Giovanni_&_Oscar_Twitter_Disasters_Final_Report_Google_Colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWM79lj2LYI6"
      },
      "source": [
        "# Twitter Disasters Final Report | April 2023"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSY5YttNUWDZ"
      },
      "source": [
        "## Authors\n",
        "\n",
        "- [Giovanni Rivera](https://github.com/grivera64); Intro to Machine Learning, Section A\n",
        "- [Oscar Jesus Zambrano](https://github.com/osc-zam22); Intro to Machine Learning, Section B"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPFnCCd7O6fN"
      },
      "source": [
        "#### Setup Dependencies\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiwopORsO-yC",
        "outputId": "3677e2f3-6091-4683-ac7e-d09a86aa2e5a"
      },
      "outputs": [
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          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pydot in /usr/local/lib/python3.9/dist-packages (1.4.2)\n",
            "Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python3.9/dist-packages (from pydot) (3.0.9)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# Ensure that we have the newest version of pip installed\n",
        "%pip install -q --upgrade pip\n",
        "\n",
        "# Install necessary libraries\n",
        "%pip install -q numpy\n",
        "%pip install -q pandas\n",
        "%pip install -q matplotlib\n",
        "%pip install -q seaborn\n",
        "%pip install -q plotly\n",
        "\n",
        "# Helps avoid showing plots in a separate line\n",
        "%matplotlib inline\n",
        "\n",
        "%pip install -q scikit-learn\n",
        "%pip install -q tensorflow\n",
        "\n",
        "# Helps run plot_model from keras\n",
        "%pip install pydot\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ycGSQ31nQRa8"
      },
      "outputs": [],
      "source": [
        "# Import the modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objs as plotly\n",
        "from sklearn import datasets\n",
        "\n",
        "# Set the styling of the plt plots to darkgrid\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "# Removes error messsages and sets precision to 3 decimal places\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "np.set_printoptions(precision=3, suppress=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a3gc2WkUWDd"
      },
      "source": [
        "## 1. Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gqckFxJUWDd"
      },
      "source": [
        "### Our Story:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RA9sUHnaUWDe"
      },
      "source": [
        "In light of recent events around the globe, we have seen the rise of misinformation being spread on social media. In order to combat this we are using real tweets from real users on Twitter to find a way to stop misinformation from spreading, and promote real useful information to those living in or have relatives in affected areas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0GVVzenUWDf"
      },
      "source": [
        "### Our Task:\n",
        "\n",
        "We are designing machine learning models using Tensorflow/Keras to identify whether a given tweet is a natural distaster.\n",
        "\n",
        "Throughout the notebook, we will refer to tweets about a natural distaster as part of the `postive class`, while tweets that aren't about a natural disaster as part of the `negative class`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJCd5JzUNNuk"
      },
      "source": [
        "#### 1.1 Load the Data in a Colab Notebook\n",
        "\n",
        "We use a dataset from the Kaggle competition [Tech Exchange 2023 ML Project](https://www.kaggle.com/competitions/techexchange-2023-ml-project).\n",
        "\n",
        "You may find the dataset by navigating to the 'Data' tab in the link aforementioned. Under 'Data Explorer', you can download the following files:\n",
        "\n",
        "- `train.csv`: Contains the training data; we will use this dataset for our training and validation data.\n",
        "- `test.csv`: Contains our testing data; we will use this dataset for making our submissions to Kaggle."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Ct512u3DuP7E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "1abeb6d5-2482-428c-8bba-d2b0441b26e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
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              "   id keyword location                                               text  \\\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Please update the Path here to the location of your train.csv and test.csv files\n",
        "path_to_csv = '/content/drive/MyDrive/Final_Project_Oscar_Giovanni'\n",
        "\n",
        "# Load the Data Frames from the Training and Testing Data Frame\n",
        "train_df = pd.read_csv(f'{path_to_csv}/train.csv')\n",
        "test_df = pd.read_csv(f'{path_to_csv}/test.csv')\n",
        "display(train_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "SPhr-ZauUWDh",
        "outputId": "7d2ae7e1-5f83-4213-de4f-62895a7427f4"
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              "   id keyword location                                               text\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...\n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada\n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b8a0a6e-86f7-419f-acbb-4234992a3ec6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
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          "output_type": "display_data",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Separate Data Frame for input and outputs\n",
        "\n",
        "input_names = ['id', 'keyword', 'location', 'text']\n",
        "input_df = train_df[input_names]\n",
        "display(input_df.head())\n",
        "\n",
        "output_names = ['target']\n",
        "output_df = train_df[output_names]\n",
        "display(output_df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEyF_7m7UWDh"
      },
      "source": [
        "### 1.2 Convert our data into numpy arrays for usage in our ML models.\n",
        "\n",
        "Numpy will help us take full advantage of our GPU power to quickly perform training operations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JoKKTOAMUWDi",
        "outputId": "dbe2c37c-e567-4525-d9e3-000184d9b80d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 nan nan\n",
            "  'Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all']\n",
            " [4 nan nan 'Forest fire near La Ronge Sask. Canada']\n",
            " [5 nan nan\n",
            "  \"All residents asked to 'shelter in place' are being notified by officers. No other evacuation or shelter in place orders are expected\"]\n",
            " [6 nan nan\n",
            "  '13,000 people receive #wildfires evacuation orders in California ']\n",
            " [7 nan nan\n",
            "  'Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours into a school ']]\n",
            "[1 1 1 1 1]\n",
            "(7613, 4)\n",
            "(7613,)\n"
          ]
        }
      ],
      "source": [
        "# Convert into numpy data\n",
        "X_data = input_df.to_numpy()\n",
        "Y_data = output_df.to_numpy().flatten()\n",
        "\n",
        "print(X_data[:5])\n",
        "print(Y_data[:5])\n",
        "\n",
        "print(X_data.shape)\n",
        "print(Y_data.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2m04sCkCUWDi"
      },
      "source": [
        "### 1.3 Split the training data into 90% training and 10% for validation.\n",
        "\n",
        "We use Sklearn's train_test_split to split our `train.csv` dataset to create a 90:10 testing/validation split to use further down this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "PB-3pHpdUWDi"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test\n",
        "X_train, X_validation, Y_train, Y_validation = train_test_split(X_data, Y_data, train_size=0.90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZA6IbFj6UWDj",
        "outputId": "0d91d8c8-a793-49ae-a81c-0f9a7eda1435"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text\n",
            "[3119 'debris' 'Campo Grande-MS'\n",
            " '[Reuters] Debris confirmed from MH370; relatives hope for discovery of crash site http://t.co/DFYaSVj7NF']\n",
            "\n",
            "Is Natural Disaster?\n",
            "1 Yes\n",
            "======================\n",
            "\n",
            "Text\n",
            "[8936 'snowstorm' 'Western New York'\n",
            " '#LakeEffect #Snowstorm Twill Denim Jackets   *** ALL MY PROCEEDS FOR ITEMS WITH THIS DESIGN WILL GO\\x89Û_ https://t.co/TxTpx4umqH']\n",
            "\n",
            "Is Natural Disaster?\n",
            "1 Yes\n",
            "======================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Going through a few examples in the training split\n",
        "\n",
        "for index in range(2):\n",
        "    print('Text')\n",
        "    print(X_train[index])\n",
        "    print()\n",
        "    \n",
        "    print('Is Natural Disaster?')\n",
        "    print(Y_train[index], 'Yes' if Y_train[index] == 1 else 'No')\n",
        "    print('======================')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QecvKfmAUWDj"
      },
      "source": [
        "## 2. Baseline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQ-HJVpkUWDk"
      },
      "source": [
        "### 2.1 Create a Simple Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "78bHdhIIUWDk"
      },
      "outputs": [],
      "source": [
        "# Returns a positive result, regardless of the input\n",
        "def baseline_model(text_inputs):\n",
        "    return 1\n",
        "\n",
        "# Vectorized version of the method to apply to numpy arrays properly\n",
        "baseline_model_np = np.vectorize(baseline_model, signature='(n) -> ()')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZCbGd1RUWDk",
        "outputId": "e984570a-1046-488c-d765-9c5ba66853b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:\n",
            "[3119 'debris' 'Campo Grande-MS'\n",
            " '[Reuters] Debris confirmed from MH370; relatives hope for discovery of crash site http://t.co/DFYaSVj7NF']\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[8936 'snowstorm' 'Western New York'\n",
            " '#LakeEffect #Snowstorm Twill Denim Jackets   *** ALL MY PROCEEDS FOR ITEMS WITH THIS DESIGN WILL GO\\x89Û_ https://t.co/TxTpx4umqH']\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[4819 'evacuation' 'Washington'\n",
            " 'Roosevelt Wash. under evacuation order due to wildfire http://t.co/FiJAPxyKRQ']\n",
            "\n",
            "Output\n",
            "1\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[2925 'danger' 'World'\n",
            " \"Permits for bear hunting in danger of outnumbering actual bears: The licenses for Florida's fir... http://t.co/FP64YOSJwx #st petersburg\"]\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[2406 'collide' nan\n",
            " 'I liked a @YouTube video from @sqwizzix http://t.co/GGqCz9AB6u Call of Duty: \\x89ÛÏThe Piano Entertainer\\x89Û\\x9d Ep. 9 \\x89ÛÒ Musicians Collide!']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Testing the baseline on the \n",
        "baseline_predictions_train = baseline_model_np(X_train)\n",
        "\n",
        "for i in range(5):\n",
        "    print('Input:')\n",
        "    print(X_train[i])\n",
        "    print()\n",
        "\n",
        "    print('Output')\n",
        "    print(Y_train[i])\n",
        "    print()\n",
        "\n",
        "    print('Prediction')\n",
        "    print(baseline_predictions_train[i])\n",
        "    print('==================')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51kTTYGVUWDk"
      },
      "source": [
        "### 2.2 Calculate the Log Loss of our baseline model\n",
        "\n",
        "Since our baseline is simple, we can use this loss value to determine whether our models are more accurate than a naive approach."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "LW3kruZrUWDl"
      },
      "outputs": [],
      "source": [
        "# Calculates Log Loss\n",
        "def calculate_loss(labels, predictions):\n",
        "    epsilon = 0.000001  # Prevents taking the natural log of non-positive values\n",
        "    ce_values = -labels * np.log(predictions + epsilon) - (1 - labels) * np.log(1 - predictions + epsilon)\n",
        "    loss = ce_values.mean()\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ui2fCF4JUWDl",
        "outputId": "ab527eee-fae9-4f9d-b7d7-9e4d11e613f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss: 7.820252075724265\n",
            "Validation Loss: 8.412593964429885\n"
          ]
        }
      ],
      "source": [
        "# Calculate the loss on the training portion of our train data\n",
        "training_loss = calculate_loss(Y_train, baseline_predictions_train)\n",
        "print('Training Loss:', training_loss)\n",
        "\n",
        "# Calculate the loss on the validation portion our our train data\n",
        "baseline_predictions_validation = baseline_model_np(X_validation)\n",
        "testing_loss = calculate_loss(Y_validation, baseline_predictions_validation)\n",
        "print('Validation Loss:', testing_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UN83Y94tUWDl",
        "outputId": "efef3c31-d2c8-4822-8af9-689fe4c179e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.4339512479929937\n",
            "Validation Accurracy: 0.3910761154855643\n"
          ]
        }
      ],
      "source": [
        "training_accuracy = (baseline_predictions_train == Y_train).sum() / len(Y_train)\n",
        "print(\"Training Accuracy:\", training_accuracy)\n",
        "\n",
        "validation_accuracy = (baseline_predictions_validation == Y_validation).sum() / len(Y_validation)\n",
        "print('Validation Accurracy:', validation_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Uf2mYUZUWDm"
      },
      "source": [
        "### 2.3 Upload the baseline model to Kaggle\n",
        "\n",
        "We can then upload a `submission.csv` file to Kaggle to find the F1 score of our baseline model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Wk9ywY3jUWDm"
      },
      "outputs": [],
      "source": [
        "# Create the submission CSV file for our Kaggle submission\n",
        "def save_to_submissions_csv(text_inputs, prediction_labels, name='submission.csv'):\n",
        "    print(f'Generating \"{name}\" file...')\n",
        "\n",
        "    # Extract the ids of the text inputs and flatten to a 1D ndarray\n",
        "    test_ids = text_inputs[:,0].flatten()\n",
        "\n",
        "    # Write the submission file and save to 'submission.csv'\n",
        "    np.savetxt(\n",
        "        name,\n",
        "        np.rec.fromarrays([test_ids, prediction_labels]),\n",
        "        fmt=['%s', '%d'],\n",
        "        delimiter=',',\n",
        "        header='id,target',\n",
        "        comments=''\n",
        "    )\n",
        "\n",
        "    # Show success!\n",
        "    print(f'Successfully created \"{name}\"')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaU7X80jUWDm",
        "outputId": "8d37641e-9088-421f-8988-357bb6feba47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating \"baseline_submission.csv\" file...\n",
            "Successfully created \"baseline_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "# Reformat the single training dataframe to an input dataframe\n",
        "input_names = ['id', 'keyword', 'location', 'text']\n",
        "test_input_df = test_df[input_names]\n",
        "\n",
        "# Reformat the input dataframe into a numpy array for running through our model\n",
        "test_input_np = test_input_df.to_numpy()\n",
        "\n",
        "# Predict by using the baseline model on the test input and save to a .csv\n",
        "baseline_predictions_test = baseline_model_np(test_input_np)\n",
        "save_to_submissions_csv(test_input_np, baseline_predictions_test, 'baseline_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zaq_LsAoUWDn",
        "outputId": "097edee2-4993-4255-d3fb-69b51c1f4434"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       1\n",
              "3   9       1\n",
              "4  11       1"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "# Look at the first few predictions to ensure things went smoothly\n",
        "pd.read_csv('baseline_submission.csv').head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mb640oh-UWDn"
      },
      "source": [
        "## 3. Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdABUj4GUWDn"
      },
      "source": [
        "### 3.1 Explore the frequency of common words\n",
        "\n",
        "Tweets can have all sorts of words inside of them, but we can always find common words that we can use for our vocabulary\n",
        "\n",
        "The motivation for this graph to see what type of words are commonly used within our tweets dataset to see what we should and should not include in our vocabulary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "4yHTQoB-UWDo"
      },
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "\n",
        "# Plots the frequency of the top commonly used words in the provided tweets in\n",
        "# the positive and negative class\n",
        "def plot_frequency(tweets_np, labels_np, max_word_num=20):\n",
        "    # Creates a counter that keeps track of the frequency of words (similar to defaultdict)\n",
        "    pos_counter = Counter()\n",
        "    neg_counter = Counter()\n",
        "    tweets_np = np.copy(tweets_np) # Deep Copy of input\n",
        "\n",
        "    # Total Counter Variables\n",
        "    pos_counter_all = 0\n",
        "    neg_counter_all = 0\n",
        "\n",
        "    # Go through the tweets dataset\n",
        "    total_words = set()\n",
        "    for entry_index in range(tweets_np.shape[0]):\n",
        "        # Flatten all of the features into a single string\n",
        "        words = ' '.join([str(feature).lower() for feature in tweets_np[entry_index]])\n",
        "        # Count the frequency of each word\n",
        "        for word in words.split():\n",
        "            # Group all links as 1 token\n",
        "            if word.startswith('http'):\n",
        "                word = '<LINK>'\n",
        "            # Group all articles as 1 token\n",
        "            elif word in ['the', 'a', 'an']:\n",
        "                word = '<ARTICLE>'\n",
        "            \n",
        "            if labels_np[entry_index]:\n",
        "                pos_counter[word] += 1  # Positive entry\n",
        "                pos_counter_all += 1\n",
        "            else:\n",
        "                neg_counter[word] += 1  # Negative entry\n",
        "                neg_counter_all += 1\n",
        "\n",
        "            # For Debug purposes, saving all of the words we encounter\n",
        "            total_words.add(word)\n",
        "    \n",
        "    # Extract at most max_word_num words that are the most common words\n",
        "    # for both classes (and removes overlap)\n",
        "    top_pos_words = [word for word, _ in pos_counter.most_common(max_word_num // 2)]\n",
        "    top_neg_words = [word for word, _ in neg_counter.most_common(max_word_num // 2)]\n",
        "    top_words = set(top_pos_words + top_neg_words)\n",
        "\n",
        "    # Create a Data Frame for the collected data\n",
        "    result = {\n",
        "        'word': [word for word in top_words],\n",
        "        'pos count': [pos_counter[word] for word in top_words],\n",
        "        'neg count': [neg_counter[word] for word in top_words],\n",
        "        '% chance is pos': [(pos_counter[word] / (pos_counter[word] + neg_counter[word])) * 100 for word in top_words]\n",
        "    }\n",
        "    word_count_df = pd.DataFrame(data=result, columns=result.keys())\n",
        "    word_count_df = word_count_df.set_index('word')\n",
        "    display(word_count_df)\n",
        "    \n",
        "    # Plot a bar graph that groups pos and neg count for a few of the most used words\n",
        "    pd.concat([word_count_df[['pos count']], word_count_df[['neg count']]], axis=1).plot.bar()\n",
        "\n",
        "    print(f'DEBUG: Total Words Len: {len(total_words)}')\n",
        "    print(f'DEBUG: Total Positive Examples: {pos_counter_all}')\n",
        "    print(f'DEBUG: Total Negative Examples: {neg_counter_all}')"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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              "           pos count  neg count  % chance is pos\n",
              "word                                            \n",
              "with             188        382        32.982456\n",
              "to               761       1195        38.905930\n",
              "<ARTICLE>       2426       3372        41.842014\n",
              "you              125        629        16.578249\n",
              "and              512        927        35.580264\n",
              "this             179        287        38.412017\n",
              "i                292       1061        21.581670\n",
              "for              399        489        44.932432\n",
              "from             247        183        57.441860\n",
              "be               113        287        28.250000\n",
              "by               279        242        53.550864\n",
              "at               305        237        56.273063\n",
              "of               957        935        50.581395\n",
              "in              1186        854        58.137255\n",
              "after            181         75        70.703125\n",
              "it               164        342        32.411067\n",
              "on               418        444        48.491879\n",
              "nan             1117       1479        43.027735\n",
              "is               348        590        37.100213\n",
              "<LINK>          2510       2196        53.336167\n",
              "as               171        160        51.661631\n",
              "my               134        566        19.142857\n",
              "that             181        357        33.643123\n",
              "new              132        292        31.132075\n",
              "-                419        396        51.411043"
            ],
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          "output_type": "stream",
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          "text": [
            "DEBUG: Total Words Len: 33580\n",
            "DEBUG: Total Positive Examples: 61864\n",
            "DEBUG: Total Negative Examples: 80205\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Display and plot at most 40 words from the X_data set\n",
        "plot_frequency(X_data, Y_data, max_word_num=40)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7fNAAq7UWDo"
      },
      "source": [
        "#### 3.1.1\n",
        "\n",
        "After reviewing this information, there appears to be a lot of words that are partially helpful, but we don't see any keywords that we expected such as \"fire\", or other natural disaster words.\n",
        "\n",
        "As a result, we will need to have a lot of words in our vocabulary to include these helpful words as well, as the expected keywords that we expected."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ty0Cd2rDUWDp"
      },
      "source": [
        "### 3.2 Explore the length of the input\n",
        "\n",
        "We were thinking that the number of tokens in a tweet might help with determining whether a tween is a disaster or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "PhNNAZ60UWDp"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "def length_plot(tweets_np , labels_np, interval_list):\n",
        "\n",
        "    # initializes maps to count based on intervals of words\n",
        "    pos_intervals = defaultdict(int)\n",
        "    neg_intervals = defaultdict(int)\n",
        "\n",
        "    # Convert the interval list into ranges for use below\n",
        "    interval_map = {}\n",
        "    for interval in interval_list:\n",
        "        # Parses interval strings into useable ranges\n",
        "        if '-' in interval:\n",
        "            start, end = map(lambda x: int(x), interval.split('-'))\n",
        "        else:\n",
        "            start, end = interval.split('+')[0], 285\n",
        "\n",
        "        interval_map[interval] = range(int(start), int(end) + 1)\n",
        "\n",
        "    # Track the counts of positive and negative inputs for each range from above\n",
        "    for entry_index in range(tweets_np.shape[0]):\n",
        "        # Flatten all of the features into a single string\n",
        "        words = ' '.join([str(feature) for feature in tweets_np[entry_index]]).split()\n",
        "\n",
        "        for interval in interval_list:\n",
        "            # Ignore words counts outside of our intervals\n",
        "            if len(words) not in interval_map[interval]:\n",
        "                continue\n",
        "\n",
        "            # Updates the counts of positive and negative entries in the dictionary\n",
        "            # based on the interval they are in\n",
        "            if labels_np[entry_index]:\n",
        "                pos_intervals[interval] += 1\n",
        "            else:\n",
        "                neg_intervals[interval] += 1\n",
        "\n",
        "    # Create a Data Frame for the collected data\n",
        "    result= {\n",
        "        'interval' : [ interval for interval in interval_list],\n",
        "        'pos intervals' : [pos_intervals[interval] for interval in interval_list],\n",
        "        'neg intervals' : [neg_intervals[interval] for interval in interval_list],\n",
        "        '% chance is pos': [(pos_intervals[interval] / (pos_intervals[interval] + neg_intervals[interval])) * 100 for interval in interval_list],\n",
        "    }\n",
        "    word_count_df = pd.DataFrame(data = result , columns=result.keys())\n",
        "    word_count_df = word_count_df.set_index('interval')\n",
        "\n",
        "    # Plot and Display the collected data\n",
        "    pd.concat([word_count_df[['pos intervals']], word_count_df[['neg intervals']]], axis=1).plot.bar()\n",
        "    display(word_count_df)\n",
        "\n",
        "    # Debugging code\n",
        "    print(neg_intervals)\n",
        "    print(pos_intervals)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "id": "_rxLPqfmUWDp",
        "outputId": "0a67ab17-272b-4781-de69-448d4554574e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          pos intervals  neg intervals  % chance is pos\n",
              "interval                                               \n",
              "0-5                   2             28         6.666667\n",
              "6-10                164            463        26.156300\n",
              "11-15               740           1017        42.117245\n",
              "16-20              1041           1165        47.189483\n",
              "21-25               973           1010        49.067070\n",
              "26-30               317            552        36.478711\n",
              "31-35                34            106        24.285714\n",
              "36+                   0              1         0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1482e36f-575d-462f-9c0b-bdc422090c7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pos intervals</th>\n",
              "      <th>neg intervals</th>\n",
              "      <th>% chance is pos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>interval</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0-5</th>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6-10</th>\n",
              "      <td>164</td>\n",
              "      <td>463</td>\n",
              "      <td>26.156300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11-15</th>\n",
              "      <td>740</td>\n",
              "      <td>1017</td>\n",
              "      <td>42.117245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16-20</th>\n",
              "      <td>1041</td>\n",
              "      <td>1165</td>\n",
              "      <td>47.189483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21-25</th>\n",
              "      <td>973</td>\n",
              "      <td>1010</td>\n",
              "      <td>49.067070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26-30</th>\n",
              "      <td>317</td>\n",
              "      <td>552</td>\n",
              "      <td>36.478711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31-35</th>\n",
              "      <td>34</td>\n",
              "      <td>106</td>\n",
              "      <td>24.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36+</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1482e36f-575d-462f-9c0b-bdc422090c7b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1482e36f-575d-462f-9c0b-bdc422090c7b 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-1482e36f-575d-462f-9c0b-bdc422090c7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "defaultdict(<class 'int'>, {'6-10': 463, '0-5': 28, '16-20': 1165, '11-15': 1017, '21-25': 1010, '26-30': 552, '31-35': 106, '36+': 1})\n",
            "defaultdict(<class 'int'>, {'16-20': 1041, '6-10': 164, '21-25': 973, '11-15': 740, '26-30': 317, '31-35': 34, '0-5': 2, '36+': 0})\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plots the count of positive and negative classes of X_data based on the given intervals\n",
        "intervals = ['0-5', '6-10' , '11-15' , '16-20' , '21-25' , '26-30' , '31-35', '36+']\n",
        "length_plot(X_data, Y_data, intervals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YB_4Xp0UWDq"
      },
      "source": [
        "#### 3.2.1\n",
        "\n",
        "It looks like there is a fair distribution based on the number of words in the tweet.\n",
        "\n",
        "After viewing this graph, we figured that it may be best to not include the number of words in our model inputs. Bucketing the tweets based on these values may not be worth the extra model complexity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59TLdwTwUWDq"
      },
      "source": [
        "## 4. Our Experiments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGwuOlIdUWDq"
      },
      "source": [
        "We were able to come up with three differing models using different algorithms learned in class, each varying in complexity. various differing variations of each model were ran however we kept the best versions of each model along with an explanation of how we reached that point"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUTkLIuGUWDq"
      },
      "source": [
        "### 4.1 Normalize our data\n",
        "\n",
        "Since tweets can contain lots of punctuation, we want to specify a standardization to sanitize our inputs. In addition, all links are changed to <LINK> as a way of reducing the creation of unique features. \n",
        "> **NOTE** Though standarization helps ensure that our model works for unusual input, there may be a lost \n",
        "> of information when applying normalizations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_71T45YUWDr"
      },
      "source": [
        "#### 4.1.1 Normalization Technique 1: Remove punctuation and Links"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaHUoeYeUWDr",
        "outputId": "30ec4965-7df7-4a65-87fd-4acba8e75b8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DEBUG: i am cool <LINK> <LINK>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=string, numpy=b'i am cool <LINK> <LINK>'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Stardardizes the input\n",
        "def normalize_punctuation_and_links(tweets_text, show_debug=False):\n",
        "    # Make all letters lowercase\n",
        "    result_tensor = tf.strings.lower(tweets_text)\n",
        "\n",
        "    # Replace links with <LINK> token\n",
        "    link_regex = r'(https?:\\/\\/)([\\da-z\\.-]+)\\.([a-z\\.]{2,6})([\\/\\w\\.-]*)'\n",
        "    result_tensor = tf.strings.regex_replace(result_tensor, link_regex, '<LINK>')\n",
        "\n",
        "    # Remove punctuation (but don't remove <> from our Link tokens)\n",
        "    punctuation_regex = r'[^\\w\\s<>]'\n",
        "    result_tensor = tf.strings.regex_replace(result_tensor, punctuation_regex, ' ')\n",
        "\n",
        "    # Remove extra spaces\n",
        "    multi_space_regex = r'\\s{2,}'\n",
        "    result_tensor = tf.strings.regex_replace(result_tensor, multi_space_regex, ' ')\n",
        "\n",
        "    if show_debug:\n",
        "        print('DEBUG: ', end='')\n",
        "        tf.print(result_tensor)\n",
        "\n",
        "    return result_tensor\n",
        "\n",
        "normalize_punctuation_and_links('I.am.cool  http://www.example.com, https://github.com/example', show_debug=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnctKzbhUWDr"
      },
      "source": [
        "### 4.2 Build Our Model(s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9fyFWq5UWDr"
      },
      "source": [
        "#### 4.2.1 FFNN Model (Bag Of Embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTpGLybBUWDr"
      },
      "source": [
        "##### 4.2.1.1\n",
        "Our first model is a fairly simple FFNN. We used the concept of \"Bag of Embeddings\", where we use embeddings with dim=3 to try to identify which words are related to each other.\n",
        "\n",
        "A problem with this model is that, as the name suggests, the words are not in any particular order (just in a messy \"bag\"), but there is a lot of information that the model can learn without word order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "vWLN5PR7UWDr"
      },
      "outputs": [],
      "source": [
        "def build_ffnn_model(tweets_np, max_vocab, max_tokens, embedding_dim):\n",
        "    # Remove randomness\n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "    \n",
        "    # Build the normalization layer and vocabulary ahead of time\n",
        "    norm_layer = tf.keras.layers.TextVectorization(\n",
        "        max_tokens,\n",
        "        standardize=normalize_punctuation_and_links,\n",
        "        split='whitespace',\n",
        "        output_mode='int',\n",
        "        encoding='utf-8',\n",
