LSTM_new.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vannestn/LSTM-Human-Activity-Recognition/blob/master/LSTM_new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction:\n",
        "This work expands upon the efforts of Guillaume Chevalier:\n",
        "> Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016,\n",
        "> https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\n",
        "\n",
        "Guillaume used a TensorFlow version 1.x model that achieved 91% accuracy to classify motion data. In this notebook I have reused code from their introduction and data wrangling efforts, before defining my own CNN-DNN model with TensorFlow version 2. Sections that used Guillaume's code are annotated for clarity.\n",
        "\n",
        "My model achieved 92.3% accuracy with validation data. Reviewing graphs, you will se that training and validation accuracies remain relatively in sync, which suggests minimal overfitting.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PLeZwJKfGj6A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup Environment for Running in Google Colab\n",
        "This step is done because some local environments may not have a GPU, and so running this notebook in colab allows users to mount a T4 GPU cheaply in order to speed up the training process"
      ],
      "metadata": {
        "id": "QgiwkIijPodx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "try:\n",
        "    from google.colab import drive\n",
        "\n",
        "    # Mount Google Drive\n",
        "    drive.mount('/content/drive')\n",
        "\n",
        "    # Change to what you expect to be the notebook's directory\n",
        "    # based on your common directory structure\n",
        "    GITHUB_FOLDER_PATH = '/content/drive/My Drive/Colab_Notebooks/github/'\n",
        "    REPO_NAME = 'LSTM-Human-Activity-Recognition'\n",
        "    os.chdir(os.path.join(GITHUB_FOLDER_PATH, REPO_NAME))\n",
        "    print(f\"Changed directory to {os.getcwd()}\")\n",
        "\n",
        "except ImportError:\n",
        "    # Local environment (like running in VS Code)\n",
        "    notebook_path = os.getcwd()\n",
        "    print(f\"Running on local machine, current directory is {notebook_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYZ5QVb8PZNr",
        "outputId": "a3d1caea-bf1b-4bf3-88db-a463e1345ba8"
      },
      "execution_count": 102,
      "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",
            "Changed directory to /content/drive/My Drive/Colab_Notebooks/github/LSTM-Human-Activity-Recognition\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjWSs4nXO1b4"
      },
      "source": [
        "### (Authored by Guillaume Chevalier)\n",
        "# <a title=\"Activity Recognition\" href=\"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/guillaume-chevalier/LSTM-Human-Activity-Recognition\" > LSTMs for Human Activity Recognition</a>\n",
        "\n",
        "Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:\n",
        "- WALKING,\n",
        "- WALKING_UPSTAIRS,\n",
        "- WALKING_DOWNSTAIRS,\n",
        "- SITTING,\n",
        "- STANDING,\n",
        "- LAYING.\n",
        "\n",
        "Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. [Other research](https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.names) on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.\n",
        "\n",
        "Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.\n",
        "\n",
        "## Video dataset overview\n",
        "\n",
        "Follow this link to see a video of the 6 activities recorded in the experiment with one of the participants:\n",
        "\n",
        "<p align=\"center\">\n",
        "  <a href=\"http:https://www.youtube.com/watch?feature=player_embedded&v=XOEN9W05_4A\n",
        "\" target=\"_blank\"><img src=\"http:https://img.youtube.com/vi/XOEN9W05_4A/0.jpg\"\n",
        "alt=\"Video of the experiment\" width=\"400\" height=\"300\" border=\"10\" /></a>\n",
        "  <a href=\"https://youtu.be/XOEN9W05_4A\"><center>[Watch video]</center></a>\n",
        "</p>\n",
        "\n",
        "## Details about the input data\n",
        "\n",
        "I will be using an LSTM on the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this:\n",
        "\n",
        "> The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.\n",
        "\n",
        "That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer  as a preprocessing step for another 3D feature as an input to help learning. If you'd ever want to extract the gravity by yourself, you could fork my code on using a [Butterworth Low-Pass Filter (LPF) in Python](https://github.com/guillaume-chevalier/filtering-stft-and-laplace-transform) and edit it to have the right cutoff frequency of 0.3 Hz which is a good frequency for activity recognition from body sensors.\n",
        "\n",
        "## What is an RNN?\n",
        "\n",
        "As explained in [this article](http:https://karpathy.github.io/2015/05/21/rnn-effectiveness/), an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. **In our case, the \"many to one\" architecture is used**: we accept time series of [feature vectors](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answer/Guillaume-Chevalier-2) (one vector per [time step](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answer/Guillaume-Chevalier-2)) to convert them to a probability vector at the output for classification. Note that a \"one to one\" architecture would be a standard feedforward neural network.\n",
        "\n",
        "> <a href=\"https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm\" ><img src=\"https://raw.githubusercontent.com/Neuraxio/Machine-Learning-Figures/master/rnn-architectures.png\" /></a>\n",
        "> [Learn more on RNNs](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm)\n",
        "\n",
        "## What is an LSTM?\n",
        "\n",
        "An LSTM is an improved RNN. It is more complex, but easier to train, avoiding what is called the vanishing gradient problem. I recommend [this course](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm) for you to learn more on LSTMs.\n",
        "\n",
        "> [Learn more on LSTMs](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### (Authored by Nick Van Nest)\n",
        "## Import Packages"
      ],
      "metadata": {
        "id": "hrJIH-6pV2N5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJgINXa9O1cE",
        "outputId": "68ef27c1-bd22-4428-d89d-0b941909fa66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit_learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.11.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (3.2.0)\n"
          ]
        }
      ],
      "source": [
        "# Standard library imports\n",
        "import os\n",
        "import sys\n",
        "\n",
        "# Third-party library imports\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score\n",
        "import tensorflow as tf\n",
        "\n",
        "# Installing scikit-learn package\n",
        "!{sys.executable} -m pip install scikit_learn\n",
        "\n",
        "# TensorFlow and Keras imports\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.models import Model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### (Modified work taken from Guillaume Chevalier)\n",
        "## Define Constants to Describe Input Data Features and Labels"
      ],
      "metadata": {
        "id": "o713fPPNbuSV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "EEKYwnkxO1cE"
      },
      "outputs": [],
      "source": [
        "# Those are separate normalised input features for the neural network\n",
        "INPUT_SIGNAL_TYPES = [\n",
        "    \"body_acc_x_\",\n",
        "    \"body_acc_y_\",\n",
        "    \"body_acc_z_\",\n",
        "    \"body_gyro_x_\",\n",
        "    \"body_gyro_y_\",\n",
        "    \"body_gyro_z_\",\n",
        "    \"total_acc_x_\",\n",
        "    \"total_acc_y_\",\n",
        "    \"total_acc_z_\"\n",
        "]\n",
        "\n",
        "# Output classes to learn how to classify\n",
        "LABELS = [\n",
        "    \"WALKING\",\n",
        "    \"WALKING_UPSTAIRS\",\n",
        "    \"WALKING_DOWNSTAIRS\",\n",
        "    \"SITTING\",\n",
        "    \"STANDING\",\n",
        "    \"LAYING\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ks1W106yO1cF"
      },
      "source": [
        "### (Modified work taken from Guillaume Chevalier)\n",
        "## Let's start by downloading the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmI-F4WHO1cF",
        "outputId": "0bcd0e23-1eb8-480e-f7d5-580ddd3938df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/github/LSTM-Human-Activity-Recognition\n",
            "data\t\tLSTM_original.ipynb\n",
            "LICENSE\t\tmae_and_loss_last_80_percent_model1.png\n",
            "LSTM_files\tmae_and_loss_model1.png\n",
            "LSTM_new_files\tREADME.md\n",
            "LSTM_new.ipynb\ttraining_validation_metrics_model1.png\n",
            "/content/drive/MyDrive/Colab_Notebooks/github/LSTM-Human-Activity-Recognition/data\n",
            " download_dataset.py   source.txt\t 'UCI HAR Dataset.zip'\n",
            " __MACOSX\t      'UCI HAR Dataset'\n",
            "\n",
            "Downloading...\n",
            "Dataset already downloaded. Did not download twice.\n",
            "\n",
            "Extracting...\n",
            "Dataset already extracted. Did not extract twice.\n",
            "\n",
            "/content/drive/MyDrive/Colab_Notebooks/github/LSTM-Human-Activity-Recognition/data\n",
