm2FERFSs7ty5g99++w3Dhw+HmZmZ1mOOjo4IDg7G+vXrtX6VCgsLQ0BAAE6fPo3hw4dj2LBhSgPp8ePHCAoKgpWVFaKiohAdHQ1LS0u0a9dOGekTExODQ4cOKbcNGzYAAJYuXap1/7Vr117LazB58mSEhYXhxIkTMDQ0xMCBA5XHoqKi0K9fP3z44Yc4d+4cli1bhvDwcMyZM0fn43h5eaF9+/bYvHmzcl9ISAhOnDiB7du348iRI5BlGR06dMDjx49hbW2Njh07Yu3atVr7iYiIQJcuXWBubl6o1/t5+/btQ5s2bTBnzhxMmDABANC9e3fcvHkTv/76K06ePAl/f3+0atVKGRFY2Ndg6tSp6NatG2JjYxEcHIxevXohISEBQOHeFwCwd+9eJCYm4vfff8eOHTtw4sQJjB49GjNnzkRiYiJ27dqF5s2b5/saz5s3DzY2NsrNxcVFx5SIiIiIqLRhM4yIiIpVUlISZFmGt7d3no97e3vj7t27uHXrlnJfhw4dMHz4cHh4eGDChAlwcHDA/v37AQDr16+HRqPBihUr4OvrC29vb6xatQopKSmIjIwEAJiammLkyJFo1qwZmjVrpjTSli9frty3cuVK2Nvbl+zJPzNnzhwEBgbCx8cHEydOxOHDh5GZmQkACA0NxcSJE9G/f3+4u7ujTZs2mDVrFpYtW1akY3l5eSmjoZKSkrB9+3asWLECzZo1Q506dRAREYG//voLW7duBQAEBwdj69atyiiw9PR0/PLLLwgODgZQuNc7x5YtW9C5c2csW7YMQ4cOBQAcOnQIx44dw4YNGxAQEABPT08sWLAAtra2ysiuwr4G3bt3x+DBg1G9enXMmjULAQEBWLRokU51WlhYYMWKFahZsyZq1qyJlJQUWFhYoGPHjqhSpQr8/PwwevTofF/fSZMmIS0tTbnlNFQ5ebCYJEmCkZER8xMU8xMfMxQb8xObJEmwsrLSdxmlhqG+CyAiov8mXeYjqF27tvLnnMsPb968CeDp/F8XL17M9T/vzMxMJCcnA3jaEDp69CjGjh2Lb775Rms7KysrLF26FH369Cnqqejs+fNxcnICANy8eROurq6IjY1FdHS01igotVqNzMxMPHz4EObm5jodS5Zl5UNpQkICDA0N8cYbbyiP29vbo0aNGsqIqg4dOsDIyAjbt29Hr169sGnTJlhbW6N169YACvd6A8DRo0exY8cObNy4UWtlydjYWGRkZORqPD569Eh5fmFfg0aNGmnto1GjRoiJidGpTl9fXxgbGyt/b9OmDapUqQJ3d3e0a9cO7dq1wzvvvJPv625iYgITE5Nc9/OLgJgkSYKtra2+y6AiYn7iY4ZiY35ikyQJ1apV03cZpQabYUREVKw8PDwgSRISEhLwzjvv5Ho8ISEB5cqVQ/ny5ZX7jIyMtLaRJElZ6SYjIwP16tVDRERErn09vw8TExMsXrwYUVFRiIuLU+4fNWrUa22EAdrnk9M0ef58QkND0bVr11zPMzU11flYCQkJqFq1aqG3NzY2xrvvvou1a9eiV69eWLt2LXr27AlDQ0OlvsK83tWqVYO9vT1WrlyJt956SznnjIwMODk55RpFBkD5AF0cr0Fh67SwsNB6zMrKCqdOnUJkZCR2796NadOmYcaMGTh+/LhOH/A5+ayYZFlWGq5saIqH+YmPGYqN+YlNlmWkpqaiQoUKUKl4kSBfASIiKlb29vZo06YNlixZgkePHmk9lpqaioiICPTs2bPQH6L8/f2RlJSEChUqwMPDQ+tmY2Ojte3Vq1cRFxcHSZKUydJ37NhRPCdWTPz9/ZGYmJjrXDw8PHT+YHL+/Hns2rUL3bp1A/D0EtQnT57g6NGjyja3b99GYmIifHx8lPuCg4Oxa9cunD17Fvv27VMukcyprzCvt4ODA/bt24eLFy+iR48eePz4sfL81NRUGBoa5nq+g4ODTq/BH3/8oXW+f/zxh3L5rS7vixcZGhqidevW+OyzzxAXF4crV65g3759Or32bIaJKeeLHPMTE/MTHzMUG/MTW04zjPk9xWYYEREVu8WLFyMrKwtBQUE4ePAgrl27hl27dqFNmzaoVKmSTpPFBwcHw8HBAZ07d0ZUVBQuX76MyMhIjB49Gn/++afWtps2bULFihWVRs+0adNw9uxZrcvm9G3atGlYs2YNQkNDcfbsWSQkJOCnn37ClClTCnzekydPkJqaiuvXryM+Ph6LFi1CYGAg6tati/HjxwMAPD090blzZwwZMgSHDh1CbGws3nvvPVSqVAmdO3dW9tW8eXNlMYOqVatqXVapy+tdoUIF7Nu3D+fPn0fv3r3x5MkTtG7dGo0aNUKXLl2we/duXLlyBYcPH8bkyZNx4sQJnV6DDRs2YOXKlbhw4QKmT5+OY8eOYeTIkTrX+bwdO3bg66+/RkxMDK5evYo1a9ZAo9EozVMiIiIi+u9jM4yIiIqdp6cnTpw4AXd3d/To0QPVqlXD0KFD8eabb+LIkSOws7Mr9L7Mzc1x8OBBuLq6omvXrvD29sagQYOQmZkJa2trrW0zMjIQGxuLtm3bwsDAAKGhodi3bx+OHz9e3KdYZEFBQdixYwd2796N+vXro2HDhvjyyy9RpUqVAp939uxZODk5wdXVFS1atMDPP/+MSZMmISoqCpaWlsp2q1atQr169dCxY0c0atQIsixj586duS7d7N27t7JK4/N0eb2BpyuE7tu3D/Hx8QgODoZGo8HOnTvRvHlzDBgwANWrV0evXr1w9epVVKxYUafXIDQ0FD/99BNq166NNWvWYN26dcoIN13rzGFra4vNmzejZcuW8Pb2xtKlS7Fu3TrUrFmzwNefiIiIiP47JJlj5IiIiBAZGYmQkBBlZUbSL0mSsGXLFq3J+UuD9PR02NjY4PDhw1yRSUCyLCMjIwOWlpac70ZAzE98zFBszE9ssizDxsYGlStXfi1zhuV8ZkpLSyvwh0p94QT6RERERDrilwAxcVl5sTE/8TFDsTE/sUmSBFdXV32XUWrwMkkiIiIiHXFgvZhkWcb9+/eZn6CYn/iYodiYn9hkWUZKSoqywnlZx5FhREREANzc3DBmzBh9l0HPlPYP2qW9PsqbLMvIzMyEhYUFR/cJiPmJjxmKjfmJTZZl3LlzB5UqVdJ3KaUCR4YRERGBzTAiIiIiorKCzTAiIiIiIiIiIioz2AwjIiIi0hEvDxGTJEkwNzdnfoJifuJjhmJjfmKTJAmOjo7M7xnOGUZERESko5o1a5bKZcKJiIiI6OU4MoyIiIhIR2q1Wt8lUBGo1WokJyczP0ExP/ExQ7ExP7ExP21shhERERFRmXH//n19l0CvgPmJjxmKjfmJjfn9i80wIiIiIiIiIiIqM9gMIyIiIiIiIiKiMoPNMCIiIiIdcSUmMUmSBBcXF+YnKOYnPmYoNuYnNuanjatJEhEREelIpeLviSJSqVSwt7fXdxlURMxPfMxQbMxPbMxPGz/JEREREemIKzGJSa1W4/z588xPUMxPfMxQbMxPbMxPG5thRERERFRmZGZm6rsEegXMT3zMUGzMT2zM719shhERERERERERUZnBZhgREREREREREZUZbIYRERER6YgT6ItJpVLB3d2d+QmK+YmPGYqN+YmN+WnjapJEREREOuKy5GKSJAnW1tb6LoOKiPmJjxmKjfmJjflpY0uQiIiISEdciUlMarUa8fHxzE9QzE98zFBszE9szE8bm2FEREREVGbwS4DYmJ/4mKHYmJ/YmN+/2AwjIiIiIiIiIqIyg80wIiIiIiIiIiIqMyRZlmV9F0FEREQkgvT0dNjY2ODevXuwsbHRdzmkI1mWkZmZCVNTUy6CICDmJz5mKDbmJ7bXnV/OZ6a0tLRSOXE/R4YRERERUZlhbGys7xLoFTA/8TFDsTE/sTG/f7EZRkRERKQjjUaj7xKoCDQaDeLj45mfoJif+Jih2Jif2JifNjbDiIiIiIiIiIiozGAzjIiIiIiIiIiIygw2w4iIiIiIiIiIqMzgapJEREREhcTVJMUmyzI0Gg1UKhVXQhMQ8xMfMxQb8xPb686Pq0kSEREREZUS2dnZ+i6BXgHzEx8zFBvzExvz+xebYUREREQ64kpMYtJoNEhMTGR+gmJ+4mOGYmN+YmN+2gz1XQARERGRaLpO+BOS4X19l0E6MlBpEOiVgfErrkGt4W/ComF+4mOGYmN+YjNQafD5YH1XUXrwHUxERERERERERGUGm2FEREREVGY84WgGoTE/8TFDsTE/sRkYGOi7hFKDq0kSERERFVLOykjNBp2BobGVvsshIiIiKrR9S1xf27G4miQRERHRfw5/SxSTDDuLR2B+omJ+4mOGYmN+YpORnp4Ojod6is0wIiIiIh0ZqPhBUkQGKhl1q9xifoJifuJjhmJjfmIzUMm4dOkSV5N8hs0wIiIiIiIiIiIqM9gMIyIiIiIiIiKiMoPNMCIiIiJd8QoRMcnAgywj5icq5ic+Zig25ic2GTA1NdV3FaUGm2FElC9dJ1eMjIyEm5vbKx0zMjISkiTh3r17r7QffZkxYwbq1q2r7zJK3JUrVyBJEmJiYgDkzi08PBy2travrZ7mzZtj7dq1r+14lFuLFi0wZswY5e9ubm5YuHCh3uopjF69eiEsLKxIz1XL/AglIrWswtFkJ+YnKOYnPmYoNuYnNrWsgpeXFwwMDPRdSqnAdzFRKXbt2jUMHDgQzs7OMDY2RpUqVfDhhx/i9u3br+X43333XbHuT5KkAm8zZswo1uO9KD09HZMnT4aXlxdMTU3h6OiI1q1bY/PmzUKtqtKiRQuEh4cX+fnZ2dn47LPPUKdOHZibm8PBwQFNmjTBqlWr8Pjx42KpsWfPnrhw4UKx7Otltm/fjr///hu9evXSuv/06dPo3r07KlasCFNTU3h6emLIkCGvra7S7tatWxg2bBhcXV1hYmICR0dHBAUFITo6WtlGkiRs3bq1UPvbvHkzZs2aVULV5vbWW28p/0YNHToUM2fO1Hr87Nmz6NatG9zc3CBJUp6NuSlTpmDOnDlIS0vT+fiSJM6/GfQvSZLhbJvB/ATF/MTHDMXG/MQmSTJu377NCfSfYTOMqJS6dOkSAgICkJSUhHXr1uHixYtYunQp9u7di0aNGuHOnTslevzY2FjMnz+/WPd548YN5bZw4UJYW1tr3Tdu3LhiPd7z7t27h8aNG2PNmjWYNGkSTp06hYMHD6Jnz5745JNPivRlWETZ2dkICgrCp59+iqFDh+Lw4cM4duwYRowYgUWLFuHs2bPFchwzMzNUqFChWPb1Ml9//TUGDBgAlerf/6Xt2LEDDRs2RFZWFiIiIpCQkIAff/wRNjY2mDp1apGPlZ2dXRwlF4osy3jy5EmJ7b9bt244ffo0Vq9ejQsXLmD79u1o0aKFzs32nNfEzs4OVlZWJVFqLrIs448//kCTJk0AAFFRUcqfczx8+BDu7u749NNP4ejomOd+atWqhWrVquHHH3/UuQYVvwgISSXJ8HK+w/wExfzExwzFxvzEppJkXLt2TahBACWJzTCiUmrEiBEwNjbG7t27ERgYCFdXV7Rv3x579uzBX3/9hcmTJyvburm5Ye7cuRg4cCCsrKzg6uqaa1TXtWvX0KNHD9ja2sLOzg6dO3fGlStX8j3+xo0bcfnyZZw6darYzsnR0VG52djYQJIkrfssLS2VbU+ePImAgACYm5ujcePGSExM1NrXtm3b4O/vD1NTU7i7uyM0NLTAxsH//vc/XLlyBUePHkX//v3h4+OD6tWrY8iQIYiJiVGOnddIGFtbW62RWH/++Sd69+4NOzs7WFhYICAgAEePHs3zuMnJyXB3d8fIkSMhyzLu3r2Lfv36oVy5cjA3N0f79u2RlJSk9ZxNmzahZs2aMDExgZubW4GXccmyjBkzZiije5ydnTF69Oh8t1+4cCEOHjyIvXv3YsSIEahbty7c3d3Rp08fHD16FJ6engCAXbt2oWnTprC1tYW9vT06duyI5OTkfPf7ohcvk8y5fPSHH36Am5sbbGxs0KtXL9y/f1/Z5v79+wgODoaFhQWcnJzw5Zdf5rr07kW3bt3Cvn370KlTJ+W+hw8fYsCAAejQoQO2b9+O1q1bo2rVqnjjjTewYMECLFu2TNn2wIEDaNCgAUxMTODk5ISJEydqvY9atGiBkSNHYsyYMXBwcEBQUJBySehvv/0GPz8/mJmZoWXLlrh58yZ+/fVXeHt7w9raGn369MHDhw+VfWVlZWH06NGoUKECTE1N0bRpUxw/flx5PGe/v/76K+rVqwcTExMcOnQIycnJ6Ny5MypWrAhLS0vUr18fe/bs0XodCvNvwPPu3buHqKgozJ8/H2+++SaqVKmCBg0aYNKkSXj77beVfQLAO++8A0mSlL/nZLlixQpUrVpVmXviZVmtWLECtra22Lt3LwDgzJkzaN++PSwtLVGxYkX07dsX//zzT77Pf15iYiJkWYaPjw/++ecfXLx4EW+88YbWNvXr18fnn3+OXr16wcTEJN99derUCT/99FO+j2dlZSE9PV3rRkRERERiYzOMqBS6c+cOfvvtNwwfPhxmZmZajzk6OiI4OBjr16/X6uqHhYUhICAAp0+fxvDhwzFs2DClgfT48WMEBQXBysoKUVFRiI6OhqWlJdq1a6eM6oiJicGhQ4eU24YNGwAAS5cu1br/2rVrr+U1mDx5MsLCwnDixAkYGhpi4MCBymNRUVHo168fPvzwQ5w7dw7Lli1DeHg45syZk+e+NBoNfvrpJwQHB8PZ2TnX45aWljA0NCxUXRkZGQgMDMRff/2F7du3IzY2Fp988kmew43j4uLQtGlT9OnTB4sXL4YkSQgJCcGJEyewfft2HDlyBLIso0OHDsrliSdPnkSPHj3Qq1cvxMfHY8aMGZg6dWq+l0Vu2rQJX375JZYtW4akpCRs3boVvr6++dYfERGB1q1bw8/PL9djRkZGsLCwAAA8ePAAH3/8MU6cOIG9e/dCpVLhnXfeeaVh1cnJydi6dSt27NiBHTt24MCBA/j000+Vxz/++GNER0dj+/bt+P333xEVFfXSZuyhQ4dgbm4Ob29v5b7ffvsN//zzDz755JM8n5PTpPvrr7/QoUMH1K9fH7Gxsfj222/x/fffY/bs2Vrbr169GsbGxoiOjsbSpUuV+2fMmIHFixfj8OHDSrN54cKFWLt2LX755Rfs3r0bixYtUrb/5JNPsGnTJqxevRqnTp2Ch4cHgoKCco3ynDhxIj799FMkJCSgdu3ayMjIQIcOHbB3716cPn0a7dq1Q6dOnZCSkqL1vIL+DXiRpaUlLC0tsXXrVmRlZeW5TU6jbtWqVbhx44ZW4+7ixYvYtGkTNm/erMwbV5DPPvsMEydOxO7du9GqVSvcu3cPLVu2hJ+fH06cOIFdu3bh77//Ro8ePQrcT8eOHWFra4uAgACkpaWhXLlyqFq1KtRqNSpXrlykeeoaNGiAY8eO5fs6zJs3DzY2NsrNxcVF52MQERERUelSuG9/RPRaJSUlQZZlrS/4z/P29sbdu3dx69Yt5VK0Dh06YPjw4QCACRMm4Msvv8T+/ftRo0YNrF+/HhqNBitWrIAkSQCefsG1tbVFZGQk2rZtC1NTU4SEhCA2NlbrWMuXL8fy5csBAAMGDMDixYtL6rS1zJkzB4GBgQCeNgfeeustZGZmwtTUFKGhoZg4cSL69+8PAHB3d8esWbPwySefYPr06bn29c8//+Du3bvw8vJ65brWrl2LW7du4fjx47CzswMAeHh45Nru8OHD6NixIyZPnoyxY8cCeJrr9u3bER0djcaNGwN42pxycXHB1q1b0b17d3zxxRdo1aqVcilf9erVce7cOXz++ecICQkB8HQEUY6UlBRl7jMjIyO4urqiQYMG+daflJSEFi1avPQ8u3XrpvX3lStXonz58jh37hxq1ar10ufnRaPRIDw8XLmUrm/fvti7dy/mzJmD+/fvY/Xq1Vi7di1atWoF4Ol7NK/m5fOuXr2KihUral0imTPS7mV5L1myBC4uLkqj0svLC9evX8eECRMwbdo0ZZ+enp747LPPlOfduHEDADB79mzl0rxBgwZh0qRJykhAAHj33Xexf/9+TJgwAQ8ePMC3336L8PBwtG/fHsDT/7Z+//13fP/99xg/fryy/5kzZ6JNmzbK3+3s7FCnTh3l77NmzcKWLVuwfft2jBw5Urm/oH8DXmRoaIjw8HAMGTIES5cuhb+/PwIDA9GrVy/Url0bAFC+fHkAT5uHL15mmJ2djTVr1ijbFGTChAn44YcfcODAAdSsWRMAsHjxYvj5+WHu3LnKditXroSLiwsuXLiA6tWr57mvFStWIDMzEx988AEaNmyIkJAQTJs2Dba2tvj4449fWktenJ2dkZ2djdTUVFSpUiXX45MmTdLad3p6+tOGGK8wEJMM3MkwZX6iYn7iY4ZiY35ik/HaprQQAUeGEZViulzPnfMFFoBy+eHNmzcBPJ3/6+LFi7CyslJGhNjZ2SEzM1O59M3LywtHjx7FiBEjcu3bysoKERERWLlyJczNzV/xrHQ/HycnJwDQOp+ZM2cq52JpaYkhQ4bgxo0bWpel5SjO6+JjYmLg5+enNMLykpKSgjZt2mDatGlKIwwAEhISYGhoqHU5l729PWrUqIGEhARlmxfnPmrSpAmSkpKgVqtzHat79+549OgR3N3dMWTIEGzZsqXAy0UL+1okJSWhd+/ecHd3h7W1tXKJ3IujkXTh5uam9T9gJycnJdNLly7h8ePHWo08GxubPBs5z3v06FGuJaILe44JCQlo1KiR0iAGnr7WGRkZ+PPPP5X76tWrl+fzn3+PVqxYEebm5kojLOe+nPNLTk7G48ePtbI1MjJCgwYNlOxzBAQEaP09IyMD48aNg7e3N2xtbWFpaYmEhIRcWRT0b0BeunXrhuvXr2P79u1o164dIiMj4e/vX6jFGapUqVKoRlhYWBiWL1+OQ4cOKY0w4Ol/w/v379f6bzineVnQ5biOjo6oVKkS/vjjDwQHB8PNzQ1HjhxBjx494ObmVqTVbHNG3+b1bwcAmJiYwNraWusGcDVJUallFWJSKjA/QTE/8TFDsTE/sallFapVq8bVJJ/hu5ioFPLw8IAkSbm+JOdISEhAuXLltL6MGhkZaW0jSZJySVtGRgbq1auHmJgYrduFCxfQp08f5TkmJiZYvHix1pdqABg1apTWdq/D8+eT06x4/nxCQ0O1ziU+Ph5JSUm5GiPA0xEutra2OH/+/EuPK0lSrmbK8yssvnjZal7Kly+PBg0aYN26dSU+v5CLiwsSExOxZMkSmJmZYfjw4WjevHm+q0JWr169UK9Dp06dcOfOHSxfvhxHjx5V5kR7lQnkC3qPFpWDgwPu3r2rdV/OqKLCnGdh5Fw6+qIX36PFdX4vHm/cuHHYsmUL5s6di6ioKMTExMDX1zdXFkU5vqmpKdq0aYOpU6fi8OHDCAkJyXN05ctqzE+zZs2gVqvx888/a92fkZGBTp065fo3KSkpCc2bN89zX3PnzoWlpSVsbW2RlpYGPz8/WFpa4uLFiwgKCoKlpSWioqIKVdfzci5TLUxz73lcSUtMkiSjavk05ico5ic+Zig25ic2SZKRmprK1SSfYTOMqBSyt7dHmzZtsGTJEjx69EjrsdTUVERERKBnz55aI1oK4u/vj6SkJFSoUAEeHh5aNxsbG61tr169iri4OEiSpIzK2bFjR/GcWDHx9/dHYmJirnPx8PDQulwuh0qlQq9evRAREYHr16/nejwjI0MZTVW+fHnlMjjg6Qip50eM1K5dGzExMQWu5mlmZoYdO3bA1NQUQUFByiTx3t7eePLkidZk+7dv30ZiYiJ8fHyUbaKjo7X2Fx0djerVq+f7K46ZmRk6deqEr7/+GpGRkThy5Aji4+Pz3LZPnz7Ys2cPTp8+neuxx48f48GDB0pNU6ZMQatWrZTLckuSu7s7jIyMtOalSktLw4ULFwp8np+fH1JTU7Xqa9u2LRwcHLQubXzevXv3ADx9rXPmbcsRHR0NKysrVK5c+RXOJrdq1aop847lePz4MY4fP65kn5/o6GiEhITgnXfega+vLxwdHQtc/OJV+Pj44MGDB8rfjYyM8hyRWFgNGjTAr7/+irlz52LBggXK/f7+/jh79izc3Nxy/TecX6Ptgw8+QExMDN5//310794dMTExmDx5Mlq0aIHY2FjExMTkGlVXGGfOnEHlypXh4OCg0/O4kpaYVM++yDE/MTE/8TFDsTE/sameNcO4muRTbIYRlVKLFy9GVlYWgoKCcPDgQVy7dg27du1CmzZtUKlSpXwni89LcHAwHBwc0LlzZ0RFReHy5cuIjIzE6NGjtS4HA55OyF6xYkXs2rULZ8+exbRp03D27FmdVhIsadOmTcOaNWsQGhqKs2fPIiEhAT/99BOmTJmS73PmzJkDFxcXvPHGG1izZg3OnTuHpKQkrFy5En5+fsjIyAAAtGzZEosXL8bp06dx4sQJfPDBB1ojbnr37g1HR0d06dIF0dHRuHTpEjZt2oQjR45oHc/CwgK//PILDA0N0b59e2RkZMDT0xOdO3fGkCFDcOjQIcTGxuK9995DpUqV0LlzZwDA2LFjsXfvXsyaNQsXLlzA6tWrsXjxYowbNy7P8woPD8f333+PM2fO4NKlS/jxxx9hZmaW59xHADBmzBg0adIErVq1wjfffIPY2FhcunQJP//8Mxo2bIikpCSUK1cO9vb2+O6773Dx4kXs27evyPMxFZaVlRX69++P8ePHY//+/Th79iwGDRoElUpVYNPXz88PDg4OWk0mCwsLrFixAr/88gvefvtt7NmzB1euXMGJEyfwySef4IMPPgAADB8+HNeuXcOoUaNw/vx5bNu2DdOnT8fHH3+cZ1P1VVhYWGDYsGEYP348du3ahXPnzmHIkCF4+PAhBg0aVOBzPT09lYnqY2Nj0adPn1f+Re/27dto2bIlfvzxR8TFxeHy5cvYsGEDPvvsM+W9CDy9tHXv3r25Go66aNy4MXbu3InQ0FAsXLgQwNPVcu/cuYPevXvj+PHjSE5Oxm+//YYBAwbk23yzs7ODh4cHzp07h/bt28PDwwNJSUlo27at0kh7fuRmdna2MuIsOzsbf/31F2JiYnDx4kWt/UZFRaFt27ZFOjciIiIiEhObYUSllKenJ06cOAF3d3f06NED1apVw9ChQ/Hmm2/iyJEjBc5Z9SJzc3McPHgQrq6u6Nq1K7y9vTFo0CBkZmYq89/kyMjIQGxsLNq2bQsDAwOEhoZi3759WiN29C0oKAg7duzA7t27Ub9+fTRs2BBffvllvg0g4OkX6T/++APvvfceZs+eDT8/PzRr1gzr1q3D559/royQCwsLg4uLC5o1a4Y+ffpg3LhxWvOkGRsbY/fu3ahQoQI6dOgAX19ffPrpp3mO2rK0tMSvv/4KWZbx1ltv4cGDB1i1ahXq1auHjh07olGjRpBlGTt37lQabv7+/vj555/x008/oVatWpg2bRpmzpypTJ7/IltbWyxfvhxNmjRB7dq1sWfPHvzf//0f7O3t89zexMQEv//+Oz755BMsW7YMDRs2RP369fH1119j9OjRqFWrFlQqFX766SecPHkStWrVwkcffYTPP/+8sPEU2RdffIFGjRqhY8eOaN26NZo0aQJvb+88L33NYWBggAEDBiAiIkLr/s6dO+Pw4cMwMjJCnz594OXlhd69eyMtLU1ZLbJSpUrYuXMnjh07hjp16uCDDz7AoEGDCmyqvopPP/0U3bp1Q9++feHv74+LFy/it99+Q7ly5Qp83hdffIFy5cqhcePG6NSpE4KCguDv7/9KtVhaWuKNN97Al19+iebNm6NWrVqYOnUqhgwZorVIRlhYGH7//Xe4uLjkuQJpYTVt2hS//PILpkyZgkWLFsHZ2RnR0dFQq9Vo27YtfH19MWbMGNja2hbYiHzy5Amio6OVSykPHDiQ72WV169fh5+fH/z8/HDjxg0sWLAAfn5+GDx4sLJNZmYmtm7diiFDhhT53IiIiIhIPJLMMXJEVEwiIyMREhJSYpdwUdny4MEDVKpUCWFhYQWOnkpNTUXNmjVx6tSpAhuiRC/69ttvsWXLFuzevbvQz0lPT4eNjQ0CB8dDZWT98idQqaKSZFR3uoMLN+ygkQs31QCVHsxPfMxQbMxPbCpJxsoJEipXrlzsV0HkJeczU1paWq4BGKWBob4LICIiAoDTp0/j/PnzaNCgAdLS0jBz5kwA0LpsLy+Ojo74/vvvkZKSwmYY6cTIyAiLFi0q0nM1ssTh9QLSyBLOX8975CyVfsxPfMxQbMxPbBpZgqurq77LKDX4OY6IiEqNBQsWoE6dOmjdujUePHiAqKioQk1s3qVLFzRr1uw1VEj/JYMHD1YWCtEVJw8Wk0qS4eV8m/kJivmJjxmKjfmJTSXJSElJ4WqSz3BkGBEVGzc3N4wZM0bfZZCg/Pz8cPLkSX2XQVQoXFZeTJIkw9n2AZJSywG8xEc4zE98zFBszE9skiTjzp27qFSpkr5LKRU4MoyIig2bYURERERERFTasRlGRERERERERERlBleTJCIiIiqknJWR7t69C1tbW32XQzrSaDS4efMmKlSo8FpW0qLixfzExwzFxvzE9rrzK+2rSbIZRkRERFRIpf2DHREREVFpUNo/M7GdS0RERKQjtVqt7xKoCNRqNZKTk5mfoJif+Jih2Jif2JifNjbDiIiIiKjMuH//vr5LoFfA/MTHDMXG/MTG/P7FZhgREREREREREZUZhrpsfO/ePWzZsgVRUVG4evUqHj58iPLly8PPzw9BQUFo3LhxSdVJRERERERERET0ygo1Muz69esYPHgwnJycMHv2bDx69Ah169ZFq1atULlyZezfvx9t2rSBj48P1q9fX9I1ExEREemVJEn6LoGKQJIkuLi4MD9BMT/xMUOxMT+xMT9thRoZ5ufnh/79++PkyZPw8fHJc5tHjx5h69atWLhwIa5du4Zx48YVa6FEREREpQWXlBeTSqWCvb29vsugImJ+4mOGYmN+YmN+2iRZluWXbXT79m2dXjRdtyciIiISQc4y4Xfu3EG5cuX0XQ7pSK1WIykpCZ6enjAwMNB3OaQj5ic+Zig25ie2151fzmemtLQ0WFtbl/jxdFWonzV1bWyxEUZEREREpVFmZqa+S6BXwPzExwzFxvzExvz+VaQx/j/88AOaNGkCZ2dnXL16FQCwcOFCbNu2rViLIyIiIiIiIiIiKk46N8O+/fZbfPzxx+jQoQPu3bsHtVoNALC1tcXChQuLuz4iIiIiIiIiIqJio3MzbNGiRVi+fDkmT56sdZ1pQEAA4uPji7U4IiIiotKIE+iLSaVSwd3dnfkJivmJjxmKjfmJjflpK9Rqks+7fPky/Pz8ct1vYmKCBw8eFEtRRERERKUZlyUXkyRJpXISXyoc5ic+Zig25ic25qdN55Zg1apVERMTk+v+Xbt2wdvbuzhqIiIiIirVcqaJILGo1WrEx8czP0ExP/ExQ7ExP7ExP206jwz7+OOPMWLECGRmZkKWZRw7dgzr1q3DvHnzsGLFipKokYiIiIioWPBLgNiYn/iYodiYn9iY3790boYNHjwYZmZmmDJlCh4+fIg+ffrA2dkZX331FXr16lUSNRIRERERERERERULnZphT548wdq1axEUFITg4GA8fPgQGRkZqFChQknVR0REREREREREVGwkWZZlXZ5gbm6OhIQEVKlSpaRqIiIiIiqV0tPTYWNjg3v37sHGxkbf5ZCOZFlGZmYmTE1NuQiCgJif+Jih2Jif2F53fjmfmdLS0krlxP06T6DfoEEDnD59uiRqISIiIiIqUcbGxvougV4B8xMfMxQb8xMb8/uXzs2w4cOHY+zYsVi8eDGOHDmCuLg4rRsRERHRf51Go9F3CVQEGo0G8fHxzE9QzE98zFBszE9szE+bzhPo50ySP3r0aOU+SZIgyzIkSeLqBEREREREREREVGrp3Ay7fPlySdRBRERERERERERU4nRuhnHifCIiIiIiIiIiEpXOq0kCQHJyMhYuXIiEhAQAgI+PDz788ENUq1at2AskIiIiKi24mqTYZFmGRqOBSqXiSmgCYn7iY4ZiY35ie935/edWk/ztt9/g4+ODY8eOoXbt2qhduzaOHj2KmjVr4vfffy+JGomIiIiIikV2dra+S6BXwPzExwzFxvzExvz+pfPIMD8/PwQFBeHTTz/Vun/ixInYvXs3Tp06VawFEhEREZUWOb9yRkdHl8pfOalgGo0Gt2/fhr29PVQqnX8TJj1jfuJjhmJjfmLTaDTQaDTw9fWFgYFBiR/vPzcyLCEhAYMGDcp1/8CBA3Hu3LliKYqIiIiIiIiIiKgk6NwMK1++PGJiYnLdHxMTgwoVKhRHTURERERERERERCVC59UkhwwZgqFDh+LSpUto3LgxACA6Ohrz58/Hxx9/XOwFEhEREREVF076LDbmJz5mKDbmJ7bXcXmkKHSeM0yWZSxcuBBhYWG4fv06AMDZ2Rnjx4/H6NGj+R8HERER/WflzH9x5MgRWFpa6rscIiIiokKrVavWaztWaZ8zTOdm2PPu378PALCysiq2goiIiIhKq5wPdocPH+bnHwHJsozHjx/DyMiIP+AKiPmJjxmKjfmJTZZlVKlSBVZWVq8lv9LeDNN5zrDLly8jKSkJwNMmWM4HwaSkJFy5cqVYiyMiIiIqjV7ht0TSI1mWkZaWxvwExfzExwzFxvzEJssyLl26BI1Go+9SSgWdm2EhISE4fPhwrvuPHj2KkJCQ4qiJiIiIiIiIiIioROjcDDt9+jSaNGmS6/6GDRvmucokERERERERERFRaaFzM0ySJGWusOelpaVBrVYXS1FERET/dbpeYhAZGQk3N7eSKaaYSJKErVu36ruM14JzpYhJkiQYGBgwP0ExP/ExQ7ExP7FJkgRTU1N9l1Fq6NwMa968OebNm6fV+FKr1Zg3bx6aNm1arMURERGVlGvXrmHgwIFwdnaGsbExqlSpgg8//BC3b99+Lcf/7rvvin2ft27dwrBhw+Dq6goTExM4OjoiKCgI0dHRxX6sorhy5corfYBu0aIFxowZo3XfV199BRMTE/z000+vWJ1u+EVATJIkwc7OjvkJivmJjxmKjfmJTZIkeHl5wcDAQN+llAqGuj5h/vz5aN68OWrUqIFmzZoBAKKiopCeno59+/YVe4FERETF7dKlS2jUqBGqV6+OdevWoWrVqjh79izGjx+PX3/9FX/88Qfs7OxK7PixsbGYP38+3n///WLdb7du3ZCdnY3Vq1fD3d0df//9N/bu3ftKDT61Wg1JkqBS6fz7WYmbPn06FixYgG3btqFdu3av9dicPFhMsiwjMzMTpqam/DInIOYnPmYoNuYnNlmWcfv2bZQrV65Ufq573XR+BXx8fBAXF4cePXrg5s2buH//Pvr164fz58+jVq1aJVEjERFRsRoxYgSMjY2xe/duBAYGwtXVFe3bt8eePXvw119/YfLkycq2bm5umDt3LgYOHAgrKyu4urrmGtV17do19OjRA7a2trCzs0Pnzp0LXGF548aNuHz5Mk6dOlVs53Tv3j1ERUVh/vz5ePPNN1GlShU0aNAAkyZNwttvv61s98UXX8DX1xcWFhZwcXHB8OHDkZGRoTweHh4OW+4Z+tQAAQAASURBVFtbbN++HT4+PjAxMUFKSgqOHz+ONm3awMHBATY2NggMDMyz/n/++QfvvPMOzM3N4enpie3bt+db89WrV9GpUyeUK1cOFhYWqFmzJnbu3PnSc5VlGaNGjcLXX3+N33//XasRtmLFCnh7e8PU1BReXl5YsmSJ8ljLli0xcuRIrX3dunULxsbG2Lt3b57HysrKQnp6utYtpwYSjyzLyMjIYH6CYn7iY4ZiY35ik2UZ165dY37PFKkd6OzsjLlz5+KXX37Bxo0bMW3atBL9BZ2IiKi43LlzB7/99huGDx8OMzMzrcccHR0RHByM9evXa31QCAsLQ0BAAE6fPo3hw4dj2LBhSExMBAA8fvwYQUFBsLKyQlRUFKKjo2FpaYl27dohOzsbABATE4NDhw4ptw0bNgAAli5dqnX/tWvXinxelpaWsLS0xNatW5GVlZXvdiqVCl9//TXOnj2L1atXY9++ffjkk0+0tnn48CHmz5+PFStW4OzZs6hQoQLu37+P/v3749ChQ/jjjz/g6emJDh065JpHNDQ0FD169EBcXBw6dOiA4OBg3LlzJ89aRowYgaysLBw8eBDx8fGYP38+LC0tCzzPJ0+e4L333sPGjRtx4MABNG7cWHksIiIC06ZNw5w5c5CQkIC5c+di6tSpWL16NQBg8ODBWLt2rdbr8+OPP6JSpUpo2bJlnsebN28ebGxslJuLi0uB9RERERFR6SfJhWwL/vPPP3jw4AGqVKmi3Hf27FksWLAADx48QJcuXdCnT58SK5SIiKg4HD16FA0bNsSWLVvQpUuXXI9/+eWX+Pjjj/H333+jQoUKcHNzQ7NmzfDDDz8AePqrmqOjI0JDQ/HBBx/gxx9/xOzZs5GQkKBcMpCdnQ1bW1ts3boVbdu2xfnz59GrVy/ExsbmW9eAAQOwePFimJub5/l4ZGQkQkJCChxxtmnTJgwZMgSPHj2Cv78/AgMD0atXL9SuXTvf52zcuBEffPAB/vnnHwBPR4YNGDAAMTExqFOnTr7P02g0sLW1xdq1a9GxY0cAT+eimDJlCmbNmgUAePDgASwtLfHrr7/meRlj7dq10a1bN0yfPj3f4zyvRYsWOHLkCICnl5p6eXlpPe7h4YFZs2ahd+/eyn2zZ8/Gzp07cfjwYWRmZsLZ2RlLly5Fjx49AAB16tRB165d860hKytLq3mWnp4OFxcXREdHw9raulB1U+mh0Whw+/Zt2Nvb8xIRATE/8TFDsTE/sWk0Gmg0Gvj6+r6WecPS09NhY2ODtLS0UvmZqdDv4JzLEXLcvHkTzZo1w/Hjx5GVlYWQkBDliwIREVFpp8sQ8eebSZIkwdHRETdv3gTwtClz8eJFWFlZKaOz7OzskJmZieTkZACAl5cXjh49ihEjRuTat5WVFSIiIrBy5cp8G2GF1a1bN1y/fh3bt29Hu3btEBkZCX9/f4SHhyvb7NmzB61atUKlSpVgZWWFvn374vbt23j48KGyjbGxca4G2t9//40hQ4bA09MTNjY2sLa2RkZGBlJSUvJ9rSwsLGBtba28Vi8aPXo0Zs+ejSZNmmD69OmIi4t76Tk2bdoUlpaWmDp1Kp48eaLc/+DBAyQnJ2PQoEFKDpaWlpg9e7aSg6mpKfr27YuVK1cCAE6dOoUzZ84gJCQk3+OZmJjA2tpa6wZwAn1RSZIEIyMj5ico5ic+Zig25ic2SZJgZWWl7zJKjUI3w/744w+tOUfWrFkDOzs7xMTEYNu2bZg7dy6++eabEimSiIiouHh4eECSJCQkJOT5eEJCAsqVK4fy5csr9xkZGWltI0kSNBoNACAjIwP16tVDTEyM1u3ChQtaI6ZNTEywePHiXE2mUaNGFevIalNTU7Rp0wZTp07F4cOHERISoox6unLlCjp27IjatWtj06ZNOHnypPL/7pxLOgHAzMws1wfd/v37IyYmBl999RUOHz6MmJgY2Nvbaz0PKPi1etHgwYNx6dIl9O3bF/Hx8QgICMCiRYsKPD9fX1/s3bsX+/fvR8+ePZWGWM68Z8uXL9fK4cyZM/jjjz+0jvn777/jzz//xKpVq9CyZUutUe+FxS8CYpIkCba2tsxPUMxPfMxQbMxPbJIkoVq1alxN8plCN8NSU1Ph5uam/H3fvn3o2rUrDA2fLkj59ttvIykpqdgLJCIiKk729vZo06YNlixZgkePHmk9lpqaioiICPTs2bPQH/T8/f2RlJSEChUqwMPDQ+tmY2Ojte3Vq1cRFxcHSZJQo0YNAMCOHTuK58Ty4ePjgwcPHgAATp48CY1Gg7CwMDRs2BDVq1fH9evXC7Wf6OhojB49Gh06dEDNmjVhYmKiXFr5KlxcXPDBBx9g8+bNGDt2LJYvX/7S59StWxd79+7FwYMH0aNHDzx+/BgVK1aEs7MzLl26lCuHqlWrKs/19fVFQEAAli9fjrVr12LgwIFFqpuTz4pJlmU8ePCA+QmK+YmPGYqN+YlNlmWkpqbm+yNlWVPoZpi1tTXu3bun/P3YsWN44403lL9LklTghL1ERESlxeLFi5GVlYWgoCAcPHgQ165dw65du9CmTRtUqlQJc+bMKfS+goOD4eDggM6dOyMqKgqXL19GZGQkRo8ejT///FNr202bNqFixYrYtWsXzp49i2nTpuHs2bPKZXyv4vbt22jZsiV+/PFHxMXF4fLly9iwYQM+++wzdO7cGcDTUXGPHz/GokWLcOnSJfzwww9YunRpofbv6emJH374AQkJCTh69CiCg4NzLUCgqzFjxuC3335TVtbcv38/vL29C/XcOnXqYN++fTh06JDSEAsNDcW8efPw9ddf48KFC4iPj8eqVavwxRdfaD138ODB+PTTTyHLMt55550i1c4vAmKSZRkPHz5kfoJifuJjhmJjfmLLaYYxv6cK3Qxr2LAhvv76a2g0GmzcuBH379/XWnnpwoULXGGJiIiE4OnpiRMnTsDd3R09evRAtWrVMHToULz55ps4cuSITiskm5ub4+DBg3B1dUXXrl3h7e2NQYMGITMzM9dkoRkZGYiNjUXbtm1hYGCA0NBQ7Nu3D8ePH3/lc7K0tMQbb7yBL7/8Es2bN0etWrUwdepUDBkyBIsXLwbwtIH0xRdfYP78+ahVqxYiIiIwb968Qu3/+++/x927d+Hv74++ffti9OjRqFChwivVrFarMWLECHh7e6Ndu3aoXr06lixZUujn+/r6Yt++fTh8+DC6d++Ofv36YcWKFVi1ahV8fX0RGBiI8PBwrZFhANC7d28YGhqid+/eMDU1faVzICIiIiLxFHo1ybi4OLRq1Qrp6el48uQJ/ve//ymrRQFA3759YWFhUehfmImIiKjwCrOaJBXOlStXUK1aNRw/fhz+/v46PTdnZSSuJikmroQmNuYnPmYoNuYnNq4mqc2wsBvWrl0bCQkJiI6OhqOjo9YlkgDQq1cv+Pj4FHuBRERERMXh8ePHuH37NqZMmYKGDRvq3Ah7HicPFpMkSTA1NWV+gmJ+4mOGYmN+YpMkCXZ2dszvmUI3wwAoc6Lk5a233iqWgoiIiIhKQnR0NN58801Ur14dGzdufKV98YOkmLisvNiYn/iYodiYn9gkSYKrq6u+yyg1dGqGERERkX64ublhzJgx+i5DaC1atCi2SWM5+ayYZFlGRkYGLC0t2dAUEPMTHzMUG/MTmyzLSElJQeXKlXmZK3SYQJ+IiIj0h82w0oXNMDHJsozMzEzmJyjmJz5mKDbmJzZZlnHnzh3m9wybYUREREREREREVGbwMkkiIiIiHXl7e6NcuXL6LoN0pFarER8fj5o1a76WlbSoeDE/8TFDsTE/seXkR0/pPDIsMDAQa9aswaNHj0qiHiIiIqJSj3OliEmSJDg6OjI/QTE/8TFDsTE/sTE/bTo3w/z8/DBu3Dg4OjpiyJAh+OOPP0qiLiIiIqJSixPPikmlUsHR0ZH5CYr5iY8Zio35iY35adP5VVi4cCGuX7+OVatW4ebNm2jevDl8fHywYMEC/P333yVRIxEREVGpolar9V0CFYFarUZycjLzExTzEx8zFBvzExvz01aklqChoSG6du2Kbdu24c8//0SfPn0wdepUuLi4oEuXLti3b19x10lERERE9Mru37+v7xLoFTA/8TFDsTE/sTG/f73S+Lhjx45h+vTpCAsLQ4UKFTBp0iQ4ODigY8eOGDduXHHVSEREREREREREVCx0Xk3y5s2b+OGHH7Bq1SokJSWhU6dOWLduHYKCgpSJ2EJCQtCuXTssWLCg2AsmIiIiIiIiIiIqKp2bYZUrV0a1atUwcOBAhISEoHz58rm2qV27NurXr18sBRIRERGVNlyJSUySJMHFxYX5CYr5iY8Zio35iY35aZNkWZZ1eUJUVBSaNWtWUvUQERERlVrp6emwsbFBWloarK2t9V0OERERUalU2j8z6Txn2PTp03Hv3r1c96enp6Nly5bFURMRERFRqcaVmMSkVqtx/vx55ico5ic+Zig25ic25qdN52bYgQMHkJ2dnev+zMxMREVFFUtRREREREQlITMzU98l0CtgfuJjhmJjfmJjfv8q9JxhcXFxAABZlnHu3DmkpqYqj6nVauzatQuVKlUq/gqJiIiIiIiIiIiKSaGbYXXr1oUkSZAkKc/LIc3MzLBo0aJiLY6IiIiIiIiIiKg4FboZdvnyZciyDHd3dxw7dkxrFUljY2NUqFABBgYGJVIkERERUWmiUuk80wSVAiqVCu7u7sxPUMxPfMxQbMxPbMxPW6GbYVWqVAEAaDSaEiuGiIiISARcllxMkiSVyhWtqHCYn/iYodiYn9iYn7ZCNcO2b9+O9u3bw8jICNu3by9w27fffrtYCiMiIiIqrbgSk5jUajXOnTsHHx8fXtEgIOYnPmYoNuYnNuanrVDNsC5duiA1NRUVKlRAly5d8t1OkiR+OCQiIiKiUoufVcXG/MTHDMXG/MTG/P5VqGbY85dG8jJJIiIiIiIiIiISlU4zpz1+/BitWrVCUlJSSdVDRERERERERERUYnRqhhkZGSEuLq6kaiEiIiISAldiEpNKpUKNGjWYn6CYn/iYodiYn9iYnzadX4X33nsP33//fUnUQkRERERUooyNjfVdAr0C5ic+Zig25ic25vevQs0Z9rwnT55g5cqV2LNnD+rVqwcLCwutx7/44otiK46IiIioNOIcqmLSaDSIj4+Hr68vV9ISEPMTHzMUG/MTG/PTpnMz7MyZM/D39wcAXLhwQesxSZKKpyoiIiIiIiIiIqISoHMzbP/+/SVRBxERERERERERUYnjzGlERERERERERFRmSLIsy7o+6cSJE/j555+RkpKC7Oxsrcc2b95cbMURERERlSbp6emwsbHBvXv3YGNjo+9ySEeyLEOj0UClUnF6DwExP/ExQ7H9P3t3Hh7T+bcB/D4TssieCElIBFkRxNai9iWoVOmvUU0RsceuVL2tvfatSFFbgmpRW1VrJ0RqJxERERESGrWERBDLzLx/aE6NbDMxkTzcn+uaq+bMmXO+Z+5JOvPkWZif2N50ftmfmdLT02FhYVHk59OVzsMk169fjx49esDX1xd79uxB27ZtcenSJfzzzz/o3LlzUdRIREREVKL4fZmCUobpxV0G6UwNU6PnePikFAB+kRMP8xMfMxQb8xObGn/MLQdjY+PiLqRE0HmY5LRp0zB//nz8/vvvMDQ0xIIFC3Dx4kX4+/vD2dm5KGokIiIiKlEMFDp3rKcSwEChxntVU5mfoJif+Jih2Jif2AwUasTHx3NF7H/p3BiWmJiIDz/8EABgaGiIhw8fQpIkjBgxAsuWLdN7gURERERERERERPqic2OYtbU1Hjx4AACoUKECzp8/DwC4f/8+Hj16pN/qiIiIiIiIiIiI9EjnOcOaNm2KvXv3wtvbG59++imGDRuGAwcOYO/evWjVqlVR1EhEREREpBfPVVxMXWTMT3zMUGzMT2wGBgbFXUKJofNqkmlpacjKyoKjoyNUKhVmzZqFv/76C25ubvj2229hbW1dVLUSERERFavslZGa9D6PUobmxV0OERERkdYOLH5z87y/datJ2tjYyP9WKBT4+uuv9VoQERERUcnHyYPFpIaNaRbSHhqDK6GJiPmJjxmKjfmJTY2MjAyYm5tDkpifVn0cMzIytL4RERERve24kpaYDBRq1K50m/kJivmJjxmKjfmJzUChxpUrV7ia5L+06hlmZWVVYMuhWq2GJElQKpV6KYyIiIiIiIiIiEjftOoZdvDgQRw4cCDfW/Y+RFT0dJzqD+Hh4XBxcSmaYgQVHh4OSZJw//794i5Fr/bv3w8vLy+h/jAxbtw49OvXT+/HnThxImrXri3fDwwMxMcff6z38+hDUdX26nGbN2+O4cOH5/ucsLAwWFlZ6b2WvNy5cwflypXD9evX39g5s3322WeYO3fuGz8vERERERUvrRrDmjVrpvWN6F2QkpKCoKAgODo6wtDQEJUqVcKwYcNw9+7dN3L+ZcuW6f2Yt2/fxsCBA+Hs7AwjIyPY29vD19cXkZGRej/X6/jnn39QunRprF+/PtfHe/fujTp16rzhqvRv6NChqFu3LoyMjDQadAry1Vdf4dtvv5VXilEqlZgxYwY8PT1hYmICGxsbvPfee1ixYkURVa6bmzdvYsGCBfjmm29ybB8yZAiqVKkCIyMjODk5wc/PD/v37y+mSnN6k41r2Y23ud1u3ryp9XG2bNmCKVOmyPddXFzw/fffa+zTtWtXXLp0SV+lF6hs2bLo0aMHJkyYoNPzPvzwQ/l3Yb9+/TB58mSNx2NjY/HJJ5/AxcUFkiTluE4A+PbbbzF16lSkp6frXjhHiIhJDTx8Upr5iYr5iY8Zio35iU0NGBsbF3cVJYbOE+gfPnw438ebNm1a6GKIRHDlyhU0bNgQ7u7u+OWXX1C5cmXExsZi9OjR2LlzJ44dO6ax0IS+RUdHY+bMmejfv79ej/vJJ5/g6dOnWL16NapUqYJ//vkH+/fvf2MNfNoqX748PvzwQ6xatQqfffaZxmMPHz7Exo0bMWPGjGKqTr+CgoJw/PhxnDt3Tqv9jxw5gsTERHzyySfytkmTJuHHH39ESEgI6tWrh4yMDJw6dQr37t0rqrJ1smLFCjRq1AiVKlWSt129ehWNGzeGlZUVZs+eDW9vbzx79gy7d+/GoEGDcPHixWKsuHjFx8fnWI2nXLlyWj9fm99NJiYmMDEx0bm219GrVy/UrVsXs2fP1qpGtVqNY8eOYdasWQCAiIgIhISEaOzz6NEjVKlSBZ9++ilGjBiR63Fq1KiBqlWr4qeffsKgQYN0qlmpVuj+IYqKnVKtwPFEh+IugwqJ+YmPGYqN+YlNqVbA09OzuMsoMbTqGfay5s2b57i1aNFCvhG97QYNGgRDQ0Ps2bMHzZo1g7OzM9q3b499+/bhxo0bGj1cXFxcMG3aNAQFBcHc3BzOzs45enWlpKTA398fVlZWsLGxQadOnXD16tU8z79p0yYkJSXhzJkzerum+/fvIyIiAjNnzkSLFi1QqVIlNGjQAGPHjsVHH30k7zdv3jx4e3vD1NQUTk5OCA4ORmZmpsaxli9fDicnJ5QpUwadO3fGvHnzNIZcJSYmolOnTihfvjzMzMxQv3597Nu3T6d6e/fujf379yM5OVlj+6+//ornz58jICAAT548wdChQ1GuXDkYGxvjgw8+wMmTJ/M85qtD6gDg+++/1xhemt0TaNq0aShfvjysrKwwefJkPH/+HKNHj4aNjQ0qVqyI0NBQjePomjEALFy4EIMGDUKVKlW0ek0AYP369WjTpo3GX3y2b9+O4OBgfPrpp6hcuTJq1aqF3r17Y9SoUfI+u3btwgcffAArKyvY2tqiY8eOSExM1Dj2X3/9hdq1a8PY2Bj16tXDtm3bIEkSoqKiALzogda7d29UrlwZJiYm8PDwwIIFC7Sq2c/PT2NbcHAwJEnCiRMn8Mknn8Dd3R3Vq1fHyJEjcezYMXm/5ORkdOrUCWZmZrCwsIC/vz/++ecfrV8vlUqF6dOnyzXXqlULmzZt0tgnNjYWHTt2hIWFBczNzdGkSRMkJiZi4sSJWL16NX777Te5h1Z4eDiAgvNWKpUYOXKk/Hp/9dVXWg99LleuHOzt7TVuCoVC6+O+PEyyefPmuHbtGkaMGCFfA5D7MMklS5agatWqMDQ0hIeHB9auXavxuCRJWLFiBTp37owyZcrAzc0N27dvlx+/d+8eAgICYGdnBxMTE7i5uWn8nFSvXh2Ojo7YunWrVq9DfHw81Go1qlWrhjt37uDy5ct47733NPapX78+Zs+ejc8++wxGRkZ5HsvPzy/PnqYA8OTJk1wXC5Ik/llcRJKkhqNVJvMTFPMTHzMUG/MTmySpcffuXU6g/y+dG8Pu3buncbt16xZ27dqF+vXrY8+ePUVRI1GJkZaWht27dyM4ODhHzwl7e3sEBARgw4YNGl9A586di3r16uHs2bMIDg7GwIEDER8fDwB49uwZfH19YW5ujoiICERGRsLMzAzt2rXD06dPAQBRUVE4cuSIfPv1118BAEuXLtXYnpKSUujrMjMzg5mZGbZt24YnT57kuZ9CocDChQsRGxuL1atX48CBA/jqq6/kxyMjIzFgwAAMGzYMUVFRaNOmDaZOnapxjMzMTHTo0AH79+/H2bNn0a5dO/j5+eVo2MpPhw4dUL58eYSFhWlsDw0NRZcuXWBlZYWvvvoKmzdvxurVq3HmzBm4urrC19cXaWlpWp8nNwcOHMDff/+Nw4cPY968eZgwYQI6duwIa2trHD9+HAMGDED//v3l+Y+0yVhfIiIiUK9ePY1t9vb2OHDgAG7fvp3n8x4+fIiRI0fi1KlT2L9/PxQKBTp37iz/jzIjIwN+fn7w9vbGmTNnMGXKFIwZM0bjGCqVChUrVsSvv/6KCxcuYPz48fi///s/bNy4Mc/zpqWl4cKFCxo1p6WlYdeuXRg0aBBMTU1zPCe7kUalUqFTp05IS0vDoUOHsHfvXly5cgVdu3Yt8HXKNn36dKxZswZLly5FbGwsRowYgS+++AKHDh0CANy4cQNNmzaFkZERDhw4gNOnTyMoKAjPnz/HqFGj4O/vj3bt2iE1NRWpqalo1KiRVnnPnTsXYWFhWLVqFY4cOYK0tDStG4Hyo+txt2zZgooVK2Ly5MnyNeRm69atGDZsGL788kucP38e/fv3R69evXDw4EGN/SZNmgR/f3+cO3cOHTp0QEBAgPzzNm7cOFy4cAE7d+5EXFwclixZgrJly2o8v0GDBoiIiMj3Gjt27AgrKyvUq1cP6enpsLa2RuXKlaFUKlGxYsVCzXXWoEEDnDhxIs/ffdOnT4elpaV8c3JyAgAo+EVASApJDU/HNOYnKOYnPmYoNuYnNoWkRkpKis7zT7+tdO7hb2lpmWNbmzZtYGhoiJEjR+L06dN6KYyoJEpISIBarYaXl1euj3t5eeHevXu4ffu2PHSpQ4cOCA4OBgCMGTMG8+fPx8GDB+Hh4YENGzZApVJhxYoVcq+M0NBQWFlZITw8HG3btoWxsTECAwMRHR2tca7ly5dj+fLlAF4MMXp1iJAuSpUqhbCwMPTt2xdLly5FnTp10KxZM3z22WeoWbOmvN/LE2+7uLjgu+++w4ABA7B48WIAwKJFi9C+fXu515G7uzv++usv7NixQ35erVq1UKtWLfn+lClTsHXrVmzfvh2DBw/Wql4DAwP07NkTYWFhGDduHCRJQmJiIiIiIrB37148fPgQS5YsQVhYGNq3bw/gxeu1d+9erFy5EqNHjy70a2VjY4OFCxdCoVDAw8MDs2bNwqNHj/B///d/AICxY8dixowZOHLkCD777DOtMtaXa9euwdHRUWPbvHnz8L///Q/29vaoXr06GjVqhE6dOsmvCwCNYZUAsGrVKtjZ2eHChQuoUaMGfv75Z0iShOXLl8PY2BjVqlXDjRs30LdvX/k5pUuXxqRJk+T7lStXxtGjR7Fx40b4+/vnWm9ycjLUarVGzZcvX4ZarS6wC/f+/fsRExODpKQkuXFizZo1qF69Ok6ePIn69evn+/wnT55g2rRp2LdvHxo2bAgAqFKlCo4cOYIff/wRzZo1ww8//ABLS0usX78epUuXBvDiPZ3NxMQET548gb29vbztp59+KjDv77//HmPHjkWXLl0AvGjY3r17d771ZqtYsaLG/UqVKiE2NhYAdD6ujY0NDAwMYG5urnENr5ozZw4CAwPl32PZPfTmzJmj0SM8MDAQ3bp1AwBMmzYNCxcuxIkTJ9CuXTskJyfDx8dHbvjMbUEPR0dHnD17Nt/rX7FiBbKysjBgwAC8//77CAwMxPjx42FlZYWRI0fm+9y8ODo64unTp7h586bGcN1sY8eO1Th2RkaG/J4jIiIiIjHp3DMsL+XLl5d7uxC97XRpTX+5MUmSJNjb2+PWrVsAXsz/dfnyZZibm8u9s2xsbJCVlSUPU/P09MTx48dznc/G3Nwc69atw6pVq1CmTJnXuqZPPvkEf//9N7Zv34527dohPDwcderU0eh9tW/fPrRq1QoVKlSAubk5unfvjrt37+LRo0cAXgxdatCggcZxX72fmZmJUaNGwcvLC1ZWVjAzM0NcXJxOPcOAF/NpJSUlyb1TQkND4eLigpYtWyIxMRHPnj1D48aN5f1Lly6NBg0aIC4uTqfzvKp69erysDTgxe8+b29v+b6BgQFsbW11ylhfHj9+nGNSzGrVquH8+fM4duwYgoKCcOvWLfj5+aFPnz7yPgkJCejWrRuqVKkCCwsLuaEiO5P4+HjUrFlT49iv5goAP/zwA+rWrQs7OzuYmZlh2bJl+eb6+PFjAJoTeWr7sxUXFwcnJyeNRolq1arByspKq4wvX76MR48eoU2bNnIuZmZmWLNmjZxLVFQUmjRpIjeEaaOgvNPT05GamqoxpK9UqVI5evTlJSIiAlFRUfLtzz//BIDXPm5+4uLiNH6WAKBx48Y5XueXf9eZmprCwsJC/jkYOHAg1q9fj9q1a+Orr77CX3/9leM8JiYm8u+SvNjb26NChQo4duwYAgIC4OLigqNHj8Lf3x8uLi6FWjU3u5dvXuc2MjKChYWFxo2IiIiIxKZzz7BXJ3JWq9VITU3FjBkzdFrxjEhErq6ukCQJcXFx6Ny5c47H4+LiYG1tDTs7O3nbq1+kJUmSh59lZmaibt26WLduXY5jvXwMIyMjhISEICIiQuNncMiQIfj8889f+7qyGRsbo02bNmjTpg3GjRuHPn36YMKECQgMDMTVq1fRsWNHDBw4EFOnToWNjQ2OHDmC3r174+nTp1o3xo0aNQp79+7FnDlz4OrqChMTE/zvf//Tecigm5sbmjRpgtDQUDRv3hxr1qxB37595d44ulIoFDkaYp49e5Zjv9zy1EfG+lC2bNlcJ8ZXKBSoX78+6tevj+HDh+Onn35C9+7d8c0336By5crw8/NDpUqVsHz5cjg6OkKlUqFGjRo6ZbJ+/XqMGjUKc+fORcOGDWFubo7Zs2fj+PHj+dYLvBh+n/1auLm5QZKkIp8kP3uuuz/++AMVKlTQeCx7fqnCTCJf1HlXrly5UEMB34T8fg7at2+Pa9eu4c8//8TevXvRqlUrDBo0CHPmzJH3T0tLy/c1mjZtGqZNmwa1Wo1Hjx7Bx8cHwIthvr6+vpAkCTt37kSTJk10qjt7KKfO+XCEgZjUQFqmMfMTFfMTHzMUG/MTm/pFZwp6QeeeYbVr14aPjw9q164t/7tDhw54+vQpVqxYURQ1EpUYtra2aNOmDRYvXiz3asl28+ZNrFu3Dl27dtW6QaZOnTpISEhAuXLl4OrqqnF7dUjytWvXcO7cOUiSBA8PDwDQGH5YFKpVq4aHDx8CAE6fPg2VSoW5c+fi/fffh7u7O/7++2+N/T08PHJMUv/q/cjISAQGBqJz587w9vaGvb19gZPJ56V3797YvHkzNm/ejBs3biAwMBAA5Im+IyMj5X2fPXuGkydPolq1arkey87ODjdv3tRoEMueHP516JLx6/Lx8cGFCxcK3C/7NXj48CHu3r2L+Ph4fPvtt2jVqpU81PdlHh4eiImJ0ZhTKbdcGzVqhODgYPj4+MDV1bXAnm9Vq1aFhYWFRs02Njbw9fXFDz/8IL/3Xnb//n0AL4Ykp6SkaMyVd+HCBdy/fz/PjF99DYyMjJCcnJwjl+zeZjVr1kRERESujaIAYGhoCKVSqbGtoLwtLS3h4OCg0Uj4/Pnz155ioLDHze0aXuXl5aXxswS8yFub1/lldnZ26NmzJ3766Sd8//33ORYTOX/+vNzAlZsBAwYgKioK/fv3x6effoqoqCh88803aN68OaKjoxEVFVWonnDnz59HxYoVc8xhVhClWm+d6+kNUqoViEoux/wExfzExwzFxvzEplQrULVqVRgYGBR3KSWCzu/ipKQkXLlyBUlJSUhKSsK1a9fw6NEj/PXXX1ymk94JISEhePLkCXx9fXH48GGkpKRg165daNOmDSpUqJBjwvj8BAQEoGzZsujUqRMiIiKQlJSE8PBwDB06VJ6APdvmzZtRvnx57Nq1C7GxsRg/fjxiY2P1MtTu7t27aNmyJX766SecO3cOSUlJ+PXXXzFr1ix06tQJwItecc+ePcOiRYtw5coVrF27FkuXLtU4zpAhQ/Dnn39i3rx5SEhIwI8//oidO3dqNA66ublhy5YtiIqKQnR0ND7//PNCr2jy6aefonTp0ujfvz/atm0rN2KYmppi4MCBGD16NHbt2oULFy6gb9++ePToEXr37p3rsZo3b47bt29j1qxZSExMxA8//ICdO3cWqq6X6ZLxyy5fvoyoqCjcvHkTjx8/lofF5ddby9fXF0eOHNHY9r///Q/z58/H8ePHce3aNYSHh2PQoEFwd3eHp6cnrK2tYWtri2XLluHy5cs4cOBAjrmXsjPq168f4uLisHv3brlHT3a2bm5uOHXqFHbv3o1Lly5h3Lhx+a7eCbzosda6descNf/www9QKpVo0KABNm/ejISEBMTFxWHhwoXy/F6tW7eGt7c3AgICcObMGZw4cQI9evRAs2bNtGoQMTc3x6hRozBixAisXr0aiYmJOHPmDBYtWoTVq1cDAAYPHoyMjAx89tlnOHXqFBISErB27Vp5SgAXFxecO3cO8fHxuHPnDp49e6ZV3sOGDcOMGTOwbds2XLx4EcHBwXIjX0Fu3bqFmzdvatyyG+sKc1wXFxccPnwYN27cwJ07d3LdZ/To0QgLC8OSJUuQkJCAefPmYcuWLRorkhZk/Pjx+O2333D58mXExsZix44dGnMvPnr0CKdPn853Dj0bGxu4urriwoULaN++PVxdXZGQkIC2bdvKDY4v9+Z7+vSpxs/NjRs3EBUVhcuXL2scNyIiolBz93ElLTFJkhqV7dKZn6CYn/iYodiYn9gkSY2bN29yNcl/6dwYVqlSJY2bk5NTjjlqiN5m2V/6q1SpAn9/f1StWhX9+vVDixYtcPToUdjY2Gh9rDJlyuDw4cNwdnZGly5d4OXlhd69eyMrKyvHvDSZmZmIjo5G27ZtYWBggEmTJuHAgQMFNjhow8zMDO+99x7mz5+Ppk2bokaNGhg3bhz69u0rT8xfq1YtzJs3DzNnzkSNGjWwbt06TJ8+XeM4jRs3xtKlSzFv3jzUqlULu3btwogRIzR+R8ybNw/W1tZo1KgR/Pz84Ovrizp16mgcZ+LEiVrN/VOmTBl89tlnuHfvHoKCgjQemzFjBj755BN0794dderUweXLl7F7925YW1vneiwvLy8sXrwYP/zwA2rVqoUTJ07o9GU/vxq1zfhlffr0gY+PD3788UdcunQJPj4+8PHxydEb72UBAQGIjY3VmL/R19cXv//+O/z8/ODu7o6ePXvC09MTe/bsQalSpaBQKLB+/XqcPn0aNWrUwIgRIzB79myN41pYWOD3339HVFQUateujW+++Qbjx48H8N98X/3790eXLl3QtWtXvPfee7h796484Xp++vTpg/Xr12v8T7lKlSo4c+YMWrRogS+//BI1atRAmzZtsH//fixZsgTAi0a43377DdbW1mjatClat26NKlWqYMOGDQWeM9uUKVMwbtw4TJ8+HV5eXmjXrh3++OMPVK5cGcCLnqAHDhxAZmYmmjVrhrp162L58uXycMC+ffvCw8MD9erVg52dHSIjI7XK+8svv0T37t3Rs2dPeUhpbsOuc+Ph4QEHBweNW3bvr8Icd/Lkybh69SqqVq2a5zDBjz/+GAsWLMCcOXNQvXp1/Pjjj/LwZG0ZGhpi7NixqFmzJpo2bQoDAwOsX79efvy3336Ds7NzgUMcnz9/jsjISDRt2hQAcOjQIfnfr/r777/ln5vU1FTMmTMHPj4+GvPlZWVlYdu2bRqLQWiLK2mJSfHvFznmJybmJz5mKDbmJzbFv41hXE3yBUldiFdi//79mD9/vjx5rpeXF4YPH47WrVvrvUAien3h4eHyvF9vWt++fXHx4kVERERo/ZyePXtCkiSNyftJO6NHj0ZGRgZ+/PHHIj3PunXr0KtXL6Snpxdqbq1sarUa7733HkaMGCGvREjvnvfffx9Dhw7V6xyI2liyZAm2bt2KPXv2aP2cjIwMWFpaonnfc5BK6XeoMxU9A4UKzTyv49DFilCqOMxHNMxPfMxQbMxPbAYKFWb3uQ9vb+83MlQy+zNTenp6iVyASOd38OLFi9GuXTuYm5tj2LBhGDZsGCwsLNChQwf88MMPRVEjEQlkzpw58op62UPOevbsqfXz1Wo1wsPDMWXKlCKs8u31zTffoFKlSnrv/rxmzRocOXIESUlJ2LZtG8aMGQN/f//XaggDXvTwWrZsGZ4/f66nSkk0d+7cQZcuXYqlMbR06dJYtGjRGz8vERERERUvnXuGVaxYEV9//TUGDx6ssf2HH37AtGnTcOPGDb0WSESv7032DPP390d4eDgePHiAKlWqYMiQIRgwYECRn5eK1qxZs7B48WLcvHkTDg4O+PjjjzF16lStVxEleltk/5WzWZ8YKEqXvL9yUv4UkhruDmm4lGoDlbpwqw9T8WF+4mOGYmN+YlNIaqwaI6FixYpQKIq+Z19J7xmmc2OYmZkZoqKi4OrqqrE9ISEBPj4+8nL1RFRyXL16Fdu2bcPw4cOLuxQiIqFlf7Br0vs8ShlyeXIiIiISx4HFzm/sXCW9MUzn5sCPPvoIW7duzbH9t99+Q8eOHfVSFBHpl4uLCxvCiIj0iJMHi0khqeHpeJf5CYr5iY8Zio35iU0hqZGcnMzVJP9VStcnVKtWDVOnTkV4eLi8xP2xY8cQGRmJL7/8EgsXLpT3HTp0qP4qJSIiIiohuKy8mCRJDUerh0i4aQ1wiI9wmJ/4mKHYmJ/YJEmNtLR7qFChQnGXUiLoPEwye8n5Ag8sSbhy5UqhiiIiIiIqibK7/KelpcHa2rq4yyEdKZVKxMTEvLGVtEi/mJ/4mKHYmJ/Y3nR+JX2YpM49w5KSkoqiDiIiIiIiIiIioiL3WksIqNVq6NixjIiIiEh4ksThISKSJAn29vbMT1DMT3zMUGzMT2zMT1OhGsPWrFkDb29vmJiYwMTEBDVr1sTatWv1XRsRERFRifQmliQn/VMoFLC3t2d+gmJ+4mOGYmN+YmN+mnR+FebNm4eBAweiQ4cO2LhxIzZu3Ih27dphwIABmD9/flHUSERERFSiKJXK4i6BCkGpVCIxMZH5CYr5iY8Zio35iY35adJ5zrBFixZhyZIl6NGjh7zto48+QvXq1TFx4kSMGDFCrwUSEREREenLgwcPirsEeg3MT3zMUGzMT2zM7z869wxLTU1Fo0aNcmxv1KgRUlNT9VIUERERERERERFRUdC5MczV1RUbN27MsX3Dhg1wc3PTS1FERERERERERERFQedhkpMmTULXrl1x+PBhNG7cGAAQGRmJ/fv359pIRkRERPS24UpMYpIkCU5OTsxPUMxPfMxQbMxPbMxPk6RWq9W6PunMmTOYN28e4uLiAABeXl748ssv4ePjo/cCiYiIiEqKjIwMWFpaIj09HRYWFsVdDhEREVGJVNI/M+nUMywjIwPHjx/H06dPMX/+fNjZ2RVVXUREREQlFldiEpNSqURCQgLc3NxgYGBQ3OWQjpif+Jih2Jif2JifJq0bw6KiotChQwf8888/UKvVMDc3x8aNG+Hr61uU9RERERER6U1WVlZxl0CvgfmJjxmKjfmJjfn9R+sJ9MeMGYPKlSvjyJEjOH36NFq1aoXBgwcXZW1ERERERERERER6pXXPsNOnT2PPnj2oU6cOAGDVqlWwsbFBRkZGiRz/SURERERERERE9Cqte4alpaWhYsWK8n0rKyuYmpri7t27RVIYERERUUmlUGj9EYpKEIVCgSpVqjA/QTE/8TFDsTE/sTE/TTpNoH/hwgXcvHlTvq9WqxEXF4cHDx7I22rWrKm/6oiIiIhKIC5LLiZJkjiiQWDMT3zMUGzMT2zMT5OkVqvV2uyoUCggSRJy2z17uyRJXF2JiIiI3lrZy4SnpaXB2tq6uMshHSmVSly4cAHVqlXjSloCYn7iY4ZiY35ie9P5ZX9mSk9PL5GNcFr3DEtKSirKOoiIiIiIihz/cCs25ic+Zig25ic25vcfrRvDKlWqVJR1EBERERERERERFTnOnEZERERERERERO8MrecMIyIiInrXZc9/cf/+fVhaWhZ3OaQjtVqNrKwsGBsbcxEEATE/8TFDsTE/sb3p/Er6nGHsGUZERERE7wxDQ8PiLoFeA/MTHzMUG/MTG/P7DxvDiIiIiHSkUqmKuwQqBJVKhZiYGOYnKOYnPmYoNuYnNuanSefGsAkTJuDatWtFUQsREREREREREVGR0rkx7LfffkPVqlXRqlUr/Pzzz3jy5ElR1EVERERERERERKR3OjeGRUVF4eTJk6hevTqGDRsGe3t7DBw4ECdPniyK+oiIiIiIiIiIiPTmtVaTfPbsGX7//XeEhoZi9+7d8PT0RO/evREYGMgVloiIiOitk70yUpPeMShlWPJWRqKCqGGgUEOpkgBwJTTxMD/xMUOxMT+xqbF3UUUoFAquJonXnEBfrVbj2bNnePr0KdRqNaytrRESEgInJyds2LBBXzUSEREREemFcWllcZdAr4H5iY8Zio35ie3p06fFXUKJUajGsNOnT2Pw4MFwcHDAiBEj4OPjg7i4OBw6dAgJCQmYOnUqhg4dqu9aiYiIiEoEA0WhO9ZTMTJQqPFe1VTmJyjmJz5mKDbmJzYDhRrx8fFcTfJfOjeGeXt74/3330dSUhJWrlyJlJQUzJgxA66urvI+3bp1w+3bt/VaKBERERERERER0esqpesT/P39ERQUhAoVKuS5T9myZdnaSEREREREREREJY7OjWHjxo2T/5099/6bmHyNiIiIiOh1PVe91pS5VMyYn/iYodiYn9gMDAyKu4QSo1Dv5JUrV6JGjRowNjaGsbExatSogRUrVui7NiIiIqISSckvA0JSqhQ4fLEi8xMU8xMfMxQb8xObUqWAt7c3G8T+pXPPsPHjx2PevHkYMmQIGjZsCAA4evQoRowYgeTkZEyePFnvRRIRERGVLJw8WExq2JhmIe2hMQCObBAP8xMfMxQb8xObGhkZGTA3N+foPhSiZ9iSJUuwfPlyTJ8+HR999BE++ugjTJ8+HcuWLcPixYuLokYiIiKiEoUraYnJQKFG7Uq3mZ+gmJ/4mKHYmJ/YDBRqXLlyhfO7/0vnxrBnz56hXr16ObbXrVsXz58/10tRRETviuy5F7UVHh4OFxeXoilGB4GBgfj444/l+82bN8fw4cOLrR5dSJKEbdu2FXcZJZJarUa/fv1gY2MDSZIQFRVV3CUREREREemdzo1h3bt3x5IlS3JsX7ZsGQICAvRSFBHRm5KSkoKgoCA4OjrC0NAQlSpVwrBhw3D37t03cv5ly5YVyXH1dV1Xr17VqlFky5YtmDJlymtU/PqaN28OSZLyvDVv3rxIz798+XLUqlULZmZmsLKygo+PD6ZPny4//moDYkm0a9cuhIWFYceOHUhNTUWNGjVy3a+gayUiIiIiKsm0mjNs5MiR8r8lScKKFSuwZ88evP/++wCA48ePIzk5GT169CiaKomIisCVK1fQsGFDuLu745dffkHlypURGxuL0aNHY+fOnTh27BhsbGyK7PzR0dGYOXMm+vfvr9fjFsd1ve7xlEolJEmCQlH4CVm3bNmCp0+fAnjRGNigQQPs27cP1atXBwAYGhq+Vo35WbVqFYYPH46FCxeiWbNmePLkCc6dO4fz58/r/VxPnz4tsmtJTEyEg4MDGjVqlOc+b/JaSzSOEBGTGnj4pDTzExXzEx8zFBvzE5saMDY2Lu4qSgytvnWcPXtWvsXExKBu3bqws7NDYmIiEhMTUbZsWdSpUwexsbFFXS8Rkd4MGjQIhoaG2LNnD5o1awZnZ2e0b98e+/btw40bN/DNN9/I+7q4uGDatGkICgqCubk5nJ2dc/TqSklJgb+/P6ysrGBjY4NOnTrh6tWreZ5/06ZNSEpKwpkzZ4rtunIbMmhlZYWwsDAAQOXKlQEAPj4++fauenWY5JMnTzBq1ChUqFABpqameO+99xAeHi4/HhYWBisrK2zfvh3VqlWDkZERkpOTER4ejgYNGsDU1BRWVlZo3Lgxrl27ptV129jYwN7eHvb29rCzswMA2NrayttebrC7c+cOOnfujDJlysDNzQ3bt2/XONb58+fRvn17mJmZoXz58ujevTvu3LmT57m3b98Of39/9O7dG66urqhevTq6deuGqVOnAgAmTpyI1atX47fffpN7qmW/HjExMWjZsiVMTExga2uLfv36ITMzUz52do+yqVOnwtHRER4eHpg8eXKuvbZq166NcePG5VnnoUOH0KBBAxgZGcHBwQFff/21PMVBYGAghgwZguTkZEiSlOdw3IKuFch92OzHH3+MwMBA+f6TJ08wZswYODk5wcjICK6urli5cqX8eGxsLDp27AgLCwuYm5ujSZMmSExMlB9fsWIFvLy8YGxsDE9PT415S58+fYrBgwfDwcEBxsbGqFSpktxzTa1WY+LEiXB2doaRkREcHR0xdOjQPF+zJ0+eICMjQ+MGAEo1V9ISkVKtwPFEB+YnKOYnPmYoNuYnNqVaAU9PT64m+S+teoYdPHiwqOsgInqj0tLSsHv3bkydOhUmJiYaj9nb2yMgIAAbNmzA4sWL5dVW5s6diylTpuD//u//sGnTJgwcOBDNmjWDh4cHnj17Bl9fXzRs2BAREREoVaoUvvvuO7Rr1w7nzp2DoaEhoqKiNBo5fv31VwDA0qVLNXrWVqpUCU5OTm/suvJz4sQJjR5W2vZIGjx4MC5cuID169fD0dERW7duRbt27RATEwM3NzcAwKNHjzBz5kysWLECtra2sLGxQe3atdG3b1/88ssvePr0KU6cOCHXefXqVVSuXBkHDx587SGPkyZNwqxZszB79mwsWrQIAQEBuHbtGmxsbHD//n20bNkSffr0wfz58/H48WOMGTMG/v7+OHDgQK7Hs7e3x6FDh3Dt2jVUqlQpx+OjRo1CXFwcMjIyEBoaCuBF493Dhw/l983Jkydx69Yt9OnTB4MHD5YbJAFg//79sLCwwN69ewEAlpaWmDRpEk6ePIn69esDePGHq3PnzmHLli251njjxg106NABgYGBWLNmDS5evIi+ffvC2NgYEydOxIIFC1C1alUsW7YMJ0+ezPODUkHXqq0ePXrg6NGjWLhwIWrVqoWkpCS5wfHGjRto2rQpmjdvjgMHDsDCwgKRkZFyw926deswfvx4hISEwMfHB2fPnkXfvn1hamqKnj17YuHChdi+fTs2btwIZ2dnpKSkICUlBQCwefNmzJ8/H+vXr0f16tVx8+ZNREdH51nn9OnTMWnSpBzbJYl/FheRJKnhYPkQqemmUKu5kpZomJ/4mKHYmJ/YJEmNu3fvwtra+rVGY7wttGoMIyJ62yQkJECtVsPLyyvXx728vHDv3j3cvn0b5cqVAwB06NABwcHBAIAxY8Zg/vz5OHjwIDw8PLBhwwaoVCqsWLFCbrwJDQ2FlZUVwsPD0bZtWxgbGyMwMDDHF+/ly5dj+fLlAIBevXohJCTkjV5Xfl7tYaWN5ORkhIaGIjk5GY6OjgBeNAbt2rULoaGhmDZtGoAXC7IsXrwYtWrVAvCiIS89PR0dO3ZE1apV5XqzlS5dGh4eHihTpoxWdeQnMDAQ3bp1AwBMmzYNCxcuxIkTJ9CuXTu5gSW7TuDF0EAnJydcunQJ7u7uOY43YcIEdOnSBS4uLnB3d0fDhg3RoUMH/O9//4NCoYCZmRlMTEzw5MkTjddx9erVyMrKwpo1a2BqagoACAkJgZ+fH2bOnIny5csDAExNTbFixQqNxkhfX1+EhobKjWGhoaFo1qwZqlSpkus1L168GE5OTggJCYEkSfD09MTff/+NMWPGYPz48bC0tIS5uTkMDAzyzbqga9XGpUuXsHHjRuzduxetW7cGAI26f/jhB1haWmL9+vUoXbo0AGi87hMmTMDcuXPRpUsXAC96MF64cAE//vgjevbsieTkZLi5ueGDDz6AJEkajXbJycmwt7dH69atUbp0aTg7O6NBgwZ51jp27FiN6SIyMjLg5OQEBRvDhKSQ1PB0TMM/GWWg5Bc54TA/8TFDsTE/sSkkNVJSUmBlZVXcpZQIbA4koneaLqs51qxZU/63JEmwt7fHrVu3ALyY/+vy5cswNzeHmZkZzMzMYGNjg6ysLHlol6enJ44fP45BgwblOLa5uTnWrVuHVatW6aWxR9dVKvUpJiYGSqUS7u7u8mthZmaGQ4cOaQxzMzQ01HhNbWxsEBgYCF9fX/j5+WHBggVITU2VH69QoQIuXryYb8OFtl4+r6mpKSwsLDSyPHjwoEbtnp6eAKBR/8scHBxw9OhRxMTEYNiwYXj+/Dl69uyJdu3a5bt8dVxcHGrVqiU3hAFA48aNoVKpEB8fL2/z9vbO0SsvuwddVlYWnj59ip9//hlBQUH5nqthw4YaPQIbN26MzMxMXL9+Pc/n6etaXxYVFQUDAwM0a9Ysz8ebNGkiN4S97OHDh0hMTETv3r01Mvruu+/kfAIDAxEVFQUPDw8MHToUe/bskZ//6aef4vHjx6hSpQr69u2LrVu35rsatpGRESwsLDRuRERERCQ29gwjoneSq6srJElCXFwcOnfunOPxuLg4WFtbyz2jAOT4Yi5JkvzlPzMzE3Xr1sW6detyHOvlYxgZGSEkJAQRERE4d+6cvH3IkCH4/PPP3/h1SZKUo+Hs2bNnr1VDZmYmDAwMcPr06RxD7czMzOR/m5iY5BiqGRoaiqFDh2LXrl3YsGEDvv32W+zdu1desEVfCsoyu2fWqxwcHPI9bo0aNVCjRg0EBwdjwIABaNKkCQ4dOoQWLVq8Vr0vN5Zl8/Pzg5GREbZu3QpDQ0M8e/YM//vf/17rPLrI71oVCkW+76tXh/C+Kr/Hs4caL1++HO+9957GY9nvtzp16iApKQk7d+7Evn374O/vj9atW2PTpk1wcnJCfHw89u3bh7179yI4OBizZ8/GoUOHcm18IyIiIqK3D3uGEdE7ydbWFm3atMHixYvx+PFjjcdu3ryJdevWoWvXrlrNqwW8+PKdkJCAcuXKwdXVVeNmaWmpse+1a9dw7tw5SJIEDw8PAMCOHTuK5brs7Ow0el8lJCTg0aNH8v3s3khKpVLrGnx8fKBUKnHr1q0cr4U2Qy19fHwwduxY/PXXX6hRowZ+/vlnrc+tD9kLwri4uOSoP7dGqbxUq1YNwIueTMCL1/LV19HLywvR0dHyPgAQGRkJhUIhvzfyUqpUKfTs2ROhoaEIDQ3FZ599lm8jkpeXF44eParRSBUZGQlzc3NUrFhR6+vKzavX+ur7SqlUaqw26e3tDZVKhUOHDuV6vJo1ayIiIiLXhtny5cvD0dERV65cyZFP9oIPAGBhYYGuXbti+fLl2LBhAzZv3oy0tDQALxrb/Pz8sHDhQoSHh8s93XTCUZJiUgNpmcbMT1TMT3zMUGzMT2zqF6NR6AU2hhHROyskJARPnjyBr68vDh8+jJSUFOzatQtt2rRBhQoVNFbHK0hAQADKli2LTp06ISIiAklJSQgPD8fQoUNzDEHbvHkzypcvj127diE2Nhbjx49HbGxsnkPwivK6WrZsiZCQEJw9exanTp3CgAEDNHrHlCtXDiYmJti1axf++ecfpKenF3h+d3d3BAQEoEePHtiyZQuSkpJw4sQJTJ8+HX/88Ueez0tKSsLYsWNx9OhRXLt2DXv27EFCQoI8b9iNGzfg6emJEydOvMarU7BBgwYhLS0N3bp1w8mTJ5GYmIjdu3ejV69eeTYKDhw4EFOmTEFkZCSuXbuGY8eOoUePHrCzs0PDhg0BvFiR9Ny5c4iPj8edO3fw7NkzBAQEwNjYGD179sT58+dx8OBBDBkyBN27d5fnC8tPnz59cODAAezatSvfIZIAEBwcjJSUFAwZMgQXL17Eb7/9hgkTJmDkyJE6TaKqzbW2bNkSf/zxB/744w9cvHgRAwcOxP379+VjuLi4oGfPnggKCsK2bdvkn5eNGzcCeLEAQ0ZGBj777DOcOnUKCQkJWLt2rTx0dNKkSZg+fToWLlyIS5cuISYmBqGhoZg3bx4AYN68efjll19w8eJFXLp0Cb/++ivs7e3llVJXrlyJ8+fP48qVK/jpp59gYmKi82IAXElLTEq1AlHJ5ZifoJif+Jih2Jif2JRqBapWrcrVJP+l1TDJV5ecz89HH31U6GKIiN4kNzc3nDp1ChMmTIC/vz/S0tJgb2+Pjz/+GBMmTICNjY3WxypTpgwOHz6MMWPGoEuXLnjw4AEqVKiAVq1a5ZhjKDMzE9HR0XJjx6RJk9CqVSucPHlSnjj+TV3X3Llz0atXLzRp0gSOjo5YsGABTp8+LT9eqlQpLFy4EJMnT8b48ePRpEkThIeHF1hDaGgovvvuO3z55Ze4ceMGypYti/fffx8dO3bM8zllypTBxYsXsXr1aty9excODg4YNGgQ+vfvD+DFMLv4+HiNnmtFwdHREZGRkRgzZgzatm2LJ0+eoFKlSmjXrl2ejUatW7fGqlWrsGTJEty9exdly5ZFw4YNsX//ftja2gJ4McdXeHg46tWrh8zMTHlVzN27d2PYsGGoX78+ypQpg08++URu1CmIm5sbGjVqhLS0tBxDBl9VoUIF/Pnnnxg9ejRq1aoFGxsb9O7dG99++61Or4821xoUFITo6Gj06NEDpUqVwogRI3IMFV2yZAn+7//+D8HBwbh79y6cnZ3xf//3fwBe9HA8cOAARo8ejWbNmsHAwAC1a9dG48aNAbxoBCxTpgxmz56N0aNHw9TUFN7e3hg+fDiAF3/1nDVrFhISEmBgYID69evjzz//hEKhgJWVFWbMmIGRI0dCqVTC29sbv//+u1y7triapJgkSQ2Xshm4eseCK6EJiPmJjxmKjfmJTZLUuHnzJsqVK8fVJAFIai1mWX71hXp1jpmXhxHpMpSGiIh0Ex4ejsDAQFy9erW4S6ESQK1Ww83NDcHBwRorHlLRycjIgKWlJZr3PQeplGXBT6ASxUChQjPP6zh0sSKUKn4REA3zEx8zFBvzE5uBQoXZfe7D29v7jfQOy/7MlJ6eXiIXINLqHaxSqeTbnj17ULt2bezcuRP379/H/fv38eeff6JOnTrYtWtXUddLREREAG7fvo2QkBDcvHkTvXr1Ku5yiIiIiIiEofNqksOHD8fSpUvxwQcfyNt8fX1RpkwZ9OvXD3FxcXotkIiIiHIqV64cypYti2XLlsHa2rq4yyEiIiIiEobOjWGJiYmwsrLKsd3S0pLDdoiIipiLi4s8LxK927SY5YCKkFotgbOliEetlvD3fVPOdSMo5ic+Zig25ic2tVqCjY2NxjRX7zKt5gx7WdOmTWFsbIy1a9fKkz//888/6NGjB7KysvJcJp2IiIhIdNnzXzTpfR6lDLk8OREREYnjwGLnN3aut2LOsJetWrUKqampcHZ2hqurK1xdXeHs7IwbN25g5cqVRVEjERERUYmi4GqSQlJIang63mV+gmJ+4mOGYmN+YlNIaiQnJ0OlUhV3KSWCzsMkXV1dce7cOezduxcXL14EAHh5eaF169bsbkdERETvhK2zKnCuNgEplUrExNyDt3fFN7KSFukX8xMfMxQb8xPbi/xiUKFCheIupUTQuTEMACRJQtu2bdG0aVMYGRmxEYyIiIiIiIiIiISg8zBJlUqFKVOmoEKFCjAzM0NSUhIAYNy4cRwmSUREREREREREJZrOjWHfffcdwsLCMGvWLBgaGsrba9SogRUrVui1OCIiIqKSiL3ixSRJEuzt7ZmfoJif+Jih2Jif2JifJp0bw9asWYNly5YhICBAY5xwrVq15DnEiIiIiN5mCoXOH6GoBFAoFLC3t2d+gmJ+4mOGYmN+YmN+mnR+FW7cuAFXV9cc21UqFZ49e6aXooiIiIhKMqVSWdwlUCEolUokJiYyP0ExP/ExQ7ExP7ExP006N4ZVq1YNERERObZv2rQJPj4+eimKiIiIiKgoPHjwoLhLoNfA/MTHDMXG/MTG/P6j82qS48ePR8+ePXHjxg2oVCps2bIF8fHxWLNmDXbs2FEUNRIREREREREREemFzj3DOnXqhN9//x379u2Dqakpxo8fj7i4OPz+++9o06ZNUdRIRERERERERESkFzr3DAOAJk2aYO/evfquhYiIiEgIXIlJTJIkwcnJifkJivmJjxmKjfmJjflpKlRjGACcOnUKcXFxAF7MI1a3bl29FUVERERUknElJjEpFArY2toWdxlUSMxPfMxQbMxPbMxPk86f5K5fv44mTZqgQYMGGDZsGIYNG4b69evjgw8+wPXr14uiRiIiIqIShSsxiUmpVOLixYvMT1DMT3zMUGzMT2zMT5POjWF9+vTBs2fPEBcXh7S0NKSlpSEuLg4qlQp9+vQpihqJiIiIiPQiKyuruEug18D8xMcMxcb8xMb8/qPzMMlDhw7hr7/+goeHh7zNw8MDixYtQpMmTfRaHBERERERERERkT7p3DPMyckJz549y7FdqVTC0dFRL0UREREREREREREVBZ0bw2bPno0hQ4bg1KlT8rZTp05h2LBhmDNnjl6LIyIiIiqJOIG+mBQKBapUqcL8BMX8xMcMxcb8xMb8NElqtVqtyxOsra3x6NEjPH/+HKVKvRhlmf1vU1NTjX3T0tL0VykRERFRMcvIyIClpSXS09NhYWFR3OUQERERlUgl/TOTznOGff/990VQBhEREZE4uBKTmJRKJS5cuIBq1arBwMCguMshHTE/8TFDsTE/sTE/TTo3hvXs2bMo6iAiIiIiKnJsyBQb8xMfMxQb8xMb8/uPzo1hycnJ+T7u7Oxc6GKIiIiIiIiIiIiKks6NYS4uLpAkKc/H2dJIREREREREREQllc6NYWfPntW4/+zZM5w9exbz5s3D1KlT9VYYERERUUnFlZjEpFAo4OHhwfwExfzExwzFxvzExvw06dwYVqtWrRzb6tWrB0dHR8yePRtdunTRS2FERERERPpmaGhY3CXQa2B+4mOGYmN+YmN+/9Fbk6CHhwdOnjypr8MRERERlVgqlaq4S6BCUKlUiImJYX6CYn7iY4ZiY35iY36adO4ZlpGRoXFfrVYjNTUVEydOhJubm94KIyIiIiIiIiIi0jedG8OsrKxyTKCvVqvh5OSE9evX660wIiIiIiIiIiIifdO5MezgwYMa9xUKBezs7ODq6opSpXQ+HBEREZFwuoy5DqnUg+Iug3RkoFChmWcmRq9IgVLFCYRFw/zExwzFxvzEZqBQYXaf4q6i5JDUarW6uIsgIiIiEkFGRgYsLS3RpHcMShlaFHc5pDM1DBRqKFUSAKnAvamkYX7iY4ZiY35iU2PvoopQKBQ5RvsVhezPTOnp6bCwKHmfmQrVlSsxMRHff/894uLiAADVqlXDsGHDULVqVb0WR0RERESkT8allXj4hKMZRMX8xMcMxcb8xPb06VMYGxsXdxklgs59G3fv3o1q1arhxIkTqFmzJmrWrInjx4+jevXq2Lt3b1HUSERERFSiGCjYsV5EBgo13quayvwExfzExwzFxvzEZqBQIz4+nqtJ/kvnJt2vv/4aI0aMwIwZM3JsHzNmDNq0aaO34oiIiIiIiIiIiPRJ555hcXFx6N27d47tQUFBuHDhgl6KIiIiIiIiIiIiKgo6N4bZ2dkhKioqx/aoqCiUK1dOHzURERERERWJ51wBTWjMT3zMUGzMT2wGBgbFXUKJofMwyb59+6Jfv364cuUKGjVqBACIjIzEzJkzMXLkSL0XSERERFTSKFWKwq1CRMVKqVLg8MWKxV0GFRLzEx8zFBvzE5tSpYC3t3dxl1Fi6Pw5bty4cTA3N8fcuXMxduxYAICjoyMmTpyIoUOH6r1AIiIiopKHkweLSQ0b0yykPTQGUPTLypO+MT/xMUOxMT+xqZGRkQFzc3NIEvPTqY/j8+fPsXbtWnz++ee4fv060tPTkZ6ejuvXr2PYsGF8QYmIqMRRq3VrtAgPD4eLi4tezh0WFgYrKyudnhMYGIiPP/74jZxLH8LDwyFJEu7fv/9Gz1tc15uNK2mJyUChRu1Kt5mfoJif+Jih2Jif2AwUaly5coWrSf5Lp8awUqVKYcCAAcjKygIAmJubw9zcvEgKIyKit0NKSgqCgoLg6OgIQ0NDVKpUCcOGDcPdu3ffyPmXLVum92MGBgZCkqQcKytv27ZN4w9DXbt2xaVLl/R+fhcXF3z//fca24rqXMXl0KFDaNmyJWxsbFCmTBm4ubmhZ8+eePr0KYC373qJiIiI6M3Refa7Bg0a4OzZs0VRCxERvWWuXLmCevXqISEhAb/88gsuX76MpUuXYv/+/WjYsCHS0tKK9PzR0dGYOXNmkRzb2NgYM2fOxL179/Lcx8TE5I0tLvMmz1XULly4gHbt2qFevXo4fPgwYmJisGjRIhgaGkKpVAJ4u66XiIiIiN4snRvDgoOD8eWXXyIkJARHjx7FuXPnNG5ERETZBg0aBENDQ+zZswfNmjWDs7Mz2rdvj3379uHGjRv45ptv5H1dXFwwbdo0BAUFwdzcHM7Ozjl6daWkpMDf3x9WVlawsbFBp06dcPXq1TzPv2nTJiQlJeHMmTN6v7bWrVvD3t4e06dPz3Of3IbyfffddyhXrhzMzc3Rp08ffP3116hdu3aO586ZMwcODg6wtbXFoEGD8OzZMwBA8+bNce3aNYwYMQKSJMk90V4918SJE1G7dm2sXbsWLi4usLS0xGeffYYHDx7I+zx48AABAQEwNTWFg4MD5s+fj+bNm2P48OHyPmvXrkW9evVgbm4Oe3t7fP7557h161a+r82RI0fQpEkTmJiYwMnJCUOHDsXDhw/zfc7L9uzZA3t7e8yaNQs1atRA1apV0a5dOyxfvhwmJiZFer2vevLkCTIyMjRuADhlmKjUwMMnpZmfqJif+Jih2Jif2NQv/phLL+jcGPbZZ58hKSkJQ4cORePGjVG7dm34+PjI/yUiIgKAtLQ07N69G8HBwXIDRjZ7e3sEBARgw4YNGnN6zZ07F/Xq1cPZs2cRHByMgQMHIj4+HgDw7Nkz+Pr6wtzcHBEREYiMjISZmRnatWsnD52LiorCkSNH5Nuvv/4KAFi6dKnG9pSUlNe+PgMDA0ybNg2LFi3C9evXtXrOunXrMHXqVMycOROnT5+Gs7MzlixZkmO/gwcPIjExEQcPHsTq1asRFhaGsLAwAMCWLVtQsWJFTJ48GampqUhNTc3zfImJidi2bRt27NiBHTt24NChQxpDO0eOHInIyEhs374de/fuRURERI6Gw2fPnmHKlCmIjo7Gtm3bcPXqVQQGBuZ7znbt2uGTTz7BuXPnsGHDBhw5cgSDBw/W6jUCXrw/UlNTcfjwYa2fo6/rfdX06dNhaWkp35ycnAAASjWXlheRUq3A8UQH5ico5ic+Zig25ic2pVoBT09PGBgYFHcpJYLOq0kmJSUVRR1ERPSWSUhIgFqthpeXV66Pe3l54d69e7h9+7Y83K1Dhw4IDg4GAIwZMwbz58/HwYMH4eHhgQ0bNkClUmHFihVyb6jQ0FBYWVkhPDwcbdu2hbGxMQIDAxEdHa1xruXLl2P58uUAgF69eiEkJEQv19i5c2fUrl0bEyZMwMqVKwvcf9GiRejduzd69eoFABg/fjz27NmDzMxMjf2sra0REhICAwMDeHp64sMPP8T+/fvRt29f2NjYwMDAQO6plR+VSoWwsDB5fs/u3btj//79mDp1Kh48eIDVq1fj559/RqtWrQC8eD0dHR01jhEUFCT/u0qVKli4cCHq16+PzMxMmJmZ5Tjn9OnTERAQIPe2cnNzw8KFC9GsWTMsWbJEq79Ifvrpp9i9ezeaNWsGe3t7vP/++2jVqhV69OgBCwuLIr3eV40dOxYjR46U72dkZMDJyQmSxD+Li0iS1HCwfIjUdFOo1Vz4STTMT3zMUGzMT2ySpMbdu3dhbW0NhYINmjq/ApUqVcr3RkRE9DJdVnOsWbOm/G9JkmBvby8PyYuOjsbly5dhbm4OMzMzmJmZwcbGBllZWUhMTAQAeHp64vjx4xg0aFCOY5ubm2PdunVYtWoVypQp85pX9Z+ZM2di9erViIuLK3Df+Ph4NGjQQGPbq/cBoHr16hp/tXNwcChwaGJuXFxcNBa6efk4V65cwbNnzzTOb2lpCQ8PD41jnD59Gn5+fnB2doa5uTmaNWsGAEhOTs71nNHR0QgLC5MzMjMzg6+vL1QqldZ/UDMwMEBoaCiuX7+OWbNmoUKFCpg2bRqqV6+eb084fVzvq4yMjGBhYaFxAwAFG8OEpJDU8HRMY36CYn7iY4ZiY35iU0hqpKSk6LzS+ttK655hKpUKsbGx8Pb2BvBiyEn2sBTgxQfXgQMHsoWRiIgAAK6urpAkCXFxcejcuXOOx+Pi4mBtbQ07Ozt5W+nSpTX2kSRJXv45MzMTdevWxbp163Ic6+VjGBkZISQkBBERERpzWQ4ZMgSff/75a1/Xq5o2bQpfX1+MHTs23+GDusjvdXiTx3n48CF8fX3h6+uLdevWwc7ODsnJyfD19dX4DPCyzMxM9O/fH0OHDs3xmLOzs071V6hQAd27d0f37t0xZcoUuLu7Y+nSpZg0aVKu++vrdSMiIiKit5vWjWHr16/H0qVL5fk7Ro8eDSsrK5Qq9eIQd+7cgbGxMXr37l00lRIRkVBsbW3Rpk0bLF68GCNGjNCYN+zmzZtYt24devToIQ95LEidOnWwYcMGlCtXLt+hcgBw7do1nDt3DpIkwd3dHfHx8dixYwemTp36WteUlxkzZqB27doF9jLy8PDAyZMn0aNHD3nbyZMndT7fy6sqFlaVKlVQunRpnDx5Um6kSk9Px6VLl9C0aVMAwMWLF3H37l3MmDFDnivr1KlT+R63Tp06uHDhAlxdXV+rvldZW1vDwcFBp4n4X6bN9RIRERHRu0HrblyhoaE5hp0cOnQISUlJSEpKwuzZs/HTTz/pvUAiIhJXSEgInjx5Al9fXxw+fBgpKSnYtWsX2rRpgwoVKujUOBUQEICyZcuiU6dOiIiIQFJSEsLDwzF06NAcE9hv3rwZ5cuXx65duxAbG4vx48cjNjZWHk6pb97e3ggICMDChQvz3W/IkCFYuXIlVq9ejYSEBHz33Xdyo50uXFxccPjwYdy4cQN37twpVM3m5ubo2bMnRo8ejYMHDyI2Nha9e/eGQqGQ63F2doahoSEWLVqEK1euYPv27ZgyZUq+xx0zZgz++usvDB48GFFRUUhISMBvv/2m0wT6P/74IwYOHIg9e/YgMTERsbGxGDNmDGJjY+Hn51dk16sTjjAQkxpIyzRmfqJifuJjhmJjfmJTQ2M6iXed1o1hFy9eRL169fJ8vFmzZjkmLCYionebm5sbTp06hSpVqsDf3x9Vq1ZFv3790KJFCxw9ehQ2NjZaH6tMmTI4fPgwnJ2d0aVLF3h5eaF3797IysrK0VMsMzMT0dHRaNu2LQwMDDBp0iQcOHCgUL2wtDV58uQCh+QFBARg7NixGDVqFOrUqYOkpCQEBgbqvMz15MmTcfXqVVStWlVjiKiu5s2bh4YNG6Jjx45o3bo1GjduDC8vL7keOzs7hIWF4ddff0W1atUwY8YMzJkzJ99j1qxZE4cOHcKlS5fQpEkT+Pj4YPz48RoT1U+cOBEuLi55HqNBgwbIzMzEgAEDUL16dTRr1gzHjh3Dtm3b5DnLiuJ6dcGVtMSkVCsQlVyO+QmK+YmPGYqN+YlNqVagatWqXE3yX5Jay9nTjI2NERsbi6pVqwIAbt++DVtbW3mOsMuXL6N69ep48uRJ0VVLRERUxMLDwxEYGIirV6++kfO1adMG9vb2WLt27Rs5X34ePnyIChUqYO7cuUU67UHPnj0hSRLCwsKK7BzaKMz1ZmRkwNLSEk37xMCgdP7DdankkSQ1XMpm4OodC66EJiDmJz5mKDbmJzZJUmPdOCOUK1fujcz1nv2ZKT09vcApToqD1nOGlS9fHvHx8XJj2Kt/iY6LiytwiXciIqJ32aNHj7B06VL4+vrCwMAAv/zyC/bt24e9e/cWSz1nz57FxYsX0aBBA6Snp2Py5MkAgE6dOhXZOdVqNcLDw3HkyJEiO0de9Hm9XElLTApJjcp26Ui+aw4lv8gJh/mJjxmKjfmJTSGpcfPmzdcaVfA20boxrFWrVpg6dSo6dOiQ4zG1Wo3p06ejVatWei2OiIjobSJJEv78809MnToVWVlZ8PDwwObNm9G6detiq2nOnDmIj4+HoaEh6tati4iICJQtW7bIzidJEq5du1Zkxy/Im75eIiIiIip5tG4M++abb1CnTh289957GDVqFNzd3QEA8fHx8gfLNWvWFFmhREREb4KLiwuGDx9eJMc2MTHBvn37iuTYheHj44PTp08XdxlvzLt2vURERESUO60bw6pWrYq9e/ciMDAQXbt2lVdeUqvV8PT0xJ49e/S+jDoREdGbVpSNYfT2UKslcICIeNRqCX/fN+VcN4JifuJjhmJjfmJTqyXY2NgUbhXtt5DWE+i/LCoqCpcuXQLwYqUwHx8fvRdGREREVNJkTwbbpPd5lDLk8uREREQkjgOLnd/YuUr6BPqFagwjIiIiehdlf7C7d+8erKysirsc0pFKpcL169dRsWLFN7KSFukX8xMfMxQb8xPbm86vpDeGafUKzJgxA48fP9bqgMePH8cff/zxWkURERERlWT8W6KY1Go10tLSmJ+gmJ/4mKHYmJ/YmJ8mrRrDLly4AGdnZwQHB2Pnzp24ffu2/Njz589x7tw5LF68GI0aNULXrl1hbs5hA0REREREREREVPJoNYH+mjVrEB0djZCQEHz++efIyMiAgYEBjIyM8OjRIwAvVmjq06cPAgMDYWxsXKRFExERERERERERFYbOc4apVCqcO3cO165dw+PHj1G2bFnUrl0bZcuWLaoaiYiIiEoEzhkmNpVKhVu3bqFcuXKc70ZAzE98zFBszE9sbzq/kj5nGCfQJyIiItJSSf9gR0RERFQSlPTPTGzOJSIiItKRUqks7hKoEJRKJRITE5mfoJif+Jih2Jif2JifJjaGEREREdE748GDB8VdAr0G5ic+Zig25ic25vcfNoYREREREREREdE7g41hRERERERERET0zih0Y9jly5exe/duPH78GADAefiJiIjoXSFJUnGXQIUgSRKcnJyYn6CYn/iYodiYn9iYnyadG8Pu3r2L1q1bw93dHR06dEBqaioAoHfv3vjyyy/1XiARERFRScMl5cWkUChga2vL/ATF/MTHDMXG/MTG/DTp/CqMGDECpUqVQnJyMsqUKSNv79q1K3bt2qXX4oiIiIhKIq7EJCalUomLFy8yP0ExP/ExQ7ExP7ExP02ldH3Cnj17sHv3blSsWFFju5ubG65du6a3woiIiIiI9C0rK6u4S6DXwPzExwzFxvzExvz+o3PPsIcPH2r0CMuWlpYGIyMjvRRFRERERERERERUFHRuDGvSpAnWrFkj35ckCSqVCrNmzUKLFi30WhwREREREREREZE+6TxMctasWWjVqhVOnTqFp0+f4quvvkJsbCzS0tIQGRlZFDUSERERlSicfFZMCoUCVapUYX6CYn7iY4ZiY35iY36adH4VatSogUuXLuGDDz5Ap06d8PDhQ3Tp0gVnz55F1apVi6JGIiIiohKFy5KLSZIkWFhYMD9BMT/xMUOxMT+xMT9NklqtVhd3EUREREQiyMjIgKWlJdLS0mBtbV3c5ZCOlEolLly4gGrVqsHAwKC4yyEdMT/xMUOxMT+xven8sj8zpaenw8LCosjPpyuthkmeO3dO6wPWrFmz0MUQERERERUlLikvNuYnPmYoNuYnNub3H60aw2rXrg1JklBQJzJJkvjiEhERERERERFRiaVVY1hSUlJR10FERERERERERFTkdJ4z7PDhw2jUqBFKldJsR3v+/Dn++usvNG3aVK8FEhEREZUU2fNf3L9/H5aWlsVdDulIrVYjKysLxsbGnEBYQMxPfMxQbMxPbG86v5I+Z5jOq0m2aNECaWlpObanp6ejRYsWeimKiIiIiKgoGBoaFncJ9BqYn/iYodiYn9iY3390bgxTq9W5tiLevXsXpqameimKiIiIqCRTqVTFXQIVgkqlQkxMDPMTFPMTHzMUG/MTG/PTpNWcYQDQpUsXAC8myQ8MDISRkZH8mFKpxLlz59CoUSP9V0hERERERERERKQnWjeGZc+LoVarYW5uDhMTE/kxQ0NDvP/+++jbt6/+KyQiIiIqYbqMuQ6p1IPiLoN0ZKBQoZlnJkavSIFSpfMACSpmzE98zFBszE9sBgoVZvcp7ipKDq0bw0JDQwEALi4uGDVqFIdEEhERERERERGRcHReTZKIiIjoXZW9MlKT3jEoZVjyVkaigqhhoFBDqZIAcCU08TA/8TFDsTE/samxd1FFKBQKriYJLXuG1alTB/v374e1tTV8fHzyfeHOnDmjt+KIiIiIiPTJuLQSD59oPTiCShjmJz5mKDbmJ7anT5/C2Ni4uMsoEbR6F3fq1EmeMP/jjz8uynqIiIiISjwDBTvWi8hAocZ7VVNx6GLFf3s2kEiYn/iYodiYn9gMFGrEx8fD29sbBgYGxV1OsdOqMWzChAkICgrCggULMGHChKKuiYiIiIiIiIiIqEhovQTE6tWr8fjx46KshYiIiIiIiIiIqEhp3RjGefaJiIiISHTPVVp//KUSiPmJjxmKjfmJjcMj/6PTzHcPHjwocLK1krhKABEREZE+KVUK3T5EUYmgVClw+GLF4i6DCon5iY8Zio35iU2pUsDb27u4yygxdGrWdXd3h7W1da43KysrWFtbF1WdRFQC6dpjNDw8HC4uLkVTTCG5uLjg+++/f6PnlCQJ27Zty/Px8PBwSJKE+/fvAwDCwsJgZWUlPz5x4kTUrl27SGvUxdWrVyFJEqKiooqtBrVajX79+sHGxqbYa8lW0nLK9ur7S1sl9XoKUnQ/4+wxLyY1bEwfg/mJivmJjxmKjfmJTY2MjAyO+vuXTo1hmzZtwoEDB3K9HTx4EAcOHCiqOokoFykpKQgKCoKjoyMMDQ1RqVIlDBs2DHfv3n0j51+2bJnejxkYGAhJkjBgwIAcjw0aNAiSJCEwMFBv5zt58iT69eunt+PpQ6NGjZCamgpLS8tcHx81ahT279+vt/NlN47kdwsPD9fb+YrCrl27EBYWhh07diA1NRU1atTIsU/2dVpbWyMrK0vjsZMnT8rXqi/6zulNKqjBVp+aN2+u8V4rX748Pv30U1y7dk0vxy+qn3GuJikmA4UatSvdZn6CYn7iY4ZiY35iM1CoceXKFahUquIupUTQqYd/48aNUa5cuaKqhYh0cOXKFTRs2BDu7u745ZdfULlyZcTGxmL06NHYuXMnjh07BhsbmyI7f3R0NGbOnIn+/fvr/dhOTk5Yv3495s+fDxMTEwBAVlYWfv75Zzg7O+v1XHZ2dno9nj4YGhrC3t4+z8fNzMxgZmamt/NlN75lGzZsGDIyMhAaGipvK8r3kj4kJibCwcEBjRo1KnBfc3NzbN26Fd26dZO3rVy5Es7OzkhOTtZbTfrO6W3Wt29fTJ48GWq1GteuXcPw4cPxxRdfICIiItf91Wo1lEolSpUq+GNMSfwZJyIiIqLixdnviAQ1aNAgGBoaYs+ePWjWrBmcnZ3Rvn177Nu3Dzdu3MA333wj7+vi4oJp06YhKCgI5ubmcHZ2ztGrKyUlBf7+/rCysoKNjQ06deqEq1ev5nn+TZs2ISkpCWfOnNH7tdWpUwdOTk7YsmWLvG3Lli1wdnaGj4+Pxr65DYGqXbs2Jk6cCODFl+aJEyfC2dkZRkZGcHR0xNChQ/N8/v3799G/f3+UL18exsbGqFGjBnbs2CE/vnnzZlSvXh1GRkZwcXHB3Llzc9QzZcoUdOvWDaampqhQoQJ++OGHHNd4584ddO7cGWXKlIGbmxu2b98uP1bQMLZXh6udPHkSbdq0QdmyZWFpaYlmzZrplEt241v2zcTEBEZGRvL9qKgotGzZElZWVrC1tUXHjh2RmJiY7zHPnz+P9u3bw8zMDOXLl0f37t1x584d+fFNmzbB29sbJiYmsLW1RevWrfHw4cM8j3fo0CE0aNAARkZGcHBwwNdff43nz58DeNGbcMiQIUhOToYkSQUOxe3ZsydWrVol33/8+DHWr1+Pnj17auyX27DA77//XuP44eHhaNCgAUxNTWFlZYXGjRvLPZpye/6qVavk94+DgwMGDx6cZ53a5CpJElasWJHnewkA/vzzT7i7u8PExAQtWrTI9+cagHx9nTt3zvX1XLt2LVxcXGBpaYnPPvsMDx48kB9TqVSYPn06KleuDBMTE9SqVQubNm3K93wAUKZMGdjb28PBwQHvv/8+Bg8erHGt2T8TO3fuRN26dWFkZIQjR44gMTERnTp1Qvny5WFmZob69etj3759Oa7n5Z9xbV6zlz158gQZGRkaNyIiIiISm9aNYZUqVeLKA0QlRFpaGnbv3o3g4GC551Q2e3t7BAQEYMOGDRrjwefOnYt69erh7NmzCA4OxsCBAxEfHw8AePbsGXx9fWFubo6IiAhERkbCzMwM7dq1w9OnTwEAUVFROHLkiHz79ddfAQBLly7V2J6SkqKXawwKCtLombRq1Sr06tVL5+Ns3rwZ8+fPx48//oiEhARs27Ytz4kjVSoV2rdvj8jISPz000+4cOECZsyYIf/uO336NPz9/fHZZ58hJiYGEydOxLhx4xAWFqZxnNmzZ6NWrVo4e/Ysvv76awwbNgx79+7V2GfSpEnw9/fHuXPn0KFDBwQEBCAtLU3n6wNeLG7Ss2dPHDlyBMeOHYObmxs6dOig0UgRGBiI5s2bF+r4Dx8+xMiRI3Hq1Cns378fCoUCnTt3zrOL9f3799GyZUv4+Pjg1KlT2LVrF/755x/4+/sDAFJTU9GtWzcEBQUhLi4O4eHh6NKlS57zF9y4cQMdOnRA/fr1ER0djSVLlmDlypX47rvvAAALFizA5MmTUbFiRaSmpuLkyZP5Xk/37t0REREh9wLbvHkzXFxcUKdOHZ1el+fPn+Pjjz9Gs2bNcO7cORw9ehT9+vXLc6jlkiVLMGjQIPTr1w8xMTHYvn07XF1d8zy+NrkC+b+XUlJS0KVLF/j5+SEqKgp9+vTB119/ne91Zb9+oaGhOV7PxMREbNu2DTt27MCOHTtw6NAhzJgxQ358+vTpWLNmDZYuXYrY2FiMGDECX3zxBQ4dOpT/i/mStLQ0bNy4Ee+9916Ox77++mvMmDEDcXFxqFmzJjIzM9GhQwfs378fZ8+eRbt27eDn51dgDz9dfv6mT58OS0tL+ebk5PTiAY4QEZMaePikNPMTFfMTHzMUG/MTmxoFLoj4LtF6mGRSUlJR1kFEOkhISIBarYaXl1euj3t5eeHevXu4ffu2PLS5Q4cOCA4OBgCMGTMG8+fPx8GDB+Hh4YENGzZApVJhxYoV8hf50NBQWFlZITw8HG3btoWxsTECAwMRHR2tca7ly5dj+fLlAIBevXohJCREL9f4xRdfYOzYsXIvm8jISKxfv17nuauSk5Nhb2+P1q1bo3Tp0nB2dkaDBg1y3Xffvn04ceIE4uLi4O7uDgCoUqWK/Pi8efPQqlUrjBs3DsCLRUUuXLiA2bNna8xj1rhxY7nBwd3dHZGRkZg/fz7atGkj7xMYGCgP05s2bRoWLlyIEydOoF27djpdHwC0bNlS4/6yZctgZWWFQ4cOoWPHjgAABweHQs8P8Mknn2jcX7VqFezs7HDhwoVc5+YKCQmBj48Ppk2bpvEcJycnXLp0CZmZmXj+/Dm6dOmCSpUqAUC+K9ssXrwYTk5OCAkJgSRJ8PT0xN9//40xY8Zg/PjxsLS0hLm5OQwMDPIdXpqtXLlyaN++PcLCwjB+/HisWrUKQUFB2r4csoyMDKSnp6Njx46oWrUqAOT5MwkA3333Hb788ksMGzZM3la/fv0899cmVyD/99KSJUtQtWpVuQejh4cHYmJiMHPmzDzPmz2s0MrKKsfrqVKpEBYWBnNzcwAvGhb379+PqVOn4smTJ5g2bRr27duHhg0bAnjx83PkyBH8+OOPaNasWZ7nXLx4MVasWAG1Wo1Hjx7B3d0du3fvzrHf5MmTNX6ObGxsUKtWLfn+lClTsHXrVmzfvj3fXne6/PyNHTsWI0eOlO9nZGTAyckJSjVXkxSRUq3A8USH4i6DCon5iY8Zio35iU2pVsDT07O4yygxOEySSGC6rARSs2ZN+d+SJMHe3h63bt0C8GL+r8uXL8Pc3Fye58jGxgZZWVnycDhPT08cP34cgwYNynFsc3NzrFu3DqtWrUKZMmVe86pesLOzw4cffoiwsDCEhobiww8/RNmyZXU+zqefforHjx+jSpUq6Nu3L7Zu3SoPr3tVVFQUKlasKDeEvSouLg6NGzfW2Na4cWMkJCRAqVTK27IbAl6+HxcXp7Ht5TxMTU1hYWEh56Grf/75B3379oWbmxssLS1hYWGBzMxMjd4x2T12CiMhIQHdunVDlSpVYGFhIQ+by6v3TXR0NA4ePCi/l8zMzOT/8SYmJqJWrVpo1aoVvL298emnn2L58uW4d+9enuePi4tDw4YNNXpcNW7cGJmZmbh+/XqhrikoKAhhYWG4cuUKjh49ioCAAJ2PYWNjg8DAQPj6+sLPzw8LFizQmHvtZbdu3cLff/+NVq1aaX18bXIF8n8vxcXF5ehh9er7UxcuLi5yQxjwopE1+1yXL1/Go0eP0KZNG43s16xZU+Cw2oCAAERFRSE6OhpHjhyBq6sr2rZtm6MXXL169TTuZ2ZmYtSoUfDy8oKVlRXMzMwQFxdXYM8wXX7+jIyMYGFhoXEDAEnin8VFJElqOFplMj9BMT/xMUOxMT+xSZIad+/e5QT6/2JjGJGAXF1dIUlSjgaWbHFxcbC2ttaYOLp06dIa+0iSJP8izMzMRN26dREVFaVxu3TpEj7//HP5OUZGRggJCdH4IgkAQ4YM0dhPX7IbLFavXp1nzx2FQpGjUfDZs2fyv52cnBAfH4/FixfDxMQEwcHBaNq0qcY+2V4dclqU8stDVz179kRUVBQWLFiAv/76C1FRUbC1tZWHuL4uPz8/pKWlYfny5Th+/DiOHz8OAHkePzMzUx6W9/ItISEBTZs2hYGBAfbu3YudO3eiWrVqWLRoETw8PN5oD+T27dvj8ePH6N27N/z8/GBra5tjn4LeW8CLHpRHjx5Fo0aNsGHDBri7u+PYsWM5jlWY95a2uerzvVSQgn6PAMAff/yhkfuFCxcKnDfM0tISrq6ucHV1RePGjbFy5UokJCRgw4YNGvuZmppq3B81ahS2bt2KadOmISIiAlFRUfD29i7wva+P10zBLwJCUkhqeDqmMT9BMT/xMUOxMT+xKSQ1UlJSdOpQ8TZjYxiRgGxtbdGmTRssXrwYjx8/1njs5s2bWLduHbp27Zrn3EWvqlOnDhISElCuXDn5C2n2zdLSUmPfa9eu4dy5c5AkCR4eHgCgMcG8PmXPWZY9p1lu7OzsNHrjZGRk5GhUMTExgZ+fHxYuXIjw8HAcPXoUMTExOY5Vs2ZNXL9+HZcuXcr1XF5eXoiMjNTYFhkZCXd3d405FV9tDDl27Fi+w+deV2RkJIYOHYoOHTrIk7O/PFn967h79y7i4+Px7bffolWrVvIQ3PzUqVMHsbGxcHFxyfF+ym7MkCQJjRs3xqRJk3D27FkYGhpi69atuR7Py8sLR48e1fgfd2RkJMzNzVGxYsVCXVepUqXQo0cPhIeH59nQamdnh5s3b2qcNyoqKsd+Pj4+GDt2LP766y/UqFEDP//8c459zM3N4eLigv3792tdoz5y9fLywokTJzS25dZY96rSpUtr9HbURrVq1WBkZITk5OQcucvzbGkp++fp1d9vr4qMjERgYCA6d+4Mb29v2NvbF7hAABERERGRzo1h+Q1J0eYDNhHpR0hICJ48eQJfX18cPnwYKSkp2LVrF9q0aYMKFSpg6tSpWh8rICAAZcuWRadOnRAREYGkpCSEh4dj6NChOX7mN2/ejPLly2PXrl2IjY3F+PHjERsbW+AwqMIwMDBAXFwcLly4kOcCHi1btsTatWsRERGBmJgY9OzZU2PfsLAwrFy5EufPn8eVK1fw008/wcTERJ6r6mXNmjVD06ZN8cknn2Dv3r1ISkrCzp07sWvXLgDAl19+if3792PKlCm4dOkSVq9ejZCQEIwaNUrjOJGRkZg1axYuXbqEH374Ab/++qvGPFH65ubmhrVr1yIuLg7Hjx9HQEBAjp5IY8eORY8ePXQ+trW1NWxtbbFs2TJcvnwZBw4c0Jg/KTeDBg1CWloaunXrhpMnTyIxMRG7d+9Gr169oFQqcfz4cUybNg2nTp1CcnIytmzZgtu3b+fZYBgcHIyUlBQMGTIEFy9exG+//YYJEyZg5MiRUCgK/zedKVOm4Pbt23k2tDZv3hy3b9/GrFmzkJiYiB9++AE7d+6UH09KSsLYsWNx9OhRXLt2DXv27EFCQkKe1zFx4kTMnTsXCxcuREJCAs6cOYNFixblWZ82uRZkwIABSEhIwOjRoxEfH4+ff/45x4IPucluuLt582aBjZ/ZzM3NMWrUKIwYMQKrV69GYmKifI2rV6/O97mPHj3CzZs3cfPmTURHR2PgwIEwNjZG27Zt832em5sbtmzZIg+x/Pzzz9n1n4iIiIgKpPO3iLZt2+a64lJkZGShJn4mosJxc3PDqVOnUKVKFfj7+6Nq1aro168fWrRogaNHj8LGxkbrY5UpUwaHDx+Gs7MzunTpAi8vL/Tu3RtZWVny/DjZMjMzER0djbZt28LAwACTJk3CgQMHClzBr7BenqMnN2PHjkWzZs3QsWNHfPjhh/j444/lycyBF5OAL1++HI0bN0bNmjWxb98+/P7777kOiwNeNPbVr18f3bp1Q7Vq1fDVV1/JPWTq1KmDjRs3Yv369ahRowbGjx+PyZMna0yeD7xoNDt16hR8fHzw3XffYd68eXk2uOjDypUrce/ePdSpUwfdu3fH0KFD5YUTsqWmphY4j1JuFAoF1q9fj9OnT6NGjRoYMWIEZs+ene9zHB0dERkZCaVSibZt28Lb2xvDhw+HlZUVFAoFLCwscPjwYXTo0AHu7u749ttvMXfuXLRv3z7X41WoUAF//vknTpw4gVq1amHAgAHo3bs3vv32W52v52WGhoYoW7Zsnj0ovby8sHjxYvzwww+oVasWTpw4odHwWaZMGVy8eBGffPIJ3N3d0a9fPwwaNAj9+/fP9Xg9e/bE999/j8WLF6N69ero2LEjEhIS8qxPm1wL4uzsjM2bN2Pbtm2oVasWli5dqrGwQV7mzp2LvXv3wsnJCT4+Plqfb8qUKRg3bhymT58OLy8vtGvXDn/88QcqV66c7/OWL18OBwcHODg4oEWLFrhz5w7+/PNPufdpXubNmwdra2s0atQIfn5+8PX11XlV0ELjCAMxqYG0TGPmJyrmJz5mKDbmJzY1NOZ+fddJah0HjAYFBeHcuXM4ePCg/EIePnwYfn5+mDhxIkaMGFEkhRKR+MLDwxEYGPhWD2NycXHB8OHDMXz48OIuhYiKQEZGBiwtLdGk93mUMuQHSiIiIhLHgcXOb+xc2Z+Z0tPT8+3cUFx07hm2YsUKODs7w8/PD0+ePMHBgwfx4YcfYvLkyWwIIyIioncCV9ISkySpUdkunfkJivmJjxmKjfmJTZLUuHnzJqeU+JfOjWHZQ2ZKly6Nli1b4qOPPsL06dOLdD4cIiIiopKEK2mJSfHvFznmJybmJz5mKDbmJzbFv41hXE3yhVLa7HTu3Lkc2yZOnIhu3brhiy++QNOmTeV9atasqd8KieitkT2E8G32Ng8BJSIiIiIiehto1RhWu3ZtSJKk0YKYff/HH3/EsmXLoFarIUmSzkuxE9G7411oDCMiIiIiIqKSTavGsKSkpKKug4iIiEgYarWE3NchpZJMrZbw931TqNVMT0TMT3zMUGzMT2xqtQQbG5s8V1J/1+i8miQRERHRu6qkr4xEREREVBKU9M9MOk+gDwCJiYkYMmQIWrdujdatW2Po0KFITEzUd21EREREJRJXYhKTSqVCcnIy8xMU8xMfMxQb8xMb89Okc2PY7t27Ua1aNZw4cQI1a9ZEzZo1cfz4cVSvXh179+4tihqJiIiIShR2rBeTWq1GWloa8xMU8xMfMxQb8xMb89Ok1ZxhL/v6668xYsQIzJgxI8f2MWPGoE2bNnorjoiIiIiIiIiISJ907hkWFxeH3r1759geFBSECxcu6KUoIiIiIiIiIiKioqBzY5idnR2ioqJybI+KikK5cuX0URMRERFRicaVmMQkSRLs7e2Zn6CYn/iYodiYn9iYnyadh0n27dsX/fr1w5UrV9CoUSMAQGRkJGbOnImRI0fqvUAiIiKikkahKNQaRFTMFAoF7O3ti7sMKiTmJz5mKDbmJzbmp0lS6zh7mlqtxvfff4+5c+fi77//BgA4Ojpi9OjRGDp0KFsZiYiI6K2VvUx4WloarK2ti7sc0pFSqcTVq1fh4uICAwOD4i6HdMT8xMcMxcb8xPam88v+zJSeng4LC4siP5+udO4ZJkkSRowYgREjRuDBgwcAAHNzc70XRkRERESkb9mfX0lMzE98zFBszE9szO8/OjeGZbt9+zbi4+MBAJ6enihbtqzeiiIiIiIiIiIiIioKOk948fDhQwQFBcHBwQFNmzZF06ZN4eDggN69e+PRo0dFUSMREREREREREZFe6NwYNnLkSBw6dAi///477t+/j/v37+O3337DoUOH8OWXXxZFjUREREQlCudIFZMkSXBycmJ+gmJ+4mOGYmN+YmN+mnSeQL9s2bLYtGkTmjdvrrH94MGD8Pf3x+3bt/VZHxEREVGJUdIngyUiIiIqCUr6Zyade4Y9evQI5cuXz7G9XLlyHCZJRERE7wSlUlncJVAhKJVKXLx4kfkJivmJjxmKjfmJjflp0rkxrGHDhpgwYQKysrLkbY8fP8akSZPQsGFDvRZHRERERKRPL3+GJfEwP/ExQ7ExP7Exv//ovJrkggUL4Ovri4oVK6JWrVoAgOjoaBgbG2P37t16L5CIiIiIiIiIiEhfdG4Mq1GjBhISErBu3TpcvHgRANCtWzcEBATAxMRE7wUSERERERERERHpi84T6BMRERG9q7Ing71//z4sLS2LuxzSkVqtxoMHD2Bubs7VtATE/MTHDMXG/MT2pvMr6RPo69wz7O7du7C1tQUApKSkYPny5Xj8+DH8/PzQtGlTvRdIREREVNLwS4CYJEkqkR/ISTvMT3zMUGzMT2zMT5PWE+jHxMTAxcUF5cqVg6enJ6KiolC/fn3Mnz8fy5YtQ8uWLbFt27YiLJWIiIioZOBKTGJSKpWIiYlhfoJifuJjhmJjfmJjfpq0bgz76quv4O3tjcOHD6N58+bo2LEjPvzwQ6Snp+PevXvo378/ZsyYUZS1EhERERG9Fn4JEBvzEx8zFBvzExvz+4/WwyRPnjyJAwcOoGbNmqhVqxaWLVuG4OBgKBQv2tOGDBmC999/v8gKJSIiIiIiIiIiel1a9wxLS0uDvb09AMDMzAympqawtraWH7e2tsaDBw/0XyEREREREREREZGeaN0YBuScLJaTxxIREdG7KLtnPIlFoVDAw8OD+QmK+YmPGYqN+YmN+WnSaTXJwMBAGBkZAQCysrIwYMAAmJqaAgCePHmi/+qIiIiIiPTI0NCwuEug18D8xMcMxcb8xMb8/qN1k2DPnj1Rrlw5WFpawtLSEl988QUcHR3l++XKlUOPHj2KslYiIiKiEkGlUhV3CVQIKpUKMTExzE9QzE98zFBszE9szE+TpFar1cVdBBEREZEIMjIyYGlpieZ9z0EqZVnc5ZCODBQqNPO8jkMXK0Kp4jAR0TA/8TFDsTE/sRkoVJjd5z68vb1hYGBQ5OfL/syUnp4OCwuLIj+frvgOJiIiIiIiIiKidwYbw4iIiIiIiIiI6J3BxjAiIiIiHSlVXFFbREqV9O/wHuYnIuYnPmYoNuYnNqVKgre3N1eT/BdfBSIiIiJ6ZxiXVhZ3CfQamJ/4mKHYmJ/Ynj59WtwllBhsDCMiIiLSkYGC6w+JyEChxntVU5mfoJif+Jih2Jif2AwUasTHx3M1yX+xMYyIiIiIiIiIiN4ZbAwjIiIiIiIiIqJ3BhvDiIiIiOid8VzFj78iY37iY4ZiY35iMzAwKO4SSgy+k4mIiN4xarVuc32Eh4fDxcWlaIopQhMnTkTt2rWL5NhKfhkQklKlwOGLFZmfoJif+Jih2Jif2JQqBby9vdkg9i++i4mIiN6wlJQUBAUFwdHREYaGhqhUqRKGDRuGu3fvvpHzL1u2TO/HDAwMhCRJGDBgQI7HBg0aBEmSEBgYmO9z87oVtiFu1KhR2L9/f6GeWzBOHiwmNWxMH4P5iYr5iY8Zio35iU2NjIwMnf8o+rZiYxgREdEbdOXKFdSrVw8JCQn45ZdfcPnyZSxduhT79+9Hw4YNkZaWVqTnj46OxsyZM4vk2E5OTli/fj0eP34sb8vKysLPP/8MZ2fnPJ+3YMECpKamyjcACA0Nle+fPHlSY39tlwU3MzODra1tIa6kYFxJS0wGCjVqV7rN/ATF/MTHDMXG/MRmoFDjypUrXE3yX2wMIyIieoMGDRoEQ0ND7NmzB82aNYOzszPat2+Pffv24caNG/jmm2/kfV1cXDBt2jQEBQXB3Nwczs7OOXp1paSkwN/fH1ZWVrCxsUGnTp1w9erVPM+/adMmJCUl4cyZM3q/tjp16sDJyQlbtmyRt23ZsgXOzs7w8fHJ83mWlpawt7eXbwBgZWUl369fvz6mTJmCHj16wMLCAv369QMAjBkzBu7u7ihTpgyqVKmCcePG4dmzZ/JxXx0mGRgYiI8//hhz5syBg4MDbG1tMWjQII3nvOrJkyfIyMjQuBERERGR2NgYRkRE9IakpaVh9+7dCA4OhomJicZj9vb2CAgIwIYNGzS6r8+dOxf16tXD2bNnERwcjIEDByI+Ph4A8OzZM/j6+sLc3BwRERGIjIyEmZkZ2rVrJ/eeioqKwpEjR+Tbr7/+CgBYunSpxvaUlBS9XGNQUBBCQ0Pl+6tWrUKvXr1e+7hz5sxBrVq1cPbsWYwbNw4AYG5ujrCwMFy4cAELFizA8uXLMX/+/HyPc/DgQSQmJuLgwYNYvXo1wsLCEBYWluf+06dPh6WlpXxzcnJ67WshIiIiouLFxjAiIqI3JCEhAWq1Gl5eXrk+7uXlhXv37uH27dvytg4dOiA4OBiurq4YM2YMypYti4MHDwIANmzYAJVKhRUrVsDb2xteXl4IDQ1FcnIywsPDAQDGxsYYPHgwmjRpgiZNmsgNacuXL5e3rVq1Sm/DCb/44gscOXIE165dw7Vr1xAZGYkvvvjitY/bsmVLfPnll6hatSqqVq0KAPj222/RqFEjuLi4wM/PD6NGjcLGjRvzPY61tTVCQkLg6emJjh074sMPP8x3XrGxY8ciPT1dvsmNhhwhIiY18PBJaeYnKuYnPmYoNuYnNvWLz4X0QqniLoCIiOhdo8vEpTVr1pT/LUkS7O3tcevWLQAv5v+6fPkyzM3NNZ6TlZWFxMREAICnpyeOHz+OL7/8Ej/88IPGfubm5li6dCk+//zzwl5KDnZ2dvjwww8RFhYGtVqNDz/8EGXLln3t49arVy/Htg0bNmDhwoVITExEZmYmnj9/DgsLi3yPU716dY1VlBwcHBATE5Pn/kZGRjAyMsqxXalW8EOUgJRqBY4nOhR3GVRIzE98zFBszE9sSrUCnp6exV1GicGeYURERG+Iq6srJElCXFxcro/HxcXB2toadnZ28rbSpUtr7CNJkjzxaWZmJurWrYuoqCiN26VLlzQauIyMjBASEqLRsAYAQ4YM0WtDWLagoCCEhYVh9erVCAoK0ssxTU1NNe4fPXoUAQEB6NChA3bs2IGzZ8/im2++KXBy/fxeT11IEv8sLiJJUsPRKpP5CYr5iY8Zio35iU2S1Lh79y4n0P8XG8OIiIjeEFtbW7Rp0waLFy/WWHERAG7evIl169aha9eukCRJq+PVqVMHCQkJKFeuHFxdXTVulpaWGvteu3YN586dgyRJ8PDwAADs2LFDPxf2iuw5y7LnNCsKf/31FypVqoRvvvkG9erVg5ubG65du1Yk58qNgl8EhKSQ1PB0TGN+gmJ+4mOGYmN+YlNIaqSkpOg0QuFtxsYwIiKiNygkJARPnjyBr68vDh8+jJSUFOzatQtt2rRBhQoVMHXqVK2PFRAQgLJly6JTp06IiIhAUlISwsPDMXToUFy/fl1j382bN6N8+fLYtWsXYmNjMX78eMTGxsrDKfXJwMAAcXFxuHDhgsaQRH1yc3NDcnIy1q9fj8TERCxcuBBbt24tknMRERER0duFjWFERERvkJubG06dOoUqVarA398fVatWRb9+/dCiRQscPXoUNjY2Wh+rTJkyOHz4MJydndGlSxd4eXmhd+/eyMrKyjF3VmZmJqKjo9G2bVsYGBhg0qRJOHDgAE6ePKnvSwQAWFhYFDh/1+v46KOPMGLECAwePBi1a9fGX3/9Ja8ySURERESUH0nNPnJERESUj/DwcAQGBuLq1avFXUqxy8jIgKWlJZr3OQeptGXBT6ASxUBSwdvpDmJSykKp5t+ERcP8xMcMxcb8xGYgqbB0pBIuLi5F1nP/ZdmfmdLT04v0D6SFxYWQiIiIiHTE1STFpFQrEJVcrrjLoEJifuJjhmJjfmJTqhWoWtWluMsoMdicS0RERKQjrqQlJklSo7JdOvMTFPMTHzMUG/MTmySpcfPmTa4m+S82hhEREVG+XFxcMHz48OIuo0ThSlpiUvz7RY75iYn5iY8Zio35iU3xb2MYZ8p6gY1hRERElC82hhERERHR24SNYURERERERERE9M7gapJEREREWspeGenevXuwsrIq7nJIRyqVCtevX0fFihWhUPBvwqJhfuJjhmJjfmJ70/mV9NUk2RhGREREpKWS/sGOiIiIqCQo6Z+Z2JxLREREpCOuxCQmlUqF5ORk5ico5ic+Zig25ic25qeJjWFEREREOmLHejGp1WqkpaUxP0ExP/ExQ7ExP7ExP01sDCMiIiIiIiIioncGG8OIiIiIiIiIiOidwcYwIiIiIh1JklTcJVAhSJIEe3t75ico5ic+Zig25ic25qeJq0kSERERaamkr4xEREREVBKU9M9M7BlGREREpCOlUlncJVAhKJVKJCYmMj9BMT/xMUOxMT+xMT9NbAwjIiIionfGgwcPirsEeg3MT3zMUGzMT2zM7z9sDCMiIiIiIiIioncGG8OIiIiIiIiIiOidwcYwIiIiIh1xJSYxSZIEJycn5ico5ic+Zig25ic25qepVHEXQERERCQahYJ/TxSRQqGAra1tcZdBhcT8xMcMxcb8xMb8NPGTHBEREZGOuBKTmJRKJS5evMj8BMX8xMcMxcb8xMb8NLExjIiIiIjeGVlZWcVdAr0G5ic+Zig25ic25vcfNoYREREREREREdE7g41hRERERERERET0zmBjGBEREZGOOIG+mBQKBapUqcL8BMX8xMcMxcb8xMb8NHE1SSIiIiIdcVlyMUmSBAsLi+IugwqJ+YmPGYqN+YmN+WlikyARERGRjrgSk5iUSiViYmKYn6CYn/iYodiYn9iYnyY2hhERERHRO4NfAsTG/MTHDMXG/MTG/P7DxjAiIiIiIiIiInpnsDGMiIiIiIiIiIjeGZJarVYXdxFEREREIsjIyIClpSXu378PS0vL4i6HdKRWq5GVlQVjY2MugiAg5ic+Zig25ie2N51f9mem9PT0EjlxP3uGEREREdE7w9DQsLhLoNfA/MTHDMXG/MTG/P7DxjAiIiIiHalUquIugQpBpVIhJiaG+QmK+YmPGYqN+YmN+WkqVdwFEBEREYnmqqcf0lQcIiIaVSkDZHRriSu/DIfiOVfUEg3zEx8zFBvzE5uqlAGw8/viLqPEYM8wIiIiIiIiIiJ6Z7AxjIiIiIiIiIiI3hlcTZKIiIhIS9krI52xeR8WCs42IRo1AHUpA0jPleAgV/EwP/ExQ7ExP7GpAVS+GQ6FQsHVJMGeYURERES647cAMUmA0tSY+YmK+YmPGYqN+YlNAp4+fVrcVZQYbAwjIiIi0pWBQXFXQIWgNjDArY8aQc38hMT8xMcMxcb8xKY2MEB8fDxXk/wXG8OIiIiIiIiIiOidwcYwIiIiIiIiIiJ6Z7AxjIiIiIjeGYpnz4u7BHoNzE98zFBszE9sBhziKmNjGBWpwixW6uLigvDw8Nc6r4uLC77//vvXOsab1Lx5cwwfPvy1jnHz5k20adMGpqamsLKy0ktdLwsPD4ckSbh//36++5Wk1z4wMBAff/yxfL8wr7MkSdi2bZte66LCu3r1KiRJQlRUlE7PCwsLK5Kfi3fFxIkTUb58ef48vER6rizuEqgQFM+VcFh/EArmJyTmJz5mKDbmJzbFcyW8vb3ZIPYvNoa9hQIDAyFJUo7b5cuX33gtK1as0OsEfXldW/bNxcVFb+d6VfaX8Nxux44dK7Lzamv+/PlITU1FVFQULl26lOs+EydOzLX+ffv2FXj8Ro0aITU1FZaWlgCKvnHh5fosLCxQv359/Pbbb691zC1btmDKlCk6PSc1NRXt27d/rfPqIiwsLN/3uCRJuHr16hurR0RvskH2dX/f6qMhvKjFxcVh0qRJ+PHHH/P8eXj196ONjQ2aNWuGiIiIYqj4zVBzJS0hqSUgy9GW+QmK+YmPGYqN+YlNLQEZGRmF6rDyNmJj2FuqXbt2SE1N1bhVrly5yM737NmzXLcvXrwYR44c0dt5FixYoHFNABAaGirfP3nypN7OlZd9+/bleG3r1q1b5OctSGJiIurWrQs3NzeUK1cuz/2qV6+eo/6mTZsWeHxDQ0PY29tDkt7c//2ysz116hQaN26M//3vf4iJiSn08WxsbGBubq7Tc+zt7WFkZFToc+qqa9euGtk0bNgQffv21djm5OSk9fHyWj45r5/ZghT2eW+zN/37NjdFuUx2YmIiAKBTp04F/jxk/348fPgwHB0d0bFjR/zzzz9FVlux4l9VhaQ2MMDdVnW4EpqgmJ/4mKHYmJ/Y1AYGuHLlCleT/Bcbw95SRkZGsLe317hld4c8dOgQGjRoACMjIzg4OODrr7/G8+f/jf3OrVdF7dq1MXHiRPm+JElYsmQJPvroI5iammLq1Kk5akhMTERUVBQ2bdqkt+uytLTUuCYAsLKyku/b2dnJ+z569AhBQUEwNzeHs7Mzli1bpnGslJQU+Pv7w8rKCjY2NujUqZNWPW5sbW1zvLalS5cG8KLnVe3atbF27Vq4uLjA0tISn332GR48eCA//+HDh+jRowfMzMzg4OCAuXPnanXtS5YsQdWqVWFoaAgPDw+sXbtWfszFxQWbN2/GmjVrIEkSAgMD8zxOqVKlctRvaGiItWvXol69ejA3N4e9vT0+//xz3Lp1S37ey8Mkw8PD0atXL6Snp8s9QV5+fxT02msrO1t3d3dMmTIFz58/x8GDB+XHdc3w1V44qamp+PDDD2FiYoLKlSvj559/zvH+f3VYWExMDFq2bAkTExPY2tqiX79+yMzMlB/PHpo5Z84cODg4wNbWFoMGDdK6EcnExCRHNmXKlJHvGxsbo3///rCzs4OFhQVatmyJ6Oho+fnZ78EVK1agcuXKMDY2lq8jt5/Z/N5X+T2vIImJiejUqRPKly8PMzMz1K9fP0cPRBcXF0ybNi3f98qJEyfg4+MDY2Nj1KtXD2fPns33vM2bN8e1a9cwYsQI+b35st27d8PLywtmZmZyI9bLVqxYAS8vLxgbG8PT0xOLFy8u8Frz+n376jBdABg+fDiaN28O4MV75dChQ1iwYIFGr7/cel1u27ZN41ryyvn+/fvo06dPnu+P3OT3np44cSL8/PwAAAqFosDG8OzfjzVq1MD//d//ISMjA8ePH5cfP3/+PNq3bw8zMzOUL18e3bt3x507d+THd+3ahQ8++ABWVlawtbVFx44d5cY44EWj3+DBg+Hg4ABjY2NUqlQJ06dPlx9PTk5Gp06dYGZmBgsLC/j7+2s0xmnzO/pVT548QUZGhsaNiIiIiMTGxrB3zI0bN9ChQwfUr18f0dHRWLJkCVauXInvvvtO52NNnDgRnTt3RkxMDIKCgvD333/jyJEj8i0kJATAi6FpL28/deqUvi8rV3PnzpW/PAcHB2PgwIGIj48H8KJ3i6+vL8zNzREREYHIyEj5y/Hr9rBITEzEtm3bsGPHDuzYsQOHDh3CjBkz5MdHjx6NQ4cO4bfffsOePXsQHh6OM2fO5HvMrVu3YtiwYfjyyy9x/vx59O/fH7169ZIbhk6ePIl27drB398fqampWLBggc51P3v2DFOmTEF0dDS2bduGq1ev5tmo1qhRI3z//fewsLCQe8KMGjVKfjy/1x540WCRX4Pdq54/f46VK1cCeNFDLbve182wR48e+PvvvxEeHo7Nmzdj2bJlGg2Ar3r48CF8fX1hbW2NkydP4tdff8W+ffswePBgjf0OHjyIxMREHDx4EKtXr0ZYWBjCwsK0vt78fPrpp7h16xZ27tyJ06dPo06dOmjVqhXS0tLkfS5fvozNmzdjy5YtGnNrvfozW9D7Kq/naSMzMxMdOnTA/v37cfbsWbRr1w5+fn5ITk7W2C+/90pmZiY6duyIatWq4fTp05g4caLG+yw3W7ZsQcWKFTF58mSNHqTAi0baOXPmYO3atTh8+DCSk5M1jrdu3TqMHz8eU6dORVxcHKZNm4Zx48Zh9erVWl2zrhYsWJCj558uvf5yy1mb98fLCnpPjxo1CqGhoQCQ4/XMz+PHj7FmzRoA//3M3r9/Hy1btoSPjw9OnTqFXbt24Z9//oG/v79GPSNHjsSpU6ewf/9+KBQKdO7cWf4L5sKFC7F9+3Zs3LgR8fHxWLdunTw8XqVSoVOnTkhLS8OhQ4ewd+9eXLlyBV27dtWoraDf0a+aPn06LC0t5ZsuGRERERFRyVSquAugorFjxw6YmZnJ99u3b49ff/0VixcvhpOTE0JCQiBJEjw9PfH3339jzJgxGD9+PBQK7dtHP//8c/Tq1Uu+/+TJE8yYMQOLFi3S2O/GjRto0qQJAMDd3R0bN258zavTTocOHRAcHAwAGDNmDObPn4+DBw/Cw8MDGzZsgEqlwooVK+SeDqGhobCyskJ4eDjatm2b53EbNWqU43V6uWeQSqVCWFiYPByve/fu2L9/P6ZOnYrMzEysXLkSP/30E1q1agUAWL16NSpWrJjvtcyZMweBgYHy9YwcORLHjh3DnDlz0KJFC9jZ2cHIyEjuVZSfmJgYjfdGtWrVcOLECY1GjipVqmDhwoWoX78+MjMzNfYHXny5tbS0hCRJuZ4vv9ceAJydneHg4JBvnQDQrVs3GBgY4PHjx1CpVHBxcZG/OL9OhgBw8eJF7Nu3DydPnkS9evUAvOgV5Obmludzfv75Z2RlZWHNmjUwNTUFAISEhMDPzw8zZ85E+fLlAQDW1tYICQmBgYEBPD098eGHH2L//v3o27dvgdecnyNHjuDEiRO4deuWPFRtzpw52LZtGzZt2oR+/foBeNF7Zs2aNRo9JYGcP7PdunXL932V1/O0UatWLdSqVUu+P2XKFGzduhXbt2/XaDzM773y888/Q6VSYeXKlTA2Nkb16tVx/fp1DBw4MM/z2tjYwMDAQO7h+LJnz55h6dKlqFq1KgBg8ODBmDx5svz4hAkTMHfuXHTp0gUAULlyZVy4cAE//vgjevbsmec58/p9WxBLS0uNnn+6ejVnbd8fL9PmPZ3dS02bGrN/Pz569AhqtRp169aVf9eFhITAx8cH06ZNk/dftWoVnJyccOnSJbi7u+OTTz7RON6qVatgZ2eHCxcuoEaNGkhOToabmxs++OADSJKESpUqyfvu378fMTExSEpKkhus1qxZg+rVq+PkyZOoX78+gPx/R+dm7NixGDlypHw/IyODDWKCK5X+sLhLoNfA/MTHDMXG/MSWPZqA2Bj21mrRogWWLFki38/+khMXF4eGDRtqDHVp3LgxMjMzcf36dTg7O2t9juwGhGxGRkZYuHAhWrdujaCgINy9e1fj8R49emDx4sVyLUWtZs2a8r+zG22ye/1ER0fj8uXLOeaPysrK0hiSk5sNGzbAy8srz8ddXFw0juvg4CCfNzExEU+fPsV7770nP25jYyM3EuUlLi4uxxfZxo0bF6oHmIeHB7Zv3y7fz/7SnN3zJjo6Gvfu3ZN7YiQnJ6NatWo6nSO/1x6A3GOkIPPnz0fr1q1x5coVjBgxAgsXLoSNjQ2A18sQAOLj41GqVCnUqVNH3ubq6gpra+s8nxMXF4datWppvIcbN24MlUqF+Ph4uTGsevXqGqu0ODg4vNZcZ9mio6ORmZkJW1tbje2PHz/WuOZKlSrlaAgDcv7Mavu+evV52sjMzMTEiRPxxx9/IDU1Fc+fP8fjx49z9AzL770SFxeHmjVravxPu2HDhjrXkq1MmTJyQxig+bP58OFDJCYmonfv3hqNls+fP5cXjchLXr9vi9qrOWv7/niZtu9pbW3YsAGenp44f/48vvrqK4SFhcnDyKOjo3Hw4MEcjevAi9+N7u7uSEhIwPjx43H8+HHcuXNH4/dQjRo1EBgYiDZt2sDDwwPt2rVDx44d5YbvuLg4ODk5aTRUVatWDVZWVoiLi5Mbw/L7HZ0bIyOjXOdJk54rAQU/RolG8VyJ8tv/Ku4yqJCYn/iYodiYn9gUz5Vw8/Qs7jJKDH6Ke0uZmprC1dW1UM9VKBQ5VpjIbb6jvL7wffTRR/jmm280/pJeoUKFIhtqlJfsL2DZJEmSv1hlZmaibt26WLduXY7n5daI8DInJ6d8X9v8zlsSGBoa5qg/e6iUr68v1q1bBzs7OyQnJ8PX17dQw0b19RrY29vD1dUVrq6uCA0NRYcOHXDhwgWUK1futTIsakX1HsjMzISDgwPCw8NzPPbyHFN5/WwWtpGmMM8bNWoU9u7dizlz5sDV1RUmJib43//+l+P99CZ/XnI7V/bvuuzencuXL9dorAZQ4PLTef2+1fZ3aWGf92ou2r4/ipKTkxPc3Nzg5uaG58+fo3Pnzjh//jyMjIyQmZkp9zh7VXZPUT8/P1SqVAnLly+Ho6MjVCoVatSoIb9v6tSpg6SkJOzcuRP79u2Dv78/WrdurdPclPp6z6kVXEpLRGqFhEdVHFHmyt+QVFxNSzTMT3zMUGzMT2xqhYS7d+/C2tpapxFhbyu+Au8YLy8vHD16VOOLVmRkJMzNzeWhenZ2dhrzwmRkZCApKUmn82T3PHJ3d4eBgQFu3LhR4MTXb1KdOnWQkJCAcuX+n707D4/p7N8Afp9JZN8kQoKQhCSC2GlRElsTe6g9RGx9/VpV+1K1UxQt6kVtCV4ElZbaWxUVVCxNxJIgQlSliCWChEzm90eaU0e2mUhMnub+XNdcl5w5c85z5j4zZr7znOcpLxdbsm8F9QJ5E9WqVUOZMmUUA0o/fPgQV65cyfdxnp6eOH78uGLZ8ePHde6xlZfY2FgkJydj/vz5aNGiBWrUqJFvTwkgq6imVquLZP/aaNKkCRo2bChfyvSmGXp4eCAjI0NxXl67dg0PHz7M8zGenp6Ijo7G06f/dA8/fvw4VCpVgb37ikKDBg2QlJQEQ0PDHMdcrlw5nbdXnOfV8ePHERQUhG7dusHLywsODg5aTVDxevvOnz+PtLQ0edlvv/1W4OMKc25WqFABFStWxPXr13M8t4WdGfL191IAijHc8mqrvb09njx5ojjPXn9cbgpzfhTnOd2jRw8YGhrKkxA0aNAAFy9ehLOzc472mZubIzk5GXFxcfj888/Rpk0beHp65vp6tLKyQu/evbFmzRps27YNO3fuxIMHD+Dp6Ylbt27h1q1b8rqXLl3Co0ePiuy9UoEfIoWkUanwqGlNaJifkJif+Jih2Jif2DQqFW7dupXjR9fSimdxKfPRRx/h1q1b+OSTTxAbG4tdu3Zh+vTpGDNmjFwdbt26NTZt2oRjx44hJiYGAwcOLLBnxKvu3buHiIgIjBs3DhcuXMCRI0fg5OSEsLCw4josnQUEBKBcuXLo2rUrjh07hoSEBISHh2PkyJH4448/8n1scnIykpKSFLdXv6znx8LCAkOGDMH48ePxyy+/4MKFCwgKCiqwMj9+/HiEhIRg5cqVuHr1Kr766iuEhYUVOJi4tqpUqQIjIyN88803uH79Onbv3o3Zs2fn+xhnZ2ekpqbi8OHDuH//Pp49e6b1/gIDAzF58mSd2zlq1Ch8++23uH379htlCAA1atRA27Zt8eGHHyIyMhK///47PvzwQ5iamuY5Y15AQABMTEwwcOBA+dz+5JNPMGDAAJ0vJyuMtm3bomnTpvD398ehQ4dw48YNnDhxAlOmTCnUxBTFeV65ubnJA7tHR0ejX79+Ove+6devHyRJwrBhw3Dp0iXs27cPixYtKvBxzs7O+PXXX3H79m3FTIUFmTlzJubNm4dly5bhypUriImJQXBwML766iud2p2tdevWOHPmDDZu3IirV69i+vTpuHDhQo62njp1Cjdu3JAvC3znnXdgZmaGzz77DPHx8diyZYtWEzAU5vwoznNakiSMHDkS8+fPx7Nnz/Dxxx/jwYMH6Nu3L06fPo34+HgcPHgQgwYNglqtRtmyZWFnZ4fVq1fj2rVr+OWXXxQ9jAHgq6++wtatWxEbG4srV65gx44dcHBwgI2NDdq2bQsvLy8EBATg3LlziIyMRGBgILy9vQt1qS8RERER/XuxGFbKVKpUCfv27UNkZCTq1q2L4cOHY8iQIfj888/ldSZPngxvb2906tQJHTt2hL+/v2KcnYKcPHkSe/bswcKFC1GmTBm0aNECUVFRJaoCbWZmhl9//RVVqlRB9+7d4enpiSFDhiAtLQ1WVlb5PrZt27ZwdHRU3H744Qet971w4UK0aNECnTt3Rtu2bfHee++hYcOG+T7G398fS5cuxaJFi1CrVi18++23CA4Oho+Pj9b7zY+9vT1CQkKwY8cO1KxZE/Pnzy+w6NCsWTMMHz4cvXv3hr29Pb788kut95eYmKj1rHSv8vPzg4uLC+bOnftGGWbbuHEjKlSogJYtW6Jbt24YNmwYLC0t8xxY0szMDAcPHsSDBw/QuHFj9OjRA23atJFnTtXWjBkz5BnwdCFJEvbt24eWLVti0KBBcHd3R58+fXDz5s1CFS7e5LxydnbGjBkz8rz/q6++QtmyZdGsWTN07twZvr6+ivHZtGFhYYEff/wRMTExqF+/PqZMmZLrJXavmzVrFm7cuIFq1arpdMns0KFDsXbtWgQHB8PLywve3t4ICQkpdM8wX19fTJ06FRMmTEDjxo3x5MkTBAYGKtYZN24cDAwMULNmTfnyZFtbW/zvf//Dvn374OXlha1bt+b7XGcrzPlRVOd0XgYOHIiXL19i+fLlqFixIo4fPw61Wo33338fXl5eGDVqFGxsbKBSqaBSqRAaGoqzZ8+idu3aGD16NBYuXKjYnqWlJb788ks0atQIjRs3xo0bN7Bv3z6oVCpIkoRdu3ahbNmyaNmyJdq2bQtXV1ds27atSI6FiIiIiP49JE1JqlAQIetLdkhISJEVeoi09ccff8DJyQk///yzPANecRg4cCAkSdKqt09J9OzZM9jZ2WH//v18nVKpk5KSAmtra5wr3xxWmRw3TDSZhgZ44F0Xtkejocp4e5f6U9FgfuJjhmJjfmLLNDSAKmIdnJ2ddbryq7CyPzM9fvxY684KbxMH0CeiUuuXX35BamoqvLy8cOfOHUyYMAHOzs5o2bJlse1To9EgPDwcERERxbaP4nbkyBG0bt2ahTAq1TibpJhUGWqUO3xO382gQmJ+4mOGYmN+YlNlqHW64uvfjpdJElGp9fLlS3z22WeoVasWunXrBnt7e4SHh+eYba4oSZKEmzdvwsnJqdj2Udw6duyIvXv36rsZRHrF2STFpFFJSKnryvwExfzExwzFxvzEplFJSEpKKraZ20XDYhiVOKNGjSrUeEpEuvL19cWFCxfw7Nkz/PXXX/j+++9RtWpVfTeLiETAmbSEpFGp8KRONc6EJijmJz5mKDbmJzaNSoWkpKQSNZa3PrF/P5U4o0aN0ncTiIiIiIiIiOhfiiVdIiIiIiIiIiIqNTibJBEREZGWsmdGevjwIWxsbPTdHNJRZmYm/vjjD1SuXBkqXuYjHOYnPmYoNuYntredX0mfTZLFMCIiIiItlfQPdkREREQlQUn/zMRyLhEREZGOOBOTmDIzM5GYmMj8BMX8xMcMxcb8xMb8lFgMIyIiItIRO9aLSaPR4MGDB8xPUMxPfMxQbMxPbMxPicUwIiIiIiIiIiIqNVgMIyIiIiIiIiKiUoPFMCIiIiIdSZKk7yZQIUiSBAcHB+YnKOYnPmYoNuYnNuanxNkkiYiIiLRU0mdGIiIiIioJSvpnJvYMIyIiItKRWq3WdxOoENRqNeLj45mfoJif+Jih2Jif2JifEothRERERFRqPHnyRN9NoDfA/MTHDMXG/MTG/P7BYhgREREREREREZUaLIYREREREREREVGpwWIYERERkY44E5OYJEmCk5MT8xMU8xMfMxQb8xMb81My1HcDiIiIiESjUvH3RBGpVCrY2dnpuxlUSMxPfMxQbMxPbMxPiZ/kiIiIiHTEmZjEpFarERsby/wExfzExwzFxvzExvyUWAwjIiIiolIjLS1N302gN8D8xMcMxcb8xMb8/sFiGBERERERERERlRoshhERERERERERUanBYhgRERGRjjiAvphUKhVcXV2Zn6CYn/iYodiYn9iYnxJnkyQiIiLSEaclF5MkSbCystJ3M6iQmJ/4mKHYmJ/YmJ8SS4JEREREOuJMTGJSq9WIiYlhfoJifuJjhmJjfmJjfkoshhERERFRqcEvAWJjfuJjhmJjfmJjfv9gMYyIiIiIiIiIiEoNFsOIiIiIiIiIiKjUkDQajUbfjSAiIiISQUpKCqytrfHo0SNYW1vruzmkI41Gg7S0NJiYmHASBAExP/ExQ7ExP7G97fyyPzM9fvy4RA7cz55hRERERFRqGBkZ6bsJ9AaYn/iYodiYn9iY3z8M9d0AIiIiItFcd+8Iq0z+Ki6aTEMD3OnbGo5bf4Eqg4MIi4b5iY8Zio35iS3T0ABP9y+Bl5cXDAwM9N0cvWPPMCIiIiIiIiIiKjVYDCMiIiIiIiIiolKDxTAiIiIiIiIiIio1OJskERERkZayZ0Y6Z/surFQcelU0GgAaQwNIGWpwxDfxMD/xMUOxMT+xaQC4JIVDpVJxNkmwZxgRERGR7vgtQEwSoDY3YX6iYn7iY4ZiY35ik4AXL17ouxUlBothRERERLriLExC0hgY4G6XZtAwPyExP/ExQ7ExP7FpDAwQFxeHzMxMfTelRGAxjIiIiIiIiIiISg0Ww4iIiIiIiIiIqNRgMayYFWZ+AmdnZ4SHhxd9Y4qQJEn44Ycf9LLvkJAQ2NjY6GXf2ZydnbFkyRK9tqGovY3n1cfHB6NGjSrWfWgrKCgI/v7++m5GqZSUlIR27drB3NxcPueK4j2loHP4xo0bkCQJUVFRb7SfkuptvTe+/jp+/f3wbf3/oM//h0hsqpcZ+m4CvQHmJz5mKDbmJzYDXuIq+1cWw4KCgiBJUo7btWvX3npb1q5dW+TX5IaHh0OSJDx69CjPdfIq1syYMQP16tUr0vYUlezc5s+fr1j+ww8/KGa76N27N65cuVIsbch+bvO7hYeH4/Tp0/jwww+LpQ3a+OOPP2BkZITatWu/9X3v2bMH3t7esLS0hJmZGRo3boyQkBDFOtqco8WtoDYsXbo0R7tLopLyWi7KQsvXX3+NO3fuICoqSn4t37lzB+3bty+S7Yvg2rVr8PX1hZWVFWxtbdG+fXvcu3fvre3fx8cnx7LCFoiL+/2wOM/1NymKSxnqom0MvRWqDDUcQ49AxfyExPzExwzFxvzEpspQw8vLiwWxv/0ri2EA4Ofnhzt37ihuLi4uxba/ly9f5rp8xYoViIiIKLb9/tuYmJhgwYIFePjwYZ7rmJqaonz58sWy/2bNminOmV69euU4l5o1awZ7e3uYmZkVSxu0ERISgl69eiElJQWnTp16a/v95ptv0LVrVzRv3hynTp3C+fPn0adPHwwfPhzjxo17a+14VWFnRLG2ttZ7D8PSKj4+Hg0bNoSbm5v8WnZwcICxsbGeW/b2fPjhh7h//z6OHj2KkydPonfv3oXqSayLK1euIDQ0VLHs3Llz2LNnzxttV9/vh/qi4UxaQtJIQFpFO+YnKOYnPmYoNuYnNo0EpKSkFPtnTlH8a4thxsbGcHBwUNyyK6BHjx5FkyZNYGxsDEdHR0yaNAkZGf9098ytJ0a9evUwY8YM+W9JkrBy5Up06dIF5ubmmDt3bo42xMfHIyoqCt99912xHGNROH36NNq1a4dy5crB2toa3t7eOHfunGKdq1evomXLljAxMUHNmjXx008/FbjdAwcO4L333oONjQ3s7OzQqVMnxMfHF/i4tm3bwsHBAfPmzctzndx6qPz4449o3LgxTExMUK5cOXTr1k2+Lz09HePGjUOlSpVgbm6Od955J8/LUI2MjBTnjKmpaY5zycjIKNfLgr799lt06tQJZmZm8PT0xMmTJ3Ht2jX4+PjA3NwczZo1y/Ec7Nq1Cw0aNICJiQlcXV0xc+ZMxbmYG41Gg+DgYAwYMAD9+vXDunXrFPdnXwoWFhaGVq1awczMDHXr1sXJkydzPI9VqlSBmZkZunXrhuTk5Hz3e+vWLYwdOxajRo3CF198gZo1a6J69eoYO3YsFi5ciMWLF+PUqVO4ceMGWrVqBQAoW7YsJElCUFCQvJ3MzExMmDABtra2cHBwULyuAODRo0cYOnQo7O3tYWVlhdatWyM6Olq+P7uXyNq1a+Hi4gITE5N8252X13uEZGZmYt68eXBxcYGpqSnq1q2reO0+fPgQAQEBsLe3h6mpKdzc3BAcHFyofReHzMxMzJo1C5UrV4axsTHq1auHAwcOyPf36NEDI0aMkP8eNWoUJElCbGwsgKyiorm5OX7++ecc2w4PD8egQYPw+PFjuYdkdm4PHz5EYGAgypYtCzMzM7Rv3x5Xr17Ns53Ozs7YuXMnNm7cqDg3Xr3krbjO4dzk9//B6tWrUbFixRy9e7t27YrBgwfLfxfmdaxSqeDr64v69evDw8MDQUFBWhX5o6Oj0apVK1haWsLKygoNGzbEmTNnFOscPHgQnp6esLCwkIv5AFCuXDkcOXIEvXr1wqNHjzBt2jRMnjwZrq6umDFjBjZs2IBdu3YpesFqo6DLxqdPnw5HR0ecP38eABAREYEWLVrA1NQUTk5OGDlyJJ4+fZrrY0NCQjBz5kxER0fL7Xq1R+f9+/fRrVs3mJmZwc3NDbt375bvU6vVGDJkiPya9vDwwNKlS+X7tT3m9PR0pKSkKG4AOJukoDQGBkhu04AzoQmK+YmPGYqN+YlNY2CA69evczbJv/1ri2F5uX37Njp06IDGjRsjOjoaK1euxLp16zBnzhydtzVjxgx069YNMTExGDx4MP78809ERETIt+XLlwMAwsLCFMtf/+KiT0+ePMHAgQMRERGB3377DW5ubujQoQOePHkCIOsLdvfu3WFkZIRTp05h1apVmDhxYoHbffr0KcaMGYMzZ87g8OHDUKlU6NatW4EvPAMDA3zxxRf45ptv8Mcff2h1DHv37kW3bt3QoUMH/P777zh8+DCaNGki3z9ixAicPHkSoaGhOH/+PHr27Ak/P798v7AXxuzZsxEYGIioqCjUqFED/fr1w3/+8x9MnjwZZ86cgUajURQjjh07hsDAQHz66ae4dOkSvv32W4SEhORaWH3VkSNH8OzZM7Rt2xb9+/dHaGhorl8kp0yZgnHjxiEqKgru7u7o27ev/AX91KlTGDJkCEaMGIGoqCi0atWqwNfAd999h5cvX+baA+w///kPLCwssHXrVjg5OWHnzp0AgLi4ONy5c0fxBXTDhg0wNzfHqVOn8OWXX2LWrFmKAmvPnj1x9+5d7N+/H2fPnkWDBg3Qpk0bPHjwQF7n2rVr2LlzJ8LCwops/Kd58+Zh48aNWLVqFS5evIjRo0ejf//+OHr0KABg6tSpuHTpEvbv34/Lly9j5cqVKFeu3BvtMygoKNfL1Qpj6dKlWLx4MRYtWoTz58/D19cXXbp0kc9zb29vxRf9o0ePoly5cvKy06dP4+XLl2jWrFmObTdr1gxLliyBlZWV3EMy+zwICgrCmTNnsHv3bpw8eRIajQYdOnTIs7fs6dOn4efnh169euU4N15X1Ofw6wr6/6Bnz55ITk7GkSNH5Mc8ePAABw4cQEBAAIDCv467du2KFStW5PjxoSABAQGoXLkyTp8+jbNnz2LSpEkoU6aMfP+zZ8+waNEibNq0Cb/++isSExPlrGxtbfHtt9+ibdu2iI6ORnx8PA4ePIiaNWti3LhxOXrC5nYu6EKj0eCTTz7Bxo0bcezYMdSpUwfx8fHw8/PDBx98gPPnz2Pbtm2IiIhQvDe+qnfv3hg7dixq1aolt6t3797y/TNnzkSvXr1w/vx5dOjQAQEBAfJ7RWZmJipXrowdO3bg0qVLmDZtGj777DNs374dALQ+5nnz5sHa2lq+OTk5vdHzQkRERET6Z6jvBhSXPXv2wMLCQv67ffv22LFjB1asWAEnJycsX74ckiShRo0a+PPPPzFx4kRMmzYNKpX29cF+/fph0KBB8t/p6emYP38+vvnmG8V6t2/fRosWLQAA7u7u8gfx4jZx4kR8/vnnimUvXrxAzZo15b9bt26tuH/16tWwsbHB0aNH0alTJ/z888+IjY3FwYMHUbFiRQDAF198UeDYPh988IHi7/Xr18Pe3h6XLl0qcJyrbt26oV69epg+fXqOXk+5mTt3Lvr06YOZM2fKy+rWrQsASExMRHBwMBITE+X2jxs3DgcOHEBwcDC++OKLArevrUGDBqFXr14Asp77pk2bYurUqfD19QUAfPrpp4rzZebMmZg0aRIGDhwIAHB1dcXs2bMxYcIETJ8+Pc/9rFu3Dn369IGBgQFq164NV1dX7NixQ9H7Kvs4O3bsKO+rVq1auHbtGmrUqIGlS5fCz88PEyZMAJB1Xp44cULRk+h1V65cgbW1NRwdHXPcZ2RkBFdXV1y5cgUGBgawtbUFAJQvXz5HL746derIx+fm5obly5fj8OHDaNeuHSIiIhAZGYm7d+/Kl8wtWrQIP/zwA7777jt5XKIXL15g48aNsLe3z7O9ukhPT8cXX3yBn3/+GU2bNgWQlUdERAS+/fZbeHt7IzExEfXr10ejRo0AZPWGeVOOjo5a/TKjzWt50aJFmDhxIvr06QMAWLBgAY4cOYIlS5bgv//9L3x8fPDpp5/i3r17MDQ0xKVLlzB16lSEh4dj+PDhCA8PR+PGjXO93M3IyAjW1taQJAkODg7y8qtXr2L37t04fvy4XETYvHkznJyc8MMPP6Bnz545tmVvbw9jY2OYmpoqtpWboj6HX1fQ/wdly5ZF+/btsWXLFrRp0wZAVlG4XLlycu/HwryOf/nlF0yaNAkzZ85Ep06dsG3bNvn/iJ07dyIoKEj+QeJ1iYmJGD9+PGrUqAEg6zX0qpcvX2LVqlWoVq0agKwfA2bNmgUgqxfflClTcP/+fdStWxfVqlVD+/btsWTJEnh4eMDU1BTp6ekF5qKNjIwM9O/fH7///jsiIiJQqVIlAFmFpYCAAHkAfjc3Nyxbtgze3t5YuXJljp6epqamsLCwgKGhYa7tCgoKQt++fQFk/d+0bNkyREZGws/PD2XKlFH8v+Di4oKTJ09i+/bt6NWrFywsLLQ65smTJ2PMmDHy3ykpKSyIEREREQnuX9szrFWrVoiKipJvy5YtAwBcvnwZTZs2VQzI3rx5c6SmpmrdEylb9pfibMbGxli2bBl27doFOzu7HOsHBgbi3LlzcqGmuI0fP17xHERFRWH48OGKdf766y8MGzYMbm5usLa2hpWVFVJTU5GYmAgg6/lycnKSC0kA5GJBfq5evYq+ffvC1dUVVlZWcuEge7sFWbBgATZs2IDLly8XuG5UVJT8RfV1MTExUKvVcHd3h4WFhXw7evSoVpdt6qJOnTryvytUqAAA8PLyUixLS0uTL7GJjo7GrFmzFO0aNmwY7ty5g2fPnuW6j0ePHiEsLAz9+/eXl/Xv3z/XouGr7ckuYN29exdAVq7vvPOOYn1tci0Kr7Yru23Z7YqOjkZqairs7OwUz0tCQoIir6pVqxZZIQzI6mn27NkztGvXTrHfjRs3yvv9v//7P4SGhqJevXqYMGECTpw48cb7ze6NVpCCXsspKSn4888/0bx5c8XjmjdvLr+GateuDVtbWxw9ehTHjh1D/fr10alTJ7nn29GjR3XupXb58mUYGhoqziU7Ozt4eHho9dotSHGfw9r8fxAQEICdO3ciPT0dQFaxr0+fPvIPJ4V5HU+aNAkff/wxxo0bh3Xr1qFz58748ccfAWS9Z7333nt5tnnMmDEYOnQo2rZti/nz5+d4HzMzM5MLYYDy9XX37l20aNEC27dvh42NDWbNmoW5c+cWy4Qko0ePxqlTp/Drr7/KhTAg6/kKCQlRPF++vr7IzMxEQkKCzvt59RwxNzeHlZWVfLwA8N///hcNGzaEvb09LCwssHr1aq3/H8pmbGwMKysrxY3EZvg498tySQzMT3zMUGzMT2yFHWLm3+hf2zPM3Nwc1atXL9RjVSpVjkHlcrvkx9zcPNfHd+nSBVOmTFH8klypUiVs2LChUO0prHLlyuV4DrJ77GQbOHAgkpOTsXTpUlStWhXGxsZo2rRpoQclz9a5c2dUrVoVa9askcfcqV27ttbbbdmyJXx9fTF58uQcPZ5eZ2pqmud9qampMDAwwNmzZ3PMmvFqz8Gi8OqlStlfrnNblt0TKDU1FTNnzkT37t1zbCuvN6ktW7YgLS1NUQTQaDTIzMzElStX4O7unm973uT6cHd3dzx+/Bh//vmnojgKZPVSio+Pl3vL5OfVdmW37dXnxNHRMddxe17tYZbXa6+wUlNTAWRdcvvqF3cAcg+19u3b4+bNm9i3bx9++ukntGnTBh9//DEWLVpUpG3JjTav5YJIkoSWLVsiPDwcxsbG8PHxQZ06dZCeno4LFy7gxIkTepsEIS9FfQ4XRufOnaHRaLB37140btwYx44dw9dffy3fX5jX8fnz5zF69GgAWefVunXr0LNnTyxfvhwhISH48ssv82zPjBkz0K9fP+zduxf79+/H9OnTERoaKo+TmNvrK/v/Mw8PD3h4eCjub9CgARo0aKDFM6Gbdu3aYevWrTh48KB8SSmQ9Xz95z//wciRI3M8pkqVKjrvJ7/3k9DQUIwbNw6LFy9G06ZNYWlpiYULFxbZpCNShhpQ/Ws/Rv1rqTLUqLD7zX/MIP1gfuJjhmJjfmJTZajh9vfVBfQvLoblxdPTEzt37oRGo5G/XB0/fhyWlpaoXLkygKzLeLIHHAayel3o+ot19iC+7u7uiI+Px+3bt/H777+jfv36RXQkReP48eNYsWIFOnToACBrkPT79+/L93t6euLWrVu4c+eO3DPjt99+y3ebycnJiIuLw5o1a+RLfwozo+b8+fNRr169HF/eXlenTh0cPnxYcQlitvr160OtVss9IkqSBg0aIC4uTqei7bp16zB27NgcBcKPPvoI69evx/z587XajqenZ44vhAXl+sEHH2DixIlYvHgxFi9erLhv1apVePr0qXy5kpGREYCsAax10aBBAyQlJcHQ0LBILkPUVs2aNWFsbIzExER4e3vnuZ69vT0GDhyIgQMHokWLFhg/fvxbKYYVxMrKChUrVsTx48cV7T9+/Lhi/Dxvb2+sWbMGxsbGmDt3LlQqFVq2bImFCxciPT09R8+yVxkZGeXI09PTExkZGTh16pR8mWT26//VSziLQ2HO4dy2UdD/ByYmJujevTs2b96Ma9euwcPDQ1E8KszruFKlSvj111/l18sHH3yA1NRUDBo0CHXq1Mn18tJXubu7w93dHaNHj0bfvn0RHBysmDREG7kVnHPLuLC6dOmCzp07o1+/fjAwMJAv323QoAEuXbqk0/NV2HZlX7770Ucfycte70n3JsesUXEqLRFpVBKeuVaE2fU/IWVyNi3RMD/xMUOxMT+xaVQSkpOTUbZsWZ2Gh/q3KnXPwEcffYRbt27hk08+QWxsLHbt2oXp06djzJgx8gnRunVrbNq0CceOHUNMTAwGDhyYo1dRfu7du4eIiAiMGzcOFy5cwJEjR+Dk5ISwsLDiOqxCc3Nzw6ZNm3D58mWcOnUKAQEBip5Wbdu2hbu7OwYOHIjo6GgcO3YMU6ZMyXebZcuWhZ2dHVavXo1r167hl19+UfSS05aXlxcCAgLkS1zzMn36dGzduhXTp0/H5cuXERMTgwULFgDI+tIYEBCAwMBAhIWFISEhAZGRkZg3bx727t2rc5uK0rRp07Bx40bMnDkTFy9exOXLlxEaGppjbKhsUVFROHfuHIYOHYratWsrbn379sWGDRsKnMEu28iRI3HgwAEsWrQIV69exfLlywsca6lKlSr48ssvsWTJEkyZMgWxsbGIj4/HV199hQkTJmDs2LFyj7WqVatCkiTs2bMH9+7dk3teFaRt27Zo2rQp/P39cejQIdy4cQMnTpzAlClTCj3xRExMjOLywldnpsxmaWmJcePGYfTo0diwYQPi4+Nx7tw5fPPNN3KPzmnTpmHXrl24du0aLl68iD179sDT01PeRps2beRJMwBg+fLlist3b9++jRo1aiAyMlJeNnnyZAQGBhbquF43fvx4LFiwANu2bUNcXBwmTZqEqKgofPrpp/I6Pj4+uHTpEi5evChfiufj44PNmzejUaNG+fa4c3Z2RmpqKg4fPoz79+/j2bNncHNzQ9euXTFs2DBEREQgOjoa/fv3R6VKldC1a9ciOa68FOYcfp02/x8AWZdK7t27F+vXr1f0cgJ0fx0DwIQJE7B69WrMnDkTsbGxOHXqFE6ePAkzMzPExsbi+PHjuT7u+fPnGDFiBMLDw3Hz5k0cP34cp0+fVpyHb8LZ2Rnnz59HXFwc7t+/n+ckCNrq1q0bNm3ahEGDBskzs06cOBEnTpyQJz64evUqdu3alecA+tntSkhIQFRUFO7fvy9fsloQNzc3nDlzBgcPHsSVK1cwdepUnD59Ose2C33M/BApJI1KhUdNa0LD/ITE/MTHDMXG/MSmUalw69atHFfBlVal7iyuVKkS9u3bh8jISNStWxfDhw/HkCFDFF9cJk+eDG9vb3Tq1AkdO3aEv7+/YgyWgpw8eRJ79uzBwoULUaZMGbRo0QJRUVEl8qRbt24dHj58iAYNGmDAgAEYOXIkypcvL9+vUqnw/fff4/nz52jSpAmGDh1a4CxpKpUKoaGhOHv2LGrXro3Ro0dj4cKFhWrfrFmzCrwsysfHBzt27MDu3btRr149tG7dWlFwCA4ORmBgIMaOHQsPDw/4+/vj9OnThbokpyj5+vpiz549OHToEBo3box3330XX3/9NapWrZrr+uvWrUPNmjXlgbNf1a1bN9y9exf79u3Tat/vvvsu1qxZg6VLl6Ju3bo4dOhQvl/es40aNQrff/89jh07hkaNGqF27drYsmULVq5cqeghValSJXlg8QoVKuT7RfdVkiRh3759aNmyJQYNGgR3d3f06dMHN2/elMdh01XLli1Rv359+dawYcNc15s9ezamTp2KefPmwdPTE35+fti7dy9cXFwAZPUgmTx5MurUqYOWLVvCwMAAoaGh8uPj4+MVvSrv37+v6IXy8uVLxMXFKcaRunPnjs7jF+Vl5MiRGDNmDMaOHQsvLy8cOHAAu3fvVgyw7uXlBRsbG9SrV0++TNjHxwdqtbrA8cKaNWuG4cOHo3fv3rC3t5cv5QsODkbDhg3RqVMnNG3aFBqNBvv27ctx+VpRK+w5/Cpt/j8Asn4gsbW1RVxcHPr166e4T9fXMZA1++q2bdvw448/omHDhujSpQtevHiB2NhYDBgwAP7+/rnOdmtgYIDk5GQEBgbC3d0dvXr1Qvv27RWDxL+JYcOGwcPDA40aNYK9vX2eRTld9OjRAxs2bMCAAQMQFhaGOnXq4OjRo7hy5QpatGiB+vXrY9q0aTkuvX7VBx98AD8/P7Rq1Qr29vbYunWrVvv+z3/+g+7du6N379545513kJycrOglBhTPMRMRERFRySdpSmKFppRzdnZGSEiIzoNZExERUfFKSUmBtbU1zpVvDqtMXiopmkxDA9zp2xqOW3+BKqNoLgumt4f5iY8Zio35iS3T0ABP9y+Bl5eXTle+FVb2Z6bHjx+XyAmISl3PMCIiIiIqvYz/TNZ3E+gNMD/xMUOxMT+xWVpa6rsJJUapG0CfiIiI6E1xNkkxqTLUKHf4nL6bQYXE/MTHDMXG/MSmylDrNPzTvx17hpVAo0aNeqsz6REREZFuOJukmDQqCSl1XZmfoJif+Jih2Jif2DQqCUlJSQWOyV1asBhWArEYRkREVMJxJi0haVQqPKlTjTOhCYr5iY8Zio35iU2jUiEpKalETuynDzyLiYiIiIiIiIio1OBskkRERERayp4Z6cGDByhbtqy+m0M6UqvViImJeWszaVHRYn7iY4ZiY35ie9v5cTZJIiIion8ZSeJ4KSKSJAm2trbMT1DMT3zMUGzMT2zMT4k9w4iIiIi0VNJ/5SQiIiIqCUr6Zyb2DCMiIiLSEWdiElNmZiYSExOZn6CYn/iYodiYn9iYnxKLYUREREQ6Ysd6MWk0Gjx48ID5CYr5iY8Zio35iY35KbEYRkREREREREREpQaLYUREREREREREVGqwGEZERESkI87EJCZJkuDg4MD8BMX8xMcMxcb8xMb8lDibJBEREZGWSvrMSEREREQlQUn/zMSeYUREREQ6UqvV+m4CFYJarUZ8fDzzExTzEx8zFBvzExvzU2IxjIiIiIhKjSdPnui7CfQGmJ/4mKHYmJ/YmN8/WAwjIiIiIiIiIqJSg8UwIiIiIiIiIiIqNVgMIyIiItIRZ2ISkyRJcHJyYn6CYn7iY4ZiY35iY35KhvpuABEREZFoVCr+nigilUoFOzs7fTeDCon5iY8Zio35iY35KfGTHBEREZGOOBOTmNRqNWJjY5mfoJif+Jih2Jif2JifEothRERERFRqpKWl6bsJ9AaYn/iYodiYn9iY3z9YDCMiIiIiIiIiolKDxTAiIiIiIiIiIio1WAwjIiIi0hEH0BeTSqWCq6sr8xMU8xMfMxQb8xMb81PibJJEREREOuK05GKSJAlWVlb6bgYVEvMTHzMUG/MTG/NTYkmQiIiISEeciUlMarUaMTExzE9QzE98zFBszE9szE+JxTAiIiIiKjX4JUBszE98zFBszE9szO8fLIYREREREREREVGpwWIYERERERERERGVGpJGo9HouxFEREREIkhJSYG1tTUePXoEa2trfTeHdKTRaJCWlgYTExNOgiAg5ic+Zig25ie2t51f9memx48fl8iB+zmbJBEREZGOrrv6wUrFj1Gi0QDQGBpAylCDX+PEw/zExwzFxvzEpgHgkhSu72aUGLxMkoiIiEhXhgb6bgEVgsbQAHf6toaG+QmJ+YmPGYqN+YlNY2iAmJgYZGZm6rspJQKLYUREREREREREVGqwGEZERERERERERKUGi2FERERERERERFRqcDZJIiIiIi1lz4x0zvZdDqAvIA7+LDbmJz5mKDbmJ7bsAfRVKhVnkwR7hhERERHpjt8CxCQBanMT5icq5ic+Zig25ic2CXjx4oW+W1FisBhGREREpCsDzqQlIo2BAe52aQYN8xMS8xMfMxQb8xObxsAAcXFxnE3ybyyGERERERERERFRqcFiGBER/SsVZkhMZ2dnhIeHF8n+JUnCDz/8UCTbKgliY2Px7rvvwsTEBPXq1dN3c4iIiIiICo3FMCIiKjJBQUGQJCnH7dq1a2+9LWvXri3ybuDh4eGQJAmPHj3KcZ+zszOWLFki/33nzh20b99eq+2KUDibPn06zM3NERcXh8OHD+e6zuv529nZwc/PD+fPn3/Lrc0/KyrdVC8z9N0EegPMT3zMUGzMT2wGvMRVxmIYEREVKT8/P9y5c0dxc3FxKbb9vXz5MtflK1asQERERLHttyAODg4wNjZ+q/vM67koCvHx8XjvvfdQtWpV2NnZ5bneq/kfPnwYhoaG6NSpU7G1S1+kDLW+m0CFoMpQwzH0CFTMT0jMT3zMUGzMT2yqDDW8vLxYEPsbi2FERFSkjI2N4eDgoLhl/6d79OhRNGnSBMbGxnB0dMSkSZOQkfHPL4yv964CgHr16mHGjBny35IkYeXKlejSpQvMzc0xd+7cHG2Ij49HVFQUvvvuu2I5Rm282tvrxYsXGDFiBBwdHWFiYoKqVati3rx5ALKOGQC6desGSZLkvwFg5cqVqFatGoyMjODh4YFNmzbl2Merz8WcOXNQvXp1LFq0SLFeVFRUvj30MjMzMWvWLFSuXBnGxsaoV68eDhw4oNjP2bNnMWvWLEiSpMjjda/mX69ePUyaNAm3bt3CvXv35HVu3bqFXr16wcbGBra2tujatStu3Lgh33/69Gm0a9cO5cqVg7W1Nby9vXHu3Dn5/hs3bkCSJERFRcnLHj16BEmSEB4ejhs3bqBVq1YAgLJly0KSJAQFBWHjxo2ws7NDenq6os3+/v4YMGBArseTnp6OlJQUxQ0ANJxJS0gaCUiraMf8BMX8xMcMxcb8xKaRgJSUlEINJfJvxGIYERG9Fbdv30aHDh3QuHFjREdHY+XKlVi3bh3mzJmj87ZmzJiBbt26ISYmBoMHD8aff/6JiIgI+bZ8+XIAQFhYmGL5mTNnivqwtLJs2TLs3r0b27dvR1xcHDZv3iwXvU6fPg0ACA4Oxp07d+S/v//+e3z66acYO3YsLly4gP/85z8YNGgQjhw5otj2q8/FkCFDMHjwYAQHByvWCQ4ORsuWLVG9evVc27d06VIsXrwYixYtwvnz5+Hr64suXbrg6tWrALIu+axVqxbGjh2LO3fuYNy4cVodd2pqKv73v/+hevXqcm+yly9fwtfXF5aWljh27BiOHz8OCwsL+Pn5ydN9P3nyBAMHDkRERAR+++03uLm5oUOHDnjy5IlW+3VycsLOnTsBAHFxcbhz5w6WLl2Knj17Qq1WY/fu3fK6d+/exd69ezF48OBctzVv3jxYW1vLNycnp6w7+KuqkDQGBkhu04AzoQmK+YmPGYqN+YlNY2CA69evczbJvxnquwFERPTvsmfPHlhYWMh/t2/fHjt27MCKFSvg5OSE5cuXQ5Ik1KhRA3/++ScmTpyIadOmQaXS/veZfv36YdCgQfLf6enpmD9/Pr755hvFerdv30aLFi0AAO7u7ti+ffsbHl2WypUr51j27NmzPNdPTEyEm5sb3nvvPUiShKpVq8r32dvbAwBsbGzg4OAgL1+0aBGCgoLw0UcfAQDGjBmD3377DYsWLZJ7PQE5n4ugoCBMmzYNkZGRaNKkCV6+fIktW7bk6C32qkWLFmHixIno06cPAGDBggU4cuQIlixZgv/+979wcHCAoaEhLCwsFG3Mzav5P336FI6OjtizZ4+c77Zt25CZmYm1a9dCkrJ+Wg4ODoaNjQ3Cw8Px/vvvo3Xr1optrl69GjY2Njh69KhWl1waGBjA1tYWAFC+fHnY2Ngonq/g4GD07NkTAPC///0PVapUgY+PT67bmjx5MsaMGSP/nZKS8k9BjIiIiIiExJ5hRERUpFq1aoWoqCj5tmzZMgDA5cuX0bRpU7kAAgDNmzdHamoq/vjjD5320ahRI8XfxsbGWLZsGXbt2pXreFaBgYE4d+4c6tatW4gjyunYsWOKY4yKikLFihXzXD8oKAhRUVHw8PDAyJEjcejQoQL3cfnyZTRv3lyxrHnz5rh8+bJi2evPRcWKFdGxY0esX78eAPDjjz8iPT1dLv68LiUlBX/++adW+9LGq/lHRkbC19cX7du3x82bNwEA0dHRuHbtGiwtLWFhYQELCwvY2toiLS0N8fHxAIC//voLw4YNg5ubG6ytrWFlZYXU1FQkJibq3J7XDRs2DIcOHcLt27cBACEhIfLA/7kxNjaGlZWV4kZEREREYmPPMCIiKlLm5uZ5Xo5XEJVKlWMcg9wGhTc3N8/18V26dMGUKVMUPXkqVaqEDRs2FKo9eXFxcVH0NgIAQ8O8/0tt0KABEhISsH//fvz888/o1asX2rZtWyRjmuX2XAwdOhQDBgzA119/jeDgYPTu3RtmZmZvvC9t2/Nq/mvXroW1tTXWrFmDOXPmIDU1FQ0bNsTmzZtzPDa7l9zAgQORnJyMpUuXomrVqjA2NkbTpk3lyyize5m9eq5oO3lA/fr1UbduXWzcuBHvv/8+Ll68iL179xb6eEk8ho+f6rsJ9AaYn/iYodiYn9hMTEz03YQSgz3DiIjorfD09MTJkycVBYzjx4/D0tJSvuzQ3t4ed+7cke9PSUlBQkKCTvvJHg/K3d0dBgYGuH37Nn7//fciOII3Y2Vlhd69e2PNmjXYtm0bdu7ciQcPHgAAypQpA7VaOTOTp6cnjh8/rlh2/Phx1KxZs8B9dejQAebm5li5ciUOHDiQ53hY2e2qWLFiofdVEEmSoFKp8Pz5cwBZhcGrV6+ifPnyqF69uuJmbW0t73vkyJHo0KEDatWqBWNjY9y/f1/eZnbR7NVz5dXB9AHAyMgIAHI8r0BWsTAkJATBwcFo27ZtoS575GySYlJlqFFh9wnOhCYo5ic+Zig25ic2VYYaNWrU4GySf2MxjIiI3oqPPvoIt27dwieffILY2Fjs2rUL06dPx5gxY+SePq1bt8amTZtw7NgxxMTEYODAgTr9h33v3j1ERERg3LhxuHDhAo4cOQInJyeEhYUV12Fp5auvvsLWrVsRGxuLK1euYMeOHXBwcJB7lzk7O+Pw4cNISkrCw4cPAQDjx49HSEgIVq5ciatXr+Krr75CWFiYVoPXGxgYICgoCJMnT4abmxuaNm2a7/rjx4/HggULsG3bNsTFxWHSpEmIiorCp59+qvOxpqenIykpCUlJSbh8+TI++eQTpKamonPnzgCAgIAAlCtXDl27dsWxY8eQkJCA8PBwjBw5Ur5c1s3NDZs2bcLly5dx6tQpBAQEwNTUVN6Hqakp3n33XcyfPx+XL1/G0aNH8fnnnyvaUbVqVUiShD179uDevXtITU2V7+vXrx/++OMPrFmzJt9CYX40Kk6lJSKNSsLT6pWYn6CYn/iYodiYn9g0KgnJyckcQP9vLIYREdFbUalSJezbtw+RkZGoW7cuhg8fjiFDhiiKGJMnT4a3tzc6deqEjh07wt/fH9WqVdN6HydPnsSePXuwcOFClClTBi1atEBUVJTep5C2tLTEl19+iUaNGqFx48a4ceMG9u3bJxcBFy9ejJ9++glOTk6oX78+AMDf3x9Lly7FokWLUKtWLXz77bcIDg7Oc6D31w0ZMgQvXrxQDK6fl5EjR2LMmDEYO3YsvLy8cODAAezevRtubm46H+uBAwfg6OgIR0dHvPPOOzh9+jR27Nght9vMzAy//vorqlSpgu7du8PT0xNDhgxBWlqaPB7XunXr8PDhQzRo0AADBgzAyJEjUb58ecV+1q9fj4yMDDRs2BCjRo3KMStppUqVMHPmTEyaNAkVKlTAiBEj5Pusra3xwQcfwMLCAv7+/jofIwBAhwkfqOTQqFR41LQmNMxPSMxPfMxQbMxPbBqVCrdu3dL75+KSQtLwmSAiIgKQ1UMrJCRE64JTSXbs2DG0adMGt27dQoUKFfTdnBKnTZs2qFWrljzBg7ZSUlJgbW2Nc+WbwyqTv4yLJtPQAHf6tobj1l94mY+AmJ/4mKHYmJ/YMg0N8HT/Enh5eb2VSyWzPzM9fvy4RE5AxAH0iYiI/kXS09Nx7949zJgxAz179mQh7DUPHz5EeHg4wsPDsWLFCn03h4iIiIj0gMUwIiKif5GtW7diyJAhqFevHjZu3Kjv5pQ49evXx8OHD7FgwQJ4eHjouzmkB8Z/Juu7CfQGmJ/4mKHYmJ/YLC0t9d2EEoOXSRIREf1tyZIl8Pf3h7Ozs76bQiVUdpf/323fhaWKvykSERGROKrdO/bW9lXSL5PkyHdERER/GzVqFAthpBXOpCUmjUpCSl1X5ico5ic+Zig25ic2jUpCUlISZ5P8G4thRERERLriTFpC0qhUeFKnGmdCExTzEx8zFBvzE5tGpUJSUhJnk/wbL5MkIiIi0lJ2l/8HDx6gbNmy+m4O6UitViMmJuatzaRFRYv5iY8Zio35ie1t58fLJImIiIiIiIiIiEoIFsOIiIiIdCRJHC9FRJIkwdbWlvkJivmJjxmKjfmJjfkp8TJJIiIiIi2V9C7/RERERCVBSf/MxJ5hRERERDriTExiyszMRGJiIvMTFPMTHzMUG/MTG/NTYjGMiIiISEfsWC8mjUaDBw8eMD9BMT/xMUOxMT+xMT8lFsOIiIiIiIiIiKjUYDGMiIiIiIiIiIhKDRbDiIiIiHTEmZjEJEkSHBwcmJ+gmJ/4mKHYmJ/YmJ8SZ5MkIiIi0lJJnxmJiIiIqCQo6Z+Z2DOMiIiISEdqtVrfTaBCUKvViI+PZ36CYn7iY4ZiY35iY35KLIYRERERUanx5MkTfTeB3gDzEx8zFBvzExvz+weLYUREREREREREVGqwGEZERERERERERKUGi2FEREREOuJMTGKSJAlOTk7MT1DMT3zMUGzMT2zMT8lQ3w0gIiIiEo1Kxd8TRaRSqWBnZ6fvZlAhMT/xMUOxMT+xMT8lfpIjIiIi0hFnYhKTWq1GbGws8xMU8xMfMxQb8xMb81NiMYyIiIiISo20tDR9N4HeAPMTHzMUG/MTG/P7B4thRERERERERERUarAYRkREREREREREpQaLYUREREQ64gD6YlKpVHB1dWV+gmJ+4mOGYmN+YmN+SpxNkoiIiEhHnJZcTJIkwcrKSt/NoEJifuJjhmJjfmJjfkosCRIRERHpiDMxiUmtViMmJob5CYr5iY8Zio35iY35KbEYRkRERESlBr8EiI35iY8Zio35iY35/YPFMCIiIiIiIiIiKjVYDCMiIiIiIiIiolKDA+gTERER6eiGWwc8kPgxSjQaCTCxMkdCylNIGn23hnTF/MTHDMXG/MSmkQCPm4c4m+Tf+CwQERER6YpfAsSkAQyepjE/UTE/8TFDsTE/sWkAIyMjfbeixGAxjIiIiEhXhgb6bgEVgsbQAHf6toaG+QmJ+YmPGYqN+YlNY2iAmJgYZGZm6rspJQKLYUREREREREREVGqwGEZERERERERERKUGi2FERERERERERFRqSBqNhsPfEREREWkhJSUF1tbWOGf7LqxUnE1SNBpkjZkiZagh6bsxpDPmJz5mKDbmJzYNAJekcKhUKkhS8SeY/Znp8ePHsLKyKvb96Yo9w4iIiIh0xW8BYpIAtbkJ8xMV8xMfMxQb8xObBLx48ULfrSgxWAwjIiIi0pUBZ9ISkcbAAHe7NIOG+QmJ+YmPGYqN+YlNY2CAuLg4zib5NxbDiIio1CjMyADOzs4IDw8v+sYUEWdnZyxZskSvbfDx8cGoUaO0Xj8oKAj+/v7F1h4iIiIiovywGEZERMUqKCgIkiTluF27du2tt2Xt2rVF/mtYeHh4rscnSRKSkpKKdF/FLb9jyb7lVhgMCwvD7Nmztd7P0qVLERIS8sbtvX37Nvr37w87OzuYmprCy8sLZ86cyfcx4eHhaNCgAYyNjVG9evUiaQcRERERiYUjvxIRUbHz8/NDcHCwYpm9vX2x7e/ly5coU6ZMjuUrVqyAh4cHWrZsWeT7jIuLyzE4aPny5Qu9vRcvXsDIyOhNm6WTZs2a4c6dO/Lfn376KVJSUhTZ2dra5mjjq8u0YW1t/cZtffjwIZo3b45WrVph//79sLe3x9WrV1G2bNk8H5OQkICOHTti+PDh2Lx5Mw4fPoyhQ4fC0dERvr6+b9wmEoPqZYa+m0BvgPmJjxmKjfmJzYCXuMrYM4yIiIqdsbExHBwcFLfs/4yPHj2KJk2awNjYGI6Ojpg0aRIyMv75oJXbZYD16tXDjBkz5L8lScLKlSvRpUsXmJubY+7cuTnaEB8fj6ioKHz33XfFcozly5fPcYwqVdZ/s7ldRujv74+goCD5b2dnZ8yePRuBgYGwsrLChx9+CACIiIhAixYtYGpqCicnJ4wcORJPnz7Nsx1r166FjY0NDh8+DAD46quv4OXlBXNzczg5OeGjjz5Campqro81MjJStN/U1FSR3apVq9CkSROsXbsWLi4uMDExyXF8n332Gd55550c265bty5mzZoFIOdlkgcOHMB7770HGxsb2NnZoVOnToiPj8/7yQawYMECODk5ITg4GE2aNIGLiwvef/99VKtWLc/HrFq1Ci4uLli8eDE8PT0xYsQI9OjRA19//XWej0lPT0dKSoriBgBShjrf9lHJpMpQwzH0CFTMT0jMT3zMUGzMT2yqDDW8vLxYEPsbi2FERKQ3t2/fRocOHdC4cWNER0dj5cqVWLduHebMmaPztmbMmIFu3bohJiYGgwcPxp9//omIiAj5tnz5cgBZl/S9urygy+repkWLFqFu3br4/fffMXXqVMTHx8PPzw8ffPABzp8/j23btiEiIgIjRozI9fFffvklJk2ahEOHDqFNmzYAAJVKhWXLluHixYvYsGEDfvnlF0yYMKHQbbx27Rp27tyJsLAwREVF5bg/ICAAkZGRimLWxYsXcf78efTr1y/XbT59+hRjxozBmTNncPjwYahUKnTr1i3fS1p3796NRo0aoWfPnihfvjzq16+PNWvW5Nv2kydPom3btoplvr6+OHnyZJ6PmTdvHqytreWbk5MTAEDDmbSEpJGAtIp2zE9QzE98zFBszE9sGglISUkp1Bi6/0YshhERUbHbs2cPLCws5FvPnj0BZF226OTkhOXLl6NGjRrw9/fHzJkzsXjxYp3H9urXrx8GDRoEV1dXVKlSBXZ2dti+fTtatGiBFi1ayL3Lbt++LS8bMmRIrpdTFkblypUVx1irVi2dt9G6dWuMHTsW1apVQ7Vq1TBv3jwEBARg1KhRcHNzQ7NmzbBs2TJs3LgRaWlpisdOnDgRS5YskXvaZRs1ahRatWoFZ2dntG7dGnPmzMH27dsLfZwvXrzAxo0bUb9+fdSpUyfH/bVq1ULdunWxZcsWednmzZvxzjvvoHr16rlu84MPPkD37t1RvXp11KtXD+vXr0dMTAwuXbqUZzuuX7+OlStXws3NDQcPHsT//d//YeTIkdiwYUOej0lKSkKFChUUyypUqICUlBQ8f/4818dMnjwZjx8/lm+3bt3KuoO/qgpJY2CA5DYNOBOaoJif+Jih2Jif2DQGBrh+/Tpnk/wbxwwjIqJi16pVK6xcuVL+29zcHABw+fJlNG3aFJL0z0+MzZs3R2pqKv744w9UqVJF6300atRI8bexsTGWLVuGtm3bYvDgwUhOTlbcHxgYiBUrVshteVPHjh2DpaWl/HdhimyvH0N0dDTOnz+PzZs3y8s0Gg0yMzORkJAAT09PAMDixYvx9OlTnDlzBq6uropt/Pzzz5g3bx5iY2ORkpKCjIwMpKWl4dmzZzAzM9O5jVWrVi1wvLeAgACsX78eU6dOhUajwdatWzFmzJg817969SqmTZuGU6dO4f79+/KHtMTERNSuXTvXx2RmZqJRo0b44osvAAD169fHhQsXsGrVKgwcOFDn48qLsbExjI2Ni2x7RERERKR/7BlGRETFztzcHNWrV5dvjo6OWj9WpVLl6M798uXLXPeRmy5dumDKlCmKZZUqVcKGDRuKrBAGAC4uLopjrFq1qnxfYY8hNTUV//nPfxAVFSXfoqOjcfXqVcXYWC1atIBarc7R4+vGjRvo1KkT6tSpg507d+Ls2bP473//CyCrh1dhaPOc9e3bF3FxcTh37hxOnDiBW7duoXfv3nmu37lzZzx48ABr1qzBqVOncOrUqQLb6OjoiJo1ayqWeXp6IjExMc/HODg44K+//lIs++uvv2BlZQVTU9MCj4uIiIiI/h3YM4yIiPTG09MTO3fuhEajkXuHHT9+HJaWlqhcuTKArFknX53hMCUlBQkJCTrtZ/fu3QAAd3d3xMfH4/bt2/j9999Rv379IjqS/L1+DGq1GhcuXECrVq3yfVyDBg1w6dKlPC8vzNakSROMGDECfn5+MDQ0xLhx4wAAZ8+eRWZmJhYvXiwP5v8ml0hqq3LlyvD29sbmzZvx/PlztGvXLs+ZNZOTkxEXF4c1a9agRYsWALImDShI8+bNERcXp1h25coVRRHydU2bNsW+ffsUy3766Sc0bdq0wP3Rv4fh47wnoKCSj/mJjxmKjfmJLXvyI2LPMCIi0qOPPvoIt27dwieffILY2Fjs2rUL06dPx5gxY+TiTevWrbFp0yYcO3YMMTExGDhwoE6z4Ny7dw8REREYN24cLly4gCNHjsDJyQlhYWFFeix3795FUlKS4pbd+6t169bYu3cv9u7di9jYWPzf//0fHj16VOA2J06ciBMnTmDEiBGIiorC1atXsWvXrlwH0G/WrBn27duHmTNnyuOjVa9eHS9fvsQ333yD69evY9OmTVi1alVRHnaeAgICEBoaih07diAgICDP9cqWLQs7OzusXr0a165dwy+//JLvJZXZRo8ejd9++w1ffPEFrl27hi1btmD16tX4+OOP5XUmT56MwMBA+e/hw4fj+vXrmDBhAmJjY7FixQps374do0eP1vn4OJukmFQZalTYfYIzoQmK+YmPGYqN+YlNlaFGjRo1OJvk31gMIyIivalUqRL27duHyMhI1K1bF8OHD8eQIUPw+eefy+tMnjwZ3t7e6NSpEzp27Ah/f3/FJYIFOXnyJPbs2YOFCxeiTJkyaNGiBaKioop8Jh0PDw84OjoqbmfPngUADB48GAMHDkRgYCC8vb3h6upaYK8wAKhTpw6OHj2KK1euoEWLFqhfvz6mTZuGihUr5rr+e++9h7179+Lzzz/HN998g7p16+Krr77CggULULt2bWzevBnz5s0r0uPOS48ePZCcnIxnz57B398/z/VUKhVCQ0Nx9uxZ1K5dG6NHj8bChQsL3H7jxo3x/fffY+vWrahduzZmz56NJUuWKApvd+7cUVw26eLigr179+Knn35C3bp1sXjxYqxduxa+vr46H59Gxam0RKRRSXhavRLzExTzEx8zFBvzE5tGJSE5OZkD6P9N0nBeTSIiojw5OzsjJCQEPj4++m4KlQApKSmwtrbGufLNYZXJLwOiyTQ0wJ2+reG49Rf2bBAQ8xMfMxQb8xNbpqEBnu5fAi8vr7fSOyz7M9Pjx49hZWVV7PvTFXuGERERERERERFRqcFiGBERERERERERlRoshhEREeVj1KhRcHZ21ncziKiIGP+ZrO8m0BtgfuJjhmJjfmKztLTUdxNKDI4ZRkRERKSl7PEvfrd9F5YqQ303h4iIiEhr1e4de2v74phhRERERP8ynElLTBqVhJS6rsxPUMxPfMxQbMxPbBqVhKSkJM4m+Tf+pElERESkI+cre1G2bFl9N4N0pFarERMTA5e3NJMWFS3mJz5mKDbmJ7bs/Ozt7fXdlBKBPcOIiIiIiIiIiKjUYDGMiIiIiIiIiIhKDRbDiIiIiHQkSRwvRUSSJMHW1pb5CYr5iY8Zio35iY35KXE2SSIiIiItlfSZkYiIiIhKgpL+mYk9w4iIiIh0xJmYxJSZmYnExETmJyjmJz5mKDbmJzbmp8RiGBEREZGO2LFeTBqNBg8ePGB+gmJ+4mOGYmN+YmN+SiyGERERERERERFRqcFiGBERERERERERlRoshhERERHpiDMxiUmSJDg4ODA/QTE/8TFDsTE/sTE/Jc4mSURERKSlkj4zEhEREVFJUNI/M7FnGBEREZGO1Gq1vptAhaBWqxEfH8/8BMX8xMcMxcb8xMb8lFgMIyIiIqJS48mTJ/puAr0B5ic+Zig25ic25vcPFsOIiIiIiIiIiKjUYDGMiIiIiIiIiIhKDRbDiIiIiHTEmZjEJEkSnJycmJ+gmJ/4mKHYmJ/YmJ+Sob4bQERERCQalYq/J4pIpVLBzs5O382gQmJ+4mOGYmN+YmN+SvwkR0RERKQjzsQkJrVajdjYWOYnKOYnPmYoNuYnNuanxGIYEREREZUaaWlp+m4CvQHmJz5mKDbmJzbm9w8Ww4iIiIiIiIiIqNRgMYyIiIiIiIiIiEoNFsOIiIiIdMQB9MWkUqng6urK/ATF/MTHDMXG/MTG/JQ4myQRERGRjjgtuZgkSYKVlZW+m0GFxPzExwzFxvzExvyUWBIkIiIi0hFnYhKTWq1GTEwM8xMU8xMfMxQb8xMb81NiMYyIiIiISg1+CRAb8xMfMxQb8xMb8/sHi2FERERERERERFRqcMwwIiIiIh3dqNEZDzI5bphoMg0NkNK3Na5vHQVVBn8dFw3zEx8zFBvzE1umoQGwf4m+m1FiSBqNRqPvRhARERGJICUlBdbW1jhn9y6sJP6mKBqNBGRYmcMw5SkkfgIWDvMTHzMUG/MTm0YCKt08BBMTk7cyEVD2Z6bHjx+XyIH7eZkkERERka74JUBMGsDgaRrzExXzEx8zFBvzE5sGMDIy0ncrSgwWw4iIiIh0ZWig7xZQIWgMDXCnb2tomJ+QmJ/4mKHYmJ/YNIYGiImJQWZmpr6bUiKwGEZERERERERERKUGi2FERERERERERFRqsBhGRERERERERESlBmeTJCIiItKSPJuk7buwUnE2SdFokDVmipShRvHPo0VFjfmJjxmKjfmJTQPAJSkcKpWKs0mCPcOIiIiIdMdvAWKSALW5CfMTFfMTHzMUG/MTmwS8ePFC360oMVgMI6JSozAdYZ2dnREeHv7G+3Z2dsaSJUvkvyVJwg8//KD148PDwyFJEh49evTGbQGy/iOsXr06Tpw4ofVjgoKC4O/vXyT7L01EfN58fHwwatQo+e/Xz99/i1WrVqFz586Fe7ABZ9ISkcbAAHe7NIOG+QmJ+YmPGYqN+YlNY2CAuLg4zib5NxbDiKhYBQUFQZKkHLdr16699basXbu2WN/8a9SoAWNjYyQlJRW47p07d9C+ffsi3f+MGTNQr149rdZdtWoVXFxc0KxZM4SEhOSa0au3GzduFGlbX/f777+jd+/ecHR0hLGxMapWrYpOnTrhxx9/LFQRUx9u3LgBSZIQFRWlWL506VKEhITopU3ZRdT8brkVe8PCwjB79uy332Ad7NixA82aNQMAnDhxAq6uror7o6Oj0bdvXzg5OcHU1BSenp5YunSpYp3Bgwfj3LlzOHbs2FtrNxERERHpH4thRFTs/Pz8cOfOHcXNxcWl2Pb38uXLXJevWLECERERxbLPiIgIPH/+HD169MCGDRsKXN/BwQHGxsbF0paCaDQaLF++HEOGDAEA9O7dW5FN06ZNMWzYMMUyJyenYmvPrl278O677yI1NRUbNmzA5cuXceDAAXTr1g2ff/45Hj9+XGz71sabdie3traGjY1N0TRGR82aNVPk2KtXrxyvx+yCEvDPsdra2sLS0lIvbdbWyZMn0bx5cwDAsWPH5H9nO3v2LMqXL4///e9/uHjxIqZMmYLJkydj+fLl8jpGRkbo168fli1b9lbbTkRERET6xWIYERU7Y2NjODg4KG4Gf3evPnr0KJo0aQJjY2M4Ojpi0qRJyMjIkB+b2+VZ9erVw4wZM+S/JUnCypUr0aVLF5ibm2Pu3Lk52hAfH4+oqCh89913xXKM69atQ79+/TBgwACsX7++wPVfv0zyxIkTqFevHkxMTNCoUSP88MMPufYyOnv2LBo1agQzMzM0a9YMcXFxAICQkBDMnDkT0dHRco+fvHojnT17FvHx8ejYsSMAwNTUVJGNkZERzMzMcs0LABYtWgRHR0fY2dnh448/VhQf09PTMW7cOFSqVAnm5uZ455138r3M9OnTpxgyZAg6duyIvXv34v3334erqys8PT0xZMgQREdHw9raWl7/woULaN++PSwsLFChQgUMGDAA9+/fl+/38fHByJEjMWHCBNja2sLBwUFxrgDAo0ePMHToUNjb28PKygqtW7dGdHS0fH92D7u1a9fCxcUFJiYmAIADBw7gvffeg42NDezs7NCpUyfEx8fLj8su8NavXx+SJMHHxwdAzssk09PTMXLkSJQvXx4mJiZ47733cPr0afn+7N5chw8fzjVrIKvXU6tWrWBpaQkrKys0bNgQZ86cyfH8GhkZKXI0NTVVvB5XrVqFJk2a5DjW1y+TfF1iYiK6du0KCwsLWFlZoVevXvjrr78U68yZMwfly5eHpaUlhg4dikmTJsk9F3/99VeUKVMmRy/KUaNGoUWLFnnu91UnTpyQC2ARERE5imGDBw/G0qVL4e3tDVdXV/Tv3x+DBg1CWFiYYr3OnTtj9+7deP78ea77SU9PR0pKiuJGYlO9zCh4JSqxmJ/4mKHYmJ/YDHiJq4zFMCLSm9u3b6NDhw5o3LgxoqOjsXLlSqxbtw5z5szReVszZsxAt27dEBMTg8GDB+PPP/9ERESEfMvuDRIWFqZYnlsBQVdPnjzBjh070L9/f7Rr1w6PHz/W6bKrlJQUdO7cGV5eXjh37hxmz56NiRMn5rrulClTsHjxYpw5cwaGhoYYPHgwgKzeXWPHjkWtWrXkHj+9e/fOdRvHjh2Du7t7oXr+HDlyBPHx8Thy5Ag2bNiAkJAQRdFtxIgROHnyJEJDQ3H+/Hn07NkTfn5+uHr1aq7bO3ToEJKTkzFhwoQ895k9282jR4/QunVr1K9fH2fOnMGBAwfw119/oVevXor1N2zYAHNzc5w6dQpffvklZs2ahZ9++km+v2fPnrh79y7279+Ps2fPokGDBmjTpg0ePHggr3Pt2jXs3LkTYWFhckHy6dOnGDNmDM6cOYPDhw9DpVKhW7du8qW3kZGRAICff/4Zd+7cyVF0yTZhwgTs3LkTGzZswLlz51C9enX4+voq9g/knTUABAQEoHLlyjh9+jTOnj2LSZMmoUyZMnk+h/nJ7Vjzk5mZia5du+LBgwc4evQofvrpJ1y/fl1xvm3evBlz587FggULcPbsWVSpUgUrV66U72/ZsiVcXV2xadMmednLly+xefNmxXG+bsuWLbCxsYGNjQ0iIyMxYMAA2NjYYN++fRg3bhxsbGywZcuWPB//+PFj2NraKpY1atQIGRkZOHXqVK6PmTdvHqytreVbdi9JKUOd/xNFJZIqQw3H0CNQMT8hMT/xMUOxMT+xqTLU8PLyYkHsb5wTnIiK3Z49e2BhYSH/3b59e+zYsQMrVqyAk5MTli9fDkmSUKNGDfz555+YOHEipk2bBpVK+3p9v379MGjQIPnv9PR0zJ8/H998841ivdu3b8s9T9zd3bF9+/Y3PDogNDQUbm5uqFWrFgCgT58+WLdundY9XLZs2QJJkrBmzRqYmJigZs2auH37NoYNG5Zj3blz58Lb2xsAMGnSJHTs2BFpaWkwNTWFhYUFDA0N4eDgkO/+bt68iYoVK+p4lFnKli2L5cuXw8DAADVq1EDHjh1x+PBhDBs2DImJiQgODkZiYqK8/XHjxuHAgQMIDg7GF198kWN7V65cAQB4eHjIy06fPo1WrVrJf4eGhqJTp05Yvnw56tevr9jO+vXr4eTkhCtXrsDd3R0AUKdOHUyfPh0A4ObmhuXLl+Pw4cNo164dIiIiEBkZibt378qXqS5atAg//PADvvvuO3z44YcAsi4X3LhxI+zt7eV9ffDBB4q2r1+/Hvb29rh06RJq164tr2tnZ5dnBk+fPsXKlSsREhIijxm3Zs0a/PTTT1i3bh3Gjx8vr5tX1iYmJkhMTMT48eNRo0YN+TgLK7djzc/hw4cRExODhIQEuTC0ceNG1KpVC6dPn0bjxo3xzTffYMiQIfJrctq0aTh06BBSU1Pl7QwZMgTBwcHyMf/4449IS0vLUdx8VZcuXdCsWTP8/PPPWLJkCfbs2YPz589j+PDh8mQQ5cqVy/WxJ06cwLZt27B3717FcjMzM1hbW+PmzZu5Pm7y5MkYM2aM/HdKSgqcnJyg4UxaQtJIQLqjHYzvJEMSYzhCegXzEx8zFBvzE5tGyvocY2lpKf/YXJqxZxgRFbtWrVohKipKvmWPz3P58mU0bdpU8WbcvHlzpKam4o8//tBpH40aNVL8bWxsjGXLlmHXrl2ws7PLsX5gYCDOnTuHunXrFuKIlNavX4/+/fvLf/fv3x87duzAkydPtHp8XFwc6tSpI1+iBgBNmjTJdd06derI/3Z0dAQA3L17V6f2Pn/+XLEvXdSqVUvxa5Kjo6O8/5iYGKjVari7u8PCwkK+HT16VHE5YUHq1KkjnytPnz6VL5uNjo7GkSNHFNvOLga9uv1Xn6PX2xgdHY3U1FTY2dkptpOQkKDYRtWqVXMUh65evYq+ffvC1dUVVlZWcHZ2BpB1yaC24uPj8fLlS8UlfWXKlEGTJk1w+fLlHM/Dq8cA/JP1mDFjMHToULRt2xbz58/X6fl9XW7Hmp/Lly/DyclJMY5czZo1YWNjIx9DXFxcjnP49b+DgoJw7do1/PbbbwCyLvXt1asXzM3N89y3hYUFnJ2dce7cOXTt2hXOzs6IiYlBhw4d4OzsDGdnZ0XhPduFCxfQtWtXTJ8+He+//36O+01NTfHs2bNc92lsbAwrKyvFDQBnkxSUxsAAyW0acCY0QTE/8TFDsTE/sWkMDHD9+nXOJvk39gwjomJnbm6O6tWrF+qxKpUqx2yCuQ2Qn9cX6C5dumDKlCmKnh2VKlXSapB7bVy6dAm//fYbIiMjFZc2qtVqhIaG5tq76028eilcdhFR1//QypUrh5iYmDfef3YbsvefmpoKAwMDnD17Nkf369wKFMA/PZri4uLw7rvvAsgqPuR2vqSmpqJz585YsGBBjvuyi0XatNHR0THXccxeHeQ+t/Opc+fOqFq1KtasWYOKFSsiMzMTtWvXfuMB9vOSX9YzZsxAv379sHfvXuzfvx/Tp09HaGgounXrpvN+8is+Fafy5cujc+fOCA4OhouLC/bv35/v+HKJiYmoWbMmACAtLQ2GhoZYunQp0tPToVKpEBoaiv79+2PVqlWKx126dAlt2rTBhx9+iM8//zzXbT948ECngiARERERiY09w4hIbzw9PXHy5ElFsev48eOwtLRE5cqVAQD29va4c+eOfH9KSgoSEhJ02s/u3bsBZF0WaWBggNu3b+P3338vgiPIGji/ZcuWiI6OVvR+GzNmDNatW6fVNjw8PBATE4P09HR52asDqmvLyMgIanXBYzjUr18fsbGxOYqMb6p+/fpQq9W4e/cuqlevrrjlddng+++/D1tb21wLXK9r0KABLl68CGdn5xzb17ag06BBAyQlJcHQ0DDHNvK6vA4AkpOTERcXh88//xxt2rSBp6cnHj58qFjHyMgIAPLNoFq1ajAyMsLx48flZS9fvsTp06flQo+23N3dMXr0aBw6dAjdu3dHcHCwTo8vLE9PT9y6dQu3bt2Sl126dAmPHj2Sj8HDwyPHOZzbOT106FBs27YNq1evRrVq1XIMgv+qihUrIioqCgcOHIChoSGioqLkcb6OHTuGqKgozJo1S/GYixcvolWrVhg4cGCuE2sAWb310tLSUL9+fe2eACIiIiISHothRKQ3H330EW7duoVPPvkEsbGx2LVrF6ZPn44xY8bI44W1bt0amzZtwrFjxxATE4OBAwfqNOjjvXv3EBERgXHjxuHChQs4cuQInJyc8hzcXBcvX77Epk2b0LdvX9SuXVtxGzp0KE6dOoWLFy8WuJ1+/fohMzMTH374IS5fvoyDBw9i0aJFAKDT9fzOzs5ISEhAVFQU7t+/ryiuvapVq1ZITU3Vqm26cHd3R0BAAAIDAxEWFoaEhARERkZi3rx5OcZpymZhYYG1a9di79696NixIw4ePIjr16/j/Pnz+PLLLwH8M+vNxx9/jAcPHqBv3744ffo04uPjcfDgQQwaNEirIiAAtG3bFk2bNoW/vz8OHTqEGzdu4MSJE5gyZUq+kymULVsWdnZ2WL16Na5du4ZffvlF0dsQyOrpZGpqKg/s//jx4xzbMTc3x//93/9h/PjxOHDgAC5duoRhw4bh2bNnGDJkiFbH8Pz5c4wYMQLh4eG4efMmjh8/jtOnT8PT01Orx7+ptm3bwsvLCwEBATh37hwiIyMRGBgIb29v+XLlTz75BOvWrcOGDRtw9epVzJkzB+fPn89xPvv6+sLKygpz5sxRjPmXm+wC5h9//IF33nkHNWrUQHJyMlxdXdGkSRNUr14d5cuXl9e/cOECWrVqhffffx9jxoxBUlISkpKScO/ePcV2jx07BldXV1SrVq2IniEq6QwfP9V3E+gNMD/xMUOxMT+xFXaolH8jFsOISG8qVaqEffv2ITIyEnXr1sXw4cMxZMgQxaVMkydPhre3Nzp16oSOHTvC399fpy+tJ0+exJ49e7Bw4UKUKVMGLVq0QFRUVJH0itq9ezeSk5NzvTTN09MTnp6eWvUOs7Kywo8//oioqCjUq1cPU6ZMwbRp0wDo9h/WBx98AD8/P7Rq1Qr29vbYunVrruvZ2dmhW7du2Lx5s9bb1lZwcDACAwMxduxYeHh4wN/fH6dPn0aVKlXyfEy3bt1w4sQJmJmZITAwEB4eHmjdujV++eUXefB8IKtn0PHjx6FWq/H+++/Dy8sLo0aNgo2NjdaTLUiShH379qFly5YYNGgQ3N3d0adPH9y8eRMVKlTI83HZl+GdPXsWtWvXxujRo7Fw4ULFOoaGhli2bBm+/fZbVKxYEV27ds11W/Pnz8cHH3yAAQMGoEGDBrh27RoOHjyIsmXLanUMBgYGSE5ORmBgINzd3dGrVy+0b98eM2fO1Orxb0qSJOzatQtly5ZFy5Yt0bZtW7i6umLbtm3yOgEBAZg8eTLGjRuHBg0aICEhAUFBQTnOZ5VKhaCgIKjVagQGBmq1//DwcLRs2RIAcPToUfnfr/vuu+9w7949/O9//4Ojo6N8a9y4sWK9rVu3FupyZs4mKSZVhhoVdp/gTGiCYn7iY4ZiY35iU2WoUaNGDc4m+TdJU9TXyRAR/Ys4OzsjJCQEPj4+b3W/mzdvxqBBg/D48WOYmpoW+fbPnz+Pdu3aIT4+Ps/xvIiKUrt27eDg4IBNmzYplg8ZMgT37t2TL2d+my5evIjWrVvjypUrsLa21uoxKSkpsLa2xrlyTWEFfpgUjUYl4ZlrRZhd/xNSJj8Ci4b5iY8Zio35iU2jklD20vcoW7as1j8kv4nsz0yPHz/+ZwKiEoQD6BMRlQAbN26Eq6srKlWqhOjoaEycOBG9evUqlkIYkDVT4YIFC5CQkAAvL69i2QeVXs+ePcOqVavg6+sLAwMDbN26FT///DN++ukneZ3Hjx8jJiYGW7Zs0UshDADu3LmDjRs3al0IU1CpAE7GJByNSoVHTWvC9EYSpEz2bBAN8xMfMxQb8xObRqXCrVu3FJNGlWYshhERlQBJSUmYNm0akpKS4OjoiJ49e+Y54HdRCQoKKtbtU+mVfTnq3LlzkZaWBg8PD+zcuRNt27aV1+natSsiIyMxfPhwtGvXTi/tfLU9RERERFR6sBhGRJSPUaNGwdnZudj3M2HCBEyYMKHY90P0NpiamuLnn3/Od53w8PC30xgiIiIiotewGEZElI9Ro0bpuwlERFSEjP9M1ncT6A0wP/ExQ7ExP7FZWlrquwklBgfQJyIiItJS9mCwv9u+C0sVf1MkIiIicVS7d+yt7YsD6BMRERH9yzjH7+cAtALKzMzE3bt3Ub58+bcykxYVLeYnPmYoNuYntszMTCQlJTG/v/EZICIiItIRO9aLSaPRICkpifkJivmJjxmKjfmJjfkpsRhGRERERERERESlBothRERERERERERUarAYRkRERKQjSZL03QQqBEmSYGtry/wExfzExwzFxvzExvyUOJskERERkZZK+sxIRERERCVBSf/MxJ5hRERERDrKzMzUdxOoEDIzM5GYmMj8BMX8xMcMxcb8xMb8lFgMIyIiItIRO9aLSaPR4MGDB8xPUMxPfMxQbMxPbMxPicUwIiIiIiIiIiIqNVgMIyIiIiIiIiKiUoPFMCIiIiIdcSYmMUmSBAcHB+YnKOYnPmYoNuYnNuanxNkkiYiIiLRU0mdGIiIiIioJSvpnJvYMIyIiItKRWq3WdxOoENRqNeLj45mfoJif+Jih2Jif2JifEothRERERFRqPHnyRN9NoDfA/MTHDMXG/MTG/P7BYhgREREREREREZUaLIYREREREREREVGpwWIYERERkY44E5OYJEmCk5MT8xMU8xMfMxQb8xMb81My1HcDiIiIiESjUvH3RBGpVCrY2dnpuxlUSMxPfMxQbMxPbMxPiZ/kiIiIiHTEmZjEpFarERsby/wExfzExwzFxvzExvyUWAwjIiIiolIjLS1N302gN8D8xMcMxcb8xMb8/sFiGBERERERERERlRoshhERERERERERUanBYhgRERGRjjiAvphUKhVcXV2Zn6CYn/iYodiYn9iYnxJnkyQiIiLSEaclF5MkSbCystJ3M6iQmJ/4mKHYmJ/YmJ8SS4JEREREOuJMTGJSq9WIiYlhfoJifuJjhmJjfmJjfkoshhERERFRqcEvAWJjfuJjhmJjfmJjfv/gZZJEREREOrpRozMeZPJSSdFkGhogpW9rXN86CqoMfiEQDfMTHzMUG/MTW6ahAbB/ib6bUWKwZxgREREREREREZUakkaj0ei7EUREREQiSElJgbW1Nc7ZvQsriR3sRaORgAwrcximPIXET8DCYX7iY4ZiY35i00hApZuHYGJi8lYmAsr+zPT48eMSOXA/e4YRERER6YpfAsSkAQyepjE/UTE/8TFDsTE/sWkAIyMjfbeixGAxjIiIiEhXhgb6bgEVgsbQAHf6toaG+QmJ+YmPGYqN+YlNY2iAmJgYZGZm6rspJQKLYUREREREREREVGqwGEZERERERERERKUGi2FERERERERERFRqcDZJIiIiIi3Js0navgsrFWeTFI0GWWOmSBlqFP88WlTUmJ/4mKHYmJ/YNABcksKhUqk4myTYM4yIiEhnPj4+GDVqVLHuozC/Vfn4+CAkJOSN9vs2ju1fgd8CxCQBanMT5icq5ic+Zig25ic2CXjx4oW+W1FisBhGRESUi6CgIEiSlON27do1hIWFYfbs2cW6/+3bt+PRo0dFtr0ZM2bkejyv3opDSEhIgfu9ceNGsey7WBlwJi0RaQwMcLdLM2iYn5CYn/iYodiYn9g0BgaIi4vjbJJ/YzGMiIgoD35+frhz547i5uLiAltbW1haWhbrvkNDQ7F79+4i2964ceMUx1G5cmXMmjVLsaw49O7dW7GPpk2bYtiwYYplTk5OxbJvXb18+VLfTSAiIiKit4DFMCIiojwYGxvDwcFBcTMwMFBcSvjZZ5/hnXfeyfHYunXrYtasWfLfa9euhaenJ0xMTFCjRg2sWLEiz/2mpqbi4MGD+O6774rsWCwsLHIch6WlpWJZtszMTEyYMAG2trZwcHDAjBkzFNt69OgRhg4dCnt7e1hZWaF169aIjo7Odb+mpqaKfRgZGcHMzEz++91338U333yjeEy9evUU+5QkCd9++y06deoEMzMzeHp64uTJk7h27Rp8fHxgbm6OZs2aIT4+XrGdlStXolq1ajAyMoKHhwc2bdqkuF+SJKxcuRJdunSBubk55s6dm6P96enpSElJUdyIiIiISGwshhEREb2BgIAAREZGKgoxFy9exPnz59GvXz8AwObNmzFt2jTMnTsXly9fxhdffIGpU6diw4YNAIAHDx4gIiJCvq1YsQLPnz/HoUOHEB4eLi8/efLkW+navmHDBpibm+PUqVP48ssvMWvWLPz000/y/T179sTdu3exf/9+nD17Fg0aNECbNm3w4MGDYmvT7NmzERgYiKioKNSoUQP9+vXDf/7zH0yePBlnzpyBRqPBiBEj5PW///57fPrppxg7diwuXLiA//znPxg0aBCOHDmi2O6MGTPQrVs3xMTEYPDgwTn2O2/ePFhbW8u3ktKLjQpP9TJD302gN8D8xMcMxcb8xGbAS1xlLIYRERHlYc+ePbCwsJBvPXv2zLFOrVq1ULduXWzZskVetnnzZrzzzjuoXr06AGD69OlYvHgxunfvDhcXF3Tv3h2jR4/Gt99+CwCwsbHB0aNH0apVK7Ro0QITJ04EkNUrKXtZr1698Pz5c6hUxf9fd506dTB9+nS4ubkhMDAQjRo1wuHDhwEAERERiIyMxI4dO9CoUSO4ublh0aJFsLGxKdKebK8bNGgQevXqBXd3d0ycOBE3btxAQEAAfH194enpiU8//RTh4eG7JDL/AACJgklEQVTy+osWLUJQUBA++ugjuLu7Y8yYMejevTsWLVqk2G6/fv0waNAguLq6okqVKjn2O3nyZDx+/Fi+3bp1CwAgZaiL7Vip+Kgy1HAMPQIV8xMS8xMfMxQb8xObKkMNLy8vFsT+xmIYERFRHlq1aoWoqCj5tmzZslzXCwgIkIthGo0GW7duRUBAAADg6dOniI+Px5AhQxSFtTlz5si9yVQqFaZMmYKjR4+iatWqObbv5+eH6OhotG7dupiOVKlOnTqKvx0dHXH37l0AQHR0NFJTU2FnZ6c4noSEhByXKRZXmypUqAAA8PLyUixLS0uTL2O8fPkymjdvrthG8+bNcfnyZcWyRo0a5btfY2NjWFlZKW4AoOFMWkLSSEBaRTvmJyjmJz5mKDbmJzaNBKSkpBRqxvJ/I0N9N4CIiKikMjc3l3t35adv376YOHEizp07h+fPn+PWrVvo3bs3gKzxvwBgzZo1OcYWe/2XuWbNmuHrr79G9+7d5WVlypTBd999B3Nz8zc9HK2VKVNG8bckSfLlmampqXB0dFT0wspmY2Oj875UKlWOD2W5DWT/apuyZ77MbZmul5EW+nk1MAA4GZNwNAYGSG7TAI5bf2HvPgExP/ExQ7ExP7FpDAxw/fp19g77G4thREREb6hy5crw9vbG5s2b8fz5c7Rr1w7ly5cHkNVjqWLFirh+/brcWyw/2TNIOjs746+//pLHDuvWrVuxHoO2GjRogKSkJBgaGsLZ2fmNt2dvb6+YyTIlJQUJCQlvvF1PT08cP34cAwcOlJcdP34cNWvWfONtExEREZHYWAwjIiIqAgEBAZg+fTpevHiBr7/+WnHfzJkzMXLkSFhbW8PPzw/p6ek4c+YMHj58iDFjxsjrZWRkYPfu3ejfvz9WrlyJGzduoE+fPggLCysxxbC2bduiadOm8Pf3x5dffgl3d3f8+eef2Lt3L7p161bgZYeva926NUJCQtC5c2fY2Nhg2rRpRfJr5fjx49GrVy/Ur18fbdu2xY8//oiwsDD8/PPPb7xtIiIiIhIbxwwjIiIqAj169EBycjKePXsGf39/xX1Dhw7F2rVrERwcDC8vL3h7eyMkJAQuLi6K9c6cOYMlS5Zg06ZNsLCwQO3atXH69GlUqlQp10sH9UGSJOzbtw8tW7bEoEGD4O7ujj59+uDmzZvyWF66mDx5Mry9vdGpUyd07NgR/v7+qFat2hu309/fH0uXLsWiRYtQq1YtfPvttwgODoaPj88bb5vEZvj4qb6bQG+A+YmPGYqN+YnNxMRE300oMSQNR08jIiL6V/Dx8UFQUBCCgoL03ZR/rZSUFFhbW+N323dhqWIHeyIiIhJHtXvH3tq+sj8zPX78WJ6AqCRhzzAiIiIiHWlUnEpLRBqVhKfVKzE/QTE/8TFDsTE/sWlUEpKTk3WebOjfisUwIiIiIl2p+BFKRBqVCo+a1oSG+QmJ+YmPGYqN+YlNo1Lh1q1bOWbxLq14FhMREf1LBAUFoV69evpuBhERERFRicbBLoiIiP4lOFYYEREREVHB2DOMiIiIiEoN4z+T9d0EegPMT3zMUGzMT2yWlpb6bkKJwZ5hRERERDqqdnVfiZwZiYiIiIgKxp5hRERERDriTExiyszMRFJSEvMTFPMTHzMUG/MTG/NTYjGMiIiISEeciUlMGo0GSUlJzE9QzE98zFBszE9szE+JxTAiIiIiIiIiIio1WAwjIiIiIiIiIqJSg8UwIiIiIh1JkqTvJlAhSJIEW1tb5ico5ic+Zig25ic25qckaXjBKBEREZFWUlJSYG1tjcePH3M2SSIiIqI8lPTPTOwZRkRERKQjzsQkpszMTCQmJjI/QTE/8TFDsTE/sTE/JRbDiIiIiHTEjvVi0mg0ePDgAfMTFPMTHzMUG/MTG/NTYjGMiIiIiIiIiIhKDRbDiIiIiIiIiIio1GAxjIiIiEhHnIlJTJIkwcHBgfkJivmJjxmKjfmJjfkpcTZJIiIiIi2V9JmRiIiIiEqCkv6ZiT3DiIiIiHSkVqv13QQqBLVajfj4eOYnKOYnPmYoNuYnNuanxGIYEREREZUaT5480XcT6A0wP/ExQ7ExP7Exv3+wGEZERERERERERKUGi2FERERERERERFRqsBhGREREpCPOxCQmSZLg5OTE/ATF/MTHDMXG/MTG/JQM9d0AIiIiItGoVPw9UUQqlQp2dnb6bgYVEvMTHzMUG/MTG/NT4ic5IiIiIh1xJiYxqdVqxMbGMj9BMT/xMUOxMT+xMT8lFsOIiIiIqNRIS0vTdxPoDTA/8TFDsTE/sTG/f7AYRkREREREREREpQaLYUREREREREREVGqwGEZERESkIw6gLyaVSgVXV1fmJyjmJz5mKDbmJzbmp8TZJImIiIh0xGnJxSRJEqysrPTdDCok5ic+Zig25ic25qfEkiARERGRjjgTk5jUajViYmKYn6CYn/iYodiYn9iYnxJ7hhERERHp6EaNzniQyd5hosk0NMDDvq1xfesnUGXwy4BomJ/4mKHYmJ/YMg0NoN6/RN/NKDHYM4yIiIiIiIiIiEoNFsOIiIiIiIiIiKjUkDQajUbfjSAiIiISQUpKCqytrXHO7l1YSRxtQjQaCciwModhylNI/AQsHOYnPmYoNuYnNo0EVLp5CCYmJm9lIqDsz0yPHz8ukQP3s2cYERERka74JUBMGsDgaRrzExXzEx8zFBvzE5sGMDIy0ncrSgwWw4iIiIh0ZWig7xZQIWgMDXCnb2tomJ+QmJ/4mKHYmJ/YNIYGiImJQWZmpr6bUiKwGEZERERERERERKUGi2FERERERERERFRqsBhGRERERERERESlRokthvn4+GDUqFHFuo/CTKTp4+ODkJCQN9rv2zi24iRJEn744Qd9N4P+5UJCQmBjY6PvZhSrGTNmoF69ejo/TvT3EH0r7PP+tmjTvhs3bkCSJERFRb2VNhWHoKAg+Pv767sZhZeh1ncLqBCkDDUct/4CifkJifmJjxmKjfmJTcpQw8vLCypViS0DvVV6fRaCgoIgSVKO27Vr1xAWFobZs2cX6/63b9+OR48eFdn2ZsyYkevxvHorTtn7+O233xTL09PTYWdnB0mSEB4ervX2SvoXxrz4+PhAkiTMnz8/x30dO3aEJEmYMWPGW2/Xs2fPMHnyZFSrVg0mJiawt7eHt7c3du3a9dbboquQkBDFeWxhYYGGDRsiLCysSLbv7OyMJUuWFMm2/k3Cw8MhSVKRvk/l5d1338Xw4cMVy1atWgVJknL8ABAUFIQWLVoUSzueP38OW1tblCtXDunp6cWyj3HjxuHw4cNFtj1ti5N5rfd64ff19hV30cjZ2Vmn/xtelVfb3ua5W5Bie/6Kf0ZyKg4SoDY3YX6iYn7iY4ZiY35ik4AXL17ouxUlht5Lgn5+frhz547i5uLiAltbW1haWhbrvkNDQ7F79+4i2964ceMUx1G5cmXMmjVLsay4OTk5ITg4WLHs+++/h4WFRbHvuyRxcnLK8QX+9u3bOHz4MBwdHfXSpuHDhyMsLAzffPMNYmNjceDAAfTo0QPJycl6aY+urKys5PP4999/h6+vL3r16oW4uLg8H1MS32xfvnyp7yaUSK1atcpREDly5AicnJxyLA8PD0fr1q0LtZ+CzomdO3eiVq1aqFGjRrH1QLWwsICdnV2xbLsovK32lcTXp1AMOJOWiDQGBrjbpRk0zE9IzE98zFBszE9sGgMDxMXFcTbJv+m9GGZsbAwHBwfFzcDAQPEL+meffYZ33nknx2Pr1q2LWbNmyX+vXbsWnp6eMDExQY0aNbBixYo895uamoqDBw/iu+++K7JjsbCwyHEclpaWimXZMjMzMWHCBNja2sLBwSFHT6VHjx5h6NChsLe3h5WVFVq3bo3o6OgC2zBw4ECEhobi+fPn8rL169dj4MCBOdadOHEi3N3dYWZmBldXV0ydOlUuFISEhGDmzJmIjo6WewO9Wly6f/8+unXrBjMzM7i5ueUoKl64cAHt27eHhYUFKlSogAEDBuD+/fsAgD179sDGxgZqdVb32qioKEiShEmTJsmPHzp0KPr37w8AuHnzJjp37oyyZcvC3NwctWrVwr59+/J9Hjp16oT79+/j+PHj8rINGzbg/fffR/ny5RXrpqenY9y4cahUqRLMzc3xzjvvKL78Z/faOHjwIDw9PWFhYSEXcXWxe/dufPbZZ+jQoQOcnZ3RsGFDfPLJJxg8eLBWbUlJSYGpqSn279+v2O73338PS0tLPHv2DABw69Yt9OrVCzY2NrC1tUXXrl1x48YNef3sXhKLFi2Co6Mj7Ozs8PHHHxdYJJIkST6P3dzcMGfOHKhUKpw/f15ex9nZGbNnz0ZgYCCsrKzw4YcfAvinwGFsbAxnZ2csXrxYfoyPjw9u3ryJ0aNH59qDsqDnPb/XffalZNu2bYO3tzdMTEywefNmnc+pTZs2oVGjRvLruV+/frh79658f3YvmMOHD6NRo0YwMzNDs2bNchQK58+fjwoVKsDS0hJDhgxBWlpanvu8ceMGWrVqBQAoW7YsJElCUFCQfH9Rv4e0atUKcXFxSEpKkpcdPXoUkyZNUrweEhIScPPmTbRq1QpqtRpDhgyBi4sLTE1N4eHhgaVLlyq2m32+zZ07FxUrVoSHh0eebQCAdevWoX///ujfvz/WrVuX4/7Y2Fi89957MDExQc2aNfHzzz/nuHQ7v/c2IGevV21eEytWrICbmxtMTExQoUIF9OjRQ37s0aNHsXTpUvn8ffX1Vhivtm/GjBnYsGEDdu3aJW8/r15c+b3vAlmvtREjRmDUqFEoV64cfH19c2zjxYsXGDFiBBwdHWFiYoKqVati3rx5b3Q8BZk5c6Z8ng4fPlxRpEtPT8fIkSNRvnx5mJiY4L333sPp06fl+ws6B/N6/nQ5zvT0dKSkpChuRERERCQ2vRfDtBEQEIDIyEjEx8fLyy5evIjz58+jX79+AIDNmzdj2rRpmDt3Li5fvowvvvgCU6dOxYYNGwAADx48QEREhHxbsWIFnj9/jkOHDiE8PFxefvLkybdSKd2wYQPMzc1x6tQpfPnll5g1axZ++ukn+f6ePXvi7t272L9/P86ePYsGDRqgTZs2ePDgQb7bbdiwIZydnbFz504AQGJiIn799VcMGDAgx7qWlpYICQnBpUuXsHTpUqxZswZff/01AKB3794YO3YsatWqJfcG6t27t/zYmTNnolevXjh//jw6dOiAgIAAuW2PHj1C69atUb9+fZw5cwYHDhzAX3/9hV69egEAWrRogSdPnuD3338HkPWFu1y5cooveEePHoWPjw8A4OOPP0Z6ejp+/fVXxMTEYMGCBQX2dDMyMkJAQICil1xISIii8JRtxIgROHnyJEJDQ3H+/Hn07NkTfn5+uHr1qrzOs2fPsGjRImzatAm//vorEhMTMW7cOPn+7GJIfl+CHRwcsG/fPjx58iTPdfJri5WVFTp16oQtW7YoHrN582b4+/vDzMwML1++hK+vLywtLXHs2DEcP35cLiK9+gXzyJEjiI+Px5EjR7BhwwaEhIToNBaeWq2WX1sNGjRQ3Ldo0SLUrVsXv//+O6ZOnYqzZ8+iV69e6NOnD2JiYjBjxgxMnTpV3l9YWFiOXpTZCnreC3rdZ5s0aRI+/fRTXL58Gb6+vjqfUy9fvsTs2bMRHR2NH374ATdu3FAUprJNmTIFixcvxpkzZ2BoaKg437Zv344ZM2bgiy++wJkzZ+Do6Jhvwd7JyUl+HcfFxeHOnTuKL/lF/R7SvHlzlClTBkeOHAEAXLp0Cc+fP8eQIUOQnJyMhIQEAFnnjomJCZo2bYrMzExUrlwZO3bswKVLlzBt2jR89tln2L59u2Lbhw8fRlxcHH766Sfs2bMnz2OOj4/HyZMn0atXL/Tq1QvHjh3DzZs35fvVarV8rp86dQqrV6/GlClTcmwnv/e2vOT3mjhz5gxGjhyJWbNmIS4uDgcOHEDLli0BAEuXLkXTpk0xbNgw+fx1cnLKd1+6GDduHHr16qXoRd2sWbMc6xX0vpttw4YNMDIywvHjx7Fq1aoc21m2bBl2796N7du3Iy4uDps3b4azs3ORHc/rDh8+jMuXLyM8PBxbt25FWFgYZs6cKd8/YcIE7Ny5Exs2bMC5c+dQvXp1+Pr6yudxQedgXs+fLsc5b948WFtby7eizJeIiIiI9MNQ3w3Ys2eP4kto+/btsWPHDsU6tWrVQt26dbFlyxZMnToVQNaX4HfeeQfVq1cHAEyfPh2LFy9G9+7dAQAuLi64dOkSvv32WwwcOBA2NjY4evQoZsyYgYyMDHnb6enpcu8LR0dH/O9//3srA8rVqVMH06dPBwC4ublh+fLlOHz4MNq1a4eIiAhERkbi7t27MDY2BpBVYPjhhx/w3Xffyb1t8jJ48GCsX78e/fv3R0hICDp06AB7e/sc633++efyv52dnTFu3DiEhoZiwoQJMDU1hYWFBQwNDRU92rIFBQWhb9++AIAvvvgCy5YtQ2RkJPz8/LB8+XLUr18fX3zxhbz++vXr4eTkhCtXrsDd3R316tVDeHg4GjVqhPDwcIwePRozZ85EamoqHj9+jGvXrsHb2xtAVkHvgw8+gJeXFwDA1dVVq+d48ODBaNGiBZYuXYqzZ8/i8ePH6NSpk6IHTWJiIoKDg5GYmIiKFSsCyPrydODAAQQHB8vH8PLlS6xatQrVqlUDkFW0erVXopmZGTw8PFCmTJk827N69WoEBATAzs4OdevWxXvvvYcePXqgefPmWrclICAAAwYMwLNnz2BmZoaUlBTs3bsX33//PQBg27ZtyMzMxNq1a+UeVsHBwbCxsUF4eDjef/99AFk9jZYvXw4DAwPUqFEDHTt2xOHDhzFs2LA82//48WP5tfr8+XOUKVMGq1evlp+TbK1bt8bYsWPlvwMCAtCmTRv5tevu7o5Lly5h4cKFCAoKgq2traIX5asKet4Let1nGzVqlLxO9nOtyzn1alHL1dUVy5YtQ+PGjZGamqp4/5o7d6583k6aNAkdO3ZEWloaTExMsGTJEgwZMgRDhgwBAMyZMwc///xznr3DDAwMYGtrCwAoX758jskEivo9xNzcHE2aNEF4eDj69u2L8PBwvPfeezA2NkazZs0QHh4OFxcXhIeHo2nTpvJ2Xy1cuLi44OTJk9i+fbuiCGNubo61a9fCyMgo3+d5/fr1aN++PcqWLQsA8PX1RXBwsPya/emnnxAfH4/w8HD5XJk7dy7atWun2E5+7215ye81kZiYCHNzc3Tq1AmWlpaoWrUq6tevDwCwtraGkZERzMzMcn2vfN2KFSuwdu1axbKMjAyYmJjkur6FhQVMTU2Rnp6e7/a1ed8Fss6VL7/8UvHYV4v4iYmJcHNzw3vvvQdJklC1atUCj+lNGBkZYf369TAzM0OtWrUwa9YsjB8/HrNnz8bz58+xcuVKhISEoH379gCANWvW4KeffsK6deswfvx4lClTJt9zMK/nT5fjnDx5MsaMGSP/nZKSwoKY4FQvMwpeiUos5ic+Zig25ic2A17iKtN7z7BWrVohKipKvi1btizX9QICAuQeMRqNBlu3bkVAQAAA4OnTp4iPj8eQIUNgYWEh3+bMmSP3JlOpVJgyZQqOHj2a64dePz8/REdHF3ocHF3VqVNH8bejo6N82VV0dDRSU1NhZ2enOJ6EhARF77i89O/fHydPnsT169fz7A0FZBVOmjdvDgcHB1hYWODzzz9HYmKizu03NzeHlZWVov1HjhxRtL1GjRoAILff29sb4eHh0Gg0OHbsGLp37w5PT09ERETg6NGjqFixItzc3AAAI0eOxJw5c9C8eXNMnz5dcVlefurWrQs3Nzd89913WL9+PQYMGABDQ2X9NyYmBmq1Gu7u7or2Hj16VPFcm5mZKYo+r+YFAE2aNEFsbCwqVaqUZ3tatmyJ69ev4/Dhw+jRowcuXryIFi1ayBNFaNOWDh06oEyZMvJlqTt37oSVlRXatm0rP/fXrl2DpaWl/HhbW1ukpaUpjqdWrVqKN8LXjyc3lpaW8uv0999/xxdffIHhw4fjxx9/VKzXqFEjxd+XL1+WC37ZmjdvjqtXr8qXyuYlv+ddm9d9Xm3S9Zw6e/YsOnfujCpVqsDS0lJRqH3Vq6+L7LHpstt7+fLlHJd7N23aNN/95qc43kN8fHzkHprh4eFy78zs12v28uwfEADgv//9Lxo2bAh7e3tYWFhg9erVOZ4XLy+vAgth2b0Nsy+PBiAX9LN768bFxcHJyUlR1GjSpEmObRXmvS2/10S7du1QtWpVuLq6YsCAAdi8ebN8WbKuAgICFP/nRUVFKQq8haXN+y6Q1Xs4P0FBQYiKioKHhwdGjhyJQ4cOvXHbEhMTFe16tWBXt25dmJmZyX83bdoUqampuHXrFuLj4/Hy5UvF+0eZMmXQpEkTXL58WV6mzTn4JsdpbGwMKysrxQ0AZ9ISlCpDDcfQI1AxPyExP/ExQ7ExP7Gp/p5NkgWxLHrvGWZubi737spP3759MXHiRJw7dw7Pnz/HrVu35Mv2UlNTAWT9Yvz6l83Xg27WrBm+/vprRS+RMmXK4LvvvoO5ufmbHo7WXu9BJEmS/IUvNTUVjo6OuY4L83rvkNzY2dmhU6dO8phE7du3z3Fp3smTJxEQEICZM2fC19cX1tbWCA0NVYzl9Cbt79y5MxYsWJDjcdkFAh8fH6xfvx7R0dEoU6YMatSoIX8Rf/jwoVxsALLGD/P19cXevXtx6NAhzJs3D4sXL8Ynn3xSYDsHDx6M//73v7h06RIiIyNz3J+amgoDAwOcPXs2x7nyao+f3I5Xo9EUuP/XlSlTBi1atECLFi0wceJEzJkzB7NmzcLEiRO1aouRkRF69OiBLVu2oE+fPtiyZQt69+4tF/lSU1PRsGFDbN68Oce+X+0dmF9+eVGpVIrXap06dXDo0CEsWLAAnTt3lpcX5esov+ddl9f9623S5Zx6+vQpfH194evri82bN8Pe3h6JiYnw9fXNMQD5q+3N7plXXJddF8d7SKtWrTB37lzcvn0b4eHh8iWp3t7e+PbbbxEfH49bt27JPxqEhoZi3LhxWLx4MZo2bQpLS0ssXLgQp06dUmxXm3Pi4MGDuH37tuJybCCrSJbd400bhX1vy+/5tLS0xLlz5xAeHo5Dhw5h2rRpmDFjBk6fPq3Ve/KrrK2tc/yf9/o4hoWhzfsuUHAWDRo0QEJCAvbv34+ff/4ZvXr1Qtu2bfMcX9PKykpxKWu2R48ewcDAAObm5rCwsEBUVJR8X3aPx6Kg7Tn4Ol2PMzcazqQlJI0EpDvawfhOMiTd/xsnPWN+4mOGYmN+YtNIWT3cLS0tc4zTXBrpvRimrcqVK8Pb2xubN2/G8+fP0a5dO/kLRIUKFVCxYkVcv35d7i2Wn+xeNc7Ozvjrr7/kscO6detWrMegrQYNGiApKQmGhoaFHqtl8ODB6NChAyZOnJhr5ffEiROoWrWqYryd17/QGBkZFdhzJzcNGjTAzp074ezsnKMnVrbsccO+/vprufDl4+OD+fPn4+HDh4rL7ICs8ZOGDx+O4cOHY/LkyVizZo1WxbB+/fph3LhxqFu3LmrWrJnj/vr160OtVuPu3bto0aKFzsf6pmrWrImMjAykpaVp3ZaAgAC0a9cOFy9exC+//II5c+bI9zVo0ADbtm1D+fLl5d4LxcnAwEAxWUNuPD09FRMZAMDx48fh7u4un5uFOdd0fd2/TttzKjY2FsnJyZg/f758adSZM2d03p+npydOnTqFwMBAedlvv/2W72Oye1Pp+twU9j2kWbNmMDIywooVK5CWlib3ImrcuDHu3buH9evXy5dTAlk5NmvWDB999JG8DW16r+Zm3bp16NOnT44xwObOnYt169ahXbt28PDwwK1bt/DXX3+hQoUKAKAYTB3Q7r2tMAwNDdG2bVu0bdsW06dPh42NDX755Rd079690O+V2tJm+9q872rLysoKvXv3Ru/evdGjRw/4+fnhwYMHuRaxPDw8EBoaivT0dPnSWQA4d+4cXFxc5CJjXj96RUdH4/nz5zA1NQWQ9ZqwsLCAk5MTypUrJ49vlt2j++XLlzh9+rQ8wY4252Bez58ux5krAwOAkzEJR2NggOQ2DeC49Rf27hMQ8xMfMxQb8xObxsAA169fZ++wv+n9MkldBAQEIDQ0FDt27Mjx5XfmzJmYN28eli1bhitXriAmJgbBwcH46quvFOtlZGRg9+7d6N+/P2JiYhAZGYlatWohLCzsbR5Kvtq2bYumTZvC398fhw4dwo0bN3DixAlMmTJF6y/hfn5+uHfvXp6X37i5uSExMRGhoaGIj4/HsmXL5HGnsjk7OyMhIQFRUVG4f/8+0tPTtdr3xx9/jAcPHqBv3744ffo04uPjcfDgQQwaNEj+QlK2bFnUqVMHmzdvli/FatmyJc6dO4crV64oeoaNGjUKBw8eREJCAs6dO4cjR47A09NTq7aULVsWd+7cweHDh3O9393dHQEBAQgMDERYWBgSEhIQGRmJefPmYe/evVrtAwAiIyNRo0YN3L59O891fHx88O233+Ls2bO4ceMG9u3bh88++wytWrWClZWV1m1p2bIlHBwcEBAQABcXF0WvqICAAJQrVw5du3bFsWPHkJCQgPDwcIwcORJ//PGH1seTG41Gg6SkJCQlJSEhIQGrV6/GwYMH0bVr13wfN3bsWBw+fBizZ8/GlStXsGHDBixfvlwxEL6zszN+/fVX3L59WzH7XUG0fd2/TpdzqkqVKjAyMsI333yD69evY/fu3fKlrbr49NNPsX79egQHB+PKlSuYPn06Ll68mO9jqlatCkmSsGfPHty7d0/uDVeQwr6HmJqa4t1338U333yD5s2bK4qVry7PLnC4ubnhzJkzOHjwIK5cuYKpU6fmKE5p4969e/jxxx8xcOBA1K5dW3ELDAzEDz/8gAcPHqBdu3aoVq0aBg4ciPPnz+P48ePy+GDZv25p896mqz179mDZsmWIiorCzZs3sXHjRmRmZsozYzo7O+PUqVO4ceMG7t+/X+S9AZ2dnXH+/HnExcXh/v37uc78qs37rja++uorbN26FbGxsbhy5Qp27NgBBweHPHvABQQEQJIkBAYG4uzZs7h27RrWr1+PJUuW5PhRIzcvXrzAkCFDcOnSJezbtw/Tp0/HiBEjoFKpYG5ujv/7v//D+PHjceDAAVy6dAnDhg3Ds2fP5LH3tDkHc3v+dD1OIiIiIvp3EaoY1qNHDyQnJ+PZs2fw9/dX3Dd06FCsXbsWwcHB8PLygre3N0JCQuDi4qJY78yZM1iyZAk2bdoECwsL1K5dG6dPn0alSpVy/YKhD5IkYd++fWjZsiUGDRoEd3d39OnTBzdv3pR7Q2izjexf1XPTpUsXjB49GiNGjEC9evVw4sQJeYDzbB988AH8/PzQqlUr2NvbY+vWrVrtu2LFijh+/DjUajXef/99eHl5YdSoUbCxsVFMTuDt7Q21Wi0Xw2xtbVGzZk04ODjIXzKBrF4xH3/8MTw9PeHn5wd3d/d8Z+F7nY2NTb6XBgUHByMwMBBjx46Fh4cH/P39cfr0aVSpUkXrfTx79gxxcXH5nkO+vr7YsGED3n//fXh6euKTTz6Br6+vYuY9bdoiSRL69u2L6OjoHEVhMzMz/Prrr6hSpYo8Dlv25bJv2lMsJSXl/9u787ia8v8P4K/bvqlEK1FUJK0yRgwRU3YG0WTJOoaGkH0sk93UiDF2wsg2xtLYBlGUfSlL1iwxajJCQtS99/dHv87X1XqJHL2ej8d9PNyzvs99n3vd++6zwNzcHObm5rC3t0dYWBhCQkIKnc3vTW5ubtiyZQs2bdqE+vXrY8qUKQgJCVGYjTEkJAR37txB7dq1C53soSilfd+/TZl7ytjYGGvWrMEff/yBevXqYc6cOQgNDS11jPl69OiByZMnY+zYsWjQoAHu3r2L77//vth9qlWrhp9++gnjx4+HqakpAgMDS3Wu9/kMadGiBZ49eya8L/M1b94cz549Uxgv7LvvvsM333yDHj16oFGjRnj06JFCC53SWrduHXR1deHl5VVgnZeXF7S1tbF+/Xqoqqpix44dyMrKQsOGDTFw4EDh/ssfgL40n23KMjQ0xLZt29CyZUvY29tj6dKl2LhxIxwcHADkTXShqqqKevXqCd1oy9KgQYNQp04duLu7w9jYuEBLS6D0n7slqVSpEubNmwd3d3c0bNhQKNwXdQxDQ0McPXoUOTk56NixI1xcXLBw4UL88ssv+O6770o8n5eXF2xtbdGsWTP06NEDHTt2VJjkZM6cOejatSt69+4NNzc33Lx5E3///bcwyUJp7sHCXj9lr5OIiIiIPi8S+bsMfFSBeXp6IiAgQOGHPBERlY/4+Hg0bdoUN2/eLDCzKdGHkJmZCQMDA5wzaQJ9GcfbEBuZmioetm0E4z0nOQC0CDF/4sccihvzJ24yNVVIDy+Fra3tR+kmmf+d6enTpx9lCB9liWbMMCIiou3bt0NPTw+2tra4efMmRowYgSZNmrAQRh+dJFcKqPBrlNio5EphGnWsvMOgd8T8iR9zKG7Mn7ip5Eph+/+zjZPIukkSEVHF9uzZMwwbNgx169ZFQEAAGjZsiJ07d5Z3WFQByVXYKkyM5CoSPLepxvyJFPMnfsyhuDF/4iZXkeDRo0cfbLZ7sWExTEkBAQFwcXEp7zCIiCqkPn364Pr168jOzsb9+/exZs0aVKlSpbzDooqI44uJklxFBU8a14Oc+RMl5k/8mENxY/7ETa6ignv37oEjZeVh+34lcawwIiIiIiIiIiLxYkmXiIiIiIiIiIgqDM4mSURERFRK+TMjZWRkoHLlyuUdDilJKpXizp07sLKy+igzaVHZYv7EjzkUN+ZP3D52/j712SRZDCMiIiIqpU/9ix0RERHRp+BT/87EbpJERERESuJMTOIkk8mQlpbG/IkU8yd+zKG4MX/ixvwpYjGMiIiISElsWC9OcrkcaWlpzJ9IMX/ixxyKG/MnbsyfIhbDiIiIiIiIiIiowmAxjIiIiIiIiIiIKgwWw4iIiIiUJJFIyjsEegcSiQRGRkbMn0gxf+LHHIob8yduzJ8iziZJREREVEqf+sxIRERERJ+CT/07E1uGERERESmJMzGJk0wmQ0pKCvMnUsyf+DGH4sb8iRvzp4jFMCIiIiIlsWG9OMnlcmRkZDB/IsX8iR9zKG7Mn7gxf4pYDCMiIiIiIiIiogqDxTAiIiIiIiIiIqowWAwjIiIiUhJnYhIniUQCMzMz5k+kmD/xYw7FjfkTN+ZPEWeTJCIiIiqlT31mJCIiIqJPwaf+nYktw4iIiIiUJJVKyzsEegdSqRTJycnMn0gxf+LHHIob8yduzJ8iFsOIiIiIqMJ49uxZeYdA74H5Ez/mUNyYP3Fj/v6HxTAiIiIiIiIiIqowWAwjIiIiIiIiIqIKg8UwIiIiIiVxJiZxkkgksLS0ZP5EivkTP+ZQ3Jg/cWP+FKmVdwBEREREYqOiwr8nipGKigqqVKlS3mHQO2L+xI85FDfmT9yYP0X8JkdERESkJM7EJE5SqRRXr15l/kSK+RM/5lDcmD9xY/4UsRhGRERERBVGdnZ2eYdA74H5Ez/mUNyYP3Fj/v6H3SSJiIiIiIiIqNxJpVLk5OSUdxifJalUCrlcjuzsbKiqqr738dTV1cvkOOWFxTAiIiIiIiIiKjdyuRxpaWl48uRJeYfy2ZLL5VBRUcHdu3fLbBB9Q0NDmJmZiXJQfhbDiIiIiJTEAfTFSUVFBbVq1WL+RIr5Ez/mUNw+ZP7yC2EmJibQ0dERZXHlUyeXyyGTyaCiovLer69cLseLFy+Qnp4OADA3Ny+LED8qFsOIiIiIlMQv6eIkkUigr69f3mHQO2L+xI85FLcPlT+pVCoUwjjboXhoa2sDANLT02FiYiK6LpMshhEREREpKdm2LfRlLIiJjUxNFf92awbTrUegksvZtMSG+RM/5lDcPlT+pNWMkTM3EKrZcmTfe1hmx6W3SCSQ2VpCW1u7zP6op6OjAwDIyckRXTGM7VOJiIiIqMKQqfNvwWLG/IkfcyhuHyR/EgkgkYB/YhIfMbeUZzGMiIiIiIiIiIgqDBbDiIiIiIiIiIjKyJEzp6Dtao8nzzLLOxQqAothRERERMqScqwbMZJIpTCJOgYJ8ydKzJ/4MYfiJsb8PczIwPCZ02DbpiUMvnCCVauv0GHoQBxLOFdm5/h6YB8E/zxLYdmXzi64feAIDPQqldl53tWgKRPQfWQgIJdDS0urvMP5ZLDDNhEREZGy5AAHNxEhOaD6PDsvfyQ+zJ/4MYfiJsL8+QUPx+vcHKwMmQ2ratWRnvEIMSdPIOPJkw96Xg11DZhVNf6g53gXYh7jq6yxZRgRERGRstTENWMS5ZGrqSLVryXkzJ8oMX/ixxyKm9jy9+RZJuLPn8XM4aPRvGEj1LSohob1nTBmwGC092wpbPP9Tz/CsoUHTJq6w2dwAC5cuyocY8bSRWjUows27NqJOm29YPpVQ/QeNwrPnj8HkNfq6ujZ0/htw+/QdrWHtqs97j74p0A3yd+jtsPsqy+w58hhOHVuA6PGrvALHoEXL19ifdQO1GnrBfNmjTBq7kxI32h59+r1a4z/ZR5qfd0cVRq74avePXDkzClhff5xDxyLg8s37VDVowE6DhuE1IfpQvzr/9qBXTHR0Ha1h4qKCmJiYj70Sy8KLIYRERERERER0WdFT1sHejo6iIqJxqvXrwvdxn9MEB5mZGDHouU4FrkVLnXroe2Qfsh4+kTY5tb9FEQdjsa2hUvw54IliDt3GqERKwAAoWMmopGTC/p/0x23DxzB7QNHUN3UrNBzvcjOxuKN67FuThiiFi3H0bOn0GP0D9gXH4sdvy7DqhlzsOrPzdh28G9hn5FzpuPkhQSsmxOG01t24JvW3ug4bBBu3r2jcNzwdauxavpcHFi1DvdSUzFh/s8AgKA+/dD1ax987fEVbh88iuTkZHh4eLznK/t5YDdJIiIiIiIiIvqsqKmpYflPszFs+hSs3LoZLnXr4asGDdHduy0c7eog/vxZnLl8ESnR8dDU0AAAzBk1Fn/FRGP7wf0Y0NUXACCTybEiZDYq6eoCAPzadcThU8fxE4JgUKkSNNTVoa2lVWK3yJzcHCycOBW1LGsAALp4eWPD7ijcjT4KPR1d2Ne2QfOGjXDk9Cl0926LlNQHWBe1Hdf3HIKFiQkAYGSf/jgQfxTrorYj5IeRwnF/nTRNOO6Qnt9i9vIlAAA9HV1oa2rh1escmFU1hszMDBr/f60VHYthRERERERERPTZ6dLqa7T5qjniz5/BqQuJ+Dv+KH5ZuwpLpkzH85cvkPXiBap5NlbY5+WrbNy6nyI8r2lhIRTCAMCsqjEeZmQoHYuOlrZQsAIAkypVUNPCAno6/zu2iVEVpD9+BAC4fPM6pFIpnDq3UTjOq5zXMDI0LPK4ZlWNkZ7xSOn4Khp2k/yMeHp6Iigo6IOeQy5XfrRET09PrFmzpuyDKUFMTAwkEgmefODBEd/Xixcv0LVrV+jr679XvB8j/58qiUSCHTt2fPDzLF++HJaWllBRUUF4ePg7H2fatGlwcXEps7gIuHPnDiQSCRISEj7KfhXd2583VlZW7/WeKM6aNWtg+MYXvk9Grnhm0qL/keRKYb7xECTMnygxf+LHHIqbWPOnpakJry+bYMLgoYhZuxG9O3TG9CW/IuvFC5hVNcbJTdsUHhe278HIPgOE/dXV1BWOJ5FIIJPJlI5DXU2xLZJEIini2Hm/ubNevICqqiqObdiqEF/Cn7sQOmZiscct9He7XA5tbW2l4/5csRgmMgEBAZBIJAUeN2/exLZt2zB9+vQPev4tW7Z80OKSt7c3VFVVcfr06RK3zS925T9MTU3RtWtX3Lp1CwDg4eGB1NRUGBgYfLB4y8LatWtx9OhRHDt2rMh4pVIp5syZg7p160JbWxtGRkZo1KgRVq5cWQ4RK+/w4cNo3749jI2NoaWlhdq1a6NHjx44cuRImRw/NTUVbdq0KXnD95CZmYnAwECMGzcO//zzDwYPHlzodm/ek/r6+mjYsCF27typsE1wcDCio6M/aLxFefnyJYyMjFC1alW8evWqxO2nTZsGiUQCHx+fAut+/vlnSCQSeHp6foBIlWNpaYnU1FTUr1//o+z3tsI+l5s2bfpex3wX+cU9VVVV/PPPPwrrUlNToaamBolEgjt37pTpeU+fPq3wnnjXAnVhRbUePXrg+vXr7xnhB8DJmMRJAkh1tZg/sWL+xI85FLfPJH91a9ngRfZLuNrXw7+P/oOamhpq16ip8KhauXKpj6ehrg6pVPniWElc6tpDKpUiPeNRgfiUmalSQ10dMlleAfNdGrd8rlgMEyEfHx+kpqYqPKytrWFkZIRKlSp90HNv2rQJUVFRH+TYKSkpOHbsGAIDA7F69epS73ft2jU8ePAAf/zxBy5fvowOHTpAKpVCQ0MDZmZmn/z0scnJybC3t0f9+vWLjPenn37C/PnzMX36dCQlJeHw4cMYPHjwJ9/qDQAWL14MLy8vVKlSBZs3b8a1a9ewfft2eHh4YOTIkUXuJ5VKS/0XFzMzM2hqapZVyIVKSUlBTk4O2rVrB3Nzc+jo6BS5bUREBFJTU3HmzBk0adIE3bp1w8WLF4X1enp6qFKlygeNtyh//vknHBwcULdu3VIXK8zNzXH48GHcv39fYfnq1atRo0aNIvb6uFRVVWFmZgY1tcJ7/8vlcuTm5iq9nzLy857/+FCflUDJ749q1aph3bp1CsvWrl2LatWqfZB4jI2Ni31PvA9tbW2Y/P84GZ8UVXHMpEWK5KqqSO/oATnzJ0rMn/gxh+Imtvw9evIYPoMDsHF3FC5ev4Y7/9zHnwf2Yf7aVWjfvCVaNvJAIycX+I4MxMHj8bj74B8cTziPqYvCcfbypVKfp6ZFNZy+dAF3H/yD/x4/fqdWY4WxrWmNnm07YODk8dgRvR93/rmP05cu4OdVy7H3aIxS8V28fg3X797G/fv3kZOTUybxiR2LYSKkqakJMzMzhYeqqqpCt5WJEyeiUaNGBfZ1dnZGSEiI8HzlypWwt7eHlpYW6tati8WLFxd53qysLPz999/YunVrmV8TkPdDsn379vj++++xceNGvHz5slT7mZiYwNzcHM2aNcOUKVOQlJSEmzdvFtpNMi4uDl999RW0tbVhaWmJ4cOH4/n/T4sLAK9evcK4ceNgaWkJTU1N2NjYYNWqVcL6S5cuoU2bNtDT04OpqSl69+6N//77r9j48osPmpqasLKyQlhYmLDO09MTYWFhOHLkSLEtbKKiojB06FB0794d1tbWcHZ2xoABAxAcHFzkeXfv3g0DAwNERkaiZcuWCAwMVFj/8OFDaGhoKNVCady4cbCzs4OOjg5q1aqFyZMnF/thmpKSgqCgIAQFBWHt2rVo2bIlatasCScnJ4wYMQJnzpwRts3vChUVFYV69epBU1MTKSkpOH36NFq3bo2qVavCwMAAzZs3x7lz5xTO82YrlPyWMdu2bUOLFi2go6MDZ2dnHD9+vNhrS0lJQadOnaCnpwd9fX34+vri33//FWJzdHQEANSqVavEljWGhoYwMzODnZ0dpk+fjtzcXBw+fFhYX1g3ydWrVwv3ibm5uUK+njx5goEDB8LY2Bj6+vpo2bIlEhMTi72eoqxatQq9evVCr169FO7t4piYmODrr7/G2rVrhWXHjh3Df//9h3bt2hXYvrjPlfz8bNmyRXgvNmzYENevX8fp06fh7u4OPT09tGnTBg8fPhT2k8lkCAkJQfXq1aGpqQkXFxfs27evwHHzuzvmv//37t2LBg0aQFNTE3FxcQViLWq/6OhouLu7Q0dHBx4eHrh27VqJr1N+3vMfRkZGpYq9sM+qhIQEhfusqPdHUfr27YuIiAiFZREREejbt2+BbUv6XHv+/Dn69OkDPT09mJubK3yG5XuzRZeVlRUAoEuXLpBIJMLz5ORkdOrUCaamptDT00PDhg1x8OBB4Rienp64e/cuRo4cKbSue/Pa37RkyRLUrl0bGhoaqFOnDn7//XeF9RKJBCtXrkSXLl2go6MDW1tbheLk48eP4e/vD2NjY2hra8PW1rbA65Xv1atXyMzMVHgQERHRp09PRxcN6zvh18i1aD2gNxp064iQxQvRr0s3zB8/Oe83xK/L0MTNHYOnToRjpzboM2E0UlIfwESJP1wH9ekHVRUVuHZtD8uWHriXllpm17B82kz4t++E8b/Mg1PntvAdGYgzSRdhaWZR6mP069IddlbWaPJtN9SsWRPx8fFlFp+YsRj2mfL398epU6eQnJwsLLt8+TIuXLiAb7/9FgAQGRmJKVOmYObMmbhy5QpmzZqFyZMnCz94MzIyEBcXJzwWL16Mly9fYv/+/YiJiRGWHz9+/L2r33K5HBEREejVqxfq1q0LGxubdyq65feBfl3I1LnJycnw8fFB165dceHCBWzevBlxcXEKRYc+ffpg48aNWLhwIa5cuYJly5ZBT08PQF5BomXLlnB1dcWZM2ewb98+/Pvvv/D19S0ynrNnz8LX1xc9e/bExYsXMW3aNEyePFkYQ23btm0YNGgQGjdujNTUVGzbtq3Q45iZmeHQoUMKxYHibNiwAX5+foiMjIS/vz8GDhyIDRs2KHSLW79+PapVq4aWLVuW6pgAUKlSJaxZswZJSUlYsGABVqxYgfnz5xe5/Z9//omcnByMHTu20PVvt4J78eIF5s6di5UrV+Ly5cswMTHBs2fP0LdvX8TFxeHEiROwtbVF27Zt8ezZs2JjnTRpEoKDg5GQkAA7Ozv4+fkV2jIIyCtWdOrUCRkZGYiNjcWBAwdw69Yt9OjRA0BeN638H+2nTp1CamoqLC0tiz0/AOTm5goFp+JmbVmyZAmGDRuGwYMH4+LFi4iKioKNjY2wvnv37khPT8fevXtx9uxZuLm5wcvLCxn/P3BnfkEnJiam2HiSk5Nx/Phx+Pr6wtfXF0ePHsXdu3dLvA4A6N+/v8LYf6tXr4a/v3+B6yrpcyXf1KlT8eOPP+LcuXNQU1PDt99+i7Fjx2LBggU4evQobt68iSlTpgjbL1iwAGFhYQgNDcWFCxfg7e2Njh074saNG8XGPX78eMyZMwdXrlyBk5NTqa4VyLt/wsLCcObMGaipqaF///6l3vdt7xr72wp7fxSlY8eOePz4sVAAjIuLw+PHj9GhQweF7UrzuTZmzBjExsZi586dwuf/2wXpN+V3c89vKZf/PCsrC23btkV0dDTOnz8PHx8fdOjQQSjqbdu2DdWrV0dISIjQuq4w27dvx4gRIzB69GhcunQJ3333Hfr166dQcAbyWtT6+vriwoULaNu2Lfz9/YX3zOTJk5GUlIS9e/fiypUrWLJkCapWrVro+WbPng0DAwPhUZr3PhEREZU/TQ0NTB8+Csc2/Im0o6fw6Pg5JG7fg6nDRkBbSwsAUElXF7+Mm4Rb+2ORefoCbuw9hIiZ82BpZg4A+HFIIE5u3q5w3B/8++Lanv81KLCtaY3YdZuQcfw8Xp6/gpoW1dDM/Qu8PH8FhpX0AQC9O3ZB2tFTCscp7NgrQmbjj/mLhOfq6uqY/P0PuLr7IDJPX8DtA0ewOexX1Le1K/K4HVu0wsvzV4TnxkZG2LVkFR4eO4fnz59/EkOcfAo4m6QI7dq1SyjQAECbNm3wxx9/KGzj4OAAZ2dnbNiwAZMnTwaQ9yO1UaNGwo/sqVOnIiwsDN988w0AwNraGklJSVi2bBn69u0LQ0NDxMbGYtq0aQpFhFevXqFFixYA8rpPrV+/Hioq71dXPXjwIF68eAFvb28AEFqt9O7du9THSE1NRWhoKKpVq4Y6derg2LFjCutnz54Nf39/ofWcra0tFi5ciObNm2PJkiVISUnBli1bcODAAbRq1QpAXiugfIsWLYKrqytmzZolLFu9ejUsLS1x/fp12NnZFYjpl19+gZeXl5ADOzs7JCUl4eeff0ZAQACMjIygo6MjdOksyi+//IJu3brBzMwMDg4O8PDwQKdOnQodJ+u3337DpEmT8Ndff6F58+YAgG+++QaBgYHYuXOn8CN3zZo1whh0pfXjjz8K/7ayskJwcDA2bdpUZLHr+vXr0NfXV7i2P//8U6F1yvHjx4VWVzk5OVi8eDGcnZ2F9W8X65YvXy7cm+3bty8y1uDgYKHV0k8//QQHBwfcvHkTdevWLbBtdHQ0Ll68iNu3bws/dNetWwcHBwecPn0aDRs2FLo1GhsbF5srAPDz84OqqipevnwJmUwGKyurYoumM2bMwOjRozFixAhhWcOGDQHkFTFOnTqF9PR0oStoaGgoduzYga1bt2Lw4MFQV1dHnTp1Suymtnr1arRp0waV/38MBG9vb0RERGDatGnF7gcA7du3x5AhQ3DkyBE0aNAAW7ZsQVxcXIEuzSV9ruQLDg4W3u8jRoyAn58foqOj0aRJEwDAgAEDFIpvoaGhGDduHHr27AkAmDt3Lg4fPozw8HD89ttvRcYdEhKC1q1bl3h9b5s5c6bw/hk/fjzatWuH7OxsaP3/F6fC5Oc93/r169G5c+d3jv1thb0/iqKuro5evXph9erVaNq0KVavXo1evXpBXV1xkNaSPtcsLCywatUqrF+/Hl5eXgDyultWr169yHMbG+eNYZHfUi6fs7OzQuzTp0/H9u3bERUVhcDAQBgZGUFVVRWVKlUq9j0WGhqKgIAADB06FAAwatQonDhxAqGhocL/TUDeGJt+fn4AgFmzZmHhwoU4deoUfHx8kJKSAldXV7i7uwP4X2u2wkyYMAGjRo0SnmdmZrIgJnIqOYX/YYTEgfkTP+ZQ3Jg/+lywZZgItWjRAgkJCcJj4cKFhW7n7++PDRs2AMhrebVx40b4+/sDyOv2kpycjAEDBkBPT094zJgxQ2hNpqKigkmTJiE2NhY1a9YscHwfHx8kJiYq1bKoKKtXr0aPHj2EcXv8/PwQHx+v0LKtKNWrV4euri4sLCzw/Plz/Pnnn4W2wklMTMSaNWsUrtfb2xsymQy3b99GQkICVFVVhR/Ahe1/+PBhhf3zCytFxXnlyhXhx32+Jk2a4MaNG5BKSz8LS7169XDp0iWcOHEC/fv3R3p6Ojp06ICBAwcqbLd161aMHDkSBw4cULgOLS0t9O7dWyhcnDt3DpcuXUJAQECpYwCAzZs3o0mTJjAzM4Oenh5+/PHHYrtqAQVbf3l7eyMhIQG7d+/G8+fPFV4HDQ2NAq13/v33XwwaNAi2trYwMDCAvr4+srKySjzvm8cxN8/7y056enqh2165cgWWlpYKP3Dr1asHQ0NDXLlypdB9ijN//nwkJCRg7969qFevHlauXCl0mXtbeno6Hjx4IBQa3paYmIisrCxUqVJF4d67ffu2cN9Vq1YNV69exRdffFFkTFKpFGvXrkWvXr2EZb169cKaNWtK1bIzv7gSERGBP/74A3Z2dgVyVZrPlXxv7mtqagoAQlE0f1l+vjIzM/HgwYNC30sl5Se/2KEsZe6ffPl5z3+0bt36vWJ/W2Hvj+L0798ff/zxB9LS0vDHH38U2rqtpM+15ORkvH79WqHbvZGREerUqaNU7EBey7Dg4GDY29vD0NAQenp6uHLlSonv5bcV9bn69uv55mulq6sLfX19IYfff/89Nm3aBBcXF4wdO7bAH0/epKmpCX19fYUHANHNpEV5VHKlMN90GCrMnygxf+LHHIob8ydycjl0dHQ++TG1Pxa2DBMhXV1dhS5URfHz88O4ceNw7tw5vHz5Evfu3RO6fWVlZQEAVqxYUWBsMdW3BkT08PDA/PnzhZYeQN4P461bt0JXV/d9LwcZGRnYvn07cnJysGTJEmG5VCrF6tWrMXPmzGL3P3r0KPT19WFiYlLsBAJZWVn47rvvMHz48ALratSogZs3bxZ7nqysLHTo0AFz584tsC7/x/KHpKKigoYNG6Jhw4YICgrC+vXr0bt3b0yaNAnW1tYAAFdXV5w7dw6rV6+Gu7u7wgfdwIED4eLigvv37yMiIkIYv6u0jh8/Dn9/f/z000/w9vaGgYEBNm3aVOj4QflsbW3x9OlTpKWlCS099PT0YGNjU+iA5dra2gU+nPv27YtHjx5hwYIFqFmzJjQ1NdG4ceNCu8K+6c0WMPnHLKvBLEtiZmYGGxsb2NjYICIiAm3btkVSUlKh3dpKmt44KysL5ubmhXaBfHscpeL8/fff+Oeff4TPgHxSqRTR0dGlaj3Vv39/NGrUCJcuXSq0sKLM50ph+Xl7WVnk610/o97l/snP+5tKM75UfsvaN2f3KWwsvsLeH8VxdHRE3bp14efnJ0zSkT82Wr6SPtdK+lxURnBwMA4cOIDQ0FDY2NhAW1sb3bp1K/G9/K7ebgX35j3Vpk0b3L17F3v27MGBAwfg5eWFYcOGITQ0tNTHl/N7pCjJJcAr8yrQTH0ECSfUEh3mT/yYQ3Fj/sRPKpVCRUWFBTGwZdhnrXr16mjevDkiIyMRGRmJ1q1bCz/GTU1NYWFhgVu3bgk/2vMf+YWVN+UPPGxlZQVtbW3k5ORg//79ZRJnZGQkqlevjsTERIVWFWFhYVizZk2JLaisra1Ru3btEmfSdHNzQ1JSUoHrtbGxgYaGBhwdHSGTyRAbG1vk/pcvX4aVlVWB/Yv6wW1vb19ggML4+HjY2dkVKA4oq169egCgMAFA7dq1cfjwYezcuRM//PCDwvaOjo5wd3fHihUrsGHDBqXHQDp27Bhq1qyJSZMmwd3dHba2tiWON9WtWzeoq6sX+kO7tOLj4zF8+HC0bdtWGGC+pEkLlGVvb4979+7h3r17wrKkpCQ8efJEeJ3f1RdffIEGDRoUWdStVKkSrKysipzIwM3NDWlpaVBTUytw3xU1xlFhVq1ahZ49eyq8xxISEtCzZ89SD6Tv4OAABwcHXLp0SRh78E3Kfq6Ulr6+PiwsLAp9L71vfj600sSe363wzTGy3i5avav+/fsjJiamyPd7SZ9rtWvXhrq6Ok6ePCns8/jxY1y/fr3Y86qrqxf47I6Pj0dAQAC6dOkCR0dHmJmZFZiIQkNDo8TP/KI+V5W9F4yNjdG3b1+sX78e4eHhWL58uVL7czZJcZKrquKRl5toZkIjRcyf+DGH4sb8iZxEojCGdEXHlmGfOX9/f0ydOhWvX78uMND5Tz/9hOHDh8PAwAA+Pj549eoVzpw5g8ePHyuMj5Kbm4uoqCj06tULS5YswZ07d9CzZ09s27YNXbp0ee8YV61ahW7duqF+/foKyy0tLTFhwgTs27ev0BnrlDVu3Dh8+eWXCAwMxMCBA6Grq4ukpCQcOHAAixYtgpWVFfr27Yv+/ftj4cKFcHZ2xt27d5Geng5fX18MGzYMK1asgJ+fH8aOHQsjIyPcvHkTmzZtwsqVKwstbo0ePRoNGzbE9OnT0aNHDxw/fhyLFi0qdtbOwnTr1g1NmjSBh4cHzMzMcPv2bUyYMAF2dnYFxsCys7PD4cOH4enpCTU1NWGGNyCvdVhgYCB0dXWVzp2trS1SUlKwadMmNGzYELt378b27duL3adGjRoICwvDiBEjkJGRgYCAAFhbWyMjIwPr168HULDFUGHn/f333+Hu7o7MzEyMGTOmxNZUymrVqhUcHR3h7++P8PBw5ObmYujQoWjevPk7d7N7U1BQELp06YKxY8eiWrVqBdZPmzYNQ4YMgYmJCdq0aYNnz54hPj4eP/zwA1q1aoXGjRujc+fOmDdvHuzs7PDgwQPs3r0bXbp0gbu7O/755x94eXlh3bp1hXaVfPjwIf766y9ERUUVeJ/16dMHXbp0QUZGRpFdOd906NAh5OTkFNkqrbSfK8oaM2YMpk6ditq1a8PFxQURERFISEhAZGTkOx/zYykpdhsbG1haWmLatGmYOXMmrl+/XmyLS2UMGjQI3bt3LzJfJX2u6enpYcCAARgzZgyqVKkCExMTTJo0qcRxIvMLvE2aNIGmpiYqV64MW1tbbNu2DR06dIBEIsHkyZMLtLazsrLCkSNH0LNnT2hqahZa8B0zZgx8fX3h6uqKVq1a4a+//sK2bdsUZqYsyZQpU9CgQQM4ODjg1atX2LVrF+zt7Uu9PxERERGJG1uGfea6deuGR48e4cWLF+jcubPCuoEDB2LlypWIiIiAo6MjmjdvjjVr1hRowXHmzBmEh4fj999/h56eHurXr4/Tp0+jWrVqhXblUcbZs2eRmJiIrl27FlhnYGAALy+vUrdaKYmTkxNiY2Nx/fp1fPXVV3B1dcWUKVNgYfG/aWmXLFmCbt26YejQoahbty4GDRoktLzKb90hlUrx9ddfw9HREUFBQTA0NCzyh6Gbmxu2bNmCTZs2oX79+pgyZQpCQkKUHqvL29sbf/31Fzp06AA7Ozv07dsXdevWxf79+wvtblinTh0cOnQIGzduxOjRo4Xlfn5+UFNTg5+fX4GBwKdNm1bsINIdO3bEyJEjERgYCBcXFxw7dkyYGKA4P/zwA/bv34+HDx+iW7duwmyQt2/fxr59+xTGiSrMqlWr8PjxY7i5uaF3794YPnx4sbPovQuJRIKdO3eicuXKaNasGVq1aoVatWph8+bNZXJ8Hx8fWFtbF9k6rG/fvggPD8fixYvh4OCA9u3bCzMNSiQS7NmzB82aNUO/fv1gZ2eHnj174u7du8JYWzk5Obh27RpevHhR6PHXrVsHXV3dQscl8/Lygra2tlCcLImurm6x3TNL+7mirOHDh2PUqFEYPXo0HB0dsW/fPkRFRcHW1va9jvsxlBS7uro6Nm7ciKtXr8LJyQlz587FjBkzyuTcampqqFq1aqGfE0DpPtd+/vlnfPXVV+jQoQNatWqFpk2bokGDBsWeNywsDAcOHIClpSVcXV0B5E0EUrlyZXh4eKBDhw7w9vaGm5ubwn4hISG4c+cOateuLbSYe1vnzp2xYMEChIaGwsHBAcuWLUNERIRSMyNpaGhgwoQJcHJyQrNmzaCqqopNmzaVen8iIiIiEjeJ/M1BSog+AE9PTwQEBChdAKKyl/8j8/Tp0wV+hPbt2xcSiURhBj8iIlKUmZkJAwMDnDNpAn0Zx9sQG5maKh62bQTjPSc5ALQIMX/ixxyK24fKn7S6CbLnBqKmsSk0JWyv88FIJJDb1YCWllaZjRmWnZ2N27dvw9raukBji/zvTE+fPhUmIPqUsJskUQWQk5ODR48e4ccff8SXX35ZoBAml8sRExODuLi4coqQiEhcJLlSQIVfo8RGJVcK06iiZw+lTxvzJ37MobgxfyInl5f5cDNixrIrUQUQHx8Pc3NznD59GkuXLi2wXiKR4O7du7C0tCyH6IiIxEeuwlZhYiRXkeC5TTXmT6SYP/FjDsWN+RO/3NxcsHNgHhbD6IMLCAiAi4tLeYdRoXl6ekIul+PatWsljtFFRESlUMIkAvRpkquo4EnjepAzf6LE/IkfcyhuzJ+4zFi6CI16vDFpmkSC169fl19Anxi276cPjmOFERERERERkbLaLtf5qOfbM7jwCak+ddqu9tj8y6/o2KKVsCyoTz9839O/HKP6tLEYRkRERERERET0GdHT0YWejm55h/HJ4mySRERERKWUPzNSRkYGKleuXN7hkJKkUinu3LkDKysrqKqqlnc4pCTmT/yYQ3H7UPkrbkbClkNTyuw8pXFocQ2ltvf09ISTkxO0tLSwcuVKaGhoYMiQIZg2bRoA4MmTJwgODsbOnTvx6tUruLu7Y/78+XB2dhaOMWPGDCxcuBAvX75Ejx49ULVqVezbtw8JCQkAgNOnT2PixIk4f/48cnJy4OLigvnz5wuTollZWeHu3bvC8WrWrIk7d+5g2rRp2LFjBxISErB//3507NgRd+7cgampqTCb5IgRI3Dx4kUcOnQIABAXF4cJEybgzJkzqFq1Krp06YLZs2dDV7fwopqYZ5NkZ18iIiIiJfFHnDipqqqidu3azJ9IMX/ixxyKG/NXuLVr10JXVxcnT57EvHnzEBISggMHDgAAunfvjvT0dOzduxdnz56Fm5sbvLy8kJGRAQCIjIzEzJkzMXfuXJw9exY1atTAkiVLFI7/7Nkz9O3bF3FxcThx4gRsbW3Rtm1bPHv2DEBesQwAIiIikJqaKjx/k5eXFwwNDbF7926hECaVSrF582b4++d1pUxOToaPjw+6du2KCxcuYPPmzYiLi0NgYOCHeeHKGVuGEREREZVS/l85Hz9+DENDw/IOh5Qkk8mQnp4OExMTqHAAaNFh/sSPORS3D5U/sbcMk0qlOHr0qLDsiy++QMuWLdG+fXu0a9cO6enp0NTUFNbb2Nhg7NixGDx4ML788ku4u7tj0aJFwvqmTZsiKytLaBn2NplMBkNDQ2zYsAHt27cHAEgkEmzfvh2dO3cWtnuzZRjwv1Zg0dHRkEgkQmuxtLQ0GBoaYuDAgVBVVcWyZcuEY8TFxaF58+Z4/vx5gdwAbBlGREREVKHwb4niJJfLkZaWxvyJFPMnfsyhuDF/hXNyclJ4bm5ujvT0dCQmJiIrKwtVqlSBnp6e8Lh9+zaSk5MBANeuXcMXX3yhsP/bz//9918MGjQItra2MDAwgL6+PrKyspCSolyh0N/fH7GxsXjw4AGAvFZp7dq1E/64l5iYiDVr1ijE6u3tDZlMhtu3byt1LjHgAPpERERERERERO9AXV1d4blEIoFMJkNWVhbMzc0RExNTYB9lWpf37dsXjx49woIFC1CzZk1oamqicePGeP36tVJxNmzYELVq1cKmTZswdOhQbN++HWvWrBHWZ2Vl4bvvvsPw4cML7FujhnIt5sSAxTAiIiIiIiIiojLk5uaGtLQ0qKmpwcrKqtBt6tSpg9OnT6NPnz7CsrfH/IqPj8fixYvRtm1bAMC9e/fw33//KWyjrq4OqVRaYkw9evTAhg0bYGlpCRUVFbRr104h3qSkJNjY2JT2EkWN3SSJiIiIlJQ/+CyJi0QigZGREfMnUsyf+DGH4sb8KadVq1Zo3LgxOnfujP379+POnTs4duwYJk2ahDNnzgAAfvjhB6xatQpr167FjRs3MGPGDFy4cEHhNba1tcXvv/+OK1eu4OTJk/D394e2trbCuaysrBAdHY20tDQ8fvy4yJi+/fZbnDt3DjNnzkS3bt0UxjIbN24cjh07hsDAQCQkJODGjRvYuXPnZzuAPluGERERESmJAz+Lk4qKymfZ1aOiYP7EjzkUt/LIn7ID2n9KJBIJ9uzZg0mTJqFfv354+PAhzMzM0KxZM5iamgLIG8fr1q1bCA4ORnZ2Nnx9fREQEIBTp04Jx1m1ahUGDx4MNzc3WFpaYtasWQgODlY4V1hYGEaNGoUVK1agWrVquHPnTqHx1KtXD1988QVOnTqF8PBwhfVOTk6IjY3FpEmT8NVXX0Eul6N27dro0aNHmb82nwLOJklERERUSpxNUtxkMhnu37+P6tWrs6ApQsyf+DGH4vah8lfcjIQVUevWrWFmZobff/+9TI8rl8vx+vVraGholFnrPs4mSURERFSB8G+J4iSXy5GRkcH8iRTzJ37Mobgxf2XvxYsX+OWXX3D58mVcvXoVU6dOxcGDB9G3b98Pcr7SjCtWUbCbJBERERERERHRR5bflXLmzJnIzs5GnTp18Oeff6JVq1blHdpnj8UwIiIiIiIiIqKPTFtbGwcPHizvMCokdpMkIiIiUhJn0hIniUQCMzMz5k+kmD/xYw7FjfkTP3V19fIO4ZPBlmFERERESuLAz+KkoqICMzOz8g6D3hHzJ37Mobgxf+ImkUhYDHsDv8kRERERKYkD0IqTVCpFcnIy8ydSzJ/4MYfixvyJm1wuR3Z2NidA+H8shhERERFRhfHs2bPyDoHeA/MnfsyhuDF/4iaTyco7hE8Gi2FERERERERERFRhsBhGREREREREREQVBothREREREriTFriJJFIYGlpyfyJFPMnfsyhuDF/4qehoQFra2uEh4eXdyjljrNJEhERESmJs0mKk4qKCqpUqVLeYdA7Yv7EjzkUt/LI36VLlz7q+erXr/9Rz1canp6ecHFxee8ClkQigZoaS0D5+E2OiIiISEmcSUucpFIprl69yvyJFPMnfsyhuDF/ny65XI7c3NwSt3n58uVHiujTx2IYEREREVUY2dnZ5R0CvQfmT/yYQ3Fj/hR5enpi+PDhGDt2LIyMjGBmZoZp06YJ6588eYKBAwfC2NgY+vr6aNmyJRITE4X1AQEB6Ny5s8Ixg4KC4OnpKayPjY3FggULIJFIIJFIcOfOHcTExEAikWDv3r1o0KABNDU1ERcXh+TkZHTq1AmmpqbQ09NDw4YNcfDgQeHYcrn8Q74cosJiGBERERERERHRO1i7di10dXVx8uRJzJs3DyEhIThw4AAAoHv37khPT8fevXtx9uxZuLm5wcvLCxkZGaU69oIFC9C4cWMMGjQIqampSE1NhaWlpbB+/PjxmDNnDq5cuQInJydkZWWhbdu2iI6Oxvnz5+Hj44MOHTogJSXlg1y7mLHDKBERERERERHRO3BycsLUqVMBALa2tli0aBGio6Ohra2NU6dOIT09HZqamgCA0NBQ7NixA1u3bsXgwYNLPLaBgQE0NDSgo6MDMzOzAutDQkLQunVr4bmRkRGcnZ2F59OnT8f27dsRFRWFYcOGve+lflZYDCMiIiJSEgfQFycVFRXUqlWL+RMp5k/8mENxY/4K5+TkpPDc3Nwc6enpSExMRFZWVoFJB16+fInk5OQyObe7u7vC86ysLEybNg27d+9GamoqcnNz8fLlS6FlWH5RjlgMIyIiIlIap5UXJ4lEAn19/fIOg94R8yd+zKG4MX+FU1dXV3gukUggk8mQlZUFc3NzxMTEFNjH0NAQQF6B8e1xvHJyckp9bl1dXYXnwcHBOHDgAEJDQ2FjYwNtbW1069YNr1+/hkQigaqqaqmP/bljMYyIiIhIScm2baEvY0FMbGRqqvi3WzOYbj0ClVzOhiY2zJ/4MYfi9qHyJ61ugpy5gXiVLQckb7U6+8gVi+yEq0ptL8t6gdyHGQr7SZ9mQSqToL5BVaSlpUF6/S5qWlRT3DHrP2Tf/w+V5Sq4kHxbYf9z8cehrqYmLFN7lYNXaf8pbPP6Zl5Lr+yL15Fd6X8FyrjoQ+jl3Q5trO0BKZCV/hR3km8h18EJ2YnXILP933hjFR3bNxIRERFRhSFT59+CxYz5Ez/mUNyYv9Jr2cgDjZxc4DsyEAePx+Pug39wPOE8pi4Kx9nLlwAAng2/xLmkS4j8awdu3r2D6Ut+RVLyDYXj1LSohtOXLuDug3/w3+PHkMlkRZ6zdo2a2HnoIBKvXcGFa1cRMGEMZPKit6/IeCcTERERERER0SfHJle8JQuJRIIdvy7D1EXhGDx1Iv57/BimVauiqZs7TP5/HLHWHk0xYdD3mLQgDNmvXqFPp2/wbbtOuHzzunCcoD79MHDyBLh2bY+X2dm4uvtgkeecN3o8vps2CS0CvkUVw8oYHTAAmc+zPvi1ipFE/nYHVSIiIiIqVGZmJgwMDHDOpAm7SYqQTE0VqX4tYb7xELtoiRDzJ37Mobh9qPxJq5sge24gahqbQvPtbpJUdiQSyGwtoa2tXWZjn2ZnZ+P27duwtraGlpaWwrr870xPnz79JMea451GREREpCwpf8SJkUQqhUnUMUiYP1Fi/sSPORQ35k/k5PICBauKjMUwIiIiImWxXb04yQHV59nMn1gxf+LHHIob8yd6nA37f1gMIyIiIlKWGqcmFyP5/3fxkTN/osT8iR9zKG7Mn8hJJHj58mV5R/HJYDGMiIiIiIiIiIgqDBbDiIiIiIiIiIiowmAxjCoET09PBAUFfdBzvMvErJ6enlizZk3ZB/OJsLKyQnh4eHmHIXqluX/XrFkDQ0PDjxLPtWvXYGZmhmfPnn2U8wHAtGnT4OLi8tHOp6zly5fD0tISKioqn/Q9HxAQgM6dO5d3GJ+M8ePH44cffijvMIiIiIjoI2MxjD4bAQEBkEgkBR43b97Etm3bMH369A96/i1btuDJkycf7Pje3t5QVVXF6dOnS7W9XC7HihUr0LhxY+jr60NPTw8ODg4YMWIEbt68WaaxfcxCzIcybdo0BAQEvNO+d+7cUbjnqlSpgq+//hrnz58vk9jevn8LKzL26NED169fL5PzlWTChAn44YcfUKlSJWHZihUr4OzsDD09PRgaGsLV1RWzZ88us3MGBwcjOjq61Nvn5yQhIaHMYihKZmYmAgMDMW7cOPzzzz8YPHhwkdsePnwY7du3h7GxMbS0tFC7dm306NEDR44cKdOYPub1l5eff/4Z3377LQBgw4YNaNmypcL6a9euoUWLFjA1NYWWlhZq1aqFH3/8ETk5OcI2wcHBWLt2LW7duqV8AGU4pTx9PJJcKcw3HoKE+RMl5k/8mENxY/5ETi6HtrZ2eUfxyWAxjD4rPj4+SE1NVXhYW1vDyMhI4Yf7h7Bp0yZERUV9kGOnpKTg2LFjCAwMxOrVq0vcXi6X49tvv8Xw4cPRtm1b7N+/H0lJSVi1ahW0tLQwY8aMDxJnRXfw4EGkpqbi77//RlZWFtq0aVMmBdLS3L/a2towMTF573OVJCUlBbt27VIoHK5evRpBQUEYPnw4EhISEB8fj7FjxyIrK6vMzqunp4cqVaqU2fHKUkpKCnJyctCuXTuYm5tDR0en0O0WL14MLy8vVKlSBZs3b8a1a9ewfft2eHh4YOTIkR85avE7fvw4mjRpAgA4evSo8O986urq6NOnD/bv349r164hPDwcK1aswNSpU4VtqlatCm9vbyxZskT5ADgZkzhJAKmuFvMnVsyf+DGH4sb8id679Gb6XLEYRp8VTU1NmJmZKTxUVVUVuplNnDgRjRo1KrCvs7MzQkJChOcrV66Evb09tLS0ULduXSxevLjI82ZlZeHvv//G1q1by/yaACAiIgLt27fH999/j40bN5Y4C8jmzZuxadMmbN68GZMnT8aXX36JGjVq4Msvv8TcuXMRERGhsH1x15rfwmTbtm1o0aIFdHR04OzsjOPHjwMAYmJi0K9fPzx9+lRoGTVt2rRC43ry5AkGDhwIY2Nj6Ovro2XLlkhMTBTW53eFW716NWrUqAE9PT0MHToUUqkU8+bNg5mZGUxMTDBz5kyF4/7yyy9wdHSErq4uLC0tMXToUIVCTH7Ltb///hv29vbQ09MTCqdF2bp1KxwdHaGtrY0qVaqgVatWeP78ebGve5UqVWBmZgZ3d3eEhobi33//xcmTJwEAf/75JxwcHKCpqQkrKyuEhYUp7Lt48WLY2tpCS0sLpqam6Natm7DuzfvX09MTd+/exciRI4XX+81rfNOSJUtQu3ZtaGhooE6dOvj9998V1kskEqxcuRJdunSBjo4ObG1tSyzobtmyBc7OzqhWrZqwLCoqCr6+vhgwYABsbGzg4OAAPz8/hTzJZDKEhISgevXq0NTUhIuLC/bt26dw7Pv378PPzw9GRkbQ1dWFu7u78PoV1k2yuPvW2toaAODq6gqJRAJPT08cOXIE6urqSEtLUzhOUFAQvvrqqyKvOSUlBZ06dYKenh709fXh6+uLf//9F0De6+7o6AgAqFWrFiQSCe7cuVPoMYKCghAUFIS1a9eiZcuWqFmzJpycnDBixAicOXNGYfu4uDh89dVX0NbWhqWlJYYPH65w/1lZWWHWrFno378/KlWqhBo1amD58uXFXn9hZDIZZs+eDWtra2hra8PZ2VnhcywmJgYSiQR///03XF1doa2tjZYtWyI9PR179+6Fvb099PX18e233+LFixdKHzc6Ohru7u7Q0dGBh4cHrl27VmQe3vZmMSwuLq5AMaxWrVro168fnJ2dUbNmTXTs2BH+/v44evSownYdOnTApk2bijzPq1evkJmZqfAAAKhyJi0xkquqIr2jB+TMnygxf+LHHIob8ydyEgmys7PLO4pPBothVOH4+/vj1KlTSE5OFpZdvnwZFy5cELrcREZGYsqUKZg5cyauXLmCWbNmYfLkyVi7di0AICMjA3FxccJj8eLFePnyJfbv34+YmBhh+fHjxyGTyd4rXrlcjoiICPTq1Qt169aFjY1NiUW3jRs3ok6dOujYsWOh6/MLKKW51nyTJk1CcHAwEhISYGdnBz8/P+Tm5sLDwwPh4eHQ19cXWuMFBwcXet7u3bsLP6LPnj0LNzc3eHl5ISMjQ9gmOTkZe/fuxb59+7Bx40asWrUK7dq1w/379xEbG4u5c+fixx9/FIokAKCiooKFCxfi8uXLWLt2LQ4dOoSxY8cqnPvFixcIDQ3F77//jiNHjiAlJaXIOFNTU+Hn54f+/fvjypUriImJwTfffKPUX1LymyC/fv0aZ8+eha+vL3r27ImLFy9i2rRpmDx5sjBe3JkzZzB8+HCEhITg2rVr2LdvH5o1a1bocbdt24bq1asjJCREeL0Ls337dowYMQKjR4/GpUuX8N1336Ffv344fPiwwnY//fQTfH19ceHCBbRt2xb+/v4K+Xjb0aNH4e7urrDMzMwMJ06cwN27d4vcb8GCBQgLC0NoaCguXLgAb29vdOzYETdu3ACQV1Bu3rw5/vnnH0RFRSExMRFjx44t8v1T0n176tQpAP9rrbdt2zY0a9YMtWrVUigK5uTkIDIyEv379y/0PDKZDJ06dUJGRgZiY2Nx4MAB3Lp1Cz169ACQ1z314MGDwjlTU1NhaWlZ4Dh//vkncnJyCtyX+d58TyYnJ8PHxwddu3bFhQsXsHnzZsTFxSEwMFBhn7CwMLi7u+P8+fMYOnQovv/+e6GYVNj1F2b27NlYt24dli5disuXL2PkyJHo1asXYmNjFbabNm0aFi1ahGPHjuHevXvw9fVFeHg4NmzYgN27d2P//v349ddflT7upEmTEBYWhjNnzkBNTa3IPOSbM2cODA0NYWhoiLS0NDRv3hyGhoa4dOkSfH19YWhoiLi4uEL3vXnzJvbt24fmzZsrLP/iiy9w//79QouY+ddiYGAgPArLLxEREVFZO3LmFLRd7fHkWWax29Vp64VfI9cWuw0VpFbeARCVpV27dkFPT0943qZNG/zxxx8K2zg4OMDZ2RkbNmzA5MmTAeT9sG7UqBFsbGwAAFOnTkVYWBi++eYbAHmtLJKSkrBs2TL07dsXhoaGiI2NxbRp05Cbmysc+9WrV2jRogUAwNzcHOvXr4eKyvvVnA8ePIgXL17A29sbANCrVy+sWrUKvXv3LnKf69evo06dOgrLgoKCsHLlSgCAoaEh7t+/X6przRccHIx27doByCugODg44ObNm6hbty4MDAwgkUhgZmZWZExxcXE4deoU0tPToampCQAIDQ3Fjh07sHXrVmGcJZlMhtWrV6NSpUqoV68eWrRogWvXrmHPnj1QUVFBnTp1MHfuXBw+fFho4ffm4PJWVlaYMWMGhgwZotBSKCcnB0uXLkXt2rUBAIGBgQotAd9szZaamorc3Fx88803qFmzJgAIrX9K48mTJ5g+fTr09PTwxRdfYNSoUfDy8hLuNzs7OyQlJeHnn39GQEAAUlJSoKuri/bt26NSpUqoWbMmXF1dCz22kZERVFVVUalSpWJf79DQUAQEBGDo0KEAgFGjRuHEiRMIDQ0V7lEgb6w9Pz8/AMCsWbOwcOFCnDp1Cj4+PoUe9+7duwWKYVOnTsU333wDKysr2NnZoXHjxmjbti26desm3P+hoaEYN24cevbsCQBCDsPDw/Hbb79hw4YNePjwIU6fPg0jIyMAEN6PhSnpvjU2Ngbwv9Z6+QYMGICIiAiMGTMGAPDXX38hOzsbvr6+hZ4nOjoaFy9exO3bt4UiyLp16+Dg4IDTp0+jYcOGQvdNY2PjInNy/fp16OvrK6z/888/Fd5jx48fh6OjI2bPng1/f3/hvra1tcXChQvRvHlzLFmyBFpaWgCAtm3bCvkdN24c5s+fj8OHD6NOnTpFXv+bXr16hVmzZuHgwYNo3LgxgLzWVHFxcVi2bJlC0WjGjBlCy6sBAwZgwoQJSE5ORq1atQAA3bp1w+HDhzFu3Diljjtz5kzh+fjx49GuXTtkZ2cL1/i2IUOGoGfPnlizZg1OnDiBpUuXYs+ePVizZg22bNkCAAWu18PDA+fOncOrV68wePBghfc9AFhYWADIu7etrKwKnHPChAkYNWqU8DwzM5MFMSIioo/kn9aDPur5qh1Y8VHPV5wvnV1w+8ARGOjlDZfye9R2jPl5NtKOnlLYLm79H9DlWGBKY8sw+qy0aNECCQkJwmPhwoWFbufv748NGzYAyGt5tXHjRvj7+wMAnj9/juTkZAwYMAB6enrCY8aMGUJrMhUVFUyaNAmxsbFCseRNPj4+SExMLDCg87tYvXo1evToATW1vNq1n58f4uPjFVq2lcakSZOQkJCAKVOmCF0IS3Ot+ZycnIR/m5ubAwDS09NLff7ExERkZWWhSpUqCue6ffu2wrmsrKwUxscyNTVFvXr1FIqKpqamCuc+ePAgvLy8UK1aNVSqVAm9e/fGo0ePFLpt6ejoCIWw/GsoKn5nZ2d4eXnB0dER3bt3x4oVK/D48eMSr9HDwwN6enqoXLkyEhMTsXnzZpiamuLKlSsFunA1adIEN27cgFQqRevWrVGzZk3UqlULvXv3RmRkpELs76Koc165ckVh2Zt51dXVhb6+frF5ffnyZYFChbm5OY4fP46LFy9ixIgRyM3NRd++feHj4wOZTIbMzEw8ePCg2HgSEhLg6uoqFMKKo8x9+7aAgADcvHkTJ06cAJDXzdHX1xe6urqFbn/lyhVYWloqFD/q1asHQ0PDAq9lSd5s/QXkTYqRkJCA3bt34/nz55BK8wajTUxMxJo1axSuzdvbGzKZDLdv3xb2fzN3+cVoZd6TN2/exIsXL9C6dWuFc61bt67Y97+pqSl0dHSEQlj+svxzv+txS/O5YmhoCCsrK5w6dQpdu3aFlZUVzp8/j44dO8LKygpWVlYF7s/Nmzfj3LlzQiu20NBQhfX5rTiLes9pampCX19f4UHippKTW/JG9Mli/sSPORQ35u/j0VDXgFlV4wLfId9mbGQEHRbDlMaWYfRZ0dXVLbY1ST4/Pz+MGzcO586dw8uXL3Hv3j2h21N+oWjFihUFxhZTfat/vIeHB+bPny+0TgHyBm3eunVrkT+ulZGRkYHt27cjJydHYYBnqVSK1atXFxg7K5+trW2BsXeMjY1hbGysMMi6Mteqrq4u/Dv/A1mZLqBZWVkwNzdHTExMgXVvjnX15nnyz1XYsvxz37lzRxhPbebMmTAyMkJcXBwGDBiA169fC4OZF3aMoro9qqqq4sCBAzh27JjQ/WvSpEk4efKkMBZTYTZv3ox69eqhSpUqSs2uWalSJZw7dw4xMTHYv38/pkyZgmnTpuH06dMffJbO4l7bwlStWrXIwmD9+vVRv359DB06FEOGDMFXX32F2NhYNGjQoMQ4lJnZRpn79m0mJibo0KEDIiIiYG1tjb179xZ6T5Y1W1tbPH36FGlpaULLJT09PdjY2AiF7nxZWVn47rvvMHz48ALHqVGjhvBvZXP3tvzXcffu3QpjwAEQWm8Wdq6S3pPvc1yg6M+Vo0ePok2bNgDyClcxMTEYOXIkXr58CXV1dcyZMwcTJ07ExIkTFfbLL2TWq1cPUqkUgwcPxujRo4V7Jb9bcH5rutKS5EoBFX6NEhuVXCnMNx0ueUP6JDF/4sccihvzV9DXA/ugno0tAGDj7iioq6lhULeemDJ0OCQSCR5nPkXwvFnYcyQGr3Je4yu3hggbOxE2Na0AAHcf/INRc2fg2PlzeJ2Tg5oWFpgVNAY+XzXHkTOn4D2oL1KPnMSFa1cxeGredxxtV3sAwKTvhuHHIYGo09YLgf598IN/X/SdEAypTIr1c+cLMebk5MD662aYO2ocBkweD5lMhjlz5mD58uVIS0uDnZ0dJk+erDBmcUXAlmFUIVWvXh3NmzdHZGQkIiMj0bp1a6FIZGpqCgsLC9y6dQs2NjYKj8IKIfkDjltZWUFbWxs5OTnYv39/mcQZGRmJ6tWrIzExUaHFW1hYGNasWSO0JHmbn58frl27hp07dxZ7fGWvtSgaGhpFxpLPzc0NaWlpUFNTK3CuqlWrlvpcbzt79ixkMhnCwsLw5Zdfws7ODg8ePHjn4+WTSCRo0qQJfvrpJ5w/fx4aGhrYvn17sftYWlqidu3aBQpY9vb2iI+PV1gWHx8POzs74Qe5mpoaWrVqhXnz5uHChQu4c+cODh06VOh5SvN6F3XOevXqFbtfSVxdXZGUlFTidvnnef78OfT19WFhYVFsPE5OTkhISCh2vLJ8pblvNTQ0AKDQ12ngwIHYvHkzli9fjtq1axdosfYme3t73Lt3D/fu3ROWJSUl4cmTJ0q9lt26dYO6ujrmzp1b4rZubm5ISkoqcG02NjbCdZWkuOvPV69ePWhqaiIlJaXAed6nG+CHOq67uzsSEhKwatUqWFpa4sKFC4iKioK2tjYuXLiAhIQEDBkypNhjyGQy5OTkKBTcLl26BHV1dTg4OCgVj5wzaYmSXAJkW1Rh/kSK+RM/5lDcmL/CRf61A2qqqjj6+xb8PGYiFq5fi4jteUP1DJ4yEeeSLuOP8N8Qs2Yj5JCj8w/fIScnBwAwcs50vHr9GgdW/Y4zf+zEjBGjoVfIzORfOrvg5zEToK+nh9sHjuD2gSMI6tOvwHY927bHniMxyHrxv4mXDhyPw4vsbHRs2RpSqRSzZs0q1diunzv+SZMqLH9/f0ydOhWvX7/G/PnzFdb99NNPGD58OAwMDODj44NXr17hzJkzePz4scLYMbm5uYiKikKvXr2wZMkS3LlzBz179sS2bdvQpUuX945x1apV6NatG+rXr6+w3NLSEhMmTMC+ffuEcbzelB9Dz549MWHCBHh7e8PU1BR3797F5s2bFVrPlPZai2NlZYWsrCxER0fD2dkZOjo6QousfK1atULjxo3RuXNnzJs3Tyha7d69G126dCkwDlVp2djYICcnB7/++is6dOiA+Ph4LF269J2Ole/kyZOIjo7G119/DRMTE5w8eRIPHz6Evb39Ox1v9OjRaNiwIaZPn44ePXrg+PHjWLRokTCm2a5du3Dr1i00a9YMlStXxp49eyCTyQqM+5bPysoKR44cQc+ePaGpqVloMXHMmDHw9fWFq6srWrVqhb/++gvbtm0TBnt/V97e3hg4cCCkUqlwH33//fewsLBAy5YtUb16daSmpmLGjBkwNjYWxowaM2YMpk6ditq1a8PFxQURERFISEhAZGQkgLwC7qxZs9C5c2fMnj0b5ubmOH/+PCwsLIRjvKmk+9bExATa2trYt28fqlevDi0tLRgYGAjXoK+vjxkzZhQYP+ptrVq1gqOjI/z9/REeHo7c3FwMHToUzZs3V+qerVGjBsLCwjBixAhkZGQgICAA1tbWyMjIwPr16wH8r1XbuHHj8OWXXyIwMBADBw6Erq4ukpKScODAASxatKhU5yvu+vNVqlQJwcHBGDlyJGQyGZo2bYqnT58iPj4e+vr6CuOZKeNDHVdbW1uYQMTT0xM2NjY4duwYmjRpAjs7uwLbR0ZGQl1dHY6OjtDU1MSZM2cwYcIE9OjRQ6FF2tGjR4WZO5Wiqgq83/woVA7kqqp45OUG842H8lr3kagwf+LHHIob81e46qbm+Dl4AiQSCeysrHH5xnX8un4dmjX4ArtiD+FQxAY0dskbDzhi5jzYtmmJqJhodG3tg3tpqejs9TXq2+Z9l7GuXvgfDjXUNWCgVwkSSGBWtejW7K0bN4WuljaiDh3Et+07AQA2792Nds1aoJKeHjIzMzF79uxSje36uWPLMKqwunXrJowr1blzZ4V1AwcOxMqVKxEREQFHR0c0b94ca9asKdBa6syZMwgPD8fvv/8OPT091K9fH6dPn0a1atWEav+7Onv2LBITE9G1a9cC6wwMDODl5YVVq1YVuq9EIsHmzZsRHh6OPXv2wMvLC3Xq1EH//v1haWmpMNtaaa+1OB4eHhgyZAh69OgBY2NjzJs3r9CY9uzZg2bNmqFfv36ws7NDz549cffuXZiampb6XG9zdnbGL7/8grlz56J+/fqIjIzE7Nmz3/l4AKCvr48jR46gbdu2sLOzw48//oiwsDChi5ay3NzcsGXLFmzatAn169fHlClTEBISgoCAAAB53US3bduGli1bwt7eHkuXLsXGjRuLbKkSEhKCO3fuoHbt2kV27ercuTMWLFiA0NBQODg4YNmyZYiIiICnp+c7XUO+Nm3aQE1NTaGo1qpVK5w4cQLdu3eHnZ0dunbtCi0tLURHRwuDyw8fPhyjRo3C6NGj4ejoiH379iEqKgq2tnnNyjU0NLB//36YmJigbdu2cHR0xJw5c4rs9ljSfaumpoaFCxdi2bJlsLCwQKdOnYR9VVRUEBAQAKlUij59+hR7vRKJBDt37kTlypXRrFkztGrVCrVq1cLmzZuVfu1++OEH7N+/Hw8fPkS3bt1ga2uLtm3b4vbt29i3b58wSYOTkxNiY2Nx/fp1fPXVV3B1dcWUKVOEgd5Lo7jrf9P06dMxefJkzJ49G/b29vDx8cHu3buVev9/zOMCQExMjDDbamxsbJEzr6qpqWHu3Ln44osv4OTkhJ9++gmBgYHCRCL5Nm3ahEGDPu7gvERERPT5+MLJWWFcr0ZOLrh57y6u3EqGmpoavnD83xipVQwrw66mNa7dyhtHdWjPXpizcilaBHyL6Ut+xcXr1wocXxlqamro+rUPNu3dBQB4/vIFdsUcQs+2HQDkzVpe2rFdP3cSeVGD5hDRB+fp6YmAgAChKEIkBr/99huioqLw999/l3co72zAgAF4+PCh0M2ZKqa9e/di9OjRuHDhQoGx24qSmZkJAwMDnDNpAn0Z+4mIjUxNFal+LWG+8RBU2KpBdJg/8WMOxe1D5U9a3QTZcwNR09gUmhLF9jqf+mySXw/sA+vqllg27X9jOf91OBrfjg3Chnnh+HZsEJ6cSFD4A++XPb9BxxZemPjdMADAvbRU7Dsai4Mn4rH3SCzmjBqLoX69FMYMM6ykX+Rskm+OGQYAxxPO4+tBfZD8dwyiTxzDmJ9n4fb+I1DX0MDJJ2nw9PRETExMoWO7KjukRXZ2Nm7fvg1ra+sCkxjlf2d6+vTpJzkBEbtJEhGRUr777js8efIEz549U5j5UwyePn2KixcvYsOGDSyEEZ4/f46IiIhSF8Lo86D29HnJG9Eni/kTP+ZQ3Ji/gk5fTFR4fupiImwsa8K+Vm3k5ubi1MULQjfJR08e4/rd26hb63+TvlmamWNQ954Y1L0nJi/8BRHb/8BQv14FzqOurg6prOQiZGMXV1Q3NcPWv/dif/wRfNPKWxgiwt7eXhjbtSJ1iSwMv/0REZFS1NTUMGnSpPIO45106tQJp06dwpAhQ9C6devyDofK2fvMmsTZJMVJJVcK06hj5R0GvSPmT/yYQ3Fj/gp3Ly0VY0PnYGC3Hjh/JQlLNkVizqixsKlphfaeXhg2fTJ+/fEnVNLRxeSFv8DC2AQdPFsCAIJ/ngXvJs1gW9MKjzOfIvbMSdSxrlXoeWqaV0PWixc4fPI4HO3qQkdLCzpFjHnao007rNy6CTdS7mLf8jV5C+VymJiYfJCxXcWI3+KIylFAQABcXFzKOwyiCiMmJqa8Q6DPhFyFXSTFSK4iwYtaFtC59QASGUcKERvmT/yYQ3Erj/wp222xPPi374TsV9n4qrcvVFVUMezb3hjQ1RcAsPynmQieNwtdh3+P17k5aOrmjh2/LhNaakmlMgTNmY5//k2Dvq4eWns0xbzg8YWep7GLKwZ164He40fh0ZMnmPTdMPw4JLDQbXu26YC5K5ehhrkFPFzchOW5ubkICQmBsbExZs+ejVu3bsHQ0BBubm6YOHFiGb8ynzaOGUZERERUShwzTNw4XpG4MX/ixxyKW3mMGfap+3pgHzjVqYvQMSIoJEkkkNlaQltbW2HA//ch5jHDxHWnERERERERERERvQd2kyQiIiJSktXVv1C5cuXyDoOUJJVK8fziRdT6eZLCzF4kDsyf+DGH4vah8pffukizkNZFnzoVPR2oGRtBy6VueYdSIrlcjpcvX5Z3GJ8MFsOIiIiIqMIQ2yy4pIj5Ez/mUNyYP0ViG49WRYWdA/OxGEZERESkJLZoECdVVVXUrl27vMOgd8T8iR9zKG7Mn7hJJBLRtbz7kFgWJCIiIlKSTCYr7xDoHchkMqSlpTF/IsX8iR9zKG4fOn+c2+/DksvlyMnJKdPXWcw5YzGMiIiISEli/vJXkcnlcqSlpTF/IsX8iR9zKG4fKn/q6uoAgBcvXpTpcamgnJycMj1efs7ycygm7CZJREREREREROVCVVUVhoaGSE9PBwDo6OhAIpGUc1SfH7lcjlevXkEikbz36yuXy/HixQukp6fD0NBQlMNHsBhGREREREREROXGzMwMAISCGJW9/G6S6urqZVZsNDQ0FHInNiyGERERESmJf7EWJ4lEAiMjI+ZPpJg/8WMOxe1D5k8ikcDc3BwmJiZl3pWP8uSP+WZmZlYms0qqq6uLskVYPomcHbaJiIiISiUzMxMGBgZ4+vQp9PX1yzscIiIiok/Sp/6diQPoExERESmJM6GJk0wmQ0pKCvMnUsyf+DGH4sb8iRvzp4jFMCIiIiIlsWG9OMnlcmRkZDB/IsX8iR9zKG7Mn7gxf4pYDCMiIiIiIiIiogqDA+gTERERlVL+X1MzMzNFPWhsRSWVSpGVlcX8iRTzJ37Mobgxf+L2sfOXmZkJ4NNtTc9iGBEREVEpPXr0CABgZWVVvoEQERERicCzZ89gYGBQ3mEUwGIYERERUSkZGRkBAFJSUj7JL3ZUvMzMTFhaWuLevXuf5MxWVDzmT/yYQ3Fj/sTtY+dPLpfj2bNnsLCw+ODnehcshhERERGVkopK3nCrBgYG/CEgYvr6+syfiDF/4sccihvzJ24fM3+f8h8OOYA+ERERERERERFVGCyGERERERERERFRhcFiGBEREVEpaWpqYurUqdDU1CzvUOgdMH/ixvyJH3MobsyfuDF/iiTyT3WeSyIiIiIiIiIiojLGlmFERERERERERFRhsBhGREREREREREQVBothRERERERERERUYbAYRkREREREREREFQaLYURERESl9Ntvv8HKygpaWlpo1KgRTp06Vd4hUSnMnj0bDRs2RKVKlWBiYoLOnTvj2rVr5R0WvaM5c+ZAIpEgKCiovEOhUvrnn3/Qq1cvVKlSBdra2nB0dMSZM2fKOywqBalUismTJ8Pa2hra2tqoXbs2pk+fDs7D9+k6cuQIOnToAAsLC0gkEuzYsUNhvVwux5QpU2Bubg5tbW20atUKN27cKJ9gyxGLYURERESlsHnzZowaNQpTp07FuXPn4OzsDG9vb6Snp5d3aFSC2NhYDBs2DCdOnMCBAweQk5ODr7/+Gs+fPy/v0EhJp0+fxrJly+Dk5FTeoVApPX78GE2aNIG6ujr27t2LpKQkhIWFoXLlyuUdGpXC3LlzsWTJEixatAhXrlzB3LlzMW/ePPz666/lHRoV4fnz53B2dsZvv/1W6Pp58+Zh4cKFWLp0KU6ePAldXV14e3sjOzv7I0daviRylnSJiIiIStSoUSM0bNgQixYtAgDIZDJYWlrihx9+wPjx48s5OlLGw4cPYWJigtjYWDRr1qy8w6FSysrKgpubGxYvXowZM2bAxcUF4eHh5R0WlWD8+PGIj4/H0aNHyzsUegft27eHqakpVq1aJSzr2rUrtLW1sX79+nKMjEpDIpFg+/bt6Ny5M4C8VmEWFhYYPXo0goODAQBPnz6Fqakp1qxZg549e5ZjtB8XW4YRERERleD169c4e/YsWrVqJSxTUVFBq1atcPz48XKMjN7F06dPAQBGRkblHAkpY9iwYWjXrp3C+5A+fVFRUXB3d0f37t1hYmICV1dXrFixorzDolLy8PBAdHQ0rl+/DgBITExEXFwc2rRpU86R0bu4ffs20tLSFD5HDQwM0KhRowr3fUatvAMgIiIi+tT9999/kEqlMDU1VVhuamqKq1evllNU9C5kMhmCgoLQpEkT1K9fv7zDoVLatGkTzp07h9OnT5d3KKSkW7duYcmSJRg1ahQmTpyI06dPY/jw4dDQ0EDfvn3LOzwqwfjx45GZmYm6detCVVUVUqkUM2fOhL+/f3mHRu8gLS0NAAr9PpO/rqJgMYyIiIiIKoxhw4bh0qVLiIuLK+9QqJTu3buHESNG4MCBA9DS0irvcEhJMpkM7u7umDVrFgDA1dUVly5dwtKlS1kME4EtW7YgMjISGzZsgIODAxISEhAUFAQLCwvmj0SN3SSJiIiISlC1alWoqqri33//VVj+77//wszMrJyiImUFBgZi165dOHz4MKpXr17e4VApnT17Funp6XBzc4OamhrU1NQQGxuLhQsXQk1NDVKptLxDpGKYm5ujXr16Csvs7e2RkpJSThGRMsaMGYPx48ejZ8+ecHR0RO/evTFy5EjMnj27vEOjd5D/nYXfZ1gMIyIiIiqRhoYGGjRogOjoaGGZTCZDdHQ0GjduXI6RUWnI5XIEBgZi+/btOHToEKytrcs7JFKCl5cXLl68iISEBOHh7u4Of39/JCQkQFVVtbxDpGI0adIE165dU1h2/fp11KxZs5wiImW8ePECKiqKZQNVVVXIZLJyiojeh7W1NczMzBS+z2RmZuLkyZMV7vsMu0kSERERlcKoUaPQt29fuLu744svvkB4eDieP3+Ofv36lXdoVIJhw4Zhw4YN2LlzJypVqiSMi2JgYABtbe1yjo5KUqlSpQLju+nq6qJKlSoc900ERo4cCQ8PD8yaNQu+vr44deoUli9fjuXLl5d3aFQKHTp0wMyZM1GjRg04ODjg/Pnz+OWXX9C/f//yDo2KkJWVhZs3bwrPb9++jYSEBBgZGaFGjRoICgrCjBkzYGtrC2tra0yePBkWFhbCjJMVhUQul8vLOwgiIiIiMVi0aBF+/vlnpKWlwcXFBQsXLkSjRo3KOywqgUQiKXR5REQEAgICPm4wVCY8PT3h4uKC8PDw8g6FSmHXrl2YMGECbty4AWtra4waNQqDBg0q77CoFJ49e4bJkydj+/btSE9Ph4WFBfz8/DBlyhRoaGiUd3hUiJiYGLRo0aLA8r59+2LNmjWQy+WYOnUqli9fjidPnqBp06ZYvHgx7OzsyiHa8sNiGBERERERERERVRgcM4yIiIiIiIiIiCoMFsOIiIiIiIiIiKjCYDGMiIiIiIiIiIgqDBbDiIiIiIiIiIiowmAxjIiIiIiIiIiIKgwWw4iIiIiIiIiIqMJgMYyIiIiIiIiIiCoMFsOIiIiIiIiIiKjCYDGMiIiIiIjoHdy5cwcSiQQJCQnlHYrg6tWr+PLLL6GlpQUXF5dyjUUikWDHjh3lGgMRUWFYDCMiIiIiIlEKCAiARCLBnDlzFJbv2LEDEomknKIqX1OnToWuri6uXbuG6OjoQrfJf90kEgnU1dVhbW2NsWPHIjs7u0xjSU1NRZs2bcr0mEREZYHFMCIiIiIiEi0tLS3MnTsXjx8/Lu9Qyszr16/fed/k5GQ0bdoUNWvWRJUqVYrczsfHB6mpqbh16xbmz5+PZcuWYerUqe983sKYmZlBU1OzTI9JRFQWWAwjIiIiIiLRatWqFczMzDB79uwit5k2bVqBLoPh4eGwsrISngcEBKBz586YNWsWTE1NYWhoiJCQEOTm5mLMmDEwMjJC9erVERERUeD4V69ehYeHB7S0tFC/fn3ExsYqrL906RLatGkDPT09mJqaonfv3vjvv/+E9Z6enggMDERQUBCqVq0Kb2/vQq9DJpMhJCQE1atXh6amJlxcXLBv3z5hvUQiwdmzZxESEgKJRIJp06YV+ZpoamrCzMwMlpaW6Ny5M1q1aoUDBw4onGv27NmwtraGtrY2nJ2dsXXrVmFd9erVsWTJEoVjnj9/HioqKrh7964Qz5vdJO/duwdfX18YGhrCyMgInTp1wp07d4TXSEVFBQ8fPgQAZGRkQEVFBT179hT2nzFjBpo2bQoAePz4Mfz9/WFsbAxtbW3Y2toWmhsiosKwGEZERERERKKlqqqKWbNm4ddff8X9+/ff61iHDh3CgwcPcOTIEfzyyy+YOnUq2rdvj8qVK+PkyZMYMmQIvvvuuwLnGTNmDEaPHo3z58+jcePG6NChAx49egQAePLkCVq2bAlXV1ecOXMG+/btw7///gtfX1+FY6xduxYaGhqIj4/H0qVLC41vwYIFCAsLQ2hoKC5cuABvb2907NgRN27cAJDXLdHBwQGjR49GamoqgoODS3Xdly5dwrFjx6ChoSEsmz17NtatW4elS5fi8uXLGDlyJHr16oXY2FioqKjAz88PGzZsUDhOZGQkmjRpgpo1axY4R05ODry9vVGpUiUcPXoU8fHx0NPTg4+PD16/fg0HBwdUqVJFKCQePXpU4TkAxMbGwtPTEwAwefJkJCUlYe/evbhy5QqWLFmCqlWrlup6iYhYDCMiIiIiIlHr0qULXFxc3rubn5GRERYuXIg6deqgf//+qFOnDl68eIGJEyfC1tYWEyZMgIaGBuLi4hT2CwwMRNeuXWFvb48lS5bAwMAAq1atAgAsWrQIrq6umDVrFurWrQtXV1esXr0ahw8fxvXr14Vj2NraYt68eahTpw7q1KlTaHyhoaEYN24cevbsiTp16mDu3LlwcXFBeHg4gLxuiWpqatDT04OZmRn09PSKvNZdu3ZBT08PWlpacHR0RHp6OsaMGQMAePXqFWbNmoXVq1fD29sbtWrVQkBAAHr16oVly5YBAPz9/REfH4+UlBQAea3FNm3aBH9//0LPt3nzZshkMqxcuRKOjo6wt7dHREQEUlJSEBMTA4lEgmbNmiEmJgYAEBMTg379+uHVq1e4evUqcnJycOzYMTRv3hwAkJKSAldXV7i7u8PKygqtWrVChw4diksvEZFArbwDICIiIiIiel9z585Fy5YtS90aqjAODg5QUflfewFTU1PUr19feK6qqooqVaogPT1dYb/GjRsL/1ZTU4O7uzuuXLkCAEhMTMThw4cLLUwlJyfDzs4OANCgQYNiY8vMzMSDBw/QpEkTheVNmjRBYmJiKa/wf1q0aIElS5bg+fPnmD9/PtTU1NC1a1cAwM2bN/HixQu0bt1aYZ/Xr1/D1dUVAODi4gJ7e3ts2LAB48ePR2xsLNLT09G9e/dCz5eYmIibN2+iUqVKCsuzs7ORnJwMAGjevDmWL18OIK8V2KxZs3D9+nXExMQgIyMDOTk5wvV///336Nq1K86dO4evv/4anTt3hoeHh9KvAxFVTCyGERERERGR6DVr1gze3t6YMGECAgICFNapqKhALpcrLMvJySlwDHV1dYXn+bMtvr1MJpOVOq6srCx06NABc+fOLbDO3Nxc+Leurm6pj1kWdHV1YWNjAwBYvXo1nJ2dsWrVKgwYMABZWVkAgN27d6NatWoK+705IL6/v79QDNuwYQN8fHyKHLQ/KysLDRo0QGRkZIF1xsbGAPLGTgsKCsKNGzeQlJSEpk2b4urVq4iJicHjx4/h7u4OHR0dAECbNm1w9+5d7NmzBwcOHICXlxeGDRuG0NDQ939xiOizx26SRERERET0WZgzZw7++usvHD9+XGG5sbEx0tLSFApiCQkJZXbeEydOCP/Ozc3F2bNnYW9vDwBwc3PD5cuXYWVlBRsbG4WHMgUwfX19WFhYID4+XmF5fHw86tWr917xq6ioYOLEifjxxx/x8uVL1KtXD5qamkhJSSkQs6WlpbDft99+i0uXLuHs2bPYunVrkV0kgbzX4caNGzAxMSlwTAMDAwCAo6MjKleujBkzZsDFxQV6enrw9PREbGwsYmJihPHC8hkbG6Nv375Yv349wsPDhVZlREQlYTGMiIiIiIg+C46OjvD398fChQsVlnt6euLhw4eYN28ekpOT8dtvv2Hv3r1ldt7ffvsN27dvx9WrVzFs2DA8fvwY/fv3BwAMGzYMGRkZ8PPzw+nTp5GcnIy///4b/fr1g1QqVeo8Y8aMwdy5c7F582Zcu3YN48ePR0JCAkaMGPHe19C9e3eoqqrit99+Q6VKlRAcHIyRI0di7dq1SE5Oxrlz5/Drr79i7dq1wj5WVlbw8PDAgAEDIJVK0bFjxyKP7+/vj6pVq6JTp044evQobt++jZiYGAwfPlyYkCB/3LDIyEih8OXk5IRXr14hOjpaGC8MAKZMmYKdO3fi5s2buHz5Mnbt2iUUIImISsJiGBERERERfTZCQkIKdGO0t7fH4sWL8dtvv8HZ2RmnTp16r7HF3jZnzhzMmTMHzs7OiIuLQ1RUlDCzYX5rLqlUiq+//hqOjo4ICgqCoaGhwvhkpTF8+HCMGjUKo0ePhqOjI/bt24eoqCjY2tq+9zWoqakhMDAQ8+bNw/PnzzF9+nRMnjwZs2fPhr29PXx8fLB7925YW1sr7Ofv74/ExER06dIF2traRR5fR0cHR44cQY0aNfDNN9/A3t4eAwYMQHZ2NvT19YXtmjdvDqlUKhTDVFRU0KxZM0gkEoXx0jQ0NDBhwgQ4OTmhWbNmUFVVxaZNm977dSCiikEif7vzPBERERERERER0WeKLcOIiIiIiIiIiKjCYDGMiIiIiIiIiIgqDBbDiIiIiIiIiIiowmAxjIiIiIiIiIiIKgwWw4iIiIiIiIiIqMJgMYyIiIiIiIiIiCoMFsOIiIiIiIiIiKjCYDGMiIiIiIiIiIgqDBbDiIiIiIiIiIiowmAxjIiIiIiIiIiIKgwWw4iIiIiIiIiIqML4P+20sb7n6kVmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 🎨 Give a new name to each book that includes the rating together with the title\n",
        "sampled_reviews[\"grouped_title\"] = sampled_reviews[\"rating\"].astype(str) + \"★ | \" + sampled_reviews[\"title\"]\n",
        "\n",
        "# 📊 Aggregate sentiment counts\n",
        "sentiment_counts = (\n",
        "    sampled_reviews.groupby([\"grouped_title\", \"sentiment_label\"])\n",
        "    .size()\n",
        "    .unstack(fill_value=0)[[\"negative\", \"neutral\", \"positive\"]]  # consistent order\n",
        ")\n",
        "\n",
        "# 💾 Save table for HF dashboard\n",
        "sentiment_counts.reset_index().to_csv(PY_TAB / 'sentiment_counts_sampled.csv', index=False)\n",
        "\n",
        "\n",
        "# ✅ Plot stacked horizontal bars\n",
        "fig, ax = plt.subplots(figsize=(12, 14))\n",
        "sentiment_counts.plot.barh(\n",
        "    stacked=True,\n",
        "    ax=ax,\n",
        "    color={\"negative\": \"royalblue\", \"neutral\": \"lightgray\", \"positive\": \"crimson\"}\n",
        ")\n",
        "\n",
        "plt.title(\"💬 Sentiment Distribution in Reviews (5 Books per Popularity Level)\", fontsize=14)\n",
        "plt.xlabel(\"Number of Reviews\")\n",
        "plt.ylabel(\"Book Title (Grouped by Popularity Score)\")\n",
        "plt.legend(title=\"Sentiment\", loc=\"lower right\")\n",
        "plt.grid(axis=\"x\", linestyle=\"--\", alpha=0.6)\n",
        "plt.tight_layout()\n",
        "plt.savefig(PY_FIG / 'sentiment_distribution_sampled_titles.png', dpi=150)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmgylC1ENCHy"
      },
      "source": [
        "## **5.** 🔮 Forecast book sales with the following  ARIMA code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFV4JE1R3FKH"
      },
      "source": [
        "### *a. Initial setup*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "Mh8Alha03H22"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "import statsmodels.api as sm\n",
        "from itertools import product\n",
        "import matplotlib.cm as cm\n",
        "import warnings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHucD8OW3U0w"
      },
      "source": [
        "### *b. Define function find_best_arima to try different ARIMA parameter values and return the best combination for each book's price forecast*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "477fa43f"
      },
      "outputs": [],
      "source": [
        "def find_best_arima(series, p_range=(0, 5), d_range=(0, 2), q_range=(0, 1)):\n",
        "    best_aic = float(\"inf\")\n",
        "    best_order = None\n",
        "    best_model = None\n",
        "\n",
        "    for p, d, q in product(range(p_range[0], p_range[1] + 1),\n",
        "                           range(d_range[0], d_range[1] + 1),\n",
        "                           range(q_range[0], q_range[1] + 1)):\n",
        "        try:\n",
        "            model = sm.tsa.ARIMA(series, order=(p, d, q))\n",
        "            results = model.fit()\n",
        "            if results.aic < best_aic:\n",
        "                best_aic = results.aic\n",
        "                best_order = (p, d, q)\n",
        "                best_model = results\n",
        "        except:\n",
        "            continue\n",
        "\n",
        "    return best_order, best_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rq5t1Hey3jkD"
      },
      "source": [
        "### *c. Plot the figure*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "DmxGdvLE3dHQ",
        "outputId": "d539f3b5-deeb-4650-ed8b-36f6a040812a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_379/4284958625.py:32: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
            "  plt.tight_layout()\n",
            "/tmp/ipykernel_379/4284958625.py:33: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
            "  plt.savefig(PY_FIG / 'arima_forecasts_sampled_titles.png', dpi=150)\n",
            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 🎨 Generate 25 highly distinct colors using HUSL (hue-saturation-lightness)\n",
        "colors = sns.color_palette(\"tab10\", len(sampled_titles))\n",
        "\n",
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "for i, title in enumerate(sampled_titles):\n",
        "    book_sales = sampled_sales[sampled_sales[\"title\"] == title].copy()\n",
        "    book_sales[\"month\"] = pd.to_datetime(book_sales[\"month\"])\n",
        "    book_sales = book_sales.sort_values(\"month\").set_index(\"month\")\n",
        "\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.simplefilter(\"ignore\")\n",
        "        best_order, best_model = find_best_arima(book_sales[\"units_sold\"])\n",
        "        if best_model is not None:\n",
        "            forecast = best_model.get_forecast(steps=6)\n",
        "            forecast_index = pd.date_range(start=book_sales.index[-1] + pd.DateOffset(months=1), periods=6, freq='MS')\n",
        "\n",
        "            # 🟦 Plot observed sales (solid line)\n",
        "            plt.plot(book_sales.index, book_sales[\"units_sold\"], color=colors[i], label=title, linewidth=2)\n",
        "\n",
        "            # 🟠 Plot forecast (dotted line, same color)\n",
        "            plt.plot(forecast_index, forecast.predicted_mean, linestyle=\"--\", color=colors[i], linewidth=2)\n",
        "\n",
        "# 📈 Final formatting\n",
        "plt.title(\"📈 ARIMA Forecasts for Sampled Books (1 per Popularity Level)\", fontsize=14)\n",
        "plt.xlabel(\"Month\")\n",
        "plt.ylabel(\"Units Sold\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
        "plt.grid(True)\n",
        "plt.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), fontsize=\"small\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(PY_FIG / 'arima_forecasts_sampled_titles.png', dpi=150)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKBcx3fyCFly"
      },
      "source": [
        "## **6.** 🏷️ Decide on price changes with a rule-based approach based on sentiment and future revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nY-vV2JJDZqu"
      },
      "source": [
        "### *a. Calculate average sales per book*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "nbDT_RHaDD2R"
      },
      "outputs": [],
      "source": [
        "avg_sales = df_sales.groupby(\"title\")[\"units_sold\"].mean().reset_index()\n",
        "avg_sales.columns = [\"title\", \"avg_units_sold\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94wi-RvkDf2z"
      },
      "source": [
        "### *b. Calculate sentiment distribution per book*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "fWjQ9IOXDk-M"
      },
      "outputs": [],
      "source": [
        "sentiment_counts = df_reviews.groupby([\"title\", \"sentiment_label\"]).size().unstack(fill_value=0)\n",
        "sentiment_counts[\"total\"] = sentiment_counts.sum(axis=1)\n",
        "sentiment_counts[\"positive_ratio\"] = sentiment_counts.get(\"positive\") / sentiment_counts[\"total\"]\n",
        "sentiment_counts[\"negative_ratio\"] = sentiment_counts.get(\"negative\") / sentiment_counts[\"total\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vm10ym_iDtEW"
      },
      "source": [
        "### *c. Merge the calculated sales and sentiment characteristics*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "T-zlh6rBDpxg"
      },
      "outputs": [],
      "source": [
        "df_decision = avg_sales.merge(sentiment_counts, on=\"title\", how=\"left\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WIWDojyD7fK"
      },
      "source": [
        "### *d. ✋🏻🛑⛔️ Create the pricing_decision function as a basic rule-based pricing decider based on sentiment and revenue*\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5qJCb46Dxfb"
      },
      "source": [
        "*   If there are 120 or more average units sold and 0.6 or higher positive ratio, the decision should be to increase price.\n",
        "*   If there are 60 or less average units sold and 0.4 or higher negative ratio, the decision should be to decrease price.\n",
        "*   Otherwise, the price should be kept the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "XBzozedwD6yx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmLEdF14EPAA"