        "        name='Normalization_Layer',\n",
        "        output_sequence_length=max_tokens,\n",
        "    )\n",
        "    norm_layer.adapt(tweets_np, batch_size=64)\n",
        "\n",
        "    # display(norm_layer.get_vocabulary())\n",
        "\n",
        "    # Build our FFNN Model using embeddings and average pooling\n",
        "    model = tf.keras.models.Sequential()\n",
        "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string))\n",
        "    model.add(norm_layer)\n",
        "    model.add(tf.keras.layers.Embedding(\n",
        "        input_dim=max_vocab,\n",
        "        output_dim=embedding_dim,\n",
        "        input_length=max_tokens,\n",
        "        name='Embeddings_Layer',\n",
        "    ))\n",
        "    # max pool layer\n",
        "    model.add(tf.keras.layers.GlobalMaxPooling1D(\n",
        "        name='Max_Pool_Layer'\n",
        "    ))\n",
        "    # two dense layer with a sigmoid activation\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=128,  \n",
        "        activation='sigmoid',\n",
        "        name='Dense_Layer_1',\n",
        "    ))\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=64,\n",
        "        activation='sigmoid',\n",
        "        name='Dense_Layer_2',\n",
        "    ))\n",
        "\n",
        "    # output layer with a sigmoid activation \n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=1,\n",
        "        activation='sigmoid',\n",
        "        name='Output_Layer',\n",
        "    ))\n",
        "\n",
        "    # Compile and return the model\n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6t5-ZZ_UWDs"
      },
      "source": [
        "A large vocabulary with seems to have a direct benefit to the model as more features can be identified. An input of 300 with a dimension of 12 was also found to have a good impact through tedious trial and error. The biggest obstacle was finding the correct amount of training. This model was very prone to overfitting data if it was trained past 11 epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "8WjLcMMDUWDs",
        "outputId": "594fc87f-31a6-488e-b6f2-d3441239f6ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Normalization_Layer (TextVe  (None, 300)              0         \n",
            " ctorization)                                                    \n",
            "                                                                 \n",
            " Embeddings_Layer (Embedding  (None, 300, 12)          420000    \n",
            " )                                                               \n",
            "                                                                 \n",
            " Max_Pool_Layer (GlobalMaxPo  (None, 12)               0         \n",
            " oling1D)                                                        \n",
            "                                                                 \n",
            " Dense_Layer_1 (Dense)       (None, 128)               1664      \n",
            "                                                                 \n",
            " Dense_Layer_2 (Dense)       (None, 64)                8256      \n",
            "                                                                 \n",
            " Output_Layer (Dense)        (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 429,985\n",
            "Trainable params: 429,985\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "ffnn_model = build_ffnn_model(X_train[:,3], 35000, 300, 12)\n",
        "display(ffnn_model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(ffnn_model, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "Lsi-TMqPU177",
        "outputId": "e594ce72-52c2-4570-d2b6-9dfaccc4a8bd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwRWCwNPUWDt"
      },
      "source": [
        "#### 4.2.2 CNN Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTY-ECm6UWDt"
      },
      "source": [
        "##### 4.2.2.1\n",
        "The second model made was a CNN. This model is also fairly standard. This model applies filters along the features to detect any patterns that are useful for our predictions. We still use embeddings since these is useful for identifying if words are related.\n",
        "\n",
        "Giovanni put this together and got it running with originally 2 conv1D layers, which later concantenated together."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Bl0MmNrbUWDt"
      },
      "outputs": [],
      "source": [
        "def build_cnn_model(tweets_np, max_vocab, max_tokens, embedding_dim):\n",
        "    # Remove randomness\n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "    \n",
        "    # Build the normalization layer ahead of time\n",
        "    norm_layer = tf.keras.layers.TextVectorization(\n",
        "        max_tokens,\n",
        "        standardize=normalize_punctuation_and_links,\n",
        "        split='whitespace',\n",
        "        output_mode='int',\n",
        "        encoding='utf-8',\n",
        "        name='Normalization_Layer',\n",
        "        output_sequence_length=max_tokens,\n",
        "    )\n",
        "    norm_layer.adapt(tweets_np, batch_size=64)\n",
        "\n",
        "    # Build our CNN Model using Keras' Functional API\n",
        "    input_layer = tf.keras.Input(shape=(1,), dtype=tf.string, name='Input Layer')\n",
        "    norm_layer = norm_layer(input_layer)\n",
        "    embedding_layer = tf.keras.layers.Embedding(\n",
        "        input_dim=max_vocab,\n",
        "        output_dim=embedding_dim,\n",
        "        input_length=max_tokens,\n",
        "        name='Embeddings_Layer',\n",
        "    )(norm_layer)\n",
        "    \n",
        "    # three conv1D layers, filter sizes increase as it progresses\n",
        "    conv_1 = tf.keras.layers.Conv1D(\n",
        "        filters=16,\n",
        "        kernel_size=3,\n",
        "        padding='same',\n",
        "        activation='relu',\n",
        "        name='Conv_Layer_1_1',\n",
        "    )(embedding_layer)\n",
        "    conv_2 = tf.keras.layers.Conv1D(\n",
        "        filters=16,\n",
        "        kernel_size=4,\n",
        "        padding='same',\n",
        "        activation='relu',\n",
        "        name='Conv_Layer_1_2',\n",
        "    )(embedding_layer)\n",
        "    conv_3 = tf.keras.layers.Conv1D(\n",
        "        filters=16,\n",
        "        kernel_size=5,\n",
        "        padding='same',\n",
        "        activation='relu',\n",
        "        name='Conv_Layer_1_3',\n",
        "    )(embedding_layer)\n",
        "\n",
        "    # combines layers into single layers (Dropout adds some randomness, while MaxPool1D helps shrink our layers)\n",
        "    concat_layer = tf.keras.layers.Concatenate(name='Concatenate_Layer')([conv_1, conv_2, conv_3])\n",
        "    dropout_layer = tf.keras.layers.Dropout(rate=0.05)(concat_layer)\n",
        "    max_pool_layer = tf.keras.layers.MaxPool1D(pool_size=max_tokens, name='Max_Pool_Layer')(dropout_layer)\n",
        "    flatten_layer = tf.keras.layers.Flatten(name='Flatten_Layer_1')(max_pool_layer)\n",
        "    \n",
        "    # hidden layer of size 32, sigmoid\n",
        "    hidden_layer = tf.keras.layers.Dense(\n",
        "        units=32,\n",
        "        activation='sigmoid',\n",
        "        name='Hidden_Layer_1',\n",
        "    )(flatten_layer)\n",
        "\n",
        "    # output layer\n",
        "    output_layer = tf.keras.layers.Dense(\n",
        "        units=1,\n",
        "        activation='sigmoid',\n",
        "        name='Output_Layer',\n",
        "    )(hidden_layer)\n",
        "\n",
        "    # adds input and output layer to model\n",
        "    model = tf.keras.Model(\n",
        "        inputs=input_layer,\n",
        "        outputs=output_layer,\n",
        "        name='CNN_Model',\n",
        "    )\n",
        "\n",
        "    # Compile and return the model\n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zL29EtlKUWDv"
      },
      "source": [
        "##### 4.2.2.2\n",
        "\n",
        "As in the previous model, a larger vocab size, and embedding Layer (input/dimension), had a huge boost. This may be due to using the same preprocessing funcion across the board. We first started with just 2 convolutional layers. After doing some research online, and seeing that common practice was to use a smaller filter size and increase through each of the convolutional layers, we had some better success. Our reasoning behind this choice was that the context of the input is widened as it is processed through the model. Our final change was adding one more convolutional layer, which brought the total of those type of layers to three.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "K2d8gjDFUWDv",
        "outputId": "b236d7fd-1a9a-453d-f80a-4724b92a77e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"CNN_Model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " Input Layer (InputLayer)       [(None, 1)]          0           []                               \n",
            "                                                                                                  \n",
            " Normalization_Layer (TextVecto  (None, 300)         0           ['Input Layer[0][0]']            \n",
            " rization)                                                                                        \n",
            "                                                                                                  \n",
            " Embeddings_Layer (Embedding)   (None, 300, 9)       180000      ['Normalization_Layer[0][0]']    \n",
            "                                                                                                  \n",
            " Conv_Layer_1_1 (Conv1D)        (None, 300, 16)      448         ['Embeddings_Layer[0][0]']       \n",
            "                                                                                                  \n",
            " Conv_Layer_1_2 (Conv1D)        (None, 300, 16)      592         ['Embeddings_Layer[0][0]']       \n",
            "                                                                                                  \n",
            " Conv_Layer_1_3 (Conv1D)        (None, 300, 16)      736         ['Embeddings_Layer[0][0]']       \n",
            "                                                                                                  \n",
            " Concatenate_Layer (Concatenate  (None, 300, 48)     0           ['Conv_Layer_1_1[0][0]',         \n",
            " )                                                                'Conv_Layer_1_2[0][0]',         \n",
            "                                                                  'Conv_Layer_1_3[0][0]']         \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 300, 48)      0           ['Concatenate_Layer[0][0]']      \n",
            "                                                                                                  \n",
            " Max_Pool_Layer (MaxPooling1D)  (None, 1, 48)        0           ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " Flatten_Layer_1 (Flatten)      (None, 48)           0           ['Max_Pool_Layer[0][0]']         \n",
            "                                                                                                  \n",
            " Hidden_Layer_1 (Dense)         (None, 32)           1568        ['Flatten_Layer_1[0][0]']        \n",
            "                                                                                                  \n",
            " Output_Layer (Dense)           (None, 1)            33          ['Hidden_Layer_1[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 183,377\n",
            "Trainable params: 183,377\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "cnn_model = build_cnn_model(X_train[:,3], 20000, 300, 9)\n",
        "display(cnn_model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(cnn_model, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TNxZNpqQUxoU",
        "outputId": "67b08fd5-39ea-4046-afda-c17e8d41e1a9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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njlXz2EdERETUFkzmEBGRWdi7d6+xQ6BmJCYmAgAWLFhg5EhM19mzZ7Fp0ybOZxNVNz5ERESmjskcIiIyC9OmTTN2CNSM9PR0AByr5mzatIl9ZMKYzCEiInPANXOIiIiIiIiIiMwIkzlERERERERERGaEyRwiIiIiIiIiIjPCZA4RERERERERkRlhMoeIiIiIiIiIyIwwmUNERE+lb775Bv369YOFhQUUCgXc3NwQFxdn7LCQkZEBLy8vKBQKKBQKaDQazJw509hhkQkLCwvTzheFQtHgfDlx4gRiYmLqza8333yzXtkJEybA3t4elpaW6N+/Py5cuGCIZrRKbW0tEhMT4efnp3P80KFDiI+PR01Njc7xAwcO6PRVly5dDBkuERGRwTCZQ0RET6WRI0fihx9+wIQJEwAA2dnZWL58uZGjAgIDA3H9+nV4e3vD0dERd+7cQWpqqrHDIhPn4uKCY8eOITs7Gx999JHOuVWrViEpKQnLli3TmV+dO3dGamoqjhw5olP++PHjSE9Px+TJk5GVlYWhQ4casil6y8nJwYsvvoiFCxfi0aNHOuf8/f2hUqkwfvx4FBYWao+/9tpryMvLw6lTpzBx4kRDh0xERGQwTOYQERF1oPLy8npPFVD768h+NoUxtLW1xSuvvILevXvDxsZGe3zDhg3Ys2cP0tLSYG9vr3NNUlISLCwsEBoaiqKiIkOH3Cbfffcdli5divDwcAwePLjBMpGRkRg0aBAmTpyIx48fAwAUCgXc3d0xduxY9OrVy5AhExERGRSTOURERB3oo48+Qn5+vrHDeOp1ZD+b6hhevXoVK1aswOrVq6FSqeqd9/PzQ1RUFG7duoVFixYZIcLWGzRoEDIyMjBjxgyd5NVvxcbGIjMzE5s2bTJgdERERMbHZA4RET1TkpOTYWdnB7VajYMHD+LVV1+Fg4MDPDw8sHv3bgC/PtGgUqnQtWtXhIWFoVu3blCpVPDz88O5c+cAABEREbC2toZGo9He+5133oGdnR0UCgXu37+PqKgoREdH49q1a1AoFPDx8WlxvKdPn4avry8cHR2hUqkwcOBAfPHFFwCAP//5z9q1Qby9vXHx4kUAwOzZs6FWq+Ho6IhDhw6hpqYGK1euhKenJ2xtbfHCCy9g7969AIAPPvgAarUa9vb2yM/PR3R0NNzd3ZGdnd2mftaXiCAhIQH9+vWDjY0NnJ2dMWXKFPz4448AWt/PHT2Gn3/+ORwcHLBu3TqD9FNDkpKSICLw9/dvtExcXBx69+6NHTt24MSJE42Wa24c9PncAGhyrnUEZ2dnvPTSS9i0aRNEpMPqISIiMjlCRERkwvbu3Stt+evqT3/6kwCQhw8fao+9//77AkC+/PJLKSoqkvz8fBk7dqzY2dlJVVWViIiEhoaKnZ2dfP/991JRUSFZWVkyYsQIsbe3l9zcXBERmTFjhri5uenUt3HjRgEg9+7dExGRwMBA8fb2rheXt7e3ODo6Nht/enq6xMbGyoMHD6SgoEBGjhwpnTt31p4PDAwUS0tLuXXrls51b7zxhhw6dEhERBYtWiQ2Njayb98+efjwoSxbtkwsLCzk22+/1emPyMhI2bx5s0ydOlV++OGHZmP7raCgIAkKCmrRNStXrhRra2vZuXOnFBYWyqVLl2To0KHSpUsXuXPnjoi0vp87cgwPHz4s9vb2smbNmha1tzXzOTQ0VNzd3esd9/LyEl9f3wav8fb2lp9++klERL7++muxsLCQ559/XkpLS0VE5NixY/Laa69py+szDvp8bpqba63xu9/9TgYNGtTo+ZiYGAEgFy9e1DkeGRmp81nRR1u/b4iIiAwkjU/mEBHRM8vPzw8ODg5wdXVFSEgIysrKkJubqz1vZWWlfVLB19cXycnJKCkpQUpKisFiDAoKwqpVq+Ds7AwXFxf4+/ujoKAA9+7dAwCEh4ejpqZGJ6bi4mJ8++23mDhxIioqKpCcnIyAgAAEBgbCyckJy5cvh1KprNeODRs2YP78+cjIyEDfvn07vG3l5eVISEjA1KlTMXPmTDg6OmLgwIHYtm0b7t+/j+3bt7e5jo4aw0mTJqG4uBgrVqxoc4ytUVZWhp9++gne3t7Nlh01ahQWLFiAGzduYOnSpfXOt3QcGvvctGSutae6tXEuX77cYXUQERGZGiZziIiIAFhbWwMAqqurGy0zfPhwqNVq7asnxqBUKgFAuyXz73//e/Tu3Rsff/yx9jWTPXv2ICQkBJaWlsjOzsajR48wYMAA7T1sbW2h0WiM2g4AyMrKQmlpKYYPH65zfMSIEbC2tta+DtWeTGEM20N+fj5EBGq1Wq/ycXFx6NOnD7Zu3YozZ87onGvLODz5uTHWXKvrg7t373ZYHURERKaGyRwiIqIWsLGx0T4VYwhHjhzBuHHj4OrqChsbG7z33ns65xUKBcLCwnD9+nV8+eWXAIBPP/0Ub7/9NoBfn+AAgOXLl2vX11EoFLh582a97Z4NrW5L6U6dOtU75+TkhJKSkg6p19Bj2BEqKioAoMnFgZ+kUqmQkpIChUKBOXPmoLy8XHuuvcbBWHPN1tYWwH/6hIiI6FnAZA4REZGeqqurUVhYCA8Pjw6t59SpU0hMTERubi4CAgKg0Whw7tw5FBUVIT4+vl75WbNmQaVSYceOHcjOzoaDgwN69OgBAHB1dQUAJCYmQkR0fs6ePduh7WiOk5MTADSYLOiofjbUGHa0ugRG3RNa+hg1ahQWLlyInJwcrF27Vnu8vcbBWHOtqqoKwH/6hIiI6FlgZewAiIiIzMXJkychIhg5ciSAX9djaeq1rNb697//DTs7O1y+fBnV1dWYN28evLy8APz6JM5vOTs7Y/r06dizZw/s7e0xd+5c7bnu3btDpVIhMzOz3eNsqwEDBqBTp044f/68zvFz586hqqoKw4YNA9C+/WyoMexoXbt2hUKhQFFRUYuuW7t2LQ4fPoyLFy/C09MTgP7j0BxjzbW6PnBzczNovURERMbEJ3OIiIgaUVtbi4cPH+Lx48e4dOkSoqKi4OnpiVmzZgEAfHx88ODBAxw4cADV1dW4d+8ebt68qXMPFxcX/PLLL7hx4wZKSkqaTBxUV1fj7t27OHnyJOzs7LS/bJ84cQIVFRXIyclpdP2S8PBwVFZW4vDhw5g8ebL2uEqlwuzZs7F7924kJyejuLgYNTU1yMvLw+3bt9vYQ22jUqkQHR2N/fv3IzU1FcXFxbh8+TLCw8PRrVs3hIaGAmhbP3fUGB47dsyoW5Or1Wp4eXkhLy+vRdfVvW5laWmpc0yfcdDn3s3NtZCQELi5ueHChQstirspdX0wcODAdrsnERGRyTPOLlpERET6ae1Wwd988430799fLCwsBIBoNBpZt26dbN26VdRqtQCQXr16ybVr12T79u3i4OAgAKRHjx5y5coVCQ0NFaVSKe7u7mJlZSUODg4yZcoUuXbtmraOgoICefnll0WlUknPnj3l3XfflcWLFwsA8fHxkdzcXLlw4YL06NFDbG1tZcyYMfLXv/5VvL29BUCTP/v37xcRkSVLloiLi4s4OTlJcHCwbNmyRQCIt7e3dnvtOkOGDJGYmJh6fVFZWSlLliwRT09PsbKyEldXVwkMDJSsrCyJj48XW1tbASDdu3eXnTt3triv67Rma/La2lrZuHGj9OrVS5RKpTg7O0tAQIBkZ2dry7Smn+/cudNhY3jnzh05evSo2NvbS1xcXIva255bk0dERIhSqZRHjx5pj+3fv187v7p06SLz589v8J6LFy/W2Zq8uXHQ93PT1FwTEQkICBAAsnLlyibbfPbsWRk9erR069ZN+5nQaDTi5+cnX331lU7ZSZMmibu7u9TW1uoc59bkRET0FEtTiPzf1hdEREQmKC0tDdOnT4eh/7oKCwtDeno6CgoKDFpvW0yaNAlbtmxBz549jVJ/cHAwACA9Pd0o9f+WKY5ha+ZzWFgYDh8+XO8pnKtXr6Jfv35ISUnBzJkz2zvUDlFbW4tx48Zh1qxZmDNnTpvvV1BQAA8PD8TFxSE6OlrnXFRUFFJTU3H//n2972es7xsiIqIWSudrVkRERI1oyeKyxvDkK1uXLl2CSqUyWiLHVJn6GOqrvLwcX3zxBXJycrQL/vr4+GDNmjVYs2YNSktLjRxh82pqanDgwAGUlJQgJCSkXe4ZGxuLwYMHIyIiAgAgIvjll19w5swZXL16tV3qICIiMkVM5hAREZmpJUuWICcnB1euXMHs2bN1diiip8uDBw/wyiuvoHfv3jpPtMTExCA4OBghISEtXgzZ0E6ePImMjAwcO3YMarW6zfdLSEhAZmYmjh49CqVSCQA4ePAg3N3dMXbsWBw5cqTNdRAREZkqJnOIiIh+Y9myZUhJSUFRURF69uyJffv2GTukBqnVavTt2xd/+MMfEBsbC19fX2OHZDLMZQz1sW3bNp1tvlNTU3XOr1u3DhEREVi/fr2RItTP+PHjsWvXLmg0mjbf6+DBg6isrMTJkyfh7OysPT5lyhSdvmrJK1ZERETmhGvmEBGRSeMaFubD1NbMMUWcz6aN40NERGaCa+YQEREREREREZkTJnOIiIiIiIiIiMwIkzlERERERERERGaEyRwiIiIiIiIiIjNiZewAiIiI9JGWlmbsEKgZeXl5ADhWTTl79iwA9pGpqhsfIiIiU8fdrIiIyKTV7S5DRGQo/OcxERGZuHQmc4iIiOipwy2miYiI6CnGrcmJiIiIiIiIiMwJkzlERERERERERGaEyRwiIiIiIiIiIjPCZA4RERERERERkRlhMoeIiIiIiIiIyIwwmUNEREREREREZEaYzCEiIiIiIiIiMiNM5hARERERERERmREmc4iIiIiIiIiIzAiTOUREREREREREZoTJHCIiIiIiIiIiM8JkDhERERERERGRGWEyh4iIiIiIiIjIjDCZQ0RERERERERkRpjMISIiIiIiIiIyI0zmEBERERERERGZESZziIiIiIiIiIjMCJM5RERERERERERmhMkcIiIiIiIiIiIzwmQOEREREREREZEZYTKHiIiIiIiIiMiMMJlDRERERERERGRGmMwhIiIiIiIiIjIjTOYQEREREREREZkRJnOIiIiIiIiIiMwIkzlERERERERERGaEyRwiIiIiIiIiIjPCZA4RERERERERkRlhMoeIiIiIiIiIyIwwmUNEREREREREZEaYzCEiIiIiIiIiMiNM5hARERERERERmREmc4iIiIiIiIiIzIiVsQMgIiIiaov8/HykpKToHLt06RIAID4+Xue4i4sL5s6da7DYiIiIiDqCQkTE2EEQERERtdbjx4+h0Wjw8OFDKJXKRstVVlYiNDQU27ZtM2B0RERERO0una9ZERERkVmzsrLC66+/DktLS1RWVjb6AwBvvPGGkaMlIiIiajsmc4iIiMjsvf7666iurm6yjEajwZgxYwwUEREREVHHYTKHiIiIzN6oUaPg4eHR6Hlra2u8+eabsLDgP32IiIjI/PFfNERERGT2FAoFZs6c2eiaOVVVVXj99dcNHBURERFRx2Ayh4iIiJ4KTb1q5eXlhSFDhhg4IiIiIqKOwWQOERERPRVeeOEF9OnTp95xa2trvPXWW0aIiIiIiKhjMJlDRERET40333yz3qtWVVVVCAkJMVJERERERO2PyRwiIiJ6asycOROPHz/W/lmhUGDQoEHo3bu3EaMiIiIial9M5hAREdFTo0ePHhg6dCgUCgUAwNLSkq9YERER0VOHyRwiIiJ6qvz3f/83LC0tAQA1NTWYNm2akSMiIiIial9M5hAREdFTZdq0aaitrYVCocDo0aPh7u5u7JCIiIiI2hWTOURERPRU0Wg0eOmllyAifMWKiIiInkoKERFjB0FERMYVHByMffv2GTsMIiJqxt69e/nqIBERpVsZOwIiIjINI0eOxIIFC4wdBlGbTJ8+HVFRURg8eDC2b9+OyMhIY4dkchITEwGAn3czNH36dGOHQEREJofnJq4AACAASURBVILJHCIiAgB4eHjwf3vJ7E2fPh2jRo3CtGnT8Mc//hHPPfecsUMyOenp6QDAz7sZYjKHiIjqcM0cIiIieioxkUNERERPKyZziIiIiIiIiIjMCJM5RERERERERERmhMkcIiIiIiIiIiIzwmQOEREREREREZEZYTKHiIhaLCMjA15eXlAoFFAoFHjzzTfrlZkwYQLs7e1haWmJ/v3748KFC0aItHkffvghunbtCoVCgW3btmmPHz16FI6Ojvjss886tH5D1dOQ346jRqPBzJkzDR6HqTHmmDwtTpw4gZiYmKfquwIAamtrkZiYCD8/P53jhw4dQnx8PGpqaowUGRERPWuYzCEiohYLDAzE9evX4e3tjc6dOyM1NRVHjhzRKXP8+HGkp6dj8uTJyMrKwtChQ40UbdMWLVqEr7/+ut5xETFI/YaqpyFPjqOjoyPu3LmD1NRUo8VjKow5Jk+DVatWISkpCcuWLXuqvitycnLw4osvYuHChXj06JHOOX9/f6hUKowfPx6FhYVGipCIiJ4lTOYQEVGbJCUlwcLCAqGhoSgqKjJ2OO1m0qRJKCoqwuTJk9vtnuXl5fX+R78j6qG2MfaYNDRPzMWGDRuwZ88epKWlwd7eXuecOX9XfPfdd1i6dCnCw8MxePDgBstERkZi0KBBmDhxIh4/fmzgCImI6FnDZA4REbWJn58foqKicOvWLSxatMjY4Zi0jz76CPn5+cYOg0ycuc6Tq1evYsWKFVi9ejVUKlW98+b8XTFo0CBkZGRgxowZsLGxabRcbGwsMjMzsWnTJgNGR0REzyImc4iIqM3i4uLQu3dv7NixAydOnGi0nIggISEB/fr1g42NDZydnTFlyhT8+OOPAIAPPvgAarUa9vb2yM/PR3R0NNzd3REeHg47OztYWFhg2LBhcHNzg1KphJ2dHYYOHYqxY8eie/fuUKlUcHJywnvvvadT7+nTp+Hr6wtHR0eoVCoMHDgQX3zxRaNxnjlzBp6enlAoFNiyZQuAX39RrVv347c///rXv5qtJyoqCtHR0bh27RoUCgV8fHwarEeffkpOToadnR3UajUOHjyIV199FQ4ODvDw8MDu3btbMYL6aap9f/7zn7X94e3tjYsXLwIAZs+eDbVaDUdHRxw6dAg1NTVYuXIlPD09YWtrixdeeAF79+4F0Pj4Z2dnd1ibfquhMdGnv5OSkqBSqdC1a1eEhYWhW7duUKlU8PPzw7lz5wAAERERsLa2hkaj0db3zjvvwM7ODgqFAvfv329wngDA559/DgcHB6xbt85gfdFSSUlJEBH4+/s3Wqa9viv0/Qw0Nd86grOzM1566SVs2rSJr+sREVHHEiIieuYFBQVJUFBQi6/z9vaWn376SUREvv76a7GwsJDnn39eSktLRUTk2LFj8tprr2nLr1y5UqytrWXnzp1SWFgoly5dkqFDh0qXLl3kzp07IiLy/vvvCwCJjIyUzZs3y9SpU+WHH36QVatWCQA5d+6clJWVyf379+WVV14RAHLkyBG5d++elJWVSUREhACQzMxMbb3p6ekSGxsrDx48kIKCAhk5cqR07txZez4nJ0cAyF//+lftsZ9//lkAyObNm7Vlli5dKmVlZSIicvv2bXF2dhY/Pz+pqanRq57AwEDx9vbW6cPf1tPSfvryyy+lqKhI8vPzZezYsWJnZydVVVUtHkdHR8dmy+nTPktLS7l165bOdW+88YYcOnRIREQWLVokNjY2sm/fPnn48KEsW7ZMLCws5Ntvv9Vp12/HX18AZO/evXqXb0hDY6JPf4eGhoqdnZ18//33UlFRIVlZWTJixAixt7eX3NxcERGZMWOGuLm56dS3ceNGASD37t0TkYbnyeHDh8Xe3l7WrFnTpraJtP7z3hwvLy/x9fVt8FxHflc0NSbNzbfW+N3vfieDBg1q9HxMTIwAkIsXL7a6jsa0x/wmIqKnQhqfzCEionYxatQoLFiwADdu3MDSpUvrnS8vL0dCQgKmTp2KmTNnwtHREQMHDsS2bdtw//59bN++Xaf8hg0bMH/+fGRkZKBv377a476+vlCr1ejcuTNef/11AICnpye6dOkCtVqt3Y2p7n/wASAoKAirVq2Cs7MzXFxc4O/vj4KCAty7d0/v9vn4+GD9+vVQq9UAgHnz5qG8vBwff/wxLCws2q2elvaTn58fHBwc4OrqipCQEJSVlSE3N1fv+lqiufaFh4ejpqYGKSkp2muKi4vx7bffYuLEiaioqEBycjICAgIQGBgIJycnLF++HEqlUucaoPHxN7bm+tvKykr7NImvry+Sk5NRUlJSr30tNWnSJBQXF2PFihVtbUKHKCsrw08//QRvb+9my7b3d0VjY9KS+daeevXqBQC4fPlyh9VBRETEZA4REbWbuLg49OnTB1u3bsWZM2d0zmVlZaG0tBTDhw/XOT5ixAhYW1trX0VpCWtrawDQWWxUqVQCAKqrqxu9rq5Ma7cRTktLwz//+U+sXr0affr0add62tJPdf3RVNvb02/b9/vf/x69e/fGxx9/rH3FZM+ePQgJCYGlpSWys7Px6NEjDBgwQHsPW1tbaDQaneSbudCnv4cPHw61Wm2W7WuJ/Px8iIg22dmcjvqueHJMjDXf6vrg7t27HVYHERERkzlERNRuVCoVUlJSoFAoMGfOHJSXl2vP1W3X26lTp3rXOTk5oaSkpMPiOnLkCMaNGwdXV1fY2NjUW1OnJQoKCvDuu+9ixIgRiI6Obvd6jNlPzWmufQqFAmFhYbh+/Tq+/PJLAMCnn36Kt99+G8CvT28AwPLly3XWHLp582a9rZ6fJjY2Ni16OsscVVRUAECTiwM/yRDfFcaab7a2tgD+0ydEREQdgckcIiJqV6NGjcLChQuRk5ODtWvXao87OTkBQIO/iBUWFsLDw6ND4snNzUVAQAA0Gg3OnTuHoqIixMfHt/p+kZGRKCwsREpKCiwtLdu9HmP1U2NOnTqFxMREvds3a9YsqFQq7NixA9nZ2XBwcECPHj0AAK6urgCAxMREiIjOz9mzZw3aLkOprq42yrgZWl0CoyVPoXX0d4Wx5ltVVRWA//QJERFRR2Ayh4iI2t3atWvRt29f7Y5GADBgwAB06tQJ58+f1yl77tw5VFVVYdiwYR0Sy+XLl1FdXY158+bBy8sLKpUKCoWiVfc6cuQIdu3ahRUrVqB///7a44sXL263eozVT43597//DTs7O73b5+zsjOnTp+PAgQP48MMPMXfuXO25uh3HMjMzDdkEozp58iREBCNHjgTw65o6hnoNzpC6du0KhUKBoqKiFl3Xkd8VxppvdX3g5uZm0HqJiOjZwmQOERG1u7pXKJ58ckWlUiE6Ohr79+9HamoqiouLcfnyZYSHh6Nbt24IDQ3tkFg8PT0BACdOnEBFRQVycnJatT5PcXExwsLCMHjwYO2irRUVFTh//jwyMzP1qsfFxQW//PILbty4gZKSkgZ/qTdWP/1WdXU17t69i5MnT8LOzq5F/RgeHo7KykocPnwYkydP1mnb7NmzsXv3biQnJ6O4uBg1NTXIy8vD7du3DdKujlZbW4uHDx/i8ePHuHTpEqKiouDp6YlZs2YB+HUh7QcPHuDAgQOorq7GvXv3cPPmTZ17NDRPjh07ZtJbk6vVanh5eSEvL69F13Xkd4U+8y0kJARubm64cOFCi+JuSl0fDBw4sN3uSUREVI+RttEiIiIT0tKtivfv3y/e3t4CQLp06SLz589vsNzixYt1thuura2VjRs3Sq9evUSpVIqzs7MEBARIdna2iIjEx8eLra2tAJDu3bvLzp07RURk06ZNolarBYA8//zzcvr0admwYYM4OjoKAHFzc5Ndu3bJnj17xM3NTQCIs7Oz7N69W0RElixZIi4uLuLk5CTBwcGyZcsWASDe3t4SFRWlvcbOzk6mTp0qmzdvFo1GIwBErVaLv7+/fPjhhwKgwZ+JEyc2W09ubq5cuHBBevToIba2tjJmzBhZvnx5vXr06aetW7dq+6NXr15y7do12b59uzg4OAgA6dGjh1y5cqVF49jUz/79+/Vq35OGDBkiMTEx9eqsrKyUJUuWiKenp1hZWYmrq6sEBgZKVlZWo+PfEmjj1s0Njb2+/R0aGipKpVLc3d3FyspKHBwcZMqUKXLt2jXt/QsKCuTll18WlUolPXv2lHfffVcWL14sAMTHx6fBeXLnzh05evSo2NvbS1xcXKvbVqejtiaPiIgQpVIpjx490h7rqO8KfcekqfkmIhIQECAAZOXKlU227ezZszJ69Gjp1q2b9nOh0WjEz89PvvrqK52ykyZNEnd3d6mtrW1VPzalrfObiIieGmkKkf/bboKIiJ5ZwcHBAID09HQjR0JPi0mTJmHLli3o2bOnQetVKBTYu3cvpk2bZtB6ASAsLAzp6ekoKCgweN0t0VGf96tXr6Jfv35ISUnBzJkz2/XeHaW2thbjxo3DrFmzMGfOnDbfr6CgAB4eHoiLi6u3QHp7MOb8JiIik5LO16yIiIiozZ58ZezSpUtQqVQGT+SYgtZud/808PHxwZo1a7BmzRqUlpYaO5xm1dTU4MCBAygpKUFISEi73DM2NhaDBw9GREREu9yPiIioMUzmEBERPWV+/PFHna2YG/tpr19gAWDJkiXIycnBlStXMHv2bJ3diejZERMTg+DgYISEhLR4MWRDO3nyJDIyMnDs2DGo1eo23y8hIQGZmZk4evQolEplO0RIRETUOCtjB0BERETtq2/fvjD0W9RqtRp9+/aFu7s7tm7dCl9fX4PWb2zLli1DSkoKqqqq0LNnT2zcuBFBQUHGDsso1q1bh+PHj2P9+vXYsGGDscNp1Pjx4zF+/Ph2udfBgwdRWVmJkydP6izmTERE1FG4Zg4REXHNHHpqcE2R5vHzbr44v4mI6P9wzRwiIiIiIiIiInPCZA4RERERERERkRlhMoeIiIiIiIiIyIwwmUNEREREREREZEa4mxUREQEA8vLykJaWZuwwiNrs7Nmzxg7BpOXl5QEAP+9ERERmjLtZERERgoODsW/fPmOHQUREzeBuVkREBCCdT+YQEREAICgoiFsVk9nj1s3N49bk5kuhUBg7BCIiMhFcM4eIiIiIiIiIyIwwmUNEREREREREZEaYzCEiIiIiIiIiMiNM5hARERERERERmREmc4iIiIiIiIiIzAiTOUREREREREREZoTJHCIiMpiQkBAoFAq9fg4fPtzu9YeGhsLOzg4KhQJKpRKDBg3CDz/8oFPm448/hqenJxQKBdzc3PDJJ5+0exytdfToUTg6OuKzzz57Kuoh83TixAnExMQgIyMDXl5e2s/sm2++Wa/shAkTYG9vD0tLS/Tv3x8XLlwwQsRNi4+PR9++fWFraws7Ozv07dsXK1asQHFxsU65M2fOYPTo0VCr1ejWrRuWLFmCyspKvcscOnQI8fHxqKmpMVjbiIjo6cVkDhERGdTx48dRWFiI6upq3L59GwDg7++PqqoqlJWVIT8/H3Pnzu2Quv/2t7/h7NmzAIBhw4bhu+++Q79+/XTKzJkzB6dPn8Zzzz2HvLw8zJo1q0NiaQ0RearqIfOzatUqJCUlYdmyZQgMDMT169fh7e2Nzp07IzU1FUeOHNEpf/z4caSnp2Py5MnIysrC0KFDjRR5406fPo25c+ciNzcXd+/exdq1axEfH4+goCBtmaysLEyYMAHjx4/HvXv3sH//fnz88ccIDw/Xu4y/vz9UKhXGjx+PwsJCg7eTiIieLkzmEBGRwSgUCowePRqOjo6wsrLSOa5UKqFWq+Hq6ophw4a1S33l5eXw8/PTOfbCCy9gzJgxOHfuXKNPCWzbtg1z5syBUqls9/rbYtKkSSgqKsLkyZPb7Z4NxdgR9Tzt2nusDXXvltiwYQP27NmDtLQ02Nvb65xLSkqChYUFQkNDUVRUZKQIW8fa2hrvvPMOXF1d0alTJwQHB2PKlCn417/+pU04r127FhqNBqtXr4adnR1GjRqFJUuW4JNPPsGPP/6od5nIyEgMGjQIEydOxOPHj43WZiIiMn9M5hARkcHs3r0barW62XKhoaH4r//6rzbX99FHHyE/P7/e8fnz5wMAtm7dWu9cVVUVPv30U4SGhnZY/abEHGI0Bx3Zj6YwRlevXsWKFSuwevVqqFSqeuf9/PwQFRWFW7duYdGiRUaIsPX2799fr03u7u4AgNLSUjx+/BhHjhzBSy+9BIVCoS3z6quvQkRw8OBBvcrUiY2NRWZmJjZt2tTBLSMioqcZkzlERGSSampqsHLlSnh6esLW1hYvvPAC9u7dCwD45JNP0KlTJygUCjg7O+PAgQM4f/48evToAUtLS7zxxhuIiopCdHQ0rl27BoVCAR8fH+29AwMD8dxzz2HPnj31XnfYt28ffve738HDw6PJGOrs3LkTw4cPh0qlgp2dHZ5//nmsXbu20fpFBAkJCejXrx9sbGzg7OyMKVOmaP/n/oMPPoBarYa9vT3y8/MRHR0Nd3d3fPTRR9q1fLZs2QLg11+wG1tz6F//+heAX18h8fX1haOjI1QqFQYOHIgvvvgCABqM8cyZM/Xq0Sfu5ORk2NnZQa1W4+DBg3j11Vfh4OAADw8P7N69u93mRXtrrl0RERGwtraGRqPRXvPOO+9o1166f/9+g/2YlJQElUqFrl27IiwsDN26dYNKpYKfnx/OnTvXpnsDwOeffw4HBwesW7fOIP2UlJQEEYG/v3+jZeLi4tC7d2/s2LEDJ06caLRce80lfT6frZWTkwMnJyf06NED169fR2lpKTw9PXXKeHt7AwAuXbqkV5k6zs7OeOmll7Bp0ya+0khERK0nRET0zAsKCpKgoCCD13v79m0BIK+99lq9c4sWLRIbGxvZt2+fPHz4UJYtWyYWFhby7bffiojI999/L2q1Wt566y3tNTExMbJjxw7tnwMDA8Xb27vBumNjYwWAJCQk6BwfM2aMnDhxQq8YEhMTBYCsX79eCgoK5MGDB/K3v/1NZsyY0Wj9K1euFGtra9m5c6cUFhbKpUuXZOjQodKlSxe5c+eOiIi8//77AkAiIyNl8+bNMnXqVPnhhx/k559/FgCyefNmERHJycmRpUuXSllZmbY/nZ2dxc/PT2pqakREJD09XWJjY+XBgwdSUFAgI0eOlM6dOzfZR7+tp6Vxf/nll1JUVCT5+fkyduxYsbOzk6qqqgbHob0BkL179+pdXp92zZgxQ9zc3HSu27hxowCQe/fuiUjD/RgaGip2dnby/fffS0VFhWRlZcmIESPE3t5ecnNz23Tvw4cPi729vaxZs0bvttZpzefdy8tLfH19Gzzn7e0tP/30k4iIfP3112JhYSHPP/+8lJaWiojIsWPHdD7j7TWXmvt8tlRVVZXk5eXJ5s2bxcbGRnbu3CkiIl999ZUAkI0bN9a7xtbWVsaPH69XmSfFxMQIALl48WKLYmzp/CYioqdWGp/MISIik1NRUYHk5GQEBAQgMDAQTk5OWL58OZRKJVJSUgAA/fr1Q2JiIv7+979j165d2L17NyorK/H222/rVUdoaCiUSiW2bdum/d/xy5cv4/79+xg/fnyzMVRXV2P16tV4+eWXsXTpUri4uMDZ2Rlvv/02RowY0WCd5eXlSEhIwNSpUzFz5kw4Ojpi4MCB2LZtG+7fv4/t27frlN+wYQPmz5+PjIwM9O3bt979fHx8sH79eu2ra/PmzUN5eTk+/vhjWFj8+ld8UFAQVq1aBWdnZ7i4uMDf3x8FBQW4d++efoPRirj9/Pzg4OAAV1dXhISEoKysDLm5uXrXZygtbVdrWFlZaZ9A8fX1RXJyMkpKSrTzuLUmTZqE4uJirFixos0xNqesrAw//fST9imTpowaNQoLFizAjRs3sHTp0nrn22su6fMd0VLdu3eHh4cHYmNj8cEHH2D69OkAoN2NytLSst41SqUS5eXlepV5Uq9evQD8+p1DRETUGkzmEBGRycnOzsajR48wYMAA7TFbW1toNBrtqxgA8D//8z8ICgpCWFgY0tLS8MEHH+hdh0ajQWBgIK5cuaJ9JeSvf/2rdueZ5mK4dOkSCgsL8ac//UnnvpaWloiMjGywzqysLJSWlmL48OE6x0eMGAFra2vt6zetkZaWhn/+859YvXo1+vTp02i5ukWdW7I9clvitra2BgBUV1frXZ+hdOR4NGb48OFQq9U689jU5efnQ0T0Wu8K+PV1qz59+mDr1q04c+aMzrn2mkv6fke0xM8//4z8/Hz84x//wN///ncMGTIE+fn52vV0GlqwuKqqCra2tnqVeVJdX969e7dVsRIRETGZQ0REJqesrAwAsHz5cp11YG7evIlHjx7plF23bh1KS0tbtUBs3ULIdU9L/POf/8Rbb72lVwzFxcUAACcnJ73rq1ufp1OnTvXOOTk5oaSkpMVtAICCggK8++67GDFiBKKjo3XOHTlyBOPGjYOrqytsbGzw3nvvtfj+HRW3sRmrXTY2Ni16MsrYKioqAPwatz5UKhVSUlKgUCgwZ84cnadS2qvPW/IdoS+lUglXV1dMmDABe/bsQVZWFv7yl79o1zSq+8zXefToESoqKtCtWze9yjypLrlT17dEREQtxWQOERGZHFdXVwBAYmIiRETn5+zZs9py1dXViIyMREJCAs6ePYu4uLgW1TN69GgMGTIEn332GdavX4/XXnsNjo6OesXw3HPPAQDu37+vd311iZ+GfmEtLCyEh4dHi+KvExkZicLCQqSkpOi85pGbm4uAgABoNBqcO3cORUVFiI+Pb/H9OypuYzNGu6qrq82uz+oSDy15mmvUqFFYuHAhcnJysHbtWu3x9upzfb8jWsvHxweWlpbIyspCz549YW9vj5s3b+qUuXr1KgDghRde0KvMk6qqqgCg3hM7RERE+mIyh4iITE737t2hUqmQmZnZZLl3330Xc+fOxYIFC7Bw4UKsXbu2xb/IvfPOO6ipqcGGDRswb948vWN4/vnn4eLiguPHj+td14ABA9CpUyecP39e5/i5c+dQVVWFYcOGtSh24Ncnb3bt2oUVK1agf//+2uOLFy/G5cuXUV1djXnz5sHLywsqlUpn22Rjxm0K9G2XlZVVu70mdvLkSYgIRo4c2e737ihdu3aFQqFAUVFRi65bu3Yt+vbti4sXL2qPtddc0vc7ojkFBQV444036h3PyclBTU0NunfvDisrK0ycOBGnTp1CbW2ttsyxY8egUCjg7++vV5kn1fWlm5tbm+InIqJnF5M5RERkclQqFWbPno3du3cjOTkZxcXFqKmpQV5eHm7fvg0A2Lp1K9zd3TF16lQAwF/+8hf4+vpixowZ2lcdXFxc8Msvv+DGjRsoKSlp8JfmN954Ay4uLhg9erTO/543F4ONjQ2WLVuGU6dOISIiArdu3UJtbS1KSkrw/fffN1i/paUloqOjsX//fqSmpqK4uBiXL19GeHg4unXrhtDQ0Bb1U3FxMcLCwjB48GDtYrMVFRU4f/48MjMztdsknzhxAhUVFcjJyam3Jok+faRSqdo1blOhb7t8fHzw4MEDHDhwANXV1bh37169JzAa68fa2lo8fPgQjx8/xqVLlxAVFQVPT0/MmjWrTfc+duyYwbYmV6vV8PLyQl5eXouuq3vd6smnxdprLunzHRESEgI3NzdcuHCh0fvY2dnh+PHj+N///V8UFxejuroaFy9exFtvvQU7OzssXLgQALBixQrcvXsXq1atQllZGc6ePYuNGzdi1qxZ2jWq9ClTp64vBw4cqH+HEhERPckIW2gREZGJMfTW5MXFxfLiiy+Ki4uLABALCwvx8fGRdevWactUVlbKkiVLxNPTU6ysrMTV1VUCAwMlKytLJk+eLAqFQlxcXOTrr78WEZEFCxaIhYWFABBHR0c5f/68XLhwQXr06CG2trYyZswY7bbHv7V48WL5xz/+Ue94UzHU2bJliwwcOFBUKpWoVCoZMmSIbN26VUSkwfpra2tl48aN0qtXL1EqleLs7CwBAQGSnZ0tIiLx8fFia2srAKR79+7a7ZE3b94sGo1GAIharRZ/f3/58MMPBUCDPxMnThQRkSVLloiLi4s4OTlJcHCwbNmyRQCIt7e35Obm1otx+fLl9eoRkWbj3rp1q6jVagEgvXr1kmvXrsn27dvFwcFBAEiPHj3kypUrbZo3+kALt25url0iIgUFBfLyyy+LSqWSnj17yrvvviuLFy8WAOLj49NgP965c0dCQ0NFqVSKu7u7WFlZiYODg0yZMkWuXbvW5nsfPXpU7O3tJS4ursV91JrPe0REhCiVSnn06JH22P79+8Xb21sASJcuXWT+/PkNXrt48WKdrcnbay419/kMCAgQALJy5com2+bv7y89e/aUTp06iY2NjXh7e0tISIhcvnxZp9xXX30l/+///T+xsbGRbt26yeLFi6WioqLFZUREJk2aJO7u7lJbW9tkbL/V0vlNRERPrTSFyP/tx0pERM+s4OBgAEB6erqRIyFqG4VCgb1792LatGnGDgVhYWFIT09HQUGBsUPR0ZrP+9WrV9GvXz+kpKRg5syZHRVau6qtrcW4ceMwa9YszJkzx9jhaBUUFMDDwwNxcXH1FixvjinNbyIiMqp0vmZFRERE1EFasmiwKfPx8cGaNWuwZs0alJaWGjucZtXU1ODAgQMoKSlBSEiIscPRERsbi8GDByMiIsLYoRARkRljMoeIiIiImhUTE4Pg4GCEhIS0eDFkQzt58iQyMjJw7NgxqNVqY4ejlZCQgMzMTBw9ehRKpdLY4RARkRljMoeIiIionS1btgwpKSkoKipCz549sW/fPmOH1C7WrVuHiIgIrF+/3tihNGn8+PHYtWsXNBqNsUPROnjwICor/z979x4XZZn/j/81DAMznAYEBBWQg2ezUDMTcz2A5IFDnqmstbZW3QenuwAAIABJREFU++RSW7qulWae0txNV9Ptk5q720lEXRFRw9FATetjImmU5fmMICogBxmY9++PvszPCQ+DDNwMvJ6PB49H3fd1X/f7vue+L+E91+EmMjIy4OXlpXQ4RERk5xyVDoCIiIioqZk/fz7mz5+vdBj1Ijo6GtHR0UqHYXfi4+MRHx+vdBhERNREsGcOEREREREREZEdYTKHiIiIiIiIiMiOMJlDRERERERERGRHmMwhIiIiIiIiIrIjnACZiIgAAN988w3GjBmjdBhEdbZ48WIkJycrHUaj9c033wAA33ciIiI7xmQOERGhT58+SodAZBOjR48GAFy+fBk//PADIiMjFY6o8Xn00UeVDoHu0+jRoxEYGKh0GERE1AioRESUDoKIiIjIltatW4dx48aBv+YQERFRE5TMOXOIiIiIiIiIiOwIkzlERERERERERHaEyRwiIiIiIiIiIjvCZA4RERERERERkR1hMoeIiIiIiIiIyI4wmUNEREREREREZEeYzCEiIiIiIiIisiNM5hARERERERER2REmc4iIiIiIiIiI7AiTOUREREREREREdoTJHCIiIiIiIiIiO8JkDhERERERERGRHWEyh4iIiIiIiIjIjjCZQ0RERERERERkR5jMISIiIiIiIiKyI0zmEBERERERERHZESZziIiIiIiIiIjsCJM5RERERERERER2hMkcIiIiIiIiIiI7wmQOEREREREREZEdYTKHiIiIiIiIiMiOMJlDRERERERERGRHmMwhIiIiIiIiIrIjTOYQEREREREREdkRJnOIiIiIiIiIiOwIkzlERERERERERHaEyRwiIiIiIiIiIjvCZA4RERERERERkR1hMoeIiIiIiIiIyI4wmUNEREREREREZEeYzCEiIiIiIiIisiNM5hARERERERER2REmc4iIiIiIiIiI7Iij0gEQERER1cXFixcRExMDo9Fo3lZaWgq9Xo9u3bpZlO3evTv+85//NHSIRERERDbFZA4RERHZtdatW6OiogI5OTk19hUWFlr8f0JCQkOFRURERFRvOMyKiIiI7N6zzz4LR8e7f0elUqnw1FNPNVBERERERPWHyRwiIiKye08++SSqqqruuF+lUqFnz54ICQlpwKiIiIiI6geTOURERGT3AgMD8eijj8LB4fa/2qjVajz77LMNHBURERFR/WAyh4iIiJqEZ555BiqV6rb7TCYTxo4d28AREREREdUPJnOIiIioSRgzZsxtt6vVagwYMAB+fn4NHBERERFR/WAyh4iIiJoEHx8fREZGQq1W19j3zDPPKBARERERUf1gMoeIiIiajPHjx0NELLY5ODhgxIgRCkVEREREZHtM5hAREVGT8cQTT0Cj0Zj/39HREcOHD4der1cwKiIiIiLbYjKHiIiImgx3d3fExsaaEzpVVVUYP368wlERERER2RaTOURERNSkPP3006isrAQA6HQ6DBs2TOGIiIiIiGyLyRwiIiJqUoYOHQpXV1cAwOjRo6HT6RSOiIiIiMi2HJUOgIiIlLN//36cO3dO6TCIbK5Xr1746quvEBgYiHXr1ikdDpHNRUREICAgQOkwiIhIISr57ZIPRETUbIwZMwbr169XOgwiIqqlpKQkjB07VukwiIhIGcnsmUNE1MyNHj0aycnJSodBhDFjxgCATZ5Hk8mEhQsXYvr06XWuqzFZt24dxo0bV2P5dWpeVCqV0iEQEZHCOGcOERERNTkODg6YOnWq0mEQERER1Qsmc4iIiKhJcnRkB2QiIiJqmpjMISIiIiIiIiKyI0zmEBERERERERHZESZziIiIiIiIiIjsCJM5RERERERERER2hMkcIiKy2oYNGxAaGgqVSnXHn+Dg4Dqfp1evXlCr1QgPD6970Ld44YUX4O7uDpVKhezsbKvLbN26FXq9HqmpqTaNp65++3n4+/tj/PjxSoelqMb6WdkTg8GA6dOn13i+nnnmmRplo6Oj4e7uDrVaja5duyIrK0uBiO9u4cKF6NSpE3Q6HVxdXdGpUyfMmDEDRUVFFuX27t2Lvn37wsXFBa1atcK0adNw8+ZNq8ts3rwZCxcuRFVVVYNdGxERNV9M5hARkdVGjRqFkydPIiwsDHq9HiICEUFlZSVKS0tx+fJluLi41Pk8Bw4cwMCBA20QsaVVq1Zh5cqVtS4jIjaPxRZ++3nk5ubi008/VTosRTXWz8pevP3221i6dCneeOMNi+fL29sbn376KdLS0izKp6enIzk5GbGxscjJyUGPHj0UivzO9uzZgxdffBFnz57F5cuXMWfOHCxcuBCjR482l8nJyUF0dDQiIyORn5+PjRs34uOPP8ZLL71kdZm4uDhotVpERkbi+vXrDX6dRETUvDCZQ0REdaZWq6HT6dCyZUt06NDBZvWqVCqb1VUXw4cPR2FhIWJjY5UOhe5B6c+qrKwMERERipy7rhYsWIC1a9di3bp1cHd3t9i3dOlSODg4YOLEiSgsLFQowvvj5OSEl19+Gb6+vnBzc8OYMWPwxBNPYMeOHbh06RIAYM6cOfD398c777wDV1dX9OnTB9OmTcO//vUvHD161Ooyr7zyCh566CEMGzYMlZWVil0zERE1fUzmEBGRTW3atMlmdWk0GpvVVc2aBFF9JpFEBMnJyfjoo4/q7RyknNWrVyMvL0/pMGrt+PHjmDFjBt555x1otdoa+yMiIvDqq6/iwoULmDJligIR3r+NGzfWuKY2bdoAAG7cuIHKykqkpaWhf//+Fu/+0KFDISJISUmxqky1WbNmITs7G0uWLKnnKyMiouaMyRwiIqoXS5YsgaurKxwcHNCzZ0/4+flBo9HA1dUVPXr0QL9+/RAYGAitVgtPT0/85S9/qVHH8ePH0alTJ7i6ukKn06Ffv37Yu3eveX9VVRVmzpyJoKAg6HQ6PPjgg0hKSjLvFxEsWrQIHTt2hLOzM/R6PaZOnWpxjnuV2bt3L4KCgqBSqfDBBx8AAFasWAFXV1e4uLggJSUFQ4cOhYeHBwICAvDFF19YxDd//nx07NgROp0OPj4+CAkJwfz58zF27FgAQGZmJh555BG4uLjAw8MD3bp1qzGXh63s2bMHXbp0gV6vh1arRbdu3fDll18C+HWuoOq5UcLCwnDo0CEAwHPPPQcXFxfo9Xps3rz5rvf8vffeg4uLC9zd3ZGXl4fXX38dbdq0wc8//1wv1/Nb9/tZLV26FFqtFi1btsSkSZPQqlUraLVaRERE4NtvvwUAJCYmwsnJCf7+/ubzvfzyy3B1dYVKpcKVK1fw6quv4vXXX8eJEyegUqnQrl07AMD27dvh4eGBefPmNch9uB9Lly6FiCAuLu6OZebOnYsOHTpg1apVMBgMdywnInj//ffRuXNnODs7w8vLC0888YS590pt3p+7vd91cezYMXh6eqJt27Y4efIkbty4gaCgIIsyYWFhAIDDhw9bVaaal5cX+vfvjyVLlnDYHxER1R8hIqJma/To0TJ69OhaHxcWFiZ6vd5i2yuvvCJHjhyx2Pb2228LAPn222+lpKRErly5IkOGDBEAkpaWJvn5+VJSUiKJiYkCQLKzs83HRkZGSmhoqJw6dUqMRqP88MMP0rt3b9FqtfLLL7+IiMiUKVPE2dlZ1q9fL9euXZM33nhDHBwc5MCBAyIi8uabb4pKpZK///3vcu3aNSktLZXly5cLADl06JDVZc6dOycAZNmyZeb43nzzTQEgO3fulMLCQsnLy5N+/fqJq6urVFRUiIjIvHnzRK1WS0pKipSWlsrBgwfFz89PBgwYICIiN27cEA8PD1m4cKGUlZVJbm6ujBw5UvLz8+v8edxOcnKyzJo1S65evSoFBQXy6KOPire3t3n/qFGjRK1Wy4ULFyyOe+qpp2Tz5s1W33MA8sorr8iyZctk5MiR8tNPP1l1Hff7PN7qfj+riRMniqurq/z4449SXl4uOTk50qtXL3F3d5ezZ8+KiMjTTz8tfn5+FudbtGiRADB/ZqNGjZKwsDCLMlu2bBF3d3eZPXt2na5NRCQpKUnq49e30NBQ6dKly233hYWFyalTp0REZN++feLg4CDBwcFy48YNERHZtm2bxMfHm8vPnDlTnJyc5JNPPpHr16/L4cOHpUePHuLj4yO5ubkiYt1ncq9nrbYqKirk/PnzsmzZMnF2dpZPPvlEREQyMzMFgCxatKjGMTqdTiIjI60qc6vp06dbtCG2BkCSkpLqpW4iIrIL69gzh4iI7kthYaHFKlb/+Mc/7li2S5cucHFxgbe3N5588kkAQFBQEHx8fODi4mJegan6m/tq7u7uCA4OhqOjI7p27YqVK1eivLwcH330EcrLy7FixQqMGDECo0aNgqenJ9566y1oNBqsWbMGZWVlWLx4MaKiovDaa6/B09MTOp0OLVq0MNdvTZl7iYiIgIeHB3x9fZGQkICSkhKcPXsWwK9Dznr27Im4uDjodDr06NED8fHx2L17NyoqKnD69GkUFRWha9eu0Gq18PPzw4YNG+Dj42P1+Wtj9OjRePvtt+Hl5YUWLVogLi4OBQUFyM/PBwC89NJLqKqqwpo1a8zHFBUV4cCBAxg2bNg97/mtFixYgMmTJ2PDhg3o1KlTvVxPbd3tswIAR0dHc2+SLl26YMWKFSguLq5xbbU1fPhwFBUVYcaMGXW9hHpRUlKCU6dOmXuZ3E2fPn3w5z//GadPn8Zf//rXGvvLysrw/vvvY+TIkRg/fjz0ej26deuGDz/8EFeuXKkxvPBOn0ltnjVrBQYGIiAgALNmzcJ7772HcePGAYB5NSq1Wl3jGI1Gg7KyMqvK3Kp9+/YAgCNHjtxXrERERPfCZA4REd2XW1ezEhG88sorVh3n5OQEABaTg1bPjWM0Gu96bLdu3aDX63H48GH8/PPPKC0txQMPPGDer9Pp4O/vj6NHj+L48eMoLS1FZGTkHeuzpkxtVF9b9XWUl5fXGGZRVVUFjUYDtVqN0NBQtGzZEuPHj8esWbNw+vRpm8Rhrer7Xr2U8qBBg9ChQwd8/PHH5rjXrl2LhIQEqNXqe95ze/Lbz+p2Hn74Ybi4uNjdtdVWXl4eRMTqlejmzp2Ljh07Yvny5RbDHoFfV3y6ceMGHn74YYvtvXr1gpOTk3nY2u3c+pnUx7N27tw55OXl4fPPP8e///1vdO/eHXl5eeb5dG43YXFFRQV0Op1VZW5VfS8vX758X7ESERHdC5M5RERkE0uWLLH4w6u+aDQaGI1GlJSUAADeeustix5CZ86cQWlpKc6fPw8A8PX1vWNd1pSpi2HDhuHgwYNISUlBWVkZvvvuO2zatAkxMTHmFcB27dqFxx57DPPmzUNoaCgSEhJqfMtvK2lpaRgwYAB8fX3h7OxcY54ilUqFSZMm4eTJk9i5cycA4D//+Q/+8Ic/AMA973lT5OzsbO651FSVl5cD+PVaraHVarFmzRqoVCo8//zzFs9r9ZLcbm5uNY7z9PREcXGxVeeoj2dNo9HA19cX0dHRWLt2LXJycjB//nzzPEi/nauqtLQU5eXlaNWqlVVlblWd3Km+t0RERLbGZA4REdmNyspKXL16FUFBQeYEzOLFiy16CIkI9u/fb/4mvXp4xO1YU6YuZs2ahUGDBmHChAnw8PDAyJEjMXbsWKxcudJcpmvXrkhNTcXFixcxbdo0JCUl4W9/