            " download_dataset.py   source.txt\t 'UCI HAR Dataset.zip'\n",
            " __MACOSX\t      'UCI HAR Dataset'\n",
            "/content/drive/MyDrive/Colab_Notebooks/github/LSTM-Human-Activity-Recognition\n",
            "data\t\tLSTM_original.ipynb\n",
            "LICENSE\t\tmae_and_loss_last_80_percent_model1.png\n",
            "LSTM_files\tmae_and_loss_model1.png\n",
            "LSTM_new_files\tREADME.md\n",
            "LSTM_new.ipynb\ttraining_validation_metrics_model1.png\n",
            "\n",
            "Dataset is now located at: data/UCI HAR Dataset/\n"
          ]
        }
      ],
      "source": [
        "DATA_PATH = \"data/\"\n",
        "\n",
        "!pwd && ls\n",
        "os.chdir(DATA_PATH)\n",
        "!pwd && ls\n",
        "\n",
        "!python download_dataset.py\n",
        "\n",
        "!pwd && ls\n",
        "os.chdir(\"..\")\n",
        "!pwd && ls\n",
        "\n",
        "DATASET_PATH = DATA_PATH + \"UCI HAR Dataset/\"\n",
        "print(\"\\n\" + \"Dataset is now located at: \" + DATASET_PATH)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzoDAztZO1cG"
      },
      "source": [
        "\n",
        "### (Modified work taken from Guillaume Chevalier)\n",
        "## Preparing dataset:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "6ZHJhld8O1cG"
      },
      "outputs": [],
      "source": [
        "# Load \"X\" (the neural network's training and testing inputs)\n",
        "def load_X(X_signals_paths):\n",
        "    X_signals = []\n",
        "\n",
        "    for signal_type_path in X_signals_paths:\n",
        "        file = open(signal_type_path, 'r')\n",
        "        # Read dataset from disk, dealing with text files' syntax\n",
        "        X_signals.append(\n",
        "            [np.array(serie, dtype=np.float32) for serie in [\n",
        "                row.replace('  ', ' ').strip().split(' ') for row in file\n",
        "            ]]\n",
        "        )\n",
        "        file.close()\n",
        "\n",
        "    return np.transpose(np.array(X_signals), (1, 2, 0))\n",
        "\n",
        "def load_y(y_path):\n",
        "    file = open(y_path, 'r')\n",
        "    # Read dataset from disk, dealing with text file's syntax\n",
        "    y_ = np.array(\n",
        "        [elem for elem in [\n",
        "            row.replace('  ', ' ').strip().split(' ') for row in file\n",
        "        ]],\n",
        "        dtype=np.int32\n",
        "    )\n",
        "    file.close()\n",
        "\n",
        "    # Substract 1 to each output class for friendly 0-based indexing\n",
        "    return y_ - 1\n",
        "\n",
        "TRAIN = \"train/\"\n",
        "TEST = \"test/\"\n",
        "\n",
        "X_train_signals_paths = [\n",
        "    DATASET_PATH + TRAIN + \"Inertial Signals/\" + signal + \"train.txt\" for signal in INPUT_SIGNAL_TYPES\n",
        "]\n",
        "X_test_signals_paths = [\n",
        "    DATASET_PATH + TEST + \"Inertial Signals/\" + signal + \"test.txt\" for signal in INPUT_SIGNAL_TYPES\n",
        "]\n",
        "\n",
        "X_train = load_X(X_train_signals_paths)\n",
        "X_test = load_X(X_test_signals_paths)\n",
        "\n",
        "\n",
        "# Load \"y\" (the neural network's training and testing outputs)\n",
        "\n",
        "y_train_path = DATASET_PATH + TRAIN + \"y_train.txt\"\n",
        "y_test_path = DATASET_PATH + TEST + \"y_test.txt\"\n",
        "\n",
        "y_train = load_y(y_train_path)\n",
        "y_test = load_y(y_test_path)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2_iEstwO1cH"
      },
      "source": [
        "### (The code below was authored by Nick Van Nest)\n",
        "## Define Utility Functions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "RlhlNG3JO1cH"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "def one_hot(y_, n_classes):\n",
        "    # This function was taken with modification from Guillaume Chevalier)\n",
        "    # Function to encode neural one-hot output labels from number indexes\n",
        "    # e.g.:\n",
        "    # one_hot(y_=[[5], [0], [3]], n_classes=6):\n",
        "    #     return [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]\n",
        "\n",
        "    y_ = y_.reshape(len(y_))\n",
        "    return np.eye(n_classes)[np.array(y_, dtype=np.int32)]  # Returns FLOATS\n",
        "\n",
        "def prepare_dataset(features, labels, batch_size, shuffle_buffer_size=1000):\n",
        "    \"\"\"\n",
        "    Prepare a shuffled and batched tf.data.Dataset given features and labels.\n",
        "\n",
        "    Args:\n",
        "    - features (ndarray): The feature data; shape should be (num_samples, 128, 9).\n",
        "    - labels (ndarray): The label data; shape should be (num_samples, 5).\n",
        "    - batch_size (int): Size of each batch.\n",
        "    - shuffle_buffer_size (int): Size of shuffle buffer.\n",
        "\n",
        "    Returns:\n",
        "    - dataset (tf.data.Dataset): Shuffled and batched dataset.\n",
        "    \"\"\"\n",
        "\n",
        "    # Create a tf.data.Dataset object from the features and labels\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n",
        "\n",
        "    # Shuffle the dataset\n",
        "    dataset = dataset.shuffle(shuffle_buffer_size)\n",
        "\n",
        "    # Batch the data\n",
        "    dataset = dataset.batch(batch_size)\n",
        "\n",
        "    # Using prefetch to improve performance\n",
        "    dataset = dataset.prefetch(1)\n",
        "\n",
        "    return dataset\n",
        "\n",
        "def plot_lr_vs_loss(epochs, lr_loss_logger):\n",
        "    # Use learning rates logged by custom logger\n",
        "    lrs = lr_loss_logger.learning_rates\n",
        "\n",
        "    # Use losses logged by custom logger\n",
        "    losses = lr_loss_logger.losses\n",
        "\n",
        "    # Plotting\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.grid(True)\n",
        "    plt.semilogx(lrs, losses)\n",
        "    plt.tick_params('both', length=10, width=1, which='both')\n",
        "    plt.axis([min(lrs), max(lrs), min(losses), max(losses)])\n",
        "    plt.xlabel('Learning Rate')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Learning Rate vs Loss')\n",
        "    plt.show()\n",
        "\n",
        "    return #np.array([lrs, losses]).T\n",
        "\n",
        "def plot_series_dual_y(x, y1, y2, title=None, xlabel=None, ylabel1=None, ylabel2=None, legend1=None, legend2=None, filename=None):\n",
        "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "    color = 'tab:red'\n",
        "    ax1.set_xlabel(xlabel)\n",
        "    ax1.set_ylabel(ylabel1, color=color)\n",
        "    ax1.plot(x, y1, color=color)\n",
        "    ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "    ax2 = ax1.twinx()\n",
        "    color = 'tab:blue'\n",
        "    ax2.set_ylabel(ylabel2, color=color)\n",
        "    ax2.plot(x, y2, color=color)\n",
        "    ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "    if legend1 or legend2:\n",
        "        ax1.legend([legend1], loc='upper left')\n",
        "        ax2.legend([legend2], loc='upper right')\n",
        "\n",
        "    plt.title(title)\n",
        "    fig.tight_layout()\n",
        "    if filename:\n",
        "        plt.savefig(filename)\n",
        "    plt.show()\n",
        "\n",
        "def plot_training_validation_metrics(history, title, filename=None):\n",
        "    # Inline plots\n",
        "    %matplotlib inline\n",
        "\n",
        "    # Font settings\n",
        "    font = {\n",
        "        'weight': 'bold',\n",
        "        'size': 18\n",
        "    }\n",
        "    plt.rc('font', **font)\n",
        "\n",
        "    # Figure size\n",
        "    plt.figure(figsize=(12, 12))\n",
        "\n",
        "    # Extract training and validation metrics from history object\n",
        "    train_mae = history.history['mae']\n",
        "    train_loss = history.history['loss']\n",
        "    val_mae = history.history['val_mae']\n",
        "    val_loss = history.history['val_loss']\n",
        "\n",
        "    # Create epoch axis and plot training metrics\n",
        "    epochs = range(1, len(train_mae) + 1)\n",
        "    plt.plot(epochs, train_mae, 'b--', label='Training MAE')\n",
        "    plt.plot(epochs, train_loss, 'r--', label='Training Loss')\n",
        "\n",
        "    # Plot validation metrics\n",
        "    plt.plot(epochs, val_mae, 'b-', label='Validation MAE')\n",
        "    plt.plot(epochs, val_loss, 'r-', label='Validation Loss')\n",
        "\n",
        "    # Labels and title\n",
        "    plt.title(title)\n",
        "    plt.legend(loc='upper right', shadow=True)\n",
        "    plt.ylabel('Progress (MAE or Loss values)')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    # Save the plot if filename is provided\n",
        "    if filename:\n",
        "        plt.savefig(filename)\n",
        "\n",
        "    # Show the plot\n",
        "    plt.show()\n",
        "\n",
        "def print_classification_metrics(X, y_true_oh, model, average_method=\"weighted\"):\n",
        "    # Make predictions on the original ordered data\n",
        "    predictions_prob = model.predict(X)\n",
        "    predictions = np.argmax(predictions_prob, axis=1)\n",
        "\n",
        "    # Converting one-hot encoded y_test to label encoded\n",
        "    true_labels = np.argmax(y_true_oh, axis=1)\n",