      },
      "source": [
        "### *e. ✋🏻🛑⛔️ Run the pricing_decision function and check out the first few decisions*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "TZ0ZhgHrEQJB"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTkP2_-EApev"
      },
      "source": [
        "\n",
        "## **7.** 💾 Save Python outputs for the Hugging Face dashboard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EIjfnokGpJv"
      },
      "source": [
        "\n",
        "This section exports **HF-ready artifacts** into a consistent folder structure:\n",
        "\n",
        "- `(root folder)py/figures/` (Python-generated visuals)\n",
        "- `(root folder)py/tables/` (tables/metrics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJJ4PMgIApev",
        "outputId": "bb002433-4b75-4124-ecef-043b1880a808"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Exports written to artifacts/:\n",
            " - common/: df_dashboard.csv, kpis.json\n",
            " - py/tables/: top_titles_by_units_sold.csv (+ optional top_titles_by_revenue.csv)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import json\n",
        "\n",
        "# -------------------------\n",
        "# 1) Dashboard table (monthly) — reuse if already built\n",
        "# -------------------------\n",
        "if \"df_monthly\" in globals() and df_monthly is not None:\n",
        "    df_dashboard = df_monthly.copy()\n",
        "else:\n",
        "    # fallback: monthly units sold only\n",
        "    df_dashboard = (\n",
        "        df_sales.groupby(\"month\", as_index=False)\n",
        "        .agg(total_units_sold=(\"units_sold\", \"sum\"))\n",
        "        .sort_values(\"month\")\n",
        "    )\n",
        "\n",
        "# Save the single overview dashboard table\n",
        "df_dashboard.to_csv(PY_TAB / \"df_dashboard.csv\", index=False)\n",
        "\n",
        "# -------------------------\n",
        "# 2) KPI summary (small json) — computed from raw df_sales + df_dashboard\n",
        "# -------------------------\n",
        "kpis = {\n",
        "    \"n_titles\": int(df_sales[\"title\"].nunique()),\n",
        "    \"n_months\": int(df_dashboard[\"month\"].nunique()),\n",
        "    \"total_units_sold\": float(df_sales[\"units_sold\"].sum()),\n",
        "}\n",
        "\n",
        "# Only include revenue KPIs if df_dashboard contains it (since you said monthly revenue already exists)\n",
        "if \"total_revenue\" in df_dashboard.columns and df_dashboard[\"total_revenue\"].notna().any():\n",
        "    kpis[\"total_revenue\"] = float(df_dashboard[\"total_revenue\"].sum())\n",
        "\n",
        "with open(PY_FIG / \"kpis.json\", \"w\", encoding=\"utf-8\") as f:\n",
        "    json.dump(kpis, f, indent=2)\n",
        "\n",
        "# -------------------------\n",
        "# 3) Python tables (title-level quick inspection)\n",
        "# -------------------------\n",
        "df_by_title_units = (\n",
        "    df_sales.groupby(\"title\", as_index=False)\n",
        "    .agg(total_units_sold=(\"units_sold\", \"sum\"))\n",
        "    .sort_values(\"total_units_sold\", ascending=False)\n",
        ")\n",
        "df_by_title_units.head(10).to_csv(PY_TAB / \"top_titles_by_units_sold.csv\", index=False)\n",
        "\n",
        "# Optional: title-level revenue table ONLY if df_sales already has per-row revenue\n",
        "if \"revenue\" in df_sales.columns and df_sales[\"revenue\"].notna().any():\n",
        "    df_by_title_rev = (\n",
        "        df_sales.groupby(\"title\", as_index=False)\n",
        "        .agg(total_revenue=(\"revenue\", \"sum\"))\n",
        "        .sort_values(\"total_revenue\", ascending=False)\n",
        "    )\n",
        "    df_by_title_rev.head(10).to_csv(PY_TAB / \"top_titles_by_revenue.csv\", index=False)\n",
        "\n",
        "print(\"✅ Exports written to artifacts/:\")\n",
        "print(\" - common/: df_dashboard.csv, kpis.json\")\n",
        "print(\" - py/tables/: top_titles_by_units_sold.csv (+ optional top_titles_by_revenue.csv)\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b4e76d3"
      },
      "source": [
        "✅ **Extra outputs for the R notebook**: `(root folder)common/r_input_title_level.csv` and `(root folder)common/r_input_monthly_revenue.csv` (these are the only two files the R portion needs)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aa10375c"
      },
      "source": [
        "# Task\n",
        "Perform data initialization, load \"synthetic_book_reviews.csv\" into `df_reviews` and \"synthetic_sales_data.csv\" into `df_sales`, then display their head. Conduct a quality check on both dataframes. Apply VADER sentiment analysis to the `review_text` column of `df_reviews`, storing the results in the `sentiment_label` column, and display the updated `df_reviews`. Finally, implement a rule-based pricing decision function using average sales and sentiment ratios to recommend price changes, and apply it to `df_decision`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "600c0681"
      },
      "source": [
        "## Initialize Environment and Load Data\n",
        "\n",
        "### Subtask:\n",
        "Run the cells for importing necessary libraries (pandas, numpy, random) and loading df_reviews and df_sales from their respective CSV files. This will ensure all dataframes are correctly defined before further operations.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ed6914a"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires running the cell to import necessary libraries (pandas, numpy, random).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b69b471e"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6f44d49f"
      },
      "source": [
        "**Reasoning**:\n",
        "The next step is to load the `df_reviews` DataFrame from the 'synthetic_book_reviews.csv' file as instructed by the subtask.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7b984677"
      },
      "source": [
        "df_reviews = pd.read_csv(\"synthetic_book_reviews.csv\")"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a279c8fe"
      },
      "source": [
        "**Reasoning**:\n",
        "The next step is to load the `df_sales` DataFrame from the 'synthetic_sales_data.csv' file as instructed by the subtask.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8710205b"
      },
      "source": [
        "df_sales = pd.read_csv(\"synthetic_sales_data.csv\")"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59a75c9d"
      },
      "source": [
        "## Display Initial Dataframes\n",
        "\n",
        "### Subtask:\n",
        "Add code to cell p8FdQFXErOqE to display the first few rows of df_reviews and df_sales using df_reviews.head() and df_sales.head() to allow for initial inspection of the loaded data.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5045ebd"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires displaying the first few rows of both `df_reviews` and `df_sales` for initial inspection. This will be done by adding `df_reviews.head()` and `df_sales.head()` to the specified code cell.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "78afc472",
        "outputId": "f4e7b8d3-e99e-4c3c-bb29-6033ea1cb28e"
      },
      "source": [
        "df_reviews.head()\n",
        "df_sales.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  title    month  units_sold sentiment_label\n",
              "0  A Light in the Attic  2024-09         100         neutral\n",
              "1  A Light in the Attic  2024-10         109         neutral\n",
              "2  A Light in the Attic  2024-11         102         neutral\n",
              "3  A Light in the Attic  2024-12         107         neutral\n",
              "4  A Light in the Attic  2025-01         108         neutral"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55a91f68-d9e7-4962-9e4b-4dd2020f9927\" 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>title</th>\n",
              "      <th>month</th>\n",
              "      <th>units_sold</th>\n",
              "      <th>sentiment_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-09</td>\n",
              "      <td>100</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-10</td>\n",
              "      <td>109</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-11</td>\n",
              "      <td>102</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-12</td>\n",
              "      <td>107</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2025-01</td>\n",
              "      <td>108</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55a91f68-d9e7-4962-9e4b-4dd2020f9927')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-55a91f68-d9e7-4962-9e4b-4dd2020f9927 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-55a91f68-d9e7-4962-9e4b-4dd2020f9927');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_sales",
              "summary": "{\n  \"name\": \"df_sales\",\n  \"rows\": 18000,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"The Grownup\",\n          \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n          \"Ayumi's Violin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"2024-09\",\n          \"2024-10\",\n          \"2025-05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"units_sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98,\n        \"min\": 0,\n        \"max\": 362,\n        \"num_unique_values\": 354,\n        \"samples\": [\n          214,\n          289,\n          205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fed0fc28"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask explicitly instructs to add `df_reviews.head()` and `df_sales.head()` to cell `p8FdQFXErOqE` for initial inspection of the loaded data. I am following these instructions directly.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "1fa35500",
        "outputId": "f8034b19-f3ab-4c46-8f95-f982ce855126"
      },
      "source": [
        "df_reviews.head()\n",
        "df_sales.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  title    month  units_sold sentiment_label\n",
              "0  A Light in the Attic  2024-09         100         neutral\n",
              "1  A Light in the Attic  2024-10         109         neutral\n",
              "2  A Light in the Attic  2024-11         102         neutral\n",
              "3  A Light in the Attic  2024-12         107         neutral\n",
              "4  A Light in the Attic  2025-01         108         neutral"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a36eb6e8-76d5-4132-95e3-9ec279566002\" 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>title</th>\n",
              "      <th>month</th>\n",
              "      <th>units_sold</th>\n",
              "      <th>sentiment_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-09</td>\n",
              "      <td>100</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-10</td>\n",
              "      <td>109</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-11</td>\n",
              "      <td>102</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-12</td>\n",
              "      <td>107</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2025-01</td>\n",
              "      <td>108</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a36eb6e8-76d5-4132-95e3-9ec279566002')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a36eb6e8-76d5-4132-95e3-9ec279566002 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-a36eb6e8-76d5-4132-95e3-9ec279566002');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_sales",
              "summary": "{\n  \"name\": \"df_sales\",\n  \"rows\": 18000,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"The Grownup\",\n          \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n          \"Ayumi's Violin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"2024-09\",\n          \"2024-10\",\n          \"2025-05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"units_sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98,\n        \"min\": 0,\n        \"max\": 362,\n        \"num_unique_values\": 354,\n        \"samples\": [\n          214,\n          289,\n          205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bdcc776"
      },
      "source": [
        "**Reasoning**:\n",
        "To ensure both `df_reviews.head()` and `df_sales.head()` are displayed in the specified cell, I will use `IPython.display.display` for each dataframe's head. This is standard practice in Jupyter environments to display multiple outputs from a single cell.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "0a85e917",
        "outputId": "fa062dec-eff7-4738-982e-e9d3496d1e1e"
      },
      "source": [
        "from IPython.display import display\n",
        "\n",
        "print(\"First few rows of df_reviews:\")\n",
        "display(df_reviews.head())\n",
        "\n",
        "print(\"\\nFirst few rows of df_sales:\")\n",
        "display(df_sales.head())"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of df_reviews:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  title sentiment_label  \\\n",
              "0  A Light in the Attic         neutral   \n",
              "1  A Light in the Attic         neutral   \n",
              "2  A Light in the Attic         neutral   \n",
              "3  A Light in the Attic         neutral   \n",
              "4  A Light in the Attic         neutral   \n",
              "\n",
              "                                         review_text rating  popularity_score  \n",
              "0            A competent effort, but lacked a spark.  Three                 3  \n",
              "1                The pacing felt a bit off at times.  Three                 3  \n",
              "2                   Standard fare with some promise.  Three                 3  \n",
              "3                               It was just... fine.  Three                 3  \n",
              "4  The concept was good, but the execution was la...  Three                 3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-11f5ed25-35dd-4be9-b5e7-d22892635afa\" 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>title</th>\n",
              "      <th>sentiment_label</th>\n",
              "      <th>review_text</th>\n",
              "      <th>rating</th>\n",
              "      <th>popularity_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>A competent effort, but lacked a spark.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>The pacing felt a bit off at times.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>Standard fare with some promise.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>It was just... fine.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>The concept was good, but the execution was la...</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11f5ed25-35dd-4be9-b5e7-d22892635afa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-11f5ed25-35dd-4be9-b5e7-d22892635afa 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-11f5ed25-35dd-4be9-b5e7-d22892635afa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_sales\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Light in the Attic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"The pacing felt a bit off at times.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Three\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First few rows of df_sales:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  title    month  units_sold sentiment_label\n",
              "0  A Light in the Attic  2024-09         100         neutral\n",
              "1  A Light in the Attic  2024-10         109         neutral\n",
              "2  A Light in the Attic  2024-11         102         neutral\n",
              "3  A Light in the Attic  2024-12         107         neutral\n",
              "4  A Light in the Attic  2025-01         108         neutral"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f5c44df-627f-4920-9b59-35806b882944\" 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>title</th>\n",
              "      <th>month</th>\n",
              "      <th>units_sold</th>\n",
              "      <th>sentiment_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-09</td>\n",
              "      <td>100</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-10</td>\n",
              "      <td>109</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-11</td>\n",
              "      <td>102</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2024-12</td>\n",
              "      <td>107</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>2025-01</td>\n",
              "      <td>108</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f5c44df-627f-4920-9b59-35806b882944')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6f5c44df-627f-4920-9b59-35806b882944 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-6f5c44df-627f-4920-9b59-35806b882944');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_sales\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"A Light in the Attic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"units_sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 100,\n        \"max\": 109,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d13d1b5a"
      },
      "source": [
        "## Perform Data Quality Check\n",
        "\n",
        "### Subtask:\n",
        "Re-run the `quality_check` function for both `df_reviews` and `df_sales` to verify data integrity.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8e9258e"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires re-running the `quality_check` function for both `df_reviews` and `df_sales` to verify data integrity. The code for this function and its calls is located in cell `VArQGPoKrfLm`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "c1dc00bf",
        "outputId": "bb253e9c-f52e-4aed-d295-5fa68693b80c"
      },
      "source": [
        "def quality_check(df, name=\"DataFrame\"):\n",
        "    print(f\"\\n🔍 Quality Check Report for: {name}\")\n",
        "    print(\"=\" * (25 + len(name)))\n",
        "\n",
        "    # Basic info\n",
        "    print(f\"\\n📏 Shape: {df.shape}\")\n",
        "    print(\"\\n🔠 Column Types:\")\n",
        "    print(df.dtypes)\n",
        "\n",
        "    # Missing values\n",
        "    print(\"\\n❓ Missing Values:\")\n",
        "    print(df.isnull().sum())\n",
        "\n",
        "    # Duplicates\n",
        "    duplicate_count = df.duplicated().sum()\n",
        "    print(f\"\\n📋 Duplicate Rows: {duplicate_count}\")\n",
        "\n",
        "    # Summary stats\n",
        "    print(\"\\n📊 Summary Statistics:\")\n",
        "    display(df.describe(include='all').transpose())\n",
        "\n",
        "    # Sample rows\n",
        "    print(\"\\n👀 Sample Rows:\")\n",
        "    display(df.sample(5))\n",
        "\n",
        "# Run checks\n",
        "quality_check(df_reviews, \"df_reviews\")\n",
        "quality_check(df_sales, \"df_sales\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔍 Quality Check Report for: df_reviews\n",
            "===================================\n",
            "\n",
            "📏 Shape: (10000, 5)\n",
            "\n",
            "🔠 Column Types:\n",
            "title               object\n",
            "sentiment_label     object\n",
            "review_text         object\n",
            "rating              object\n",
            "popularity_score     int64\n",
            "dtype: object\n",
            "\n",
            "❓ Missing Values:\n",
            "title               0\n",
            "sentiment_label     0\n",
            "review_text         0\n",
            "rating              0\n",
            "popularity_score    0\n",
            "dtype: int64\n",
            "\n",
            "📋 Duplicate Rows: 0\n",
            "\n",
            "📊 Summary Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    count unique  \\\n",
              "title               10000    999   \n",
              "sentiment_label     10000      3   \n",
              "review_text         10000    135   \n",
              "rating              10000      5   \n",
              "popularity_score  10000.0    NaN   \n",
              "\n",
              "                                                                top  freq  \\\n",
              "title                                        The Star-Touched Queen    20   \n",
              "sentiment_label                                            positive  4380   \n",
              "review_text       A captivating read that offered both escapism ...   112   \n",
              "rating                                                          One  2260   \n",
              "popularity_score                                                NaN   NaN   \n",
              "\n",
              "                   mean       std  min  25%  50%  75%  max  \n",
              "title               NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "sentiment_label     NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "review_text         NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "rating              NaN       NaN  NaN  NaN  NaN  NaN  NaN  \n",
              "popularity_score  3.282  1.028874  1.0  3.0  3.0  4.0  5.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ddd3dae-aa45-4c5d-9436-7d26bf1eca08\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>10000</td>\n",
              "      <td>999</td>\n",
              "      <td>The Star-Touched Queen</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment_label</th>\n",
              "      <td>10000</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "      <td>4380</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>review_text</th>\n",
              "      <td>10000</td>\n",
              "      <td>135</td>\n",
              "      <td>A captivating read that offered both escapism ...</td>\n",
              "      <td>112</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rating</th>\n",
              "      <td>10000</td>\n",
              "      <td>5</td>\n",
              "      <td>One</td>\n",
              "      <td>2260</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popularity_score</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.282</td>\n",
              "      <td>1.028874</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ddd3dae-aa45-4c5d-9436-7d26bf1eca08')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2ddd3dae-aa45-4c5d-9436-7d26bf1eca08 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-2ddd3dae-aa45-4c5d-9436-7d26bf1eca08');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"quality_check(df_sales, \\\"df_sales\\\")\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"10000\",\n        \"max\": \"10000\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"10000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 999,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"20\",\n        \"max\": \"4380\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"4380\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.282,\n        \"max\": 3.282,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0288740730458223,\n        \"max\": 1.0288740730458223,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0288740730458223\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 4.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 5.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "👀 Sample Rows:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                  title sentiment_label  \\\n",
              "5576  The Girl You Left Behind (The Girl You Left Be...         neutral   \n",
              "2417                          The Marriage of Opposites        positive   \n",
              "3700  Raspberry Pi Electronics Projects for the Evil...        positive   \n",
              "3341  Barefoot Contessa at Home: Everyday Recipes Yo...        positive   \n",
              "2525                                    The Dovekeepers         neutral   \n",
              "\n",
              "                                            review_text rating  \\\n",
              "5576   An average book — not great, but not bad either.    One   \n",
              "2417  The characters felt like old friends by the en...   Four   \n",
              "3700  This book is a masterpiece! Full of wisdom, hu...   Four   \n",
              "3341  This book will stay with me for a long time. I...   Five   \n",
              "2525  I can see why some might like it, but it wasn'...    One   \n",
              "\n",
              "      popularity_score  \n",
              "5576                 3  \n",
              "2417                 4  \n",
              "3700                 4  \n",
              "3341                 5  \n",
              "2525                 3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe162ffc-8a86-4de1-8d6b-125c3e7f223b\" 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>title</th>\n",
              "      <th>sentiment_label</th>\n",
              "      <th>review_text</th>\n",
              "      <th>rating</th>\n",
              "      <th>popularity_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5576</th>\n",
              "      <td>The Girl You Left Behind (The Girl You Left Be...</td>\n",
              "      <td>neutral</td>\n",
              "      <td>An average book — not great, but not bad either.</td>\n",
              "      <td>One</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2417</th>\n",
              "      <td>The Marriage of Opposites</td>\n",
              "      <td>positive</td>\n",
              "      <td>The characters felt like old friends by the en...</td>\n",
              "      <td>Four</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3700</th>\n",
              "      <td>Raspberry Pi Electronics Projects for the Evil...</td>\n",
              "      <td>positive</td>\n",
              "      <td>This book is a masterpiece! Full of wisdom, hu...</td>\n",
              "      <td>Four</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3341</th>\n",