+5vNYti9ezcWL16Ms2fPYsSIEfD398e3336LwsJCLFy4sEb5CRMmQKvVYtWqVfj555/h4eGBtm3bAsA973lTYzQacf36dQQEBCgdSr2qTjxU99CyRp8+ffDaa6/h2LFjmDNnjnm7p6cnANw2aVObe1nfz1q7du2gVquRk5ODkJAQuLu748yZMxZljh8/DgB48MEHrSpzq4qKCgCo0WOHiIjIVpjMISIim7p06RKee+65eqn7q6++gslkQo8ePcwrYWVnZ9+27AMPPAAHBwdkZmbesT5rytRFTk4OTpw4gfz8fBiNRpw9exYrVqyAl5cXAODixYv48ccfAfz6x+u7776LHj16mLfZwsGDB+Hq6oojR47AaDTif/7nfxAaGgqtVnvbJdi9vLwwbtw4bNq0CX/729/w4osvmvfd6543NRkZGRARPProowB+nVPnXkMB7VHLli2hUqlQWFhYq+PmzJmDTp06mVc+A359p9zc3PDdd99ZlP32229RUVGBnj17WlW3rZ61goICPPXUUzW2Hzt2DFVVVQgMDISjoyOGDRuG3bt3w2Qymcts27YNKpUKcXFxVpW5VfW99PPzq1P8REREd8JkDhER2YSIoKysDBs2bICHh4dN6qyoqEBhYSEqKyuRlZWFxMREtG3b1tx75LnnnsMXX3yBFStWoKioCFVVVTh//jwuXboEX19fjBo1CuvXr8fq1atRVFSEw4cPW0zAak2Zupg8eTKCgoJw48aN2+6/ePEiJk2ahKNHj6KiogKHDh3CmTNnzMmDujAajbh8+TIyMjLg6upqXlLZYDCgvLwcx44du+P8JS+99BJu3ryJLVu2IDY21rz9Xvfc3plMJly7dg2VlZU4fPgwXn31VQQFBWHChAkAfu3NcfXqVWzatAlGoxH5+fk1emq0aNECFy9exOnTp1FcXAyj0Yht27Y16qXJXVxcEBoaah52aK3q4Va3Tgqs1Wrx+uuvY+PGjfj0009RVFSEI0eO4KWXXkKrVq0wceJEq+u+17OWkJAAPz8/ZGVl3bEeV1dXpKenY9euXSgqKoLRaMShQ4fw+9//Hq6urnjttdcAADNmzMDly5fx9ttvo6SkBPv378eiRYswYcIEdOzY0eoy1arvZbdu3ay/oURERLXRsKtnERFRY1LbpaA3btwoYWFhAuCuP2+99ZYsWbJEXFxcBIAEBwfLnj17ZMGCBaLX6wWA+Pn5yWeffSZr164VPz8/ASBeXl7yxRdfiIjImjVrZODAgdKyZUtxdHQUb29vefLJJ+XMmTPmeG7evCnTpk2ToKAgcXR0FF9fXxk1apTk5OSIiEhxcbG88MIL4u3tLW5ubvLYY4/JzJkzBYAEBATI999/f88yL774ovj7+wsAcXFxkbi4OFm+fLn52tq3by8nTpyQjz76SDw8PASAtG3bVn755RfZtWuXeHt7W9wbjUYjnTt3lg0bNsjp06clIiJCvLy8RK1WS+vWreXNN9+UyspKm34eGzduFBGRadOmSYsWLcTT01PGjBkjH3zwgQCQsLAw8/Lb1bp37y7Tp0+vcc673fOFCxeKTqcTABIYGGhe+tladV2afNmyZff9WU2cOFE0Go20adNGHB0dxcPDQ5544gk5ceKEuf6CggIZOHCgaLVaCQkJkT/96U8ydepUASDt2rWTs2fPSlZWlrRt21Z0Op089thjkpubK1u3bhV3d3eZO3fufV9btfpamjwxMVE0Go2Ulpaat936fPn4+MjkyZNve+zUqVMtliY3mUyyaNEiad++vWg0GvHy8pIRI0bIzz//LCJi9Wdyr/d7xIgRAkBmzpx512uLi4uTkJAQcXNzE2dnZwkLC5OEhAQ5cuSIRbnMzEx55JFHxNnZWVq1aiVTp06V8vLyWpcRERk+fLi0adNGTCbTXWO7X+DS5EREzd06lchvltkgIqJmY8yYMQCA5ORkhSNpmlasWIFjx45h8eLF5m0VFRX461//ihUrVuDatWuNdk6N4cOH44MPPkBISEiDnVPJ53HSpElITk5GQUFBg5+7NtatW4dx48bVWCWtro4fP47OnTtjzZo1GD9+vE3rri8mkwkDBgzAhAkT8PzzzysdjllBQQECAgIwd+5cvP766/VyDpVKhaSkJIwdO7Ze6iciokYvmcOsiIiI6kFubi4SExPNK0FVc3JyQlBQEIxGY6Oaf+XWWA4fPgytVtugiZzGoDYTADc17dq1w+zZszF79uw7DgtsTKqqqrBp0yYUFxcjISFB6XAszJo1C+Hh4UhMTFQ6FCIiasKYzCEiIqoHOp0OGo0Gq1evxuXLl2E0GnHx4kWsWrUKM2fOREJCwh3nFjp69KjFcsx3+rHlH7HTpk3DsWPH8Msvv+C5556zWKGImofp06djzJgxSEhIqPVkyA0tIyMDGzZswLZt2+Di4qJ0OGbvv/8+srOzsXXrVmg0GqXDISKiJsxR6QCIiIiaIr1ej/T0dMyePRsdOnRASUkJ3Nzc0LVrVyxYsAB//OMf73hsp06dbD6M5l5cXFzQqVMntGnTBsuXL0eXLl0a9PxKeuONN7BmzRpUVFQgJCQEixYtwujRo5UOSxHz5s1Deno63n33XSxYsEDpcO4oMjISkZGRSodhISUlBTdv3kRGRobFpNBERET1gXPmEBE1Y5wzhxoTPo/3Vl9z5pB94Zw5RETNHufMISIiIiIiIiKyJ0zmEBERERERERHZESZziIiIiIiIiIjsCJM5RERERERERER2hKtZERE1c99884154lkiJX3zzTcAwOfxLs6fPw+A94iIiKi5Y88cIiIiIiIiIiI7wp45RETN3KOPPsqloKlR4NLk91a9NDnvUfOmUqmUDoGIiBTGnjlERERERERERHaEyRwiIiIiIiIiIjvCZA4RERERERERkR1hMoeIiIiIiIiIyI4wmUNEREREREREZEeYzCEiokZrw4YNCA0NhUqluuNPcHBwnc/Tq1cvqNVqhIeH1z3oW7zwwgtwd3eHSqVCdna21WW2bt0KvV6P1NRUm8ZDZDAYMH369Brv1jPPPFOjbHR0NNzd3aFWq9G1a1dkZWUpEPHdLVy4EJ06dYJOp4Orqys6deqEGTNmoKioyKLc3r170bdvX7i4uKBVq1aYNm0abt68Wesy1jAajZg5cyZCQ0Ph5OSENm3aYMqUKSgrKwMAbN68GQsXLkRVVdX9XzgRETV7TOYQEVGjNWrUKJw8eRJhYWHQ6/UQEYgIKisrUVpaisuXL8PFxaXO5zlw4AAGDhxog4gtrVq1CitXrqx1GRGxeSxEb7/9NpYuXYo33njD4t3y9vbGp59+irS0NIvy6enpSE5ORmxsLHJyctCjRw+FIr+zPXv24MUXX8TZs2dx+fJlzJkzBwsXLsTo0aPNZXJychAdHY3IyEjk5+dj48aN+Pjjj/HSSy/Vqoy1Xn31VSxatAjz589HQUEBPvvsM6xcuRIvvPACACAuLg5arRaRkZG4fv163W8CERE1S0zmEBGR3VGr1dDpdGjZsiU6dOhgs3pVKpXN6qqL4cOHo7CwELGxsUqH0myUlZUhIiLC7uq21oIFC7B27VqsW7cO7u7uFvuWLl0KBwcHTJw4EYWFhQpFeH+cnJzw8ssvw9fXF25ubhgzZgyeeOIJ7NixA5cuXQIAzJkzB/7+/njnnXfg6uqKPn36YNq0afjXv/6Fo0ePWl3GGidPnsSHH36IZ599FgkJCXB3d8eAAQOQmJiIzz//HD/99BMA4JVXXsFDDz2EYcOGobKy0vY3hoiImjwmc4iIyK5t2rTJZnVpNBqb1VXNmgRRfSaRRATJycn46KOP6u0cTcHq1auRl5dnd3Vb4/jx45gxYwbeeecdaLXaGvsjIiLw6quv4sKFC5gyZYoCEd6/jRs31rimNm3aAABu3LiByspKpKWloX///hbv2dChQyEiSElJsaqMtQ4cOACTyYTevXtbbB8yZAgA4MsvvzRvmzVrFrKzs7FkyRLrL5iIiOj/YTKHiIiahCVLlsDV1RUODg7o2bMn/Pz8oNFo4Orqih49eqBfv34IDAyEVquFp6cn/vKXv9So4/jx4+jUqRNcXV2h0+nQr18/7N2717y/qqoKM2fORFBQEHQ6HR588EEkJSWZ94sIFi1ahI4dO8LZ2Rl6vR5Tp061OMe9yuzduxdBQUFQqVT44IMPAAArVqyAq6srXFxckJKSgqFDh8LDwwMBAQH44osvLOKbP38+OnbsCJ1OBx8fH4SEhGD+/PkYO3asze51YyIieP/999G5c2c4OzvDy8sLTzzxhLk3RWJiIpycnODv728+5uWXX4arqytUKhWuXLmCV199Fa+//jpOnDgBlUqFdu3aYenSpdBqtWjZsiUmTZqEVq1aQavVIiIiAt9++22d6gaA7du3w8PDA/Pmzav3e7R06VKICOLi4u5YZu7cuejQoQNWrVoFg8Fwx3L3ut+1eVbv9i7VxbFjx+Dp6Ym2bdvi5MmTuHHjBoKCgizKhIWFAQAOHz5sVRlrOTj8+qu1Tqez2N6+fXsAMPfMAQAvLy/0798fS5Ys4dBKIiKqPSEiomZr9OjRMnr0aKXDuKewsDDR6/UW21555RU5cuSIxba3335bAMi3334rJSUlcuXKFRkyZIgAkLS0NMnPz5eSkhJJTEwUAJKdnW0+NjIyUkJDQ+XUqVNiNBrlhx9+kN69e4tWq5VffvlFRESmTJkizs7Osn79erl27Zq88cYb4uDgIAcOHBARkTfffFNUKpX8/e9/l2vXrklpaaksX75cAMihQ4esLnPu3DkBIMuWLTPH9+abbwoA2blzpxQWFkpeXp7069dPXF1dpaKiQkRE5s2bJ2q1WlJSUqS0tFQOHjwofn5+MmDAABt/IvXjfp7HmTNnipOTk3zyySdy/fp1OXz4sPTo0UN8fHwkNzdXRESefvpp8fPzszhu0aJFAkDy8/NFRGTUqFESFhZmUWbixIni6uoqP/74o5SXl0tOTo706tVL3N3d5ezZs3Wqe8uWLeLu7i6zZ8+u1fUmJSVJbX99Cw0NlS5dutx2X1hYmJw6dUpERPbt2ycODg4SHBwsN27cEBGRbdu2SXx8vLm8Nffbmmf1Xu9SbVVUVMj58+dl2bJl4uzsLJ988omIiGRmZgoAWbRoUY1jdDqdREZGWlXGWocPHxYAMmPGDIvtlZWVAkBGjBhhsX369OkW7761AEhSUlKtjiEioiZlHXvmEBGRXSgsLLRYxeof//jHHcvWzwpfAAAgAElEQVR26dIFLi4u8Pb2xpNPPgkACAoKgo+PD1xcXDB+/HgAqDEXhru7O4KDg+Ho6IiuXbti5cqVKC8vx0cffYTy8nKsWLECI0aMwKhRo+Dp6Ym33noLGo0Ga9asQVlZGRYvXoyoqCi89tpr8PT0hE6nQ4sWLcz1W1PmXiIiIuDh4QFfX18kJCSgpKQEZ8+eBfDrkLOePXsiLi4OOp0OPXr0QHx8PHbv3o2Kigqrz2EvysrK8P7772PkyJEYP3489Ho9unXrhg8//BBXrlyxydAyR0dHcy+ULl26YMWKFSguLsaaNWvqVO/w4cNRVFSEGTNm1DnGuykpKcGpU6fMvUzupk+fPvjzn/+M06dP469//WuN/bW933d6Vu/1Lt2PwMBABAQEYNasWXjvvfcwbtw4ADCvRqVWq2sco9FoUFZWZlUZa3Xr1g1DhgzB8uXLsWvXLpSXlyM3NxcbN26ESqWC0Wi0KF/dY+fIkSNWn4OIiAjgMCsiIrITt65mJSJ45ZVXrDrOyckJACwmGa2eG+e3f1j9Vrdu3aDX63H48GH8/PPPKC0txQMPPGDer9Pp4O/vj6NHj+L48eMoLS1FZGTkHeuzpkxtVF9b9XWUl5fXGK5RVVUFjUZz2z9U7V1OTg5u3LiBhx9+2GJ7r1694OTkZB4OZUsPP/wwXFxcajUprpLy8vIgIlav+jZ37lx07NgRy5cvtxhiCNTtft/6rN7rXbof586dQ15eHj7//HP8+9//Rvfu3ZGXl2eeT+d2kwxXVFRAp9NZVaY21q5dizFjxuDZZ59FixYt0LdvX/z3v/+FiMDb29uibPXncvny5Vqdg4iIiMkcIiKyS0uWLLH4Y7C+aDQaGI1GlJSUAADeeustix5CZ86cQWlpKc6fPw8A8PX1vWNd1pSpi2HDhuHgwYNISUlBWVkZvvvuO2zatAkxMTFNMplTvayzm5tbjX2enp4oLi6ul/M6OzsjPz+/Xuq2tfLycgC/xmwNrVaLNWvWQKVS4fnnn7folWKr+32vd+l+aDQa+Pr6Ijo6GmvXrkVOTg7mz59vns+oqKjIonxpaSnKy8vRqlUrq8rUhl6vx4cffojz58+jtLQUJ06cwN///ncAQOvWrS3KVieKqj8nIiIiazGZQ0REdAeVlZW4evUqgoKCzAmYxYsXW/QQEhHs37/f/O1+9ZCN27GmTF3MmjULgwYNwoQJE+Dh4YGRI0di7NixWLlyZb2cT2menp4AcNskwvXr1xEQEGDzcxqNxnqruz5UJwuqqqqsPqZPnz547bXXcOzYMcyZM8e83Vb3+17vUl21a9cOarUaOTk5CAkJgbu7O86cOWNR5vjx4wCABx980KoydXXgwAEAwMCBAy22Vw9/rG3vHyIiIiZziIjIrl26dAnPPfdcvdT91VdfwWQyoUePHuaVsLKzs29b9oEHHoCDgwMyMzPvWJ81ZeoiJycHJ06cQH5+PoxGI86ePYsVK1bAy8urXs6ntAceeABubm747rvvLLZ/++23qKioQM+ePQH8Ou/NvYbUWSsjIwMigkcffdTmddeHli1bQqVSobCwsFbHzZkzB506dcKhQ4fM26y93/dyr3fJWgUFBXjqqadqbD927BiqqqoQGBgIR0dHDBs2DLt374bJZDKX2bZtG1QqFeLi4qwqU1crV65ESEgI+vfvb7G9+nPx8/Or8zmIiKh5YTKHiIjskoigrKwMGzZsgIeHh03qrKioQGFhISorK5GVlYXExES0bdsWEyZMgFarxXPPPYcvvvgCK1asQFFREaqqqnD+/HlcunQJvr6+GDVqFNavX4/Vq1ejqKgIhw8ftpgU1poydTF58mQEBQXhxo0bNqmvsdNqtXj99dexceNGfPrppygqKsKRI0fw0ksvoVWrVpg4cSKAX3tqXL16FZs2bYLRaER+fn6NXhgtWrTAxYsXcfr0aRQXF5sTNCaTCdeuXUNlZSUOHz6MV199FUFBQZgwYUKd6t62bVuDLE3u4uKC0NBQ8xA/a1UPt7p1eJ6199uauu/2LgFAQkIC/Pz8kJWVdcd6XF1dkZ6ejl27dqGoqAhGoxGHDh3C73//e7i6uuK1114DAMyYMQOXL1/G22+/jZKSEuzfvx+LFi3ChAkT0LFjR6vLWBMTADzyyCM4c+YMKisrcfr0aUyZMgUGgwGrV682zx1Urfpz6datm1X3joiIyKzhV9AiIqLGorEvTb5x40YJCwsTAHf9eeutt2TJkiXi4uIiACQ4OFj27NkjCxYsEL1eLwDEz89PPvvsM1m7dq34+fkJAPHy8pIvvvhCRETWrFkjAwcOlJYtW4qjo6N4e3vLk08+KWfOnDHHc/PmTZk2bZoEBQWJo6Oj+Pr6yqhRoyQnJ0dERIqLi+WFF14Qb29vcXNzk8cee0xmzpwpACQgIEC+//77e5Z58cUXxd/fXwCIi4uLxMXFyfLly83X1r59ezlx4oR89NFH4uHhIQCkbdu28ssvv8iuXbvE29vb4t5oNBrp3LmzbNiwQZHPsDbu53k0mUyyaNEiad++vWg0GvHy8pIRI0bIzz//bC5TUFAgAwcOFK1WKyEhIfKnP/1Jpk6dKgCkXbt2cvbsWcnKypK2bduKTqeTxx57THJzc2XixImi0WikTZs24ujoKB4eHvLEE0/IiRMn6lz31q1bxd3dXebOnVur672fpckTExNFo9FIaWmpedut75aPj49Mnjz5tsdOnTrVYmnye91va5/Ve71LI0aMEAAyc+bMu15bXFychISEiJubmzg7O0tYWJgkJCTIkSNHLMplZmbKI488Is7OztKqVSuZOnWqlJeX16qMtTENHjxYPD09xdHRUby8vGT48OF3XHJ9+PDh0qZNGzGZTHet87fApcmJiJq7dSqR3yx7QUREzcaYMWMAAMnJyQpHQrawYsUKHDt2DIsXLzZvq6iowF//+lesWLEC165da9RzczS253HSpElITk5GQUGB0qGYrVu3DuPGjauxatndHD9+HJ07d8aaNWswfvz4eozOdkwmEwYMGIAJEybg+eefVzocALaPqaCgAAEBAZg7dy5ef/31Wh2rUqmQlJSEsWPH1jkOIiKyS8kcZkVERNQE5ObmIjExEX/4wx8stjs5OSEoKAhGo7FRz+3SWNVm4uDGql27dpg9ezZmz55tF0PwqqqqsGnTJhQXFyMhIUHpcADUT0yzZs1CeHg4EhMTbVIfERE1L0zmEBERNQE6nQ4ajQarV6/G5cuXYTQacfHiRaxatQozZ85EQkKCzeYWIvszffp0jBkzBgkJCbWeDLmhZWRkYMOGDdi2bRtcXFyUDgeA7WN6//33kZ2dja1bt0Kj0dggQiIiam6YzCEiImoC9Ho90tPT8cMPP6BDhw7Q6XTo0qUL1qxZgwULFuDf//630iHalTfeeANr1qxBYWEhQkJCsH79eqVDqrN58+YhMTER7777rtKh3FVkZCQ+++wz+Pv7Kx2KmS1jSklJwc2bN5GRkdFkV5ojIqL656h0AERERGQb/fr1w44dO5QOo0mYP38+5s+fr3QYNhcdHY3o6Gilw2jW4uPjER8fr3QYRERk59gzh4iIiIiIiIjIjjCZQ0RERERERERkR5jMISIiIiIiIiKyI0zmEBERERERERHZESZziIiIiIiIiIjsCFezIiJq5tavXw+VSqV0GERmfB7vjfeIiIioeVOJiCgdBBERKWP//v04d+6c0mE0WiKCs2fPIjs7G99//z2OHj0Kk8mE0NBQhIeHY9SoUVCr1UqHSbexf/9+LFmyBElJSUqHQndw4cIFpKWlITs7GwUFBXB3d0e3bt0QHh6Ohx56CJ6enkqH2KhFREQgICBA6TCIiEgZyUzmEBER3eLq1avYuXMnDAYDtm3bhnPnzsHHxwcDBw5EVFQUYmJi0Lp1a6XDpHtYt24dxo0bB/6aYx9OnjwJg8GA1NRUGAwGlJeXIzQ0FDExMYiNjcXvfvc7ODk5KR0mERFRY8FkDhERNW8mkwmHDh2CwWCAwWBAZmYmTCYTwsPDzcmbiIgIODhwmjl7wmSO/SorK8PXX39tficPHjwIV1dX9OnTBzExMYiPj0dwcLDSYRIRESmJyRwiImp+8vPzkZGRYe4JcOnSJfj5+SE6OhqxsbEYPHgwh3jYOSZzmo5Tp05hx44dMBgMSE9PR2FhIUJDQxEVFYWoqCgMHToUbm5uSodJRETUkJjMISKipq+qqgrZ2dlITU3Fli1bkJWVBbVajd69eyM2NhZRUVHo0aMHJ5VtQpjMaZoqKyvxzTffYMuWLTAYDMjKyoJWq0Xfvn3NyZ2ePXsqHSYREVF9YzKHiIiaptzcXKSnp2PLli3YsWMHrl+/bvFt/uOPPw4PDw+lw6R6wmRO85CXl4fMzExzovbatWsICQnB4MGDERUVhejoaOj1eqXDJCIisjUmc4iIqGkoLy/H3r17zfNsZGVlQafTISIiAlFRUYiNjUWXLl2UDpMaCJM5zU91D7zqNiAjIwMigvDwcPNEyuyBR0RETQSTOUREZL+qV8AxGAzYvn07iouLLVbA6devH5ydnZUOkxTAZA4VFBRg165dMBgMSEtLw4ULF9CyZUv0798fUVFRiIuLg7+/v9JhEhER3Q8mc4iIyH6UlpZi3759MBgMSElJwdGjR+Hm5oYBAwYgNjYWQ4cORWBgoNJhUiPAZA79Vk5Ojnmund27d6OyshLdu3c3D73s378/NBqN0mESERFZg8kcIiJq3E6ePGmeD2PPnj0wGo38A4zuickcupuSkhLs378fqampSElJwZkzZ+Dt7Y1BgwYhKioKw4YNQ0BAgNJhEhER3QmTOURE1LhcvXoVO3fuhMFgwLZt23Du3Dn4+Phg4MCBiIqKQkxMDFq3bq10mNTIMZlDtVE9ZDM1NRUGgwHl5eUWQzZ/97vfwcnJSekwiYiIqjGZQ0REyjKZTDh06JB57pvMzEyYTCaEh4ebkzcRERFwcHBQOlSyI0zm0P0qKyvD119/bW6TDh48CFdXV/Tp0wcxMTGIj49HcHCw0mESEVHzxmQOERE1vPz8fGRkZJi/Cb906RL8/PwQHR2N2NhYDB48GJ6enkqHSXaMyRyylVOnTmHHjh0wGAxIT09HYWEhQkNDzUM9hwwZAnd3d6XDJCKi5oXJHCIiqn/VSwZXz32TlZUFtVqN3r17IzY2FlFRUVwymGyKyRyqD5WVlfjmm2/MEylnZWVBq9Wib9++5uROz549lQ6TiIiaPiZziIiofuTm5iI9PR1btmzBjh07cP36dYtvsx9//HF4eHgoHSY1UUzmUEPIy8tDZmYmUlNTkZaWhqtXryI4OBjR0dGIiopCdHQ09Hq90mESEVHTw2QOERHZRnl5Ofbu3Wsxz4SLiwsiIiIQFRWF2NhYdOnSRekwqZlgMocaWnUPxOo2MCMjAyKC8PBw80TK7IFIREQ2wmQOERHdv+oVYAwGA7Zv347i4mKLFWD69esHZ2dnpcOkZojJHFJaQUEBdu3aBYPBgLS0NFy4cAEtW7ZE//79ERUVhbi4OPj7+ysdJhER2Scmc4iIyHqlpaXYt28fDAYDUlJScPToUbi5uWHAgAGIjY3F0KFDERgYqHSYREzmUKOTk5Njnmtn9+7dqKysRPfu3c1DT/v37w+NRqN0mEREZB+YzCEiors7efKkeeLiPXv2wGg08g8QavSYzKHGrKSkBPv370dqaipSUlJw5swZeHt7Y9CgQYiKisKwYcMQEBCgdJhERNR4MZlDRESWrl69ip07d8JgMGDr1q04f/48fH19MWDAAERFRSEmJgatW7dWOkyiu2Iyh+xJ9ZDV1NRUGAwGlJeXc8gqERHdDZM5RETNnclkwqFDh8xz32RmZsJkMiE8PNycvImIiICDg4PSoRJZjckcsldlZWX4+uuvbzuZfExMDOLj4xEcHKx0mEREpCwmc4iImqP8/HxkZGSYvwm+dOkS/Pz8EB0djdjYWAwePBienp5Kh0l035jMoabi1KlT2LFjBwwGA9LT01FYWIjQ0FDzUNchQ4bA3d1d6TCJiKhhMZlDRNQcVC+ZWz33TVZWFtRqNXr37o3Y2FhERUVxyVxqUpjMoaaosrIS33//vUVbrtVq0bdvX3Nyp2fPnkqHSURE9Y/JHCKipio3Nxfp6enYsmULduzYgevXr/PbXGo2mMyh5iAvLw+ZmZlITU1FWloarl69iuDgYERHRyMqKgrR0dHQ6/VKh0lERLbHZA4RUVNRXl6OvXv33naehaioKMTFxaFz585Kh0nUIJjMoeamugdm9b8BGRkZEBGEh4ebJ1JmD0wioiaDyRwiIntWvQKKwWDA9u3bUVxczBVQiMBkDlFBQQF27doFg8GAtLQ0XLhwwWJlwri4OPj7+ysdJhER3R8mc4iI7ElpaSn27dsHg8GAlJQUHD16FG5ubhgwYABiY2MxdOhQBAYGKh0mkeKYzCGylJOTgy1btsBgMGD37t2orKxE9+7dzUNv+/fvD41Go3SYRERkHSZziIgau5MnT5onu9yzZw+MRiN/ASe6ByZziO6spKQE+/fvR2pqKlJSUnDmzBl4e3tj0KBBiIqKwrBhwxAQEKB0mEREdGdM5hARNTZXr17Fzp07YTAYsHXrVpw/f96ia3xMTAxat26tdJhEjRqTOUTWqx6ym5qaCoPBgPLycg7ZJSJq3JjMISJSmslkwqFDh8xz32RmZsJkMiE8PNycvImIiICDg4PSoRLZDSZziO5PWVkZvv7669tOph8TE4P4+HgEBwcrHSYRUXPHZA4RkRLy8/ORkZFh/ib00qVL8PPzQ3R0NGJjYzF48GB4enoqHSaR3WIyh8g2Tp06hR07dsBgMCA9PR2FhYUIDQ01D/UdMmQI3N3dlQ6TiKi5YTKHiKghVC8ZWz33TVZWFtRqNXr37o3Y2FhERUVxyVgiG2Iyh8j2Kisr8f3331v8W6bVatG3b19zcqdnz55Kh0lE1BwwmUNEVF9yc3ORnp6OLVu2YMeOHbh+/Tq/zSRqIEzmENW/vLw8ZGZmIjU1FWlpabh69Sr8/f0xePBgxMbGIjo6Gnq9XukwiYiaIiZziIhs5W7zDERFRSEuLg6dO3dWOkyiZoHJHKKGVd0DtfrfwIyMDIgIwsPDzRMpswcqEZHNMJlDRFQX1SuAGAwGbN++HcXFxVwBhKiBlZeX4+LFixbb0tLSkJiYiBMnTlhsV6vVaNu2bUOGR9QsFRQUYNeuXTAYDEhLS8OFCxcsVmaMi4uDv7+/0mESEdkrJnOIiGqjtLQU+/btQ2pqKjZv3ozTp0/Dzc0NAwYMQGxsLIYOHYrAwEClwyRqVq5duwY/Pz8YjcZ7lh02bBjS0tIaICoiulVOTg62bNkCg8GA3bt3o7KyEt27dzcPPe7fvz80Go3SYRIR2Qsmc4iI7uXkyZPmyR737NkDo9HIX0CJGpmYmBhs27YNJpPpruX+85//4JlnnmmgqIjodkpKSrB//36kpqYiJSUFZ86cgbe3NwYNGoSoqCgMGzYMAQEBSodJRNSYMZlDRPRbV69exc6dO2EwGLB161acP3/eomt4TEwMWrdurXSYRHSLtWvX4qmnnrrrHDnOzs64cuUK3NzcGjAyIrqX6iHLqampMBgMKC8v55BlIqK7YzKHiMhkMuHQoUPmuW8yMzNhMpkQHh5uTt5ERETAwcFB6VCJ6A5KS0vh4+ODsrKy2+53dHTEiBEjsG7dugaOjIhq426LCcTExCA+Ph7BwcFKh0lEpDQmc4jIvpSWlsLFxaXO9eTn5yMjI8P8TeClS5fg5+eH6OhoxMbGYvDgwfD09LRBxETUUJ5++mkkJyffdu4clUqF//73v4iPj1cgMiK6X6dOncKOHTtgMBiQnp6OwsJChIaGmoc6DxkyBO7u7nU6R1VVFSorK9n7h4jsCZM5RGQ/Pv74Y7z33nv46aefar20afWSqdVz32RlZUGtVqN3796IjY1FVFQUl0wlsnNpaWmIiYm57T43NzdcuXKFf6wR2bHKykp8//33Fv+Wa7Va9O3b15zc6dmzZ63r/frrrzFp0iR8/vnn6NatWz1ETkRkc0zmEFHjd+XKFfzhD3/A5s2bAQCHDx+26pet3NxcpKenY8uWLdixYweuX79u82/ziKjxMBqN8PX1RWFhocV2jUaDZ599FqtWrVIoMiKqD3l5ecjMzERqairS0tJw9epV+Pv7Y/DgwbXqZTtz5kzMmTMHjo6OWLBgAf785z9zaDURNXZM5hBR47Zjxw6MHz8e165dg9FohEajwbx58zB16tQaZe82zj4qKgpxcXHo3LmzAldBRA1l0qRJWLNmDSoqKiy279y5E4MGDVIoKiKqb9U9cKt/B8jIyICIIDw83DyR8p164Hbv3h3Z2dkAAAcHBzz22GP49NNPERgY2NCXQURkLSZziKhxKi8vx6xZs/Dee+9BpVKZlxtWqVTo168fMjMzAfz/K2AYDAZs374dxcXFXAGDqBnLzMzEgAEDLLb5+PggNzcXarVamaCIqMEVFBRg165dMBgMSEtLw4ULFyxWpoyLi4O/vz+uXLmCli1bWqyEp9Fo4OTkhH/+85945plnFLwKIqI7YjKHiBqfI0eOYOzYsTh+/DgqKytr7Fer1ZgwYQJ27tyJ06dPw8vLC4MHD8bjjz+OIUOGcNlwombMZDKhdevWuHz5MoBf/yibPHky3n//fYUjIyKliAgOHTqEL7/8Etu3b8e+ffsgIujVqxdat26NTZs2mb80upVKpcLIkSPx0UcfoUWLFgpETkR0R0zmEFHjYTKZsGzZMkyZMgUAbpvIAX795ap9+/YYN24chg4dikceeYTfuBOR2dSpU7F06VLzUKv/+7//Q69evRSOiogai6KiIhgMBnz55ZdYv349iouLb7sKHvBrQtjb2xuff/45Bg4c2MCREhHdEZM5RNQ4nDlzBk899RS++eab2347disnJye8+OKL+OCDDxooOiKyJwcPHsTDDz8MAAgMDMSZM2e4Uh0R1SAi8PHxwdWrV+9aTq1Ww2QyYfLkyVi0aBGHbxNRY5DMadqJSHHJycl44IEHcODAgXsmcgCgoqICmzZtaoDIiMge9ezZE+3atQMATJgwgYkcIrqtrKyseyZygF8nVxYR/POf/8SDDz5oniyZiEhJNXrm7N+/n+PKiahB3Lx5EwcPHsTFixfv6/ghQ4bAzc3NxlE1fsnJyfVSL9t/akp+/PFH/Pjjj4iOjoaHh4fS4RDZBNt/2zp69Ch+/PFHq75IupWDgwMeeOABtG/fnsliImoQt2n/a/bMOXfuHNavX98wEVGTd/78eT5PVli/fj3Onz+vdBgNymQy4fTp03ByckLr1q3h4+MDDw8P6HQ6ODo63vE4lUoFB4dfm67c3NyGCrdRqO/3ie0/2ZLS7X9QUBA8PT0bfSKnObb/VHts/+vHpUuXICLm3ytuR6VSQaPRwMXFBXq9Hr6+vmjdujVu3LhhnmidLCnd/tsLtv9kjbu9TzV65qxbtw7jxo0Dp9IhW+DzZB2VSoWkpCSMHTtW6VAalevXr+P69esoLCw0//etPx06dMCTTz6pdJgNpr7fJ76vZEuN4XkyGAyIiopS7PzWYPtP1mD7b3smkwkzZsyAm5sbPD094enpCb1eb/7v6h8XFxelQ7U7zfF5uh9s/8kad3mfku/89TcRkcKqf5EiIrofjT2RQ0TKcXBwwLx585QOg4jovnECZCIiIiIiIiIiO8JkDhERERERERGRHWEyh4iIiIiIiIjIjjCZQ0RERERERERkR2yazPn555/xpz/9CV27doW7uzscHR2h1+vRoUMHDB8+HPv377fl6Wxiw4YNCA0NhUqlgkqlgr+/P8aPH690WHVmMpmwePFiRERENIp66mrr1q3Q6/VITU1VNA57YTAYMH369BrP9zPPPFOjbHR0NNzd3aFWq9G1a1dkZWUpEPHdLVy4EJ06dYJOp4Orqys6deqEGTNmoKioyKLc3r170bdvX7i4uKBVq1aYNm0abt68Wesy1jAajZg5cyZCQ0Ph5OSENm3aYMqUKSgrKwMAbN68GQsXLkRVVdX9X7gdYfvfeNS13Z49eza6dOkCDw8PODs7o127dvjLX/6CGzdu2DhS67D9rx22/2z/Gxrb/8ajru2/te9bQ2H7Xzts/5th+y+/kZSUJLfZfE+rVq0SjUYjv/vd72T79u1y7do1KS8vlxMnTsjatWslIiJC/vd//7fW9TaUsLAw0ev1SodhE7/88ov07dtXAMhDDz2kaD33+zz91pYtW8TDw0M2b95c57oaIwCSlJRkk7pmzpwpsbGxUlRUZN4WFhYm3t7eAkC2bNlS45ht27ZJfHy8Tc5fH4YPHy5/+9vfJC8vT4qLi2XdunWi0Whk8ODB5jI//PCD6HQ6mTFjhty4cUP27dsnPj4+8txzz9WqjLX+53/+R7RarXzxxRdSVFQkX331lXh4eMhTTz1lLrNkyRLp37+/XLt2rW434P+x1ftk6/rZ/jcetmi3+/fvL8uXL5eCggIpKiqSpKQk0Wg0MmTIkFrVw/bfOmz/747t/6/Y/tcPtv+WrHnfrMH23zps/++O7f+v7vI+rbNJMmf//v2iVqtl0KBBYjQab1tm+/btsmzZslrV25CaSmOenZ0tI0eOlE8//VTCw8PvuzG3VT31/ctHQyktLZU+ffrUW/22aszfffdd6dChg5SVlVlsDwsLk88++0wcHBykTZs2cv36dYv9jb0xHzFiRI1rGjNmjACQixcviojIuHHjJCQkREwmk7nMokWLRKVSyU8//WR1GWucOHFCHBwc5I9//KPF9rfeeksAyI8//mjelpiYKH369Llj21gbjfGXebb/jYet2u3hw4dLZWWlxbaxY8cKADl79qzV9bD9tw7b/7tj+/8rtuGGwx4AACAASURBVP/1g+2/JWveN2uw/bcO2/+7Y/v/q7slc2wyzGru3LmoqqrCu+++C0dHx9uWefzxxzF58mRbnI7u4qGHHsKGDRvw9NNPw9nZWfF6morVq1cjLy9P6TDu6vjx45gxYwbeeecdaLXaGvsj/j/27jwsynr9H/h7YIYZhh1R4aAg4EJuqbnicUvUCjXJDE6d80uzIs9JMpdMSzO3Mkz8up0WzW9p7vrVIlHTXDLJUwkXiickXHBJcAERkP3+/eHF1Mg2AwOz8H5dl9c5PfOZ57mHz/O8xXueJSQEU6ZMwdWrVzF9+nQzVFh3u3btqvSZfH19AQB5eXkoLS3FN998g0GDBkGhUOjGPP744xAR7Nmzx6Axhvrpp59QXl6OPn366C1/7LHHAAD79+/XLZs3bx6SkpKwfPlywz+wFWH+Ww5T5XZcXBzs7e31lnl5eQEACgoK6lWjNWL+mxfz33Ix/y2HqfK/tuOtqWH+mxfzv3b1buYUFxfj0KFDaNasGXr37m3Qe0QEy5Ytw0MPPQS1Wg0PDw+MGTMGv/76KwBgzZo1cHJyglarxZ49e/D444/D1dUVrVq1wubNmwEADz30EBQKBezs7PDII4/ofsF844034ObmBo1Gg//93/+t78er5Pvvv0fHjh112+jSpYtu4l588UXdtYlBQUFITEwEAEyYMAFarRZubm746quvUFZWhrlz58LPzw+Ojo7o2rUrtm7dCgD44IMPoNVq4eLigqysLEybNg2+vr5ITU01+WexFsePH4efnx8UCgVWrVoFwLB9ZMWKFdBoNGjRogVeeeUV+Pj4QKPRICQkBCdPngQAREdHw8HBAd7e3rrt/etf/4KTkxMUCgVu3ryJKVOmYNq0aUhPT4dCoUDbtm0BAPv27YOrqysWLVrUyD+Rqq1YsQIigtGjR1c7ZuHChWjfvj3Wrl2LgwcPVjvOFMcogBr39fpKS0uDu7s7/P39cf78eeTl5cHPz09vTFBQEAAgOTnZoDGGsrO7H52Ojo56y9u1awcA+O9//6tb5uHhgUGDBmH58uUQEYO3YQ2Y/00n/69evQpHR0cEBAQ06naZ/4Zh/jP/Gxvzv+nk/5+Pt8bE/DcM87+J578Rp/FU6dy5cwJA+vbta/B75s6dKw4ODrJhwwbJycmR5ORk6dGjh3h5ecn169dFROStt94SAHLo0CG5c+eOZGVlyYABA8TJyUmKi4ultLRU2rRpI35+fpVOB3/99dclNjbW4HpEDD/Ncvv27TJv3jy5ffu23Lp1S/r27SvNmjXTvT527Fixt7eXq1ev6r3v2Wef1V3vOX36dFGr1bJjxw7Jzs6W2bNni52dnfz00096n/21116TlStXylNPPWXUKWAV+vTpU6975phiPaY6zfLy5csCQO9U3dr2ERGRqKgocXJykrNnz0phYaGkpKRIr169xMXFRXe5wHPPPSctW7bU215MTIwAkBs3bojI/XkNCgrSGxMXFycuLi4yf/78en8+mOA0y8DAQOnYsWOVrwUFBcmFCxdEROTEiRNiZ2cnbdq0kby8PBGpfJqlKY5Rkdr3dWMVFxfLlStXZOXKlaJWq2XDhg0iInL06FEBIDExMZXe4+joKEOHDjVojKGSk5MFgMyZM0dveWlpqQCQ8PBwveWzZs0SAJKYmGjwNqpiaafZM/9tP/9FRPLz88XFxUWio6ONeh/z3zDMf8Mw/5n/zH/DmCL/qzveDMX8Nwzz3zDM/wa8Z87PP/8sACQ0NNSg8QUFBeLs7CyRkZF6y//zn/8IAN2BUbGj/Pk6udWrVwsA+e2330REJDY2VgDItm3bdGPy8/PFz89P7ty5Y/BnEKn7NbOLFy8WAJKVlSUiIgcPHhQAsnDhQt2YO3fuSLt27aS0tFTu3bsnWq1W7/MXFBSIWq2Wf/7zn9V+9rpoKs2cmvaRqKioSvP6008/CQB59913RaTuYW5K9Q3zvLw8USgUMmrUqCpf/3OYi4hMmzZNAMirr74qIvphbqpj1JB93VgtW7YUANKsWTP5n//5H91fGgcOHBAAsmzZskrvcXV1lZCQEIPGGOOxxx4TT09POXTokNy7d09+//132bZtmygUChk5cqTe2M8++0wAyBdffGHUNh5kab/MM/9tP/8ramrfvr3eTRUNwfw3DPPfMMx/5j/z3zCmyP/qjjdDMf8Nw/w3DPO/Ae+Z4+zsDMDw6+hTUlKQl5eHnj176i3v1asXHBwcdKe/VcXBwQHA/UeCAfdPa3Rzc9O7Fm3jxo0YM2YMXF1djfocdaVSqQBA9/ixRx99FO3bt8dnn32mO6Vqy5YtiIyMhL29PVJTU1FQUIDOnTvr1uHo6Ahvb2/dKWxUdw/uI1Xp2bMntFqtTf28s7KyICLQarUGjV+4cCE6dOiA1atX4/jx43qvmeoYbYh9/fLly8jKysKmTZvw+eefo3v37sjKytJdT1taWlrpPcXFxXB0dDRojDG2bNmCcePG4f/9v/8HT09P9O/fH//3f/8HEUGzZs30xlbMS2ZmplHbsHTMf9vP/127dmHbtm3Yv38/XFxczF1OjZj/zP8HMf8bDvPftvO/uuPNUjH/mf8Pair5X+9mTps2baDRaHDu3DmDxufk5AD44y+BP3N3d8fdu3cN3razszNefvllnDhxAv/5z38AAP/+978RHR1t8DqM9c0332Dw4MFo3rw51Go13njjDb3XFQoFXnnlFZw/fx6HDh0CAHzxxReYOHEiACA/Px8A8Pbbb+uur1UoFLh06VKTvLGkuajVaty4ccPcZZhMYWEhABh80zuNRoP169dDoVDghRdewL1793SvmeoYbYh9XaVSoXnz5hg+fDi2bNmClJQULF68WHfNc25urt74goICFBYWwsfHx6AxxnBzc8NHH32EK1euoKCgAOnp6fjwww8BAH/5y1/0xlb8RVExT7aC+W/b+b9lyxa8//77OHLkCNq0aWPuckyG+c/8r2qMMZj/zH9bz//qjjdrx/xn/lc1xhiWlv/1buao1WqMGDECN2/exA8//FDtuNu3b+PFF1+Eu7s7AFS5Q+Tk5KBVq1ZGbT86OhoqlQqxsbE4duwYWrdurbuhkakcO3YMsbGxyMjIQHh4OLy9vXHy5EncuXMHS5YsqTR+/Pjx0Gg0WLt2LVJTU+Hq6qq7aVjz5s0BALGxsRARvT8JCQkmrZuqVlJSUqd9zZJVhEXFN0SG6NevH6ZOnYq0tDQsWLBAt9xUx2hD7+tt27aFvb09UlJSEBAQABcXF1y6dElvzG+//QYA6Nq1q0Fj6uunn34CAAwZMkRveXFxMYDKN0yzdsx/283/lStXYuPGjfjuu+8q/XJizZj/9zH/K4+pL+Z/1Zj/1pf/D/rz8WbNmP/3Mf8rj6kvc+a/SR5NPm/ePKjVakydOlWvw/dnZ86cgVKpROfOneHs7Iyff/5Z7/WTJ0+iuLgYjzzyiFHbbtWqFZ555hns2LEDc+bMwZQpU+r8Oarzyy+/wMnJCadPn0ZJSQn++c9/IjAwEBqNRu8RZxU8PDwQERGB3bt3Y+nSpXjppZd0r7Vu3RoajQZJSUkmr5MMc+TIEYgI+vbtCwBQKpU1npZpDVq0aAGFQoE7d+4Y9b4FCxYgODhY9+QFACY7Rk21r9+6dQvPPvtspeVpaWkoKytD69atoVQq8cQTT+DYsWMoLy/XjYmPj4dCocDo0aMNGlNfn376KQICAjBo0CC95RXz0rJly3pvw9Iw//VZe/6LCGbOnInTp09j9+7dVX5DZ82Y/39g/jP/64v5r8/a89+Q482aMf//wPy3nfw3STOnW7du+PLLL3HmzBkMGDAAe/fuxZ07d1BSUoILFy7g008/xcSJE6FSqaDRaDBt2jTs2rULGzduRG5uLk6fPo1JkybBx8cHUVFRRm9/2rRpKC0tRXZ2Nh599FFTfCQA9zu4mZmZOHLkCJycnHSPNDt48CAKCwuRlpZW7fWDkyZNQlFREeLi4jBq1Cjdco1GgwkTJmDz5s1Ys2YNcnNzUVZWhitXruD33383We30h/LycmRnZ6O0tBTJycmYMmUK/Pz8MH78eAD3O7y3b9/G7t27UVJSghs3blTq3np6euLatWu4ePEi7t69i5KSEsTHx1vMowm1Wi0CAwNx5coVo95Xcbqlvb293jJTHKOG7OuRkZFo2bIlTp06Ve16nJyccODAAXz33XfIzc1FSUkJEhMT8fzzz8PJyQlTp04FAMyZMweZmZl45513kJ+fj4SEBMTExGD8+PHo0KGDwWMMqQkAevfujUuXLqG0tBQXL17E9OnTcfDgQaxbt0537XCFinnp0qWLQT87a8L8r8ya8//s2bP44IMP8Omnn0KlUumdIq1QKLB06VJzl2gU5n/1mP/M//pi/ldmzflv6PFmLZj/1WP+21D+G3G35FplZGTI9OnTpUuXLuLs7Cz29vbi7u4u3bt3l4kTJ8oPP/wgIiLl5eUSExMj7dq1E5VKJR4eHhIeHi6pqakicv+O2FqtVgBIu3btJD09XT755BNxdXUVAOLv7y/nzp3T2/aQIUNk7dq1Rte8a9cuCQoKEgA1/tm1a5eIiMycOVM8PT3F3d1dxo0bJ6tWrRIAEhQUpHvUXYXu3bvLrFmzKm2zqKhIZs6cKX5+fqJUKqV58+YyduxYSUlJkSVLloijo6MAkNatWxv9KMCEhATp37+/+Pj46Gr39vaWkJAQOXr0aKOvxxR3s1+5cqV4e3sLANFqtTJ69GiD95GoqChRqVTi6+srSqVSXF1dZcyYMZKenq5b/61bt2TIkCGi0WgkICBAJk+eLDNmzBAA0rZtW8nIyJBTp06Jv7+/ODo6yl//+le5fv267N27V1xcXPSeXFBXMMGjCaOjo0WlUklBQYFu2Z/3by8vL93d6x80Y8YMvUcTmuoYrWlfFxEJDw8XADJ37twaP9vo0aMlICBAnJ2dRa1WS1BQkERGRsrp06f1xh09elR69+4tarVafHx8ZMaMGVJYWGjUGENrGjZsmLi7u4tSqRQPDw8JCwur9pGLYWFh4uvrK+Xl5TWuszaW9jSTP2P+20b+nz59usafRVWP9qwO898wzH/mvyGY/8z/mpjq93ZDj7faMP8Nw/xn/huiQR9NTtV74okn5Pz58+Yuw6zMvT9FRUWJp6en2bZvKFOEeVpamiiVSqN/ATCnsrIyGTBggKxbt87cpeiYuqabN2+KRqORpUuX1ntdlvzLPOlj/pt/f2L+Wzbmv3GY/9aD+W/+/Yn5b9mY/8Zp0EeT0x/+fN1lcnIyNBoNAgICzFgRAcbdFMyatW3bFvPnz8f8+fORl5dn7nJqVVZWht27d+Pu3buIjIw0dzkAGqamefPmoVu3bg36lA0yP+a/ZWL+WybmP9kS5r9lYv5bJua/adlsM+fXX3+tdK1/VX9MuRPNnDkTaWlpOHfuHCZMmKB3h/D6MNVnMcfPhBrXrFmzMG7cOERGRhp9M7TGduTIEezcuRPx8fHQarXmLgeA6WtatmwZkpKSsHfvXqhUKhNUSIZg/jfceshyMf/rh/lvG5j/DbceslzM//qx5vxXNujazSg4OBgi0qjb1Gq1CA4Ohq+vL1avXo2OHTuaZL2m+izm+JmY0+zZs7F+/XoUFxcjICAAMTExePrpp81dVoNbtGgRDhw4gPfeew/vv/++ucup1tChQzF06FBzl6HHlDXt2bMHRUVFOHLkiN4N5qjhMf8bbj3WgvnP/DcW8982MP8bbj3WgvnP/DeWNee/Qh44urdt24aIiIgmddBTw+H+ZBiFQoGtW7fimWeeMXcpZMEa+nji8UqmxP3JMMx/MgTzn6wJ9yfDMP/JEDUcT9tt9jIrIiIiIiIiIiJbxGYOEREREREREZEVYTOHiIiIiIiIiMiKsJlDRERERERERGRF2MwhIiIiIiIiIrIi1T6aXKFQNGYdZOO4P9UuIiICERER5i6DiMcrmRT3p9ox/8lS8HglU+L+VDvmP9VHtc2crVu3NmYdZKMSEhKwfPly7k+1iIiIwJQpU9CvXz9zl0IWrOJ4amg8XskUmP+GYf6TIZj/ZE2Y/4Zh/pMhasr/aps5fN49mcry5cu5P9UiIiIC/fr148+JatUYv8xzPyRTYf7XjvlPhmL+kzVh/teO+U+Gqi7/ec8cIiIiIiIiIiIrwmYOEREREREREZEVYTOHiIiIiIiIiMiKsJlDRERERERERGRF2MwhIiIiIiIiIrIiNtfMSU1NxeTJk9GpUye4uLhAqVTCzc0N7du3R1hYGBISEsxWW3l5OWJjYxESEqK3fOfOnQgMDIRCodD74+DggBYtWmDw4MGIiYlBdna2mSonS3bw4EHMmjWr0n70j3/8o9LY4cOHw8XFBfb29ujUqRNOnTplhoprtmTJEgQHB8PR0RFOTk4IDg7GnDlzkJubqzfu+PHj6N+/P7RaLXx8fDBz5kwUFRUZPcYY1R3DFUpKSrB48WK0bdsWDg4OcHd3R+fOnXHx4kV89dVXWLJkCcrKyuq8faoZ85+aGuY/85/uY/5TU8P8Z/4DAOQBW7dulSoWW4W1a9eKSqWSgQMHyr59+yQ7O1sKCwslPT1dtmzZIiEhIfLxxx+bpbZz585J//79BYA8/PDDVY4JCgoSNzc3EREpLy+X7OxsOXz4sIwfP14UCoX4+PjITz/91Jhl15s170+NCYBs3brV6PfNnTtXRo0aJbm5ubplQUFB0qxZMwEgcXFxld4THx8vTz75ZL3qbUhhYWGydOlSycrKkrt378q2bdtEpVLJsGHDdGPOnDkjjo6OMmfOHMnLy5MTJ06Il5eXTJgwwagxxjDkGA4PD5cOHTrIjz/+KCUlJXLt2jUZPXq0nD59WkREli9fLoMGDZLs7Ow61dDQx5M1H6/Mf8tjzftTY2L+/4H5Xz3mf/WY/5bHmvenxsT8/wPzv3o1HE/bbKaZk5CQIPb29vLoo49KSUlJlWP27dsnK1eubOTKRJKSkuSpp56SjRs3Srdu3QwK8wdt375d7OzspEWLFpKTk9OQ5ZqUOfengoIC6devn1Wsuy5h/t5770n79u3l3r17esuDgoLkyy+/FDs7O/H19a20v1h6mIeHh1f6TOPGjRMAcu3aNRERiYiIkICAACkvL9eNiYmJEYVCIf/9738NHmMoQ47hzZs3i0KhkOTk5BrXFR0dLf369as2p2rCX+arxvy3TMx/wzD//8D8rx7zv2rMf8vE/DcM8/8PzP/qNYlmTlhYmACQkydPmruUGvXp06dOYS4i8sILLwgAef/99xuqPJMz5/60cuVKCQoKsop1GxvmaWlpolQqZfPmzZVeCwoKkgsXLsjUqVMFgLz44ot6r1t6mFdlypQpAkDOnTsnJSUl4uzsLOPHj9cbc+bMGd3xYciYuqruGB44cKA88sgjtb7/9u3b4ujoKDExMUZvm7/MV435b5mY/4Zh/teM+X8f879qzH/LxPw3DPO/Zsz/+2pq5tjEPXOKi4tx6NAhNGvWDL179zboPSKCZcuW4aGHHoJarYaHhwfGjBmDX3/9FQCwZs0aODk5QavVYs+ePXj88cfh6uqKVq1aYfPmzQCAhx56CAqFAnZ2dnjkkUdQUFAAAHjjjTfg5uYGjUaD//3f/zXZ5xw/fjwAID4+3mTrtES1zU10dDQcHBzg7e2te8+//vUvODk5QaFQ4ObNm5gyZQqmTZuG9PR0KBQKtG3bFitWrIBGo0GLFi3wyiuvwMfHBxqNBiEhITh58mS91g0A+/btg6urKxYtWtTgP6MVK1ZARDB69OhqxyxcuBDt27fH2rVrcfDgwWrHmeJYAICysjLMnTsXfn5+cHR0RNeuXbF161aTfN60tDS4u7vD398f58+fR15eHvz8/PTGBAUFAQCSk5MNGmNKxcXF+PHHH9GtW7dax3p4eGDQoEFYvnw5RMSkdTRFzH/bwvyvHfOf+U/3Mf9tC/O/dsx/5n8lRnR+LNa5c+cEgPTt29fg98ydO1ccHBxkw4YNkpOTI8nJydKjRw/x8vKS69evi4jIW2+9JQDk0KFDcufOHcnKypIBAwaIk5OTFBcXS2lpqbRp00b8/PyktLRUb/2vv/66xMbGVtpufTrzubm5AkBat25t8Oc0t7rsT4bMzXPPPSctW7bUe19MTIwAkBs3boiIyNixYyt1z6OiosTJyUnOnj0rhYWFkpKSIr169RIXFxfJyMio17rj4uLExcVF5s+fb9TnFTG+Mx8YGCgdO3as8rWKzryIyIkTJ8TOzk7atGkjeXl5IlK5M2+KY0FEZPr06aJWq2XHjh2SnZ0ts2fPFjs7uzpf511cXCxXrlyRlStXilqtlg0bNoiIyNGjRwVAlZ1tR0dHGTp0qEFj6qqqY/jChQsCQLp16yaDBw8Wb29vUavVEhwcLKtWrdI71VNEZNasWQJAEhMTjdo2v5mtjPlvuZj/hmH+V8b8r4z5Xxnz33Ix/w3D/K+M+V+ZzZ+ZU3GXa2dnZ4PG37t3D8uWLcNTTz2Fv//973Bzc0OXLl3w0Ucf4ebNm/jkk0/0xoeEhMDV1RXNmzdHZGQk8vPzkZGRAXt7e7z22mvIyMjArl27dOMLCgqwc+dOvPDCC6b7kABcXFygUChw9+5dk67Xkhg7N3WhVCp1XeiOHTtizZo1uHv3LtavX1+v9YaFhSE3Nxdz5sypd401yc/Px4ULF3Rd5pr069cPr7/+Oi5evIg333yz0uumOhYKCwuxZs0ahIeHY+zYsXB3d8fbb78NlUpV559r69at0apVK8ybNw8ffPABIiIiAEB3N3p7e/tK71GpVLh3755BY0wpLy8PANC8eXMsWrQIKSkpyMzMxJgxY/Dqq69i06ZNeuPbtWsHADh9+rRJ62iKmP+2g/lfO+Y/85/+wPy3Hcz/2jH/mf9VsYlmTkWIV5zmWJuUlBTk5eWhZ8+eest79eoFBwcH3Sl3VXFwcABw/xFkAPDiiy/Czc0Ny5cv143ZuHEjxowZA1dXV6M+R23y8/MhIiZfryWpz9zUVc+ePaHVanWnFVq6rKwsiAi0Wq1B4xcuXIgOHTpg9erVOH78uN5rpjoWUlNTUVBQgM6dO+ted3R0hLe3d51/rpcvX0ZWVhY2bdqEzz//HN27d0dWVhY0Gg0AoLS0tNJ7iouL4ejoaNAYU1Kr1QCATp06ISQkBJ6ennBzc8O7774LNze3Sn8pVsxdZmamSetoipj/toP5XzvmP/Of/sD8tx3M/9ox/5n/VbGJZk6bNm2g0Whw7tw5g8bn5OQAqLqT7+7ublTn29nZGS+//DJOnDiB//znPwCAf//734iOjjZ4HYaq+HzBwcEmX7elMOXcGEOtVuPGjRsNsm5TKywsBPBHgNRGo9Fg/fr1UCgUeOGFF/S60qb6eefn5wMA3n77bSgUCt2fS5cuGfxL1oNUKhWaN2+O4cOHY8uWLUhJScHixYt11zNXfCNXoaCgAIWFhfDx8TFojClVrO/mzZt6yx0cHODv74/09HS95RV/mVTMJdUd8992MP9rx/xn/tMfmP+2g/lfO+Y/878qNtHMUavVGDFiBG7evIkffvih2nG3b9/Giy++CHd3dwCockfNyclBq1atjNp+dHQ0VCoVYmNjcezYMbRu3dqgU+CMtW/fPgDA448/bvJ1WwpTz40hSkpKGmzdDaEiCMrKygx+T79+/TB16lSkpaVhwYIFuuWm+nk3b94cABAbGwsR0fuTkJBgcJ3Vadu2Lezt7ZGSkoKAgAC4uLjg0qVLemN+++03AEDXrl0NGmNKzs7OaNeuHc6ePVvptdLSUri5uektKy4uBgCTf0PQFDH/bQfzv3bMf+Y//YH5bzuY/7Vj/jP/q2ITzRwAmDdvHtRqNaZOnVrt9XBnzpyBUqlE586d4ezsjJ9//lnv9ZMnT6K4uBiPPPKIUdtu1aoVnnnmGezYsQNz5szBlClT6vw5qnP9+nXExsaiVatWJr8W15IYOjdKpVJ3qmt9HTlyBCKCvn37mnzdDaFFixZQKBS4c+eOUe9bsGABgoODkZiYqFtmqmOhdevW0Gg0SEpKMqqmB926dQvPPvtspeVpaWkoKytD69atoVQq8cQTT+DYsWMoLy/XjYmPj4dCocDo0aMNGmNqERERSExMxPnz53XLCgoKcOnSJXTp0kVvbMXctWzZ0uR1NEXMf9vA/K8d85/5T/qY/7aB+V875j/zvyo208zp1q0bvvzyS5w5cwYDBgzA3r17cefOHZSUlODChQv49NNPMXHiRKhUKmg0GkybNg27du3Cxo0bkZubi9OnT2PSpEnw8fFBVFSU0dufNm0aSktLkZ2djUcffbTOn0NEkJeXh/LycogIbty4ga1bt6J///6wt7fH7t27bfqaWUPnpm3btrh9+zZ2796NkpIS3Lhxo1IX1tPTE9euXcPFixdx9+5dXUCXl5cjOzsbpaWlSE5OxpQpU+Dn56d79GNd1x0fH98ojybUarUIDAzElStXjHpfxemWf74pmKmOBY1GgwkTJmDz5s1Ys2YNcnNzUVZWhitXruD3338HAERGRqJly5Y4depUtetxcnLCgQMH8N133yE3NxclJSVITEzE888/DycnJ0ydOhUAMGfOHGRmZuKdd95Bfn4+EhISEBMTg/Hjx6NDhw4GjzGkJkNNnToV/v7+GD9+PDIyMnDr1i3MnDkT9+7dq3TzuYq5ezDkqW6Y/7aB+V875j/zn/Qx/20D8792zH/mf5WMePSVVcjIyJDp06dLly5dxNnZWezt7cXd3V26d+8uEydOlB9++EFERMrLyyUmJkbatWsnKpVKPDw8JDw8XFJTU0VEZPXq1aLVagWAtGvXTtLT0+WTTz4RV1dXASD+/v5y7tw5vW0PGTJE1q5dW6mmhIQE6d+/v/j4+AgAASDe3t4SEhIiR48erWxmsQAAIABJREFUla+++kq6du0qWq1WHBwcxM7OTgCIQqEQd3d36d27t8yfP19u3brV8D9AE6vL/lTb3IiI3Lp1S4YMGSIajUYCAgJk8uTJMmPGDAEgbdu2lYyMDDl16pT4+/uLo6Oj/PWvf5Xr169LVFSUqFQq8fX1FaVSKa6urjJmzBhJT0+v97r37t0rLi4usnDhQqN/TjDy0YTR0dGiUqmkoKBAt2zXrl0SFBQkAMTLy0teffXVKt87Y8YMvUcTmupYKCoqkpkzZ4qfn58olUpp3ry5jB07VlJSUkREJDw8XADI3Llza/xso0ePloCAAHF2dha1Wi1BQUESGRkpp0+f1ht39OhR6d27t6jVavHx8ZEZM2ZIYWGhUWMMram2Y7jC5cuX5W9/+5t4eHiIWq2W3r17S3x8fKX1hYWFia+vb6VHFtaGj6atGfPfsjD/DcP8/wPzv3rM/5ox/y0L898wzP8/MP+rV9OjyW2umUOWxdL2p6ioKPH09DR3GZUYG+ZpaWmiVCplw4YNDViVaZWVlcmAAQNk3bp15i5Fxxw13bx5UzQajSxdutTo9/KXebImlrY/Mf/Nh/l/H/OfmgpL25+Y/+bD/L+vgfJ/m81cZkVkKGNuHGap2rZti/nz52P+/PnIy8szdzm1Kisrw+7du3H37l1ERkaauxwA5qtp3rx56NatW4M88YKIasb8b3zM/z8w/4nMh/nf+Jj/f2io/Gczh8hKzZo1C+PGjUNkZKTRN0NrbEeOHMHOnTsRHx8PrVZr7nIAmKemZcuWISkpCXv37oVKpWqUbRKR7WH+1w/zn4isFfO/fmwt/9nMoSZj9uzZWL9+Pe7cuYOAgADs2LHD3CXV26JFixAdHY333nvP3KXUaOjQofjyyy/h7e1t7lJ0GrumPXv2oKioCEeOHIGHh0ejbJOI7mP+mw/zn/lPZE7Mf/Nh/jd8/itNvkYiC7V48WIsXrzY3GWY3PDhwzF8+HBzl0G1ePLJJ/Hkk0+auwyiJon5T+bE/CcyH+Y/mVND5z/PzCEiIiIiIiIisiJs5hARERERERERWRE2c4iIiIiIiIiIrAibOUREREREREREVqTaGyBv27atMesgG5WQkACA+5MhKn5WTU1JSQlyc3PRrFkzc5di8RprH+HxSqbA/DdcU81/Mhzz33KICLKystCyZUtzl2KxmP+GY/5TbWraRxQiIn9esG3bNkRERDR4UUREVDcPxLbJMP+JiCwb85+IqGmqIv+3V2rmEBE1ptLSUvz444+Ii4vDwYMHcerUKWg0GvTv3x+hoaEIDQ3FI488Yu4yicjKVPzjlL/mEJGxysrKkJSUhIMHD+Lrr79GQkIC7Ozs0KdPH4waNQqhoaHo0aMHFAqFuUsloqaLzRwisiyZmZnYv3+/rrmTnZ2NgIAADBs2DKGhoRg+fDjc3NzMXSYRWTg2c4jIGH/+/ePbb79FTk4OAgMDdV8sjRgxAq6uruYuk4ioAps5RGS5+M0YEdUVmzlEVBOeGUxEVo7NHCKyHjdv3sThw4dx8OBBxMXF4dq1a2jRogUGDRqEkSNHYtSoUfDw8DB3mURkAdjMIaIHnT9/HgcPHsTBgwexf/9+5Obm6s6+GTlyJIYNGwaNRmPuMomIDMFmDhFZr5SUFN03akePHkV5eTm6deum+0Zt8ODBUCqrfWgfEdkwNnOIqKCgACdOnNCd4Xv27Fk4OTlhyJAhGDVqFB577DH4+fmZu0wiorpgM4eIbENeXh4OHz6MuLg47Nu3DxkZGWjWrBkeffRRhIaGIiwsDL6+vuYuk4gaCZs5RE1Txdk3X3/9Nb799lsUFRWhY8eOusuzBw4cCAcHB3OXSURUX2zmEJFt4i9zRE0bmzlETcOfv8yJj4/H5cuX4eXlhSFDhvDLHCKyZWzmEJHtq+406379+mHkyJEYM2YM/P39zV0mEZkQmzlEtqm8vByJiYm6e988eJn1yJEjERISAjs7O3OXSkTUkNjMIaKmhzdAJLJ9bOYQ2Y6qHoDQsmVLDBw4kA9AIKKmis0cImra+GhSItvEZg6R9SorK0NSUpLujNqEhATY2dmhT58+usule/ToAYVCYe5SiYjMhc0cIqI/y8zMxP79+3XNnezsbAQEBGDYsGEIDQ3F8OHD4ebmZu4yiagWbOYQWZeq/v6tOGs2NDQUI0aMgKurq7nLJCKyFGzmEBFVh98MElkvNnOILBvPjCUiqhc2c4iIDFXVNfstWrTAoEGDeM0+kYVhM4fI8vCedUREJsNmDhFRXaWkpOi+UXzwaRqhoaEYPHgwlEqlucskapLYzCEyv9qeJhkeHg4/Pz9zl0lEZI3YzCEiMoW8vDwcPnwYcXFx2LdvHzIyMuDl5YUhQ4YgNDQUYWFh8PX1NXeZRE0GmzlE5lFx9s3XX3+Nb7/9FkVFRejYsaPu8uSBAwfCwcHB3GUSEVk7NnOIiBoCf5klMi82c4gaB7/MICIyCzZziIgaWm2nmY8ZMwb+/v7mLpPIprCZQ9QwysvLkZiYqLv3zYOXGY8cORIhISGws7Mzd6lERLaMzRwiosbGG0ASNTw2c4hMp6oHALRs2RIDBw7kAwCIiMyDzRwiInPio1mJGgabOUR1V1ZWhqSkJN0ZpQkJCbCzs0OfPn10lwv36NEDCoXC3KUSETVVbOYQEVmSzMxM7N+/X9fcyc7O1p21ExoaiuHDh8PNzc3cZRJZPDZziIxT298/I0aMgKurq7nLJCKi+9jMISKyVPxmlKju2MwhqhnPDCUismps5hARWYuq7lnQokULDBo0iPcsIHoAmzlElfGebURENoPNHCIia5WSkqL7RvXBp4mEhoZi8ODBUCqV5i6TyCzYzCHi0xSJiGwYmzlERLYgLy8Phw8fRlxcHPbt24eMjAx4eXlhyJAhCA0NRVhYGHx9fc1dJlGjYTOHmqqKs2++/vprfPvttygqKkLHjh11l+cOHDgQDg4O5i6TiIjqh80cIiJbxF/mqaljM4eaCjbziYiaJDZziIhsHU+zp6aIzRyyVeXl5UhMTNTd++bBy2xHjhyJkJAQ2NnZmbtUIiJqOGzmEBE1NbwBJjUFbOaQLeEN8ImI6AFs5hARNWV8NC3ZKjZzyJqVlZUhKSlJd0ZlQkIC7Ozs0KdPH93lsj169IBCoTB3qUREZB5s5hAR0R8yMzOxf/9+XXMnOztbd9ZOaGgoRowYAVdXV3OXSVQrNnPI2jB/iYjICGzmEBFR1fjNMFkzNnPI0vHMSCIiqgc2c4iIyDC8ZwNZEzZzyBLxnmVERGQibOYQEVHdpKSk6L5R5tNUyNKwmUOWgE8TJCKiBsJmDhER1V9eXh4OHz6MuLg47Nu3DxkZGfDy8sKQIUMQGhqKsLAw+Pr6mrtMakLYzCFzqTj75uuvv8a3336LoqIidOzYUXd56sCBA+Hg4GDuMomIyLqxmUNERKbHf8yQubGZQ42FzWwiIjIDNnOIiKhh8TIDamhZWVlYv3693rLk5GRs2rQJ77//vt5yT09PvPTSS41ZHtkgXmZKRERmxmYOERE1Lt4AlEyttLQU3t7eyM7OhkqlqnZcUVERoqKi8NFHHzVidWQLeAN4IiKyMGzmEBGR+fDRvGQqkydPxscff4ySkpIaxx09ehQDBw5spKrIWpWVlSEpKUl3RmFCQgLs7OzQp08f3eWiPXr0gEKhMHepRETUNLGZQ0REliMzMxP79+/XNXeys7N1Z+2EhoZixIgRcHV1NXeZZIFOnDiB/v371zjG29sbV69e5eUvVCXmDxERWRE2c4iIyDLxm3EyhojAz88PV65cqfJ1BwcHvPbaa/jggw8auTKyVDwzkIiIrBibOUREZB0a+p4Vd+7cwW+//cZ/vFmxWbNm4cMPP6z2UqtTp06he/fujVwVmYKI4MiRIxgyZEi91sN7dhERkY1gM4eIiKyTqZ8ms2vXLkRERGDevHl48803YW9v34DVU0NITk7Gww8/XOVrgYGBSE9Pb+SKyBSuX7+O8ePH49ixY7h9+7ZRzRY+TY+IiGwUmzlERGT98vLycPjwYcTFxWHfvn3IyMiAl5cXhgwZgtDQUISFhcHX17fGdbz88stYt24dAKBXr17YtGkTAgMDG6N8MqHg4GCkpqbqLXNwcMBbb72FuXPnmqkqqqvdu3fjhRdeQF5eHkpKSnDgwAEMGzasxvdUnH3z9ddf49tvv0VRURE6duyouzxz4MCBcHBwaKRPQERE1CDYzCEiIttTl3/M+fj44Pr16wAApVIJlUqF5cuX4+WXXzbHR6A6WrRoEd59991Kl1qlpqaiffv2ZqqKjFVQUICZM2di1apVsLOzQ3l5ORwcHDB58mQsXbpUb6wpmrlERERWhs0cIiKybYZcZpGXl4fOnTtXeq9CocCoUaOwbt06eHl5maF6MtalS5cQEBCAil9vFAoFunbtiqSkJDNXRoY6efIkIiMjcfXq1UpNuXbt2uHcuXMmv8ySiIjIyrCZQ0RETUtaWhr279+Pffv24fDhwygoKIC3tzdu3ryJ0tLSSuNVKhVcXFzw+eefY+TIkWaomIzVs2dPnDp1CiICpVKJDz74AK+//rq5y6JalJaW4sMPP8Rbb70F4P4T7arSrFkz3Lp1C76+vnjssccwYsQIhIaG1usG6ERERFaGzRwiImq6CgsL8f3332PSpEm4cOECysvLqxxXcZnHSy+9hNjYWDg5OTVypWSMFStWYNq0aSgtLYVCocDly5d5mY2FO3/+PP72t7/hl19+qbaJAwD29vYIDw/H3Llz0aVLl0askIiIyKJs5/mnRETUZGk0GvTv3x8ZGRnVNnIA6F5bv349Hn74Yfzyyy+NVSLVwTPPPIPy8nIoFAr079+fjRwL98UXX6BTp05ITEyssZFToby8nI0cIiJq8tjMISKiJu3w4cOV7stRndLSUly6dAm9e/fGvHnzDPqHJzU+b29vDBo0CCKC559/3tzlUDWysrIQFhaG8ePHo7Cw0KDjsKysDPv27TP4mCUiIrJVvMyKyMaNGzcOO3bsMHcZRERERNRItm7dimeeecbcZRBRw9muNHcFRNTw+vbty5t/ktlFRERgypQp6Nevn7lL0RMdHY3MzEzdf6tUKjg6OkKr1cLJyQkuLi5wdnbW/XfF/1b88fT0NNllPLGxsQDA49UE7t27h08++QSvvfaauUuhB5SVlSE9PR35+fnIz89HQUEBCgoKdP8/Ly8Pd+/eRV5enm5ZYWGh3qWQTz31FCIiIsz4KYgsF48NoqaBzRyiJqBVq1b8dobMLiIiAv369bOofbG8vByBgYFwd3eHm5sb3N3doVKpzFbP9u3bAcCifkbWbNiwYfjLX/5i7jLIRAoKCpCTk4OcnByoVCq0a9fO3CURWSQ2c4iaBjZziIioybKzs0PPnj3NXQY1EDZybItWq4VWq+W8EhERgTdAJiIiIiIiIiKyKmzmEBERERERERFZETZziIiIiIiIiIisCJs5RERERERERERWhM0cIqpWamoqJk+ejE6dOsHFxQVKpRJubm5o3749wsLCkJCQYO4SbdbOnTsRGBgIhUIBhUIBb29v/P3vfzd3WWa3d+9euLm54euvvzZ3KRbjlVde0e0nCoWiyv3k4MGDmDVrVqX96h//+EelscOHD4eLiwvs7e3RqVMnnDp1qjE+hlGWLFmC4OBgODo6wsnJCcHBwZgzZw5yc3P1xh0/fhz9+/eHVquFj48PZs6ciaKiIqPHGKO8vByxsbEICQmp8vWSkhIsXrwYbdu2hYODA9zd3dG5c2dcvHgRX331FZYsWYKysrI6b//POO+cd8679c377t279TLdy8urzvURkY0TIrJpTz/9tDz99NNGv2/t2rWiUqlk4MCBsm/fPsnOzpbCwkJJT0+XLVu2SEhIiHz88ccNUDH9WVBQkLi5uZm7DJMAIFu3bq3XOuLi4sTV1VW++uorE1VlWepyvEZFRYmnp6fEx8dLamqqFBYW6r0+d+5cGTVqlOTm5uqWBQUFSbNmzQSAxMXFVVpnfHy8PPnkk3X7EI0gLCxMli5dKllZWXL37l3Ztm2bqFQqGTZsmG7MmTNnxNHRUebMmSN5eXly4sQJ8fLykgkTJhg1xhjnzp2T/v37CwB5+OGHqxwTHh4uHTp0kB9//FFKSkrk2rVrMnr0aDl9+rSIiCxfvlwGDRok2dnZdaqhAued816B8274GGM01LyXl5fLlStX5NixY/LEE09Is2bNjK7NFH/fEpHF28ZmDpGNq8s/DhMSEsTe3l4effRRKSkpqXLMvn37ZOXKlaYo0WQKCgqkX79+NrVdNnMsS0PvY3Vt5vj6+lb52nvvvSft27eXe/fu6S0PCgqSL7/8Uuzs7MTX11dycnL0Xrf0f9yFh4dX+kzjxo0TAHLt2jUREYmIiJCAgAApLy/XjYmJiRGFQiH//e9/DR5jqKSkJHnqqadk48aN0q1btyr/cbd582ZRKBSSnJxc47qio6OlX79+1eZvbTjvnPc/47xb77y/9tprbOYQUXW28TIrIqpk4cKFKCsrw3vvvQelUlnlmBEjRuDVV19t5Mpqtm7dOmRlZTWZ7VLjs6a5/u233zBnzhy8++670Gg0lV4PCQnBlClTcPXqVUyfPt0MFdbdrl27Kn0mX19fAEBeXh5KS0vxzTffYNCgQVAoFLoxjz/+OEQEe/bsMWiMMR5++GHs3LkTzz33HNRqdZVj/v3vf6NHjx7o0qVLjeuaN28ekpKSsHz5cqNqADjvnHfOe4WmMO9E1LSxmUNEeoqLi3Ho0CE0a9YMvXv3Nug9IoJly5bhoYceglqthoeHB8aMGYNff/0VALBmzRo4OTlBq9Viz549ePzxx+Hq6opWrVph8+bNeuvasGEDevbsCY1GAycnJ7Rp0wYLFiwAAHz//ffo2LEj3NzcoNFo0KVLF+zfvx8AMGXKFEybNg3p6elQKBRo27YtAKCsrAxz586Fn58fHB0d0bVrV2zdutWouky93YZQU40vvvii7tr7oKAgJCYmAgAmTJgArVYLNzc3fPXVVzXW/MEHH0Cr1cLFxQVZWVmYNm0afH19kZqa2mCf6UHHjx+Hn58fFAoFVq1aBcCwOVyxYgU0Gg1atGiBV155BT4+PtBoNAgJCcHJkycBANHR0XBwcIC3t7due//617/g5OQEhUKBmzdvVjvX+/btg6urKxYtWtRoPwtDrFixAiKC0aNHVztm4cKFaN++PdauXYuDBw9WO85Ux3hDHhdpaWlwd3eHv78/zp8/j7y8PPj5+emNCQoKAgAkJycbNMaUiouL8eOPP6Jbt261jvXw8MCgQYOwfPlyiIhR2+G8c96rw3m3vXknoiau8c8GIqLGZOxlG+fOnRMA0rdvX4PfM3fuXHFwcJANGzZITk6OJCcnS48ePcTLy0uuX78uIiJvvfWWAJBDhw7JnTt3JCsrSwYMGCBOTk5SXFwsIiKxsbECQN577z25deuW3L59Wz7++GN57rnnRERk+/btMm/ePLl9+7bcunVL+vbtq3f68dixYyUoKEivtunTp4tarZYdO3ZIdna2zJ49W+zs7OSnn34yuK6G2K6hDL3MypAa7e3t5erVq3rve/bZZ3X3nzH0Z/Xaa6/JypUr5amnnjLq1HSY4LTvy5cvCwC9S/wMmcOoqChxcnKSs2fPSmFhoaSkpEivXr3ExcVFMjIyRETkueeek5YtW+ptLyYmRgDIjRs3RKTquY6LixMXFxeZP39+vT6biGkvswoMDJSOHTtW+Z6goCC5cOGCiIicOHFC7OzspE2bNpKXlycilS+7MNUxbqrjokJxcbFcuXJFVq5cKWq1WjZs2CAiIkePHhUAEhMTU+k9jo6OMnToUIPG1