        "\n",
        "    # Calculating metrics\n",
        "    accuracy = accuracy_score(true_labels, predictions)\n",
        "    precision = precision_score(true_labels, predictions, average=average_method)\n",
        "    recall = recall_score(true_labels, predictions, average=average_method)\n",
        "    f1 = f1_score(true_labels, predictions, average=average_method)\n",
        "\n",
        "    print(f\"Testing Accuracy: {accuracy * 100}%\")\n",
        "    print(f\"Precision: {precision * 100}%\")\n",
        "    print(f\"Recall: {recall * 100}%\")\n",
        "    print(f\"F1 Score: {f1 * 100}%\")\n",
        "\n",
        "def plot_normalized_confusion_matrix(X, y_true_oh, model, labels, cmap=plt.cm.rainbow):\n",
        "    # Make predictions on the original ordered data\n",
        "    predictions_prob = model.predict(X)\n",
        "    predictions = np.argmax(predictions_prob, axis=1)\n",
        "\n",
        "    # Converting one-hot encoded y_test to label encoded\n",
        "    true_labels = np.argmax(y_true_oh, axis=1)\n",
        "\n",
        "    # Generate confusion matrix\n",
        "    confusion_mtx = confusion_matrix(true_labels, predictions)\n",
        "    normalised_confusion_matrix = np.array(confusion_mtx, dtype=np.float32)/np.sum(confusion_mtx)*100\n",
        "\n",
        "    # Create a plot\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.imshow(normalised_confusion_matrix, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(\"Confusion Matrix (Normalized)\")\n",
        "    plt.colorbar()\n",
        "\n",
        "    tick_marks = np.arange(len(labels))\n",
        "    plt.xticks(tick_marks, labels, rotation=90)\n",
        "    plt.yticks(tick_marks, labels)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True Label')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define Callback Functions to Modify Training"
      ],
      "metadata": {
        "id": "VnSsIZu7aZ4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lr_scheduler(epoch, lr, initial_lr=1e-6, final_lr=1e-2, epochs=10):\n",
        "    factor = (final_lr / initial_lr) ** (1 / (epochs - 1))\n",
        "    return initial_lr * (factor ** epoch)\n",
        "\n",
        "class LearningRateLossLogger(tf.keras.callbacks.Callback):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.learning_rates = []\n",
        "        self.losses = []\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        lr = float(tf.keras.backend.get_value(self.model.optimizer.lr))\n",
        "        self.learning_rates.append(lr)\n",
        "        self.losses.append(logs['loss'])\n",
        "\n",
        "class StopAtThresholdCallback(tf.keras.callbacks.Callback):\n",
        "    def __init__(self, threshold):\n",
        "        super(StopAtThresholdCallback, self).__init__()\n",
        "        self.threshold = threshold  # Threshold for stopping the training\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        val_accuracy = logs.get('val_accuracy')\n",
        "        if val_accuracy is not None and val_accuracy > self.threshold:\n",
        "            print(f\"\\nReached {self.threshold * 100}% validation accuracy. Stopping training.\")\n",
        "            self.model.stop_training = True"
      ],
      "metadata": {
        "id": "ctQaSbMiCaxR"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TRAINING_BATCH_SIZE = 512\n",
        "TESTING_BATCH_SIZE = 32\n",
        "NUMBER_OF_CLASSES = 6\n",
        "WINDOW_SIZE = len(X_train[0])\n",
        "TIME_STEP_PARAMETER_SIZE = len(X_train[0][0])\n",
        "\n",
        "# Prepare the dataset\n",
        "y_train_oh = one_hot(y_train, NUMBER_OF_CLASSES)\n",
        "y_test_oh = one_hot(y_test, NUMBER_OF_CLASSES)\n",
        "prepared_training = prepare_dataset(X_train, y_train_oh, TRAINING_BATCH_SIZE)\n",
        "prepared_testing = prepare_dataset(X_test, y_test_oh, TESTING_BATCH_SIZE)\n"
      ],
      "metadata": {
        "id": "LNCVNmYEX0e1"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBcBlJNfO1cI",
        "outputId": "5771c2b1-1145-4c9d-ec66-39ffa533de74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_1 (Conv1D)           (None, 128, 64)           1792      \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 128, 128)          98816     \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 128)               131584    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 244998 (957.02 KB)\n",
            "Trainable params: 244998 (957.02 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Build the Model\n",
        "def create_model_v1():\n",
        "  model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv1D(filters=64, kernel_size=3,\n",
        "                        strides=1,\n",
        "                        activation=\"relu\",\n",
        "                        padding='causal',\n",
        "                        kernel_regularizer=regularizers.l2(0.001),\n",
        "                        input_shape=[WINDOW_SIZE, TIME_STEP_PARAMETER_SIZE]),\n",
        "    tf.keras.layers.LSTM(128, return_sequences=True),\n",
        "    tf.keras.layers.LSTM(128),\n",
        "    tf.keras.layers.Dense(64, kernel_regularizer=regularizers.l2(0.001), activation=\"relu\"),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Dense(64, kernel_regularizer=regularizers.l2(0.001), activation=\"relu\"),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Dense(NUMBER_OF_CLASSES, activation=\"softmax\"),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "def create_model_v2():\n",
        "  # This model is representitive of the original TensorFlow 1.x model, but not exactly the same\n",
        "  model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.LSTM(64, return_sequences=True, input_shape=[WINDOW_SIZE, TIME_STEP_PARAMETER_SIZE], activation='tanh'),\n",
        "    tf.keras.layers.LSTM(64, activation='tanh'),\n",
        "    tf.keras.layers.Dense(NUMBER_OF_CLASSES, activation=\"softmax\"),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "# Print the model summary\n",
        "model = create_model_v1()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Determine the optimal learning rate for the model"
      ],
      "metadata": {
        "id": "fhLmGzyUaiHH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Xldh-HbLO1cI",
        "outputId": "73cce956-1643-4317-a620-b62fd1f76ac1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "15/15 [==============================] - 14s 246ms/step - loss: 1.9495 - accuracy: 0.2259 - val_loss: 1.9459 - val_accuracy: 0.3010 - lr: 1.0000e-05\n",
            "Epoch 2/30\n",
            "15/15 [==============================] - 2s 117ms/step - loss: 1.9447 - accuracy: 0.2481 - val_loss: 1.9392 - val_accuracy: 0.3227 - lr: 1.3738e-05\n",
            "Epoch 3/30\n",
            "15/15 [==============================] - 2s 112ms/step - loss: 1.9372 - accuracy: 0.2758 - val_loss: 1.9298 - val_accuracy: 0.3621 - lr: 1.8874e-05\n",
            "Epoch 4/30\n",
            "15/15 [==============================] - 2s 145ms/step - loss: 1.9257 - accuracy: 0.3058 - val_loss: 1.9156 - val_accuracy: 0.3821 - lr: 2.5929e-05\n",
            "Epoch 5/30\n",
            "15/15 [==============================] - 2s 162ms/step - loss: 1.9082 - accuracy: 0.3230 - val_loss: 1.8906 - val_accuracy: 0.3651 - lr: 3.5622e-05\n",
            "Epoch 6/30\n",
            "15/15 [==============================] - 2s 147ms/step - loss: 1.8717 - accuracy: 0.3384 - val_loss: 1.8298 - val_accuracy: 0.3634 - lr: 4.8939e-05\n",
            "Epoch 7/30\n",
            "15/15 [==============================] - 2s 152ms/step - loss: 1.7728 - accuracy: 0.3456 - val_loss: 1.6665 - val_accuracy: 0.3899 - lr: 6.7234e-05\n",
            "Epoch 8/30\n",
            "15/15 [==============================] - 2s 144ms/step - loss: 1.6495 - accuracy: 0.3502 - val_loss: 1.5955 - val_accuracy: 0.3359 - lr: 9.2367e-05\n",
            "Epoch 9/30\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 1.5731 - accuracy: 0.3641 - val_loss: 1.5303 - val_accuracy: 0.4567 - lr: 1.2690e-04\n",
            "Epoch 10/30\n",
            "15/15 [==============================] - 1s 82ms/step - loss: 1.4811 - accuracy: 0.4070 - val_loss: 1.2987 - val_accuracy: 0.5986 - lr: 1.7433e-04\n",
            "Epoch 11/30\n",
            "15/15 [==============================] - 1s 100ms/step - loss: 1.2828 - accuracy: 0.4848 - val_loss: 1.0708 - val_accuracy: 0.4897 - lr: 2.3950e-04\n",
            "Epoch 12/30\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 1.1849 - accuracy: 0.5052 - val_loss: 1.0401 - val_accuracy: 0.4968 - lr: 3.2903e-04\n",
            "Epoch 13/30\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 1.1031 - accuracy: 0.5192 - val_loss: 1.0302 - val_accuracy: 0.5134 - lr: 4.5204e-04\n",
            "Epoch 14/30\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 1.0483 - accuracy: 0.5424 - val_loss: 0.9956 - val_accuracy: 0.5928 - lr: 6.2102e-04\n",
            "Epoch 15/30\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.9704 - accuracy: 0.5846 - val_loss: 1.1718 - val_accuracy: 0.5704 - lr: 8.5317e-04\n",
            "Epoch 16/30\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 1.0647 - accuracy: 0.5578 - val_loss: 1.0006 - val_accuracy: 0.5426 - lr: 0.0012\n",
            "Epoch 17/30\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 0.9213 - accuracy: 0.5808 - val_loss: 0.8672 - val_accuracy: 0.5891 - lr: 0.0016\n",
            "Epoch 18/30\n",
            "15/15 [==============================] - 1s 82ms/step - loss: 0.9127 - accuracy: 0.5536 - val_loss: 0.9151 - val_accuracy: 0.5151 - lr: 0.0022\n",
            "Epoch 19/30\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.8749 - accuracy: 0.5458 - val_loss: 0.9023 - val_accuracy: 0.5365 - lr: 0.0030\n",
            "Epoch 20/30\n",
            "15/15 [==============================] - 2s 139ms/step - loss: 0.8087 - accuracy: 0.6088 - val_loss: 0.8450 - val_accuracy: 0.6318 - lr: 0.0042\n",
            "Epoch 21/30\n",
            "15/15 [==============================] - 1s 99ms/step - loss: 0.7817 - accuracy: 0.6265 - val_loss: 0.7922 - val_accuracy: 0.6240 - lr: 0.0057\n",
            "Epoch 22/30\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.7214 - accuracy: 0.6721 - val_loss: 1.1991 - val_accuracy: 0.5741 - lr: 0.0079\n",
            "Epoch 23/30\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 1.4645 - accuracy: 0.4257 - val_loss: 1.9024 - val_accuracy: 0.1805 - lr: 0.0108\n",
            "Epoch 24/30\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 1.9080 - accuracy: 0.1842 - val_loss: 1.9185 - val_accuracy: 0.1805 - lr: 0.0149\n",
            "Epoch 25/30\n",
            "15/15 [==============================] - 1s 82ms/step - loss: 1.9026 - accuracy: 0.1836 - val_loss: 1.8897 - val_accuracy: 0.1822 - lr: 0.0204\n",
            "Epoch 26/30\n",
            "15/15 [==============================] - 1s 81ms/step - loss: 1.8645 - accuracy: 0.1922 - val_loss: 1.8510 - val_accuracy: 0.1822 - lr: 0.0281\n",
            "Epoch 27/30\n",
            "15/15 [==============================] - 1s 82ms/step - loss: 1.8329 - accuracy: 0.1923 - val_loss: 1.8266 - val_accuracy: 0.1822 - lr: 0.0386\n",
            "Epoch 28/30\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 1.8160 - accuracy: 0.1921 - val_loss: 1.8149 - val_accuracy: 0.1822 - lr: 0.0530\n",
            "Epoch 29/30\n",
            "15/15 [==============================] - 1s 81ms/step - loss: 1.8042 - accuracy: 0.1914 - val_loss: 1.8059 - val_accuracy: 0.1822 - lr: 0.0728\n",
            "Epoch 30/30\n",
            "15/15 [==============================] - 2s 135ms/step - loss: 1.7983 - accuracy: 0.1835 - val_loss: 1.8033 - val_accuracy: 0.1822 - lr: 0.1000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "EPOCHS = 30\n",
        "\n",
        "# Initialize the optimizer\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-8)  # Start with the initial learning rate\n",
        "\n",
        "# Initialize the Model\n",
        "model = create_model_v1()\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=['accuracy']\n",
        "              )\n",
        "\n",
        "# Set the callbacks\n",
        "lr_loss_logger = LearningRateLossLogger()\n",
        "scheduler_callback = tf.keras.callbacks.LearningRateScheduler(lambda epoch, lr: lr_scheduler(epoch, lr, initial_lr=1e-5, final_lr=1e-1, epochs=EPOCHS))\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    prepared_training,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks=[scheduler_callback, lr_loss_logger],\n",
        "    validation_data = prepared_testing\n",
        ")\n",
        "\n",
        "# Plot the learning rate versus loss\n",
        "plot_lr_vs_loss(epochs=EPOCHS, lr_loss_logger=lr_loss_logger)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clear the session and use the learning rate to perform training in bulk"
      ],
      "metadata": {
        "id": "k8Nv4jXDar4g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "EPOCHS = 300\n",
        "LEARNING_RATE = 0.000325\n",
        "\n",
        "# Initialize the optimizer\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n",
        "\n",
        "# Initialize the Model\n",
        "model = create_model_v1()\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=['accuracy','mae']\n",
        "              )\n",
        "\n",
        "# Set the callbacks\n",
        "lr_loss_logger = LearningRateLossLogger()\n",
        "stop_at_threshold = StopAtThresholdCallback(threshold=0.92)\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    prepared_training,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks=[lr_loss_logger, stop_at_threshold],\n",
        "    validation_data = prepared_testing\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjmlRP5SGCxX",
        "outputId": "5674e7cc-c82e-49a2-beb2-cd178d4338ca"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "15/15 [==============================] - 7s 141ms/step - loss: 1.9016 - accuracy: 0.2729 - mae: 0.2744 - val_loss: 1.7717 - val_accuracy: 0.4191 - val_mae: 0.2656\n",
            "Epoch 2/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 1.6465 - accuracy: 0.3698 - mae: 0.2471 - val_loss: 1.4166 - val_accuracy: 0.4584 - val_mae: 0.2181\n",
            "Epoch 3/300\n",
            "15/15 [==============================] - 1s 93ms/step - loss: 1.4388 - accuracy: 0.4520 - mae: 0.2139 - val_loss: 1.3244 - val_accuracy: 0.5202 - val_mae: 0.2037\n",
            "Epoch 4/300\n",
            "15/15 [==============================] - 1s 98ms/step - loss: 1.3048 - accuracy: 0.5166 - mae: 0.1958 - val_loss: 1.1692 - val_accuracy: 0.6033 - val_mae: 0.1851\n",
            "Epoch 5/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 1.1878 - accuracy: 0.5585 - mae: 0.1812 - val_loss: 1.0351 - val_accuracy: 0.6325 - val_mae: 0.1651\n",
            "Epoch 6/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 1.0784 - accuracy: 0.5911 - mae: 0.1671 - val_loss: 1.0178 - val_accuracy: 0.5874 - val_mae: 0.1625\n",
            "Epoch 7/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 1.0055 - accuracy: 0.6118 - mae: 0.1557 - val_loss: 0.9955 - val_accuracy: 0.5891 - val_mae: 0.1568\n",
            "Epoch 8/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.9558 - accuracy: 0.6238 - mae: 0.1491 - val_loss: 0.9290 - val_accuracy: 0.6013 - val_mae: 0.1479\n",
            "Epoch 9/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.9205 - accuracy: 0.6409 - mae: 0.1429 - val_loss: 0.8952 - val_accuracy: 0.6050 - val_mae: 0.1440\n",
            "Epoch 10/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.8904 - accuracy: 0.6466 - mae: 0.1382 - val_loss: 0.8885 - val_accuracy: 0.6108 - val_mae: 0.1394\n",
            "Epoch 11/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.8949 - accuracy: 0.6477 - mae: 0.1366 - val_loss: 0.8903 - val_accuracy: 0.6176 - val_mae: 0.1403\n",
            "Epoch 12/300\n",
            "15/15 [==============================] - 1s 87ms/step - loss: 0.8859 - accuracy: 0.6551 - mae: 0.1359 - val_loss: 0.9080 - val_accuracy: 0.6325 - val_mae: 0.1368\n",
            "Epoch 13/300\n",
            "15/15 [==============================] - 1s 99ms/step - loss: 0.8785 - accuracy: 0.6699 - mae: 0.1338 - val_loss: 0.8284 - val_accuracy: 0.6695 - val_mae: 0.1327\n",
            "Epoch 14/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.8199 - accuracy: 0.6892 - mae: 0.1296 - val_loss: 0.8478 - val_accuracy: 0.6753 - val_mae: 0.1318\n",
            "Epoch 15/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.8085 - accuracy: 0.6922 - mae: 0.1274 - val_loss: 0.8095 - val_accuracy: 0.6899 - val_mae: 0.1295\n",
            "Epoch 16/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.7823 - accuracy: 0.6984 - mae: 0.1244 - val_loss: 0.8032 - val_accuracy: 0.7017 - val_mae: 0.1249\n",
            "Epoch 17/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.7842 - accuracy: 0.7002 - mae: 0.1236 - val_loss: 0.9190 - val_accuracy: 0.5962 - val_mae: 0.1383\n",
            "Epoch 18/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.8561 - accuracy: 0.6451 - mae: 0.1332 - val_loss: 0.8446 - val_accuracy: 0.6230 - val_mae: 0.1361\n",
            "Epoch 19/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.8338 - accuracy: 0.6542 - mae: 0.1322 - val_loss: 0.9801 - val_accuracy: 0.5911 - val_mae: 0.1453\n",
            "Epoch 20/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.8691 - accuracy: 0.6421 - mae: 0.1344 - val_loss: 0.8778 - val_accuracy: 0.6237 - val_mae: 0.1455\n",
            "Epoch 21/300\n",
            "15/15 [==============================] - 2s 141ms/step - loss: 0.8364 - accuracy: 0.6575 - mae: 0.1415 - val_loss: 0.7947 - val_accuracy: 0.6902 - val_mae: 0.1401\n",
            "Epoch 22/300\n",
            "15/15 [==============================] - 2s 142ms/step - loss: 0.7438 - accuracy: 0.7150 - mae: 0.1283 - val_loss: 0.7676 - val_accuracy: 0.6949 - val_mae: 0.1257\n",
            "Epoch 23/300\n",
            "15/15 [==============================] - 1s 99ms/step - loss: 0.7159 - accuracy: 0.7319 - mae: 0.1171 - val_loss: 0.7780 - val_accuracy: 0.6932 - val_mae: 0.1203\n",
            "Epoch 24/300\n",
            "15/15 [==============================] - 2s 141ms/step - loss: 0.6682 - accuracy: 0.7592 - mae: 0.1095 - val_loss: 0.8479 - val_accuracy: 0.6685 - val_mae: 0.1233\n",
            "Epoch 25/300\n",
            "15/15 [==============================] - 1s 101ms/step - loss: 0.6942 - accuracy: 0.7359 - mae: 0.1107 - val_loss: 0.8121 - val_accuracy: 0.6994 - val_mae: 0.1181\n",
            "Epoch 26/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.6320 - accuracy: 0.7628 - mae: 0.1044 - val_loss: 0.7718 - val_accuracy: 0.7007 - val_mae: 0.1132\n",
            "Epoch 27/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.5895 - accuracy: 0.7719 - mae: 0.0983 - val_loss: 0.7486 - val_accuracy: 0.7173 - val_mae: 0.1037\n",