              "      <td>Barefoot Contessa at Home: Everyday Recipes Yo...</td>\n",
              "      <td>positive</td>\n",
              "      <td>This book will stay with me for a long time. I...</td>\n",
              "      <td>Five</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2525</th>\n",
              "      <td>The Dovekeepers</td>\n",
              "      <td>neutral</td>\n",
              "      <td>I can see why some might like it, but it wasn'...</td>\n",
              "      <td>One</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe162ffc-8a86-4de1-8d6b-125c3e7f223b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe162ffc-8a86-4de1-8d6b-125c3e7f223b 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-fe162ffc-8a86-4de1-8d6b-125c3e7f223b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"quality_check(df_sales, \\\"df_sales\\\")\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"The Marriage of Opposites\",\n          \"The Dovekeepers\",\n          \"Raspberry Pi Electronics Projects for the Evil Genius\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"positive\",\n          \"neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"The characters felt like old friends by the end. Wonderful!\",\n          \"I can see why some might like it, but it wasn't for me.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"One\",\n          \"Four\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔍 Quality Check Report for: df_sales\n",
            "=================================\n",
            "\n",
            "📏 Shape: (18000, 4)\n",
            "\n",
            "🔠 Column Types:\n",
            "title              object\n",
            "month              object\n",
            "units_sold          int64\n",
            "sentiment_label    object\n",
            "dtype: object\n",
            "\n",
            "❓ Missing Values:\n",
            "title              0\n",
            "month              0\n",
            "units_sold         0\n",
            "sentiment_label    0\n",
            "dtype: int64\n",
            "\n",
            "📋 Duplicate Rows: 0\n",
            "\n",
            "📊 Summary Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   count unique                     top  freq        mean  \\\n",
              "title              18000    999  The Star-Touched Queen    36         NaN   \n",
              "month              18000     18                 2024-09  1000         NaN   \n",
              "units_sold       18000.0    NaN                     NaN   NaN  168.024167   \n",
              "sentiment_label    18000      3                positive  7884         NaN   \n",
              "\n",
              "                       std  min   25%    50%    75%    max  \n",
              "title                  NaN  NaN   NaN    NaN    NaN    NaN  \n",
              "month                  NaN  NaN   NaN    NaN    NaN    NaN  \n",
              "units_sold       98.656354  0.0  84.0  148.0  262.0  362.0  \n",
              "sentiment_label        NaN  NaN   NaN    NaN    NaN    NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ea890b8-4345-4297-a187-9aa44a89ca57\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>18000</td>\n",
              "      <td>999</td>\n",
              "      <td>The Star-Touched Queen</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>18000</td>\n",
              "      <td>18</td>\n",
              "      <td>2024-09</td>\n",
              "      <td>1000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>units_sold</th>\n",
              "      <td>18000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>168.024167</td>\n",
              "      <td>98.656354</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>362.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment_label</th>\n",
              "      <td>18000</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "      <td>7884</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ea890b8-4345-4297-a187-9aa44a89ca57')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3ea890b8-4345-4297-a187-9aa44a89ca57 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-3ea890b8-4345-4297-a187-9aa44a89ca57');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"quality_check(df_sales, \\\"df_sales\\\")\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"18000\",\n        \"max\": \"18000\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"18000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 999,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"The Star-Touched Queen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"36\",\n        \"max\": \"7884\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 168.02416666666667,\n        \"max\": 168.02416666666667,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          168.02416666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 98.65635350480927,\n        \"max\": 98.65635350480927,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          98.65635350480927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 84.0,\n        \"max\": 84.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          84.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 148.0,\n        \"max\": 148.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          148.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 262.0,\n        \"max\": 262.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          262.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 362.0,\n        \"max\": 362.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          362.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "👀 Sample Rows:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                        title    month  units_sold sentiment_label\n",
              "3764           Ayumi's Violin  2024-11         303        positive\n",
              "16630             The Odyssey  2026-01         254        positive\n",
              "227               Set Me Free  2025-08         147         neutral\n",
              "9363     Nightstruck: A Novel  2024-12         284        positive\n",
              "12865  The Undomestic Goddess  2025-10         335        positive"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb954409-aa4a-44de-b7ec-a3fef5749b4b\" 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>title</th>\n",
              "      <th>month</th>\n",
              "      <th>units_sold</th>\n",
              "      <th>sentiment_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3764</th>\n",
              "      <td>Ayumi's Violin</td>\n",
              "      <td>2024-11</td>\n",
              "      <td>303</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16630</th>\n",
              "      <td>The Odyssey</td>\n",
              "      <td>2026-01</td>\n",
              "      <td>254</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>Set Me Free</td>\n",
              "      <td>2025-08</td>\n",
              "      <td>147</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9363</th>\n",
              "      <td>Nightstruck: A Novel</td>\n",
              "      <td>2024-12</td>\n",
              "      <td>284</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12865</th>\n",
              "      <td>The Undomestic Goddess</td>\n",
              "      <td>2025-10</td>\n",
              "      <td>335</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb954409-aa4a-44de-b7ec-a3fef5749b4b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bb954409-aa4a-44de-b7ec-a3fef5749b4b 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-bb954409-aa4a-44de-b7ec-a3fef5749b4b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0111639"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires applying the `get_sentiment_label` function to the `review_text` column of `df_reviews` and storing the result in the `sentiment_label` column. This will be done using the `.apply()` method.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b20b8464"
      },
      "source": [
        "df_reviews[\"sentiment_label\"] = df_reviews[\"review_text\"].apply(get_sentiment_label)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "264fa216"
      },
      "source": [
        "**Reasoning**:\n",
        "The previous code failed because the `get_sentiment_label` function was not defined. I need to re-execute the cell that defines this function (cell `89809e6f`) before it can be applied.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6a6d96ec"
      },
      "source": [
        "def get_sentiment_label(text):\n",
        "    score = analyzer.polarity_scores(text)[\"compound\"]\n",
        "    if score >= 0.05:\n",
        "        return \"positive\"\n",
        "    elif score <= -0.05:\n",
        "        return \"negative\"\n",
        "    else:\n",
        "        return \"neutral\""
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21a05869"
      },
      "source": [
        "**Reasoning**:\n",
        "Now that the `get_sentiment_label` function is defined, I can apply it to the `review_text` column of `df_reviews` to create the `sentiment_label` column, as stated in the subtask.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31e89509"
      },
      "source": [
        "df_reviews[\"sentiment_label\"] = df_reviews[\"review_text\"].apply(get_sentiment_label)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1e8afce1"
      },
      "source": [
        "**Reasoning**:\n",
        "The previous code failed because the `analyzer` object was not defined, even though the `get_sentiment_label` function was. I need to ensure the VADER `SentimentIntensityAnalyzer` is initialized before attempting to use it.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5be9d4b6"
      },
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "# 🧠 Initialize VADER analyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9934cd5"
      },
      "source": [
        "**Reasoning**:\n",
        "The previous code failed with a `ModuleNotFoundError` for `vaderSentiment`. This indicates the package is not installed or accessible. I need to explicitly install `vaderSentiment` to resolve this issue.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cb2fa95",
        "outputId": "8fd4a890-a7b8-4bd3-f5db-c84b6d93cbfc"
      },
      "source": [
        "!pip install vaderSentiment"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.12/dist-packages (3.3.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from vaderSentiment) (2.32.4)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->vaderSentiment) (2026.2.25)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7c89dc94"
      },
      "source": [
        "**Reasoning**:\n",
        "Since `vaderSentiment` has been successfully installed, the next step is to re-import the `SentimentIntensityAnalyzer` and initialize the `analyzer` object so it can be used by the `get_sentiment_label` function.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "376342d9"
      },
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "# 🤖 Initialize VADER analyzer\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f200a9e0"
      },
      "source": [
        "**Reasoning**:\n",
        "Now that `vaderSentiment` is installed and the `analyzer` object is correctly initialized, I can proceed to apply the `get_sentiment_label` function to the `review_text` column of `df_reviews` to populate the `sentiment_label` column, as per the subtask instructions.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5b1b27df"
      },
      "source": [
        "df_reviews[\"sentiment_label\"] = df_reviews[\"review_text\"].apply(get_sentiment_label)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4dded10a"
      },
      "source": [
        "# Task\n",
        "I'll add the necessary code to cell `ODGyfjBSyZEO` to display the first few rows of `df_reviews`, then execute the cell. This will allow for inspection of the `df_reviews` dataframe after the sentiment analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "392ac8d2"
      },
      "source": [
        "## Ensure Environment Setup and Data Loading\n",
        "\n",
        "### Subtask:\n",
        "Ensure the necessary libraries are imported and `df_reviews` and `df_sales` are loaded correctly.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c45fa3ac"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires ensuring the necessary libraries are imported. This is done by executing cell `eBDXPQz18Xrs` which imports `pandas`, `numpy`, and `random`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35c2ff91"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5cca174"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires loading the `synthetic_book_reviews.csv` file into `df_reviews`. This is done by executing cell `fdgjghfO8uuq`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "230ce515"
      },
      "source": [
        "df_reviews = pd.read_csv(\"synthetic_book_reviews.csv\")"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53aa12c7"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires loading the `synthetic_sales_data.csv` file into `df_sales`. This is done by executing cell `6XZs3P7fHgQe`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d79323f"
      },
      "source": [
        "df_sales = pd.read_csv(\"synthetic_sales_data.csv\")"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52b09eff"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires loading the `synthetic_sales_data.csv` file into `df_sales`. This is done by executing cell `6XZs3P7fHgQe`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14541cf0"
      },
      "source": [
        "df_sales = pd.read_csv(\"synthetic_sales_data.csv\")"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbcbd2e3"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires loading the `synthetic_sales_data.csv` file into `df_sales`. This is done by executing cell `6XZs3P7fHgQe`.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3184844"
      },
      "source": [
        "df_sales = pd.read_csv(\"synthetic_sales_data.csv\")"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9df206ae"
      },
      "source": [
        "## Apply VADER Sentiment Analysis\n",
        "\n",
        "### Subtask:\n",
        "Display the first few rows of the updated `df_reviews` DataFrame after sentiment analysis.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c8ad4631"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires displaying the first few rows of the updated `df_reviews` DataFrame to inspect the sentiment analysis results. This will be done by adding `df_reviews.head()` to the specified cell.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "644f8bca",
        "outputId": "c3118ef4-908b-469e-ed71-1d097ae1b55a"
      },
      "source": [
        "df_reviews.head()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  title sentiment_label  \\\n",
              "0  A Light in the Attic         neutral   \n",
              "1  A Light in the Attic         neutral   \n",
              "2  A Light in the Attic         neutral   \n",
              "3  A Light in the Attic         neutral   \n",
              "4  A Light in the Attic         neutral   \n",
              "\n",
              "                                         review_text rating  popularity_score  \n",
              "0            A competent effort, but lacked a spark.  Three                 3  \n",
              "1                The pacing felt a bit off at times.  Three                 3  \n",
              "2                   Standard fare with some promise.  Three                 3  \n",
              "3                               It was just... fine.  Three                 3  \n",
              "4  The concept was good, but the execution was la...  Three                 3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-156a4d49-8859-46a6-9a2d-912e9acc3560\" 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>title</th>\n",
              "      <th>sentiment_label</th>\n",
              "      <th>review_text</th>\n",
              "      <th>rating</th>\n",
              "      <th>popularity_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>A competent effort, but lacked a spark.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>The pacing felt a bit off at times.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>Standard fare with some promise.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>It was just... fine.</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A Light in the Attic</td>\n",
              "      <td>neutral</td>\n",
              "      <td>The concept was good, but the execution was la...</td>\n",
              "      <td>Three</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-156a4d49-8859-46a6-9a2d-912e9acc3560')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-156a4d49-8859-46a6-9a2d-912e9acc3560 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-156a4d49-8859-46a6-9a2d-912e9acc3560');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_reviews",
              "summary": "{\n  \"name\": \"df_reviews\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"The Grownup\",\n          \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n          \"Ayumi's Violin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 135,\n        \"samples\": [\n          \"A fairly unremarkable book.\",\n          \"A vibrant and immersive experience. I felt like I was right there with the characters.\",\n          \"A frustrating and irritating read. I just wanted it to be over.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"One\",\n          \"Two\",\n          \"Four\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7c28c38"
      },
      "source": [
        "## Implement Pricing Decision Logic\n",
        "\n",
        "### Subtask:\n",
        "Define the `pricing_decision` function in cell `XBzozedwD6yx` and execute it.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5bd9dac"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires defining the `pricing_decision` function in cell `XBzozedwD6yx` with the specified logic. This function will take a row as input and return a pricing decision based on the average units sold and sentiment ratios.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "752b1130"
      },
      "source": [
        "def pricing_decision(row):\n",
        "    if row[\"avg_units_sold\"] >= 120 and row[\"positive_ratio\"] >= 0.6:\n",
        "        return \"Increase Price\"\n",
        "    elif row[\"avg_units_sold\"] <= 60 and row[\"negative_ratio\"] >= 0.4:\n",
        "        return \"Decrease Price\"\n",
        "    else:\n",
        "        return \"Keep Same\""
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8670ceaa"
      },
      "source": [
        "**Reasoning**:\n",
        "The subtask requires applying the `pricing_decision` function to the `df_decision` DataFrame to generate pricing recommendations and then displaying the first few rows to review these decisions.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "51abc3eb",
        "outputId": "3fa933fb-abd6-49f9-de82-a371d7af8951"
      },
      "source": [
        "df_decision[\"pricing_recommendation\"] = df_decision.apply(pricing_decision, axis=1)\n",
        "df_decision.head()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  avg_units_sold  \\\n",
              "0  \"Most Blessed of the Patriarchs\": Thomas Jeffe...      285.555556   \n",
              "1                                          #GIRLBOSS       47.944444   \n",
              "2  #HigherSelfie: Wake Up Your Life. Free Your So...      226.777778   \n",
              "3                                       'Salem's Lot      246.055556   \n",
              "4  (Un)Qualified: How God Uses Broken People to D...      294.444444   \n",
              "\n",
              "   negative  neutral  positive  total  positive_ratio  negative_ratio  \\\n",
              "0         0        0        10     10             1.0             0.0   \n",
              "1        10        0         0     10             0.0             1.0   \n",
              "2         0        0        10     10             1.0             0.0   \n",
              "3         0        0        10     10             1.0             0.0   \n",
              "4         0        0        10     10             1.0             0.0   \n",
              "\n",
              "  pricing_recommendation  \n",
              "0         Increase Price  \n",
              "1         Decrease Price  \n",
              "2         Increase Price  \n",
              "3         Increase Price  \n",
              "4         Increase Price  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-164727f2-7ae2-4ea9-99da-f004aedc73b9\" 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>title</th>\n",
              "      <th>avg_units_sold</th>\n",
              "      <th>negative</th>\n",
              "      <th>neutral</th>\n",
              "      <th>positive</th>\n",
              "      <th>total</th>\n",
              "      <th>positive_ratio</th>\n",
              "      <th>negative_ratio</th>\n",
              "      <th>pricing_recommendation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>\"Most Blessed of the Patriarchs\": Thomas Jeffe...</td>\n",
              "      <td>285.555556</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Increase Price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>#GIRLBOSS</td>\n",
              "      <td>47.944444</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Decrease Price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>#HigherSelfie: Wake Up Your Life. Free Your So...</td>\n",
              "      <td>226.777778</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Increase Price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>'Salem's Lot</td>\n",
              "      <td>246.055556</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Increase Price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Un)Qualified: How God Uses Broken People to D...</td>\n",
              "      <td>294.444444</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Increase Price</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-164727f2-7ae2-4ea9-99da-f004aedc73b9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-164727f2-7ae2-4ea9-99da-f004aedc73b9 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-164727f2-7ae2-4ea9-99da-f004aedc73b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_decision",
              "summary": "{\n  \"name\": \"df_decision\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"My Name Is Lucy Barton\",\n          \"The Martian (The Martian #1)\",\n          \"Dress Your Family in Corduroy and Denim\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_units_sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 97.93180096680425,\n        \"min\": 10.055555555555555,\n        \"max\": 331.72222222222223,\n        \"num_unique_values\": 898,\n        \"samples\": [\n          24.0,\n          234.16666666666666,\n          246.88888888888889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"negative\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neutral\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"positive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 10,\n        \"max\": 20,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          20,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"positive_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.496318954755443,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"negative_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4243581971385502,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pricing_recommendation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Increase Price\",\n          \"Decrease Price\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76f1ac5a"
      },
      "source": [
        "## Final Task\n",
        "\n",
        "### Subtask:\n",
        "Confirm that all placeholder cells have been filled, all necessary computations performed, and the notebook is ready for review.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6667bedc"
      },
      "source": [
        "## Summary:\n",
        "\n",
        "### Data Analysis Key Findings\n",
        "\n",
        "*   The `df_reviews` and `df_sales` DataFrames were successfully loaded from their respective CSV files.\n",
        "*   Sentiment analysis was successfully applied to `df_reviews`, as evidenced by the presence of a new `sentiment_label` column in the DataFrame.\n",
        "*   A `pricing_decision` function was defined and applied to the `df_decision` DataFrame, generating a `pricing_recommendation` column. This column provides strategic pricing adjustments ('Increase Price', 'Decrease Price', or 'Keep Same') based on predefined conditions involving average units sold and positive/negative sentiment ratios. For example, a book with `avg_units_sold` of 285.55 and `positive_ratio` of 1.0 was recommended for an 'Increase Price', while one with `avg_units_sold` of 47.94 and `negative_ratio` of 1.0 was recommended for a 'Decrease Price'.\n",
        "\n",
        "### Insights or Next Steps\n",
        "\n",
        "*   The integration of sentiment analysis results directly into the pricing recommendation logic provides a data-driven approach to product strategy, leveraging customer feedback alongside sales performance.\n",
        "*   The next logical step would be to visualize the distribution of `pricing_recommendation` categories and further analyze the characteristics of books falling into each category to validate the business rules and identify any outlier cases for manual review.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "jpASMyIQMaAq",
        "NZd99NpKkKyp",
        "TTxUKDYINPxV",
        "Qy3Hqm-FojvT",
        "rmgylC1ENCHy",
        "SKBcx3fyCFly",
        "WTkP2_-EApev"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}