FWfPn0qXXZx4cIFASDdunWTwYMHi7e3t6jVagkODpZVq1bpXfohIjJr1iwBIImJiUZtm/POeX8Q5916552XWRFRDXiZFRHpq3g6hLOzs0Hj7927h2XLluGpp57C3//+d7i5uaFLly746KOPcPPmTXzyySd640NCQuDq6ormzZsjMjIS+fn5yMjIQElJCd59910MGTIEb775Jjw9PeHh4YGJEyeiV69eAICnn34a77zzDjw8PODp6YnRo0fj1q1buHHjRpW1FRYWYs2aNQgPD8fYsWPh7u6Ot99+GyqVCuvXrzeoroberqnUVuOkSZNQVlamt/3c3Fz89NNPeOKJJ4yq+f3338err76KnTt3Ijg4uEE+T13UNIcAoFQqdd80d+zYEWvWrMHdu3frPSdhYWHIzc3FnDlz6vsRTCY/Px8XLlzQfetck379+uH111/HxYsX8eabb1Z63VTHeEMcF61bt0arVq0wb948fPDBB4iIiAAA3dNp7O3tK71HpVLh3r17Bo0xpby8PABA8+bNsWjRIqSkpCAzMxNjxozBq6++ik2bNumNb9euHQDg9OnTBm+D8855rw3n3XbmnYiIzRwi0lPRxCkoKDBofEpKCvLy8tCzZ0+95b169YKDg4PuMpaqODg4ALj/6M7k5GTk5ORgxIgRemPs7e3x2muvVfl+lUoFANU+zjU1NRUFBQXo3LmzbpmjoyO8vb11p4vXVldjbteUHqzx0UcfRfv27fHZZ5/pTuPesmULIiMjYW9vbxE1m1JtcwgAPXv2hFartcrPV5usrCyICLRarUHjFy5ciA4dOmD16tU4fvy43mumOsYbYh+7fPkysrKysGnTJnz++efo3r07srKydPfXKC0trfSe4uJiODo6GjTGlCruq9GpUyeEhITA09MTbm5uePfdd+Hm5lbpH8kVc5eZmWnwNjjvnHdDcN6rHmNKjTHvRERs5hCRnjZt2kCj0eDcuXMGjc/JyQFQ9Zk87u7uuHv3rkHrqTgjyN3dvdox33zzDQYPHozmzZtDrVbjjTfeqHGd+fn5AIC3335bd88YhUKBS5cuGdysMud2jVFbjQqFAq+88grOnz+PQ4cOAQC++OILTJw40Ww1WwK1Wl3tGVbWrLCwEACqvTHngzQaDdavXw+FQoEXXnhB71tqUx3jDbGPqVQqNG/eHMOHD8eWLVuQkpKCxYsX6+59VJErFQoKClBYWAgfHx+DxphSxfpu3rypt9zBwQH+/v5IT0/XW17xj8uKuTQE553zbgjOe9VjTKkx5p2IiM0cItKjVqsxYsQI3Lx5Ez/88EO1427fvo0XX3xR13yp6he8nJwctGrVyqDt/uUvfwFQ+RefChkZGQgPD4e3tzdOnjyJO3fuYMmSJTWus3nz5gCA2NhYiIjen4SEBIPqMtd2DXHs2DHExsYaXOP48eOh0Wiwdu1apKamwtXVFf7+/o1asyUpKSkxah+1JhX/MKju7LGq9OvXD1OnTkVaWprupuMATHaMN/Q+1rZtW9jb2yMlJQUBAQFwcXHBpUuX9Mb89ttvAICuXbsaNMaUnJ2d0a5dO5w9e7bSa6WlpXBzc9NbVlxcDABGnTHAeee8G4rzXnmMKTXGvBMRsZlDRJXMmzcParUaU6dOrfY68jNnzkCpVKJz585wdnbGzz//rPf6yZMnUVxcjEceecSgbbZp0waenp44cOBAla+fPn0aJSUl+Oc//4nAwEBoNBq9x4tWpXXr1tBoNEhKSjKoBkvariF++eUXODk5GVyjh4cHIiIisHv3bixduhQvvfRSo9dsSY4cOQIRQd++fQHcv6dOTZdlWZMWLVpAoVDgzp07Rr1vwYIFCA4O1j3xDIDJjnFT7WO3bt3Cs88+W2l5WloaysrK0Lp1ayiVSjzxxBM4duwYysvLdWPi4+OhUCgwevRog8aYWkREBBITE3H+/HndsoKCAly6dKnS44sr5q5ly5YGr5/zznk3BufduuediIjNHCKqpFu3bvjyyy9x5swZDBgwAHv37sWdO3dQUlKCCxcu4NNPP8XEiROhUqmg0Wgwbdo07Nq1Cxs3bkRubi5Onz6NSZMmwcfHB1FRUQZtU61WY/bs2Th27Biio6Nx9epVlJeX4+7duzh79qzucaIHDx5EYWEh0tLSKl277+npiWvXruHixYu4e/cu7O3tMWHCBGzevBlr1qxBbm4uysrKcOXKFfz+++8G1WWu7dakpKQEmZmZOHLkCJycnAyqscKkSZNQVFSEuLg4jBo1Srdco9E0aM2WoLy8HNnZ2SgtLUVycjKmTJkCPz8/jB8/HsD9b3pv376N3bt3o6SkBDdu3Kj0Le6Dc11SUoL4+HiLezS5VqtFYGAgrly5YtT7Ki6/+PNNQk11jBuyj0VGRqJly5Y4depUtetxcnLCgQMH8N133yE3NxclJSVITEzE888/DycnJ0ydOhUAMGfOHGRmZuKdd95Bfn4+EhISEBMTg/Hjx6NDhw4GjzGkJkNNnToV/v7+GD9+PDIyMnDr1i3MnDkT9+7dq3Qz2oq5q/hHnyF1cN4578bgvFvuvBMRGaRRHppFRGZTl0cdV8jIyJDp06dLly5dxNnZWezt7cXd3V26d+8uEydOlB9++EFERMrLyyUmJkbatWsnKpVKPDw8JDw8XFJTU0VEZPXq1aLVagWAtGvXTtLT0+WTTz4RV1dXASD+/v5y7tw5ERFZtWqVdOnSRTQajWg0GunevbusXr1aRERmzpwpnp6e4u7uLuPGjZNVq1YJAAkKCpKMjAw5deqU+Pv7i6Ojo/z1r3+V69evS1FRkcycOVP8/PxEqVRK8+bNZezYsZKSkmJwXaberiF27dolQUFBAqDGP7t27TLoZ/Nn3bt3l1mzZlXaZk01L1myRBwdHQWAtG7dWvdIWGOgno9KXblypXh7ewsA0Wq1Mnr0aIPnMCoqSlQqlfj6+opSqRRXV1cZM2aMpKen69Z/69YtGTJkiGg0GgkICJDJkyfLjBkzBIC0bdu22rneu3evuLi4yMKFC+v82SqY8tHk0dHRolKppKBhzHXcAAAgAElEQVSgQLfsz/uVl5eXvPrqq1Wuc8aMGXqPKjbVMV7bcREeHi4AZO7cuTV+5tGjR0tAQIA4OzuLWq2WoKAgiYyMlNOnT+uNO3r0qPTu3VvUarX4+PjIjBkzpLCw0KgxhtaUkJAg/fv3Fx8fH93x6e3tLSEhIXL06FHduMuXL8vf/vY38fDwELVaLb1795b4+PhK6wsLCxNfX1/dI4wNrYPzznmvwHm33nmvwEeTE1ENtrGZQ2Tj6tPMIdv0xBNPyPnz5xt9u+b85TIqKko8PT3Nsm1jmLKZk5aWJkqlsk6NN3MpKyuTAQMGyLp168xdio45arp586ZoNBpZunSp0XVw3k2D897wOO/3VTXvFdjMIaIabONlVkRENu7P94FJTk6GRqNBQECAGSsyD2NuDmpt7t27h/379yMtLU13I822bdti/vz5mD9/PvLy8sxcYe3Kysqwe/du3L17F5GRkeYuB4D5apo3bx66deuG6Ohoo+vgvNcf573hcd7/8OC8iwiuXbuG48eP627QTERUFTZziIga0a+//qr3mNbq/pjyF8mZM2ciLS0N586dw4QJE/SeXEK24fbt23jsscfQvn17vPDCC7rls2bNwrhx4xAZGWn0zVEb25EjR7Bz507Ex8dDq9WauxwA5qlp2bJlSEpKwt69e6FSqepUB+e9fjjvDY/zfl9V875nzx74+vpiwIAB+OabbxqlDiKyTgoREXMXQUQNZ9y4cQCA7du3m7kSMpe3334b7733Hnx9fbF69Wq9Gx83JoVCga1bt+KZZ55p1O3Onj0bH374IYqLi9GmTRvExMTg6aefbtQaDNVQx2vFDUTff/99k66XTGvPnj04e/Ys3njjDb2b0tYV5906cN6bJlPP+5+Z6+9bImpU29nMIbJxbOaQpeAvl7Xj8UpERPXFv2+JmoTtvMyKiIiIiIiIiMiKsJlDRERERERERGRF2MwhIiIiIiIiIrIibOYQEREREREREVkRpbkLIKKGd+XKFWzbts3cZRAhISHB3CVYtCtXrgAAj1ciIiIiqhGfZkVk48aNG4cdO3aYuwwiIiIiaiR8mhWRzdvOM3OImoCnn36ajzoms+OjUmvHR5MTEVF9KRQKc5dARI2A98whIiIiIiIiIrIibOYQEREREREREVkRNnOIiIiIiIiIiKwImzlERERERERERFaEzRwiIiIiIiIiIivCZg4RERERERERkRVhM4eIzC41NRWTJ09Gp06d4OLiAqVSCTc3N7Rv3x5hYWFISEgwd4lERFbn4MGDmDVrFnbu3InAwEAoFAooFAr84x//qDR2+PDhcHFxgb29PTp16oRTp06ZoeKaLVmyBMHBwXB0dISTkxOCg4MxZ84c5Obm6o07fvw4+vfvD61WCx8fH8ycORNFRUVGj6mLwsJCBAcH4+2339ZbvmnTJvTq1QsuLi7w9/fHhAkTcP36dQDAV199hSVLlqCsrKze2ycioqaDzRwiMqt169ahS5cuSE5OxrJly3D58mXk5+cjMTERCxYsQE5ODk6fPm3uMomIrMo777yDFStWYPbs2Rg7dizOnz+PoKAgNGvWDBs3bsQ333yjN/7AgQPYvn07Ro0ahZSUFPTo0cNMlVfv+++/x0svvYSMjAxkZmZiwYIFWLJkCZ5++mndmJSUFAwfPhxDhw7FjRs3sGvXLnz22WeYNGmSUWPq6q233kJqaqresq1bt+K5557DuHHjcOXKFezZswfHjh3D448/jtLSUowePRoajQZDhw5FTk5OvWsgIqKmgc0cIjKbH3/8EVFRURgwYAAOHTqEESNGwN39/7N359FRlXn+xz+VtZLKigKJZDELDdigouCBqD9UenCAIYIkkgacxhVt7RCBdIQAsqrADDBBMorY0XHBsDU4Ddg29CCiyNEDDJAekUUkrIEg2UO25/eHJ3WMSaCSkFQqvl/n5A/u/d57P/XUrQK+ufe5QfL29lZ0dLTGjBmjWbNmqaKiwtlR6ygrK1NcXNwv5riurjXHjfcE7dGrr76qDz/8UGvWrJG/v3+ddRkZGXJzc9PEiRNVUFDgpITN4+Xlpeeee06dO3eWn5+fEhMTNXLkSP3tb3/T2bNnJUnz5s1TSEiI5syZI5vNpoEDByotLU1vv/22vvnmG4drmuOLL77QoUOH6i1/4403dNNNNyk1NVWBgYG6/fbbNXnyZO3fv1979uyRJE2aNEm33Xabhg0bpqqqqmZnAAD8ctDMAeA08+fPV3V1tV555RV5eHg0WPPggw/q+eefb+NkV/fWW28pLy/vF3NcV9ea48Z7gvbm6NGjmjlzpubMmSOr1VpvfVxcnFJSUnT69GlNnTrVCQmbb8OGDfVeU7du3SRJxcXFqqqq0ubNmzVo0CBZLBZ7zdChQ2WM0aZNmxyqaY6ysjKlpqZq2bJl9dbl5uYqNDS0zvHCw8MlSd9//7192ezZs7V///4G9wEAwM/RzAHgFBUVFdq+fbtuuOEG3XXXXQ5tY4zRkiVL1KtXL3l7eys4OFgjR460/yY1MzNTNptNvr6+2rRpk4YOHaqAgACFhYVp9erVdfb17rvvql+/frJarbLZbLr55ps1b948ST9eyn/LLbcoMDBQVqtVffr00V//+ldJUkpKiqZMmaJjx47JYrEoNjZWklRdXa1Zs2YpIiJCPj4+uvXWW5Wdnd2kXNf7uK7uWu93cnKyvLy8FBISYt/mueeek81mk8Vi0cWLFxsct4yMDFmtVnXp0kXPPPOMQkNDZbVaFRcXZ/8teXP3LUkff/yxAgICtGDBgjYcLeBHGRkZMsYoPj6+0Zr58+frV7/6lVatWqVt27Y1Wne9vnNb83vqyJEjCgoKUmRkpI4fP67i4mJFRETUqYmJiZEkHThwwKGa5khPT7dfNfRz0dHR9Zq+tfPlREdH25cFBwdr0KBBWrZsmYwxzcoBAPgFMQA6tISEBJOQkODsGPV8++23RpIZMGCAw9vMmjXLeHl5mXfffddcvnzZHDhwwNxxxx3mxhtvNOfOnTPGGJOenm4kme3bt5uCggKTl5dn7r33XmOz2UxFRYUxxpilS5caSeaVV14x+fn55tKlS+aNN94w48aNM8YYs3btWjN79mxz6dIlk5+fbwYMGGBuuOEGe47Ro0ebmJiYOtmmTp1qvL29zbp168wPP/xgpk+fbtzc3MxXX33lcK7WOG57IslkZ2c7XO/I+z1u3DjTtWvXOtstXrzYSDIXLlwwxjQ8bhMnTjQ2m8384x//MOXl5SYnJ8f079/f+Pv7m5MnT7Zo33/5y1+Mv7+/mTt3rsOvtVZ7/bzCdURHR5tbbrmlwXUxMTHmu+++M8YY88UXXxg3Nzdz8803m+LiYmOMMVu3bjUPPfSQvf56fede7++piooKc+rUKbN8+XLj7e1t3n33XWOMMZ9++qmRZBYvXlxvGx8fHzN48GCHappq165dJj4+3hhjzIULF4wkk56ebl+/Y8cO4+npaTIyMkxhYaE5dOiQ6dWrl3nwwQfr7WvatGlGktm3b1+TcwC1mvr3LQCXtIYrcwA4Re3TR/z8/ByqLysr05IlS/Twww9r/PjxCgwMVJ8+ffT666/r4sWLWrlyZZ36uLg4BQQEqHPnzkpKSlJJSYlOnjypyspKzZkzR/fff79efPFFderUScHBwXriiSfUv39/SVJCQoJeeuklBQcHq1OnToqPj1d+fr4uXLjQYLby8nJlZmZq1KhRGj16tIKCgjRjxgx5enoqKyvLoVytfVxX09T3uzk8PDzsVxzccsstyszMVFFRUYvHbvjw4SosLNTMmTNbnBFoipKSEn333Xf2q0yuZuDAgXrhhRd04sQJvfjii/XWX6/v3Nb4ngoPD1dYWJhmz56tRYsWacyYMZJkfxqVu7t7vW08PT1VVlbmUE1TlJWVKSUlRZmZmY3WDBo0SGlpaUpOTlZAQIB69+6toqIirVq1ql5t9+7dJYmJ/wEA10QzB4BT1DZxSktLHarPyclRcXGx+vXrV2d5//795eXlZb89piFeXl6SpMrKSh04cECXL1/Wgw8+WKfG3d1dkyZNanB7T09PSWr0sbGHDx9WaWmpevfubV/m4+OjkJCQq06m+dNcbXlcV9CS97u5+vXrJ19fX5cfO/xy5eXlyRgjX19fh+rnz5+vHj16aMWKFdq1a1edddfrO7c1vqdyc3OVl5enDz74QO+884769u2rvLw8+3w6DU0gXFFRIR8fH4dqmmL69Ol6+umn7XP3NCQ9PV0rV67U9u3bVVxcrOPHjysuLk4DBw5Ubm5undra9+78+fNNygEA+OWhmQPAKW6++WZZrVZ9++23DtXXPq61oSt5goKCVFRU5NB+aq8ICgoKarRm8+bNuu+++9S5c2d5e3vrj3/841X3WVJSIkmaMWOGLBaL/ef77793uFnlzOO2R9fr/W4qb2/vRq+EAtq78vJyST+ex46wWq3KysqSxWLR448/XueqlOv1GWyN7ylPT0917txZQ4YM0YcffqicnBy9/PLL9jmuar/na5WWlqq8vFyhoaEO1Thq165dOnjwoJ588slGa86ePauFCxfq6aef1gMPPCCbzaaoqCi9+eabOnPmjBYvXlynvraZVPteAgDQGJo5AJzC29tbDz74oC5evKjPP/+80bpLly7pySeftDdfGvoPxOXLlxUWFubQcW+66SZJ0sWLFxtcf/LkSY0aNUohISHas2ePCgoKtHDhwqvus3bCy6VLl8oYU+dn9+7dDuVy1nHbq+v1fjdFZWVlq+0baAu1jYDGruZryMCBAzV58mQdOXLEPgm8dP0+g639PRUbGyt3d3fl5OQoKipK/v7+dZ4QJf34hC9JuvXWWx2qcdRbb72l7du3y83Nzd6kqn29CxYskMVi0erVq1VdXW3/u6dWQECAOnXqpJycnDrLKyoqJKnJVwgBAH55aOYAcJrZs2fL29tbkydPbnSegkOHDsnDw0O9e/eWn5+fvv766zrr9+zZo4qKCt15550OHfPmm29Wp06d9MknnzS4/uDBg6qsrNTvf/97RUdHy2q11nmcbEPCw8NltVq1f/9+hzK0p+O2V46+3x4eHo3eptZUO3bskDFGAwYMuO77BtpCly5dZLFYVFBQ0KTt5s2bp549e2rfvn32ZdfrO/d6fU/l5+dr7Nix9ZYfOXJE1dXVCg8Pl4eHh4YNG6adO3eqpqbGXrN161ZZLBbFx8c7VOOorKyseg2q2iv70tPTZYzRyJEjJf14hc5PFRUV6dKlS/ZHlNeqfe+6du3qcA4AwC8TzRwATnP77bfr/fff16FDh3Tvvfdqy5YtKigoUGVlpb777ju9+eabeuKJJ+Tp6Smr1aopU6Zow4YNeu+991RYWKiDBw/q2WefVWhoqCZOnOjQMb29vTV9+nTt3LlTycnJOn36tGpqalRUVKR//OMf9sfVbtu2TeXl5Tpy5Ei9uSE6deqkM2fO6MSJEyoqKpK7u7see+wxrV69WpmZmSosLFR1dbVOnTpV7x/wjXHWcdsrR9/v2NhYXbp0SRs3blRlZaUuXLhQ7zfuPx+32gZNTU2NfvjhB1VVVenAgQNKSUlRRESEJkyY0KJ9b926lUeTwyl8fX0VHR2tU6dONWm72tutfjop8PX6zrVardf8nkpKSlLXrl21d+/eRvdjs9n0ySef6O9//7sKCwtVWVmpffv26Xe/+51sNpsmT54sSZo5c6bOnz+vl156SSUlJdq9e7cWL16sCRMmqEePHg7XOJLJEVFRUbr//vv15ptvaufOnSorK1Nubq59/J544ok69bXvXZ8+fVp0XADAL0AbPz4LQBtzhUcdnzx50kydOtX06dPH+Pn5GXd3dxMUFGT69u1rnnjiCfP5558bY4ypqakxixcvNt27dzeenp4mODjYjBo1yhw+fNgYY8yKFSuMr6+vkWS6d+9ujh07ZlauXGkCAgKMJBMZGWm+/fZbY4wxr732munTp4+xWq3GarWavn37mhUrVhhjjElLSzOdOnUyQUFBJjEx0bz22mtGkomJiTEnT540e/fuNZGRkcbHx8fcc8895ty5c+bKlSsmLS3NREREGA8PD9O5c2czevRok5OT43Cu633c9kZNfFTqtd5vY4zJz883999/v7FarSYqKsr84Q9/MKmpqUaSiY2NbXTcJk6caDw9PU23bt2Mh4eHCQgIMCNHjjTHjh1r8b63bNli/P39zfz585s8Rq7weUX7lpycbDw9PU1paal92YYNG0xMTIyRZG688Ubz/PPPN7htampqnUeTX6/v3Gt9T40aNcpIMrNmzbrqa4uPjzdRUVHGz8/PeHt7m5iYGJOUlGQOHjxYp+7TTz81d911l/H29jahoaEmNTXVlJeXN6nG0Uw/19CjyS9evGhSUlJMbGys8fb2Nn5+fubuu+82f/7zn+ttP3z4cNOtWzdTU1PTpOMCP9XUv28BuKQ1FmOMafsWEoC2kpiYKElau3atk5Pgl85isSg7O1uPPPKIs6PomWee0dq1a5Wfn+/sKHXweUVLHT16VL169VJWVpbGjx/v7DgOqamp0X333acJEybo8ccfd3YcSc7JlJ+fr7CwMM2fP19Tpkxpk2OiY2pPf98CaDVruc0KAPCL1JRJYgFXERsbq7lz52ru3LkqLi52dpxrqq6u1saNG1VUVKSkpCRnx5HkvEyzZ8/W7bffruTk5DY7JgDAddHMAQAA6ECmTZumxMREJSUlNXky5La2Y8cOrV+/Xlu3bpWvr6+z40hyTqYlS5Zo//792rJlizw9PdvkmAAA10YzBwDwizJ9+nRlZWWpoKBAUVFRWrdunbMjAdfdggULlJycrFdeecXZUa5q8ODBev/99xUSEuLsKHZtnWnTpk26cuWKduzYoeDg4DY5JgDA9Xk4OwAAAG3p5Zdf1ssvv+zsGECrGzJkiIYMGeLsGLiGhx56SA899JCzYwAAXAxX5gAAAAAAALgQmjkAAAAAAAAuhGYOAAAAAACAC6GZAwAAAAAA4EKYABn4Bfjyyy+VmJjo7BiAli5dqrVr1zo7Rrv15ZdfShKfVwAAAFwVzRyggxs4cKCzIwCSpISEBGdHaPcGDBjg7Agdxvnz53Xo0CENHjzY2VEAoE0lJCQoPDzc2TEAtDKLMcY4OwQAAMD1tGbNGo0ZM0b8MwcAAHRAa5kzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIXQzAEAAAAAAHAhNHMAAAAAAABcCM0cAAAAAAAAF0IzBwAAAAAAwIV4ODsAAABAS5w5c0b/8i//osrKSvuy0tJSBQYGqk+fPnVq+/btq//6r/9q64gAAADXFc0cAADg0m666SZVVFQoJyen3rqCgoI6f05KSmqrWAAAAK2G26wAAIDL+9d//Vd5eFz9d1QWi0Vjx45to0QAAACth2YOAABweb/97W9VXV3d6HqLxaI777xTUVFRbZgKAACgddDMAQAALi88PFwDBgyQm1vD/7Rxd3fXv/7rv7ZxKgAAgNZBMwcAAHQIjz76qCwWS4Prampq9Mgjj7RxIgAAgNZBMwcAAHQIiYmJDS53d3fXfffdp65du7ZxIgAAgNZBMwcAAHQIN954owYPHix3d/d66x599FEnJAIAAGgdNHMAAECHMX78eBlj6ixzc3PTqFGjnJQIAADg+qOZAwAAOoyRI0fK09PT/mcPDw8NHz5cgYGBTkwFAABwfdHMAQAAHYa/v79GjBhhb+hUV1dr/PjxTk4FAABwfdHMAQAAHcq4ceNUVVUlSfLx8dGwYcOcnAgAAOD6opkDAAA6lKFDh8pms0mSEhIS5OPj4+REAAAA15eHswMAAFBr9+7dys3NdXYMdAD9+/fX//zP/yg8PFxr1qxxdhx0AHFxcQoLC3N2DAAAJEkW8/NHPgAA4CSJiYlat26ds2MAQD3Z2dl65JFHnB0DAABJWsttVgCAdiUhIUHGGH5a+CP9+J9PZ+dw1k91dbVefvnlq9YkJCRwvvHj0A8AAO0NzRwAANDhuLm5KTU11dkxAAAAWgXNHAAA0CF5eDA1IAAA6Jho5gAAAAAAALgQmjkAAAAAAAAuhGYOAAAAAACAC6GZAwAAAAAA4EJo5gAAOqQnn3xS/v7+slgs2r9/v7PjuKQtW7YoMDBQ//3f/+3sKO3Stm3bNG3aNK1fv17R0dGyWCyyWCx69NFH69UOGTJE/v7+cnd3169//Wvt3bvXCYmvbuHCherZs6d8fHxks9nUs2dPzZw5U4WFhXXqdu3apbvvvlu+vr4KDQ1VWlqarly50uSa5igvL1fPnj01Y8aMOss/+OAD9e/fX/7+/oqMjNRjjz2mc+fOSZI++ugjLVy4UNXV1S0+PgAA7QXNHABAh7Rq1Sq9+eabzo7h0owxzo7Qbr300kvKyMjQ9OnTNXr0aB0/flwxMTG64YYb9N5772nz5s116j/55BOtXbtWI0aMUE5Oju644w4nJW/cZ599pqeeekonT57U+fPnNW/ePC1cuFAJCQn2mpycHA0ZMkSDBw/WhQsXtGHDBv3pT3/Ss88+26Sa5kpPT9fhw4frLMvOzta4ceOUmJioU6dOadOmTdq5c6eGDh2qqqoqxcfHy2q1avDgwbp8+XKLMwAA0B7QzAEAoJ0pKytTXFycs2No+PDhKigo0IgRI5xy/PYyDj/36quv6sMPP9SaNWvk7+9fZ11GRobc3Nw0ceJEFRQUOClh83h5eem5555T586d5efnp8TERI0cOVJ/+9vfdPbsWUnSvHnzFBISojlz5shms2ngwIFKS0vT22+/rW+++cbhmub44osvdOjQoXrL33jjDd10001KTU1VYGCgbr/9dk2ePFn79+/Xnj17JEmTJk3SbbfdpmHDhqmqqqrZGQAAaC9o5gAAOiyLxeLsCM3y1ltvKS8vz9kxnK49jsPRo0c1c+ZMzZkzR1artd76uLg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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2qBvUCi-UWDv"
      },
      "source": [
        "#### 4.2.3 RNN model\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwGkOZrFUWDv"
      },
      "source": [
        "##### 4.2.3.1\n",
        "\n",
        "The last model we tried was an RNN. Redundant neural networks are still new to us, so it was really difficult to build this.\n",
        "\n",
        "In comparison to the other models, RNNs process input data using a Bidirectional layer (bi meaning 2 directions, forwards and backwards). This ensures that the model can use information from previous training and future training to change its weights in the back propagation process.\n",
        "\n",
        "Oscar was able to get this model working and we trust that this model will help us predict the best label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ej2XXh8RUWDv"
      },
      "outputs": [],
      "source": [
        "def build_rnn_model(tweets_np, max_vocab, max_tokens, embedding_dim):\n",
        "    \n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "\n",
        "\n",
        "    # create normalized vocab\n",
        "    norm = tf.keras.layers.TextVectorization(\n",
        "        max_tokens,\n",
        "        standardize=normalize_punctuation_and_links,\n",
        "        split='whitespace',\n",
        "        output_mode='int',\n",
        "        encoding='utf-8',\n",
        "        name='Normalization_Layer',\n",
        "        output_sequence_length=max_tokens,\n",
        "    )\n",
        "\n",
        "    norm.adapt(tweets_np , batch_size=64)\n",
        "\n",
        "    # Uses Sequential api with guidance from the tutorial https://www.tensorflow.org/text/tutorials/text_classification_rnn\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(tf.keras.Input(shape=(1,), dtype=tf.string, name='Input Layer'))\n",
        "    model.add(norm)\n",
        "    model.add(tf.keras.layers.Embedding(\n",
        "        input_dim=max_vocab,\n",
        "        output_dim=embedding_dim,\n",
        "        input_length=max_tokens,\n",
        "        name='Embeddings_Layer',\n",
        "    ))\n",
        "\n",
        "    # bidirectional layers\n",
        "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences= True), name='Bidirectional_Layer_1'))\n",
        "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True ), name='Bidirectional_Layer_2'))\n",
        "    model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64), name='Bidirectional_Layer_3'))\n",
        "    \n",
        "    # hidden sigmoid layers\n",
        "    model.add(tf.keras.layers.Dense(128, activation='sigmoid', name='hidden_layer_1'))\n",
        "    model.add(tf.keras.layers.Dense(64, activation='sigmoid', name='hidden_layer_2'))\n",
        "    \n",
        "    # output layers\n",
        "    model.add(tf.keras.layers.Dense(\n",
        "        units=1,\n",
        "        activation='sigmoid',\n",
        "        name='Output_Layer',\n",
        "    ))\n",
        "\n",
        "    \n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "X0-phMUAUWDv",
        "outputId": "4fe161e5-6cf8-43ca-d14e-46df727d7dd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Normalization_Layer (TextVe  (None, 300)              0         \n",
            " ctorization)                                                    \n",
            "                                                                 \n",
            " Embeddings_Layer (Embedding  (None, 300, 9)           270000    \n",
            " )                                                               \n",
            "                                                                 \n",
            " Bidirectional_Layer_1 (Bidi  (None, 300, 128)         37888     \n",
            " rectional)                                                      \n",
            "                                                                 \n",
            " Bidirectional_Layer_2 (Bidi  (None, 300, 128)         98816     \n",
            " rectional)                                                      \n",
            "                                                                 \n",
            " Bidirectional_Layer_3 (Bidi  (None, 128)              98816     \n",
            " rectional)                                                      \n",
            "                                                                 \n",
            " hidden_layer_1 (Dense)      (None, 128)               16512     \n",
            "                                                                 \n",
            " hidden_layer_2 (Dense)      (None, 64)                8256      \n",
            "                                                                 \n",
            " Output_Layer (Dense)        (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 530,353\n",
            "Trainable params: 530,353\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "rnn_model = build_rnn_model(X_train[:,3], 30000, 300, 9)\n",
        "display(rnn_model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(rnn_model, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "id": "PURsFGEFU4Ic",
        "outputId": "0c809f23-e251-48cf-9de1-695742e2c6c3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQcugso8UWDv"
      },
      "source": [
        "## 5. Error Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "gLW2wNlzUWDv"
      },
      "outputs": [],
      "source": [
        "def plot_history(history, epochs):\n",
        "    history = pd.DataFrame(history)\n",
        "\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Loss vs Epoch')\n",
        "\n",
        "    plt.plot(list(range(1, epochs + 1)), history['loss'], label=\"Train\")\n",
        "    plt.plot(list(range(1, epochs + 1)), history['val_loss'], label=\"Validation\")\n",
        "\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print('Loss:', history['loss'].iloc[-1])\n",
        "    print('Val Loss:', history['val_loss'].iloc[-1])\n",
        "\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy (in %)')\n",
        "    plt.title('Accuracy vs Epoch')\n",
        "\n",
        "    plt.plot(list(range(1, epochs + 1)), history['accuracy'] * 100, label=\"Train\")\n",
        "    plt.plot(list(range(1, epochs + 1)), history['val_accuracy'] * 100, label=\"Validation\")\n",
        "\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print('Accuracy:', history['accuracy'].iloc[-1])\n",
        "    print('Val Accuracy:', history['val_accuracy'].iloc[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "X5mQkhXFUWDx"
      },
      "outputs": [],
      "source": [
        "def train_and_analyze_model(model, tweet_features, labels, num_of_epochs=16, num_per_batch=16, validation=0.1):\n",
        "    # Remove randomness\n",
        "    tf.keras.backend.clear_session()\n",
        "    tf.keras.utils.set_random_seed(0)\n",
        "        \n",
        "    history = model.fit(\n",
        "        tweet_features[:,3],\n",
        "        labels,\n",
        "        epochs=num_of_epochs,\n",
        "        batch_size=num_per_batch,\n",
        "        validation_split=validation,\n",
        "        verbose=1,\n",
        "    )\n",
        "\n",
        "    plot_history(history.history, num_of_epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "CJ8wBEYNUWDx"
      },
      "outputs": [],
      "source": [
        "def plot_predictions(predictions, actual):\n",
        "    from matplotlib.ticker import PercentFormatter\n",
        "    plt.title('Prediction Distribution')\n",
        "    plt.xlabel('Positive Confidence Level (in %)')\n",
        "    plt.ylabel('# of predictions')\n",
        "    plt.gca().xaxis.set_major_formatter(PercentFormatter())\n",
        "\n",
        "    plt.hist([predictions[actual == 0] * 100, predictions[actual == 1] * 100], label=['negative', 'positive'])\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "97dZcTSRUWDx"
      },
      "outputs": [],
      "source": [
        "def plot_difference(predictions, actual):\n",
        "    plt.title('Prediction Difference Distribution')\n",
        "    plt.xlabel('Positive Confidence Level (in %)')\n",
        "    plt.ylabel('Difference')\n",
        "\n",
        "    diff = abs(predictions - actual)\n",
        "\n",
        "    plt.hist([diff[actual == 0] * 100, diff[actual == 1] * 100], label=['negative', 'positive'])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "LGvqRKPvUWDx"
      },
      "outputs": [],
      "source": [
        "def print_confusion_matrix(predictions, actual, threshold):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "\n",
        "    predictions[predictions >= threshold] = 1\n",
        "    predictions[predictions < threshold] = 0\n",
        "    tn, fp, fn, tp = confusion_matrix(actual, predictions).ravel()\n",
        "\n",
        "    print('True Positives:', tp)\n",
        "    print('True Negatives:', tn)\n",
        "    print('False Positives:', fp)\n",
        "    print('False Negatives:', fn)\n",
        "    print()\n",
        "\n",
        "    accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
        "    precision = tp / (tp + fp)\n",
        "    recall = tp / (tp + fn)\n",
        "\n",
        "    print('Accuracy:', accuracy)\n",
        "    print('Precision:', precision)\n",
        "    print('Recall:', recall)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08MxVSoiUWDx"
      },
      "source": [
        "### 5.1 Train and Anaylze the FFNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "13Bj_mYpUWDy",
        "outputId": "7d7b9d0e-a23d-4564-cad7-0a10ff06d189"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "97/97 [==============================] - 5s 29ms/step - loss: 0.6859 - accuracy: 0.5578 - val_loss: 0.6820 - val_accuracy: 0.5671\n",
            "Epoch 2/10\n",
            "97/97 [==============================] - 2s 24ms/step - loss: 0.6678 - accuracy: 0.5813 - val_loss: 0.6406 - val_accuracy: 0.7230\n",
            "Epoch 3/10\n",
            "97/97 [==============================] - 3s 33ms/step - loss: 0.5828 - accuracy: 0.7221 - val_loss: 0.5690 - val_accuracy: 0.7259\n",
            "Epoch 4/10\n",
            "97/97 [==============================] - 3s 35ms/step - loss: 0.5218 - accuracy: 0.7476 - val_loss: 0.5457 - val_accuracy: 0.7464\n",
            "Epoch 5/10\n",
            "97/97 [==============================] - 3s 27ms/step - loss: 0.4900 - accuracy: 0.7671 - val_loss: 0.5339 - val_accuracy: 0.7522\n",
            "Epoch 6/10\n",
            "97/97 [==============================] - 2s 23ms/step - loss: 0.4667 - accuracy: 0.7818 - val_loss: 0.5323 - val_accuracy: 0.7638\n",
            "Epoch 7/10\n",
            "97/97 [==============================] - 2s 25ms/step - loss: 0.4542 - accuracy: 0.7888 - val_loss: 0.5337 - val_accuracy: 0.7624\n",
            "Epoch 8/10\n",
            "97/97 [==============================] - 3s 28ms/step - loss: 0.4454 - accuracy: 0.7946 - val_loss: 0.5291 - val_accuracy: 0.7668\n",
            "Epoch 9/10\n",
            "97/97 [==============================] - 2s 18ms/step - loss: 0.4382 - accuracy: 0.7946 - val_loss: 0.5330 - val_accuracy: 0.7653\n",
            "Epoch 10/10\n",
            "97/97 [==============================] - 2s 19ms/step - loss: 0.4339 - accuracy: 0.7998 - val_loss: 0.5438 - val_accuracy: 0.7536\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Nyr2n+GDTMY6n5fHu90eZszmO0Z3rMaZLfcICrG58ByIi4mliYhry7bdr6NWrD2AwY8Z/cDi8pyenvDTQpArZGg8kp/0EAIJXP4rP8e/OK+NjNjG8fTSLJlzBi0Na0SwigGybndk/Hmf49E28se4gSVn5VV11ERHxUI888hjBwSH84Q938/TTj9G9+1W0bBnr7mp5PMPpTRf8yig5OZMK/3ScDoL/+zB+B77AaQkg7aZFFNbpVGJxh9PJdwdSmPXjMX5OzALAx2wwvF00469oQL1QvwquYOkYBkREBFfOZyRlpvbwLGoP9ysosJGSkkB4eF18fKxYLCYKq3jsj5Tsctrj9217rrPnXmmoB6iqGSYyB76BLaYPRmEOoV+Ox5x2qMTiJsPgmhYRfDiuM2+PbEfn+iEU2J0s2ZHAzTM3MXnVrxw5nVOFb0BERKT6UwByB7MvGYOnUxDZAVPeaUK/GIsp++K3KxqGwVWNa/P+bZ1479YOXNkoDLsTlu9JZPTsLTy7bC/7TmVV0RsQERGp3hSA3MRpDSL9ho8oDG2MOTOO0GW3Y+Snl2rbLjG1+Nct7flgXGf6NgvHCazel8y4OT/x2NLd7KriSb5ERESqGwUgN3IGRJA+fB72gCgsKb8QsvxuKMwt9fZto4P5501tmT++K9fFRmIA6w+d5u7523nwk51sPZ7mVXM6iIiIlJYCkJs5QhqSPuxjHNZgrAk/EvLfh8Fx8cmrfq95ZCAv3dCaTyZ0Y1jbOphNBpuPpfGHRTu5Z8EOvj98WkFIRETkHApAHsAe0YaMIbNwmn3xPfwVQd/+H+W5daRR7QD+dn0sSydewS0d62I1G+w8kcGjn+5m/MfbWLNfK9CLiIiAApDHKKh/FRnX/gunYcJ/7zwCNv2z3PuqG+LH0wNb8Pk93RnXNQY/i4lfTmXx9Bd7ue3Draz8OZFCTZIlIiJeTAHIg9iaDSGr798BCNzyFn47Z1/W/iKCfHn0mqYsu7cHd1/ZkCBfM4dTcvjbil+5ZdZmlu5MwKZ5MURExAspAHmYvLa3k939SQCC/vc3fPcvu+x91grw4YFejVl2bw8e7N2YWv4+xKfn8fev9zNi5iYW/BRPXoH9so8jIiJSXSgAeaCcbn8it/2dGDgJXv1HfI7/r0L2G+RrYUKPhnxxb3ceu6YpkUFWTmXZeG3tQW6csYkPNx0nK79sA7BFRMSzPPzwfbz11muun2+5ZRiLFs276Da9e3fju+/WXfaxK2o/VUEByBMZBlm9nyev2Q0YjgJCVt6D5dTOCtu9v4+ZsV1jWDqxO88MbE69EF9O5xQw7X+HuXHGJt7/4QjpuQUVdjwRESmdSZMe4/HHH7ngazt2bKN3724cOLC/TPucPv0jhg+/uSKq5zJz5nvcddfY857//PNVXHllzwo9VmVRAPJUJjOZ176FrX4vTAXZhH55x0WXzCgPX4uJkR3rseTuK3ju+pY0CvMnI6+Q6RuOMXz6Jv713SFSsm0VekwRESnZDTfcyJYtP3LqVOJ5ry1f/gWtWrWhefMWZdpnWFgYfn5Vs25keHgEVqv10gU9gAKQJzP7kjFkBgUR7TDlphC67HZM2eefFJfLYjZxQ9toFt7Vjb/f0JoWkYHkFNj5aHMcN87YxD/XHOBkRl6FH1dERIrr2bM3tWqFsWJF8fGfOTk5rF37DX369OW55/6Pm24azIABvRg//la+/nrVRff5+0tgx48f46GH7qV//57cfvsoNm/eeN42//7329x2280MGNCLUaNuZPr0/1BYWDREYsWKZcyePZ0DB/bRu3c3evfu5qrv7y+BHTx4gD/+8Q/079+LIUMG8PLLL5GT89v6lS+9NJlnn32CefPmcOONgxgyZACvvfay61iVyVLpR5DL4rQGkz5sDmFLbsKccZTQZXeQNmIxTt+QCj+W2WRwbWwkA1tGsP7QaWb9eIzdCZks3HaCJTsSGNq2Dnd1b0BMLf8KP7aISJVwOss0436FsPgXLVNemqIWC9dfP4SVK7/kzjsnYpzZbu3a1TgcdgYNGsKaNau5/fY7CQgIZMOG9bz44nPUrx9DmzbtLrl/h8PBn//8FGFh4bz33gdkZ2fx9tuvnVcuICCAP//5OSIiIjl48ACvvPISAQEBjBt3JwMGXMuhQwf58ccfePPNfwMQFBR03j5yc3N5/PGHadeuPTNmfEhqair/+MeLvPHGKzz33POucj/9tIXw8Ajefvs94uKO89xzz9KiRUuGDx9Rqs+svBSAqgFnQCRpw+cStmQElpS9hKy4m/RhH4Olcro0DcOgT7NwejetzeZjacz68Rhbj6fz+a6TLNt9kutaRTGhRwMiIoIr5fgiIpXC6aTWpyPwObmlSg9bUPcK0kZ8WuoQNHTojcybN4dt27bSpUs3oKjX5Zpr+hMdXZexY+9wlb3lltvYtGkja9asLlUA2rJlE0ePHuH116cREREJwH33PcSTT/6xWLm77rrH9fe6detx7NhRvvnmv4wbdye+vn74+/tjNlsID48o8Vhff70Km83GX/7yPP7+Rb84P/74Uzz99OM8/PCfCA0NAyA4OITHHpuE2WymUaPGXHVVb7Zu3aQAJEUcoY1JHzaH0KW3YD2xkZCvHyFj0LtgMlfaMQ3DoHujMLo3CmNHfDqzfjzGD4dTWfXzKVb9fIq/3tCGG1uV/I9fRMTjlDKEuFOjRo1p374Dy5d/QZcu3YiLO86OHduYOPFd7HY7c+bMZs2ar0lKSqKwsACbzYavb+l+IT5y5DBRUdGu8APQrl2H88p9881/Wbx4AfHx8eTm5mC32wkICCzT+zh69DDNm7dwhR+A9u074XA4OHbsCO3bFwWgJk2aYjb/9n9ZeHgEhw4dKNOxykMBqBopjGxHxpCZhC67A99DKwn67s9k9Z1aJSd0x/qhvHVze35OzGTmhmN8ezCFN77ex4AmtQi06p+RiFQDhlHUE+PBl8DOGjr0Rt5881WeeOJpli//gvr1Y+jcuSsff/whn3wynz/+8QmaNm2Ov78/b7/9GoWFFXfn7u7dO3n++b9y99330aPHVQQGBvHNN/9lwYKPK+wY57JYiv8fYhgGDkflT9KrQdDVTEFMLzKufRsnBv57PiZg8+tVevzWdYJ55cY2NK7tT1Z+IV/sPlmlxxcRuSyGAT4BVfsoxy+p/ftfi2GY+O9/V/HVVysYOnQ4hmGwa9cOevfuy6BBQ2jRoiX16tXn2LFjpd5v48ZNOHXqJMnJya7n9uzZVazMrl07qVMnmjvvnEirVm1o0KAhJ08mFCvj4+ODw3HxCXQbNWrCgQP7yc39LXDu2rUdk8lEw4aNS13nyqIAVA3Zmt/w25IZm9/Ab/dHVXp8k2Ewpmt9ABZsjceudcVERCpUQEAAAwZcy3vvvUNKSjJDhgwDoEGDBmze/CO7du3gyJHDvPrq30lNTSn1frt1606DBo146aXn2L9/Hzt2bOP99/9drEyDBg1ITDzJ6tVfER8fxyefLDhvcsPo6HokJJxg//5fSUtLw2Y7f8qU664bjNVq5aWXnuPQoQP89NMW3njjVQYNGkJ4eHjZP5QKpgBUTeW1u4PsKx4DIOjbP2M98GWVHn9omzqEBfhwIiOfbw8kX3oDEREpkxtuuJHMzAy6d7/SNWbnzjsnEhvbiscff4RHHrmf2rXD6dPnmlLv02Qy8fe/v0p+fj733Xcn//jHC9x334PFyvTu3Zdbbx3LG2+8wl13jWX37h3cddfEYmWuuaY/PXpcxSOP/IEbbhjI6tVfnXcsPz8/Xn99GhkZGdxzz5385S9P07Vrdx57bFLZP4xKYDidTv36XoLk5Ew8+tNxOgn69v/w3zMHp8lK+rA5FMT0qpJDGwZ8sPUE09YeoGO9EGaM6VQlx5ULMwyIiAj2/H+zXkLt4X4FBTZSUhIID6+Lj48Vi8VEoRZ/9hiX0x6/b9tznT33SkM9QNWZYZB19YvkNxuC4bARsmIilqTdVXb48Vc1wmIy2HEigz0nM6vsuCIiIpdLAai6M5nJGPg2tvpXYSrIInTZHZjSj1TJoaNC/LiuVVG37PytcVVyTBERkYqgAFQTWPzIGDyTwvA2mHKTqPXFOIycpCo59NiuMQCs3pes5TJERKTaUACqIZy+IaQN+xh7SEPXkhmGrfIvS7WqE0TXBqHYHU4+2X6i0o8nIiJSERSAahBnYBRpw+bi8I/AJ3k3ISsmgj2/0o87pktRL9DSnSfJsV18XggRkaqk+3xqnopqUwWgGsZRqwnpw+bg8AnEGv8DIV//ES4xWdXl6tOsNg1q+ZGZX8iXeyp+tXoRkbI6u7SCzVb5vwRK1Trbpmbz5a1CoDUMaqDCyPZkDJ5J6Jfj8T24nKD//Y2sq1+stCUzTIbBbV3q8+qagyz4KY5bOtXFVA3W2xGRmstkMuPvH0RWVioAAQF+2NVB7TEcDgO7vWw9OU6nE5stn6ysVPz9gzCZLq8PRwGohipo0JvMgW8R/N8H8d/9IY6ACHLOTJxYGW5oG8273x/leFoe/zt4mr7N3T/Lp4h4t5CQ2gBkZaWSk2OqkvWlpHRMpvK3h79/kKttL4cCUA2W32IYRl4Kwd/9hcBNr+HwjySv3e2VcqwAq5kRHaL5aHMc83+KUwASEbczDIPQ0HBCQsIICfElNTVbE1N6AMOAsLDAcrWH2Wy57J6fsxSAari89ndhykkicMtbBH33fzj8a2NrNqRSjjWqUz3mbolj6/F0fj2VRWxUUKUcR0SkLEwmE35+fvj4FCgAeQDDwCPaQ4OgvUBO9yfJbTMOw+kg5L8P4xP/Q6UcJzrEjwEtNTGiiIh4PgUgb2AYZPX9O/lNr3ctmWFO2lMphxp7ZpX4r35JIjlLd1+IiIhnUgDyFiYzGddOw1avByZbJqFf3oEp/WiFH6Zt3RA61guhUBMjioiIB1MA8iYWPzKGzKIwvDXmnFOELhuHkZNc4Yc52wu0ZEcCeQW671RERDyP2wPQ3Llz6d+/P+3bt2fUqFHs3LnzouUzMjKYMmUKvXv3pl27dgwaNIhvv/3W9fq//vUvYmNjiz2uv/76yn4b1YbTN5T0YR9jD26AJf0IoV/egWHLqtBj9G0eQb0QX9LzClnx86kK3beIiEhFcGsAWrFiBVOnTuWhhx5i6dKltGrViokTJ5KSknLB8jabjQkTJhAfH89bb73FqlWreOGFF6hTp06xci1atGD9+vWux7x586ri7VQbjsA6pA+fi8M/HJ+kXYSsvKdCl8wwmwxu7VLUC7Rga7ymohcREY/j1gA0e/ZsRo8ezciRI2nevDlTpkzBz8+PJUuWXLD8kiVLSE9P55133qFr167ExMTQvXt3WrVqVayc2WwmMjLS9ahd+/InTKpp7LWakn7DR0VLZsStJ3j1o+CsuEnChreLJtBq5vDpHDYcSa2w/YqIiFQEtwUgm83Gnj176Nmz52+VMZno2bMn27Ztu+A2a9asoVOnTjz//PP07NmTG264gXfffRf77+Y3P3r0KL1792bAgAE88cQTnDihwbgXUhjVkYzB03GafPA7sIyg//2NipqUIcjXwo3towGYp1viRUTEw7htIsTU1FTsdjvh4cVnDA4PD+fQoUMX3Ob48eNs3LiRYcOG8f7773Ps2DGmTJlCYWEhDz/8MAAdOnRg6tSpNGnShKSkJN555x3GjRvHsmXLCAoq28R83rCcVWHDq8kc+CbB/30Y/10f4AiMIrfbHy+53dnP5mKf0W1d6rPgp3h+PJrGweRsmkcGVlCt5fdK0x5SddQenkXt4Vkqsz3Kss9qNRO00+kkPDycF154AbPZTLt27UhMTGTmzJmuANS3b19X+VatWtGxY0f69evHypUrGTVqVJmOFx4eXKH191gRt4M5G1ZOInDjKwRG1oeud5Vq04t9RhERwQxqG83K3SdZuucUL9/SoYIqLCXxmn+z1YTaw7OoPTyLu9vDbQEoLCwMs9l83oDnlJQUIiIiLrhNZGQkFosFs9nseq5p06YkJSVhs9mwWq3nbRMSEkLjxo05duxYmeuYkpLpPdOmNxtLQNfjBGz9F84vHyPTHoitacl3zxlG0T/eS31GI9vXKQpA2+KYeEV9agee30Zy+UrbHlI11B6eRe3hWSqzPc7uuzTcNgbIarXStm1bNmzY4HrO4XCwYcMGOnfufMFtunTpwrFjx4qtIHvkyBEiIyMvGH4AsrOzOX78OJGRkWWuo9PpXY/sHpPIbX0bhtNB8FcPYYnfeNHypfmMOtQNoW10MDa7k8U7Etz+Hmvywxv/zXryQ+3hWQ+1h2c9KrM9Ssutd4FNmDCBRYsWsXTpUg4ePMjkyZPJzc3l5ptvBmDSpEm89tprrvJjxowhLS2Nl156icOHD7Nu3Tree+89xo0b5yrz8ssvs2nTJuLi4vjpp594+OGHMZlM3HDDDVX+/qodwyDrmn+Q32QQhj2fkOV3Y07ee5m7NFwTIy7efoL8woq700xERKS83DoGaMiQIZw+fZq3336bpKQkWrduzYwZM1yXwBISEoote1+3bl1mzpzJ1KlTGT58OHXq1GH8+PHce++9rjInT57k8ccfJy0tjdq1a9O1a1cWLVqkW+FLy2Qh47pp1PpiHD4JmwhddgdpI5fiCGlY7l32bxFBVJCVU1k2vvrlFMPbRVdghUVERMrOcGqWuhIlJ3vv9WIjL41aS0diOf0rhaFNSBv5GU7/3+7YM4yiQc6l/Yw+2nScf/3vMM0jApk3vguGbseoUGVtD6lcag/PovbwLJXZHmf3XRpuXwpDPJPTrxbpw+diD47Bkn6Y0C/HX9aSGTd1iMbPYuJAcjabj6VVXEVFRETKQQFISuQIjCZ92FwcfrXxObWDkFX3gd1Wrn2F+Pm4Ln3N2xpfkdUUEREpMwUguSh7WDPSb/gQpyUA6/HvCP7mMcq7ZMZtXepjAN8fPs2RlJyKraiIiEgZKADJJRXW6Uz64Ok4TRb89n9O4PrJZbvX8IwGYf70aVY0jmjBNvUCiYiI+ygASakUNOxL5oA3AAjYOQv/re+Uaz9nb4n/ck8iabkFFVY/ERGRslAAklLLbzmCrN6TAQjc+A/YsaDM++gSE0rLyEDyCx0s3ZlQwTUUEREpHQUgKZPcjveQ0+XBoh9WPYthyyzT9oZhMK5bDACLtp2gwK6JEUVEpOopAEmZZfeYRGGtppB7Gr/t08u8/bWxkUQEWknOtvH1r0mVUEMREZGLUwCSsjNZyOnxFAD+297HyD1dps19zCZGdaoHwPyt8WguThERqWoKQFIutuZDIbo9poIsAn4q+4DomzvUxddi4pdTWWyLT6+EGoqIiJRMAUjKxzDBgOcA8N/1AaasE2XavFaAD0Pb1AGKeoFERESqkgKQlF/zgRTU7Y5hzydgy9tl3nxMl6Jb4r89kMLx1NyKrp2IiEiJFICk/AyD7KueBsDv5wWY0g6XafPG4QH0bBKGE1ioiRFFRKQKKQDJZSms14P8hv0wHIUEbnqtzNuP7VJ0S/wXu0+SmVdY0dUTERG5IAUguWw5V04CwHf/55iT95Zp2+6NatEsIoDcAgef7dLEiCIiUjUUgOSyFUa2J6/ZDRg4Cfzx1TJtaxiGqxdo4bYTFDp0S7yIiFQ+BSCpEDk9nsJpmPA98jWWk1vLtO2g1lHUDvAhMTOfNfs0MaKIiFQ+BSCpEPawZuS1GgWcWSesDJMb+lpMjOxYF4D5P2kwtIiIVD4FIKkwOVc8jtNkxRq/AZ+49WXadmTHeviYDXYnZLLzREYl1VBERKSIApBUGEdwfXLb3QGUvRcoPNDK4NZRAMzfGlcp9RMRETlLAUgqVE7Xh3FaAvA5tQPr4VVl2nbMmcHQa/YncyI9rzKqJyIiAigASQVzBkSS0/EeAAI3vgoOe6m3bR4ZSPeGtXA4NTGiiIhULgUgqXC5ne/H4RuKJXUfvvuWlmnbsV2LeoE+33WSbJsmRhQRkcqhACQVzukbSk6XBwGKZoe220q97VVNwmgU5k+2zc4XuxMrq4oiIuLlFICkUuS2vxt7QBTmzOP47Z1f6u1MhsHYrkWLpC74KR67JkYUEZFKoAAklcPHn5xufwIgYMtbUJBT6k2HtKlDqJ+FE+l5fHswpbJqKCIiXkwBSCpNXpsx2IMbYM45hf+u2aXezs/HzM1nJ0bULfEiIlIJFICk8pitZHd/AoCAn/6NkZ9e6k1HdaqHxWSwPT6DvSczK6uGIiLipRSApFLltxxBYVhLTPnp+G97r9TbRQb5cl2rSADmqRdIREQqmAKQVC6TmewrnwIgYMcMjJzSL3Z6dpX41fuSSczMr5TqiYiId1IAkkpna3I9BVEdMQpzCNg6rdTbxdYJoktMKHaHk0XbTlRiDUVExNsoAEnlMwyyr3waAP/dczBllP6S1tlb4j/blUBuQelnlRYREbkYBSCpEgUxfbDV74nhsBGw5Y1Sb9e7aTgxtfzIyCvkyz2aGFFERCqGApBUjXN6gfx++QRz6sFSbWY2GYzp8tvEiI4yrDAvIiJSEgUgqTKF0V3Jb3wthtNBwI+vlnq7G9pGE+xr4VhqLusPna7EGoqIiLdQAJIqld3jKZwY+B38EkvSrlJtE2A1c1P7aEATI4qISMVQAJIqZY9oQ36LGwEI2PhKqbcb3bkeZgO2HE/n11NZlVU9ERHxEgpAUuWyuz+B02TB99hafE78WKptokP86N+yaGLE+T/FV2b1RETECygASZVz1GpCXuvbAAjc+DKUcmDzuDO3xH/18ymSszQxooiIlJ8CkLhFTrc/4TT74pOwCeuxtaXapm3dEDrUC6HQ4eSTHQmVXEMREanJFIDELRxBdcltfxdwZiyQ01Gq7c5OjPjpjgTyNDGiiIiUkwKQuE1Ol4dw+AThk7wb3wPLS7VN3+YR1A3xJS23gJU/n6rkGoqISE2lACRu4/SvTW6n+wAI2PQqOAovuY3FZHDbmYkR5/8Uj1MTI4qISDkoAIlb5Xa6F4dfGJa0Q/j9srhU2wxvF02g1czhlBw2Hk2t5BqKiEhNpAAkbuW0BpPT9REAAja/DoV5l9wmyNfC8HZFEyPO26Jb4kVEpOwUgMTtctvdgT0wGnPWCfz3fFyqbW7tUg+TARuPpnIwObuSaygiIjWNApC4n8WfnCseBSBg67/AdulAUz/Un77NIwBNjCgiImWnACQeIa/VrRSGNsaUm0LAzhml2ubsxIgr9yaSmmOrzOqJiEgNowAknsHsQ073JwHw3/YuRt6lBzd3qBdCm+hgbHYnSzQxooiIlIECkHiM/BbDKQxvhcmWScC2/1yyvGEYjD1zS/wn209gKyzdZIoiIiIKQOI5DBPZPZ4GwH/nLEzZiZfcZEDLCKKCrJzOKeCrXzQxooiIlI4CkHgUW+OBFER3xSjMI2DL25csbzGbuLWzJkYUEZGyUQASz2IYZF9Z1Avkt3cupvSjl9zkpg7R+FlM7E/KZsvxtEquoIiI1AQKQOJxCur3xNbgagxHIYGbX79k+RA/H4adnRhxq26JFxGRS1MAEo90thfI99dPMaf8esnyt3WpjwGsP3SaI6dzKrl2IiJS3SkAiUcqjOpIftPBGDgJ3PTqJcs3DPOnd9PaACzQxIgiInIJbg9Ac+fOpX///rRv355Ro0axc+fOi5bPyMhgypQp9O7dm3bt2jFo0CC+/fbby9qneKbsHk/hNEz4HlqFJXH7JcuP6xYDwPI9iaTnFlRy7UREpDpzawBasWIFU6dO5aGHHmLp0qW0atWKiRMnkpKScsHyNpuNCRMmEB8fz1tvvcWqVat44YUXqFOnTrn3KZ7LXrsl+bEjAQjc+PIly3eJCaVlZCB5hQ6W7tTEiCIiUjK3BqDZs2czevRoRo4cSfPmzZkyZQp+fn4sWbLkguWXLFlCeno677zzDl27diUmJobu3bvTqlWrcu9TPFv2FY/hNPlgjfsfPnHfX7SsYRiM7VrUC7Ro+wkK7JoYUURELsxtAchms7Fnzx569uz5W2VMJnr27Mm2bdsuuM2aNWvo1KkTzz//PD179uSGG27g3XffxW63l3ufF2MYelzsURWfkTO0IXltxwJFvUAGzouWH9Q6kvBAK0lZNr7Zl+T2z6imtYceao/q+lB7eNajMtujtCxlTgUVJDU1FbvdTnh4eLHnw8PDOXTo0AW3OX78OBs3bmTYsGG8//77HDt2jClTplBYWMjDDz9crn1eTHh4cJm38TZV8hld92f4eRE+iT8RkbIeWg25aPG7ejbmta/3sWjHSW7v0wyjLGdENad/s55F7eFZ1B6exd3t4bYAVB5Op5Pw8HBeeOEFzGYz7dq1IzExkZkzZ/Lwww9X+PFSUjLRxMIXZhhF/3ir5jMKIKDD3QT89A6F/51MWngvMEruvBzcIpxpa03sik9n9Y54OseEVnYF3a5q20MuRe3hWdQenqUy2+PsvkvDbQEoLCwMs9l83uDklJQUIiIiLrhNZGQkFosFs9nseq5p06YkJSVhs9nKtc+LcTrRyXIJVfUZ5XR+AL/dc7Cc/hXrvs/JbzmixLKh/j4MaRPF0p0nmbsljk71a34AOkv/Zj2L2sOzqD08i7vbw21jgKxWK23btmXDhg2u5xwOBxs2bKBz584X3KZLly4cO3YMh+O3wa1HjhwhMjISq9Varn1K9eD0q0Vu5wcACPzxn2C/+G3uY7oUDYb+9kAKcWm5lV4/ERGpXtx6F9iECRNYtGgRS5cu5eDBg0yePJnc3FxuvvlmACZNmsRrr73mKj9mzBjS0tJ46aWXOHz4MOvWreO9995j3Lhxpd6nVF85He7G4R+BOeMofj8vvGjZJuEBXNU4DCeaGFFERM7n1jFAQ4YM4fTp07z99tskJSXRunVrZsyY4bpclZCQgMn0W0arW7cuM2fOZOrUqQwfPpw6deowfvx47r333lLvU6oxayA5XR8haP1zBGx5g7xWI8HiX2LxcV1j2HAklWW7E/lDr8YE+VarIW8iIlKJDKdTV0RLkpysAXMlMQyIiAiu+s/Ink/tj6/GnBVPVs+/kNv5DyUWdTqdjPloKweTc/hT36bcfmam6JrIbe0hF6T28CxqD89Sme1xdt+l4falMETKxOxLdvfHAQjYOg3DllliUcMwGNOlPgALf4qn0KFvPhERKaIAJNVOfuxICms1w5Sfhv/29y9a9vrWdQjz9+FkZj5r9ydXUQ1FRMTTKQBJ9WOykN3jKQD8t7+PkVvyOm++FhMjO9YFYP7WuCqpnoiIeD4FIKmWbM2GUBDZHlNBNgFb37lo2Vs61cPHbLArIZNdJzKqqIYiIuLJFICkejJMZPeYBID/7g8xZZ0osWh4oJXrW0UBMG+rbokXEREFIKnGChpeg61uDwx7PgGb37xo2bOrxK/Zn0RCRl4V1E5ERDyZApBUX4ZB9pVPA+D380LMaSUveNs8MpArGtbC4YSFP5XcWyQiIt5BAUiqtcJ63clv1B/DaSdg02sXLTvuTC/QZ7sSyLYVVkX1RETEQykASbWXc2YskN/+zzEn7y2x3FVNwmgU5k+2zc6y3YlVVT0REfFACkBS7RVGtiOv+XAAAn98pcRyJsNgTNeiiREX/BSPXRMjioh4LQUgqRFyejyJ0zDje2Q1loQtJZYb2qYOoX4W4tPz+O5gyfMHiYhIzaYAJDWCvVZT8lqNAiBw4z8oaYEZPx8zIzpoYkQREW+nACQ1Rs4Vj+M0WbGe2IjP8e9KLDe6cz0sJoNt8Rn8nFjyWmIiIlJzKQBJjeEIrkdu+/EABG58ucReoMggX66NjQQ0MaKIiLdSAJIaJafLwzgtAfgk7cR6aGWJ5caeGQz99a9JnMrMr6rqiYiIh1AAkhrFGRBBTqd7AQj88VVw2C9YrlWdYDrHhGJ3OFm0XRMjioh4GwUgqXFyO92PwzcUS+p+fPd9WmK5cWd6gRb+FK/lMUREvIwCkNQ4Tt8Qcro8BEDgptfAfuFLXFc3C6dz/RDyCh28tuZgVVZRRETcTAFIaqTc9hOwB9TBnBmH3555FyxjGAZPD2yB2WTw7cEUvj2QXMW1FBERd1EAkprJx5+cK/4EQOCWt6Eg54LFmkUEcnu3ojXC/rnmILkFFx4zJCIiNYsCkNRYea1vwx7SEFNuEv47Z5VY7p4rG1I3xJeTmfnM2HC0CmsoIiLuogAkNZfZSnb3JwAI2PYfjLy0Cxbz8zHzVP/mAMzdGs+B5OyqqqGIiLiJApDUaPktbqKwdiym/HT8t79XYrk+zcK5pnk4doeTl1fvx1HCJIoiIlIzKABJzWYyk93jKQACdszAyEkqsegT/Zrh72Nie3wGX+5OrKoaioiIGygASY1nazKIgqhOGIW5BGx5u8Ry0SF+3NezMQBvf3eItJyCKqqhiIhUNQUgqfkMg+wrnwHAf8/HmDJKXgX+ts71aB4RSHpeIf/636GqqqGIiFQxBSDxCgUNemOr3wvDUUDA5jdKLGcxm3hmYNGA6C92J7I9Lr2qqigiIlVIAUi8RvaVkwDw+/UTzKkHSizXsX4oN7WPBmDq6v0U2h1VUj8REak6CkDiNQqju5LfZBCG01G0UOpFPNynCbX8fTiUksO8rfFVVEMREakqCkDiVbJ7PIkTA9+Dy7Gc2lliuVB/H/7UtwkA0zcc1WKpIiI1jAKQeBV7eGvyW94EQOCPr1y07NA2degcE0peoYN/arFUEZEaRQFIvE529ydwmixYj63D58TGEssZhsEzA5tjNhl8p8VSRURqFAUg8TqO0MbktR4DQODGl+Eisz43DQ/kjjOLpb665iA5Ni2WKiJSEygAiVfK6fZHnGZffBI2Yz265qJlJ17ZkHohviRqsVQRkRpDAUi8kiOoLrkdJgAQvPqP+O35GJwXvt3dz8fMUwOK5gaatzWOA0laLFVEpLorVwBKSEjg5MmTrp937tzJSy+9xMKFCyusYiKVLafLwxREdcSUn07wumeo9ekIzCk/X7Bs76ZnFkt1wj+0WKqISLVXrgD0xBNPsHFj0eDRpKQkJkyYwK5du3jjjTeYNm1ahVZQpLI4/WqRNvJzsnpPxuETiM/JrYQtvJ7AH16Egpzzyp9dLHXHiQyW7T55gT2KiEh1Ua4AtH//fjp06ADAypUradGiBQsWLOCf//wnS5curdAKilQqk4XcjveQOnYt+U0HYzjtBGx7l9rz+2M9srpY0egQP+4/s1jqv747rMVSRUSqsXIFoMLCQqxWKwA//PAD/fv3B6Bp06YkJSVVXO1EqogjqB4Zg6eTPvQD7EH1MWfGEbr8LkJW3osp64Sr3K1d6tMismix1Le/02KpIiLVVbkCUPPmzVmwYAFbtmzhhx9+4Oqrrwbg1KlT1KpVqyLrJ1KlbI0HcnrsWnI6P4DTMON7aCVh8/rhv2MGOAqxmAyeGdgCgGV7EtmmxVJFRKqlcgWgJ598koULF3LHHXcwdOhQWrVqBcCaNWtcl8ZEqi2fALJ7/pnUW1dREN0VU0E2QesnU2vxMCyJ2+lQL8S1WOo/Vu+nQIuliohUO4bTWb7bWex2O1lZWYSGhrqei4uLw9/fn/Dw8AqroDslJ2debI48r2YYEBERXPM/I6cDv73zCNwwFVN+Ok4M8trfSULHx7h57j7Scgt4uE8T7uzewK3V9Jr2qCbUHp5F7eFZKrM9zu67NMrVA5SXl4fNZnOFn/j4eD744AMOHz5cY8KPCACGiby2t3N67DryWo7AwIn/rg9o9Ol1vNH6IOBk+oajnEjXYqkiItVJuQLQgw8+yGeffQZARkYGo0ePZvbs2Tz00EPMmzevIusn4hGcAZFkXvsv0obPpzC0CeacRPrtfYYlwW8QZU/g1TUHKGdnqoiIuEG5AtCePXvo1q0bAF999RXh4eGsXbuWl19+mTlz5lRoBUU8SUGDPqTe9jXZVzyG02Sla8EWvrZOosPR2Xy3T3MDiYhUF+W+BBYYGAjA+vXrue666zCZTHTq1IkTJ05cYmuRas7iR073J0i97Wts9XviZxQwyWchnVffjP1YyavLi4iI5yhXAGrYsCGrV68mISGB9evX06tXLwBSUlIICgqq0AqKeCp7WDPSb1xIyjVvkEoIzThO9LJbCFrzJEZeqrurJyIiF1GuAPTQQw/xyiuv0L9/fzp06EDnzp0B+P7772ndunWFVlDEoxkGjraj+GHAcuYX9gPA/+cF1J7bF99fFqNbTkREPFO5b4NPSkoiKSmJVq1aYTIV5aidO3cSGBhIs2bNKrSS7qJbJkum20rPN+mLvWQc+J7XAj6gsf0oALb6V5HVdyr2sOaVemy1h2dRe3gWtYdnqda3wQNERkbSpk0bTp065VoZvkOHDjUm/IiU1RP9mvGzpQ0Ds19gU+OHcVr8sMZvIGzBdQT8+CoU6lZ5ERFPUa4A5HA4mDZtGl27dqVfv37069ePbt268c477+BwaFZc8U51gn25r2cjCrFw75GrOXzTV+Q36o/hsBG45S3CFgzE5/h37q6miIgAlvJs9MYbb7B48WKeeOIJunTpAsDWrVuZNm0aNpuNxx57rEIrKVJd3NqlPsv3JrI/KZvXtxXy3NAPsR5cTtD657CkH6HWF2PJa3EjWb2ewxkY5e7qioh4rXL1AC1dupQXX3yRsWPH0qpVK1q1asW4ceN44YUX+PTTTyu6jiLVhsVk8OzAFhjAl3sS+Sk+HVvzG0gdu46cDnfjNEz47f+c2vOuwW/3HHCqx1RExB3KFYDS09Np2rTpec83bdqU9HStji3erX29EG7qcHax1AMU2B04rcFk93metFuWURDZAZMtg+Bvn6XWkpswJ+91c41FRLxPuQJQq1atmDt37nnPz507l9jY2MuulEh191DvJoT5+3A4JYe5W+JczxdGdSTtlmVk9Z6CwycIn8SfCFs0mMDvX4CCHDfWWETEu5TrNvhNmzZx//33U7duXTp16gTA9u3bSUhIYPr06a5lMqo73TJZMt1Wemkr9iby3Mpf8bWYWHRXN+qF+hV73ZSVQND6yfgeXA6APag+WVe/iK3JtWU+ltrDs6g9PIvaw7NU69vgu3fvzqpVq7j22mvJzMwkMzOTa6+9luXLl/P555+XeX9z586lf//+tG/fnlGjRrFz584Sy3766afExsYWe7Rv375YmWeeeea8MhMnTixzvUQux+DWUXRtEEp+oeOCi6U6guqScf17pA/9EHtwA8xZ8YSumEDIynswZWpJGRGRylSuu8AA6tSpc97dXr/88guLFy/mhRdeKPV+VqxYwdSpU5kyZQodO3bkww8/ZOLEiaxatYrw8PALbhMUFMSqVatcPxuGcV6ZPn36MHXqVNfPVqu11HUSqQiGYfD0gBaM/Wgr6w+dZt2BFPq1iDivnK3xAE7X70ngljfw3/4+vodW4XP8f+R0f5LcDhPAVO7TVERESlDuiRAryuzZsxk9ejQjR46kefPmTJkyBT8/P5YsWVLiNoZhEBkZ6XpERJz/n4rVai1WJjQ0tDLfhsgFNQkP4I4rYgD455oD5NjsFy7o40/2Vf9H6uiVFER3w1SQTdD3U6j1yVAsiduqsMYiIt7Brb9a2mw29uzZw/333+96zmQy0bNnT7ZtK/lLPycnh379+uFwOGjTpg2PP/44LVq0KFZm06ZNXHXVVYSEhHDllVfy6KOPEhYWVqb6XaBjSc44+9noM7q0iVc25L+/JBGfnsf0DUd59Jrz76A8yxHRmvSRn+K7dwGBP7yET/Ieai0eTl778eRc+TRO35ALbqf28CxqD8+i9vAsldkeZdlnudcCu5BffvmFESNG8PPPP5eqfGJiIldffTULFixwLagK8Morr7B582Y++eST87bZtm0bR48eJTY2lszMTGbNmsXmzZtZvnw50dFFtx4vX74cPz8/YmJiOH78OK+//joBAQEsXLgQs9lcMW9WpAzW/nKKCR9sxmwy+PKR3rSue+EgU0xWEvz3L7BzQdHPQXXg+qnQ9mZ9k4uIXKYy9QA9/PDDF309IyPjsipTGp07dy4Wljp37syQIUNYsGABjz76KABDhw51vX52EPTAgQNdvUKllZKiOwZKYhgQHh6sz6iU2kf4079FBGv2J/P0JzuYMaYjpkuGGD+4+p/4NB1B4LpnsaQdgsV3Y9v0EVl9X8IR2shVUu3hWdQenkXt4Vkqsz3O7rs0yhSAgoMvvtPg4GDq169f6v2FhYVhNptJSUkp9nxKSsoFx/VciI+PD61bt+bYsWMllmnQoAFhYWEcPXq0TAHI6UQnyyXoMyq9x/s1Y+ORVHaeyOCznScZ0aFuqbaz1e+F7bavCdj6DgFbp2E9to6wef3J6fYoOZ3vB/NvA/zVHp5F7eFZ1B6exd3tUaYAdO5dVRXBarXStm1bNmzYwMCBA4GihVY3bNjA7bffXqp92O129u3bR9++fUssc/LkSdLS0oiMjKyQeouUR51gX+7v1Yg31h1i2v8Oc03zcMICSnl3otmXnO6Pk9/yJoK+/T+scesJ/PFlfPctJeuaqRTW71G5lRcRqWHcfhfYhAkTWLRoEUuXLuXgwYNMnjyZ3Nxcbr75ZgAmTZrEa6+95io/bdo01q9fz/Hjx9mzZw9PPfUUJ06cYNSoUQBkZ2fz8ssvs337duLi4tiwYQMPPvggjRo1ok+fPm55jyJnje5cnxaRgWTkFfLWd4fLvL29VlPSh88nY+DbOPwjsKTuo9bSkQR980TRmCERESkVt08wMmTIEE6fPs3bb79NUlISrVu3ZsaMGa5LYAkJCZhMv+W0jIwM/vrXv5KUlERoaCht27ZlwYIFNG/eHACz2cy+ffv47LPPyMzMJCoqil69evGnP/1JcwGJ21lMBv93bQvunred5XsSGda2Dl0b1CrbTgyD/NibsTXqT+CGqfjvnYvfzwvhl8WE1L+S/CaDsDUZhCO49JejRUS8TYXeBVbTaNr0kmlq+csz9ev9fLozgSa1A5g7vgs+5vJ3xloSthC0/jl8Tu0o9nxBZHtsTQaR33QQ9tqtdOdYFdL54VnUHp7FU5bCUAC6CJ0sJdMXyuXJyCvglllbSM0t4MHejZnQo+Fl7c8wIMKUTNZPS7EeXIXPyc0YTofrdXtIo6KeoaaDKIjuBiZNB1GZdH54FrWHh3A6MacdxHpsLUE5Rzjd/kHsQRXbU60AVEF0spRMXyiX79zFUhfe1ZX6of7l3tfv28PIScb3yGqsh7/Cevw7DHu+q6zDP5z8xtdia3o9tpheYCn/ceXCdH54FrWHGxXkYo3/AeuxNViPrsWc8dsd21l9nie3w90VejgFoAqik6Vk+kK5fE6nkwc+2cnW4+n0alKbN0a0veC6dqVx0fawZWM9/i2+h7/CemQ1pvz03+pgCcDW6Jqi3qFGA3D61Sr/GxIXnR+eRe1RtUxph7EeW4vv0TX4xG8o9guY02SloP5VWNsNJbnxKJwmnwo9tgJQBdHJUjJ9oVSMIyk5jPloK4UOJy8Pb0P/CyyWWhqlbg97AT4nfsT38Cqsh7/CnJXgeslpslBQ79xB1PXKVRfR+eFp1B6VrDAPnxM/Yj26BuvRNVjSi9/hag+qj63xAGwN+2GL6YVhDdAYIE+nk6Vk+kKpOP9Zf5hZPx4nKsjKogndCLSW/ebMcrWH04klaRfWw1/he2gVltO/Fnu5ILIDtqaDyG9yPfbaLTWIugx0fngWtUfFM2Ucx3psbVHoifseozDX9ZrTZKGgbg9sjfpha9Qfe1iLYt8fGgRdDehkKZm+UCpOXoGd2z7cSnx6HmO71uexa5qVeR8V0R6mtMP4Hv4vvoe/wpKwGYPfdlQY2vjMHWXXU1iniwZRX4LOD8+i9qgAdhs+CZt/6+VJ3V/85cBoV+ApiOmN01pyCFEAqgZ0spRMXygV6/vDp3n0092YDfjo9i60jAoq0/YV3R5GTlLRIOpDq7DGrf/dIOoI8ptci63J2UHUfpd/wBpG54dnUXuUjynrBNajRb08PnHrMRVku15zGmYKorv91ssT3rrUvcQKQNWATpaS6Qul4j2zbC/f7Eumfd1gZozpVIrFUn9TqV8otix8jq07M4j6G0y23xY9LhpE3a9o3FDjATh9Qyv24NWUzg/PovYoJXsBPolbz/TyrMWS8nOxlx3+kUXne6P+FDToU+7z3VMCkNtnghaRIo9f04wNh1PZlZDJZ7tOcnMpF0utbE5rELbmN2BrfsOZQdQbiwZRH/oKc/ZJfA8ux/fg8qLr/vV7nhlEfS2OIA2iFvF0puzEol9wjq7B5/j/iv+CY5gorNMZW6P+2Br1pzCiLRhuX0GrwqgH6CL020LJ9BtV5Zi3NY431h0ixM/CJxO6UbuUi6W6pT2cTixJO7Ee+qpo3NDvB1FHdcTW5Pqimah/NwiyptP54VnUHudw2LEkbnMNYPZJ2lX8Zb/a2BpeUxR6GvbF6RdW4VXwlB4gBaCL0MlSMn2hVI5Ch5M7P/6JfUnZDG0TxeTBrUq1nSe0hznt0G9h6OTW3w2ibuK6o6wwukuN+i3yQjyhPeQ33t4eRm7KmcCzFuuxbzHlpxV7vSCq45nA04/CqI6VfpODAlA14K0nS2l4+xdKZdqdkMHd87bjBN4d3aFUi6V6WnsY2afwPfL1mZmo12M4bK7XHP6RRYOoz85EbfZ1Y00rh6e1h7fzuvZwOrCc2uEawGw5taPYLyQO39CiXp6G/Yp6eQIiq7R6CkDVgNecLOXgdV8oVewfq/ezZEcCjWv7M29810sulurJ7eEaRH1oFdaj32CyZbpec/gEFv3m2WQQtkb9cfqGuLGmFceT28MbeUN7GHmpWI9/VzSA+dg6TLkpxV4viGjnumOrsE5nMLlvCLCnBCANghbxQA/2bsza/ckcOZ3Lx1viLnuxVHcqPojahk/8hqI7yg5/hTk7Eb8Dy/A7sAynyYeC+j0pqNMJp08gTp8AnJYAnD4BYPE/87N/seedPv5g9vOq8UUVzukERwFGYW7RZHaFeUV/L8jFsOeBYcIeVB9HUF23/qcpv+N0Ykne89u8PIk/FVsA2WENpqBBH2wN+2NrdA2OwGg3VtYzqQfoImrybwuXyxt+o3K3cxdLXXBnV2JqlbxoabVsjzPd9L6HisLQ7ydWK/VuDBNOiz+cE4pcAcly5udiYer851zlzglY+JwpX47xShXSHk4HFOa7golxJpjwu5+Nglyw5xUFlsIzoaXgd2Vc2+YV259rX+f8x1lidQwzjqC62IPr4whugD04BkdwDPaQM38Pqgvm0g3ar2rV8vw4l9OJYcvAlHUSc+r+M2N51mLOOVWsWGHtWNcdWwXR3cBcsetsVRRP6QFSALqIanuyVIFq/4VSDTidTh78ZCdbjqfTs0kYb45oV+JiqTWhPcypB7Ee/i/mzOMYBTkYhTkYBTln/jPPKfacUZBbbFxRZXKafX/X6xRwgaBUPHRhDSAoyJ+s9DQoyPtdGMktek/nhpZzw8jZcudMPllVnIb5TJj0x2nxK/q7w4Y588QlP28nBo6g6DPhqD724AY4QmKK/gyujz24vtvGe3n0+VGYiyn7FObsk5iyEzEV+7Po7+bsk0X/Rn7HaQnA1qBP0aWthv2rzfp9nhKA1J8p4qEMw+DpgS0Y+9FWfjicytr9yfRvWbWDFauSPawZuWEPlH4DR2HxoFSY97uQdPbvueeEqfOfKyqXW3zbc9Y1Muz5Z8JIapnfU9nm8y6Z0+x7JpD4nRdQzj4o9rPfmcuG/mXarsQeA6cDU84pTJnxmDOOY8qMw5wZhznzOKaMor8b9nzMWQmYsxLwSbjwbuyBdYp6jYJjXL1I9pCzf68HlpJ7Oasdhx1TblJRmMk6iSnnzJ9nAo0r4OSnl36XvqHYg2MoqN+rqJen3hU18iaCqqIAJOLBGtcO4I4rGjBr4zFeW3uQHo3DyrVYao1ksuD0DamcgdOuy0+/D1PnhKWCHLjAc0Zh0cPXx0y+wwfH2TByoYByzs9Oiz/4+OM0nynnczaU+Lp/7TXDhCMwGkdgNIXRXc9/3enEyE3GnHEcc2acKyCZMo5jzowv6tUrzMWcnYg5OxGfk1sveBiHfyT2c3uNQhq4ApM9OAZ8Air5jZaC04mRn1ast8Z8oZ6bnKRSXVqEooDrCIzGHlT0GTsC6hT1pgXWKXo+sA6OwDo1KyB6AH2Tini4Cd0b8NXPp4hPz+P9H46Wa7FUKSPDVBRGfPxx+oeXfXMDfCOCyfTESy6VwTBwBkRSGBBZNM/T7zmdGHmnzwlFcb8FpTM9SqaC7KIek9wkfBK3XfAwDv/w38YenRl/9FuPUgxO62X2uRXkYso+ifmc3priQaco3JT28qTTMOMIiHQFmaJHHeyB0ec8V6doSQkN5K9yCkAiHs7Px8ykAc3506e7WfhTPEPa1CG2jIuliriVYeD0D6fQPxyiOp7/+plelXN7jUyZxzFnnA1KxzHZMjHlphTd3n1qxwUP4/CtdV4osocUjUXCUg9L4mGMrN8HmnNCTpkuR9Vy9dLYXeHmbLA5E278I9zfeyclUgASqQZ6NqnNwJYRrN6XzD9W72dmGRdLFfFohoHTL4xCvzCIbH/hIvnprjFI5szj51xmOzMWKT8dU34apqQ0+N3yDmfVKkVVnBa/cwJNST03dcDiV+63K55BAUikmnjsmmZsOJLK7oRMPtuZwM0dq8cdHyIVwekbit03FHtEmwu+btgyz1xSiys+SPvswG1bBvaAyGLja+yBdYv32ATWwWkN0eUoL6EAJFJNRAX7cn+vxry+9iDT/neEa1pElHqxVJGazmkNxh7eGnt46/NeMwyICA8iNSXLO8ZkSanU7BUJRWqYUZ3qERsVRGZ+IW99e8jd1RGpPtSrI7+jACRSjVhMBs8ObI4BrNh7ii3H0txdJRGRakkBSKSaaVs3hJs71gWKFk21FZZurhEREfmNApBINfRQ7ybUDvDhaGrRYqkiIlI2CkAi1VCwn4VHr2kKwKwfjxGXlnuJLURE5FwKQCLV1PWtoujWsBb5hQ5e+eYAWtdYRKT0FIBEqinDMHh6QHN8zAY/HE5l1e6T7q6SiEi1oQAkUo01rh3A+CsaAPD0kp1sPlb2FctFRLyRApBINXdX9wZ0rB9CRl4hDy/ezWc7E9xdJRERj6cAJFLN+fmY+feoDtzYqR52h5OXvt7Pm+sOYXdoTJCISEkUgERqAF+LiTdv7cT9PRsBMHdrHJO+2EuOze7mmomIeCYFIJEawjAM7u3ZiJeGtsJqNvjuYAr3LNjOyYw8d1dNRMTjKACJ1DDXtYri3dEdqR3gw/6kbO6at509JzPdXS0REY+iACRSA7WvF8IH4zrTLCKAlGwb9y/cwTf7ktxdLRERj6EAJFJD1Q3xY8ZtnejZJIz8QgfPLPuZ2T8e04SJIiIoAInUaEG+Fl67qR23dakPwL/XH2Hyql+1gKqIeD0FIJEazmIyeKJfM54e0ByzASv2nuKhxTtJyylwd9VERNxGAUjES9zSqR5v3tyOQKuZ7fEZ3DVvG4dTctxdLRERt1AAEvEiVzauzayxnagX6kd8eh53z9/Gj0e0fIaIeB8FIBEv0zQ8kA/GdqJT/RCy8u386dNdLN5+wt3VEhGpUgpAIl4oLMDKO7d0YEibKOxOePmbA7y29qCWzxARr6EAJOKlrBYTk6+P5cHejQFY8FM8T3y2h6z8QvdWTESkCigAiXgxwzCY0KMh/xjWGl+Lie8Pn+aeBdtJ0PIZIlLDKQCJCANaRvLerR0JD7RyMDmHu+ZuY3dChrurJSJSaRSARASAttHBfDC2Ey0iAzmdU8D9C3fw319OubtaIiKVQgFIRFyizyyf0adpbWx2J39e/gvTNxzV8hkiUuMoAIlIMQFWM6/e2JZxXWMAeP+Ho/x1xS/ka/kMEalBFIBE5Dxmk8Gj1zTl/65tgdlk8NUvSTywaCenc2zurpqISIVQABKREo3oUJd/jWxHsK+FXQkZTJi7jQPJ2e6ulojIZVMAEpGLuqJhGLPGdqJBLT9OZORzz/zt/HD4tLurJSJyWRSAROSSGtcOYNbYznSJCSXbZuexpbtZtC3e3dUSESk3BSARKZVa/j5Mu6U9w9rWweGEV9cc5JVvDlCo5TNEpBryiAA0d+5c+vfvT/v27Rk1ahQ7d+4sseynn35KbGxssUf79u2LlXE6nbz11lv07t2bDh06cNddd3HkyJFKfhciNZ+P2cRfB7XkkT5NMIBPtp/gsaW7tXyGiFQ7bg9AK1asYOrUqTz00EMsXbqUVq1aMXHiRFJSUkrcJigoiPXr17sea9euLfb69OnTmTNnDpMnT2bRokX4+/szceJE8vPzK/vtiNR4hmEwvnsDXh7eBj+LiY1HUrl7/nbi03PdXTURkVJzewCaPXs2o0ePZuTIkTRv3pwpU6bg5+fHkiVLStzGMAwiIyNdj4iICNdrTqeTjz76iAceeICBAwfSqlUrXnnlFU6dOsXq1aur4i2JeIV+LSKYfltHIoOsHE7JYcLc7eyIT3d3tURESsWtAchms7Fnzx569uzpes5kMtGzZ0+2bdtW4nY5OTn069ePvn378sADD7B//37Xa3FxcSQlJRXbZ3BwMB07drzoPkWk7FrVCeaDsZ2JjQoiNbeABz7ZycqfE91dLRGRS7K48+CpqanY7XbCw8OLPR8eHs6hQ4cuuE2TJk34+9//TmxsLJmZmcyaNYvbbruN5cuXEx0dTVJSkmsfv99ncnJymepnGGUq7lXOfjb6jDyDO9ujTogvM27ryF9X/MK6Ayn8bcWvHDudy329GmHy0n8gOj88i9rDs1Rme5Rln24NQOXRuXNnOnfuXOznIUOGsGDBAh599NEKPVZ4eHCF7q8m0mfkWdzZHrPu7sHLX/3Ce98eYsbGYyTkFPDaqI74+ZjdVid30/nhWdQensXd7eHWABQWFobZbD5vwHNKSkqxcT0X4+PjQ+vWrTl27BgAkZGRrn1ERUUV22erVq3KVL+UlEy0BuSFGUbRP159Rp7BU9rj3itiiPKz8Pev97N8ZwJHTmXx2oi2RARa3VcpN/CU9pAiag/PUpntcXbfpeHWMUBWq5W2bduyYcMG13MOh4MNGzYU6+W5GLvdzr59+1zBJyYmhsjIyGL7zMrKYseOHaXe51lOpx4Xe+gz8qyHp7TH8HbRvHNLe0L9LOw5mcldH29j36kst9fLW9tDD7WHJz4qsz1Ky+13gU2YMIFFixaxdOlSDh48yOTJk8nNzeXmm28GYNKkSbz22muu8tOmTWP9+vUcP36cPXv28NRTT3HixAlGjRoFnLlFd/x4/vOf//DNN9/w66+/MmnSJKKiohg4cKBb3qOIt+naoBazxnamYZg/JzPzuWf+DtYfKnlqCxGRqub2MUBDhgzh9OnTvP322yQlJdG6dWtmzJjhugSWkJCAyfRbTsvIyOCvf/0rSUlJhIaG0rZtWxYsWEDz5s1dZe69915yc3P529/+RkZGBl27dmXGjBn4+vpW+fsT8VYNw/yZNaYTzyzby5bj6Tzx2R4evaYZt3Wuh6HRqCLiZobTWZYOI++SnKzrxSUxDIiICNZn5CE8uT0K7Q7+8c0BPt91EoCRHevyZL9mWMxu74CuNJ7cHt5I7eFZKrM9zu67NGruN5CIeASL2cSfr23Bn/o2xQCW7Ejg0aW7yczT8hki4j4KQCJS6QzD4PZuMbx6Y1v8fUz8eDSNu+dvIy5Ny2eIiHsoAIlIlenbPJzpt3UiKsjKkdO53DV3G9vitHyGiFQ9BSARqVKxUUF8OK4zresEkZ5XyIOf7GT5Hi2fISJVSwFIRKpcRJAv79/akQEtIyh0OJm86lf+vf4wDo1QFZEqogAkIm7h52Pm7ze05u4eDQCY/eNxnl32M3kFdjfXTES8gQKQiLiNyTB4oHcTJl8fi8VksGZ/MnfP387ek5nurpqI1HAKQCLidkPb1uHfozpQy9+H/UnZTJi3jX+uOUBWvm6VF5HKoQAkIh6hc0woC+7syqBWkTicsHDbCUZ/sIVv9iWh+VpFpKIpAImIxwgPtPLi0NZMG9meBrX8SMqy8cyyn3ls6R5OpOe5u3oiUoMoAImIx+nROIx547sy8cqGWEwG3x8+zegPtvDhpuMU2h3urp6I1AAKQCLikfx8zPyhV2Pmje9Kl5hQ8gsdTPvfYW7/+Cd2xGvyRBG5PApAIuLRmoQH8O7oDjx3fUtC/SwcTM7hngU7eOm/+0jPLXB39USkmlIAEhGPZxgGN7SNZvHdVzC8XR0APtt1klGzt7Bib6IGSYtImSkAiUi1Ucvfh78OiuW9WzvQpHYAqbkFPLfyVx5avIujp3PcXT0RqUYUgESk2ukSU4u547vwYO/G+FpMbD6WxtiPtjJ9w1FshRokLSKXpgAkItWSj9nEhB4NWXBnV65sFIbN7uT9H44y9qOtbDmW5u7qiYiHUwASkWotppY/b49sx0tDW1E7wIejqbk88MlOJq/8hdQcm7urJyIeSgFIRKo9wzC4rlUUiydcwciOdTGA5XtPMWr2Fj7flaBV5kXkPApAIlJjBPtZeGZgC2aN7USLyEDS8wp58b/7uX/hDg4mZ7u7eiLiQRSARKTGaVc3hI9u78Kf+jbFz2Jie3wG4+b8xDv/O0xegd3d1RMRD6AAJCI1ksVkcHu3GD6Z0I2rm4Vjdzj5YNNxbv1wKz8cPu3u6omImykAiUiNFh3ix2s3teXV4W2ICrJyIj2PP326m2eX/UxyVr67qycibqIAJCJe4ZoWESya0I2xXetjMmD1viRumb2FRdtOYHdokLSIt1EAEhGvEWi18Ng1zfhoXBfaRAeTbbPz6poD3D1/O78mZrm7eiJShRSARMTrxNYJYtaYTjzVvzmBVjN7T2Yyfu5PvLHuIDk2DZIW8QYKQCLilcwmg9Gd6/HJhG4MbBmJwwnztsYzavZm1u1Pdnf1RKSSKQCJiFeLDPJl6rDWvHVzO+qF+nEqy8ZTX+zlic/2cDIjz93VE5FKogAkIgL0bFKbhXd25a7uDTCbDL47mMLoD7bw8ZY4CjVIWqTGUQASETnDz8fMQ32aMPeOLnSqH0JugYO3vj3E+I9/YndChrurJyIVSAFIROR3mkUE8t6tHfnLdS0I8bOwPymbu+dt5+XV+8nKL3R39USkAigAiYhcgMkwuLF9XT6Z0I0hbaJwAot3JHDL7C3895dTOLXAqki1pgAkInIRtQOsTBncin+Pak/DMH9Ssm38efkv/HHJbuLSct1dPREpJwUgEZFSuKJhGPPHd+W+qxrhYzbYeDSV2z7cyqyNxyiwO9xdPREpIwUgEZFSslpM3NuzEfPHd6Vbw1rkFzr4z/dHGPfRT/wUl+bu6olIGSgAiYiUUaPaAfz7lvZMGRxLmL8Ph0/ncP/CnTy/6lfScgvcXT0RKQUFIBGRcjAMgyFt6vDJhG7c1D4agGV7Ehk1ewtf7j6pQdIiHk4BSETkMoT6+/Dn61oy47aONIsIIC23gMmr9jHi3z/wybYTJGfb3F1FEbkAw6lfU0qUnJyJPp0LMwyIiAjWZ+Qh1B6eodDuYO7WeKZvOEp+YdHAaAPoFBPKwJYR9G8RQUSQr3sr6YV0fniWymyPs/suVVkFoJLpZCmZvlA8i9rDsyRl5bP+eDqf/xTPnpOZrucNoFP9EPq3jKR/iwiighWGqoLOD8+iAFQN6GQpmb5QPIvaw7Oc2x4n0vP4Zl8ya/YlsSshs1i5jvVC6N8yggEtI6mjMFRpdH54FgWgakAnS8n0heJZ1B6epaT2OJmRx5r9yaz+NZldv1tbrH3dEAbGFl0miw7xq+Ia12w6PzyLAlA1oJOlZPpC8SxqD89SmvZIzMxnzf5kvvk1iR0nioehdnWDGdAykgEtI6irMHTZdH54FgWgakAnS8n0heJZ1B6epaztcSozn7X7k/lmXxLb4zM4d5M20cFFA6hbRlA/1L/S6lyT6fzwLApA1YBOlpLpC8WzqD08y+W0R3JWPmv2p/DNviS2xaUXC0Ot6wS5eoZiaikMlZbOD8+iAFQN6GQpmb5QPIvaw7NUVHskZ9tYd6Zn6Ke4dBzn7KtVVBD9W0YwsGUkDcIUhi5G54dnUQCqBnSylExfKJ5F7eFZKqM9UrJtfHsgmdX7ktl6PK1YGGoZGcjA2KJb6xvVDqiYA9YgOj88iwJQNaCTpWT6QvEsag/PUtntkZpjY+2BFNbsS2LLsTTs5xyjRWQgA1pGMKBFJI3DFYZA54enUQCqBnSylExfKJ5F7eFZqrI90nIKWHcgmW/2J7P5WBr2c7qGmkUEMKBlJANbRtLEi8OQzg/PogBUDehkKZm+UDyL2sOzuKs90nIL+O5ACt/sT+LHo8XDUJPwAAaemXSxWURg1VXKA+j88CwKQNWATpaS6QvFs6g9PIsntEdGXgHfHkjhm33J/Hg0lcJzw1DtANcA6mYRARiG4Z5KVhFPaA/5jQJQNaCTpWT6QvEsag/P4mntkZlXyHcHU1i9L4kfj6ZScM6goUZh/gyIjWRAiwhaRAbWyDDkae3h7RSAqgGdLCXTF4pnUXt4Fk9uj6z8ojD0zb5kNh45je2cMNQwzN81gLplVM0JQ57cHt5IAaga0MlSMn2heBa1h2epLu2RlV/I+kOn+WZfEj8cLh6GGtTyo3/LSPo1Dye2TjAWU/UNQ9WlPbyFAlA1oJOlZPpC8SxqD89SHdsj21bI94dOs3pfMj8cPk1+ocP1mr+PifZ1Q+gUE0qn+iG0qxuCv4/ZjbUtm+rYHjWZpwQgS8UeWkREqqNAq4XrWkVxXasocmx2vj9c1DP049FUsvLtbDqWxqZjaQCYDYitE0yn+iF0qh9Kx/oh1A6wuvcNiJSRRwSguXPnMnPmTJKSkmjVqhV//etf6dChwyW3W758OY8//jgDBgzg3//+t+v5Z555hqVLlxYr27t3b2bOnFnhdRcRqWkCrGaujY3k2thIHE4nB5Oz2R6fwY74dLbFpXMqy8bek5nsPZnJvK3xQNH4oU71Q+hYP5TO9UOJqeVXY8YQSc3k9gC0YsUKpk6dypQpU+jYsSMffvghEydOZNWqVYSHh5e4XVxcHC+//DLdunW74Ot9+vRh6tSprp+tVv12IiJSVibDoEVkEC0igxjVqR4AJzPy2Bafzo74DLbHp3MwOYdjqbkcS83li92JANQO8HH1DnWOCaVFZFC1HkckNY/bA9Ds2bMZPXo0I0eOBGDKlCmsW7eOJUuWcN99911wG7vdzpNPPskjjzzC1q1bycjIOK+M1WolMjKyUusuIuKNokP8GBzix+DWdQBIzy1g54kMVy/R3sRMTucUsGZ/Mmv2JwPnjCOqH0qnmOo3jkhqHrcGIJvNxp49e7j//vtdz5lMJnr27Mm2bdtK3O6dd94hPDycUaNGsXXr1guW2bRpE1dddRUhISFceeWVPProo4SFhZWpfuq9LdnZz0afkWdQe3gWb2uPWgE+XN08nKubF/Xa5xc6+PlkJtvi09kel8GOE+mlGkcUHlg5PfXe1h6erjLboyz7dGsASk1NxW63n3epKzw8nEOHDl1wmy1btrB48WI+++yzEvfbp08frr32WmJiYjh+/Divv/469957LwsXLsRsLv1vHOHhpRtJ7s30GXkWtYdn8eb2qB8dysBOMQA4HE72ncpk8+HTbD6SyuYjp0lIzztvHFHTiEC6NQ6jW+PaXNG4No3DK3aWam9uD0/k7vZw+yWwssjKymLSpEm88MIL1K5du8RyQ4cOdf09NjaW2NhYBg4c6OoVKq2UFN0yWRLDKPrHq8/IM6g9PIva43wRFoPBLcIZ3KLoF96TGXlsj89gW1y6axzRoeRsDiVns2hLHADhAT50PHPJrFP9UFpGlW8ckdrDs1Rme5zdd2m4NQCFhYVhNptJSUkp9nxKSgoRERHnlT9+/Djx8fE88MADruccjqK5Ktq0acOqVato2LDheds1aNCAsLAwjh49WqYA5HSik+US9Bl5FrWHZ1F7lKxOsB+DWvkxqFUUULR22dlxRNvjisYRpVxkHFHHM/MRBVhL36uv9vAs7m4PtwYgq9VK27Zt2bBhAwMHDgSKAs2GDRu4/fbbzyvftGlTli1bVuy5N998k+zsbP785z8THR19weOcPHmStLQ0DYoWEfFQIX4+9G4aTu+mxccRbY9PZ8eJDHbEZ5CZX1jiOKKO9UPpWK/yxhFJzeP2S2ATJkzg6aefpl27dnTo0IEPP/yQ3Nxcbr75ZgAmTZpEnTp1eOKJJ/D19aVly5bFtg8JCQFwPZ+dnc20adMYNGgQERERHD9+nFdffZVGjRrRp0+fqn1zIiJSLr4WU9HM0zGhADicTg4l57A9Pv3MI4PEzPyLzkfUqX4oDTQfkZTA7QFoyJAhnD59mrfffpukpCRat27NjBkzXJfAEhISMJlMpd6f2Wxm3759fPbZZ2RmZhIVFUWvXr3405/+pLmARESqKZNh0DwykOaRgdxyznxE28/MRbQjPoODydklzkfUs2UkMYE+tIwMItjP7f/1iQfQWmAXoXVjSqa1dTyL2sOzqD3c49xxRDvi09lzMpMC+/kNUC/Uj9ioIGKjAmkZGURsVBCRQVb1FFURrQUmIiJSgUoaR7TjRDr7UnLZFZdGQkY+J9LzOJGex9ozg6sBwvx9iI0KouWZYBQbFUSDMH9MCkU1lgKQiIjUSGfHEXVuEOrqcUjLKWBfUha/nspm36ksfj2VxZHTOaTmFrDxaCobj6a6tvf3MdHiTA/R2VDUNDwQq6X0wzLEcykAiYiI1wj19+GKhmFc0fC3lQHyCuwcTM7m11NnglFSFvuTssktcLDzRAY7T/y23JLZZNA0POBMT9Fvl9GCfPXfaXWjFhMREa/m52Ombd0Q2tYNcT1X6HByLDWnKBQlZvNrUhb7TmWRkVfI/qRs9idls3xPoqt8TC0/13iis8EoIsjXHW9HSkkBSERE5HcsJoOm4YE0DQ9kcOui55xOJ4mZ+Wd6in67jHYyM5+4tDzi0vJckzZC0R1ov40rKnrE1PLTuCIPoQAkIiJSCoZhEB3iR3SIH32b/7ZaQVpugWs80a+nsth3KpujqTmczilgw5FUNhz5bVxRoNVMi8ii8URne4yaRgTgY9a4oqqmACQiInIZavn70L1RGN0bFR9XdMA1rqiot+hgcjbZNvuZuYt+G1dkOTOu6GwvUcuoIFpEBmpcUSXTpysiIlLB/HzMtKtbtF7ZWYUOJ0dP55zTU1QUjDLzC9mXlM2+pGyWnTOuqEEtv2KX0FpGBRGhpT4qjAKQiIhIFbCYDJpFBNIsIpAhbeoAReOKEjLyi11C+/VUFqeybBxPy+N4Wh6r9xUfV1Qv1I86wb6uR3RI0c/Rwb6EBfhojFEpKQCJiIi4iWEY1Av1o16oH9e0OGdcUU6B686zs6Ho6OlcTucUcDqngN0JmRfcn9VsEHUmDNUJ9qXOOeGoKCz5EmjVf/2gACQiIuJxagX40KNRGD3OGVeUW2DnyOkcEjPyOZmZz8mMfBIz80nMzCMxM5+kLBs2u9N1R1pJgnzNRAf7uQJRnXPCUZ1gX6KCfL1iULYCkIiISDXg72OmdZ1gWte58FpXhXYHSdk2TmbkczIzzxWUEs95ZOQVkpVv50B+NgeSsy+4HwMID7QWC0dnL7Wd7UmqHeBT7ddOUwASERGpASxmE3VD/Kgb4geEXrBMjs1OYmZRQDrbg+QKSRlFPUk2u5PkbBvJ2bYafanNs2snIiIiFSbAaqZJeABNwgMu+LrT6SQ1t6AoGJ3tQcr47TLbycx8kkt5qS3Y13LeZbY6wb7UDfGlo9UHc2W9yVJSABIRERGgaFB27QArtQOsJV5qK7A7SMqyndeTdPZxMiOfzPxC1+NCl9oMA14Z3oZrzplQsqopAImIiEip+ZhNrjvXSrrUlm0rdIWhC15mczgJdvNEjwpAIiIiUqECrRaahltoGh543muGARERwSQnZ+J0uqFyZ9T8+9xEREREfkcBSERERLyOApCIiIh4HQUgERER8ToKQCIiIuJ1FIBERETE6ygAiYiIiNdRABIRERGvowAkIiIiXkcBSERERLyOApCIiIh4HQUgERER8ToKQCIiIuJ1FIBERETE61jcXQFPZhjuroHnOvvZ6DPyDGoPz6L28CxqD89Sme1Rln0aTqfTWfFVEBEREfFcugQmIiIiXkcBSERERLyOApCIiIh4HQUgERER8ToKQCIiIuJ1FIBERETE6ygAiYiIiNdRABIRERGvowAkIiIiXkcBSERERLyOApCU2nvvvcfIkSPp3LkzV111FQ8++CCHDh1yd7XkjPfff5/Y2Fheeukld1fFqyUmJvLkk0/So0cPOnTowLBhw9i1a5e7q+WV7HY7b775Jv3796dDhw4MHDiQd955B60AVTU2b97MH/7wB3r37k1sbCyrV68u9rrT6eStt96id+/edOjQgbvuuosjR45UWf0UgKTUNm3axLhx41i0aBGzZ8+msLCQiRMnkpOT4+6qeb2dO3eyYMECYmNj3V0Vr5aens6YMWPw8fFh+vTpLF++nKeffprQ0FB3V80rTZ8+nfnz5/O3v/2NFStW8OSTTzJjxgzmzJnj7qp5hZycHGJjY3nuuecu+Pr06dOZM2cOkydPZtGiRfj7+zNx4kTy8/OrpH5aDV5KbebMmcV+/sc//sFVV13Fnj17uOKKK9xUK8nOzuapp57ixRdf5D//+Y+7q+PVpk+fTnR0NFOnTnU916BBAzfWyLtt27aNAQMGcM011wAQExPD8uXL2blzp3sr5iX69u1L3759L/ia0+nko48+4oEHHmDgwIEAvPLKK/Ts2ZPVq1czdOjQSq+feoCk3DIzMwH0262bPf/88/Tt25eePXu6uypeb82aNbRr144//vGPXHXVVdx0000sWrTI3dXyWp07d2bjxo0cPnwYgF9++YWtW7dy9dVXu7lmEhcXR1JSUrHvreDgYDp27Mi2bduqpA7qAZJycTgc/P3vf6dLly60bNnS3dXxWsuXL2fv3r0sXrzY3VUR4Pjx48yfP58JEybwhz/8gV27dvHiiy/i4+PDiBEj3F09r3PfffeRlZXF4MGDMZvN2O12HnvsMYYPH+7uqnm9pKQkAMLDw4s9Hx4eTnJycpXUQQFIymXKlCns37+fefPmubsqXishIYGXXnqJWbNm4evr6+7qCEXd+u3atePxxx8HoE2bNuzfv58FCxYoALnBypUrWbZsGa+99hrNmzfn559/ZurUqURFRak9RAFIyu75559n3bp1fPzxx0RHR7u7Ol5rz549pKSkcPPNN7ues9vtbN68mblz57Jr1y7MZrMba+h9IiMjadasWbHnmjZtyldffeWmGnm3V155hfvuu881niQ2NpYTJ07w3nvvKQC5WWRkJAApKSlERUW5nk9JSaFVq1ZVUgcFICk1p9PJCy+8wNdff82cOXM0uNPNrrzySpYtW1bsuWeffZamTZty7733Kvy4QZcuXVzjTc46cuQI9evXd1ONvFteXh6GYRR7zmw26zZ4DxATE0NkZCQbNmygdevWAGRlZbFjxw7GjBlTJXVQAJJSmzJlCl9++SX//ve/CQwMdF3DDQ4Oxs/Pz8218z5BQUHnjb8KCAigVq1aGpflJnfeeSdjxozh3XffZfDgwezcuZNFixbx/PPPu7tqXqlfv368++671KtXz3UJbPbs2YwcOdLdVfMK2dnZHDt2zPVzXFwcP//8M6GhodSrV4/x48fzn//8h0aNGhETE8Nbb71FVFSU666wymY4FYWllEqaY2bq1KnFLsOI+9xxxx20atWKP//5z+6uitdau3Ytr7/+OkeOHCEmJoYJEyYwevRod1fLK2VlZfHWW2+xevVq16WWoUOH8tBDD2G1Wt1dvRrvxx9/ZPz48ec9P2LECP7xj3/gdDp5++23WbRoERkZGXTt2pXnnnuOJk2aVEn9FIBERETE62geIBEREfE6CkAiIiLidRSARERExOsoAImIiIjXUQASERERr6MAJCIiIl5HAUhERES8jgKQiEgJYmNjWb16tburISKVQEthiIhHeuaZZ1i6dOl5z/fu3ZuZM2e6oUYiUpMoAImIx+rTpw9Tp04t9pyWMBCRiqBLYCLisaxWK5GRkcUeoaGhQNHlqXnz5nHPPffQoUMHBgwYwKpVq4pt/+uvvzJ+/Hg6dOhAjx49+Otf/0p2dnaxMosXL2bo0KG0a9eO3r17n7dwaWpqKg899BAdO3bkuuuu45tvvnG9lp6ezhNPPMGVV15Jhw4duO6661iyZEklfRoiUpEUgESk2nrrrbcYNGgQn3/+OcOGDePxxx/n4MGDAOTk5DBx4kRCQ0NZvHgxb775Jj/88AMvvPCCa/t58+bx/PPPM3r0aJYtW8a///1vGjZsWOwY06ZNY/DgwXzxxRdcffXVPPnkk6SlpbmOf/DgQaZPn86KFSuYPHkyYWFhVfb+RaT8dAlMRDzWunXr6Ny5c7Hn7r//fv7whz8AcP311zNq1CgAHn30UX744QfmzJnD5MmT+fLLL7HZbLz88ssEBAQA8Le//Y0//OEPPPnkk0RERPCf//yHCRMmcOedd7r236FDh2LHGzFiBDfccAMAjz/+OHPmzGHnzp1cffXVnDhxgtatW9O+fXsAYmJiKueDEJEKpwAkIh6rR48eTJ48udhzZy+BAeeFo06dOvHzzz8DcPDgQWJjY13hB6BLly44HA4OHz6MYRicOnWKq6666qJ1iI2Ndf09ICCAoKAgTp8+DcCYMWP44x//yN69e+nVqxcDBw6kS5cu5XqvIlK1FIBExGP5+/vTqFGjStm3r69vqcr5+PgU+9kwDBwOBwB9+/Zl7dq1fPvtt3z//ffcddddjBs3jqeffrrC6ysiFUtjgESk2tq+fXuxn3fs2EGzZs0AaNasGb/++is5OTmu13/66SdMJhNNmjQhKCiI+vXrs2HDhsuqQ+3atRkxYgT//Oc/+b//+z8WLlx4WfsTkaqhACQiHstms5GUlFTscfbyE8CqVatYvHgxhw8f5u2332bnzp3cfvvtAAwbNgyr1cozzzzDvn372LhxIy+88AI33ngjERERADzyyCPMnj2bjz76iCNHjrBnzx7mzJlT6vq99dZbrF69mqNHj7J//37WrVvnCmAi4tl0CUxEPNb//vc/evfuXey5Jk2auG53f+SRR1ixYgVTpkwhMjKS1157jebNmwNFl89mzpzJSy+9xC233IK/vz/XXXcdzzzzjGtfI0aMID8/nw8++IBXXnmFWrVqcf3115e6fj4+Prz++uvEx8fj5+dH165def311yvgnYtIZTOcTqfT3ZUQESmr2NhY3nnnHQYOHOjuqohINaRLYCIiIuJ1FIBERETE6+gSmIiIiHgd9QCJiIiI11EAEhEREa+jACQiIiJeRwFIREREvI4CkIiIiHgdBSARERHxOgpAIiIi4nUUgERERMTrKACJiIiI1/l/YcLj3IMVeXkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.4338725805282593\n",
            "Val Loss: 0.5438029766082764\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7998377680778503\n",
            "Val Accuracy: 0.7536442875862122\n"
          ]
        }
      ],
      "source": [
        "train_and_analyze_model(\n",
        "    ffnn_model,\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    num_of_epochs=10,\n",
        "    num_per_batch=64,\n",
        "    validation=0.1,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7u_8Fl2UWDy"
      },
      "source": [
        "#### 5.1.1\n",
        "Here above we see that the model converges pretty well. One small issue that we have noticed is that it does not generalize for all data too well, but the results are acceptable for a simple model that is not too complex.\n",
        "\n",
        "**Notes**\n",
        "\n",
        "After multiple runs with different parameters and layers/layer sizes, we noticed that it trained and converged faster then the rest of the models. Adding more hidden layers with more nuerons did not have a significant impact on this model's accuracy, in fact it may have hurt it. As the writing of this, it is the best performing model we have however, it does not have much room to grow. One way that can maybe help this function is the using another process to pre-process the data that is far more complex that also can handle potential typos that can occur in online discourse."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YWHMk15UWDy",
        "outputId": "dd3d6b56-ddeb-4abf-e3ed-f5a590cb2324"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "215/215 [==============================] - 1s 3ms/step\n",
            "Train Loss: 0.43618570592540146\n",
            "24/24 [==============================] - 0s 3ms/step\n",
            "Val Loss: 0.5010729948965603\n"
          ]
        }
      ],
      "source": [
        "ffnn_train_predictions = ffnn_model.predict(X_train[:, 3]).flatten()\n",
        "ffnn_train_loss = calculate_loss(Y_train, ffnn_train_predictions)\n",
        "print(f'Train Loss: {ffnn_train_loss}')\n",
        "\n",
        "ffnn_validation_predictions = ffnn_model.predict(X_validation[:, 3]).flatten()\n",
        "ffnn_validation_loss = calculate_loss(Y_validation, ffnn_validation_predictions)\n",
        "print(f'Val Loss: {ffnn_validation_loss}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "089MnzJMUWDy",
        "outputId": "a31ca1f8-2a47-43ac-d312-cadb97663e02"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_predictions(ffnn_train_predictions, Y_train)\n",
        "plot_difference(ffnn_train_predictions, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_ZlsrD-UWDz",
        "outputId": "4c4fc844-4d40-49b9-dd56-b5f9032277e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positives: 1564\n",
            "True Negatives: 3731\n",
            "False Positives: 147\n",
            "False Negatives: 1409\n",
            "\n",
            "Accuracy: 0.7728798715515983\n",
            "Precision: 0.9140853302162478\n",
            "Recall: 0.5260679448368651\n"
          ]
        }
      ],
      "source": [
        "print_confusion_matrix(ffnn_train_predictions, Y_train, 0.7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gl88ONsUWDz"
      },
      "source": [
        "#### 5.1.2\n",
        "The performance of this model was middle of the pack after modifications were made. The data tables above show the distribution of predictions using two different metrics. Looking at the raw distribution of predictions, we can see that the model might fair best using a threshold between .7 and .8 to get the maximum results. In addition, when looking at the difference in distributions, we can see the model is making a lot of errors in which many false negatives are being predicted. This can be derived from both graphs. The prediciton distribution graph shows a fair amount of positives near the middle of the graph, which will all be missed with a high threshold. In the second graph we see larger amount of positives to the right of the graph signaling false negatives. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4meQc51GUWDz",
        "outputId": "6c203078-e28e-420b-b467-1edd101e2605"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102/102 [==============================] - 0s 3ms/step\n",
            "Generating \"ffnn_submission.csv\" file...\n",
            "Successfully created \"ffnn_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "ffnn_predictions = ffnn_model.predict(test_input_np[:, 3]).flatten()\n",
        "\n",
        "# Apply threshold\n",
        "THRESHOLD = 0.7\n",
        "ffnn_predictions[ffnn_predictions >= THRESHOLD] = 1\n",
        "ffnn_predictions[ffnn_predictions < THRESHOLD] = 0\n",
        "\n",
        "save_to_submissions_csv(test_input_np, ffnn_predictions, 'ffnn_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KC7DD_DAUWDz",
        "outputId": "72543735-9094-4b7b-a3d5-132790326ea4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       0\n",
              "3   9       0\n",
              "4  11       1"
            ],
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              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
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              "      fill: #174EA6;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-de97aaa6-0cc4-4d77-8a5d-eaf1a60b665c 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-de97aaa6-0cc4-4d77-8a5d-eaf1a60b665c');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "pd.read_csv('ffnn_submission.csv').head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-0kOMCgUWD0"
      },
      "source": [
        "### 5.2 Train and Evaluate the CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "y5sEnPZ9UWD0",
        "outputId": "0cad329a-51ce-4caa-bdaf-3bed339a2250"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "193/193 [==============================] - 9s 38ms/step - loss: 0.7297 - accuracy: 0.5275 - val_loss: 0.6654 - val_accuracy: 0.5773\n",
            "Epoch 2/10\n",
            "193/193 [==============================] - 7s 36ms/step - loss: 0.6245 - accuracy: 0.6672 - val_loss: 0.6140 - val_accuracy: 0.6618\n",
            "Epoch 3/10\n",
            "193/193 [==============================] - 6s 34ms/step - loss: 0.5602 - accuracy: 0.7184 - val_loss: 0.5543 - val_accuracy: 0.7303\n",
            "Epoch 4/10\n",
            "193/193 [==============================] - 8s 41ms/step - loss: 0.4884 - accuracy: 0.7669 - val_loss: 0.5275 - val_accuracy: 0.7507\n",
            "Epoch 5/10\n",
            "193/193 [==============================] - 15s 75ms/step - loss: 0.4657 - accuracy: 0.7869 - val_loss: 0.5258 - val_accuracy: 0.7551\n",
            "Epoch 6/10\n",
            "193/193 [==============================] - 14s 72ms/step - loss: 0.4551 - accuracy: 0.7912 - val_loss: 0.5289 - val_accuracy: 0.7493\n",
            "Epoch 7/10\n",
            "193/193 [==============================] - 15s 78ms/step - loss: 0.4489 - accuracy: 0.7937 - val_loss: 0.5288 - val_accuracy: 0.7536\n",
            "Epoch 8/10\n",
            "193/193 [==============================] - 11s 56ms/step - loss: 0.4421 - accuracy: 0.7981 - val_loss: 0.5292 - val_accuracy: 0.7566\n",
            "Epoch 9/10\n",
            "193/193 [==============================] - 8s 41ms/step - loss: 0.4376 - accuracy: 0.7990 - val_loss: 0.5332 - val_accuracy: 0.7624\n",
            "Epoch 10/10\n",
            "193/193 [==============================] - 6s 31ms/step - loss: 0.4324 - accuracy: 0.8015 - val_loss: 0.5412 - val_accuracy: 0.7434\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.4323958158493042\n",
            "Val Loss: 0.5412139892578125\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8014598488807678\n",
            "Val Accuracy: 0.7434402108192444\n"
          ]
        }
      ],
      "source": [
        "train_and_analyze_model(\n",
        "    cnn_model,\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    num_of_epochs=10,\n",
        "    num_per_batch=32,\n",
        "    validation=0.1,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Qoydro5UWD0"
      },
      "source": [
        "#### 5.2.1 \n",
        "Using the scatter plots above, we can see that this model might actually be overfitting the data. During the validation, it does not generalize new data well and struggles a little."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZFJ2OFsUWD0",
        "outputId": "75e76d25-4a29-4183-cbc3-41213126843a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "215/215 [==============================] - 3s 14ms/step\n",
            "Train Loss: 0.4326018375011386\n",
            "24/24 [==============================] - 0s 13ms/step\n",
            "Val Loss: 0.4924989306359378\n"
          ]
        }
      ],
      "source": [
        "cnn_train_predictions = cnn_model.predict(X_train[:, 3]).flatten()\n",
        "cnn_train_loss = calculate_loss(Y_train, cnn_train_predictions)\n",
        "print(f'Train Loss: {cnn_train_loss}')\n",
        "\n",
        "cnn_validation_predictions = cnn_model.predict(X_validation[:, 3]).flatten()\n",
        "cnn_validation_loss = calculate_loss(Y_validation, cnn_validation_predictions)\n",
        "print(f'Val Loss: {cnn_validation_loss}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "C5JZxh3qUWD0",
        "outputId": "3fd980ae-27ef-4197-9d9a-b81e4b6aa5a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_predictions(cnn_train_predictions, Y_train)\n",
        "plot_difference(cnn_train_predictions, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKo-nTIcUWD1",
        "outputId": "a42b8bf0-69f4-430a-b779-9e8f6c8752e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positives: 1544\n",
            "True Negatives: 3770\n",
            "False Positives: 108\n",
            "False Negatives: 1429\n",
            "\n",
            "Accuracy: 0.7756531893154284\n",
            "Precision: 0.9346246973365617\n",
            "Recall: 0.5193407332660612\n"
          ]
        }
      ],
      "source": [
        "print_confusion_matrix(cnn_train_predictions, Y_train, 0.7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cDBi13MUWD1"
      },
      "source": [
        "#### 5.2.2\n",
        "This model has a similar problem to the first model, the same pattern appears here where an decent chunk of false negatives are being mislabeled. One reason for this may be the text processing techinque and use of embeddings in combination. In contrast, negative results are being predicted with a reasonable amount of accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CD4ZHhmUWD1",
        "outputId": "7f650f09-8515-47c6-8985-465d22dd4288"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102/102 [==============================] - 1s 11ms/step\n",
            "Generating \"cnn_submission.csv\" file...\n",
            "Successfully created \"cnn_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "cnn_predictions = cnn_model.predict(test_input_np[:, 3]).flatten()\n",
        "\n",
        "# Apply threshold\n",
        "THRESHOLD = 0.7\n",
        "cnn_predictions[cnn_predictions >= THRESHOLD] = 1\n",
        "cnn_predictions[cnn_predictions < THRESHOLD] = 0\n",
        "\n",
        "save_to_submissions_csv(test_input_np, cnn_predictions, 'cnn_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EVmAiJMtUWD2",
        "outputId": "371aec7b-41ce-4013-e012-43fd4f93d1ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       1\n",
              "3   9       0\n",
              "4  11       1"
            ],
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2a84a04-124e-4722-a7c0-ce6bb04d89e0 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-f2a84a04-124e-4722-a7c0-ce6bb04d89e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "pd.read_csv('cnn_submission.csv').head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TI0_eA_HUWD2"
      },
      "source": [
        "### 5.3 Train and Evaluate the RNN Model\n",
        "\n",
        "> **NOTE:** This RNN model has the longest training time (can take from 30 to 39 minutes). If you are in a time crunch and are only interested in viewing our results, please view the already saved output for the next few cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Z8E8rsuYUWD2",
        "outputId": "01200c85-3785-4d3b-b94e-05ecb151c530"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "193/193 [==============================] - 251s 1s/step - loss: 0.6217 - accuracy: 0.6487 - val_loss: 0.5543 - val_accuracy: 0.7259\n",
            "Epoch 2/10\n",
            "193/193 [==============================] - 226s 1s/step - loss: 0.5061 - accuracy: 0.7599 - val_loss: 0.5771 - val_accuracy: 0.7201\n",