            "Epoch 28/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.5847 - accuracy: 0.7799 - mae: 0.0948 - val_loss: 0.7514 - val_accuracy: 0.7122 - val_mae: 0.1062\n",
            "Epoch 29/300\n",
            "15/15 [==============================] - 2s 138ms/step - loss: 0.5536 - accuracy: 0.7892 - mae: 0.0930 - val_loss: 0.7593 - val_accuracy: 0.7275 - val_mae: 0.0998\n",
            "Epoch 30/300\n",
            "15/15 [==============================] - 2s 140ms/step - loss: 0.5380 - accuracy: 0.7941 - mae: 0.0868 - val_loss: 0.7324 - val_accuracy: 0.7343 - val_mae: 0.0995\n",
            "Epoch 31/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.5298 - accuracy: 0.7912 - mae: 0.0877 - val_loss: 0.7290 - val_accuracy: 0.7333 - val_mae: 0.0978\n",
            "Epoch 32/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.5121 - accuracy: 0.8082 - mae: 0.0839 - val_loss: 0.7047 - val_accuracy: 0.7679 - val_mae: 0.0950\n",
            "Epoch 33/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.4971 - accuracy: 0.8192 - mae: 0.0831 - val_loss: 0.7002 - val_accuracy: 0.7689 - val_mae: 0.0942\n",
            "Epoch 34/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.4811 - accuracy: 0.8335 - mae: 0.0787 - val_loss: 0.7316 - val_accuracy: 0.7849 - val_mae: 0.0904\n",
            "Epoch 35/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.4799 - accuracy: 0.8405 - mae: 0.0765 - val_loss: 0.8452 - val_accuracy: 0.7791 - val_mae: 0.0978\n",
            "Epoch 36/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.5127 - accuracy: 0.8190 - mae: 0.0832 - val_loss: 0.9654 - val_accuracy: 0.7652 - val_mae: 0.1019\n",
            "Epoch 37/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.5086 - accuracy: 0.8328 - mae: 0.0813 - val_loss: 0.7150 - val_accuracy: 0.7869 - val_mae: 0.0883\n",
            "Epoch 38/300\n",
            "15/15 [==============================] - 1s 87ms/step - loss: 0.4447 - accuracy: 0.8772 - mae: 0.0717 - val_loss: 0.7290 - val_accuracy: 0.8039 - val_mae: 0.0870\n",
            "Epoch 39/300\n",
            "15/15 [==============================] - 1s 101ms/step - loss: 0.4544 - accuracy: 0.8644 - mae: 0.0720 - val_loss: 0.7018 - val_accuracy: 0.8127 - val_mae: 0.0834\n",
            "Epoch 40/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.4445 - accuracy: 0.8736 - mae: 0.0687 - val_loss: 0.7186 - val_accuracy: 0.8252 - val_mae: 0.0774\n",
            "Epoch 41/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.4262 - accuracy: 0.8799 - mae: 0.0658 - val_loss: 0.8206 - val_accuracy: 0.7920 - val_mae: 0.0867\n",
            "Epoch 42/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.4144 - accuracy: 0.8886 - mae: 0.0636 - val_loss: 0.7017 - val_accuracy: 0.8293 - val_mae: 0.0738\n",
            "Epoch 43/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.3975 - accuracy: 0.9037 - mae: 0.0582 - val_loss: 0.7825 - val_accuracy: 0.8113 - val_mae: 0.0789\n",
            "Epoch 44/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.4114 - accuracy: 0.8851 - mae: 0.0641 - val_loss: 0.7009 - val_accuracy: 0.8103 - val_mae: 0.0802\n",
            "Epoch 45/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.3804 - accuracy: 0.9086 - mae: 0.0563 - val_loss: 0.8577 - val_accuracy: 0.8008 - val_mae: 0.0793\n",
            "Epoch 46/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.3789 - accuracy: 0.9085 - mae: 0.0517 - val_loss: 0.7353 - val_accuracy: 0.8215 - val_mae: 0.0712\n",
            "Epoch 47/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.3847 - accuracy: 0.8964 - mae: 0.0551 - val_loss: 0.6902 - val_accuracy: 0.8432 - val_mae: 0.0688\n",
            "Epoch 48/300\n",
            "15/15 [==============================] - 1s 102ms/step - loss: 0.3902 - accuracy: 0.8991 - mae: 0.0550 - val_loss: 0.6149 - val_accuracy: 0.8578 - val_mae: 0.0627\n",
            "Epoch 49/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.3634 - accuracy: 0.9093 - mae: 0.0510 - val_loss: 0.6799 - val_accuracy: 0.8616 - val_mae: 0.0623\n",
            "Epoch 50/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.3452 - accuracy: 0.9212 - mae: 0.0481 - val_loss: 0.7286 - val_accuracy: 0.8531 - val_mae: 0.0617\n",
            "Epoch 51/300\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 0.3209 - accuracy: 0.9287 - mae: 0.0423 - val_loss: 0.6986 - val_accuracy: 0.8388 - val_mae: 0.0646\n",
            "Epoch 52/300\n",
            "15/15 [==============================] - 1s 92ms/step - loss: 0.3898 - accuracy: 0.8973 - mae: 0.0558 - val_loss: 0.7430 - val_accuracy: 0.7998 - val_mae: 0.0816\n",
            "Epoch 53/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.3989 - accuracy: 0.8969 - mae: 0.0575 - val_loss: 0.6468 - val_accuracy: 0.8470 - val_mae: 0.0658\n",
            "Epoch 54/300\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3659 - accuracy: 0.9154 - mae: 0.0523 - val_loss: 0.6080 - val_accuracy: 0.8426 - val_mae: 0.0681\n",
            "Epoch 55/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.3204 - accuracy: 0.9301 - mae: 0.0476 - val_loss: 0.5611 - val_accuracy: 0.8673 - val_mae: 0.0557\n",
            "Epoch 56/300\n",
            "15/15 [==============================] - 1s 101ms/step - loss: 0.2934 - accuracy: 0.9373 - mae: 0.0390 - val_loss: 0.5477 - val_accuracy: 0.8863 - val_mae: 0.0491\n",
            "Epoch 57/300\n",
            "15/15 [==============================] - 1s 101ms/step - loss: 0.2828 - accuracy: 0.9368 - mae: 0.0376 - val_loss: 0.5908 - val_accuracy: 0.8890 - val_mae: 0.0483\n",
            "Epoch 58/300\n",
            "15/15 [==============================] - 2s 141ms/step - loss: 0.2967 - accuracy: 0.9411 - mae: 0.0360 - val_loss: 0.5180 - val_accuracy: 0.8826 - val_mae: 0.0500\n",
            "Epoch 59/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.3458 - accuracy: 0.9377 - mae: 0.0371 - val_loss: 0.5336 - val_accuracy: 0.8744 - val_mae: 0.0509\n",
            "Epoch 60/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.3176 - accuracy: 0.9350 - mae: 0.0388 - val_loss: 0.4950 - val_accuracy: 0.8962 - val_mae: 0.0467\n",
            "Epoch 61/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2962 - accuracy: 0.9358 - mae: 0.0399 - val_loss: 0.4793 - val_accuracy: 0.8935 - val_mae: 0.0469\n",
            "Epoch 62/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2725 - accuracy: 0.9425 - mae: 0.0371 - val_loss: 0.5061 - val_accuracy: 0.8819 - val_mae: 0.0478\n",
            "Epoch 63/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2618 - accuracy: 0.9442 - mae: 0.0338 - val_loss: 0.4966 - val_accuracy: 0.8890 - val_mae: 0.0456\n",
            "Epoch 64/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2426 - accuracy: 0.9501 - mae: 0.0315 - val_loss: 0.5097 - val_accuracy: 0.8914 - val_mae: 0.0442\n",
            "Epoch 65/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2399 - accuracy: 0.9490 - mae: 0.0304 - val_loss: 0.4950 - val_accuracy: 0.8914 - val_mae: 0.0441\n",
            "Epoch 66/300\n",
            "15/15 [==============================] - 1s 100ms/step - loss: 0.2391 - accuracy: 0.9480 - mae: 0.0304 - val_loss: 0.4725 - val_accuracy: 0.8938 - val_mae: 0.0447\n",
            "Epoch 67/300\n",
            "15/15 [==============================] - 1s 97ms/step - loss: 0.2328 - accuracy: 0.9472 - mae: 0.0306 - val_loss: 0.5542 - val_accuracy: 0.8884 - val_mae: 0.0467\n",
            "Epoch 68/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2311 - accuracy: 0.9495 - mae: 0.0295 - val_loss: 0.4964 - val_accuracy: 0.8945 - val_mae: 0.0435\n",
            "Epoch 69/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2302 - accuracy: 0.9464 - mae: 0.0297 - val_loss: 0.5474 - val_accuracy: 0.8945 - val_mae: 0.0438\n",
            "Epoch 70/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2279 - accuracy: 0.9510 - mae: 0.0281 - val_loss: 0.5309 - val_accuracy: 0.8931 - val_mae: 0.0440\n",
            "Epoch 71/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2313 - accuracy: 0.9470 - mae: 0.0289 - val_loss: 0.4639 - val_accuracy: 0.8979 - val_mae: 0.0435\n",
            "Epoch 72/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2249 - accuracy: 0.9482 - mae: 0.0288 - val_loss: 0.5274 - val_accuracy: 0.8823 - val_mae: 0.0468\n",
            "Epoch 73/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2451 - accuracy: 0.9438 - mae: 0.0292 - val_loss: 0.4821 - val_accuracy: 0.8870 - val_mae: 0.0459\n",
            "Epoch 74/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.2338 - accuracy: 0.9387 - mae: 0.0319 - val_loss: 0.4653 - val_accuracy: 0.8914 - val_mae: 0.0443\n",
            "Epoch 75/300\n",
            "15/15 [==============================] - 1s 99ms/step - loss: 0.2319 - accuracy: 0.9430 - mae: 0.0294 - val_loss: 0.4531 - val_accuracy: 0.8884 - val_mae: 0.0450\n",
            "Epoch 76/300\n",
            "15/15 [==============================] - 1s 98ms/step - loss: 0.2205 - accuracy: 0.9501 - mae: 0.0290 - val_loss: 0.4652 - val_accuracy: 0.8972 - val_mae: 0.0422\n",
            "Epoch 77/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2134 - accuracy: 0.9539 - mae: 0.0270 - val_loss: 0.4723 - val_accuracy: 0.8938 - val_mae: 0.0415\n",
            "Epoch 78/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2082 - accuracy: 0.9517 - mae: 0.0262 - val_loss: 0.4648 - val_accuracy: 0.8962 - val_mae: 0.0412\n",
            "Epoch 79/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2083 - accuracy: 0.9508 - mae: 0.0266 - val_loss: 0.5411 - val_accuracy: 0.8873 - val_mae: 0.0457\n",