            "Epoch 3/10\n",
            "193/193 [==============================] - 226s 1s/step - loss: 0.4959 - accuracy: 0.7645 - val_loss: 0.5452 - val_accuracy: 0.7420\n",
            "Epoch 4/10\n",
            "193/193 [==============================] - 226s 1s/step - loss: 0.4779 - accuracy: 0.7753 - val_loss: 0.5284 - val_accuracy: 0.7507\n",
            "Epoch 5/10\n",
            "193/193 [==============================] - 229s 1s/step - loss: 0.4705 - accuracy: 0.7841 - val_loss: 0.5239 - val_accuracy: 0.7464\n",
            "Epoch 6/10\n",
            "193/193 [==============================] - 230s 1s/step - loss: 0.4659 - accuracy: 0.7789 - val_loss: 0.5327 - val_accuracy: 0.7449\n",
            "Epoch 7/10\n",
            "193/193 [==============================] - 232s 1s/step - loss: 0.4629 - accuracy: 0.7856 - val_loss: 0.5211 - val_accuracy: 0.7595\n",
            "Epoch 8/10\n",
            "193/193 [==============================] - 258s 1s/step - loss: 0.4551 - accuracy: 0.7880 - val_loss: 0.5319 - val_accuracy: 0.7580\n",
            "Epoch 9/10\n",
            "193/193 [==============================] - 229s 1s/step - loss: 0.4523 - accuracy: 0.7904 - val_loss: 0.5332 - val_accuracy: 0.7376\n",
            "Epoch 10/10\n",
            "193/193 [==============================] - 226s 1s/step - loss: 0.4512 - accuracy: 0.7906 - val_loss: 0.5418 - val_accuracy: 0.7289\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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SSEpKYt68eZSUlDB69GgA7rnnHmJiYpg8eTIAV155Je+88w5PPPEE11xzDTt27OC1117j2muv9Zxz2rRpfP7557z88ssEBgaSkZEBQHBwMH5+fuzcuZPPPvuMQYMGERYWxl9//cWMGTM444wziI+PP/U3QURE5DgNHNj7qPvHj7+RCRMmHbVMTWbPfkvT3RzGqwnS8OHDyc7OZubMmWRkZNClSxfeeOMNTxdbWlpaldHnsbGxzJkzhxkzZjBq1ChiYmK47rrruPHGGz1l3nvvPYAqSRPAjBkzGD16NBaLhZUrV/LWW29RXFxMbGwsQ4cO5ZZbbjkF7/jY3G43dqcbmwYKiojIYT75ZKnn+6+//oo5c17l3XcXebb5+x+c78/tduN0OjGbj/1ffXh4eN0G2ggY3G71wtZGZmbdDrB8/aftvPnzLv7vqtOJj2m4T7cYDBAVFVzn90dOnurEt6g+vM/hsJOVlUZkZCwWi7XBDdJesuQzZs58jqVLlwPw+++/cccdN/Gvf73I7NmvkJq6heefn0VMTHP+85/nSUlZT2lpCW3btmPSpFs544w+nnP97W8jGTv2SsaOvQqoaKm6996H+OmnH/nll5VERzfjttvuZODAQafs/dWmPg6v20NVfvaOef2TurLUm/T8MpwuN99uzmzQCZKISEPldrspPYWJkp/ZWKsZnw/36quzuO22f9CiRSuCg4PZt28fffsOYOLEW7BYrCxduph7772Ld99ddNSHmObOnc3NN9/Orbf+g4UL32fatKksWvQZISGhRzymMVGC5GMSWoTwWco+1qUVeDsUEZEmx+12c8OCNazdm3/Krtm9RQizr+heZ0nSDTdM4owz+npeh4SE0qnTwbkAb7zxZr7//ltWrPiOMWMur+kUAFx44UWcf/4FAEyadCsLFy5gw4YU+vbtXydx+jolSD4mMbai1WhDegFOlxuTUdPsioicSg39t258fNcqr4uLi3nzzddZufJHsrIycTqdlJWVsW9f+lHP06FDJ8/3/v7+BAYGkpOTXS8x+yIlSD6mfWQgARYTRXYn27KK6Rgd6O2QRESaDIPBwOwrujfoLjY/v6pPo7300gv8+uvP3HrrnbRq1RqbzcZDD92Lw1F+1PMcPrjbYDDQlIYtK0HyMSajga6xwfy2M5d1aflKkERETjGDwYC/peb1PRuidevWMHz4SAYNOheoaFFKT98L9PJuYD5Oz5L7oMputvVpp64PXEREGqdWrdrw3XffsHnzX2zevIlp0x7E5Wo6LUEnSwmSD0qIDQFg3V4N1BYRkdq5/fZ/Ehwcwk03/Z177/0nZ57Zj86d47wdls/TPEi1VB9zmOQU2xn6yioAvrm1P8F+Da8nVHO8+B7ViW9RfXhfQ58HqbHz9jxIakHyQeEBVlqF+QGQkq5uNhERkVNNCZKPUjebiIiI9yhB8lGJlQmSBmqLiIicckqQfFRii4r+0ZT0AlwaoCAiInJKKUHyUZ2iArGZjeSXlrMzu8Tb4YiIiDQpSpB8lNlkpGtMEKBuNhERkVNNCZIPqxyovV4L14qIiJxSSpB8WEILDdQWERHxBiVIPqxyyZGtmUUU2Y++qKCIiIjUHSVIPiw6yEbzYBsuN2xML/R2OCIi0kjcdttEXnzxOc/rv/1tJB988O5Rjxk4sDfff7+81teuq/PUNyVIPi5B8yGJiMgh7rnnn9x11+017luzJpmBA3uzZcvmEzrn7NlvMWrU6LoIz2POnNcYN+6qats/+WQpffv2r9Nr1QclSD6ucj6kdXuVIImICFx00cX89tvP7N+/r9q+xYs/JT6+Kx07djqhc4aHh+Pn51dXIR5VZGQUVqv12AW9TAmSjzs4o3YBWldYRET69x9IWFg4S5Z8VmV7cXEx3377NWedNYhHHnmASy65kPPOG8B1113OV18tPeo5D+9i27VrJ7feeiODB/fnmmsu49dfV1U75uWXZ3LFFaM577wBXHbZxcye/Qrl5RXjZZcs+Yy5c2ezZcsmBg7szcCBvT3xHt7FtnXrFu644yYGDx7A8OHn8fTTT1BcXOzZ/8QTj3L//ZN59923ufjiYQwffh7PPfe051r1peEtE9/ExDULwmIykFviYE9eKa3C/L0dkohI4+Z2Q/kpnKDX7F+xxPzxFjebueCC4Xzxxedcf/0EDAeO/fbbZbhcToYNG8433yzjmmuuJyAgkJUrf+Txxx+hZctWdO2acMzzu1wuHnzwbsLDI3nttf+jqKiQmTOfq1YuICCABx98hKioaLZu3cIzzzxBQEAAV199Peeddz6pqVv5+eefeOGFlwEICgqqdo6SkhLuuus2EhISeeONeeTk5PDUU4/z738/wyOPPOYp9/vvvxEZGcXMma+xe/cuHnnkfjp16syoUZce9307UUqQfJzVbCS+WRDr0gpYl5avBElEpD653YR9eCmW9N9O2SUdsWeQe+mHJ5QkjRhxMe+++zbJyavp2bM3UNFqc845g2nePJarrrrWU/Zvf7uCX35ZxTffLDuuBOm3335hx47tPP/8LKKiogGYOPFWpky5o0q5ceNu8HwfG9uCnTt38PXX/+Pqq6/HZvPD398fk8lMZGTUEa/11VdLsdvtPPTQY/j7V/z/dtddd3PvvXdx223/IDQ0HIDg4BD++c97MJlMtG17Gv36DWT16l+UIDV1CbEhFQnS3gIu7BLj7XBERBq3E0hUvKVt29NITExi8eJP6dmzN7t372LNmmQmTHgVp9PJ22/P5ZtvviIjI4Pycgd2ux2b7fjGGG3fvo1mzZp7kiOAhISkauW+/vp/LFy4gD179lBSUozT6SQgIPCE3seOHdvo2LGTJzkCSEw8HZfLxc6d20lMrEiQ2rVrj8lk8pSJjIwiNXXLCV3rRClBagASDsyHtF5PsomI1C+DoaI1x4e72CqNGHExL7zwLyZPvpfFiz+lZctW9OjRi3femcd///sed9wxmfbtO+Lv78/Mmc9RXu6os5DXr1/LY49N5e9/n0ifPv0IDAzi66//x4IF79TZNQ5lNldNVwwGAy6Xq16u5blmvZ5d6kTSgRm1N2UUUepw4mcxHeMIERE5aQYDWAK8HcUxDR58Pi+++Bz/+99SvvxyCZdcMgaDwcC6dWsYOHAQw4YNBzjQGrOTdu3aHdd5TzutHfv3p5OZmUlUVEX3WErKuipl1q1bS0xMc66/foJnW3p6WpUyFosFl8t51Gu1bduOJUs+p6SkxNOKtG7dHxiNRtq0Oe244q0veoqtAYgJthEVaMXpcrNxnyaMFBGRikHS5513Pq+99hJZWZkMHz4SgNatW/Prrz+zbt0atm/fxr/+9SQ5OVnHfd7evc+kdeu2PPHEI2zevIk1a5J5/fWXq5Rp3bo1+/als2zZl+zZs5v//ndBtckfmzdvQVraXjZv/ovc3Fzsdnu1aw0deiFWq5UnnniE1NQt/P77b/z73/9i2LDhREZGnvhNqUNKkBoAg8GgbjYREanmoosupqAgnzPP7OsZM3T99ROIi4vnrrtu5/bbJxEREclZZ51z3Oc0Go08+eS/KCsrY+LE63nqqelMnHhLlTIDBw7i8suv4t//foZx465i/fo1jBs3oUqZc84ZTJ8+/bj99pu46KIhLFv2ZbVr+fn58fzzs8jPz+eGG67noYfupVevM/nnP+858ZtRxwxuTa5TK5mZBZyKO/j2r7uY+f02zu0UxTOjutb/BWvJYICoqOBTdn/k2FQnvkX14X0Oh52srDQiI2OxWKyYzUbKy+t3XIscv9rUx+F1e6jKz96xqAWpgfAsObI3XxNGioiI1DMlSA1El5ggTEYDmUV29hWUeTscERGRRk0JUgPhZzHRObpifol1aQVejkZERKRxU4LUgBzazSYiIiL1RwlSA6In2URE6p7GdTY+dVGnSpAakMoJI//cX4hdT1qIiNRK5dIVdrvGdTY2lXVqMp38fNiaSbsBaRnqR5i/hdwSB3/tLyTxQMIkIiInzmg04e8fRGFhDgABAX44jz7xs5xCLpcBp/PEWoLcbjd2exmFhTn4+wdhNJ58O5ASpAakcsLIH1OzWZeWrwRJRKSWQkIiACgszKG42Fjv63vJ8TMaT74+/P2DPHV7spQgNTBJLUL4MTWb9XqSTUSk1gwGA6GhkYSEhBMSYiMnp0gTd/oAgwHCwwNPqj5MJnOtWo4qeT1Bmj9/PnPmzCEjI4P4+HimTp1KUlLSEcvn5+fz73//m6+++orc3FxatmzJAw88wKBBg477nGVlZTz11FMsWbIEu93OwIEDeeSRRzyL8vmyyoHaepJNRKTuGI1G/Pz8sFgcSpB8gMGA1+vDq4O0lyxZwowZM7j11lv56KOPiI+PZ8KECWRl1byont1uZ/z48ezZs4cXX3yRpUuXMn36dGJiYk7onE8++STffvstL7zwAm+//Tb79+/ntttuq/f3Wxe6Ng/GaID0gjIyCjWwUEREpD54NUGaO3cuY8eOZcyYMXTs2JFp06bh5+fHokWLaiy/aNEi8vLyeOmll+jVqxetWrXizDPPJD4+/rjPWVBQwKJFi7jvvvvo168fCQkJPPnkkyQnJ/PHH3+cirddK4FWMx2iKiaMVDebiIhI/fBagmS320lJSaF///4HgzEa6d+/P8nJyTUe880333D66afz2GOP0b9/fy666CJeffVVnAceOziec65fvx6Hw1GlTIcOHWjRokWDSJBA3WwiIiL1zWtjkHJycnA6nURGRlbZHhkZSWpqao3H7Nq1i1WrVjFy5Ehef/11du7cybRp0ygvL+e22247rnNmZmZisVgICQmpViYjI+OE34fBcMKH1FpibAgfrU1nfXq+V65/PCrj8tX4miLViW9RffgW1Ydvqc/6ON5zen2Q9olwu91ERkYyffp0TCYTCQkJ7Nu3jzlz5nhtDFFkZPApv+bZ3Qzw5SY27iskNDwQi8l35/v0xv2Ro1Od+BbVh29RffgWb9aH1xKk8PBwTCZTtQHZWVlZR3yaLDo6GrPZ7Jn9FKB9+/ZkZGRgt9uP65xRUVE4HA7y8/OrtCJlZWURHR19wu8jK6vglI+wDzG4CbaZKSgrZ9XGdLo0970PtMFQ8YPtjfsjNVOd+BbVh29RffiW+qyPynMfi9eaHqxWK926dWPlypWebS6Xi5UrV9KjR48aj+nZsyc7d+6sMnHU9u3biY6Oxmq1Htc5ExISsFgsVcqkpqayd+9eTj/99BN+H273qf8yYKDbgXFIa/cWeCWG4/ny1v3Rl+qkoXypPnzrS/XhW1/1WR/Hw6t9M+PHj+eDDz7go48+YuvWrTz66KOUlJQwevRoAO655x6ee+45T/krr7yS3NxcnnjiCbZt28by5ct57bXXuPrqq4/7nMHBwYwZM4annnqKVatWsX79eh544AF69OhxUgmStyTFVrR+aeFaERGRuufVMUjDhw8nOzubmTNnkpGRQZcuXXjjjTc83WFpaWlVZsOMjY1lzpw5zJgxg1GjRhETE8N1113HjTfeeNznBHjggQcwGo3ccccdVSaKbEgSWhx4kk0JkoiISJ0zuN3H29gkNcnM9E5/dX6pg/Nequgm/N/NfQkPsJ76II7CYICoqGCv3R+pTnXiW1QfvkX14Vvqsz4qz30svvv4kxxViJ+FdhEBgCaMFBERqWtKkBowz4SR6mYTERGpU0qQGrCEFhUDtdepBUlERKROKUFqwCqfZNuQVoDTpU5zERGRuqIEqQFrFxlAgMVEscNJalaRt8MRERFpNJQgNWAmo4GunnFI6mYTERGpK0qQGrikAwnS+r0aqC0iIlJXlCA1cAmxlQO1lSCJiIjUFSVIDVzlo/7bs0vIL3V4ORoREZHGQQlSAxceYKV1mB8AKekahyQiIlIXlCA1Ap5uNo1DEhERqRNKkBqBg+OQ1IIkIiJSF5QgNQJJLSrGIaWkFeDSKosiIiK1pgSpEegYFYjNbKSgrJwd2SXeDkdERKTBU4LUCJhNRrrGBAF63F9ERKQuKEFqJBIPLFy7XgmSiIhIrSlBaiQOPsmmgdoiIiK1pQSpkUg8MGHk1swiiuzlXo5GRESkYVOC1EhEBdmIDbHhBjZowkgREZFaUYLUiKibTUREpG4oQWpEKtdl05NsIiIitaMEqRFJ8jzJVoBbE0aKiIicNCVIjUjn6CAsJgO5JQ5255Z6OxwREZEGSwlSI2I1G4lvpgkjRUREaksJUiOTeEg3m4iIiJwcJUiNzMEn2dSCJCIicrKUIDUylRNGbs4opNTh9HI0IiIiDZMSpEYmJthGdJAVpxs27iv0djgiIiINkhKkRsZgMKibTUREpJaUIDVCiZowUkREpFaUIDVCiZUtSJowUkRE5KQoQWqE4mOCMBkNZBXZSS8o83Y4IiIiDY4SpEbIz2Kic3QgoHFIIiIiJ0MJUiNV2c2mCSNFREROnBKkRiqhhQZqi4iInCwlSI1UZQvSX/sLsZe7vByNiIhIw6IEqZFqGepHuL8Fh9PNX/s1YaSIiMiJUILUSFVMGOnFbjZHCexYCW61XomISMOjBKkRS2xROaP2qR2obSxMI2zhSJh7AYHLHzil1xYREakLZm8HADB//nzmzJlDRkYG8fHxTJ06laSkpBrLfvjhh9x///1VtlmtVtatW+d5HRcXV+Oxd999NzfccAMAgwcPZs+ePVX2T548mYkTJ9bmrfiUg0+ynboWJFP2ZkI/uwZTYcW99U95h/LoREq7XX3KYhAREaktrydIS5YsYcaMGUybNo3u3bszb948JkyYwNKlS4mMjKzxmKCgIJYuXep5bTAYquz/8ccfq7z+/vvvefDBBxk2bFiV7XfccQdjx471vA4MDKzt2/EpXZoHYTRAekEZGYVlRAfZ6vV65vTVhH5+PcayXJyh7TB1uRBWvUzQ91Mpj+xCefOe9Xp9ERGRuuL1Lra5c+cyduxYxowZQ8eOHZk2bRp+fn4sWrToiMcYDAaio6M9X1FRUVX2H7ovOjqar7/+mj59+tC6desq5QIDA6uUCwgIqJf36C2BVjMdog5MGFnP8yFZt31F2CeXYyzLxRHTg9y/fQLDnqSs/YUYXHZClk7EULS/XmMQERGpK15tQbLb7aSkpDBp0iTPNqPRSP/+/UlOTj7iccXFxZx77rm4XC66du3KXXfdRadOnWosm5mZyXfffcdTTz1Vbd/s2bN55ZVXiI2N5aKLLmLcuHGYzSd2Sw5rvPI5iS1C2JxRxPq0fM7rHHXsA06CLeVdgpbfh8Htwt52MPkXvIrBGgAGA4Xn/xvTB1sw52wm9MubyLtkAZis9RKHHF3lz6qv/8w2FaoP36L68C31WR/He06vJkg5OTk4nc5qXWmRkZGkpqbWeEy7du148skniYuLo6CggDfffJMrrriCxYsX07x582rlP/roIwIDAxk6dGiV7ddeey1du3YlNDSU5ORknn/+eTIyMqqNbzqWyMjgEyp/qvXrFM2Ha9L4M6OIqKg6jtXthu+egeVPVrw+/RqsI18gymTxFImMjYWrF8Dsc7Gk/ULU6qdg+L/qNg45Ib7+M9vUqD58i+rDt3izPrw+BulE9ejRgx49elR5PXz4cBYsWMCdd95ZrfyiRYsYOXIkNlvV8Tfjx4/3fB8fH4/FYuGRRx5h8uTJWK3H38KRlVWA233i7+NUaRdc8V7W7s4jfV8eZlMd9aq6nAR+9yD+Ke8AUNz7Dor73A05pUApBkPFD3ZWVgFuYrAOeZGQxePhl9cpCO5CWZfL6iYOOW5V6sSHf2abCtWHb1F9+Jb6rI/Kcx+LVxOk8PBwTCYTWVlZVbZnZWVVG1d0JBaLhS5durBz585q+3777Te2bdvGCy+8cMzzdO/enfLycnbv3k379u2P69pQ0Yjiyx+m1uH+hPiZyS8tZ1NGEV1i6iAbLy8h5H+3Ydv2JW4MFJ79OKWJ11fsO+xeVN6fstPOp+iMuwj89XmClt9HeUQc5c1qflJR6pev/8w2NaoP36L68C3erA+vDtK2Wq1069aNlStXera5XC5WrlxZpZXoaJxOJ5s2bSI6OrravoULF9KtWzfi4+OPeZ6NGzdiNBqP+ORcQ2U0GOjW/MCEkXtr/7i/oTSHsE+vqkiOTDbyL3j1YHJ0DMVn3EnZaedjcJYR8sUNGEqyjn2QiIiIF3j9Kbbx48fzwQcf8NFHH7F161YeffRRSkpKGD16NAD33HMPzz33nKf8rFmz+PHHH9m1axcpKSncfffd7N27l8suq9plU1hYyNKlS6ttB0hOTub//u//+PPPP9m1axeffvopM2bMYNSoUYSGhtbvG/aCyvmQavskm7FgL2EfjsGS9isuawh5o+Zj7zDi+E9gMFIw5EXKw9pjKtxLyJc3gau8VjGJiIjUB6+PQRo+fDjZ2dnMnDmTjIwMunTpwhtvvOHpYktLS8NoPJjH5efnM3XqVDIyMggNDaVbt24sWLCAjh07Vjnv4sWLcbvdXHTRRdWuabVaWbJkCbNmzcJut9OqVSvGjRtXZVxSY5LYoqIFqTYTRpqy/qyYALIoHWdgc/JGvo0zsssJn8dtCyH/wjcIWzgS656VBP70JEUDHz7puEREROqDwe1Wb2ttZGb6/oC+gtJyBr/0EwBf3tyXiIATe8zesncVIUsmYCzLozy8E3kj5+MKbnHUYwwGiIoKPuL9sW5dQujSilnL88//D2WdLz2hmOTEHatO5NRSffgW1Ydvqc/6qDz3sXi9i03qX7CfmXYRFZNgrj/Bbjbr1iWEfno1xrI8HLFnkDv6w2MmR8fD3mE4Rb1ur4jv27sxZaTU+pwiIiJ1RQlSE3Ey3Wx+6+YRsnQSBmcZZe2GkTvqXdx+4XUWU/GZU7C3OQdDeSmhX9yAoTSnzs4tIiJSG0qQmoiEyoHax/Mkm9tNwKpnCP7+QQy4Kel6NfkXvAZm/7oNymgi//xZOEPaYirYRcj/bgWXs26vISIichKUIDURlU+ypaQX4HQdpUPXVU7Qt1MIXD0TgKIzJ1N4zlNgrJ/x/G6/MPKGv4Hb7I911/cE/vx0vVxHRETkRChBaiLaRQYQaDVR4nCRmlVUcyFHMSFLJuC/8X3cBiMF5zxN8Rn/rPfFiZyRXSgY/DwAAb+/jG3zZ/V6PRERkWNRgtREmIwGuh5lwkhDSTZhn1yObcfXFRNAXvgGpd2uPmXxlXUaSXGPmwAI/mYypqw/T9m1RUREDqcEqQlJjD2QIB32JJsxfxdhH16CZV8yLlsYuRe/j73d0JpOUa+K+t6HvdVZGMqLCV0yAUNp7imPQUREBJQgNSmJLSrGIR36JJspcwNhiy7GnJuKM6gluaM/ojy2t3cCNJrJH/YyzuDWmPJ3EPzV7Rq0LSIiXqEEqQlJaF6RIG3PLiGvxIFl9wrCPhqDqXg/5ZHx5I75GGdEJ6/G6PYLJ//C2bhNNmw7vyXg1+e9Go+IiDRNSpCakLAAC63D/ADISV5I6GfXYrQXYG/Rl9xLF+EKivVyhBXKoxMoOPcZAAJ/exFr6lIvRyQiIk2NEqQmJrFFCONNX9A7+R4MLjtlHUaQN/Id3DbfWqS3LG4Mxd1vACB42Z2Ysjd7OSIREWlKlCA1JW4XN5TN4xHL2xUTQCZeT/7Ql8Hs5+3IalTU70HsLfpidBQS8sUNGMpOfrFdERGRE6EEqalwOgj++p/03PM2AC+4ryB/4HQwmrwc2FGYLOQPexVnUCzm3K0EL7sT3C5vRyUiIk2AEqSmwF5E6JJx+P21CLfBxH3Om3ihbBQ7ckq9HdkxuQOiyL/gwKDt7f8j4LeZ3g5JRESaACVIjZyhOJOwT8Zi3fkdbrM/+SPm8lfMSOA412XzAeUxp1MwaAYAAb88h3X7Mi9HJCIijZ0SpEbMmLed8EUXY9m/BpdfBLmXfIC97eBDJoxsGAkSQFmXsZQkXI8BN8Ff3Y4pN9XbIYmISCOmBKmRMu9fS/iiSzDl78AZ3JrcMR9THtMDOLhw7frDZtT2dYUDH8ERewZGewEhS27AYC/0dkgiItJIKUFqhCy7vif048swlmTiiOpGzphPcIa19+xPONCCtDWziMKycm+FeeJMVvKGvYYzMAZzziaCv7kL3G5vRyUiIo2QEqRGxvbXIkI/vw6jowh7q4HkXboQd2CzKmWigmzEhthwAxvSG1YrkjuwGfkXvI7baMG2dQn+v7/k7ZBERKQRUoLUWLjd+Ce/Ssiyf2BwlVPa6WLyLnoLtzW4xuINtZsNoLx5LwrPng5A4Kqnsez41ssRiYhIY6MEqTFwuwhc8RhBPz0OQHH3iRSc/x8wWY94SMKBhWsb0kDtQ5V2u4aSrldhwE3IV7dhzNvh7ZBERKQRUYLU0DnLCP7qdgLWzAagsP9UigY+DIajV63nSba9+bgb6DiewrOn44jpgbEsj9AvbgBHsbdDEhGRRkIJUgNmsBcQ+tl1+G3+BLfRQv6QmZT0mHRcx8Y1C8JqMpBXWs7uXN+fMLJGJhv5F7yOyz8ac9ZGgr+9W4O2RUSkTihBaqCMRfsI/ehvWPeswGUJJO+ieZTFjT7u4y0mI3HNGt58SIdzBcWSf8GruI1m/DZ/gv8fr3s7JBERaQSUIDVAptxUwhZdgiUzBZd/FHmXLsTR+uwTPk9ii4PdbA2Zo0UfCgc+CkDgyiew7F7h3YBERKTBU4LUwJjTfyds0SWYCnZRHnoaOWM+pjw68aTO1ZCfZDtcacL1lMaPxeB2EfLlTRjzd3s7JBERacCUIDUg1u1fE/bJ5RhLs3E0607u6I9xhZ520uernDByc0YhJQ5nHUXpJQYDBYOexBGdhLE0h5AvboDyEm9HJSIiDZQSpAbCtvF9Qpb8HUN5CfY2g8i9+APcAVG1OmfzED+aBVlxumHjvobfioTZj/wLZ+Pyi8CSuZ7g5fdp0LaIiJwUJUi+zu0m4Lf/EPLNZAxuJ6VxfyNv+P+BNbBOTp9Q2c22txEkSIAruCX5w17BbTDh99ci/NbN9XZIIiLSAClB8mUuJ0E/PETgz08DUNzzVgrO+zeYLHV2icputob8JNvhHK0GUNT/IQCCfpyGZc9KL0ckIiINjRIkX1VeSsj/bsZ/3TzcGCgcOI2ifveDwVCnl0nyzKhd0GAnjKxJSfcbKO10CQa3s2LQdsFeb4ckIiINiBIkH2QoyyP0s6uxbV2C22ilYOjLlHSfUC/XimsWhMloIKvITnpBWb1cwysMBgrO/RflkV0xlmQRsvRGKG+gE2KKiMgppwTJxxiL0gn7cDTWvT/jsgaTN/JtyjqNrLfr+VlMdI6uGM/U0OdDqsbiT97wN3DZwrDsX0PQ9w9q0LaIiBwXJUg+JuCX5zFn/4UzIIbcSxfhaDWg3q95aDdbY+MKaUP+sJdxG4z4b3wfv5R3vB2SiIg0AEqQfExZ3GhKul5J7piPcUZ1PSXX9DzJ1ogGah/K0fpsivreB0DQDw9jTvvNyxGJiIivU4LkYxwt+lJ47r9whbQ+ZdesfJLtz32FlJW7Ttl1T6WSHjdT2uEiDC4HIUsnYixK93ZIIiLiw5QgCS1D/YgIsFDucvPX/kJvh1M/DAYKBj9HeUQcpuL9hCydBE67t6MSEREfpQRJMBgMjb6bDQBrIHkXvoHLFoolfTVBPzzi7YhERMRH+USCNH/+fAYPHkxiYiKXXXYZa9euPWLZDz/8kLi4uCpfiYlVF2u97777qpWZMKHqY/K5ublMnjyZnj170rt3bx544AGKiorq5f01BJ4JIxvbk2yHcYW1o2DITNwY8E95G78N73k7JBER8UFmbwewZMkSZsyYwbRp0+jevTvz5s1jwoQJLF26lMjIyBqPCQoKYunSpZ7XhhomTzzrrLOYMWOG57XVaq2yf8qUKWRkZDB37lwcDgcPPPAADz/8MM8991wdvbOGpTE/yXY4+2nnUdznbgJ/foag7x6kPDKe8pge3g5LRER8iNdbkObOncvYsWMZM2YMHTt2ZNq0afj5+bFo0aIjHmMwGIiOjvZ8RUVVX7TVarVWKRMaGurZt3XrVn744Qcef/xxunfvTu/evXnooYdYvHgx+/btq5f36eu6xARjNMC+gjL2N6YJI4+guNdtlLUbhsFlJ+SLGzEUZ3g7JBER8SFebUGy2+2kpKQwadIkzzaj0Uj//v1JTk4+4nHFxcWce+65uFwuunbtyl133UWnTp2qlPnll1/o168fISEh9O3blzvvvJPw8HAAkpOTCQkJqdI1179/f4xGI2vXruX8888/7vdQxyt/eE2gzUTHqEA2ZRSxPj2f80Kia3W+yvvis/fHYKTw/Bcw/Xck5pwthH55E3kXL6jTde58jc/XSROj+vAtqg/fUp/1cbzn9GqClJOTg9PprNaVFhkZSWpqao3HtGvXjieffJK4uDgKCgp48803ueKKK1i8eDHNmzcHKrrXzj//fFq1asWuXbt4/vnnufHGG3n//fcxmUxkZmYSERFR5bxms5nQ0FAyMk6sJSEyMviEyvuyM9pHsimjiK25ZVweVTfvy7fvTzBcvQBePxfL3p+JWv0UDH/G20HVO9+uk6ZH9eFbVB++xZv14fUxSCeqR48e9OjRo8rr4cOHs2DBAu68804ARowY4dlfOUh7yJAhnlalupSVVdBoVq/oFO4HwM9bM8nMrN1YJIOh4gfb9+9Pc6znv0jI4r/DL69RENKFsvi/eTuoetFw6qRpUH34FtWHb6nP+qg897F4NUEKDw/HZDKRlZVVZXtWVlaN44pqYrFY6NKlCzt37jximdatWxMeHs6OHTvo168fUVFRZGdnVylTXl5OXl4e0dEn1rXkdjee5b26HXjU/899hdjLXVhMtR+i1hDuT9lpQynqfSeBv71A0Lf3Uh7emfJmSd4Oq940hDppSlQfvkX14Vu8WR9eHaRttVrp1q0bK1eu9GxzuVysXLmySivR0TidTjZt2nTUxCY9PZ3c3FxPmR49epCfn8/69es9ZVatWoXL5SIpqfH+x3gsbcP9CfEzU1buYnNG05ryoPjMuyg7bQgGZ1nFoO2SrGMfJCIijZbXn2IbP348H3zwAR999BFbt27l0UcfpaSkhNGjRwNwzz33VHn0ftasWfz444/s2rWLlJQU7r77bvbu3ctll10GQFFREU8//TR//PEHu3fvZuXKldxyyy20bduWs846C4AOHTpw1llnMXXqVNauXcvq1auZPn06I0aMICYm5tTfBB9RMWFk05gPqRqDkYIhL1Ie2g5T4R5CvrwFXOXejkpERLzE62OQhg8fTnZ2NjNnziQjI4MuXbrwxhtveLrY0tLSMBoP5nH5+flMnTqVjIwMQkND6datGwsWLKBjx44AmEwmNm3axMcff0xBQQHNmjVjwIAB/OMf/6gyF9Kzzz7L9OnTuf766zEajQwdOpSHHnro1L55H5QQG8JP23JYl5bP5bT0djinlNsWSv7wOYQtHIl1zwoCV86gaMBUb4clIiJeYHC71dtaG5mZjWtA38/bc7ht0Tpahvrx8Q1nnvR5DAaIigpukPfHunUxoUsrpp7IP38WZZ0v8W5AdaQh10ljpPrwLaoP31Kf9VF57mPxeheb+JZuscEYgD15pWQXN83FXO0dRlDc8zYAgr+dgilzg5cjEhGRU00JklQRZDNzWmQAAOv2Nv5lR46kqM/d2NsMwlBeSugXN2AoyT72QSIi0mgoQZJqkg487r8+rYkN1D6U0UT++bNwhrTFlL+T8PeHYt3+tbejEhGRU0QJklTjeZKtKSdIgNsvnLwRcyuebCtKJ3Tx9QQv+weG0hxvhyYiIvVMCZJUk9CiogVpQ3oB5a6mPVrRGdGZnMv/R/Hpk3AbjPj9tYjw987DmrrU26GJiEg9UoIk1bSPDCDQaqLE4SI1s2lNGFkjiz9FA6aSO/ojysM7YireT+gXNxD85S2aUFJEpJFSgiTVGA0GujVXN9vhypv3ImfsUop73orbYMJvy6dEvDcY2+bPtDaBiEgjowRJalTZzbYurek+yVYjsx9F/e4n92+fUh4Zj7Eki5D/3UzI0okYivZ7OzoREakjJ5UgpaWlkZ6e7nm9du1annjiCd5///06C0y8y/MkW1NbcuQ4lTfrTs5lSyjqfSduoxlb6hcVrUl/LVJrkojIyXK7MKf9SuD3D8PHt4KjxGuhnFSCNHnyZFatWgVARkYG48ePZ926dfz73/9m1qxZdRqgeEe3A0+y7cgpIa/E4eVofJTJSnGfKeRctgRHVALGslxClv2DkCXjMRameTs6EZGGwe3GvC+ZwB8fI+KtPoR/eCn+a9+EP97BVLjHa2GdVIK0efNmz6r3X3zxBZ06dWLBggU8++yzfPTRR3UaoHhHmL+FNuH+AKxPVzfb0TijupL7t88o6nMPbqMV2/ZlhL93Hn4bFqg1SUSkJm435oz1BK58koh3BhC+cCQBa17HVJiGyxJEadwYuP5znOEdvRbiSS1WW15e7ln49aeffmLw4MEAtG/fnoyMjLqLTrwqMTaYnTklrN+bz4B2Ed4Ox7eZLBT3voOydsMI/mYylv1/EPztFGxbP6PgnGdwBTethX9FRGpiyvoL25ZPsW35DHNuqme72+xPWbuhlHUcib3NORgsfvhFBUOm9/5AP6kEqWPHjixYsIBzzjmHn376iTvvvBOA/fv3ExYWVofhiTclxIaweMN+Pcl2ApyRceSO+Rj/P2YT+MuzWHd+R/h751HU/yFKu10FBj0XISJNiyk3FdvmA0lR9l+e7W6TDXvbwZR2uhh72/PA4u/FKKs7qQRpypQp3HbbbcyZM4dLLrmE+Ph4AL755htP15s0fImeJUcKcLndGA0GL0fUQBjNlPS8GXu7oRWtSem/Efzdfdi2fErBuf/CFdrW2xGKiNQrY/5OT1JkyUzxbHcbLdjbnFPRUtRuKG5rkBejPLqTSpD69OnDqlWrKCwsJDQ01LN97Nix+Pv7VgYoJ69DdCB+ZiNFdifbs4tpHxno7ZAaFGd4B3IvXYT/urkErnoK656fiFgwhMJ+91OaOE6tSSLSqBgL9mLb+jm2zZ9i2f+HZ7vbaMbRaiClHUdhbz8Mty30yCfxISeVIJWWluJ2uz3J0Z49e/jqq6/o0KEDZ511Vp0GKN5jNhro2jyY33fnsW5vvhKkk2E0UdL9BspOG0LwN1Ow7l1F8A8P47flcwoGP4szrL23IxQROWmGov3Ytn6O35bPsKT96tnuNhhxtOhHWadRlHUYjtsv3ItRnpyTSpBuueUWzj//fK688kry8/MZO3YsZrOZnJwc7rvvPq666qq6jlO8JCE2pCJBSivg4sRYb4fTYLlCTyPvkg/wS3mHwJ+ewJL2C+ELzqeozz2UdL8BjCZvhygiclwMJdnYti7BtuUTLHtWYaDiaV03BhyxZ1LWaSRlHUbgDoj2cqS1c1IJUkpKCvfffz8AX375JZGRkXz88cd8+eWXzJw5UwlSI5LUomI+pPUaqF17BiOlCddhbzOY4OX3YN31PUE/Tce29XMKBj+HM6KztyMUEamRoTQXW+rSijFFu3/E4HZ69jlieh5oKRqBK6jx/CF90l1sgYEV3S0//vgjQ4cOxWg0cvrpp7N37946DVC8q9uBgdqpmcUUlpUTZDupHxk5hCukFXkj5+O3cQGBKx7Dsi+Z8PcvoOjMuyjpcRMYdY9FxPsM9gKs277EtvkzrLu+x+A6OGmwIzqRso4jKes4EldIay9GWX9O6jdxmzZtWLZsGeeffz4//vgj48aNAyArK4ugIN8dkS4nLirQSosQG3vzy0hJL6BP24bXj+yTDAZKu16Jvc0ggpbfh23HNwStegrb1iUVY5Oiuno7QhFpihzF2LYvw7b5E6w7l2Nwlnl2lUfGU9ZxFGUdL2oS4ydPKkG69dZbmTJlCjNmzKBv37706NEDgBUrVtClS5c6DVC8L7FFCHvzM1iflq8EqY65glqQP2Ietk2LCPrhESwZawn/7wiKe91Oca/bwGT1dogi0tiVl2Dd8S22LZ9h274MQ/nB9c/KwzpUdJ91HNnkhgGcVIJ0wQUX0KtXLzIyMjxzIAH069ePIUOG1Flw4hsSYkP48s8M1u3VkiP1wmCgLO5vOFqdRdB3D2Db9iWBvz6PLfULCs57nvLoRG9HKCKNjbMM687vsW35FOu2/2F0FB3cFdKWso4jKe00CmdkF2iic+Cd9GCH6OhooqOjSU9PB6B58+aaJLKRSow9OFDb7XZjaKIflvrmCowh/8I3sG35lKDvH8KctZGw/15Ecc9bKT7jH2CyeTtEEWnInA4se1bgt/lTrKlLMdoPPnzjDGpJWceLKOs0ivLopCabFB3qpBIkl8vFyy+/zNy5cykuLgYgMDCQ8ePHc/PNN2M0agK8xqRzsyCsJgN5peXsyi31LGIr9cBgoKzTxdhbDiDoh6n4bfmMwNUzsaUupeC85yiP6eHtCEWkIXE5sexdVTGrdeoSjKU5nl3OgBjKOo6grNPFFb9bNHltFSeVIP373/9m4cKFTJ48mZ49ewKwevVqZs2ahd1u55///GedBineZTEZiY8JZu3efNbtzVeCdAq4A6IoGPYKZR1HEvzdA5hzNhG26GJKTp9I0ZmTwaw6EJEjcLswp/2G35ZPsG1ZgrHk4CLyLv9IyjqMoKzjSByxZ2oOtqM4qQTpo48+4vHHH+e8887zbIuPjycmJoZp06YpQWqEEmIPJEhp+YzoFuPtcJoMe4fhZLfsR9APj+C36UMCkl/Fuu1/FAx+jvLYM7wdnojUF7cbykswOIow2AsxOIoxOgo93xschRX7DtlvOLDfvH8NpqJ0z6lctlDK2l9IWaeLcbTsp6lEjtNJ3aW8vDzat6/+iF/79u3Jy8urdVDie5JahPDu6j2sT9NA7VPN7RdOwfkzKes0iqDl92LOTSXsw9GUJP2dor73giXA2yHKCTAW7sW25XOsO76GyLaYuoyjPFLTOjR4LufBhMWTtBz6uuhgUmOv3F542P7KbcUV37tdJx+ONRh7u2EVi8K2PktPxJ6Ek0qQ4uPjmT9/Pg899FCV7fPnzycuLq5OAhPfknBgwsgtGYWUOJz4W9Qse6rZTxtCzpXfELjiMfw3vk/A2jnYti+jYPC/cLTs7+3w5CgMxZnYti7GtvlTrGk/H9yxewXha97F3noQxT1uwtFqoAbHeomxcC/k7cGamQH2GlpmDiQxxsoEpkoCVIihvLRe4nJjwG0JrPiyBuK2BOG2BOC2Bh3cXmVbEK6gWOytBoDZr15iaioMbrfbfaIH/fLLL0yaNInY2FhOP/10AP744w/S0tKYPXs2vXv3rus4fVZmZgEnfgcbphGvrWJ/oZ1XxybRq3XYUcsaDBAVFdyk7s+pZNm5nOBv78FUWDFzfUnCdRT1ewC39cgTtapOTi1DWR7W1KX4bf602tIM9tg+2DtcSFDuOtwpH3laChxR3SjpcRNlHUeqG+RUcLux7F6B/7q5WLd/VasWG88pDaZDkpcakhnrgW2WoKrfH5YAuQ7sx+zXJAdP1+fvq8pzH7PcySRIAPv27ePdd98lNTUVqOheu/zyy3nllVeYPn36yZyyQWpK/9nc99kGvt6UyW1nteP6M48+tbz+M65/BnsBgT89gX/KOwA4g1tRcO4zOFqfXXN51Un9cxRj2/5VRUvRjm8xuOwHdzXrfmAW4pG4glt46iM7NQW/5Nn4b1zgmaDPGdyKku43UNLlSrAGeuvdNF72Ivw2LcJ/7f9hztl0cHtUHA5zcPXWGksQrirJzMHWmsMTIEw2tQLWgQadINXkzz//5NJLL2Xjxo11dUqf15T+s3nnt928+F0qgzpE8uwl3Y5aVv8ZnzqW3SsI/vZuTPk7ASjpeiVF/afitoVUKac6qSfOMqw7lmPb/Am27V9VnYU4Io6yThdT2nEkrrB2VQ47vD4MpTn4r38L/7VzMZZkAhWDa0sSrqMkcTzuwGan9G01Rsbcbfivn4ffxg88cwC5zQGUxl9GadI4wjv31OfDR/hCgqQ2XDlulRNGrtOEkT7F0WoA2Zd/ReCqpwhYNxf/De9h3fEthec8jf208459AjlxrnIsu3+secK9kLaUdrqYsk6jcEbGH+UkVbn9winu/Q+KT5+I31+L8E9+DXPeNgJX/4eAP16nNG40JaffhDO8Q328o8bL7cKy87uKbrQd32Kg4n/b8tDTKE0cR2n8WNy2EDX6SDVKkOS4xTULwmw0kF3sIC2/jBahGgDoM6yBFJ09HXvHiwj6ZgrmvG2ELr6e0ri/UTjwEdx+WkOv1twuLGm/VEy4t+VzjKXZnl3OwOYV3WedRlHerHvtuljM/pR2u4bSLldi3f4/ApJfxZK+Gv8N7+G/4T3K2g2juMdNmubhGAz2Avw2foDf+nmYc1M928vanEtp0njsbc5pkmN75PgpQZLj5mcx0blZEBvSC1i3N18Jkg9ytOhDzuX/I/CXZ/FfMxu/vxZi2fU9hYOexNHhAm+H1/C43Zj3rzmQFH1adW4Zv4gDSzNcjCP2jLr/z9Zowt7+QuztL8Sc9isBv7+Cbfv/sG37Etu2L3E070Vxj5uwtxum/+gPYcrZgv+6udj+XOhZX8xlDaY0fiylidc3iVXopW6cUIJ02223HXV/fn7+UfdLw5cYG1yRIKXlM6yLxkT4JIs/RQOmUtZhOMHfTMGcs5nQL26grNPFcNatGMytcVuO3f/elJmy/sS2+VP8Nn+CKX+HZ7vLGoy9/YWUdroYR6sBp+xJs/LYM8gfcUbFf/5/vIbfn4uwpK8m9IsbKQ9rT8npEymN+1vTfazb5cS681v8187Fuus7z+by8I6UJI6nLG7MUZ/wFKnJCX26g4OP/ks1ODiYli1b1iog8W2JsSG8n7xXE0Y2AOXNe5Ez9gsCf30B/+RXsG3+BDZ/QiQVT0mVR8ThjIyn/MCXM6xDk55Mzpi7Db8tn2Lb/Cnm7L88291mf8raDaWs4yjsbQZ5NQlxhnek8Nx/UXzmFPzXzsUv5W3MuakEL7+PwJ+fpSRpPCUJ1zWZLlVDWR5+G9/Hf908TyLrxoD9tCGUJI3H0eosPVEmJ61On2JripraEw978kq45I1fMRsNLL99ADZzzU37emLKt5j3ryXgtxewZaVA/p4ay7iNFpxh7Q8kTF0qkqeIeFzBLRvtfzLGgr3YtnyGbcunWPav8Wx3G63Y255LWadRlJ12fr3MVl4XnxGDvRC/jQvw/2M2psKKenWbAyjpegUl3W/EFXL06TgaKlPWn/iv+z/8/lrkeWrQZQultMsVlCRchyu07QmfU7+zfIsvPMWmBKmWmtqHye12c8Grq8gudvDGFd3p3jK0xnL6ZeN7Kuska/cuTFl/Ycr6E3PWn5iz/8SU9SdGe82tgi5rMM6IuIMtTQcSJ7df2Kl9A3XkSLNauw0mHK0GUtppFPb2F+C21fyzXWdx1OVnxOnAtvVz/JNfxZKZAlS8n7KOF1HS4ybKoxNrH7C3ucqxbv+qohttz0+ezeURcZQkjae08+haJbL6neVbfCFB8olB2vPnz2fOnDlkZGQQHx/P1KlTSUpKqrHshx9+yP33319lm9VqZd26dQA4HA5eeOEFvv/+e3bt2kVQUBD9+/dn8uTJxMQcXGR18ODB7NlT9S/pyZMnM3HixDp+d42LwWAgMTaE77ZmsT6t4IgJkvgut18YjhZ9cLToc8hGN8bCvZiz/sSUtfFg4pSzFaO9AGP6b1jSf6tyHmdg8wPJUtzBFqfwjj45DuZYs1qXdRpFWYcRuAOivBhlLZgslHW+lLJOl2DZ/UPFosa7vsdv8yf4bf4Ee8sBFPe8GUfrQQ2uNdBQmoPfhnfxX/fWwVYygxF7u2EV3Wgt+jW49yQNg9cTpCVLljBjxgymTZtG9+7dmTdvHhMmTGDp0qVERkbWeExQUBBLly71vD50Pp7S0lI2bNjAzTffTHx8PPn5+TzxxBPcfPPNfPjhh1XOc8cddzB27FjP68BAzVh7PBJig/luaxbr0jQov9EwGHAFt8Qe3BIOnTvJaceUm1qRMGX9iSn7wL8FuzEVpWMqSse6c7mnuNtgwhnWjvKI+Crjm1whbU79k1aVs1pv+gTrzuVHndW60TAYcLQ+m7zWZ2PKSCHgj1crWsr2rMC6ZwXlkV0o7jGJso4Xg8ni7WiPypSRgv+6N/Hb9DEGZxkALr9wSrteVdGNFqzxrlK/vJ4gzZ07l7FjxzJmzBgApk2bxvLly1m0aNERW3MMBgPR0dE17gsODmbu3LlVtk2dOpXLLruMvXv30qLFwV+GgYGBRzyPHFlii4oZmtftVYLU6JmsOA90q5UdstlgL8CU9deBlqaNnu46Y1ke5pwtmHO2wNbPPeXd5gDKIzof7KKL7EJ5ZDxu/5r/CDppzjKsO76teCz/BGa1boyc0d0oOP8/FPW5F/+1b+Cf8i7mrI2ELLsT56qnKel+I6Vdr/Ktp7ucDmypS/FfNxdL2i+ezY6oBEqSxlPWaRSY/b0YoDQlXk2Q7HY7KSkpTJo0ybPNaDTSv39/kpOTj3hccXEx5557Li6Xi65du3LXXXfRqVOnI5YvLCzEYDAQElJ16YXZs2fzyiuvEBsby0UXXcS4ceMwm0/sljTFlt1uzYMxGmB/oZ39hWXEBNuqlam8L03x/viqOq0TWzDOFr1xtuh9MHFyuzEWpXuSJc+/OVswlBdj2f8Hlv1/VDmNKyC6ImmKiKc8Ms7TZYflBP4TdJVj2fVjRUtJDbNal3WuPqu1L/xYnqrPiDu0FcVnPUrJGXfit/4d/NfOwVSYRtCKxwj49QVKE66hNOnvuIKa128gR2EozsQvZT5+69/2zDXlNpqxdxhOSdJ4ypv3BoOhXutNv7N8S33Wx/Ge06sJUk5ODk6ns1pXWmRkpGcR3MO1a9eOJ598kri4OAoKCnjzzTe54oorWLx4Mc2bV/+Al5WV8eyzzzJixAiCgg7+pXTttdfStWtXQkNDSU5O5vnnnycjI6Pa+KZjiYxsmvPJxDcPYUNaPjsKHXRrd+RxG031/viyeq2T6BA4rTMw6uA2Zzlkb4V9KbB/A+zbUPFvznaMxRlYizNg1w+HnMQAEe2gWVeI6Xbw34j2YDRVFHG5YOdKWL8INnwMxVkHDw9uAQmjIWE0phY9CTAYqPtn0OrOqfuMBEOr++G8f8La9+Gn/2DM2kzA7y8T8MdsSLoc+t8OzY5/eZRa27Mafn4dUj4E54Eu0MBo6DUeQ+/x2EJaUP3Pr/ql31m+xZv14fUuthPVo0cPevToUeX18OHDWbBgAXfeeWeVsg6Hg3/84x+43W6mTZtWZd/48eM938fHx2OxWHjkkUeYPHkyVuvxzwWTldU0n3jo0iyQDWn5/PTXfs6Mrd5EbzBU/GA31fvji7xaJ4YW0LwFND//4DZ7EeacTdVanIwlWZCdWvH15yHddCYbzojOOENPw5z2a82zWne+uGIJjsrxTlmFp+odnjCv1kfb0dDmEqzbl+H/+6sV3Vl/vAN/vIP9tCEVS5m06FM/f7477di2LMZv7Vws+373bHbEnE5p0njKOl4EJhvYgcxTN9+afmf5lvqsj8pzH4tXE6Tw8HBMJhNZWVlVtmdlZREVdXxPk1gsFrp06cLOnTurbHc4HNx5553s3buXefPmVWk9qkn37t0pLy9n9+7dtG9//FPRu900yQ9TYmwIi9aksW5v/lHff1O9P77MZ+rEEoijWQ8czXpU2Wwozjg4KPzA03Tm7L8wlJdizliHOaPiidWjzmrtC+/vOHmvPoyUnTaUstOGYk5fXfHkW+pSrNuXYd2+DEez0yuWMml/4cGWu9pcrWgffinv4JcyH1PxfqBi7q2yjhdRkjie8uY9Dxb2Yv35zOdDAO/Wh1cTJKvVSrdu3Vi5ciVDhgwBwOVysXLlSq655prjOofT6WTTpk0MGjTIs60yOdqxYwdvvfUW4eHHnlV248aNGI3GIz45J1UlxFZk3xv3FeBwurCYtBaU1A13QDSOgGgcrc86uNHlxJS/A1PWn5hyU3GGd6xYbNQHpxRoiMqb9yL/wtmYclPx/+N1/P78L5b9fxD65U04Q9pS3GMSpfGXnfgAabcb877f8V/7JratizG4ygFwBsRQmnANJV2vxh2oJYvEN3m9i238+PHce++9JCQkkJSUxLx58ygpKWH06NEA3HPPPcTExDB58mQAZs2axemnn07btm3Jz89nzpw57N27l8suuwyoSI7uuOMONmzYwGuvvYbT6SQjIwOA0NBQrFYrycnJrFmzhr59+xIYGEhycjIzZsxg1KhRhIZqXp/j0Sbcn1A/M3ml5WzKKKJbc/XbSz0ymnCGtddCo/XMGdaewnOeoujMKfivm+tZwiP4uwcqljJJHEdJ4jjc/hFHP1F5KbYtn+G/di6WjLWezY7YMyhJHEdZ+wub9LI20jB4PUEaPnw42dnZzJw5k4yMDLp06cIbb7zh6WJLS0vDaDzYOpGfn8/UqVPJyMggNDSUbt26sWDBAjp27AjAvn37+OabbwC4+OKLq1zrrbfeok+fPlitVpYsWcKsWbOw2+20atWKcePGVRmXJEdnMBhIiA1hxbZs1u/NV4Ik0oi4A6Io7nM3xT1vxW/jAgL+mI2pYBeBvz5PQPLLlHa5nOLuE6st6WEs3Ivf+rfxT5mPsTS74lwmG6WdLqE0aVzjmNFbmgwtNVJLTXla+jdW7uC1n3YwLD6ax0d0qbJP0/b7HtWJb2lQ9eEqx7Z1Cf7Jr2A5MAbMbTBS1n44JT1uwuAsw3/tm1hTl3pmKXcGtaAk4bqKuZaO1eLkAxpUfTQBWmpEGrTE2AMTRqaduidNRMQLjOaK5Vg6jsSy5ycCkl/BunM5fls/x++QCUEB7C36UpI0Hnu7YVUHzos0MPrplZPWLTYYA7A3r5SsIjuRgRpTINKoGQw4Wg0gr9UATJkbCPjjNWybPwGjmdLOoylJHIczqqu3oxSpE0qQ5KQF2cy0iwwgNauY9Wn5DOrYQBf6FJET5ozqSsGQFyk8+3HcBhNYfHk6TpETp2ezpVbUzSbStLmtwUqOpFFSgiS1ktiiYqDb+jQtXCsiIo2HEiSplYQDLUgpaQWUu/Toh4iINA5KkKRW2kUGEGg1UVruYmtmkbfDERERqRNKkKRWjAaDZ9kRdbOJiEhjoQRJaq2ym23dXiVIIiLSOChBklrTk2wiItLYKEGSWut2oIttZ04JuSUOL0cjIiJSe0qQpNbC/C20CfcHKp5mExERaeiUIEmdSDzQirROA7VFRKQRUIIkdSKxRcU4JD3JJiIijYESJKkTlU+yrU8rwOXWhJEiItKwKUGSOtEhKhB/i5Eiu5NtWcXeDkdERKRWlCBJnTAbDXRtrgkjRUSkcVCCJHXm4ISRepJNREQaNiVIUmf0JJuIiDQWSpCkzlS2IG3LKqawrNzL0YiIiJw8JUhSZyIDrbQI9cNNxdNsIiIiDZUSJKlTld1sGqgtIiINmRIkqVOJGqgtIiKNgBIkqVMJh8yo7daEkSIi0kApQZI61Tk6EJvZSF5pOdsyi7wdjoiIyElRgiR1ymIyEt8sCIDknbneDUZEROQkKUGSOlf5uP/vO3O8HImIiMjJUYIkdS6xRcWTbGpBEhGRhkoJktS5yifZ/kzPp8Tu9HI0IiIiJ04JktS5ZsE2YoKtuNyQvCfP2+GIiIicMCVIUi8qW5Hu/HA9kz9OYcW2bJwuPfYvIiINg9nbAUjjdGP/thQ4XPy8LZvvt2bx/dYsYkNsXJIYy6iEGKKCbN4OUURE5IgMbs3mVyuZmQXoDlZnMEBUVDC//rWPD9eksXjDPvJLKxawNRkNDOoQyeikWM5oG4bRYPBytE1DZZ3oZ9Y3qD58i+rDt9RnfVSe+5jllCDVjj5MNTv8h7vU4eTrTZl8uDaNtXsPrtPWKsyPSxNjGZkQQ3iA1YsRN376D8C3qD58i+rDtyhBagT0YarZ0X64t2QU8eHaNJZs2EfRgafczEYDgztFMbp7LD1bhWJQq1Kd038AvkX14VtUH75FCVIjoA9TzY7nh7vE4eR/f+7nw7XpbEg/uLjtaRH+XJoUy4iuMYT6W05RxI2f/gPwLaoP36L68C1KkBoBfZhqdqI/3H/uK+DDtWks3bifEocLAKvJwJC4aEYnxZLUIkStSrWk/wB8i+rDt6g+fIsSpEZAH6aanewPd2FZOV/+uZ9Fa9LYnHFwsdsOUQGMTopleNcYgmx6+PJk6D8A36L68C2qD9/iCwmST8yDNH/+fAYPHkxiYiKXXXYZa9euPWLZDz/8kLi4uCpfiYmJVcq43W5efPFFBg4cSFJSEuPGjWP79u1VyuTm5jJ58mR69uxJ7969eeCBBygq0urz3hZkMzOmewvmX9uTuVedzshuMdjMRrZmFvOvb7Zy4aurmP7lX6SkF6DcXkRE6ovXE6QlS5YwY8YMbr31Vj766CPi4+OZMGECWVlZRzwmKCiIH3/80fP17bffVtk/e/Zs3n77bR599FE++OAD/P39mTBhAmVlZZ4yU6ZMYcuWLcydO5dXX32V3377jYcffrje3qecGIPBQEJsCA9fEMcXk/oy5dwOtI8MoLTcxafr9zFufjLXvpPMh2v2UmQv93a4IiLSyHg9QZo7dy5jx45lzJgxdOzYkWnTpuHn58eiRYuOeIzBYCA6OtrzFRUV5dnndrt56623uPnmmxkyZAjx8fE888wz7N+/n2XLlgGwdetWfvjhBx5//HG6d+9O7969eeihh1i8eDH79u2r9/csJybYz8zlPVuy4PpezL68Oxd2aYbVZOCv/YXMWLaF4a/+zIyvNvPXvkJvhyoiIo2EVxMku91OSkoK/fv392wzGo3079+f5OTkIx5XXFzMueeey6BBg7j55pvZvHmzZ9/u3bvJyMiocs7g4GC6d+/uOWdycjIhISFVuub69++P0Wg8aveeeJfBYOD0VqE8NjyexZP6cueg9rQJ96fY4eTDtWlc887vjJufzKfr0ilxaJFcERE5eV4d7ZqTk4PT6SQyMrLK9sjISFJTU2s8pl27djz55JPExcVRUFDAm2++yRVXXMHixYtp3rw5GRkZnnMcfs7MzEwAMjMziYiIqLLfbDYTGhrqOf546cGqmlXel/q6P+EBFq45oxVX927J6l15LFqTxrebM0lJLyAlvYB/f7eV4V1jGJ0US8fowPoJooGp7zqRE6P68C2qD99Sn/VxvOdscI8D9ejRgx49elR5PXz4cBYsWMCdd955yuOJjDz2SPim7FTcnwuiQ7igZ2syC8v472+7ee+XnezMLuaD5L18kLyX3m3DubpvGy5MiMXPYqr3eHydfmZ9i+rDt6g+fIs368OrCVJ4eDgmk6nagOysrKwq44qOxmKx0KVLF3bu3AlAdHS05xzNmjWrcs74+HgAoqKiyM7OrnKe8vJy8vLyPMcfr6wsPRJaE4Oh4gf7VN+fyxKaMaZbNL/syGHRmnS+35LJbzty+G1HDo9+ksKIbjGM7h7LaREBpy4oH+GtOpGaqT58i+rDt9RnfVSe+1i8miBZrVa6devGypUrGTJkCAAul4uVK1dyzTXXHNc5nE4nmzZtYtCgQQC0atWK6OhoVq5cSZcuXQAoLCxkzZo1XHnllUBFq1N+fj7r168nISEBgFWrVuFyuUhKSjqh9+B2ow/TUXjj/hgw0KdtBH3aRpBRWMan69P5eG066QVlvLt6D++u3kOv1qGMTorlnI5RWM1ef1bhlNLPrG9RffgW1Ydv8WZ9eL2Lbfz48dx7770kJCSQlJTEvHnzKCkpYfTo0QDcc889xMTEMHnyZABmzZrF6aefTtu2bcnPz2fOnDns3buXyy67DKgYyHvdddfxyiuv0LZtW1q1asWLL75Is2bNPElYhw4dOOuss5g6dSrTpk3D4XAwffp0RowYQUxMjHduhNSL6CAbE/q2ZdyZbVi5PZsP16SxYls2q3flsXpXHuH+FkYmxHBpUiytwvy9Ha6IiPgIrydIw4cPJzs7m5kzZ5KRkUGXLl144403PF1saWlpGI0H/8LPz89n6tSpZGRkEBoaSrdu3ViwYAEdO3b0lLnxxhspKSnh4YcfJj8/n169evHGG29gs9k8ZZ599lmmT5/O9ddfj9FoZOjQoTz00EOn7o3LKWUyGhjYPpKB7SNJzy/lk3XpfLI+nYxCO2/9upu3ft1Nn7ZhjO7egrPbR2A2Na1WJRERqUpLjdSSpqWvWUOYtr/c5ebHrVl8uDaNVdtzqAwzMtDKxQkxXJIUS2yIn1djrEsNoU6aEtWHb1F9+BZfWGrE6y1IIt5iNho4p1MU53SKYk9eCR+vTefT9elkFdl58+ddzP15F/3bRTC6eywD20dg1PO/IiJNhhIkEaBlqD+3ntWOif3b8t2WilalX3fmsmJbNiu2ZdOzVSjTLoyjeSNqURIRkSPTQAuRQ1hMRobERfPyZUks+vsZXNO7Ff4WI7/vzuPKt1bzvz/3eztEERE5BZQgiRxBm3B//jGoPfOv7UVCbDCFZU4eXPwnU5f8SWGZFsgVEWnMlCCJHEPrcH9mX96dG/q2wWiApRv3c9Vbq0neneft0EREpJ4oQRI5DmaTkUkDTmP2FafTMtSPtPwybvpgDS//uA2H0+Xt8EREpI4pQRI5AUktQph/XU9GdovB5Ya5P+9iwnt/sD272NuhiYhIHVKCJHKCAq1mHr4gjqdGdiHEz8zGfYVc8/bvLFqzF00rJiLSOChBEjlJ53WO5r3renFGmzDKyl08tWwLkz9OIbvY7u3QRESklpQgidRCs2Abs/6WyD/PaY/FZOCH1GyunLeaFanZ3g5NRERqQQmSSC0ZDQau6tWKeVf3oENUANnFDu78aD1PL9tMqcPp7fBEROQkKEESqSOdooOYd3VPrujZEoCFa9K49p3f+WtfoZcjExGRE6UESaQO2cxGJp/bgf+MSSAq0Mr27BLGvZvMvF924XRpALeISEOhBEmkHvQ9LYL3ruvFOR0jKXe5mfXDNm7571rS80u9HZqIiBwHJUgi9SQswMIzo7oydWjnKuu5fblR67mJiPg6JUgi9chgMDAqsXmV9dweWqL13EREfJ0SJJFToHI9txv7HVzP7cp5q/l9d663QxMRkRooQRI5RcwmIxP7H1zPLb2gjJveX8tLP2g9NxERX6MESeQUO3Q9Nzfwf79oPTcREV+jBEnECyrXc3ta67mJiPgkJUgiXjT4wHpuZx6ynttdWs9NRMTrlCCJeFmzYBv/OWQ9tx8PrOf2Y2qWt0MTEWmylCCJ+IDK9dzeurqnZz23f36UovXcRES8RAmSiA/pGB3IvKt7cuVh67n9ua/Ay5GJiDQtSpBEfIzNbOSuczswa0yiZz238e/+ofXcREROISVIIj6qz2nhvHd9L87tFKX13ERETjElSCI+LMzfwtMjuzB1WGcCLCat5yYicoooQRLxcQaDgVEJzZl/XU8SD1vPraBU67mJiNQHJUgiDUSrMH9ev+J0JvZri+nAem5XvaX13ERE6oMSJJEGxGw0cGP/trx+2Hpus7Sem4hInVKCJNIAVa7nNiqhYj23eZXruWVpPTcRkbqgBEmkgQq0mpk6LI6nR3UltHI9t3e0npuISF1QgiTSwA3uFMW7Ws9NRKROKUESaQQOXc/NqvXcRERqTQmSSCNRuZ7bvKt70jEq0LOe21Naz01E5IQpQRJpZDpGB/J/V/fgql4V67kt0npuIiInTAmSSCNkMxv55zkV67lFB1Ws53b9/D/4x4Jklm7cT26Jw9shioj4NINbj7vUSmZmAbqD1RkMEBUVrPvjA3JLHMz4ajPfbM70bDMaICE2hAHtIhjQLoLOzQIxGAxejLLp0WfEt6g+fEt91kfluY9ZTglS7ejDVDP9svEtbrebtWn5rE4rZFlKOpsziqrsjwq00r9dOAPaRXBm23CCbGYvRdp06DPiW1QfvkUJ0gHz589nzpw5ZGRkEB8fz9SpU0lKSjrmcYsXL+auu+7ivPPO4+WXX/Zsj4uLq7H83XffzQ033ADA4MGD2bNnT5X9kydPZuLEiScUuz5MNdMvG99zaJ2k55exYls2P6Vm88vOHEocB2fhNhkN9GgZQv92EQxoH0G7iAC1LtUDfUZ8i+rDt/hCguT1PxOXLFnCjBkzmDZtGt27d2fevHlMmDCBpUuXEhkZecTjdu/ezdNPP03v3r2r7fvxxx+rvP7+++958MEHGTZsWJXtd9xxB2PHjvW8DgwMrOW7EWkYYoJtjE6KZXRSLPZyF8l78vhpWzYrUrPZkVPCb7vy+G1XHjO/30ZsiK0iWWoXwRltwvCzmLwdvohIvfN6gjR37lzGjh3LmDFjAJg2bRrLly9n0aJFR2zNcTqdTJkyhdtvv53Vq1eTn59fZX90dHSV119//TV9+vShdevWVbYHBgZWKyvS1FjNRvq0DadP23D+eU4HdueW8NO2bH5MzWb1rlzS8stYtCaNRWvSsJoM9GodVjF2qX0ErcL8vR2+iEi98GqCZLfbSUlJYdKkSZ5tRqOR/v37k5ycfMTjXnrpJSIjI7nssstYvXr1Ua+RmZnJd999x1NPPVVt3+zZs3nllVeIjY3loosuYty4cZjNJ3ZL1PNQs8r7ovvjO463TlqH+3N5eEsu79mSUoeT33bmsuJAwpSWX8bK7Tms3J7Ds99upU24PwPaV7Qu9WwVitWsB2OPlz4jvkX14Vvqsz6O95xeTZBycnJwOp3VutIiIyNJTU2t8ZjffvuNhQsX8vHHHx/XNT766CMCAwMZOnRole3XXnstXbt2JTQ0lOTkZJ5//nkyMjK4//77T+g9REYeux+zKdP98T0nWietYsO4pM9puN1utmYU8s2f+/n2zwx+3Z7NzpwSdq7ew3ur9xBgNTGgYxTnxjXjnLhoWqh16bjoM+JbVB++xZv14fUuthNRWFjIPffcw/Tp04mIiDiuYxYtWsTIkSOx2WxVto8fP97zfXx8PBaLhUceeYTJkydjtVqPO6asLA3oq4nBUPGDrfvjO+qiTsKMMLprM0Z3bUZhWTm/7KhoXVqRmk1mkZ2vNuzjqw37AOgYFXigdSmcpBYhmE1qXTqUPiO+RfXhW+qzPirPfSxeTZDCw8MxmUxkZVVdLyorK4uoqKhq5Xft2sWePXu4+eabPdtcroqnb7p27crSpUtp06aNZ99vv/3Gtm3beOGFF44ZS/fu3SkvL2f37t20b9/+uN+D240+TEeh++N76qpOAq1mzu0UxbmdonC73WzaX1SRLG3LZn1aPlsyi9iSWcS8X3YRZDPRt20EA9qH0++0CCIDj/+PkMZOnxHfovrwLd6sD68mSFarlW7durFy5UqGDBkCVCQ8K1eu5JprrqlWvn379nz22WdVtr3wwgsUFRXx4IMP0rx58yr7Fi5cSLdu3YiPjz9mLBs3bsRoNB71yTkRqZnBYCAuJoi4mCD+3rcNuSUOft6eUzGVwLZs8krLWbYpg2WbMgDoEhPkGejdJSYYk1EDP0TEt3i9i238+PHce++9JCQkkJSUxLx58ygpKWH06NEA3HPPPcTExDB58mRsNhudO3eucnxISAhAte2FhYUsXbqUe++9t9o1k5OTWbNmDX379iUwMJDk5GRmzJjBqFGjCA0Nrad3KtJ0hPlbGNalGcO6NMPpcrNxXwE/plYkSxv3FXq+3li1kzB/i2eSyj5twwn1t3g7fBER7ydIw4cPJzs7m5kzZ5KRkUGXLl144403PF1saWlpGI0nPnZh8eLFuN1uLrroomr7rFYrS5YsYdasWdjtdlq1asW4ceOqjEsSkbphMhpIiA0hITaEmwacRmaRnZUHWpZWbs8ht8TBkg37WbJhP0YDJMaGMKB9BP3bRdA5WkugiIh3+MRM2g2ZZl2tmWal9T2+WCflThdr9uZXTFK5LZutmcVV9kcHWT2TVJ7ZNoxAq9f/pqszvlgfTZnqw7f4wkzaSpBqSR+mmumXje9pCHWSnl96IFnK4ZcdOZSWH1wCxWw0cHqrUAa0i6B7ixA6RAUSYG24s3o3hPpoSlQfvsUXEqTG8+eYiDR4zUP8GN29BaO7t6Cs3EXy7lxWbMvhp20Vcy79tjOX33bmAmAAWoX50TE6iE5RgXSKDqRjdCAtQv0wqltORGpJLUi1pL82aqa/xnxPQ6+TnTklB8YtZfPX/iKyiuw1lguwmOgQFUjnZoF0PJA4dYgKJMjmW38PNvT6aGxUH75FLUgiIsepTbg/bcJbckXPlgBkF9vZnFHElowiNmdW/JuaVUSxw8m6tHzWpVVdo7FFqB+doipamTpHB9IxOohWYWptEpGaKUESkQYpIsBKn7ZW+rQN92wrd7rYkVPiSZo2ZxSyJaOI/YV29uaVsjevlO+2HpyY1s9spGP0wZamjtGBdIoKIthPvxpFmjr9FhCRRsNsMtIhqqJLbdgh23NLHIe0NBWyOaOI1KxiSstdrE8rYH1aQZXzNA+2VWlp6hQVSOtwf01oKdKEKEESkUYvzN9C7zZh9G4T5tlW7nKzO6fE09JU2V2XXlDm+foxNdtT3mY20j4y4EBLU1BF8hQVqIktRRopJUgi0iSZjQZOiwzgtMgAzo+L9mwvKC1nc2ZF19zmA19bM4soLXd5ZgCHfZ7yzYKsdIoOOtA9V9FN1zYiALNam0QaNCVIIiKHCPYz07NVGD1bhXm2OV1u9uSVsiWjkE2HDAzfm1fK/kI7+wsrJrqsZDUZaBd5yIDwA2OcwgO0SK9IQ6EESUTkGExGw4Gn6PwZ3Plga1NhWTlbMytambZkFrFpf0VrU7HDyV/7C/lrfyGLDzlPZKCVTgdamjo1CySpnRM/l5Nwf4uephPxMUqQREROUpDNTPeWoXRveXCRa5fbzd680oNddAcGhu/OLSWryE5WkZ1V23MOlP4LqGhxigm2ERPiR2ywjeYhNpqH+NE8uOLfmGAbNvOJr0kpIidPCZKISB0yGgy0CvOnVZg/53SK8mwvtjsrWpsOzNm0JbOQtHw7+/JLsTvd7MotZVdu6RHPGxFgoXmIH7EhNmIOJE6eZCrYj1B/sxb2FalDSpBERE6BAKuJxBYhJLYIAQ7O5pu+L499BXbS8kvZV1BGen4ZafmlpBeUse/A96XlLrKLHWQXO9iQXlDj+f3MRk+yVNECVfX7ZkE2LCa1QokcLyVIIiJeZDYZaRHqR4tQvxr3u91u8krLPclS+oEkKr2g9MC/ZWQV2Sktd7E9u4Tt2SU1nscARAdZiTmQNFW0RB38vnmwH0E2k1qhRA5QgiQi4sMMBgNh/hbC/C3ExQTVWKas3MX+goqkKS2/ouXJ831BGekHuvEqnrizsy6t5msFWk1VW56CDx0LZSMqyKbpC6TJUIIkItLA2cxGWof70zrcv8b9breb7GLHgW67isQp/UDiVNkKlVvioMjuZGtmMVszi2s8j8kA0UEHWp8OJE6xITY6Nwuia/NgPYknjYoSJBGRRs5gMBAZaCUy0Eq35jWvYl7icFZpeTo8mdpXUIbT5fbMMs6eqosBRwRYOKtDJIM6RHJGmzD8LKZT8dZE6o0SJBERwd9i8swsXhOny01Wkb1ay9PevFL+2JNHdrGDT9al88m6dPzMRvqeFs7ZHSI5q30kYQFajkUaHiVIIiJyTCajgWbBNpoF20g68CReJYfTxe+78vhuaxbfb81iX0EZy7dksXxLFkYDJLUI4ewOkZzdIZK2ETUnYCK+xuB2u93eDqIhy8wsQHewuspHmHV/fIfqxLc01vpwu91s2l/Ed1sz+X5rNn/tL6yy/7QIf87uEMXZHSJIiA3B5CODvhtrfTRU9Vkflec+FrUgiYhInTEYDMTFBBEXE8TE/qeRnl/K9wdaln7blXdgKoJdvPXrLiICLAxsH8HZHaLo01bjlsS3qAWplvTXRs3015jvUZ34lqZYH4Vl5fy0LZvvt2axYls2hWVOzz6b2UiftuEM6hDJwA4RRJzihX2bYn34MrUgiYhIkxFkMzM0vhlD45vhcLpI3p3H91uz+G5LFukFZZ6WJgOQ2CKEQQfGLR1p4LhIfVILUi3pr42a6a8x36M68S2qj4PcbjebM4oqBnlvyeLPw8YttQn35+wDUwgktqifcUuqD9/iCy1ISpBqSR+mmumXje9RnfgW1ceR7atsTdqSxW+7cil3HbxBYf4V45YGdYikz2nh+NfRuCXVh2/xhQRJXWwiIuJTYoJtXHZ6Cy47vQWFZeWs3J5TMW4pNZvcEgefp+zj85R92MxGzmgTdmDcUiRRgad23JI0bkqQRETEZwXZzJwfF835cdGUO10k78nj+63ZfL8lk735ZfyYms2PqdkYvtpMQmxwxWzeHSNpFxGghXelVtTFVktqjq2Zmqt9j+rEt6g+asftdrMls8gzyHvjvqrjllqH+XmSpaQWocdcZFf14Vt8oYtNCVIt6cNUM/2y8T2qE9+i+qhb+wvK+CG1Iln6bVcuDufBmxrqZ66Yb6ljFH3bhhNgrT5uSfXhW3whQVIXm4iINHjNgm2M6d6CMd1bUGQvZ9WBcUs/pmaTV1rO4g37WbxhP1aTgTPahHN2x0jObh9BVJDN26GLj1KCJCIijUqg1cx5naM5r3M05S43a/YcnG9pT14pK7Zls2JbNjOAbs2DK6YQ6BhJZGSQt0MXH6IutlpSc2zN1Fzte1QnvkX1ceq53W62ZhXzw4FkKSW9oMr+8AALzYJsxARX/2oWbKVZkA2Lyeil6JsWdbGJiIicIgaDgY5RgXSMCmR8nzZkFJbxQ2o232/J4tedOeQUO8gpdlRbYNdzPBARaK2aOAVVfR0VZDvmgHBpGJQgiYhIkxQdZGN0Uiyjk2IpdTjJx8Bfu3LYl1/GvoKqX/sLy3A43WQV2ckqsrPhsNanSkYDRAVaiQn2IybYSrMDiVPzQ5KoiEArRk1B4POUIImISJPnbzXROiqYGKuxxi4dl9tNbomjImHKr548Vfxrx+lys7/Qzv5CO+vSar6WyWio1vJU0RplIyak4vtwf4vmcfIyJUgiIiLHYDQYiAiwEhFgpUtMzeNXnC432cX2imSpoIz0ygTqkGQqs6giiUrLLyMtv+yI17OaDJ7WpyONiwrxMyuJqkdKkEREROqAyWggOshGdJANYmsuU+5yk1l4eAuUvcrr7CI7dqeb3bml7M4tPeL1/MxGTxJ16FdEgJUAqxF/iwk/iwl/i5EAiwl/iwmb2aik6jgpQRIRETlFzEYDzUP8aB7id8QyDqeL/YVl7C+wVx8LdeDfnBIHpeUuduaUsDOn5LivbwD8LBXJ08EvI34WEwEW02H7qpbzsxgJsJqqJF6H7reaDI0q+fKJBGn+/PnMmTOHjIwM4uPjmTp1KklJScc8bvHixdx1112cd955vPzyy57t9913Hx999FGVsgMHDmTOnDme17m5uUyfPp1vv/0Wo9HI0KFDefDBBwkMDKy7NyYiInKCLCYjLUP9aRnqf8QyZeWuKl13+w9plcopdlDicFLqcFLscFHicFJW7gLADZQ4XJQ4XICjTuM2GqiWdB0x0bLUkGhZD34fYDHhF3zk938qeD1BWrJkCTNmzGDatGl0796defPmMWHCBJYuXUpkZOQRj9u9ezdPP/00vXv3rnH/WWedxYwZMzyvrdaqqzxPmTKFjIwM5s6di8Ph4IEHHuDhhx/mueeeq5s3JiIiUk9sZiOtw/1pHX58SYTL7abU4aL4QOJU4nAeSJSclNidlJRXvC51OCm2H/y+5PCyDme189gPLOvickOR3UmR3Vkn7zHQauL9cb2ICT5ya1t98nqCNHfuXMaOHcuYMWMAmDZtGsuXL2fRokVMnDixxmOcTidTpkzh9ttvZ/Xq1eTn51crY7VaiY6OrvH4rVu38sMPP7Bw4UISExMBeOihh5g4cSL33HMPMTExdfTuREREvM9oMBBgNdW4Dl1tlbvc1ZKuitarqolWsb0iuSqpIfEqPfB98SHfNwux4Wep+3iPl1cTJLvdTkpKCpMmTfJsMxqN9O/fn+Tk5CMe99JLLxEZGclll13G6tWrayzzyy+/0K9fP0JCQujbty933nkn4eHhACQnJxMSEuJJjgD69++P0Whk7dq1nH/++cf9HhpRd2udqrwvuj++Q3XiW1QfvkX1cfIsJgMWk5lgv7pLKQwGiIwMJiurfmbSPh5eTZBycnJwOp3VutIiIyNJTU2t8ZjffvuNhQsX8vHHHx/xvGeddRbnn38+rVq1YteuXTz//PPceOONvP/++5hMJjIzM4mIiKhyjNlsJjQ0lIyMjBN6D5GRx56uvCnT/fE9qhPfovrwLaoP3+LN+vB6F9uJKCws5J577mH69OnVEpxDjRgxwvN9XFwccXFxDBkyxNOqVJfqI7ttDOoz+5eTozrxLaoP36L68C313YJ0PImXVxOk8PBwTCYTWVlZVbZnZWURFRVVrfyuXbvYs2cPN998s2eby1UxMr9r164sXbqUNm3aVDuudevWhIeHs2PHDvr160dUVBTZ2dlVypSXl5OXl3fEcUtH4najD9NR6P74HtWJb1F9+BbVh2/xZn14NUGyWq1069aNlStXMmTIEKAi4Vm5ciXXXHNNtfLt27fns88+q7LthRdeoKioiAcffJDmzZvXeJ309HRyc3M9yU+PHj3Iz89n/fr1JCQkALBq1SpcLtdxTS8gIiIijZvXu9jGjx/PvffeS0JCAklJScybN4+SkhJGjx4N4HmqbPLkydhsNjp37lzl+JCQEADP9qKiImbNmsWwYcOIiopi165d/Otf/6Jt27acddZZAHTo0IGzzjqLqVOnMm3aNBwOB9OnT2fEiBF6gk1ERES8nyANHz6c7OxsZs6cSUZGBl26dOGNN97wdLGlpaVhNBqP+3wmk4lNmzbx8ccfU1BQQLNmzRgwYAD/+Mc/qsyF9OyzzzJ9+nSuv/56z0SRDz30UJ2/PxEREWl4DG63eltrIzNTA/pqYjBAVFSw7o8PUZ34FtWHb1F9+Jb6rI/Kcx/L8TfNiIiIiDQRSpBEREREDqMESUREROQwSpBEREREDqMESUREROQwSpBEREREDqMESUREROQwXp8osqEzGLwdgW+qvC+6P75DdeJbVB++RfXhW+qzPo73nJooUkREROQw6mITEREROYwSJBEREZHDKEESEREROYwSJBEREZHDKEESEREROYwSJBEREZHDKEESEREROYwSJBEREZHDKEESEREROYwSJBEREZHDKEGSOvXaa68xZswYevToQb9+/bjllltITU31dlhywOuvv05cXBxPPPGEt0Npsvbt28eUKVPo06cPSUlJjBw5knXr1nk7rCbL6XTywgsvMHjwYJKSkhgyZAgvvfQSWoXr1Pj111+56aabGDhwIHFxcSxbtqzKfrfbzYsvvsjAgQNJSkpi3LhxbN++/ZTEpgRJ6tQvv/zC1VdfzQcffMDcuXMpLy9nwoQJFBcXezu0Jm/t2rUsWLCAuLg4b4fSZOXl5XHllVdisViYPXs2ixcv5t577yU0NNTboTVZs2fP5r333uPhhx9myZIlTJkyhTfeeIO3337b26E1CcXFxcTFxfHII4/UuH/27Nm8/fbbPProo3zwwQf4+/szYcIEysrK6j02c71fQZqUOXPmVHn91FNP0a9fP1JSUjjjjDO8FJUUFRVx99138/jjj/PKK694O5wma/bs2TRv3pwZM2Z4trVu3dqLEUlycjLnnXce55xzDgCtWrVi8eLFrF271ruBNRGDBg1i0KBBNe5zu9289dZb3HzzzQwZMgSAZ555hv79+7Ns2TJGjBhRr7GpBUnqVUFBAYD+Qvayxx57jEGDBtG/f39vh9KkffPNNyQkJHDHHXfQr18/LrnkEj744ANvh9Wk9ejRg1WrVrFt2zYA/vzzT1avXs3ZZ5/t5chk9+7dZGRkVPm9FRwcTPfu3UlOTq7366sFSeqNy+XiySefpGfPnnTu3Nnb4TRZixcvZsOGDSxcuNDboTR5u3bt4r333mP8+PHcdNNNrFu3jscffxyLxcKll17q7fCapIkTJ1JYWMiFF16IyWTC6XTyz3/+k1GjRnk7tCYvIyMDgMjIyCrbIyMjyczMrPfrK0GSejNt2jQ2b97Mu+++6+1Qmqy0tDSeeOIJ3nzzTWw2m7fDafLcbjcJCQncddddAHTt2pXNmzezYMECJUhe8sUXX/DZZ5/x3HPP0bFjRzZu3MiMGTNo1qyZ6qSJU4Ik9eKxxx5j+fLlvPPOOzRv3tzb4TRZKSkpZGVlMXr0aM82p9PJr7/+yvz581m3bh0mk8mLETYt0dHRdOjQocq29u3b8+WXX3opInnmmWeYOHGiZzxLXFwce/fu5bXXXlOC5GXR0dEAZGVl0axZM8/2rKws4uPj6/36SpCkTrndbqZPn85XX33F22+/rQGoXta3b18+++yzKtvuv/9+2rdvz4033qjk6BTr2bOnZ6xLpe3bt9OyZUsvRSSlpaUYDIYq20wmkx7z9wGtWrUiOjqalStX0qVLFwAKCwtZs2YNV155Zb1fXwmS1Klp06bx+eef8/LLLxMYGOjpQw4ODsbPz8/L0TU9QUFB1cZ/BQQEEBYWpnFhXnD99ddz5ZVX8uqrr3LhhReydu1aPvjgAx577DFvh9ZknXvuubz66qu0aNHC08U2d+5cxowZ4+3QmoSioiJ27tzpeb179242btxIaGgoLVq04LrrruOVV16hbdu2tGrVihdffJFmzZp5nmqrTwa30mSpQ0eaY2fGjBlVunnEe6699lri4+N58MEHvR1Kk/Ttt9/y/PPPs337dlq1asX48eMZO3ast8NqsgoLC3nxxRdZtmyZpytnxIgR3HrrrVitVm+H1+j9/PPPXHfdddW2X3rppTz11FO43W5mzpzJBx98QH5+Pr169eKRRx6hXbt29R6bEiQRERGRw2geJBEREZHDKEESEREROYwSJBEREZHDKEESEREROYwSJBEREZHDKEESEREROYwSJBEREZHDKEESETlJcXFxLFu2zNthiEg90FIjItIg3XfffXz00UfVtg8cOJA5c+Z4ISIRaUyUIIlIg3XWWWcxY8aMKtu0PISI1AV1sYlIg2W1WomOjq7yFRoaClR0f7377rvccMMNJCUlcd5557F06dIqx//1119cd911JCUl0adPH6ZOnUpRUVGVMgsXLmTEiBEkJCQwcODAagvL5uTkcOutt9K9e3eGDh3K119/7dmXl5fH5MmT6du3L0lJSQwdOpRFixbV090QkbqkBElEGq0XX3yRYcOG8cknnzBy5Ejuuusutm7dCkBxcTETJkwgNDSUhQsX8sILL/DTTz8xffp0z/Hvvvsujz32GGPHjuWzzz7j5Zdfpk2bNlWuMWvWLC688EI+/fRTzj77bKZMmUJubq7n+lu3bmX27NksWbKERx99lPDw8FP2/kXk5KmLTUQarOXLl9OjR48q2yZNmsRNN90EwAUXXMBll10GwJ133slPP/3E22+/zaOPPsrnn3+O3W7n6aefJiAgAICHH36Ym266iSlTphAVFcUrr7zC+PHjuf766z3nT0pKqnK9Sy+9lIsuugiAu+66i7fffpu1a9dy9tlns3fvXrp06UJiYiIArVq1qp8bISJ1TgmSiDRYffr04dFHH62yrbKLDaiWPJ1++uls3LgRgK1btxIXF+dJjgB69uyJy+Vi27ZtGAwG9u/fT79+/Y4aQ1xcnOf7gIAAgoKCyM7OBuDKK6/kjjvuYMOGDQwYMIAhQ4bQs2fPk3qvInJqKUESkQbL39+ftm3b1su5bTbbcZWzWCxVXhsMBlwuFwCDBg3i22+/5bvvvmPFihWMGzeOq6++mnvvvbfO4xWRuqUxSCLSaP3xxx9VXq9Zs4YOHToA0KFDB/766y+Ki4s9+3///XeMRiPt2rUjKCiIli1bsnLlylrFEBERwaWXXsqzzz7LAw88wPvvv1+r84nIqaEESUQaLLvdTkZGRpWvyu4tgKVLl7Jw4UK2bdvGzJkzWbt2Lddccw0AI0eOxGq1ct9997Fp0yZWrVrF9OnTufjii4mKigLg9ttvZ+7cubz11lts376dlJQU3n777eOO78UXX2TZsmXs2LGDzZs3s3z5ck+CJiK+TV1sItJg/fDDDwwcOLDKtnbt2nke57/99ttZsmQJ06ZNIzo6mueee46OHTsCFd1zc+bM4YknnuBvf/sb/v7+DB06lPvuu89zrksvvZSysjL+7//+j2eeeYawsDAuuOCC447PYrHw/PPPs2fPHvz8/OjVqxfPP/98HbxzEalvBrfb7fZ2ECIidS0uLo6XXnqJIUOGeDsUEWmA1MUmIiIichglSCIiIiKHURebiIiIyGHUgiQiIiJyGCVIIiIiIodRgiQiIiJyGCVIIiIiIodRgiQiIiJyGCVIIiIiIodRgiQiIiJyGCVIIiIiIodRgiQiIiJymP8HJmQoosTwCrQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.45115020871162415\n",