            "Epoch 80/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2193 - accuracy: 0.9498 - mae: 0.0273 - val_loss: 0.5276 - val_accuracy: 0.8982 - val_mae: 0.0417\n",
            "Epoch 81/300\n",
            "15/15 [==============================] - 1s 83ms/step - loss: 0.2192 - accuracy: 0.9452 - mae: 0.0289 - val_loss: 0.5141 - val_accuracy: 0.8999 - val_mae: 0.0429\n",
            "Epoch 82/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2168 - accuracy: 0.9490 - mae: 0.0285 - val_loss: 0.4509 - val_accuracy: 0.9043 - val_mae: 0.0408\n",
            "Epoch 83/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2097 - accuracy: 0.9544 - mae: 0.0264 - val_loss: 0.4799 - val_accuracy: 0.9002 - val_mae: 0.0397\n",
            "Epoch 84/300\n",
            "15/15 [==============================] - 1s 93ms/step - loss: 0.2045 - accuracy: 0.9540 - mae: 0.0254 - val_loss: 0.4843 - val_accuracy: 0.8982 - val_mae: 0.0409\n",
            "Epoch 85/300\n",
            "15/15 [==============================] - 2s 140ms/step - loss: 0.2051 - accuracy: 0.9521 - mae: 0.0254 - val_loss: 0.4729 - val_accuracy: 0.8996 - val_mae: 0.0385\n",
            "Epoch 86/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2048 - accuracy: 0.9499 - mae: 0.0266 - val_loss: 0.4637 - val_accuracy: 0.9006 - val_mae: 0.0401\n",
            "Epoch 87/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2089 - accuracy: 0.9453 - mae: 0.0275 - val_loss: 0.5213 - val_accuracy: 0.8945 - val_mae: 0.0448\n",
            "Epoch 88/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.2103 - accuracy: 0.9450 - mae: 0.0290 - val_loss: 0.5244 - val_accuracy: 0.8958 - val_mae: 0.0409\n",
            "Epoch 89/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2204 - accuracy: 0.9408 - mae: 0.0277 - val_loss: 0.4921 - val_accuracy: 0.8985 - val_mae: 0.0416\n",
            "Epoch 90/300\n",
            "15/15 [==============================] - 1s 102ms/step - loss: 0.2061 - accuracy: 0.9438 - mae: 0.0272 - val_loss: 0.7051 - val_accuracy: 0.8894 - val_mae: 0.0430\n",
            "Epoch 91/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.2116 - accuracy: 0.9450 - mae: 0.0262 - val_loss: 0.4816 - val_accuracy: 0.8999 - val_mae: 0.0403\n",
            "Epoch 92/300\n",
            "15/15 [==============================] - 1s 103ms/step - loss: 0.2137 - accuracy: 0.9410 - mae: 0.0291 - val_loss: 0.3904 - val_accuracy: 0.9063 - val_mae: 0.0403\n",
            "Epoch 93/300\n",
            "15/15 [==============================] - 1s 98ms/step - loss: 0.2161 - accuracy: 0.9459 - mae: 0.0285 - val_loss: 0.5091 - val_accuracy: 0.8758 - val_mae: 0.0494\n",
            "Epoch 94/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2116 - accuracy: 0.9486 - mae: 0.0277 - val_loss: 0.6068 - val_accuracy: 0.8809 - val_mae: 0.0470\n",
            "Epoch 95/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2110 - accuracy: 0.9436 - mae: 0.0287 - val_loss: 0.4451 - val_accuracy: 0.9013 - val_mae: 0.0406\n",
            "Epoch 96/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2020 - accuracy: 0.9461 - mae: 0.0276 - val_loss: 0.5987 - val_accuracy: 0.8907 - val_mae: 0.0406\n",
            "Epoch 97/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1977 - accuracy: 0.9476 - mae: 0.0246 - val_loss: 0.5797 - val_accuracy: 0.8928 - val_mae: 0.0405\n",
            "Epoch 98/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1970 - accuracy: 0.9476 - mae: 0.0269 - val_loss: 0.4752 - val_accuracy: 0.9013 - val_mae: 0.0430\n",
            "Epoch 99/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1909 - accuracy: 0.9558 - mae: 0.0261 - val_loss: 0.5633 - val_accuracy: 0.8948 - val_mae: 0.0404\n",
            "Epoch 100/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1920 - accuracy: 0.9548 - mae: 0.0237 - val_loss: 0.5826 - val_accuracy: 0.8985 - val_mae: 0.0401\n",
            "Epoch 101/300\n",
            "15/15 [==============================] - 1s 97ms/step - loss: 0.1860 - accuracy: 0.9540 - mae: 0.0244 - val_loss: 0.5896 - val_accuracy: 0.8972 - val_mae: 0.0403\n",
            "Epoch 102/300\n",
            "15/15 [==============================] - 1s 94ms/step - loss: 0.1799 - accuracy: 0.9566 - mae: 0.0235 - val_loss: 0.6040 - val_accuracy: 0.8904 - val_mae: 0.0408\n",
            "Epoch 103/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1882 - accuracy: 0.9516 - mae: 0.0241 - val_loss: 0.5871 - val_accuracy: 0.8938 - val_mae: 0.0420\n",
            "Epoch 104/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1892 - accuracy: 0.9495 - mae: 0.0269 - val_loss: 0.5426 - val_accuracy: 0.8985 - val_mae: 0.0421\n",
            "Epoch 105/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1937 - accuracy: 0.9559 - mae: 0.0253 - val_loss: 0.8173 - val_accuracy: 0.8748 - val_mae: 0.0465\n",
            "Epoch 106/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2324 - accuracy: 0.9455 - mae: 0.0274 - val_loss: 0.4898 - val_accuracy: 0.8914 - val_mae: 0.0433\n",
            "Epoch 107/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1960 - accuracy: 0.9482 - mae: 0.0282 - val_loss: 0.3976 - val_accuracy: 0.9002 - val_mae: 0.0422\n",
            "Epoch 108/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1906 - accuracy: 0.9535 - mae: 0.0263 - val_loss: 0.5781 - val_accuracy: 0.8890 - val_mae: 0.0420\n",
            "Epoch 109/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2392 - accuracy: 0.9497 - mae: 0.0269 - val_loss: 0.4213 - val_accuracy: 0.9030 - val_mae: 0.0387\n",
            "Epoch 110/300\n",
            "15/15 [==============================] - 1s 96ms/step - loss: 0.2043 - accuracy: 0.9506 - mae: 0.0258 - val_loss: 0.4564 - val_accuracy: 0.9036 - val_mae: 0.0375\n",
            "Epoch 111/300\n",
            "15/15 [==============================] - 2s 141ms/step - loss: 0.1852 - accuracy: 0.9520 - mae: 0.0248 - val_loss: 0.3878 - val_accuracy: 0.9060 - val_mae: 0.0370\n",
            "Epoch 112/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1919 - accuracy: 0.9535 - mae: 0.0272 - val_loss: 0.4423 - val_accuracy: 0.8985 - val_mae: 0.0440\n",
            "Epoch 113/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1837 - accuracy: 0.9553 - mae: 0.0260 - val_loss: 0.3884 - val_accuracy: 0.9114 - val_mae: 0.0349\n",
            "Epoch 114/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1856 - accuracy: 0.9517 - mae: 0.0234 - val_loss: 0.5056 - val_accuracy: 0.9050 - val_mae: 0.0398\n",
            "Epoch 115/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1916 - accuracy: 0.9498 - mae: 0.0270 - val_loss: 0.4435 - val_accuracy: 0.9009 - val_mae: 0.0421\n",
            "Epoch 116/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1886 - accuracy: 0.9523 - mae: 0.0263 - val_loss: 0.3872 - val_accuracy: 0.9063 - val_mae: 0.0377\n",
            "Epoch 117/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1768 - accuracy: 0.9561 - mae: 0.0237 - val_loss: 0.4276 - val_accuracy: 0.9030 - val_mae: 0.0369\n",
            "Epoch 118/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1801 - accuracy: 0.9498 - mae: 0.0236 - val_loss: 0.4146 - val_accuracy: 0.9033 - val_mae: 0.0374\n",
            "Epoch 119/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1886 - accuracy: 0.9410 - mae: 0.0262 - val_loss: 0.5063 - val_accuracy: 0.8890 - val_mae: 0.0452\n",
            "Epoch 120/300\n",
            "15/15 [==============================] - 1s 99ms/step - loss: 0.1951 - accuracy: 0.9412 - mae: 0.0294 - val_loss: 0.4336 - val_accuracy: 0.9016 - val_mae: 0.0394\n",
            "Epoch 121/300\n",
            "15/15 [==============================] - 1s 100ms/step - loss: 0.1968 - accuracy: 0.9380 - mae: 0.0284 - val_loss: 0.3651 - val_accuracy: 0.9033 - val_mae: 0.0395\n",
            "Epoch 122/300\n",
            "15/15 [==============================] - 1s 87ms/step - loss: 0.1818 - accuracy: 0.9444 - mae: 0.0274 - val_loss: 0.3876 - val_accuracy: 0.9046 - val_mae: 0.0364\n",
            "Epoch 123/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1741 - accuracy: 0.9483 - mae: 0.0236 - val_loss: 0.3907 - val_accuracy: 0.9043 - val_mae: 0.0360\n",
            "Epoch 124/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1753 - accuracy: 0.9497 - mae: 0.0246 - val_loss: 0.3955 - val_accuracy: 0.8989 - val_mae: 0.0369\n",
            "Epoch 125/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1776 - accuracy: 0.9480 - mae: 0.0254 - val_loss: 0.3759 - val_accuracy: 0.9070 - val_mae: 0.0356\n",
            "Epoch 126/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1690 - accuracy: 0.9548 - mae: 0.0228 - val_loss: 0.3920 - val_accuracy: 0.9019 - val_mae: 0.0362\n",
            "Epoch 127/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1791 - accuracy: 0.9497 - mae: 0.0263 - val_loss: 0.3898 - val_accuracy: 0.9128 - val_mae: 0.0390\n",
            "Epoch 128/300\n",
            "15/15 [==============================] - 1s 87ms/step - loss: 0.1744 - accuracy: 0.9516 - mae: 0.0248 - val_loss: 0.4165 - val_accuracy: 0.9019 - val_mae: 0.0354\n",
            "Epoch 129/300\n",
            "15/15 [==============================] - 1s 98ms/step - loss: 0.1987 - accuracy: 0.9455 - mae: 0.0251 - val_loss: 0.4948 - val_accuracy: 0.8700 - val_mae: 0.0488\n",
            "Epoch 130/300\n",
            "15/15 [==============================] - 1s 92ms/step - loss: 0.2171 - accuracy: 0.9332 - mae: 0.0317 - val_loss: 0.5226 - val_accuracy: 0.8670 - val_mae: 0.0485\n",
            "Epoch 131/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2105 - accuracy: 0.9278 - mae: 0.0333 - val_loss: 0.5122 - val_accuracy: 0.8826 - val_mae: 0.0465\n",