            "Val Loss: 0.5418084859848022\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ROTWGGQIADB48AEuXLpK6DCIiIru59ARgZxQX1+E/Hx8z5nGMHfuk3dedN28hlErOHSIiIufDMONkVq3aYPv/33/fhO++m41Fi5bbjrm5udv+XxRFmM1mKBQ3b2ZfX986czcTERFVnSiKEAFYROv/X/+3RRQhXv83yj8OiJC5Sf+LMMOMk/H3D7D9v6enJwRBsB37++/9mDBhHD7+eAbmzv0ap0+fxPTpXyA4uB5mzZqOo0ePQKcrRcOGjfHkk8+gY8fOtmsNHjwADz44HA8+OAKAtQdo0qQ3kJT0J/bu3YXAwCCMH/8c4uK61+4XTETkxERRRIHOhEuFemQW6nGpwPp3ZqEOV0qMsPwrPFQaIoDygcIWPqr2udd/XNn5t7vCmCAAHwxsjl7NAm//BbtFDDOVEEURulrspdAoZNV6983s2V9g/PiJCAkJg5eXFzIzM9Glyx144omnoVSqsGHDWkya9AIWLVqOevXq3fA68+bNxVNPPYtnnpmIZct+xttvv4nlyxPh7a2ttlqJiJyZzmi2BZVMW1DR41KhzhZeavPnSW0QAMgE612jMgHwdlPCx00paU0MM/8iiiIeW5KMQxkFtfacrUO8Mfeh1tUWaB577El07NjF9rG3txbNmkXaPn788aewY8dW7Ny5HUOGDLvhde6551707dsPAPDkk89g2bIl+Oefo+jSpVu11ElE5MhMFhHZReV7VGzBpVCPSwU65OuqtpaVr5sSwV5q1PNWI9jL+sffQwWlXGYNBrgWDv79twwCBAGQCdf+rup5tr8Buz+3svPK6iwjCEBAgBeyswsl3duPYaYSzr5CSXR0i3Ifl5SU4Pvv52DXrj+Rk5MNs9kMvV6PzMxL/3mdiIhmtv93c3ODh4cHcnOv1EjNRES1SRRF5JUa/zOoZBcbrs4L+W/uSrk1oFwNKvW8rgWWet4aBHmqoFHKa/6LcmEMM/8iCALmPtTaqYeZNBq3ch9/+eXn2LdvD5555jmEhTWAWq3GG29MgtH4379R/HvisCAIEOvCttpEVOeVGMzXhnsKygeVsj/6Knyfl8sEBHuqEOytKRdUru9h8VIruFCnxBhmKiEIAtzqUIo+fDgZ/fsPRPfuPQFYe2ouXcoA0F7awoiIboHRbEFGvg4XC3T/mlR77U9BFYd//NyVqPcfQcXPXQW5jEHF0THMuICwsHBs374Fd9xxJwAB3377NSxV6TslIqplFlFEbonxurt/rIHl8nXDQNnFhirNz/BUy68N93hpKgSVIE81VAquHVsXMMy4gGeffR7Tpr2DceP+D1qtD0aOHIXi4mKpyyIiF1SkN11394+uwlyVzEI9jOabJxWlXKgwP+XfQ0Geav6IcxWC6CKTICqbaW00GpCTcxH+/vWhVKqkKcxBKBSyOrVonjO3raPcHUBWbI+qM5gsuFzh7h/ddZNq9Sg2mG96HQFAgKfKFkqCrk6kDfZSo763Gs0b+kPU6SE4/e0azq8m/32UXbsqGFuJiOimLKKInGLDf979c6XEWKVreWsU1w3/qG13ApUNBQV5qqCQVz78IwhAgJca2fqqDTWRa2CYISJycaIoolBvqjyolM1ZKTLAVIW5dmqFrEJQuTZPxRpW3FV15wYLcgwMM0RELiC/1IgTl4v+FVSu9ayUGG8+/CMTgEDPGwUVa8+K1o23KVPtY5ghIqqjLhXosP1kDradzMaBC/m42bxan7JVaisJKsFeagR4qqHgbcrkgBhmiIjqCFEUcSqnBNtPZmNbag6OXy4q93gDHw1CtW7lVqu9PrxwlVpyVgwzREROzCKKOJxRgG0nc7D9ZDbS8nS2xwQArUO90aNpALo39UeYj9uNL0TkxBhmiKhaiaKIg+kFWH3kEvQmC9qEeqNdmA+aBLhDxrkU1cJgsmBfWh62n8zG9pM55e4iUsoFdG7oi+4R/ohv6g8/d+damoDoVjDMEFG1KDaYsP6fy1iWnIFT2SW245tOZAEAtBoF2oZp0TZMi3ZhWjQL9OQy8XYo0puQdOYKtp3MQdKZK+XWa/FQyRHXxA89mgaga2NfeKj4rZ1cC9/xRHRbTmYXY/nBDKz757Ltjhi1QoZ+zYNQ31uNAxfykZxegHydCdtO5mDbyRwA1qXm24Rq0TZUi3YNtIgO8rzh2iKuKrvYgB2ncrAtNRv7zueVuzU6wEOF7k390aOpP9o38IGSrx25MIYZFzV+/BNo1iwKEye+CAAYPHgAHnxwOB58cMQNPycurgPef/8TxMf3uK3nrq7rkHSMZgu2pmZjWfJFHLiQbzve0NcNQ9uEYECLYHhprn17MZktOJZZhL8v5OPvC3lITi9Akd6MP09fwZ+nrwAA3JQytA6xBpt2YVo0D/ZyyX1zzueWWifwnszB4YwCXH8DUrivG3o0DUDPZv5oUc+Lw3ZEVzHMOKFXXnkeJpMJ06fPqvBYcvIBPPPM4/jhh8Vo2rRZla85b95CKJXq6iwT3333Df74Yzt++GFRueOrVm2Al5d3tT4X1Y5LBTqsPHwJvx66aJunIReA7k0DMLRNfXRo4FPpGiMKuQytQrzRKsQbozo1gMkiIjWrCH+n5ePvC/k4mJ6PAp0Ju8/lYve5XADW3p1W9b3QLswH7Rpo0bKeV52820YURRy/XIRtqdYAczqnpNzjLet5Xe2BCUBjf3eJqiRybAwzTujeewfhjTdeweXLmQgKCi732Nq1qxEd3cKuIAMAvr6+tbY3k79/QK08D1UPiyhi37k8LEvOwI5TOSgb6QjwUCEhth4Gt6qPIC/7grBCJqB5sBeaB3thZIcwWEQRp7KLbeHmwIV85JYasT8tH/vT8oFd1omtLet5oV2YFu3CfNAqxNtpV5I1mS04kJ5/dQ2YHGQW6m2PyWUC2odp0aNZAOIj/BFs52tL5IoYZpxQt25x8PHxxbp1iRg9+jHb8ZKSEmzd+jsefngUpkx5HcnJB1BYWIDQ0DA88sgY9O3b74bX/PcwU1raeXzwwbs4duwoQkJCbcNR1/vqq5nYsWMbsrIy4ecXgLvu6ocxYx6HQqHAunWJmDdvLgDrsBIAvP76FPTvP7DCMNOpUycxY8YnOHLkMDQaDbp374Vnn30e7u7W30Lfe+8tFBUVolWrNvj554UwGk3o3fsuTJz4IhQKvoVrSoHOiDVHM7E8+SLO55bajndooMXQNiHoHuFfbXNcZIKAZoGeaBboiWHtQiGKIs5cKcGBC/m2gJNdbMDB9AIcTC/A93vSIJcJaB7saQs3rUO9HXqXZJ3RjF1nc7H9ZDb+OH0FBTqT7TGNQoZujf3Qvak/4pr4wVujlLBSIucj6b/8Xr16IT09vcLxESNGYMqUKcjKysJHH32EpKQkFBcXo3Hjxhg3bhzuvvvumi1MFAFT6c3Pqy4KN+vuaVU9XaFAv379sX79GowaNdbWrb9162ZYLGbcfXd/bNmyGQ8/PAru7h7YtetPTJ06BaGhYWjRIuam17dYLPjf/16Gr68/vvnmBxQXF2HmzE8rnOfu7o7//W8KAgICcerUSXz00Xtwd3fHyJGj0Lt3X5w+fQp79iTh88+/AgB4enpWuEZpaSleeGE8YmJa4dtvf0Rubi4++GAqPvvsI/zvf2/Zzvv77/3w9w/AzJnf4MKFNEyZ8hqaNYvEffclVPl1o6o5llmIXw5k4LcTWdBf7a3zUMlxb8tg3N+6Ppr4e9R4DYIgoIm/B5r4e2BI6xCIooi0PB0OXMizzrtJy8elQj2OXCzEkYuFmL/vAmQCEBXkabtbqk2oFlo3aUNBXqkRf57OwbbUHOw+l2t7PQHrarvxEX7o3jQAncJ96uQQGlFtkTTMLFu2DGbztdsLU1NTMWbMGPTrZ+1BmDRpEgoKCvD111/D19cXiYmJeO6557B8+XK0aNGiZooSRfisSIDy0v6auX4ljPU7Ii9hhV2BZsCAQVi0aAEOHPgL7dpZez7WrUtEjx69UK9efYwY8Yjt3KFDH8LevbuxZcvmKoWZ/fv34ty5s5g+/QsEBAQCAJ544hm89NKEcudd3ytUv34Izp8/h99//w0jR46CWq2Bm5sb5HLFfw4rbdq0AQaDAW+88Q7c3KwLer3wwsuYNOkFPPXUs/Dz8wcAeHl54/nnX4FcLkfDho3QtWsc/vprL8NMNdEZzdh0IgvLki/in0uFtuPNAj0wtE0I+kUHSTqkIwgCwn3dEO7rhkGt6gMAMvJ1+PtCnrX35kI+LuTpcCyzCMcyi7Dor3QIAJoGetjulmobpq2VNVcuXt1CYHslWwiEeKvRvWkAejTzR2yIllsDEFUTScOMn59fuY/nzJmD8PBwdOrUCQBw4MABTJkyBbGxsQCAp59+Gj/++COOHj1ac2EGsCtUSKVhw0Zo1SoWa9euRrt2HXDhQhqSkw9g7NjZMJvNWLBgHrZs2YSsrCyYTEYYDAao1ZoqXfvs2TMICqpnCzIAEBMTW+G833//DcuWLUF6ejpKS0tgNpvh7m7fb+3nzp1B06bNbEEGAFq1agOLxYLz58/Zwkzjxk0gl1/7YervH4DTp0/a9VxUUVpuKZYnX0Ti0Uu2YQ+lXEDvyEAMbV0fsSHeDrtpYIhWgxBtPdzbsh4A4HKh3jbf5u8LeTh7pRSpWcVIzSrG0oMZAIDGfu7WYHM14AR63v58lLItBLalWhew+/cWAs0CPdDj6gTeZoEeDvt6EjkzhxlgNhgMWL16NcaMGWP7x962bVusX78ePXr0gLe3N9avXw+9Xm8LO/ao7PtHpd9TBMHaS+LAw0xlBgwYhM8//xgvvjgJa9euRmhoGNq2bY+FC3/EL78sxoQJL6JJk6Zwc3PDzJmfwmQy3vyiVXTkyCG8886b+L//ewKdO3eFh4cnfv/9NyxZsrDanuN6/54bIwgCLJabT1gWBKfIpuWU1VtTdZstIv48fQXLDmZg19lc2/H63moMaV0f98XUg5+H860aG+ytxj0tgnBPiyAAQE6xAQcu5OOvNGu4OZVdgjNXrH+WJ18EYN2rqF0Dn6vzbrSor60Y+CtrD7NFxOGLBdiemoOtJ7Nx4botBGQCtxCoSTX974PsU5PtYc81HSbMbN68GYWFhUhIuDZs8Pnnn+P5559H586doVAooNFo8MUXX6Bhw4Z2X9/f36vCMZ1OhytXZJDLBSj+vZ6FsuL8Dkdz1113Y+bMT/H77xuxceM63H//UCiVchw5koz4+B4YMOBeANY5MGlp59G4cRPb1ykIAmQylPu6ZTLr6xAR0QSXL2ciLy/H1jtz/PgRALC9VkePHka9evUxduzjts/PzLwE4No11WoVRNFS8bW97jqNGzfB+vVrYDTqbb0z//xzCDKZDI0bN4ZCIYMgCBAE4V+1osKx61ksAmQyGXx9PaDRVK1HytFU9p69HVmFeizdn4ZFe84jPc8a1gUB6B4ZiEe6NESPqKA6tSJvQAAQ1dAfD139OLfYgL1nr2DP6SvYcyYH/1wsQFqeDml5l7DqsPW9G+rjhs5N/NClsT86N/FDuJ+77ZcrT607kk7m4Ld/LmHTP5eRXXTtDiSVQoY7mwbgrpbB6N08GAHV0OND/626/33Q7ZG6PRwmzCxfvhzx8fEIDr52q/GMGTNQUFCAH374Ab6+vti8eTOee+45/PTTT4iKirLr+jk5hRDF8seMRgMsFgvMZrHWbkuuTiqVBr169cVXX32BkpJi9Ot3L0wmC8LCGmDr1t9x4MABeHl54+eff8KVKzlo1Kix7esURREWC8p93RaL9XVo27YjGjQIx9tvT8bTT09ESUkxvv76SwCwvVahoWG4dOkiNmxYj+bNWyIp6U9s374VwLVrBgXVR0ZGOo4dO4bAwGC4u7tDpVKVu06fPv0wd+5svP22tZcnLy8Pn3zyIe6+uz+0Wuvt4qIoQhTFf9WKCseuZzaLsFgsyM0thlJZfT1StUEQrN8YKnvP2ksURSSnF+CXgxn4PSXbtoKs1k2BQTH1cH/r+raeg9wrRf91qTqhfbAH2gd74OmuDVCoMyE5PR9/XZ1QfDyzEOl5pVjxdzpW/G29MSHIU4W2DbRQKBXYeuzaCseAdQXjO5v4o3tTf3Rr7HdtTpHOgGydQYovzyVU578Pun012R5l164Khwgz6enpSEpKwqxZ1xaBO3/+PBYuXIg1a9agWTPrminR0dHYv38/fvrpJ7zzzjt2PYcoosILXRf+Idx77yCsWbMKXbveYetFGTVqLDIy0vHCC89Co9HgvvsScOedPVBcXLUfVjKZDO+//zE++OBdPPHEKNSrVx/PPfcyXnzxWds5cXHdMWzYCHz22UcwGIzo1u0OjB49Ft9/P8d2To8evbBjxxY8++w4FBUV2m7Nvp5Go8H06V9gxoxP8Nhjo8rdml0dKmt3Z3E7tRcbTNhw7DKWHbyIk9nFtuOt6nthaJsQ9I4MhPpqr5azvj63y1OtwB1N/HFHE+u8rBKDGYcy8m13Sx29VIjLRQZsPJZl+5xATxXiI/zRs2kA2jXQlttCwFVfR6k487/tukjq9hBEUfq3w6xZs/Dzzz9j27ZttrkRJ06cwH333Yd169YhIiLCdu7YsWMREhKCd999167nyM6uvGcmJ+ci/P3rQ6l0vjkC1UmhkDll79SNOHPbCgIQEOBV6Xv2Zk5lF2N58kWs+yfTthFh2T5JQ1vXR3Qwu+arSmc048jFQhxIz4dGo0S7+p5oHswtBKR2O/8+qPrVZHuUXbsqJO+ZsVgsWLFiBQYPHlxukmeTJk3QsGFDTJ48GZMmTYKPjw82b96MnTt34ptvvpGwYiLHUrZP0vLki/j7un2Swm37JAVxEbZboFHK0SHcBx0b+vCHJ5GDkzzMJCUlISMjA0OGDCl3XKlUYs6cOfj0008xbtw4lJSUIDw8HB988AG6d+8uUbVEjuNG+yTFNw3A0Nb10TG88n2SiIjqGsnDTFxcHE6cOFHpY40aNSo3j4bI1d1onyR/DxUSWtXD4Nj63MuHiFyO5GGGiG7uRvsktW+gxdDWIejRtPr2SSIicjYMM7Devkp1S11p02OZhVh2MAMbj5ffJ2lAi2AMaVM7+yQRETk6lw4zZcvjGwx6qFTsmq9LDAbrgmZyufO9xXVGM5b9dQHz/jiNo//eJ6l1ffRrHizpPklERI7G+b7TVyOZTA43N08UFVmXdFep1C47YdJiEWA2O39vhiiKMBj0KCrKhZubJ2Qy5xl60RnN+OVgBubvu4C8UuuEXqVcQK9mAXigTYhD75NERCQllw4zAODtbd3ssizQuCqZTFalvY6chZubp61tHZ3BZMHKQxcxb28acoqtK8eG+rhhcKtg6z5JtbDTMxGRM3P5MCMIArRaf3h5+cJsNkldjiQEAfD19UBubnGdWEdDLlc4RY+MyWzB6qOZ+H73eWQWWofF6nur8XjXhng0PgJ5daQ9iIhqmsuHmTIymQwymWv+BiwI1m0FlEojf3jWApNFxIZjmfh213mk51t3Ww7yVOH/uoTjvph6UClkvDOJiMgODDNEtcQiith8Igtzks7h3NXbq/3clRjdORz3x9a37ZVERET2YZghqmGiKGLbyRx8k3QWp7JLAABajQKPdmyAB9qGwE3JO5OIiG4HwwxRDRFFEUlncjF751kcv2zdsdxTLcfI9mF4qF0oPNX850dEVB343ZSomomiiH3n8zB751kcvmhdJ8ZdKcdD7UIwskMYN30kIqpmDDNE1ejAhXzM3nnWtnu1WiHDA21C8GjHMPjyFmsiohrBMENUDY5eLMDsneew+5x1vSKlXMD9sfUxulMDBHhydWkioprEMEN0G05cLsI3O8/ij9NXAABymYD7YoLxf53DUc9bI3F1RESugWGG6BaczinGnKRz+D0lGwAgE4B7WgTjsS7hCPNxk7g6IiLXwjBDZIfzuaWYu+scNh67DBGAAKBvVCAe79YQjfzcpS6PiMglMcwQVUFGvg7f7T6HtUczUbYfZ89mAXiiW0M0DfCQtjgiIhfHMEP0Hy4X6vH9nvNYdfgSTBZriolr4ocnuzVEdLCXxNURERHAMENUqZxiA37cm4blyRkwXO2K6RTugyfvaITYEG+JqyMiousxzBBdJ6/UiAX7LmDpgXToTBYAQNtQbzx5RyO0b+AjbXFERFQphhkiAIU6Exb9dQGL/05HscEMAGhZzwvj7miIzg19IQiCxBUSEdGNMMyQSysxmPHzgXQs2HcBhXoTACAy0APj7miEuCZ+DDFERE6AYYZcks5oxrLki/hxbxrySo0AgMb+7hjXrSF6NAuAjCGGiMhpMMyQSzGYLPj18EV8vycNOcUGAEADHw2e6NYIfaMCIZcxxBARORuGGXIJJrMFiUcz8d3u88gs1AMA6nur8VjXhujfIhgKhhgiIqfFMEN1mtkiYsOxy5i76xzS83UAgCBPFf6vSzjui6kHpVwmcYVERHS7GGaoTrKIIjafyMKcpHM4l1sKAPBzV2J053DcH1sfagVDDBFRXcEwQ3WKKIrYfjIH3ySdw8nsYgCAVqPAox0b4IG2IXBTyiWukIiIqhvDDNUJRXoT9p/Pw/d7zuNYZhEAwFMtx8j2YXioXSg81XyrExHVVfwOT07HZBFxKqsYRy4V4OjFQhy5VIizOSW4uv8j3JVyPNQuBCM7hMFbo5S0ViIiqnkMM+TQRFFEZqEeRy4W4sjFQhy9VIBjmUXQX91q4Hoh3mr0jgzEIx3D4OuukqBaIiKSAsMMOZQivQnHMq8Gl6u9LmXrwVzPQyVHy3peiKnvhZb1vdGynhf8PRhgiIhcEcMMScZkEXEquxhHLxXi6MUCHLlYiDPXDReVkcsENAvwQMv6V8NLPW809HPjKr1ERASAYYZqSdlw0dFLZb0u1uEiXSXDRfW91WhZzxsxV8NLVJAnNLwLiYiIboBhhmpEscGEY5eKcORigS3AZN9guKhFvWs9LjH1OVxERET2YZih22ayiDiTU3zdPJcCnM6uZLhIAJoGeiKmvpctwDTyc+dwERER3RZJw0yvXr2Qnp5e4fiIESMwZcoUAMCBAwfw2Wef4dChQ5DJZGjevDm+++47aDSa2i6Xrsos1NvmuBy5VIjjmYUoNVYcLqrnpbZN0I2p54XoYA4XERFR9ZM0zCxbtgxms9n2cWpqKsaMGYN+/foBsAaZxx57DE8++STefPNNyOVyHD9+HDIZl6KvLSUGs+3uorIho6yiyoeLmtfzQsx1dxgFcLiIiIhqgaRhxs/Pr9zHc+bMQXh4ODp16gQAmDZtGh555BE88cQTtnOaNGlSqzW6ErNFxOnsEhy5WIAjl6xDRqdzimH513iRXAAiyu4uqueNlleHi+TceZqIiCTgMHNmDAYDVq9ejTFjxkAQBOTk5CA5ORkDBw7EQw89hPPnz6NJkyZ47rnn0KFDB7uvz2kZldMZzViw7wKSLxXiUFo+SozmCucEXx0usv7xRnSQJ9xUHC6qKWXvVb5nHQPbw7GwPRxLTbaHPdd0mDCzefNmFBYWIiEhAQCQlpYGAPjiiy/wyiuvoHnz5vj1118xevRorFmzBo0aNbLr+v7+XtVdcp2w7K8L+CbpnO1jD5UcsWE+aBPugzYNfNC2gQ+CvDk/SQp8zzoWp2+Pi8nA+d2A2gtw87X+0fhc/X8fQKGWukK7OH171DFSt4fDhJnly5cjPj4ewcHBAACLxTqhdNiwYRgyZAgAoEWLFti1axeWL1+OF1980a7r5+QUQvz37TWEPalZAIB7YuphdIfQisNFBiOys40SVeeaBMH6jYHvWcfg9O1hNsB973S4/f0VBLHiRP0yosINFo0PRLUPRLX26v+X/e0LUaOFRe0DUeMDi1oLsexclVetdpM4fXvUMTXZHmXXrgqHCDPp6elISkrCrFmzbMcCAwMBABEREeXOjYiIQEZGht3PIYrgG78SJy5bd5i+u2U9RAR48HVyIGwLx+KM7SHPOQbvTROhyPkHAGAIvQOQKSDo8yDT5UHQ50HQF0CACMFUCnlRKVB00a7nEAWZNfRcDUHWsONzLfyofWwhyfKvxyG/9d4gZ2yPukzq9nCIMLNixQr4+/ujR48etmNhYWEICgrCmTNnyp179uxZxMfH13KFdZNFFJGaZQ0zLUO8Ja6GiKqNxQy3g3PgsedjCBYDLBo/FPb4AIaI/hXPFS3WQKPPg0yfD0GXB5k+D4I+/2rguf5Ynu2YTJ8HwaSDIFog6HIh0+XaXea13qB/9fr8OwRptLaPodECFo/bf42oTpE8zFgsFqxYsQKDBw+GQnGtHEEQMHbsWMyaNQvR0dFo3rw5Vq5cidOnT2PmzJkSVlx3pOWWotRogVohQ5NAT+ReKZK6JCK6TbL8c/D+/XkoL+4FAOgb9UVhjw8hegRV/gmCzDpkpPHBjQehbsCkqxCAKoahqx/r/hWQbqM3CB6BUPaZCUPYnfZWTHWU5GEmKSkJGRkZtnkx1xs9ejQMBgOmTZuG/Px8REdH4/vvv0d4eLgEldY9ZUNMTQM8eFs1kbMTRWiOLYbHn29DZiyGRemB4ri3oWs+rObmtCg0sCg0gEcwKt4H+V+1WiAYCq8Gn/zyPT624a9/9QKVnWsqBYqz4L1mNAr6zYGhUe+a+drIqUgeZuLi4nDixIkbPv7EE0+UW2eGqk9KVjEAICrYU+JKiOh2CMWX4bXtFajPbgYAGEI6o7D3Z7B4O+gvflfn2Yhqrd29QYKpBAHbX4BwfA281z+Ggru+rHz4jFwKl9J1YWU9M1FBHH8mclaqU2vht6Q31Gc3Q5SpUNTtTeQPWuq4QeZ2Kd2BB36AvtkgCBYjvDc+BXXKSqmrIolJ3jND0hBFESm2MMOeGSJnI+jz4bnjTWhSVgAAjAEtUdhnBsz+0RJXVgvkShT2nQlRrobm+FJ4bZoAwaSHrsVDUldGEmGYcVHZxQZcKTFCJljnzBCR81Cm/QGvLS9AXnQRoiBDSbvxKOn4HCB3of3QZHIU9voEokIDtyPz4bX1JcCsg67VaKkrIwkwzLiolMvW+TIN/dy5kzWRszCWwmPX+3A/PA8AYNI2QmGfGTDVay9xYRIRZCiKfw+iXA335Lnw2vEGBJMepW2flLoyqmUMMy6qbL5MZCB7ZYicgSLzALw2PwdF3ikAQGnMKBR1+591DokrEwQU3zEZokIDj79mwTPpXQhmHUo6TJS6MqpFDDMu6gTnyxA5B7MR7vtnwP2vWRBEM8wewSjs9SmM4T2krsxxCAJKukwCFBp47PkYHns+Bkw6lHR+hTtSugiGGReVksUwQ+To5FdS4bV5IpRZhwAAumaDrMMqGh9pC3NQJR0mQpRr4Jn0Ljz+mgXBpEPxHZMZaFwAw4wLKtKbcCFPBwCIZJghcjyiBW6HvofHrmkQzHpY1FoUdZ8GfbP7pK7M4ZW2fRKiQgOvHf+De/JcCGY9iuKnAgJXIqnLGGZcUFmvTLCXGj5uSomrIaLryQrT4fX781ClJwEA9OE9UdTrY1g86klcmfPQtRoFyNXw3Poy3I7Mh2DSobDnx4CMNzvUVQwzLqjsTiYOMRE5EFGE+sQyeP4xGTJDIUSFG4rumAJdy5EcJrkFuhYPQVSo4bX5OWiOLwXMehT2/hyQ8xe4uohhxgXxTiYixyKUZMNr2ySoz2wEABjrtUdB789h8WkscWXOTR+ZAFGugvdv46FJXQXBrEfBXV8CcrXUpVE14yCiC+KdTESOQ3XmN/gt6QP1mY0QZUoUdXkVeQkrGGSqiSFiAAru+RaiXA316Q3wXv84YCqVuiyqZgwzLsZotuBMTgkAbjBJJCXBUAjPLS9Cu+7/ICvNhskvCrlD16C0/XjO7ahmhka9kT/gB4gKDdTntkC7ZjRgLJG6LKpGDDMu5nR2CUwWEd4aBep5sauVSArK9F3wXdIXbsd+hggBJW2fQu6D62AObCl1aXWWscGdyB/4EyxKD6jSd8In8WEIhkKpy6JqwjkzLub6+TICJxU6FtEC5YWd0Jz4BVApoNG2gDGoNUwBMYDSTerqqDqYdPDY/RHckudCgAizdzgKe38GY0hnqStzCcaQzsi/bzG0ax6B8uJeaFcNR/7AhVy3pw5gmHExtjDD+TIOQ9DlQXP8F2iOzIci/4zteFkLiYIcZr9IGINiYQpqA1NQa5j8o11rU8E6QJF1GF6bJkKRmwIAKG0xwroMv4r/FmuTqV475A/6GdrVw6G8fBDaVcOQf98iiG7+UpdGt4FhxsVw5V/Hobh8CJojP1rvsjBZFzG0KD2hjx4Ct4AG0J/dC0VmMuQlmVDkHIMi5xhw7GcAgChXw+TfHKbg1jBeDThmnwjOtXBEFhPc//4S7vs+g2AxweIWiMJeH8PQqI/UlbksU2AM8gb/Ap9Vw6HMPgqflQ8gf9BiWDyCpS6NbhHDjAuxiKJtjRn2zEjEVAp1aiLcjvwI5eXka4f9m6M0ZhR0kQkQ1B5wC/BCYctCiCIgK7oIxeVkKC4nQ3n1b5k+H8rLB6G8fBBu+BEAYFF6wBTYytpzE9QaxqDWsHiHc40SCcnzTlu3I8g8AADQR/RHYfcPILr5SVwZmf2jkZewDNpVw6DITYF25VDkD/oZFq8QqUujW8Aw40Iu5OlQYjRDrZChkZ+L77Rby2R5Z+B2dCE0x5ZAps8HAIgyFfRNB6A0ZhRM9drfMHRYPOvD4Fkfhib9rAdEEbL8s1eDzSFryMk6BJmxGKqM3VBl7L72uRpfmIJibb03pqDW/O2zNogiNEd+hGfSVAgmHSwqbxTFvwt95P0Mlw7E7BuBvITl8Fn1EBT5Z+CzcgjyBv9s/SWAnArDjAtJuTpfJiLAAwoZv6HWOIsJqrO/w+3IfKjSttsOm73CUNryYeiaPwTRPcD+6woCLD6NofdpDH3k4KvPZYY8N7Vc740i+x/IdLlQnd8O1fnrnt8j2Db3xhjUGqagWIga39v8YqmMrCgDXltegiptBwDAEBaHwl7T+Ru/g7JoGyIvYTm0qx6EIv8sfFbcj/zBS2H2aSJ1aWQHhhkXwpV/a4dQfBlux5ZAc3Qh5EUZAAARAgwNe0IXMwqG8B7VP7dFJofZPxpm/2jomw+zHjProcg5bg02mclQXj4IeW4q5MWZkJ/ZaFttFgDM3g1hDL7We2MMiAFUfJ/YRRShTv0VnjvegEyfD1GhQVHX/1n3CeImhw7N4hWC/ITl0K4aDkVuCnxWDEHeoMUw+0dLXRpVEcOMC+HKvzVIFKG8uAeaw/OhPr0OgsUEwDrMo2v+EEpbPgyLtmHt1iRX28IJYq4eMxRDmX3k2hyczIOQF5yz/UHqKuuXI8hg9m12Xe9Na5gCmnMZ+BsQdLnw3PYaNKfWAACMQa1R2GcmzL4REldGVWXxCEZegnVSsCLnH/j8+gDy71sMU2DMzT+ZJMcw40JSsrjBZHUTDIVQn1gBtyPzobhywnbcWK89SmMehT5iAKDQSFjhv6g8YAzpXG5dE0GXC0XWYSgzk6G4fBCKy8mQF1+C4soJKK6csG7SB0CUKWEKaFFueMrsG+nyd1Cpzv4Oz60vQ15yGaJMgZIOE1HS/llAxm+vzkZ080fe4KXQJo6E8nKy9bbtexfAVK+d1KXRTfBfm4vILjYgp9gAAUBTDjPdNnn2P3A7sgDqlBWQGa0hUVS4QReZAF3Mo07125yo8YWxQTyMDeJtx2TFmdfdQXUQisxkyPR5UF6dk1O2hJ+ocIMpsJWt98YY1BoWbSPXmORqKIbnznfg9s9PAACTbzMU9vnc2hNGTkvU+CB/0BJo14yyLqy3ejgK7p3PhQ0dHMOMiygbYmro5wY3pWv/Jn3LzHqoT62H25H5UF7cazts8m1q7YWJGgJRrZWwwOpj8QiGofFdMDS+y3pAFCErTCvXe6PIOgyZsRjKi3vLvR4WtdY6LBUYC5NvU4gaH1jUWohqH1g0PtbXyMkX/FNc3Afvzc9Zh+YAlLR+HMVdXgEUXKm5LhBVXsgbuBDatWOgSt8JbeLDyO//PYwN7pS6NLoBhhkXkcL5MrdMVnDh6m3ViyErzQFgXZXX0KQfSmMehTG0W93viRAEWLzDofcOh77ZQOsxixnyvFPXem8uH7LeQaXPhypth+1unsqICndbsLH9rfa5Gnx8ygWfa8e0EFVe0r7WZj089n4Kt7+/tm5H4BmKwt7TYQy7Q7qaqGYo3ZF/7w/wXv8E1Oe3Qrt2NAr6zYGhUW+pK6NKMMy4iBTbnUwMM1UiWqA8v916W/W53yGIFgDW25p1LR+GrsVwWDzqSVykxGTWbRbMfpHQRz9gPWY2WOfaXO3BkRddhKDPg0yXB0GfB0FfAAEiBFMJ5EUlwNW7vapKFOQQ1d624FM+BGkhanxtwad8SNLe9uRlefY/1u0Ico4BAHTRD6Io7i2Iau/bui45MIUbCvp/C++NT0N9ZiO81z+Ggru+hCGiv9SV0b8wzLgI3slUNULpFWiO/Qy3owttQwiAda2Q0phHYWjUF5ArJazQwclV1lWIA1sBeLji4xYzBEPhdQEnHzJ9HgRdHmT6/Kt/W49f+/+r55r1EEQzBF0uZLpcIN++0kSF29WAUxZ8fK4LPr4Q/x2Crp4LlTvw52fw2fIeBIsRFjd/FPb48NoihlS3ydUouHs2vH5/DprUVfDe+BQK+3wOfWSC1JXRdRhmXECR3oS0POvePwwzlRBFKDL/tk7oPZkIwawHAFhU3tA1fxC6lo/wFtvqIpNbe1Q0PrDYO73IVHpd4LH+LegrhqCyXqCKvUGlkBeVAkUX7XpaEYLtv/pGd6Gw50e3ttghOS+5EoV9ZgJyNTTHl8Jr0wQIJj10LR6SujK6imHGBZy8ekt2kKcKPu7sVbAxlkCT+is0h+dDmX3k2uGAGOhajYKu2SBAyW0fHIbCDRaFG+BRD2Z7Pk+0WAPNjYJPhR6i60KSSQcBIqDyQmHcW9BFP1j350dR5WRyFPb6BKJCA7cj8+G19SXArLcuikiSY5hxAbaVf9krAwCQ556E5sgCaI7/ApmhAIB1F2p9s/tQ2vIRmILb8gdWXSLIrvUG2fu5plLI9fnwCw2DPt8EiDVRIDkNQYai+PcgytVwT54Lrx3/g2DWo7TNE1JX5vIYZlwA58sAMBuhOvsb3A7Phyp957XD3g1RGvMIdM2HcX8iqkjhBovSDVC6ASiUuhpyBIKA4jsmQ1Ro4PHXLHjufAeCqRQlHSZKXZlLY5hxAa688q+s+BI0RxdB889PkBdnArAu1W9o2AelrR61LhTHfXOIyB6CgJIukwCFBh57PobHno8Bkw4lnV9hr65EGGbqOKPZglPZ1jATGeQiK/+KIpTpSXA78iNUpzdCEK0zLCxuAShtMRy6FiNh8Q6TuEgicnYlHSZClGvgmfQuPP6aBcGkQ/EdkxloJMAwU8edzimBySLCS61AiLcD7RFUAwR9PjTHl0FzdAEUuSdtxw31O0PX6lHom9zj9CvPEpFjKW37JESFGl473oB78lwIZj2K4qeyx7eWMczUcbbF8oI8INTh3xaUGXvgvXaMbUKvRekBfdQQlMY8ArN/c4mrI6K6TNdqNCDXwHPry3A7Mh8w6VHU8yOX34S1NkkaZnr16oX09PQKx0eMGIEpU6bYPhZFEY8//jj++OMPfPnll+jTp09tlunUTrjAyr+KS3/Be82jkBmLYfJthtLYMdBH3g9RVXe/ZiJyLLoWD1l7aDY/B7fjP0Mw61DY+3MusllL7AozFosFe/fuxf79+5GRkQGdTgc/Pz80b94c3bp1Q/369e168mXLlsFsvrZiRGpqKsaMGYN+/cqvrPnjjz/W6V6FmlTX92RSZB2GNvERyIzFMITegfx7f+Bmf0QkCX1kAkS5Ct6/jYcmdRUEsx4Fd33F4e1aUKVBPZ1Oh6+++grdu3fHE088gT/++AOFhYWQyWQ4d+4cZs2ahd69e+Pxxx/HwYMHq/zkfn5+CAwMtP3ZunUrwsPD0alTJ9s5x44dw/fff4/333/f7i/O1VlEsU7fySTPOQbtquGQGQpgrN8J+QPmMcgQkaQMEQNQcM+3EOVqqE9vgPf6xwCTTuqy6rwq9czcfffdaNOmDaZOnYpu3bpBqazYbZaeno41a9bghRdewLhx4/Dggw/aVYjBYMDq1asxZswYWy9MaWkpXnzxRUyePBmBgYF2XY+AjHwdig1mqOQCGvnVrR/y8iup8Fn1EGT6PBiD2iD/3h+5Wi8ROQRDo97IH/ADtOvGQH1uC7RrRyO///f8HlWDqhRmvv/+e0RE/PfeNKGhoXjyySfxf//3f7h40b69TwBg8+bNKCwsRELCtc27pk2bhrZt21bLHBlXHKUqmy8TEeABpeLGnXBlr42zvEayvDPQrnoIstIcmAJjUHDfQkDtBScp/6acrT3qOraHY3GW9jCF34n8gQvhvWYUVBf+hE/iwygY+CNElZfUpVWrmmwPe65ZpTBzsyBzPaVSifDw8KpXcNXy5csRHx+P4OBgAMDvv/+O3bt3Y+XKlXZfqzL+/nXrDVQVF/7KAAC0DvdFQMDNv36neI3yzgOJI4CSTCCoBRSjVsPfw1/qqmqEU7SHC2F7OBanaI+AvoDfKmDhECgv7oX/2oeBh5cDbnVvtXGp2+OW72YymUz4+eefsWfPHlgsFrRr1w4jR46EWq22+1rp6elISkrCrFmzbMd2796N8+fPo2PHjuXOffbZZ9GhQwcsWLDArufIySmE6GL7qhw4ewUA0NBbjezsGy/FLgjWN6Kjv0ayogxoVzwAeUEaTD4RyL/3J4ilKqC0bi0z7yzt4SrYHo7F6drDLRryQUugXT0CsvS/YPpuAPIHLYLoVjd+CavJ9ii7dlXccpiZOnUqzp49i759+8JkMmHVqlU4cuQIpk+fbve1VqxYAX9/f/To0cN27IknnsADDzxQ7ryBAwfitddeQ8+ePe1+DlGEc7zxq1HZMFOzQI8qfe2O/BoJxZfh/etDkBecg9m7IfIHLYHFLbBOb/znyO3hitgejsWZ2sMU2Ap5g3+Bz6rhUGQfhXbFA8gftBgWj2CpS6s2UrdHlcPMpk2b0LdvX9vHO3fuxIYNGyCXWxcFiouLw7Bhw+wuwGKxYMWKFRg8eDAUimvllN3h9G8hISFo0KCB3c/janKKDcguNkAA0MzJ15gRSnPgs+ohKPJOw+wZirxBP8Piad8yAEREUjL7RyMvYRm0q4ZBkZsC7cqhyB/0MyxeIVKXVidUeb3l5cuX4+mnn0ZmpnWzvhYtWmDKlCnYsWMHtmzZgo8//hitWrWyu4CkpCRkZGRgyJAhdn8u3VhKlrVXJtzXDe4q512FUtDlQbt6BBS5KTB7BCNv8M/cV4mInJLZNwJ5Ccth9moARf4Z+KwcAtXpDYDFJHVpTk8Qxap3DK1btw4zZszAww8/jEGDBuGrr77C3r17YTab0a5dOzz77LPw8/OryXpvWXa2k4yvVpMf9pzHl3+eRd+oQLx/738v5y8IQECAl8O9RoKhENpVD0F5ORkWtwDkJSyD2bep1GXVOEdtD1fF9nAsdaE9ZIUZ0K56EIr8swAAs2d96FqMhK7FcKcbeqrJ9ii7dpXOtSfMAEBBQQE+/vhjHD9+HG+//TZatGhxS0XWNmd+49+K1xKPYXNKFsbf2RijOv33sJxDfnMwFMNnzcNQXtwHi8YXeYN/gdk/WuqqaoVDtocLY3s4lrrSHkLpFbgfnAPNscWQleYAAESZAvom90AX8yiMIV0c//5zOE6YsXsCsLe3N959913s27cPkyZNQlxcHJ577rlbuouJak7ZMFNUkIfEldwCYym060Zbg4xai/z7FrlMkCEi1yC6+aG466so7vQ81KfWwe3IfCgv7oPmZCI0JxNh8o1Eacwj0EcNgaj2lrpch1flOTMZGRmYOHEiBg4ciBdffBENGzbE8uXL4ebmhvvuuw/bt2+vyTrJDiUGM9JySwEAkc62jYFZD+36x6BK3wWL0hP59y6AKdD+uVhERE5BroY+MgF596/ElWG/obTlIxAV7lDkpsDrjzfh/0MHeG57FfLsf6Su1KFVOcxMmjQJMpkML7/8Mvz9/TF58mSoVCpMmDABX331FebMmYOJEyfWZK1URalZRRABBHqq4OfuRBucmQ3w3jAOqrTtEBVuyL93Pkz12kldFRFRrTAHtEBRj2nIGfMXCuOnwuQbCcFUArejC+H3813wWT4Y6hMrALNe6lIdTpWHmY4cOYJVq1YhPDwcd955J3r37m17LCIiAj/99BN+/vnnGimS7HPishNuLmkxwXvTs1Cf3QRRrkb+gB9gCul0888jIqpjRJUXdK1GQxczCsqLe6A5PB/q0+ugvLQfykv7Ydn5NnTNH0Jpy4dh8eZSJYAdYaZly5aYMWMGEhISkJSUhMjIyArn3Mo6M1T9Uq4ulhcZ6CTzZSxmeG1+DupTayHKVMi/51sYw+6QuioiImkJAowhXWAM6YKi4stwO7YYmqMLIS+6CPe/v4Tb31/B0LAXdDGPwhDeA5A57zIct6vKw0wffvghjEYjpk2bhszMTLz99ts1WRfdhrKVf52iZ0a0wHPbK9Ck/gpRpkDB3V/D2ND+FZ6JiOoy0SMIJR0m4soju5B/z7cwNOgOASLU536Hdu0o+P10J9z+/gpC6RWpS5VElXtmQkNDMXPmzJqshaqByWzBqRzrMJPDT/4VRXjueANux36GKMhQ0PcLGJrcLXVVRESOS6aAoUk/GJr0gzzvNDRHFkJz/GfIC87Dc9f78NjzCfRN70Vpq1EwBbdzitu7q0OVe2bIOZy5UgKjWYSHSo4QrUbqcm5MFOGx8x24HZkPEQIKe38GQ9N7pa6KiMhpmH2aoDhuMnJG7UdBr09hDGoNwWKAJmUFfJcPgs/SftAc/Qkwlkhdao1jmKljyoaYIoM8IXPURC6K8Nj9IdyT5wIAinp+CH0Ut7MgIrolSjfomw9D3gNrkTt0DUqjh0GUq6HMPgqvbZPg/0N7eOx4E/IrqVJXWmMYZuoYZ7iTyX3/DLj//QUAoDB+KnQtRkhcERFR3WAKboOi3p8iZ/R+FN0xGSZtI8gMhXA/PA9+i3tC++uDUJ1cA5iNUpdarexeAZgcW8plx1751+3vr+Gx9xMAQNEdk6FrNVragoiI6iBR44vSNk+gtPVjUF74E26Hf4Tq7Cao0pOgSk+C2T0YuhbDoWs5AhZP59+5m2GmDhFF0baNQWSg4/XMaA59D89d7wEAijtPQmmbJySuiIiojhNkMDaIh7FBPGSFGdD88xPcji6CvCQTHvs/h/tfs2BofBdKYx6FMSzOaScM2x1mSkpKMGfOHOzevRs5OTmwWCzlHv/999+rrTiyT3q+DkV6M5RyAU383aUupxzN0YXw+mMyAKC4w0SUdHhW4oqIiFyLxSsEJZ1fRkmHiVCf3gjNkR+hytgN9en1UJ9eD5NPE+hiHoUuaihEjY/U5drF7jDzxhtvYO/evRg0aBACAwMhOGmKq4tSsqzzZSL8PaCQO850KPXxX+C57TUAQEmbJ1HS6SWJKyIicmFyFfTNBkLfbCDkOSfgdnQ+1MeXQ5F3Gp5/vgWP3R9A12wwdDGPwhQUK3W1VWJ3mNmxYwe++eYbtG/fvibqodtw7U4mx5kvo05dBa8tL0KAiNJWo1Hc7Q2n7cYkIqprzP5RKIp/D8VdXoM65Ve4HfkBipzjcDu2BG7HlsAY1AalrUZB3/ReQOEmdbk3ZPev797e3vDx8amBUuh2pTjYyr+q0+vhtWkCBNGC0hYjUHTnOwwyREQOSFR5QhfzMHKHbULu/SuhazYYokwJ5eWD8P79efj/0BEeO9+FLP+s1KVWyu4wM3HiRMyYMQOlpaU1UQ/dBkcKM6qzv8N749MQRDN0UUNQ1OMDQHCcoS8iIqqEIMBUvyMK7/oCOaP2oajLqzB7hUGmz4P7wW/gvzAO2sSRUJ35DbCYpa7Wxu5hpnnz5uH8+fPo1q0bwsLCoFCUv8TKlSurrTiqutwSAy4XGSAAaCrxBpPKtD/gveEJCBYjdE0HorDXpwwyRERORnQPQGn78Sht+xRU57dCc/hHqM5vg+r8dqjOb4fZMxS6mIeBuMcASDsEZXeY6dOnT03UQbepbL5MA183eKiku+NembEb2nVjIJj10De+G4V9ZgIyrgBAROS0ZHIYGvWBoVEfyPLPwe3oQmiOLYG8KB0euz8E9n0G5cAFMITeIVmJdv+UGT9+fE3UQbcp5erKv1KuL6O49Be814yCYNJBH94TBXd/BciVktVDRETVy6JtiOJu/0NxpxehPrUGbofnQ5n5NwRjsaR18VfmOkLqO5kUlw9Bm/gIZMZiGMLiUHDPHECulqQWIiKqYQoN9FFDYYgeigBfDQy5OkCUsJyqnNSpUyds2LABfn5+6Nix43+uLbN3795qK46q7oSEk3/l2f9Au3oEZIYCGOp3Rn7/7x36Fj4iIqpGciUAnaQlVCnMvPbaa/D0tP6QfP3112u0ILJfqdGM87nWu8tqO8zIr6TCZ/VwyPR5MAa3RcG9PwBKx1p9mIiI6rYqhZmEhIRK/58cQ2pWMUQAAR4q+Huoau155XmnoV31EGSlOTAGtkL+wIUQVV619vxERERAFdeZKSkpseui9p7vtCwmQJRwkPAqKebLyArSoF31EOQlmTD5RyP/vkUQ1dpae34iIqIyVQozd911F+bMmYPLly/f8BxRFLFz50489thjWLBgQbUV6KiU6UkImB0BvwXd4LntVahOrYOgL5CkltqeLyMryoDPqmGQF2XA5NsUefcthqjxrZXnJiIi+rcqDTPNnz8fn332GWbNmoXo6GjExMQgKCgIarUa+fn5OHXqFA4ePAi5XI4nnngCDz30UE3XLTlRrgHkasgL0+B2dCHcji6EKMhhCm4LQ3h3GBrEwxTUBpDJa7yW2lz5V1acCe2vwyAvOA+zd0PkD1oC0T2wxp+XiIjoRgRRrPo4SUZGBjZs2ID9+/cjIyMDOp0Ovr6+aNGiBeLi4hAfHw+5vOZ/eN+K7OzC6h8RMhRDlbELyvPboUrbDkXe6XIPW9RaGMPiroabHrB4hVRzAYDJbEH3WTthMItY8X8d0cDX/ruIBAEICPC66WsklObAZ+UDUOSmwOwVhryE5bB4hd5G9VSZqrYH1Q62h2NheziWmmyPsmtX6Vx7wowzq403vqwgDaq07VCl7YAy7U/IDOWHnUy+TWFo0B3GBvEwhHatlrt+TmYVY/j8v+ChkmPL+G6Q3cJGjlV5Mwq6XPj8OgyKnH9g9qiHvIRlsGgb3V7xVCl+s3YsbA/HwvZwLI4SZrhoXjWyeDeAruXD0LV8GLCYoLicbN3HIm0HFJkHoMg9CUXuSeDQdxBlKhjrd7zaa9Md5oAWt7SjdErW1cm/gR63FGSqQtAXQJv4MBQ5/8DiFoj8QT8zyBARkcNgmKkpMgVM9drDVK89Sjq9CEGXB2X6TtsGXfKidKjSd0KVvhPY9T4sboEwNLjTNt+mqvNQrt3JVEPzZQzF0K55FMrLybBofJE3aDHMvhE181xERES3gGGmlogaHxgiBsAQMQAQRcjzTkOZZg02qvQkyEqzoElZAU3KCgCAMaAljFd7bYz1O9xwa4AaDTPGUmjXjYby0n5Y1Frk37cYZv/o6n8eIiKi28AwIwVBgNk3AmbfCOhi/w8w66G8uB+qtO1Qnt8BZfYRKLOPQpl9FO5/fwVR4QZDaFcYG3SHIbw7zD4RgCBAFEXbBpPVfieTSQft+segSt8Fi9IT+QMXwhQYU73PQUREVA0YZhyBXA1j2B0wht0BdH0dQkkWVGl/WCcTn98BWWkW1Oe2QH1uCwDA7BkKQ3g8Mv27QdCroZB5oYl/NW4hYDbAe+NTUKVth6hwR/7ABTAFt62+6xMREVUju8NMr169cP/99+P+++9HSEj132pMgOgeCH3U/dBH3W8dkso5Zh2OStsOZcZeyIvS4fbPYjTCYhxQCzghj4R2/z0whPeAKbgNILuNjGoxwXvTeKjPboIoVyN/wDyY6nestq+NiIioutl9a/YPP/yAlStXIjU1FZ07d8bQoUPRt29fqFS1tyfQragzt/EZS61r26TtQPHxzQjUny33sEXlDWPYHba7pCzeDW56SdutdZfz4LlpIjSpv0KUqZA/4HsYw3vUzNdBN8RbTx0L28OxsD0ci6Pcmn3L68wcPXoUK1euxJo1a2CxWHDvvfdiyJAhaNmyZZWv0atXL6Snp1c4PmLECEycOBGzZs3Cn3/+iYsXL8LPzw99+vTBxIkT4eVl/2aGdfGN/8LKIzh5OhVTW1zCHbJDUKXtgEyfX+4ck08T67o24T1gCOkKqCru3yQIQICfB3S/jIPm2M8QZQoU9JsDQ+O7autLoevwm7VjYXs4FraHY3H6MFPGaDRi0aJF+OSTT2AymRAZGYlHHnkEQ4YMgXCTdU+uXLkCs9ls+zg1NRVjxozB/Pnz4evri1mzZiEhIQFNmzZFeno63nrrLURFRWHmzJl211kX3/gDvtmNy0UGzBnWGm3DtIDFDEXWoatDUjuguPQXBPHa6yvKlDDW72C9Qyq8O0wBLQFBBgEiAva8Bez/DqIgQ8FdX8HQ9F7pvjAXx2/WjoXt4VjYHo7F6cOM0WjEpk2bsGLFCiQlJaF169YYOnQoLl26hEWLFqFLly749NNP7brme++9h23btuG3336rNAitX78eL7/8Mg4ePAiFwr55IXXtjZ9XYkTfr3cBALaO7wZPdcXXQ9AXQJmeZJtvIy84X+5xi5s/DGF3AjIZNCdWQISAwj4zrHN1SDL8Zu1Y2B6Ohe3hWBwlzNg9U/To0aNYsWIF1qxZA5lMhsGDB+O1115DRMS1hdT69u2LoUOH2nVdg8GA1atXY8yYMTfs0SkqKoKnp6fdQQa4pcV1HVpKtnV9mQY+GnhpbvB6aLxhjOgHY0Q/FAOQ5Z2xbrVwfjuUF3ZCVpoDTeqvttOLe38MQ/T9qGMvldMpe6/Wtfess2J7OBa2h2Opyfaw55p2p4KhQ4eiW7dueOutt9CnTx8olcoK54SFhWHAgAF2XXfz5s0oLCxEQkJCpY9fuXIFX331FYYNG2ZvyQAAf3/759k4sgtHLwMAWjXwqXJyRUAs0DQWwHjAbATS9gKntgBpe4DWw+HZdiRqft9tqqq69p51dmwPx8L2cCxSt4fdw0zp6ekIDa3+nZLHjh0LpVKJ2bNnV3isqKgIY8aMgVarxddff11pgLqZnJy61SX5vzXHsPF4Fp6Oa4T/6xJ+W9cSBOsbsa69Rs6K7eFY2B6Ohe3hWGqyPcquXRV298zk5OQgOzsbrVu3Lnc8OTkZMpkMrVq1sveSSE9PR1JSEmbNmlXhsaKiIjz22GPw8PDAl19+eUtBBgBEEXXqjV+28m9kkGe1fV117TVydmwPx8L2cCxsD8cidXvI7P2Ed955BxcvXqxwPDMzE++8884tFbFixQr4+/ujR48e5Y4XFRXZemy+/vprqNWV70/kanRGM87llgAAogIr3mpNRETkSuzumTl16lSla8k0b94cJ0+etLsAi8WCFStWYPDgweUm9hYVFeH//u//UFpaio8//hhFRUUoKrJOevXz84NcLrf7ueqK1KxiWETAz12JAE8GPCIicm12hxmVSoXs7Gw0aFB+ZdmsrKxbussoKSkJGRkZGDJkSLnjR48eRXJyMgDr3VHX+/333xEWFmb3c9UVKVnWUFftm0sSERE5IbvTxx133IHp06fjq6++sq3EW1BQgM8++wzdunWzu4C4uDicOHGiwvHOnTtXepyAE5etYSaSYYaIiMj+MDNp0iSMHDkSPXv2RPPmzQEAx48fh7+/Pz766KNqL5AqOnF18i97ZoiIiG4hzAQHB2P16tVITEzE8ePHodFoMGTIEAwYMOCW7zSiqjNZRJzKZpghIiIqY/8kFwDu7u63vHgd3Z5zV0qgN1ngrpQjzEcjdTlERESSu6UwAwAnT55ERkYGjEZjueO9e/e+7aLoxsrmyzQL9ICM63kTERHZH2bS0tLwzDPPICUlBYIgoGwB4bL9lI4dO1a9FVI5KZwvQ0REVI7di+a99957CAsLQ1JSEjQaDdauXYuFCxciJiYGCxYsqIka6ToneFs2ERFROXaHmQMHDmDChAnw8/ODTCaDIAjo0KEDXnjhBUydOrUmaqSrRFFEiu22bK78S0REBNxCmLFYLPDwsP4g9fX1xeXL1t2bQ0NDcebMmeqtjsq5VKhHgc4EuUxAE3+GGSIiIuAW5sw0a9YMJ06cQIMGDdC6dWt8++23UCqVWLp0aYVVgal6lfXKNPF3h0phdw4lIiKqk+z+ifjUU0/BYrEAACZMmIALFy5g5MiR2L59O/73v/9Ve4F0DVf+JSIiqsjunpk777zT9v8NGzbEhg0bkJeXB61Wa7ujiWoGV/4lIiKqyK6eGaPRiBYtWiAlJaXccR8fHwaZWlA2zBTFyb9EREQ2doUZpVKJ+vXr24aZqPbklRpxqVAPAIgMZM8MERFRGbvnzIwbNw7Tp09HXl5eDZRDN1LWKxOq1cBTfcsLNxMREdU5dv9U/Omnn3Du3DnceeedCAkJgbu7e7nHV65cWW3F0TUpWZwvQ0REVBm7w0yfPn1qog66iRNcLI+IiKhSdoeZ8ePH10QddBMnLnMbAyIiospw5TUnoDOace5KCQCGGSIion+zu2cmOjr6P2/D5q7Z1e9UdjEsIuDrpkSAh0rqcoiIiByK3WHmiy++KPexyWTCsWPHsHLlSjz77LPVVhhdc/0QE9fzISIiKq9aJgD369cPTZs2xbp16/DAAw9US2F0TdnKv9zGgIiIqKJqmzPTpk0b7N69u7ouR9dJyeLKv0RERDdSLWFGp9Nh/vz5CAoKqo7L0XXMFhGpWeyZISIiuhG7h5k6duxYbt6GKIooLi6GRqPBxx9/XK3FEXAutwR6kwVuShnCfd2kLoeIiMjh2B1mXnvttXJhRhAE+Pn5oXXr1tBqtdVaHAEpV+fLNAv0hIyTf4mIiCqwO8zcf//9NVEH3YBt5d9AzpchIiKqjN1zZpYvX47169dXOL5+/Xruy1QDuPIvERHRf7M7zMyZMwe+vr4Vjvv7+2P27NnVUhRZiaJo2y07KphhhoiIqDJ2h5mMjAyEhYVVOB4SEoKLFy9WS1FklVmoR77OBLkANPHnMBMREVFl7A4z/v7+OHHiRIXjx48fh4+PT3XURFeVLZbX2N8DagW30SIiIqqM3ROABwwYgPfeew8eHh7o2LEjAGDv3r14//33MWDAgGov0JVxsTwiIqKbszvMTJw4Eenp6Rg9ejQUCuunWywWDBo0CM8//3y1F+jKyubLcLE8IiKiG7M7zKhUKnz++ec4e/Ysjh07Bo1Gg8jISISGhtZEfS6NdzIRERHdnN1hpkyjRo3QqFGjaiyFrlegM+JigR4AEBnIMENERHQjds8qffbZZzFnzpwKx+fOnYsJEyZUS1F0beXfEK0GXppbzpxERER1nt1hZt++fejevXuF4/Hx8di/f3+1FEVc+ZeIiKiq7P6Vv6SkBEqlsuKFFAoUFRXZda1evXohPT29wvERI0ZgypQp0Ov1+OCDD7Bu3ToYDAbExcVhypQpCAgIsLdsp8P5MkRERFVjd89MZGQk1q1bV+H4unXr0LRpU7uutWzZMvz555+2P/PmzQMA9OvXDwDw/vvvY+vWrfj888+xYMECXL58GePHj7e3ZKd07bZshhkiIqL/YnfPzNNPP41nn30WaWlp6NKlCwBg165dWLt2LWbMmGHXtfz8/Mp9PGfOHISHh6NTp04oLCzE8uXL8cknn6Br164ArOGmf//+OHjwINq0aWNv6U5DZzTjbE4JAN6WTUREdDN2h5levXrhyy+/xOzZs7Fx40ao1WpERUVh3rx56NSp0y0XYjAYsHr1aowZMwaCIODIkSMwGo3o1q2b7ZyIiAiEhITcUpgRhFsurdadzimBWQR83JQI9lLVeO1l13em16guY3s4FraHY2F7OJaabA97rnlLt8n06NEDPXr0qHA8JSUFkZGRt3JJbN68GYWFhUhISAAAZGdnQ6lUwtvbu9x5/v7+yMrKsvv6/v5et1SXFDJO5wIAWoVpERjofZOzq48zvUaugO3hWNgejoXt4Vikbo/bvue3qKgIa9euxS+//IKjR4/i2LFjt3Sd5cuXIz4+HsHBwbdbUqVycgohijVy6Wr316lsAEAjHw2yswtr/PkEwfpGdKbXqC5jezgWtodjYXs4lppsj7JrV8Uth5l9+/bhl19+waZNmxAUFIS+ffti8uTJt3St9PR0JCUlYdasWbZjAQEBMBqNKCgoKNc7k5OTg8DAQLufQxThNG98251MgZ61WrMzvUaugO3hWNgejoXt4Vikbg+7wkxWVhZWrlyJZcuWoaioCPfccw8MBgO+/PJLu+9kut6KFSvg7+9fbugqJiYGSqUSu3btwt133w0AOH36NDIyMur05F+zRURqlnXBPN7JREREdHNVDjPjxo3Dvn370KNHD7z++uu48847IZfLsWTJktsqwGKxYMWKFRg8eLBt40oA8PLywpAhQ/DBBx9Aq9XC09MTU6dORdu2bet0mEnLLYXOZIFGIUMDXzepyyEiInJ4VQ4zO3bswCOPPILhw4dX655MSUlJyMjIwJAhQyo89vrrr0Mmk2HChAnlFs2ry8qGmJoFekAu43R9IiKim6lymFm0aBGWLVuG+++/HxERERg0aBD69+9/2wXExcXhxIkTlT6mVqsxZcqUOh9grle2WB7XlyEiIqqaKq8A3KZNG0ydOhV//vknhg0bhrVr1yI+Ph4WiwU7d+60eysDqpxtTyaGGSIioiqxezsDd3d3DB06FIsXL7Ytcjd37lx069YN48aNq4kaXYYoijhxmZN/iYiI7GF3mLlekyZN8Morr2D79u2YPn16ddXksi4XGZBXaoRcAJoGcLdsIiKiqrjtRfMAQC6Xo0+fPujTp091XM5lpVwdYmrk7w614rZyJhERkcvgT0wHYpsvE8ghJiIioqpimHEgtpV/OV+GiIioyhhmHEgKV/4lIiKyG8OMgyjUmZCRrwNgXTCPiIiIqoZhxkGULZZX31sNrZtS4mqIiIicB8OMg+B8GSIiolvDMOMgUngnExER0S1hmHEQZSv/chsDIiIi+zDMOAC9yYIzV0oAAFFBnPxLRERkD4YZB3A6pxhmiwitRoFgL7XU5RARETkVhhkHcCLz2k7ZgiBIXA0REZFzYZhxALyTiYiI6NYxzDgArvxLRER06xhmJGa2iEjNKhtm4uRfIiIiezHMSCwtrxSlRgvUChka+rpLXQ4REZHTYZiRWNliec0CPSCXcfIvERGRvRhmJGZbLI8r/xIREd0ShhmJpdjuZOJ8GSIiolvBMCMhURR5WzYREdFtYpiRUHaxAbmlRsgEICKAPTNERES3gmFGQmW9Mg393KFRyiWuhoiIyDkxzEgo5TIXyyMiIrpdDDMSKuuZiQzkEBMREdGtYpiRECf/EhER3T6GGYkU6U1Iz9cBsO6WTURERLeGYUYiKVf3Ywr2UsPHTSlxNURERM6LYUYiJzj5l4iIqFowzEjkBFf+JSIiqhYMMxJJ4eRfIiKiasEwIwGDyYLTOSUAOPmXiIjodjHMSOB0TjHMFhHeGgXqeamlLoeIiMipMcxIoGzl38ggTwiCIHE1REREzk3yMJOZmYmXXnoJnTt3RmxsLAYOHIjDhw/bHi8uLsY777yD+Ph4xMbGon///li8eLGEFd8+rvxLRERUfRRSPnl+fj6GDx+Ozp07Y+7cufD19cW5c+eg1Wpt53zwwQfYvXs3Pv74Y4SGhmLnzp14++23ERQUhN69e0tY/a3jyr9ERETVR9IwM3fuXNSrVw/Tpk2zHWvQoEG5cw4cOIDBgwejc+fOAIBhw4bh559/xqFDh5wyzFhEEalZXGOGiIioukg6zLRlyxbExMRgwoQJ6Nq1KwYPHoylS5eWO6dt27bYsmULMjMzIYoidu/ejTNnziAuLk6iqm/PhTwdSoxmqBUyNPRzl7ocIiIipydpz0xaWhoWL16MMWPGYNy4cTh8+DCmTp0KpVKJhIQEAMCbb76JN998E/Hx8VAoFBAEAVOnTkXHjh3tei5HmWdbtr5MRIAHlHLHKKrstXGU18jVsT0cC9vDsbA9HEtNtoc915Q0zIiiiJiYGLzwwgsAgBYtWiA1NRVLliyxhZkFCxbg4MGD+PrrrxESEoL9+/fb5sx069atys/l7+9VI1+DvdKK0gEArcN9ERDgGDWVcZTXiKzYHo6F7eFY2B6ORer2kDTMBAYGIiIiotyxJk2aYOPGjQAAnU6Hzz77DF988QV69OgBAIiOjsaxY8fw3Xff2RVmcnIKIYrVVvotO3j2CgCgobcK2dmFEldjJQjWN6KjvEauju3hWNgejoXt4Vhqsj3Krl0VkoaZdu3a4cyZM+WOnT17FqGhoQAAk8kEo9FYYS0WuVwO0c5XTRThEG/847bbsj0dop7rOcprRFZsD8fC9nAsbA/HInV7SDoBeNSoUUhOTsbs2bNx7tw5JCYmYunSpRgxYgQAwNPTE506dcLHH3+MPXv2IC0tDStWrMCvv/6KPn36SFn6Lcku0uNKiREyAWjGNWaIiIiqhaQ9M7Gxsfjiiy8wffp0fPnllwgLC8Prr7+O++67z3bO9OnTMX36dLz00kvIz89HSEgInn/+eQwfPlzCym/Niau3ZDf0dYdGKZe4GiIiorpB0jADAD179kTPnj1v+HhgYGC5dWicWdmdTJFB7JUhIiKqLpJvZ+BKuPIvERFR9WOYqUXXemYYZoiIiKoLw0wtKdKbkJanAwBEBTLMEBERVReGmVpSth9TkKcKPu5KiashIiKqOxhmakkK58sQERHVCIaZWnKC82WIiIhqBMNMLeGdTERERDWDYaYWGM0WnM4pAcAwQ0REVN0YZmrB6ZwSmCwivNQK1PdWS10OERFRncIwUwtOXLfy7783zSQiIqLbwzBTC3gnExERUc1hmKkFtpV/uVgeERFRtWOYqWEWUUTK1QXz2DNDRERU/Rhmalh6ng7FBjNUcgGN/NykLoeIiKjOYZipYSlZ1iGmiAAPKOR8uYmIiKobf7rWMK78S0REVLMYZmoYV/4lIiKqWQwzNSzlMif/EhER1SSGmRqUU2xAdrEBAoCmAR5Sl0NERFQnMczUoLIhpnBfN7ir5BJXQ0REVDcxzNQgrvxLRERU8xhmatCJq/NleCcTERFRzWGYqUFla8xEBXG+DBERUU1hmKkhxQYT0nJLAbBnhoiIqCYxzNSQk1nFEAEEeqrg566SuhwiIqI6i2GmhnCxPCIiotrBMFNDuI0BERFR7WCYqSFc+ZeIiKh2MMzUAJPZglM5V2/LDuSdTERERDWJYaYGnM4pgdEswlMtR6hWI3U5REREdRrDTA0oW18mMtATgiBIXA0REVHdxjBTA7jyLxERUe1hmKkB127L5nwZIiKimsYwU81EUeQGk0RERLWIYaaapefrUGwwQykX0NjPXepyiIiI6jyGmWpW1isT4e8BhZwvLxERUU3jT9tqdiKLi+URERHVJsnDTGZmJl566SV07twZsbGxGDhwIA4fPlzunFOnTmHcuHFo37492rRpgyFDhiAjI0Oiiv9bim0bA07+JSIiqg0KKZ88Pz8fw4cPR+fOnTF37lz4+vri3Llz0Gq1tnPOnz+PESNGYMiQIZgwYQI8PT2RmpoKtVotYeU3xg0miYiIapekYWbu3LmoV68epk2bZjvWoEGDcud89tlniI+PxyuvvGI7Fh4eXms12uNKiQFZRQYIAJpyGwMiIqJaIWmY2bJlC+Li4jBhwgTs27cPwcHBGDFiBB588EEAgMViwbZt2/DYY49h7Nix+OeffxAWFoYnn3wSffr0seu5amMh3tSrK/828HWDp1rSl9YuZa8NFyt2DGwPx8L2cCxsD8dSk+1hzzUl/YmblpaGxYsXY8yYMRg3bhwOHz6MqVOnQqlUIiEhATk5OSgpKcHcuXPx3HPP4aWXXsIff/yB8ePHY/78+ejUqVOVn8vf36sGvxKrtCOXAQCtGvggIKDmn6+61cZrRFXH9nAsbA/HwvZwLFK3h6RhRhRFxMTE4IUXXgAAtGjRAqmpqViyZAkSEhJgsVgAAL1798bo0aMBAM2bN8fff/+NJUuW2BVmcnIKIYrV/iWU8/eZHABAY60a2dmFNftk1UgQrG/E2niN6ObYHo6F7eFY2B6OpSbbo+zaVSFpmAkMDERERES5Y02aNMHGjRsBAL6+vlAoFBXOiYiIwF9//WXXc4kiavyNf+1OJk+n/EdWG68RVR3bw7GwPRwL28OxSN0ekt6a3a5dO5w5c6bcsbNnzyI0NBQAoFKp0KpVq/88x1GUGMw4n1sKwLpbNhEREdUOScPMqFGjkJycjNmzZ+PcuXNITEzE0qVLMWLECNs5Y8eOxfr167F06VKcO3cOCxcuxNatWzF8+HAJK68oNasIIoAADxX8PVRSl0NEROQyJB1mio2NxRdffIHp06fjyy+/RFhYGF5//XXcd999tnP69u2Lt956C3PmzMHUqVPRuHFjzJw5Ex06dJCw8opSuPIvERGRJCS/f7hnz57o2bPnf54zdOhQDB06tJYqujUnuPIvERGRJCTfzqCuSOHKv0RERJJgmKkGJrMFp7I5zERERCQFhplqcPZKKQxmER4qOUK0GqnLISIicikMM9XANl8m0AMyrrFNRERUqxhmqkFK1rXF8oiIiKh2McxUgxOXGWaIiIikwjBzm0RRRMplTv4lIiKSCsPMbcoo0KFQb4JCJqCJv7vU5RAREbkchpnbVNYrExHgAaWcLycREVFt40/f23T9nUxERERU+xhmbtMJrvxLREQkKYaZ28RtDIiIiKTFMHMb8kqMuFxkAAA05TATERGRJBhmbkPZEFMDHw081ZJvQE5EROSSGGZuQ9nKvxxiIiIikg7DzG3gyr9ERETSY5i5DQwzRERE0mOYuUWlRjPOXSkFwGEmIiIiKTHM3KKTWcUQAfi5KxHgoZK6HCIiIpfFMHOLTmZzc0kiIiJHwDBzi6KCPBHmo8GgVvWkLoWIiMilcXGUW9SinhdWju0kdRlEREQujz0zRERE5NQYZoiIiMipMcwQERGRU2OYISIiIqfGMENEREROjWGGiIiInBrDDBERETk1hhkiIiJyagwzRERE5NQYZoiIiMipMcwQERGRU2OYISIiIqfGMENEREROjWGGiIiInJpC6gJqiyBIXYHjKntt+Bo5BraHY2F7OBa2h2Opyfaw55qCKIpi9ZdAREREVDs4zEREREROjWGGiIiInBrDDBERETk1hhkiIiJyagwzRERE5NQYZoiIiMipMcwQERGRU2OYISIiIqfGMENEREROjWGGiIiInBrDjIv65ptvMGTIELRt2xZdu3bF008/jdOnT0tdFl01Z84cREVF4b333pO6FJeWmZmJl156CZ07d0ZsbCwGDhyIw4cPS12WSzKbzfj888/Rq1cvxMbGok+fPvjyyy/BHXlqx759+zBu3DjExcUhKioKmzdvLve4KIqYMWMG4uLiEBsbi9GjR+Ps2bO1Vh/DjIvau3cvRo4ciaVLl2LevHkwmUwYO3YsSkpKpC7N5R06dAhLlixBVFSU1KW4tPz8fAwfPhxKpRJz587F2rVrMWnSJGi1WqlLc0lz587F4sWLMXnyZKxbtw4vvfQSvv32WyxYsEDq0lxCSUkJoqKiMGXKlEofnzt3LhYsWIC33noLS5cuhZubG8aOHQu9Xl8r9bnMrtlU3nfffVfu4w8++ABdu3bF0aNH0bFjR4mqouLiYrz88suYOnUqvv76a6nLcWlz585FvXr1MG3aNNuxBg0aSFiRaztw4AB69+6NHj16AADCwsKwdu1aHDp0SNrCXET37t3RvXv3Sh8TRRHz58/HU089hT59+gAAPvroI3Tr1g2bN2/GgAEDarw+9swQAKCwsBAA+FunxN555x10794d3bp1k7oUl7dlyxbExMRgwoQJ6Nq1KwYPHoylS5dKXZbLatu2LXbv3o0zZ84AAI4fP46//voL8fHxEldGFy5cQFZWVrnvW15eXmjdujUOHDhQKzWwZ4ZgsVjw/vvvo127doiMjJS6HJe1du1a/PPPP1i2bJnUpRCAtLQ0LF68GGPGjMG4ceNw+PBhTJ06FUqlEgkJCVKX53KeeOIJFBUV4Z577oFcLofZbMbzzz+P++67T+rSXF5WVhYAwN/fv9xxf39/ZGdn10oNDDOEt99+G6mpqVi0aJHUpbisixcv4r333sP3338PtVotdTkEa9d5TEwMXnjhBQBAixYtkJqaiiVLljDMSGD9+vVITEzEp59+iqZNm+LYsWOYNm0agoKC2B7EMOPq3nnnHWzbtg0LFy5EvXr1pC7HZR09ehQ5OTm4//77bcfMZjP27duHn376CYcPH4ZcLpewQtcTGBiIiIiIcseaNGmCjRs3SlSRa/voo4/wxBNP2OZfREVFISMjA9988w3DjMQCAwMBADk5OQgKCrIdz8nJQXR0dK3UwDDjokRRxLvvvotNmzZhwYIFnNgosS5duiAxMbHcsddeew1NmjTB448/ziAjgXbt2tnmZ5Q5e/YsQkNDJarItel0OgiCUO6YXC7nrdkOICwsDIGBgdi1axeaN28OACgqKkJycjKGDx9eKzUwzLiot99+G2vWrMFXX30FDw8P25inl5cXNBqNxNW5Hk9Pzwrzldzd3eHj48N5TBIZNWoUhg8fjtmzZ+Oee+7BoUOHsHTpUrzzzjtSl+aSevbsidmzZyMkJMQ2zDRv3jwMGTJE6tJcQnFxMc6fP2/7+MKFCzh27Bi0Wi1CQkLw6KOP4uuvv0bDhg0RFhaGGTNmICgoyHZ3U00TRMZal3SjNUymTZtWbqiDpPPII48gOjoa//vf/6QuxWVt3boV06dPx9mzZxEWFoYxY8bgwQcflLosl1RUVIQZM2Zg8+bNtuGMAQMG4JlnnoFKpZK6vDpvz549ePTRRyscT0hIwAcffABRFDFz5kwsXboUBQUFaN++PaZMmYLGjRvXSn0MM0REROTUuM4MEREROTWGGSIiInJqDDNERETk1BhmiIiIyKkxzBAREZFTY5ghIiIip8YwQ0RERE6NYYaIXEJUVBQ2b94sdRlEVAO4nQER1bhXX30VK1eurHA8Li4O3333nQQVEVFdwjBDRLXizjvvxLRp08od4zL0RFQdOMxERLVCpVIhMDCw3B+tVgvAOgS0aNEiPPbYY4iNjUXv3r2xYcOGcp9/4sQJPProo4iNjUXnzp3x5ptvori4uNw5y5Ytw4ABAxATE4O4uLgKm0Lm5ubimWeeQevWrXHXXXfh999/tz2Wn5+PF198EV26dEFsbCzuuusuLF++vIZeDSKqTgwzROQQZsyYgbvvvhurVq3CwIED8cILL+DUqVMAgJKSEowdOxZarRbLli3D559/jqSkJLz77ru2z1+0aBHeeecdPPjgg0hMTMRXX32F8PDwcs/xxRdf4J577sHq1asRHx+Pl156CXl5ebbnP3XqFObOnYt169bhrbfegq+vb619/UR06zjMRES1Ytu2bWjbtm25Y08++STGjRsHAOjXrx8eeOABAMBzzz2HpKQkLFiwAG+99RbWrFkDg8GADz/8EO7u7gCAyZMnY9y4cXjppZcQEBCAr7/+GmPGjMGoUaNs14+NjS33fAkJCbj33nsBAC+88AIWLFiAQ4cOIT4+HhkZGWjevDlatWoFAAgLC6uZF4KIqh3DDBHVis6dO+Ott94qd6xsmAlAhaDTpk0bHDt2DABw6tQpREVF2YIMALRr1w4WiwVnzpyBIAi4fPkyunbt+p81REVF2f7f3d0dnp6euHLlCgBg+PDhmDBhAv755x/ccccd6NOnD9q1a3dLXysR1S6GGSKqFW5ubmjYsGGNXFutVlfpPKVSWe5jQRBgsVgAAN27d8fWrVuxfft27Ny5E6NHj8bIkSMxadKkaq+XiKoX58wQkUM4ePBguY+Tk5MREREBAIiIiMCJEydQUlJie/zvv/+GTCZD48aN4enpidDQUOzateu2avDz80NCQgI++eQTvP766/j5559v63pEVDsYZoioVhgMBmRlZZX7UzbEAwAbNmzAsmXLcObMGcycOROHDh3Cww8/DAAYOHAgVCoVXn31VaSkpGD37t149913MWjQIAQEBAAAnn32WcybNw/z58/H2bNncfToUSxYsKDK9c2YMQObN2/GuXPnkJqaim3bttnCFBE5Ng4zEVGt+OOPPxAXF1fuWOPGjW23YD/77LNYt24d3n77bQQGBuLTTz9F06ZNAViHqL777ju89957GDp0KNzc3HDXXXfh1VdftV0rISEBer0eP/zwAz766CP4+PigX79+Va5PqVRi+vTpSE9Ph0ajQfv27TF9+vRq+MqJqKYJoiiKUhdBRK4tKioKX375Jfr06SN1KUTkhDjMRERERE6NYYaIiIicGoeZiIiIyKmxZ4aIiIicGsMMEREROTWGGSIiInJqDDNERETk1BhmiIiIyKkxzBAREZFTY5ghIiIip8YwQ0RERE6NYYaIiIic2v8Dn9qziV7XqgIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7905920743942261\n",
            "Val Accuracy: 0.728863000869751\n"
          ]
        }
      ],
      "source": [
        "train_and_analyze_model(\n",
        "    rnn_model,\n",
        "    X_train,\n",
        "    Y_train,\n",
        "    num_of_epochs=10,\n",
        "    num_per_batch=32,\n",
        "    validation=0.1,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hbn07P4FUWD3"
      },
      "source": [
        "#### 5.3.1\n",
        "\n",
        "This model is the most chaotic of all three. As we can see, it is not performing well in its current state. The model does not converge well, however it seems to generalize well. It is possible that if left training for a long enough time it will perform slightly better.\n",
        "\n",
        "After several trial and errors while working on this model, we expanded our bidirectional layers to three. This however was not the best decision. The thought process was that adding more layers can help the model learn word contexts of the tweets, rather then making predictions on single words, such as the FFNN. Another unforseen effect was the amount of time it would take to fully train a model. Some variations lasted for up to 30 minutes. We also learned that more complexity does not mean better results. Three bidirectional layers performed worse then a model with two layers.\n",
        "\n",
        "Out of all our experiments, this one is the worst in terms of accuracy and time taken to train. As we trained with different variations, it was noted that adding complexity does not necessarily add any benefit and may instead hinder the learning process. Despite these current results, we still think that using an RNN model as a basis may still help us.\n",
        "\n",
        "As of writing this, the model is still a work in progess and has shown promise thus far. We will continue to work on this model to try to get the highest kaggle score possible as it still is promising."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtUBqJJMUWD3",
        "outputId": "094f49a1-e1a8-4032-d465-30c84f621337"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "215/215 [==============================] - 3s 12ms/step\n",
            "Train Loss: 0.4326018375011386\n",
            "24/24 [==============================] - 0s 8ms/step\n",
            "Val Loss: 0.4924989306359378\n"
          ]
        }
      ],
      "source": [
        "rnn_train_predictions = cnn_model.predict(X_train[:, 3]).flatten()\n",
        "rnn_train_loss = calculate_loss(Y_train, rnn_train_predictions)\n",
        "print(f'Train Loss: {rnn_train_loss}')\n",
        "\n",
        "rnn_validation_predictions = cnn_model.predict(X_validation[:, 3]).flatten()\n",
        "rnn_validation_loss = calculate_loss(Y_validation, rnn_validation_predictions)\n",
        "print(f'Val Loss: {rnn_validation_loss}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "IW4EFbmxUWD3",
        "outputId": "e6cc301e-6661-4017-fb47-31c001e8230e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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4b29vHnzwQb799lvOnz+faX1ubkUQGhpKmTJlmDFjBlevXrVZl/EcBgYGcvfdd/Pll19mec1PXm6BcOrUKYYOHYqLi8stb3SZmJiYqT/+/v6YzWZSUlKsy0qXLp3nU6cZdu7cyd69e60/nzlzhrVr19KkSRPr0Zm7776bS5cusX//fuu48+fPs3r16kzzZbe2jL+T8+bNs/l79P3333Pp0iVdPC2Z6EiRyG08+OCDPPvss8yYMYN9+/bRpEkTXFxcOH78OL/88gvvvvsubdu2pVy5crz00kvMmDGDV155hebNm/PPP//wf//3f3h5ed1yG2azmVGjRtG3b1+efPJJOnbsSPny5Tl69CiHDx9m9uzZwLXwBNc+Hh4aGoqTk5PN3Yuv9/rrr7N582a6dOlCly5dcHJy4ttvvyUlJYW33nrLvk0Cfv31V0qXLk1qaqr1jtZ//fUXNWvWvOl1O7nVs2dPfvvtN/r06UOHDh2oU6cOly9f5uDBg/z666+sXbv2tqc1R44cSZcuXQgPD+eZZ56hatWqxMTE8Pfff3P27FnrEa7scnNz45133mH48OE89dRTPP7443h4eLB//36uXLnC+++/j9lsZuzYsbz88ss8/vjjdOzYkYoVK3Lu3Dm2bduGm5vbbT+pCNcuzP/xxx8xDIOEhAR2797N//73P0wmExMnTrTeuDErW7duJSIigrZt21KtWjXS09P58ccfcXJyok2bNtZxderUYcuWLcyZM4cKFSrg6+ub6TYO2eXv70/Pnj1tPpIP2NyZ/bHHHmPSpEn069ePrl27cuXKFRYuXEj16tVtAlVOaitXrhyvvPIK06ZNo1evXrRo0YJjx47xzTffULdu3WwdUZM7i0KRSDZEREQQGBjIokWL+Pjjj3FycqJKlSq0b9+eBg0aWMe9/vrruLq6smjRIrZt20ZQUBBffvklr7zyym230bRpU+bOncunn37Kl19+iWEYVK1a1XovFrj2KaquXbuycuVKli9fjmEYNw1F9913HwsWLODDDz9kxowZGIZBUFAQH3zwQa7f3G5l1KhRwLVrTby8vKhVqxbjx4/P9nef5USpUqWYN28eM2bM4JdffuGHH37Azc2NatWq0b9//2xdMFyjRg2WLFnCtGnTWLZsGRcvXqRcuXLUrl2b1157LVd1Pf3003h7e/PFF1/w2Wef4ezszL333kuPHj2sYxo1asS3337LZ599xvz580lOTqZ8+fIEBQXx7LPPZms7P/30Ez/99JP1u8/uueceunfvnq3vPgsICCA0NJTff/+dc+fOUapUKQICApg5cyb169e3jhs6dCgjRoxg8uTJXLlyhQ4dOuT6dZNxM9JPP/2U06dPU6NGDSIjI23Cm5eXF9OmTWPChAl88MEH+Pr68sYbb/Dvv/9mCkU5qa1///6UK1eO+fPnExkZiaenJ8888wxvvPFGjr/6RIo/k5HXqyhFREREigFdUyQiIiKCQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAC6o3WOxMZeIre3ujSZwNvbPU9zSM6o546hvhc89dwx1HfHyEnfM8Zml0JRDhgGeX7h22MOyRn13DHU94KnnjuG+u4Y+dF3nT4TERERQaFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBABnRxcgRZfZbMJsNuV5HovFwGIx7FCRiIhI7ikUSa6YzSY8y5bG2SnvBxvT0i3EX0xWMBIREYdSKJJcMZtNODuZGbhoJ4fPJ+Z6nhoV3JjyXDBms0mhSEREHEqhSPLk8PlE9p5OcHQZIiIieaYLrUVERERQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARwcCjavn07ffr0ITQ0lICAANasWWNdl5qaygcffEB4eDj169cnNDSUIUOGcO7cOZs5Ll68yJtvvkmDBg1o2LAhw4YNIykpyWbM/v376dKlC3Xr1qV58+bMnDmzQPZPREREig6HhqLk5GQCAgIYOXJkpnVXrlzhn3/+oW/fvixdupRp06Zx7Ngx+vbtazNu8ODBHD58mDlz5jB9+nT+/PNPRowYYV2fmJhIz549qVy5MkuXLmXIkCFMmzaNb7/9Nt/3T0RERIoOZ0duvHnz5jRv3jzLde7u7syZM8dm2XvvvcfTTz/N6dOnqVy5MkeOHGHDhg18//331K1bF4Dhw4fTu3dvhgwZQsWKFVm+fDmpqamMHz8eV1dX7rvvPvbt28ecOXN49tln830fRUREpGhwaCjKqcTEREwmEx4eHgDs3LkTDw8PayACCAkJwWw2ExUVRatWrfj7779p2LAhrq6u1jGhoaHMnDmT+Ph4PD09s719kyn3tWc8Ni9zFHf27o167hjqe8FTzx1DfXeMnPQ9p89NkQlFV69eZdKkSYSFheHm5gZATEwM5cqVsxnn7OyMp6cn0dHR1jG+vr42Y3x8fKzrchKKvL3d87ILdpujOPLyKpNvc6vnjqG+Fzz13DHUd8fIj74XiVCUmprKwIEDMQyD0aNHO6yO2NhLGEbuHmsyXXsC8zJHYeLkZLZrkLlwIYn0dIvd5oPi1/OiQn0veOq5Y6jvjpGTvmeMza5CH4pSU1N5/fXXOX36NHPnzrUeJYJrR3zi4uJsxqelpREfH0/58uWtY2JiYmzGZPycccQouwyDPL/w7TFHcZVffVHPHUN9L3jquWOo746RH30v1PcpyghE//77L1999RVeXl4264ODg0lISGDPnj3WZVu3bsVisRAUFARA/fr1+fPPP0lNTbWO2bx5M9WrV8/RqTMREREp3hwaipKSkti3bx/79u0D4OTJk+zbt4/Tp0+TmprKgAED2LNnD5MmTSI9PZ3o6Giio6NJSUkBwM/Pj6ZNm/Lee+8RFRXFjh07GDNmDGFhYVSsWBGA8PBwXFxcePfddzl06BCrVq3i66+/5sUXX3TYfouIiEjh49DTZ3v27KFbt27WnyMjIwHo0KED/fr147fffgPgiSeesHnc119/TaNGjQCYNGkSY8aMoXv37pjNZlq3bs3w4cOtY93d3Zk9ezYRERF07NgRLy8vXn31VX0cX0RERGw4NBQ1atSIAwcO3HT9rdZlKFu2LB9++OEtx9SsWZNvvvkmx/WJiIjInaNQX1MkIiIiUlAUikRERERQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBFIpEREREAAeHou3bt9OnTx9CQ0MJCAhgzZo1NusNw2DKlCmEhoYSFBREjx49OH78uM2Yixcv8uabb9KgQQMaNmzIsGHDSEpKshmzf/9+unTpQt26dWnevDkzZ87M710TERGRIsahoSg5OZmAgABGjhyZ5fqZM2cyb948Ro0axeLFiylVqhQ9e/bk6tWr1jGDBw/m8OHDzJkzh+nTp/Pnn38yYsQI6/rExER69uxJ5cqVWbp0KUOGDGHatGl8++23+b5/IiIiUnQ4O3LjzZs3p3nz5lmuMwyDr7/+mr59+9KyZUsAJk6cSEhICGvWrCEsLIwjR46wYcMGvv/+e+rWrQvA8OHD6d27N0OGDKFixYosX76c1NRUxo8fj6urK/fddx/79u1jzpw5PPvsswW2ryIiIlK4Fdprik6ePEl0dDQhISHWZe7u7tSrV4+dO3cCsHPnTjw8PKyBCCAkJASz2UxUVBQAf//9Nw0bNsTV1dU6JjQ0lGPHjhEfH19AeyMiIiKFnUOPFN1KdHQ0AN7e3jbLvb29iYmJASAmJoZy5crZrHd2dsbT09P6+JiYGHx9fW3G+Pj4WNd5enpmuyaTKWf7kNVjs5rDbDZhysvk/x/DMLBYjDzP4yh2aEGW89l7Xrk19b3gqeeOob47Rk76ntPnptCGosLI29s9X+ZItxg4mfP+t8pe8ziCl1eZfJvbHs+b5Jz6XvDUc8dQ3x0jP/peaENR+fLlAYiNjaVChQrW5bGxsdSsWRO4dsQnLi7O5nFpaWnEx8dbH+/j42M9spQh4+eMI0bZFRt7CSOXB2JMpmtP4I1zODmZ8fIqw8BFOzl8PjF3kwM1Krgx5blgLlxIIj3dkut5siujbnvJj7pv1nPJX+p7wVPPHUN9d4yc9D1jbHYV2lDk6+tL+fLl2bJlC7Vq1QKufZJs165ddO7cGYDg4GASEhLYs2cPgYGBAGzduhWLxUJQUBAA9evXZ/LkyaSmpuLi4gLA5s2bqV69eo5OnQEYBnl+4d9sjsPnE9l7OiFvk1+3jaIov+q2x/MmOae+Fzz13DHUd8fIj7479ELrpKQk9u3bx759+4BrF1fv27eP06dPYzKZ6NatG59//jlr167lwIEDDBkyhAoVKlg/jebn50fTpk157733iIqKYseOHYwZM4awsDAqVqwIQHh4OC4uLrz77rscOnSIVatW8fXXX/Piiy86bL9FRESk8HHokaI9e/bQrVs368+RkZEAdOjQgQkTJvDyyy9z+fJlRowYQUJCAvfffz+zZs2iRIkS1sdMmjSJMWPG0L17d8xmM61bt2b48OHW9e7u7syePZuIiAg6duyIl5cXr776qj6OLyIiIjYcGooaNWrEgQMHbrreZDIxcOBABg4ceNMxZcuW5cMPP7zldmrWrMk333yT6zpFRESk+Cu09ykSERERKUgKRSIiIiIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigIO/EFbsz8nJPjnXYjGwWAy7zCUiIlIUKBQVE+XdSoAlHQ+PUnaZz0hPI+7iFQUjERG5YygUFRMepZzB7ARLekHMwbxN5uOPqdMszGaTQpGIiNwxFIqKm5iDcGaXo6sQEREpcnShtYiIiAgKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiACFPBSlp6czefJkWrRoQVBQEC1btuTTTz/FMAzrGMMwmDJlCqGhoQQFBdGjRw+OHz9uM8/Fixd58803adCgAQ0bNmTYsGEkJSUV8N6IiIhIYVaoQ9HMmTNZuHAhI0aMYNWqVQwePJhZs2Yxb948mzHz5s1j1KhRLF68mFKlStGzZ0+uXr1qHTN48GAOHz7MnDlzmD59On/++ScjRoxwxC6JiIhIIVWoQ9HOnTt59NFHefjhh/H19aVt27aEhoYSFRUFXDtK9PXXX9O3b19atmxJzZo1mThxIufPn2fNmjUAHDlyhA0bNjB27Fjq1atHw4YNGT58OCtXruTcuXOO3D0REREpRJwdXcCtBAcHs3jxYo4dO0b16tXZv38/O3bsYOjQoQCcPHmS6OhoQkJCrI9xd3enXr167Ny5k7CwMHbu3ImHhwd169a1jgkJCcFsNhMVFUWrVq2yXY/JlPt9yXhsXuZwhIKs197bKqo9L+rU94KnnjuG+u4YOel7Tp+bQh2KevfuTWJiIu3atcPJyYn09HQGDRpE+/btAYiOjgbA29vb5nHe3t7ExMQAEBMTQ7ly5WzWOzs74+npaX18dnl7u+d2V+w6R0Hx8ipTLLZVlHpenKjvBU89dwz13THyo++FOhT9/PPPrFixgg8//JAaNWqwb98+IiMjqVChAh06dCjwemJjL3HdNd45YjJdewJvnMPJyVyg4SMnLlxIIj3dkuU6e9d9q23l1s16LvlLfS946rljqO+OkZO+Z4zNrkIdiiZOnEjv3r0JCwsDICAggNOnTzNjxgw6dOhA+fLlAYiNjaVChQrWx8XGxlKzZk0AfHx8iIuLs5k3LS2N+Ph46+OzyzDI8wvfHnMUpIKsNb+2VdR6Xlyo7wVPPXcM9d0x8qPvebrQ+t9//2XDhg1cuXIFwOaj8vZw5coVTDecEHRycrJux9fXl/Lly7Nlyxbr+sTERHbt2kVwcDBw7bqkhIQE9uzZYx2zdetWLBYLQUFBdq1XREREiq5cHSm6cOECgwYNYuvWrZhMJv73v/9RtWpVhg0bhqenp/VC6Lx65JFHmD59OpUrV7aePpszZw6dOnUCwGQy0a1bNz7//HPuuecefH19mTJlChUqVKBly5YA+Pn50bRpU9577z1Gjx5NamoqY8aMISwsjIoVK9qlThERESn6cnWkKDIyEicnJ9atW0fJkiWtyx977DE2bNhgt+KGDx9OmzZtGD16NI899hjvv/8+zz77LAMHDrSOefnll3nhhRcYMWIETz31FMnJycyaNYsSJUpYx0yaNIl7772X7t2707t3bxo0aEBERITd6hQREZGiL1dHijZt2sTs2bOpVKmSzfJq1apx+vRpuxQG4Obmxrvvvsu777570zEmk4mBAwfaBKUblS1blg8//NBudYmIiEjxk6sjRcnJyTZHiDJcvHgRV1fXPBclIiIiUtByFYoaNmzIDz/8YLPMYrEwa9YsGjVqZI+6RERERApUrk6fvfXWW/To0YM9e/aQmprKBx98wOHDh4mPj2fhwoX2rlFEREQk3+UqFPn7+/Prr78yf/58ypQpQ3JyMq1ateL555+3uV+QiIiISFGR65s3uru707dvX3vWIiIiIuIwubqmaMmSJfz888+Zlv/8888sW7Ysz0WJiIiIFLRchaIvvvgCLy+vTMu9vb2ZPn16nosSERERKWi5CkWnT5/G19c30/LKlStz5syZPBclIiIiUtByFYq8vb05cOBApuX79++nbNmyea1JREREpMDl6kLrsLAwxo0bR5kyZXjggQcA+OOPPxg/frz1G+1FREREipJchaKBAwdy6tQpevTogbPztSksFgtPPPEEgwYNsmuBIiIiIgUhV6HI1dWVyZMnc+zYMfbv30/JkiXx9/enSpUq9q5PREREpEDk+j5FANWrV6d69er2qkVERETEYXIVitLT01m6dClbt24lNjYWi8Vis/7rr7+2S3EiIiIiBSVXoWjcuHEsW7aM5s2bc99992Eymexdl4iIiEiBylUoWrlyJZMnT6Z58+b2rkdERETEIXJ1nyIXFxfuvvtue9ciIiIi4jC5CkUvvfQSX3/9NYZh2LseEREREYfI1emzHTt2sG3bNv7v//6P++67z3qvogzTpk2zS3EiIiIiBSVXocjDw4NWrVrZuxYRERERh8lVKIqMjLR3HSIiIiIOlatrigDS0tLYvHkzixYtIjExEYBz586RlJRkt+JERERECkqujhSdOnWKXr16cebMGVJSUmjSpAlubm7MnDmTlJQUIiIi7F2niIiISL7K1ZGicePGERgYyB9//EGJEiWsy1u1asXWrVvtVpyIiIhIQcn1p88WLlyIq6urzfIqVapw7tw5uxQmIiIiUpByFYosFkum7zsDOHv2LGXKlMlzUSL5yWw2YTbn/atpLBYDi0X36hIRKS5yFYqaNGnC3LlzGTNmjHVZUlISU6dO1Vd/SKFmNpvwLFsaZ6dcf8bAKi3dQvzFZAUjEZFiIleh6O2336ZXr1489thjpKSkMHjwYI4fP46XlxcfffSRvWsUsRuz2YSzk5mBi3Zy+HxiruepUcGNKc8FYzabFIpERIqJXIWiu+66ix9//JFVq1axf/9+kpOTeeqppwgPD6dkyZL2rlHE7g6fT2Tv6QRHlyEiIoVIjkNRamoq7dq1Y8aMGbRv35727dvnR10iIiIiBSrHF1a4uLhw9erV/KhFRERExGFydbXp888/z8yZM0lLS7N3PSIiIiIOkatrinbv3s2WLVvYuHEjAQEBlCpVymb9tGnT7FKciIiISEHJVSjy8PCgTZs29q5FRERExGFyFYoiIyPtXYeIiIiIQ+X6DnZpaWls3ryZRYsWkZh47X4v586dIykpyW7FiYiIiBSUXB0pOnXqFL169eLMmTOkpKTQpEkT3NzcmDlzJikpKURERNi7ThEREZF8lasjRePGjSMwMJA//viDEiVKWJe3atWKrVu32q04ERERkYKSqyNFO3bsYOHChbi6utosr1KlCufOnbNLYSIiIiIFKVdHiiwWCxaLJdPys2fPUqZMmTwXJSIiIlLQchWKmjRpwty5c22WJSUlMXXqVJo3b26XwkREREQKUq5C0dChQ/nrr7947LHHSElJYfDgwbRo0YJz584xePBge9coIiIiku9ydU1RpUqV+PHHH1m1ahX79+8nOTmZp556ivDwcEqWLGnvGkVERETyXbZDUYcOHfjqq6/w9PRk2rRp9OzZk/bt29O+ffv8rE9ERESkQGT79NmRI0e4fPkyAJ9++inJycn5VtT1Mk7JNWrUiKCgIMLDw9m9e7d1vWEYTJkyhdDQUIKCgujRowfHjx+3mePixYu8+eabNGjQgIYNGzJs2DDdZFJERERsZPtIUa1atXjnnXe4//77MQyD2bNnU7p06SzH9uvXzy7FxcfH07lzZxo1asTMmTPx8vLi33//xdPT0zpm5syZzJs3jwkTJuDr68uUKVPo2bMnq1atst5DafDgwURHRzNnzhxSU1MZNmwYI0aM4MMPP7RLnSIiIlL0ZTsURUZGMnXqVH7//XdMJhMbNmzAyckp0ziTyWS3UDRz5kwqVapk811rVatWtf6/YRh8/fXX9O3bl5YtWwIwceJEQkJCWLNmDWFhYRw5coQNGzbw/fffU7duXQCGDx9O7969GTJkCBUrVrRLrSIiIlK0ZTsU3XvvvXz88ccA1KxZk6+++gpvb+98Kwzgt99+IzQ0lAEDBrB9+3YqVqxIly5deOaZZwA4efIk0dHRhISEWB/j7u5OvXr12LlzJ2FhYezcuRMPDw9rIAIICQnBbDYTFRVFq1atsl2PyZT7fcl4bF7mcISCrNfe2yqonhe15zS/FdXXelGmnjuG+u4YOel7Tp+bXF1o3a9fv5ueOrOn//77j4ULF/Liiy/Sp08fdu/ezdixY3FxcaFDhw5ER0cDZApn3t7exMTEABATE0O5cuVs1js7O+Pp6Wl9fHZ5e7vnYW/sN0dB8fIquBtx5ue28rPnBdmjoqYovdaLC/XcMdR3x8iPvmc7FGVcaO3p6cmnn35K586dKVWqlN0Lup5hGAQGBvLGG28AULt2bQ4dOsSiRYvo0KFDvm47K7GxlzCM3D3WZLr2BN44h5OTudC+sV64kER6euY7l4P9677VtnIrq54XhbqLupu91iX/qOeOob47Rk76njE2uwr1hdbly5fHz8/PZtm9997Lr7/+al0PEBsbS4UKFaxjYmNjqVmzJgA+Pj7ExcXZzJGWlkZ8fLz18dllGOT5hW+POQpSQdaaX9vK754XpeezIBW113pxoJ47hvruGPnR90J9oXWDBg04duyYzbLjx49TpUoVAHx9fSlfvjxbtmyhVq1aACQmJrJr1y46d+4MQHBwMAkJCezZs4fAwEAAtm7disViISgoyC51ioiISNFXqC+07t69O507d2b69Om0a9eOqKgoFi9eTEREBHAtgHXr1o3PP/+ce+65x/qR/AoVKlg/jebn50fTpk157733GD16NKmpqYwZM4awsDB98kxERESscvU1H/v377d3HVkKCgpi2rRpfPTRR3z66af4+voybNgwm7tov/zyy1y+fJkRI0aQkJDA/fffz6xZs6z3KAKYNGkSY8aMoXv37pjNZlq3bs3w4cMLZB9ERESkaMh2KFq7di3NmjXDxcWFtWvX3nLso48+mufCMjzyyCM88sgjN11vMpkYOHAgAwcOvOmYsmXL6kaNIiIickvZDkWvvfYamzZtwtvbm9dee+2m40wmE/v27bNLcSIiIiIFJduh6PpTZgV1+kxERESkoOT4miKLxcLSpUtZvXo1p06dwmQy4evrS5s2bXjiiScw6daeIiIiUgTlKBQZhkHfvn1Zv349NWvWxN/fH8MwOHLkCEOHDuV///sfn332WX7VKiIiIpJvchSKli5dyvbt2/nqq6946KGHbNZt2bKF1157jR9++IEnn3zSnjWKiIiI5DtzTgavXLmSPn36ZApEAI0bN6Z3796sWLHCbsWJiIiIFJQchaIDBw7QtGnTm65v1qyZLsIWERGRIilHoSg+Pv6Wd7H29vYmPj4+z0WJiIiIFLQchaL09HScnW9+GZKTkxPp6el5LkpERESkoOX402dDhw7F1dU1y/UpKSl2KUpERESkoOUoFHXo0OG2Y/TJMxERESmKchSKIiMj86sOEREREYfK0TVFIiIiIsWVQpGIiIgICkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICFDEQtEXX3xBQEAA48aNsy67evUqo0ePplGjRgQHB9O/f39iYmJsHnf69Gl69+5NvXr1aNy4Me+//z5paWkFXb6IiIgUYkUmFEVFRbFo0SICAgJslo8fP57ff/+dyZMnM2/ePM6fP0+/fv2s69PT03nllVdITU1l0aJFTJgwgWXLlvHJJ58U9C6IiIhIIVYkQlFSUhJvvfUWY8eOxdPT07r80qVLLFmyhKFDh9K4cWMCAwMZP348O3fu5O+//wZg48aNHD58mA8++IBatWrRvHlzBg4cyIIFC0hJSXHQHomIiEhhUyRCUUREBM2bNyckJMRm+Z49e0hNTbVZ7ufnR+XKla2h6O+//8bf3x8fHx/rmNDQUBITEzl8+HCO6jCZ8vYnqzkKu1vtS0Fty549Lyp1F/U/6ot6fqf8Ud8Lf99zwtk+bwv5Z+XKlfzzzz98//33mdbFxMTg4uKCh4eHzXJvb2+io6OtY64PRID154wx2eXt7Z6j8fk1R0Hx8ipTLLaVnz0vyB4VNUXptV5cqOeOob47Rn70vVCHojNnzjBu3Di+/PJLSpQo4ehyiI29hGHk7rEm07Un8MY5nJzMhfaN9cKFJNLTLVmus3fdt9pWbmXV86JQd1F3s9e65B/13DHUd8fISd8zxmZXoQ5Fe/fuJTY2lo4dO1qXpaens337dhYsWMDs2bNJTU0lISHB5mhRbGws5cuXB64dFYqKirKZN+PTaRljssswyPML3x5zFKSCrDW/tpXfPS9Kz2dBKmqv9eJAPXcM9d0x8qPvhToUPfTQQ6xYscJm2TvvvMO9997Lyy+/zF133YWLiwtbtmyhTZs2ABw9epTTp09Tv359AOrXr8/06dOJjY3F29sbgM2bN+Pm5kaNGjUKdH9ERESk8CrUocjNzQ1/f3+bZaVLl6Zs2bLW5Z06dWLChAl4enri5ubG2LFjCQ4Otoai0NBQatSowZAhQ3jrrbeIjo5m8uTJPP/887i6uhb0LomIiEghVahDUXYMGzYMs9nMgAEDSElJITQ0lJEjR1rXOzk5MX36dEaNGsWzzz5LqVKl6NChAwMGDHBg1SIiIlLYFLlQNG/ePJufS5QowciRI22C0I2qVKnCzJkz87s0ERERKcKKxH2KRERERPJbkTtSJHKnMptNmM05vBNZFiwWA4tFH5UREbmRQpFIEWA2m/AsWxpnp7wf3E1LtxB/MVnBSETkBgpFIkWA2WzC2cnMwEU7OXw+Mdfz1KjgxpTngjGbTQpFIiI3UCgSKUIOn09k7+kER5chIlIs6UJrERERERSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERAcDZ0QWIADg52SefWywGFothl7myo6jWLSIimSkUiUOVdysBlnQ8PErZZT4jPY24i1fyPWAU1bpFROTmFIrEoTxKOYPZCZb0gpiDeZvMxx9Tp1mYzaZ8DxdFtW4REbk5hSIpHGIOwpldjq4i54pq3SIikokutBYRERFBoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhEREREAnB1dgIiIiL2YzSbMZpNd5rJYDCwWwy5zSdGgUCQiIsWC2WyiXNmSmJzs89ZmpKcRd/GKgtEdRKFIRESKBbPZdC0QLekFMQfzNpmPP6ZOszCbTQUSinSEq3BQKBIRkeIl5iCc2eXoKrJNR7gKD4UiERHJREcuCk5RPsJV3CgUiUi+stebq95YC46OXDhIETvCVRwpFIlIvjGbTXiWLY2zU97v/pGWbiH+YrLeWAuAjlzInUqhSETyjdlswtnJzMBFOzl8PjHX89So4MaU54L1xlrQdORC7jAKRSKS7w6fT2Tv6QRHlyEickuF+o7WM2bMoFOnTgQHB9O4cWNeffVVjh49ajPm6tWrjB49mkaNGhEcHEz//v2JiYmxGXP69Gl69+5NvXr1aNy4Me+//z5paWkFuSsiIiJSyBXqUPTHH3/w/PPPs3jxYubMmUNaWho9e/YkOTnZOmb8+PH8/vvvTJ48mXnz5nH+/Hn69etnXZ+ens4rr7xCamoqixYtYsKECSxbtoxPPvnEEbskUig4OZlxds77H3t9OklEpDAo1KfPZs+ebfPzhAkTaNy4MXv37uWBBx7g0qVLLFmyhEmTJtG4cWPgWkh67LHH+Pvvv6lfvz4bN27k8OHDzJkzBx8fH2rVqsXAgQOZNGkS/fr1w9XV1RG7JuIQ5d1KgCUdD49SdpmvoD9V5GSHC7ZBn2QTkawV6lB0o0uXLgHg6ekJwJ49e0hNTSUkJMQ6xs/Pj8qVK1tD0d9//42/vz8+Pj7WMaGhoYwaNYrDhw9Tu3btgt0JEQfyKOUMZqci96mioh7mRKRoKDKhyGKxMH78eBo0aIC/vz8AMTExuLi44OHhYTPW29ub6Oho65jrAxFg/TljTHaZ8nCmIOOxeZnDEYpavRlMpqLZ8wKr1c6fKrq+7vzoe36FOcMomK9vMNmpGYaR9RGuovJaL+z13czN6i7sfS+sdeVVTvqe0x4UmVA0evRoDh06xDfffOOwGry93QvFHAXFy6uMo0vIlRvrLio9Ly79zpAvfbdjmCuwflvSrwW6ApirML/Wi9vr+3qFse9Ftd85kR99LxKhKCIignXr1jF//nwqVapkXe7j40NqaioJCQk2R4tiY2MpX768dUxUVJTNfBmfTssYk12xsZfI7T8sTaZrT+CNczg5mQvti/fChSTS0y1ZrisKdWfV86JQd1aKUt3X991sLjp15wfr82anI1x0mpVl3Tf7/ZJb+fF6K9B+29Gt6rZX34tqvx0lJ33PGJtdhToUGYbBmDFjWL16NfPmzaNq1ao26wMDA3FxcWHLli20adMGgKNHj3L69Gnq168PQP369Zk+fTqxsbF4e3sDsHnzZtzc3KhRo0YO6yHPv3DsMUdBKkq1Xu/6uotSz4tKnTfKqu6isC8FVqOdT1ferO7C/lovzLXdyu3qLqx9L4w12VN+9L1Qh6LRo0fz008/8dlnn1GmTBnrNUDu7u6ULFkSd3d3OnXqxIQJE/D09MTNzY2xY8cSHBxsDUWhoaHUqFGDIUOG8NZbbxEdHc3kyZN5/vnn9ckzERERsSrUoWjhwoUAdO3a1WZ5ZGQkHTt2BGDYsGGYzWYGDBhASkoKoaGhjBw50jrWycmJ6dOnM2rUKJ599llKlSpFhw4dGDBgQMHtiIiIiBR6hToUHThw4LZjSpQowciRI22C0I2qVKnCzJkz7VmaiIiIFDOF+o7WIiIiIgVFoUhEREQEhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBFAoEhEREQEUikREREQAhSIRERERQKFIREREBCjkXwgrIuIoZrMJs9mUpzmcnPTvTpGiRKFIROQGZrMJz7KlcVaoEbmjKBSJiNzAbDbh7GRm4KKdHD6fmOt5Hg4oz1ttatqxMhHJTwpFIiI3cfh8IntPJ+T68X7ly9ixGhHJbzo2LCIiIoJCkYiIiAigUCQiIiICKBSJiIiIALrQWkRERHLJHvfzymCxGFgshl3myi2FIhEREckxs9lEubIlMTnZJ0oY6WnEXbzi0GCkUCQiIiI5ZjabrgWiJb0g5mDeJvPxx9RpFmazSaFIREREiqiYg3Bml6OrsAtdaC0iIiKCjhSJiBQr+iJbkdxTKBIRKSbMZhMenvoiW5HcUigSESkmTCZ9ka1IXigUiYgUM/oiW5Hc0TFWERERERSKRERERACFIhERERFA1xSJiEghoFsJSGGgUCQiIg5lNpvwLKtbCYjjKRSJiIhDmc26lYAUDgpFIiJSKOhWAuJoOlYpIiIigo4UiYiI5EleL/DWBeKFh0KRiIhILpjNJtItBl5eOm1XXCgUiYiI5ILJZMLJbNIF4sWIQpGIiEge6ALx4kMnMkVERETQkSIREZE7ju4gnjWFIhERkTuI7iB+c3dUKFqwYAGzZ88mOjqamjVr8t577xEUFOToskRERAqM7iB+c3dMKFq1ahWRkZGMHj2aevXqMXfuXHr27Mkvv/yCt7e3o8sTEREpULpAPLM75tjZnDlzeOaZZ+jUqRM1atRg9OjRlCxZkiVLlji6NBERESkE7ohQlJKSwt69ewkJCbEuM5vNhISEsHPnTgdWJiIiIoXFHXH67MKFC6Snp2c6Tebt7c3Ro0ezPY/ZDIaRuxpMplvPUaeyB6VcnXI3OeBX3u3a/1QKApfSuZ4HAO8a1v813yY2F+a6b9Xzwlz3rRSFuq/ve4aiUHdWikrdGT3P+G9RqftGRa1u9dsxdd/u/TSrsdllMozcvs0XHefOnaNZs2YsWrSI4OBg6/KJEyeyfft2vvvuOwdWJyIiIoXBHXH6zMvLCycnJ2JjY22Wx8bG4uPj46CqREREpDC5I0KRq6srderUYcuWLdZlFouFLVu22Bw5EhERkTvXHXFNEcCLL77I22+/TWBgIEFBQcydO5fLly/TsWNHR5cmIiIihcAdE4oee+wx4uLi+OSTT4iOjqZWrVrMmjVLp89EREQEuEMutBYRERG5nTvimiIRERGR21EoEhEREUGhSERERARQKBIREREBFIpEREREAIWiArFgwQJatGhB3bp1efrpp4mKinJ0ScXGjBkz6NSpE8HBwTRu3JhXX3010/fZXb16ldGjR9OoUSOCg4Pp378/MTExDqq4ePriiy8ICAhg3Lhx1mXqe/44d+4cgwcPplGjRgQFBREeHs7u3but6w3DYMqUKYSGhhIUFESPHj04fvy44wou4tLT05k8eTItWrQgKCiIli1b8umnn3L9B7fV87zbvn07ffr0ITQ0lICAANasWWOzPjs9vnjxIm+++SYNGjSgYcOGDBs2jKSkpBzVoVCUz1atWkVkZCSvvfYay5Yto2bNmvTs2TPTV45I7vzxxx88//zzLF68mDlz5pCWlkbPnj1JTk62jhk/fjy///47kydPZt68eZw/f55+/fo5sOriJSoqikWLFhEQEGCzXH23v/j4eDp37oyLiwszZ85k5cqVvP3223h6elrHzJw5k3nz5jFq1CgWL15MqVKl6NmzJ1evXnVg5UXXzJkzWbhwISNGjGDVqlUMHjyYWbNmMW/ePJsx6nneJCcnExAQwMiRI7Ncn50eDx48mMOHDzNnzhymT5/On3/+yYgRI3JWiCH56qmnnjJGjx5t/Tk9Pd0IDQ01ZsyY4cCqiq/Y2FjD39/f+OOPPwzDMIyEhASjTp06xs8//2wdc/jwYcPf39/YuXOng6osPhITE43WrVsbmzZtMl544QVj7NixhmGo7/nlgw8+MDp37nzT9RaLxWjSpIkxa9Ys67KEhAQjMDDQ+OmnnwqixGKnd+/exjvvvGOzrF+/fsabb75pGIZ6nh/8/f2N1atXW3/OTo8zfr9ERUVZx6xfv94ICAgwzp49m+1t60hRPkpJSWHv3r2EhIRYl5nNZkJCQti5c6cDKyu+Ll26BGD9l/OePXtITU21eQ78/PyoXLkyf//9tyNKLFYiIiJo3ry5TX9Bfc8vv/32G4GBgQwYMIDGjRvz5JNPsnjxYuv6kydPEh0dbdN3d3d36tWrp985uRQcHMzWrVs5duwYAPv372fHjh00a9YMUM8LQnZ6vHPnTjw8PKhbt651TEhICGazOUeXrNwxX/PhCBcuXCA9PR1vb2+b5d7e3pmue5G8s1gsjB8/ngYNGuDv7w9ATEwMLi4ueHh42Iz19vYmOjraEWUWGytXruSff/7h+++/z7ROfc8f//33HwsXLuTFF1+kT58+7N69m7Fjx+Li4kKHDh2svc3qd46u58qd3r17k5iYSLt27XByciI9PZ1BgwbRvn17APW8AGSnxzExMZQrV85mvbOzM56enjn6naNQJMXG6NGjOXToEN98842jSyn2zpw5w7hx4/jyyy8pUaKEo8u5YxiGQWBgIG+88QYAtWvX5tChQyxatIgOHTo4uLri6eeff2bFihV8+OGH1KhRg3379hEZGUmFChXU82JIp8/ykZeXF05OTpkuqo6NjdUX0dpZREQE69atY+7cuVSqVMm63MfHh9TUVBISEmzGx8bGUr58+YIus9jYu3cvsbGxdOzYkdq1a1O7dm3++OMP5s2bR+3atdX3fFK+fHn8/Pxslt17772cPn3auh7Q7xw7mjhxIr179yYsLIyAgACefPJJunfvzowZMwD1vCBkp8c+Pj7ExcXZrE9LSyM+Pj5Hv3MUivKRq6srderUYcuWLdZlFouFLVu2EBwc7MDKig/DMIiIiGD16tXMnTuXqlWr2qwPDAzExcXF5jk4evQop0+fpn79+gVcbfHx0EMPsWLFCn744Qfrn8DAQMLDw63/r77bX4MGDazXtmQ4fvw4VapUAcDX15fy5cvb9D0xMZFdu3bpd04uXblyBZPJZLPMycnJ+pF89Tz/ZafHwcHBJCQksGfPHuuYrVu3YrFYCAoKyva2dPosn7344ou8/fbbBAYGEhQUxNy5c7l8+TIdO3Z0dGnFwujRo/npp5/47LPPKFOmjPXcsbu7OyVLlsTd3Z1OnToxYcIEPD09cXNzY+zYsQQHB+vNOQ/c3Nys121lKF26NGXLlrUuV9/tr3v37nTu3Jnp06fTrl07oqKiWLx4MREREQCYTCa6devG559/zj333IOvry9TpkyhQoUKtGzZ0sHVF02PPPII06dPp3LlytbTZ3PmzKFTp06Aem4vSUlJnDhxwvrzyZMn2bdvH56enlSuXPm2Pfbz86Np06a89957jB49mtTUVMaMGUNYWBgVK1bMdh0mw7juDlSSL+bPn8/s2bOJjo6mVq1aDB8+nHr16jm6rGLhxnvjZIiMjLQGz6tXrzJhwgRWrlxJSkoKoaGhjBw5Uqdx7Kxr167UrFmTd999F1Df88vvv//ORx99xPHjx/H19eXFF1/kmWeesa43DINPPvmExYsXk5CQwP3338/IkSOpXr26A6suuhITE5kyZQpr1qwhNjaWChUqEBYWxmuvvYarqyugntvDtm3b6NatW6blHTp0YMKECdnq8cWLFxkzZgy//fYbZrOZ1q1bM3z4cMqUKZPtOhSKRERERNA1RSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAigUiYiIiAAKRSJFxrZt2wgICMj0Jas3atGiBV999VXBFFUALl++TP/+/WnQoIF1/7OzjwEBAaxZs6ZgiizChg4dyquvvnrbcW+99RbTp0+3/uyo19mgQYP48ssvC3y7cmdQKBKxo6FDhxIQEEBAQACBgYG0atWKadOmkZaWlue5g4OD2bhxI+7u7gAsXbqUhg0bZhr3/fff8+yzz+Z5e7fz77//8s4779CsWTMCAwNp0aIFb7zxBrt377brdpYtW8aff/7JokWLrPtfUPuY37IbdB1t//79/N///R9du3a1LrPHczB79mwaN25M48aNMwWdXbt20bFjx0x/d/r27cv06dO5dOlSnrYtkhV9IayInTVt2pTIyEhSUlJYv349ERERuLi48Morr+RpXldX12x9b1i5cuXytJ3s2L17Nz169OC+++4jIiKCe++9l6SkJNauXcv777/P/Pnz7bat//77Dz8/P5svoC2IfZT/37x582jTpo3Nd0jl9TnYv38/n3zyifXo0yuvvEKTJk0ICAggLS2NkSNHEhERgbOz7duUv78/VatWZfny5Tz//PN5qkHkRjpSJGJnGeGlSpUqdOnShZCQEH777TcA4uPjGTJkCA888AD16tWjV69eHD9+3PrYU6dO0adPHx544AHq169PWFgY69evB2yPKmzbto133nmHS5cuWY9MTZ06FbA9rfHmm2/y+uuv29SXmppKo0aN+OGHHwCwWCzMmDGDFi1aEBQURPv27fnll19uun+GYfDOO+9wzz338M033/Dwww9z9913U6tWLfr168dnn31mHXvgwAG6detGUFAQjRo14r333iMpKcm6PuPUzezZswkNDaVRo0bWb7iGa18y++WXX7J9+3YCAgKsRypuPHVz/Phxnn/+eerWrctjjz3Gpk2bMtV95swZBg4cSMOGDXnwwQfp27cvJ0+ezHYtACkpKXzwwQc0b97ceiTwu+++s64/ePAgvXr1Ijg4mJCQEN566y3i4uJu2svbSUlJ4f3336dp06bUr1+fp59+mm3btgHXvqg0KCjI+vrIsHr1aoKDg7l8+XK29vt20tPT+fXXX2nRooXN8hufg4CAAL777jtee+016tWrR+vWrVm7du1N5z169CgBAQHWI0UBAQEcPXoUuHYEqWHDhgQFBWX52EceeYSVK1dmex9EskuhSCSflShRwvrGOnToUPbs2cPnn3/Ot99+i2EY9O7d27o+IiKClJQU5s+fz4oVKxg8eDClS5fONGdwcDDDhg3Dzc2NjRs3snHjRl566aVM48LDw/n9999tgsjGjRu5cuUKLVu2BGDGjBn88MMPjB49mpUrV9KjRw/eeust/vjjjyz3Z9++fRw6dIiXXnoJsznzrxAPDw8AkpOT6dmzJ56ennz//fdMnjyZzZs3M2bMGJvx27Zt48SJE8ydO5cJEyawbNkyli1bBsDUqVN55plnrKcOM4Lf9SwWC/3798fFxYXvvvuO0aNHM2nSJJsxqamp9OzZkzJlyrBgwQIWLlxI6dKl6dWrFykpKdmqBWDIkCGsXLmS4cOH8/PPPxMREWE9epKQkED37t2pXbs233//PbNmzSI2NjZTKM2JiIgIdu7cyccff8zy5ctp27atNUi7ubnx8MMP89NPP9k8ZsWKFbRs2ZJSpUple79v5cCBA1y6dInAwMDbjp02bRrt2rVj+fLlNGvWjMGDB3Px4sUsxwYEBHD8+HFOnz7NqVOnOH78OP7+/pw4cYKlS5fesm9BQUFERUVlex9Ess0QEbt5++23jb59+xqGYRgWi8XYtGmTERgYaEyYMME4duyY4e/vb+zYscM6Pi4uzggKCjJWrVplGIZhPP7448bUqVOznHvr1q2Gv7+/ER8fbxiGYSxZssS4//77M4175JFHjDlz5hiGYRipqalGo0aNjGXLllnXv/HGG8brr79uGIZhXL161ahXr57x119/2cwxbNgw44033siyjpUrVxr+/v7G3r17b9mLb7/91njggQeMpKQk67J169YZNWvWNKKjow3DuNavRx55xEhLS7OOGTBggLU+wzCMsWPHGi+88MJN93HDhg1G7dq1jbNnz1rXr1+/3vD39zdWr15tGIZh/PDDD0abNm0Mi8ViHXP16lUjKCjI2LBhQ7ZqOXr0qOHv729s2rQpy/399NNPjZdeeslm2ZkzZwx/f3/j6NGjWT7mxuf0eqdOnTJq1apls1+GYRjdu3c3PvzwQ8MwDGP16tVG/fr1jeTkZMMwDOPSpUtG3bp1jfXr1+dovzNes1lZvXq1UatWLZs5DMP2OTAMw/D39zc+/vhj689JSUmGv7+/tZasfPPNN0br1q2N1q1bG9988411/1avXm38/PPPRlhYmPHEE08Yf/zxh83j9u3bZ/j7+xsnT5686dwiuaFrikTsbN26dQQHB5OamophGDz++OP079+fLVu24OzsTL169axjvby8qF69OkeOHAGgW7dujBo1io0bNxISEkLr1q2pWbNmrmtxdnamXbt2rFixgieffJLk5GTWrl3LRx99BFy7WPry5cuZjjKlpqZSq1atXG8X4MiRIwQEBNgc6WrQoAEWi4Vjx47h4+MDQI0aNXBycrKOKV++PAcPHszRdipVqkTFihWty4KDg23G7N+/nxMnTtCgQQOb5VevXuXEiRPWn29Vy759+3BycuKBBx7Iso79+/ezbdu2TNsGOHHiBNWrV8/2PsG1U3Hp6em0bdvWZnlKSgply5YFoFmzZri4uPDbb78RFhbGr7/+ipubGyEhITna71u5cuUKrq6umEym244NCAiw/n/p0qVxc3O75enDzp0707lzZ+vPy5Yto0yZMtSvX5+2bdvy/fffc/bsWQYNGsRvv/2Gq6srACVLlrTWJmJPCkUidtaoUSNGjRqFi4sLFSpUyHSh6K08/fTThIaGsm7dOjZt2sQXX3zB22+/bfOpn5wKDw+na9euxMbGsmnTJkqUKEHTpk2Ba6e44NoptOtDBWB9A7pRtWrVgGvXhNSuXTvXdWW4sT8mkwnDMPI87/WSk5OpU6dOptNqYHvB8K1qyXgjvtU2HnnkEQYPHpxpXXYukM9qPicnJ5YsWWIT1ABr0HR1daVNmzasWLGCsLAwfvrpJx577DHrfmR3v2/Fy8uLy5cvk5KSctPXRAYXFxebn00mExaLJVvbiYuLY9q0aSxYsIBdu3ZRrVo165+0tDSOHTtmDV3x8fHW2kTsSdcUidhZqVKluOeee6hcubLNm6yfnx9paWns2rXLuuzChQscO3aMGjVqWJfddddddO7cmWnTpvHiiy+yePHiLLfj4uJCenr6betp0KABlSpVYtWqVaxYsYK2bdta37z8/PxwdXXl9OnT3HPPPTZ/7rrrriznq1WrFjVq1ODLL7/M8g0v4+Plfn5+HDhwwBq8AP766y/MZnOOj5rcip+fH2fPnuX8+fPWZX///bfNmDp16vDvv//i7e2daT8zbnFwO/7+/lgsFrZv357l+jp16nDo0CGqVKmSaRtZXRd2O7Vq1SI9PZ24uLhM810fssLDw9m4cSOHDh1i69athIeH23W/M44YZhzNzC+RkZH06NGDSpUqYbFYbD6Kn56ebvNaO3jwIJUqVdKnEMXuFIpECki1atV49NFHee+99/jzzz/Zv38/b731FhUrVuTRRx8FYNy4cWzYsIH//vuPvXv3sm3bNvz8/LKcr0qVKiQnJ7Nlyxbi4uKsnzbKyuOPP86iRYvYvHmzzZumm5sbL730EpGRkSxbtowTJ06wd+9e5s2bZ3OB8fVMJhORkZEcP36cLl26sH79ev777z/279/P559/br0RYHh4OK6urgwdOpSDBw+ydetWxowZwxNPPGE9dWYPISEhVKtWjaFDh7J//37+/PNPPv74Y5sx4eHheHl50bdvX/7880/+++8/tm3bxtixYzl79my2tuPr60uHDh0YNmwYa9assc6xatUqALp06UJ8fDxvvPEGUVFRnDhxgg0bNvDOO+/cNrwePHiQffv2Wf/s37+f6tWrEx4ezpAhQ/jf//7Hf//9R1RUFDNmzGDdunXWxz7wwAP4+PgwePBgfH19bU7P2mO/y5UrR506ddixY0e2xufGpk2brJ8gBKhbty5Hjx5l/fr1fPvtt5mC9I4dO2jSpEm+1SN3Lp0+EylAkZGRjBs3jj59+pCamkrDhg354osvrEduLBYLERERnD17Fjc3N5o2bco777yT5VwNGjTgueee4/XXX+fixYv069eP/v37Zzm2ffv2TJ8+nSpVqnD//ffbrHv99dcpV64cM2bM4OTJk7i7u1O7dm369Olz0/0ICgpiyZIlTJ8+neHDh3PhwgUqVKhg/VQcXDtiNnv2bMaNG8dTTz1FqVKlaN26NUOHDs1N627KbDYzbdo03n33XZ566imqVKnC8OHD6dWrl3VMqVKlmD9/PpMmTaJfv34kJSVRsWJFGjdujJubW7a3NWrUKD766CNGjRrFxYsXqVy5svX+UxUrVmThwoVMmjSJnj17kpKSQuXKlWnatGmWn9K73o3323FycuKff/4hMjKSzz//nAkTJnD+/HnKli1L/fr1efjhh61jTSYTYWFhzJo1i9dee81mHnvt91NPPcWPP/7ICy+8kO3HZNeVK1eIiIhg8uTJ1j5VqlSJ9957j2HDhuHq6sr7779vPX159epV1qxZw6xZs+xei4jJsPfJexERKVauXLlC27Zt+fjjj7O8kLwgffPNN6xZs0Zf9SH5QqfPRETklkqWLMn777/PhQsXHF0KLi4uDB8+3NFlSDGlI0UiIiIi6EiRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICAD/Dxz6UDYPYyH0AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_predictions(rnn_train_predictions, Y_train)\n",
        "plot_difference(rnn_train_predictions, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QquGxMgUUWD3",
        "outputId": "c381c73c-c53b-411d-cd85-3f2ce8ad1702"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Positives: 1544\n",
            "True Negatives: 3770\n",
            "False Positives: 108\n",
            "False Negatives: 1429\n",
            "\n",
            "Accuracy: 0.7756531893154284\n",
            "Precision: 0.9346246973365617\n",
            "Recall: 0.5193407332660612\n"
          ]
        }
      ],
      "source": [
        "print_confusion_matrix(rnn_train_predictions, Y_train, 0.7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhxIx_RrUWD4"
      },
      "source": [
        "#### 5.3.2\n",
        "Here the same problem emerges, a large amount of false positves are being predicted. Interestingly however, it has done a very good job of prediciting negative values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fObi2nCUWD4",
        "outputId": "36e1e597-996f-42e0-e6f5-e1342a9c5782"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102/102 [==============================] - 32s 279ms/step\n",
            "Generating \"rnn_submission.csv\" file...\n",
            "Successfully created \"rnn_submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "rnn_predictions = rnn_model.predict(test_input_np[:, 3]).flatten()\n",
        "\n",
        "# Apply threshold\n",
        "THRESHOLD = 0.7\n",
        "rnn_predictions[rnn_predictions >= THRESHOLD] = 1\n",
        "rnn_predictions[rnn_predictions < THRESHOLD] = 0\n",
        "\n",
        "save_to_submissions_csv(test_input_np, rnn_predictions, 'rnn_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2yD3p-oQUWD4",
        "outputId": "0a382443-1bd8-433d-ea11-f010b988f4cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target\n",
              "0   0       0\n",
              "1   2       0\n",
              "2   3       1\n",
              "3   9       0\n",
              "4  11       1"
            ],
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              "  <div id=\"df-1ac3d148-8a03-49a1-9c87-f1a5c8036b8b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ac3d148-8a03-49a1-9c87-f1a5c8036b8b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "pd.read_csv('rnn_submission.csv').head()"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "3GZTVNw2UWD4"
      },
      "source": [
        "## 6. Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DauZq7JtUWD5"
      },
      "source": [
        "### 6.1 Overall Results\n",
        "\n",
        "Overall, our models much better than the baseline, showing that they have learned something from the training process, rather than predicting 1 or 0 all the time. Below is a chart that shows the accuracy of all of our models.\n",
        "\n",
        "> **Note:** Some of these values change due to some inner randomness of each layer (despite using a set global seed), but the values are still very close each training time.\n",
        "\n",
        "\n",
        "Model | Training Loss | Training Accuracy | Validation Loss | Validation Accuracy | F1 Score\n",
        "---|---|---|---|---|---\n",
        "Baseline | 7.900914820187636 | 0.4308860020434973 | 7.687370260599765 | 0.41863517060367456 | 0.42966\n",
        "FFNN | 0.43176037073135376 | 0.8029196858406067 | 0.5280683636665344 | 0.7434402108192444 | 0.75176\n",
        "CNN | 0.4304109811782837 | 0.8103811740875244 | 0.5113658905029297 | 0.7594752311706543 | 0.74287\n",
        "RNN | 0.44599589705467224 | 0.7956204414367676 | 0.5282750725746155 | 0.7448979616165161 | 0.72816\n",
        "\n",
        "**Most General:** RNN\n",
        "\n",
        "**Highest F1:** FFNN\n",
        "\n",
        "**Smallest Validation Loss:** CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNeTLWvGUWD5"
      },
      "source": [
        "### 6.2 Future Work\n",
        "After sifting through the graphs on all the models, the main hurdle we seem to struggle with the most is the amount of false negatives the models continuously miss. The root of the problem may be the use of custom made pre-processing function for this project. It is present in all models, so it may be the biggest factor that hinders the learning process. One possibility for a better pre-processing function is to break apart words into n-grams, that way links can be broken down to individual websites that may appear frequeantly (e.g.common news site).\n"
      ]
    }
  ],
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