            "Epoch 132/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.2208 - accuracy: 0.9410 - mae: 0.0302 - val_loss: 0.3918 - val_accuracy: 0.9002 - val_mae: 0.0430\n",
            "Epoch 133/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2147 - accuracy: 0.9505 - mae: 0.0293 - val_loss: 0.5490 - val_accuracy: 0.8728 - val_mae: 0.0493\n",
            "Epoch 134/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.2136 - accuracy: 0.9479 - mae: 0.0282 - val_loss: 0.5346 - val_accuracy: 0.8907 - val_mae: 0.0435\n",
            "Epoch 135/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1968 - accuracy: 0.9493 - mae: 0.0274 - val_loss: 0.4708 - val_accuracy: 0.9036 - val_mae: 0.0388\n",
            "Epoch 136/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1849 - accuracy: 0.9516 - mae: 0.0261 - val_loss: 0.4148 - val_accuracy: 0.9026 - val_mae: 0.0386\n",
            "Epoch 137/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1807 - accuracy: 0.9518 - mae: 0.0259 - val_loss: 0.4406 - val_accuracy: 0.9077 - val_mae: 0.0382\n",
            "Epoch 138/300\n",
            "15/15 [==============================] - 2s 138ms/step - loss: 0.1730 - accuracy: 0.9542 - mae: 0.0252 - val_loss: 0.4987 - val_accuracy: 0.9036 - val_mae: 0.0389\n",
            "Epoch 139/300\n",
            "15/15 [==============================] - 1s 99ms/step - loss: 0.1965 - accuracy: 0.9397 - mae: 0.0281 - val_loss: 0.4684 - val_accuracy: 0.8992 - val_mae: 0.0438\n",
            "Epoch 140/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1843 - accuracy: 0.9455 - mae: 0.0300 - val_loss: 0.5502 - val_accuracy: 0.8935 - val_mae: 0.0413\n",
            "Epoch 141/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1717 - accuracy: 0.9533 - mae: 0.0249 - val_loss: 0.4829 - val_accuracy: 0.8992 - val_mae: 0.0391\n",
            "Epoch 142/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1759 - accuracy: 0.9510 - mae: 0.0261 - val_loss: 0.6446 - val_accuracy: 0.9002 - val_mae: 0.0419\n",
            "Epoch 143/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1649 - accuracy: 0.9551 - mae: 0.0249 - val_loss: 0.6379 - val_accuracy: 0.8928 - val_mae: 0.0390\n",
            "Epoch 144/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1670 - accuracy: 0.9536 - mae: 0.0230 - val_loss: 0.5822 - val_accuracy: 0.9009 - val_mae: 0.0376\n",
            "Epoch 145/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1655 - accuracy: 0.9521 - mae: 0.0243 - val_loss: 0.5181 - val_accuracy: 0.8985 - val_mae: 0.0385\n",
            "Epoch 146/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1597 - accuracy: 0.9542 - mae: 0.0227 - val_loss: 0.5586 - val_accuracy: 0.8951 - val_mae: 0.0380\n",
            "Epoch 147/300\n",
            "15/15 [==============================] - 2s 139ms/step - loss: 0.1641 - accuracy: 0.9497 - mae: 0.0240 - val_loss: 0.5161 - val_accuracy: 0.9084 - val_mae: 0.0375\n",
            "Epoch 148/300\n",
            "15/15 [==============================] - 1s 87ms/step - loss: 0.1569 - accuracy: 0.9542 - mae: 0.0226 - val_loss: 0.5694 - val_accuracy: 0.8965 - val_mae: 0.0385\n",
            "Epoch 149/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1662 - accuracy: 0.9520 - mae: 0.0232 - val_loss: 0.5069 - val_accuracy: 0.8992 - val_mae: 0.0376\n",
            "Epoch 150/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1688 - accuracy: 0.9464 - mae: 0.0262 - val_loss: 0.4296 - val_accuracy: 0.9060 - val_mae: 0.0379\n",
            "Epoch 151/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1653 - accuracy: 0.9517 - mae: 0.0252 - val_loss: 0.4479 - val_accuracy: 0.9033 - val_mae: 0.0367\n",
            "Epoch 152/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1591 - accuracy: 0.9559 - mae: 0.0227 - val_loss: 0.4675 - val_accuracy: 0.9023 - val_mae: 0.0354\n",
            "Epoch 153/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1568 - accuracy: 0.9573 - mae: 0.0214 - val_loss: 0.4854 - val_accuracy: 0.9002 - val_mae: 0.0365\n",
            "Epoch 154/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1642 - accuracy: 0.9505 - mae: 0.0245 - val_loss: 0.5101 - val_accuracy: 0.9080 - val_mae: 0.0378\n",
            "Epoch 155/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1571 - accuracy: 0.9540 - mae: 0.0232 - val_loss: 0.5773 - val_accuracy: 0.8931 - val_mae: 0.0380\n",
            "Epoch 156/300\n",
            "15/15 [==============================] - 1s 98ms/step - loss: 0.1561 - accuracy: 0.9559 - mae: 0.0224 - val_loss: 0.5501 - val_accuracy: 0.9002 - val_mae: 0.0373\n",
            "Epoch 157/300\n",
            "15/15 [==============================] - 1s 101ms/step - loss: 0.1559 - accuracy: 0.9518 - mae: 0.0233 - val_loss: 0.5161 - val_accuracy: 0.9084 - val_mae: 0.0358\n",
            "Epoch 158/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1494 - accuracy: 0.9555 - mae: 0.0214 - val_loss: 0.5751 - val_accuracy: 0.8948 - val_mae: 0.0388\n",
            "Epoch 159/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1706 - accuracy: 0.9498 - mae: 0.0228 - val_loss: 0.4755 - val_accuracy: 0.9084 - val_mae: 0.0369\n",
            "Epoch 160/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1636 - accuracy: 0.9504 - mae: 0.0259 - val_loss: 0.4617 - val_accuracy: 0.9026 - val_mae: 0.0382\n",
            "Epoch 161/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1565 - accuracy: 0.9555 - mae: 0.0236 - val_loss: 0.4423 - val_accuracy: 0.9030 - val_mae: 0.0360\n",
            "Epoch 162/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1663 - accuracy: 0.9502 - mae: 0.0246 - val_loss: 0.4751 - val_accuracy: 0.9094 - val_mae: 0.0389\n",
            "Epoch 163/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1727 - accuracy: 0.9502 - mae: 0.0261 - val_loss: 0.6136 - val_accuracy: 0.8843 - val_mae: 0.0425\n",
            "Epoch 164/300\n",
            "15/15 [==============================] - 1s 86ms/step - loss: 0.1803 - accuracy: 0.9479 - mae: 0.0250 - val_loss: 0.4302 - val_accuracy: 0.8928 - val_mae: 0.0394\n",
            "Epoch 165/300\n",
            "15/15 [==============================] - 2s 139ms/step - loss: 0.1643 - accuracy: 0.9525 - mae: 0.0246 - val_loss: 0.4298 - val_accuracy: 0.9026 - val_mae: 0.0397\n",
            "Epoch 166/300\n",
            "15/15 [==============================] - 1s 100ms/step - loss: 0.1674 - accuracy: 0.9528 - mae: 0.0254 - val_loss: 0.3882 - val_accuracy: 0.9040 - val_mae: 0.0370\n",
            "Epoch 167/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1604 - accuracy: 0.9553 - mae: 0.0234 - val_loss: 0.4410 - val_accuracy: 0.9009 - val_mae: 0.0372\n",
            "Epoch 168/300\n",
            "15/15 [==============================] - 1s 84ms/step - loss: 0.1590 - accuracy: 0.9531 - mae: 0.0229 - val_loss: 0.4434 - val_accuracy: 0.8955 - val_mae: 0.0392\n",
            "Epoch 169/300\n",
            "15/15 [==============================] - 1s 85ms/step - loss: 0.1633 - accuracy: 0.9516 - mae: 0.0235 - val_loss: 0.3377 - val_accuracy: 0.9057 - val_mae: 0.0365\n",
            "Epoch 170/300\n",
            "15/15 [==============================] - ETA: 0s - loss: 0.1645 - accuracy: 0.9509 - mae: 0.0263\n",
            "Reached 92.0% validation accuracy. Stopping training.\n",
            "15/15 [==============================] - 1s 87ms/step - loss: 0.1645 - accuracy: 0.9509 - mae: 0.0263 - val_loss: 0.2714 - val_accuracy: 0.9230 - val_mae: 0.0339\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot Mean Absolute Error (MAE) and Loss During Training"
      ],
      "metadata": {
        "id": "10kNYQuta4WK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "QX6zJJEhO1cJ",
        "outputId": "16298216-174e-488c-cb48-41446a411bdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get mae and loss from history log\n",
        "mae = history.history['mae']\n",
        "loss = history.history['loss']\n",
        "\n",
        "# Get number of epochs\n",
        "epochs = range(len(loss))\n",
        "\n",
        "# Plotting the full graph\n",
        "plot_series_dual_y(\n",
        "    x=epochs,\n",
        "    y1=mae,\n",
        "    y2=loss,\n",
        "    xlabel='Epochs',\n",
        "    ylabel1='MAE',\n",
        "    ylabel2='Loss',\n",
        "    legend1='MAE',\n",
        "    legend2='Loss',\n",
        "    title='MAE and Loss across Epochs',\n",
        "    filename='mae_and_loss_model1.png')\n",
        "\n",
        "# Plotting the last 80% of epochs. This is done because plotting\n",
        "# the first 20% of epochs reduces the resolution of the graph for\n",
        "# the latter 80%.\n",
        "zoom_split = int(len(epochs) * 0.2)\n",
        "epochs_zoom = epochs[zoom_split:]\n",
        "mae_zoom = mae[zoom_split:]\n",
        "loss_zoom = loss[zoom_split:]\n",
        "\n",
        "plot_series_dual_y(\n",
        "    x=epochs_zoom,\n",
        "    y1=mae_zoom,\n",
        "    y2=loss_zoom,\n",
        "    xlabel='Epochs',\n",
        "    ylabel1='MAE',\n",
        "    ylabel2='Loss',\n",
        "    legend1='MAE',\n",
        "    legend2='Loss',\n",
        "    title='MAE and Loss across the last 80% of Epochs',\n",
        "    filename='mae_and_loss_last_80_percent_model1.png'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Monitor MAE and Loss in Training and Validation Data for any discrepancies"
      ],
      "metadata": {
        "id": "9p0uYN54bJN0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "Tfe9x8r7O1cJ",
        "outputId": "c2c674d4-6896-47f9-f0ac-8fa01e3d62e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_training_validation_metrics(history, \"Training and Validation progress over epochs\", filename=\"training_validation_metrics_model1.png\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate Classification Metrics to Determine Performance and Graph Confusion Matrix"
      ],
      "metadata": {
        "id": "B9zPI5ktbc70"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "JSV8QFjVO1cO",
        "outputId": "cc5a8217-44d0-478a-9aee-ac9572252c5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "93/93 [==============================] - 1s 8ms/step\n",
            "Testing Accuracy: 92.29725144214456%\n",
            "Precision: 92.33560384578922%\n",
            "Recall: 92.29725144214456%\n",
            "F1 Score: 92.2360829337984%\n",
            "93/93 [==============================] - 1s 7ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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OCYIgCA8ePBD8/Px05m3RooXOf/cyMzOV32l9h7W1tbBw4UK9vxND36cDBw4oX58yZYrG68Z+7/OO1B41BaHPu5IeqT1qvi47NVU4duyY8u9f21G7dm0hISFB9G+ZIaVKlRIACHXr1jWYtzjjQgFE9EYTBAH79u1TnoeEhEhWdkhICNavXw8AsLe3x8CBA9G0aVNYWVnhzJkzWL58OZ4/f44//vgDqamp2LVrl969OdLS0tC5c2dcu3YNH3zwATp06IBSpUrh5s2bWLJkCe7cuYPbt29jwIABGkt05vVSrF+/Hhs2bAAATJs2De+++64yT+nSpSV773kEQUDv3r3x33//AQDq1auHXr16oXLlyrCxsUFKSgpiY2Nx4MABXLhwwex6JkyYgJkzZwIArKys0LdvX7Ru3RoODg64dOkSfv/9dzx8+BDR0dFo1aoVTp48CXt7e53l5eTkIDg4GEePHkWrVq3QvXt3lCtXDvfu3UNERASuXLmClJQU9O3bF1euXIGtra3ZbVc3cOBATJo0Cbt27cKjR4/g4eEhev3vv/9GcnIyAOOHnr169QqCIKB27dpo1aoVatasCXd3d7x69QqJiYnYsmULzp49i8ePH6NLly64cOGCaHloDw8PbN68GY8ePcKnn34K4HVP3xdffKFRl3p782zYsAG7d++Gq6srBg4ciIYNG8LKygoxMTEmPeVesmQJjh8/jlu3bmHu3Llo37492rRpI8rz+eef4+bNmwCAr7/+Gu3atTO6fHX//vuv8udGjRqZfP1PP/2k7Mn97rvvzF5l6vz582jdujUyMjIAvJ4T179/f1SqVAlJSUn4448/cPToUTx58gRdunTBP//8Y3Dxkjlz5mDXrl3w9PREaGgo3n33XeTk5ODUqVNah6cuWrQIGzduhK+vLwYNGoRq1arh2bNnWLVqFY4ePYqUlBT06tULly5dQufOnXHu3Dl06tQJXbp0gbu7O+Li4vDLL7/gyZMnOHz4MKZPn44ff/xRox6FQoHMzEyUL18e7dq1Q506dVC2bFnI5XIkJibi2LFj2LZtG3JycjBy5EiUL1/e6B5LUxnTwxsZGYlt27ZZpH5VN27cQIcOHZCWlgbg9WqVAwYMgJeXFx48eIB169bh1KlT6NOnj0lL9Tdu3Bi7du1CTEwMkpKS4Onpaam3ULgKOagiIrKoq1evKp9wOTg4CNnZ2ZKUu379emW5ZcuWFa5cuaKRJyEhQfD19VXm0/XEESpP4WxtbYXt27dr5ElOThaVdfLkSa1lqT911UWqnprTp08rX+vSpYuQk5Ojs5wrV64IycnJJrfl2LFjyl4IR0dH4eDBgxp5njx5InpqPGbMGK1tyHu6mXf873//08iTkZEhNG7cWJlnw4YNOt+TIdqe7N+5c0eQy+UCACE8PFzjmm7dugkAhGrVqinTDPXUJCQkCBcvXtTblrVr1yrrDQ0NNdjegQMHGnx/qj01AIQaNWoI9+7d03uNoSfrgvD6d25tbS0AEMqXLy/63qxZs0Z5fYMGDYTMzEyD7dSnWrVqAgDB2dlZyM3N1ZtX2+9TEASha9euyvStW7dqXGeopyY3N1eoXbu2Ms/o0aO1tuWHH35Q5vHy8hIyMjI08qj+GwBAaN68uZCamqrzPann79Kli0a5ubm5Qvv27ZV5GjZsKMjlcmHNmjUa5V27dk1wcHAQAAglS5YUXr16pZEnJydH2LVrl6BQKHS2KyYmRvD09BQACJUrV9b5u8lvT40hu3fvFvU+pvaqJQj93pP0SO31unde9b0MGjRI4/9XCoVC+Prrr0W/L2N6asLCwvR+P98UnFNDRG+0e/fuKX/29vaGtbU0HdSzZs1S/hwZGYlatWpp5PH29sb69euVvTNz5sxBbm6u3nInTZqELl26aKS7u7uLxuzv2bPH3KZLKq+HBgAGDx4MKysrnXlr1aoFd3d3k+uYM2eOcknWOXPmoGXLlhp5SpUqhY0bNyrnQyxduhTPnj3TW+7gwYMxbNgwjXR7e3vR02WpP2svLy+0bt0awP+tcJbn0aNHyvkdAwcONLpMb29vg3sQ9evXDx9//DGA170qUm/KKpPJsH79epQvXz7fZQUEBCj3W7p//z6GDBkC4PXcn88++wzA632Y1q5dm69etMzMTOXk6apVqyqXzzbV9OnTlddOnDjR5OW3//77b1y5cgUA0KRJE8yfP19rW77//nt07twZwOuVDg3NkXB0dMSGDRuMnkPo4eGB1atXa/RyyuVy5ZLXAHD27Fl8+umn6N+/v0YZ1apVU37Pnj17hlOnTmnksbKyQocOHfT2XNepU0c5Z+nmzZuFss9KbGwsPvzwQ+Tm5hbIwisHDx4E8PozXLp0qcb/r2QyGebOnWtyj2LNmjWVP1+8eDH/DS2iGNQQ0RvtyZMnyp9LliwpSZkJCQk4f/48gNfDAzp27Kgzb6NGjZQ3sLdv39a7QpKVlRVGjhyp8/W8cgDg6tWrpjbbIlQnVefdlEkpMzNTeZPv7u6uvLnVxtvbW7nST3p6Ov755x+9ZY8ePVrnay1btlTeUFjis84bVnbx4kWcO3dOmb569Wrk5ORALpcbvbSwKZo2bQoAyMjIkPzmpkWLFqhbt65k5U2cOBEtWrQAAGzduhULFy7ERx99pByas2DBAlSvXj1fdSQmJioD5lKlSpldznvvvaf87l2+fBlr16416fq//vpL+fPYsWP13uyr7j2lep02wcHBJgWZISEhOocK+vv7i1aBHDFihM5ymjdvrvw5P38/ed9X4PWCLAXpyZMn6Nq1q3JhhkWLFr1+QS7/v71qpDrUAthRo0bpDNZlMplRm/eqcnNzU/58+/Ztk64tThjUEBGZSPXJ4/vvv28wv2oeff9jrlatmuh/PuoqVKig/FnXKmgFrXnz5nBwcAAATJ06Fd98842kN8sxMTHKVZSCgoIMPpU39rPOW6VJF1tbW+UcJEt81j179lQ+PVftrVHdmyZvfwlTnDx5EqNGjYK/vz/c3d1ha2srWsFp+PDhyrxSr+6WF4BIRS6XY/Xq1cqHEaNGjVI+rQ8ODsYnn3yS7zqePn2q/Dk/QQ0A/PDDD8qb/ilTppjUE5b3b4pMJjM4P6hp06ZwcnICYPhG39TfSePGjXW+Zm1trexpdXR01No7nads2bLKn/X9/SQkJGDq1Klo1aoVypUrBwcHB9H3VXVDU0usRqhLdnY2goODlauvTZ48Gb179379YgEs6aw+h0ydodfVqfaQF5X/d1gCgxoieqOp/mNuaDiSsR48eKD8uVq1agbzq+ZRvVadoYn8qsMfXr16ZbDeglCqVCnMnz8fMpkMOTk5mDdvHurWrYuyZcuiR48emDdvHmJjY80u31Kftbu7u96n4cD/fd6W+KxV96xZu3YtsrOzce7cOWVAaOreNFlZWRgwYACaNGmChQsX4syZM3j69KneG+u8Hg+pqAbdUqlUqRJ+++03UZqXlxeWLVsmSfmqyw47Ozvnq6zKlSsrA62bN2+a1Ma876qnp6fBdsjlcrzzzjsAXgdl+vY7MvV3Ymh4aN7fRKlSpfT+/Rjzb9WCBQtQo0YNhIWFITo6GklJSXr/1qT+vuozfPhw5VCw3r17a10K2pKqVKmi93V3d3eTRh6oDj/MW4jiTcTVz4jojaY69OL27dvIycnJ97ya58+fK392dHQ0mD/vqar6terMHc9f2D799FPUqFED06ZNw4EDB6BQKPDo0SNs2bIFW7ZswTfffIOmTZti/vz5Jo8Ff5M/69DQUERERCA5ORl///03Dhw4AMC0vWnyjBgxAqtWrQLw+oayU6dO8Pf3R4UKFeDo6Kic67R//378+uuvAGBwfpep8nrspPbOO+/A2toaOTk5AF4/pdbXo2kK1ZtvKW6av//+e0RFRSEjIwPTpk1DaGioUfve5H1XjfmOA5rfc13BiKm/E2P/LvL797NmzRp89dVXyvMWLVogMDAQPj4+cHZ2VvbIqq7GJ/X3VZe5c+fi999/BwD4+fkp92tSyhsyJiUrQfmjtbW1UZs9Ozo6Gv2gTnVvI0v9nRYFDGqI6I1Ws2ZNlCpVCk+fPkVGRgYuXLgAPz+/fJWp+iT1xYsXBvOnp6drvba4MGbSc2BgIAIDA5VLuR4/fhwHDx7E6dOnoVAocOzYMTRv3tyoZWhVvcmfdbNmzVC1alXEx8fjt99+w5kzZwAAffr0MenGIyEhAcuXLwcAVKxYEQcPHkTlypW15lVdOKM4ePHiBfr3768MaIDXG3727t0bnTp1ynf5qkPOVIeimatcuXL44osvMGvWLCQlJeGXX34RzYHRxdnZGc+ePTPqOw4Ur++5Nt9//z2A1zfw27Zt0zkv0RLz9PT5+++/MW7cOACve7m2bt1a4EFATk4OsrOzDQY2xn5XAGmHWRZlhf+oiojIgmQyGdq2bas8z3uanR/lypVT/py3cpI+qnmkWBlKCqpPqPUNXwGg3DPFGO7u7ujevTtmzZqFEydO4M6dO8oVkrKzszFmzBiT2vkmfNb65K1wtnv3bpP3psmzf/9+5WT38ePH6wxogOI3SfjLL7/EtWvXAABdu3ZV3ugNGjQIDx8+zHf5lSpVUvY6SBHUAMC4ceOUQ4Nmz55t1NP0vO95UlKSKGDRRhAE5VyPvHlTxcnNmzdx69YtAED37t31LrRSkN/XS5cuoV+/flAoFChRogS2bt2q/d+QAphTo7qqpDZPnjwxaTi16nfb29vb6OuKGwY1RPTGU13lKjIyMt//o1QdQqW6cZ8uqqtwmbO5nyWojse+f/++zny5ubnKHgRzVKhQAStWrFBu9nb27FmTxnTXrVtXGYBFR0cbnHxdFD9rfQYMGCAaylOtWjXRik/GUL25z5troYuh5alV25IXKBWWTZs2ISIiAsDr1cX+/PNPTJ06FcDrYUmDBg3KdxttbW1RtWpVAK9vJE1dilkbNzc3jB07FsDrSdmzZ882eE3ed1UQBOzdu1dv3mPHjikDn+LwHVcn5fdVKo8ePULXrl2Rnp4OmUyGlStXomHDhgVStzb79+/X+7rqhtLGUJ3XWKdOHbPaVBwwqCGiN17Tpk2VQ1WeP3+Ofv366Z1voW7BggWiPRJ8fHzQoEEDAK9X59IX2Jw5c0b5Pyhvb+9C/R+lKtWVi/T9D3T9+vV4/PhxvuqytrYWreSlOpTIEDs7O+W+HMnJyYiKitKZNzExEevWrQPwes5B+/btzWtwAfLy8sKQIUPQuHFjNG7cGF9//bXJZajO2ch7gq/N1q1bDa5MpzpXw5ThLVK7e/eucg8he3t7rFu3DnZ2dhg3bpxy+OKuXbvwyy+/5LuuvBW/nj9/Ltny3aNHj1auAPbzzz8jKSlJb/7g4GDlz3PnztUbrKnukaV6XXFh7Pc1MTERkZGRFm9PZmYmunfvrnzY9cMPP+j/XKVezlnLHJ2FCxfqfIAjCALmz59v0ntUXSWvOAbCxmJQQ0RvhRUrVihvrI8fP47mzZvjxIkTeq85deoU3n//fXz11VcaQ7Tyxl0Dr4cQxcXFaVx/584d9O3bV/n0d+zYsXo3pyxI7dq1U7Zl0aJFWnuvzpw5g1GjRuktZ82aNYiMjNTb+3LixAnlvj6VK1c2eQ7A2LFjlT0I33zzDY4ePaqRJyUlBb169VLeiA8fPlznfhtFzW+//YYTJ07gxIkTyknRpvD391f+PHfuXK1Ltp48eRKDBw82WFapUqWUn9uFCxcKpbdGoVAgJCREOWRm7ty5qF27NoDXPUmrVq1SLhQwbtw4XLp0KV/1qS6hrG2jSHM4Ojpi0qRJAICXL18qe5x06dy5s/I9Hj16FGPHjtXaa/TTTz9h+/btAF4HxB999JEk7S1INWvWVC6IsHXrVq2f+cOHD9G9e3eTHj6Z65NPPsHx48cBAP3791f+3gpDXsAeFxeHzz//XGNxBEEQMG7cOIP/71KX9xnXq1dPtNz2m4YLBRDRW6F06dLYt28funbtiuvXr+PixYsICAhA48aN0a5dO/j4+MDFxQVPnz7FjRs3sHv3br03S3369MHmzZuxfv16PHjwAA0aNEBoaCgCAgJgZWWFM2fOYPny5coVld5//318/vnnBfV2DSpfvjz69++PVatW4enTp/D398fnn3+OWrVqIT09HdHR0Vi3bh1KlSqF1q1b6+zNiY+Px9SpUzFq1Ci0a9cO/v7+8PLygp2dHR49eoTDhw9jy5Ytyv85f/fddya3tUmTJhg3bhxmzJiB58+fIzAwEP369UPr1q3h4OCAy5cvIyIiQjmspU6dOvjhhx/M/3CKmYCAADRs2BBnz55FQkICatSogeHDh6N69erIyMjA/v37sWHDBgDARx99hDVr1ugtr3Xr1ti8eTNu3LiBDz/8ED179hQNVwwMDLTo5OlZs2YhOjoawOubffVNHitWrIhly5ahV69eyMzMRL9+/XDmzBnY29ubVV+nTp1ga2uLrKwsREdHGxX8GWPYsGEIDw9HQkKCwV6vvD15mjZtioyMDISHh+PAgQP46KOPULFiRTx8+BB//PEHjhw5AgCwsbHBypUrzX7PhcnW1haffvop5s2bh+zsbLRs2RKDBw9Wbu557tw5REZG4tmzZxgwYABWrlxpsbZs3rwZq1evBvA6oO/atSu2bNmiNe/Lly9f/2Als8DqZ68D2Hnz5iEoKAhpaWmIiIjAqVOnMGDAAHh5eSEpKQlr167FyZMn0ahRI9y9e1fv0OE8MTExygcE3bt3l7bdRY1ARPQWSUlJET777DPB1tZWAGDw8PT0FBYtWiRkZ2drlJWdnS188sknBsvo1auX8PLlS51tyssXGBhosP2G8k6ZMkWZ58CBA3rLevLkiVCvXj2d7S5Xrpxw4sQJYeDAgcq0W7duicoICwsz6nO0sbERZs6cqbUd+spX9f333wtWVlZ66wkMDBSSk5N1luHt7S0AELy9vfV+Nqbm1eXWrVvKtrVv397scuzs7PS2JT4+XvDy8tL5udjb2wvLly8XIiMjlWmRkZFayzp//rzg4OCgsyzV35Ex5akLDAxUXqPu1KlTgo2NjfJv79GjRzrLUf3bGzFihFF169K9e3cBgODk5CS8ePFCZz5Tf58rVqzQ+Pz0fU7Hjh0TypUrp/c7XqpUKWH37t06yzDl3wBT8xv7N3HgwAFlmVOmTNF4PSMjQ2jVqpXe9/npp58KN27cUJ4PHDhQa136vk+G2qL6/TX2SB3aUBBGNJb0SB3a8HXZqanC0aNHhdKlS+usv3bt2sLt27eN/l18++23ymvj4+P15i3uOPyMiN4qJUuWxOLFi3Hjxg3Mnz8fXbp0QeXKleHi4qLcMbt+/foYNmwYtm7disTERHz++eda97axtrbGsmXLcPz4cQwZMgRVqlSBo6MjHBwc4Ovri48//hj79u3Dn3/+WST3BihVqhSOHTuGmTNnon79+nByclLuFD5x4kTExMTo3WEcACZOnIjo6Gh8//33aN++PXx8fODg4ABra2u4ubmhUaNGGDduHK5evSoasmeOH374ARcvXsSoUaNQq1YtODs7w87ODhUrVkRwcDA2bdqE6OhogxsIvomqVKmC8+fPY8KECahZsybs7e3h5OSE6tWrY+TIkTh79qzRPRD16tXD2bNn8cknn6B69epG7bMihfT0dPTv3x/Z2dmQyWSIjIxEmTJldOZfsGCBcrPVRYsW4e+//za77rxe1PT0dJ1P6s3x8ccfK4eVGSMgIADx8fGYN28eAgMDUaZMGdjY2MDd3R0BAQGYPn06bty4USzmi+ljb2+Pf/75B4sXL0ZAQIDyb9nb2xt9+vTBnj17sHTp0iKxn5QGucwyx//XtGlTxMbGYvz48ahRowYcHBxQsmRJNGzYELNnz8apU6dQqVIlo5oqCIJynmG7du0MbupZ3MkEoZCXNyEiIiIqZHXr1sXFixfx/vvvF9iqW1R8pKWlwdXVFamfNYKLnbSzN9Iyc+C65BRSU1Ph4uIiWbn79+9HmzZtALxeWKNDhw6SlV0UFcEQmIiIiKhghYWFAXi9LPjZs2cLtzFEEvjpp58AvO4BfNMDGoBBDRERERF69OiBgIAAAP8X4BBpKIDNN6Vw5MgR5X42M2fOlLz8oohBDRERERFe7w8il8vx999/52vTWaLClheY9+3bFy1btizcxhQQLulMREREBKBBgwYae4MQiWjZLFOSMiW2d+9eycss6thTQ0RERERExRp7aoiI3jIKhQL379+Hs7MzZDLpx3ITERUFgiDg+fPnKF++vHTLQ1tiDowF5tS8jRjUEBG9Ze7fvw8vL6/CbgYRUYFITExExYoVC7sZksvJycGRI0dw+vRp5ZGQkCDKM3DgQERFReksw8fHB7dv3zaqvoYNG0oy1+zo0aP4/fffcejQITx48AAAUK5cOQQGBmLw4MFo2rSpWeUyqCEiess4OzsDABLfrwwXG6tCbo00vj98urCbIDn79MJugbTkCj6NpoKViTTMh5fy3zxJyOWvDymZWd7du3fRqlUradtiQTk5Ofjss88QERGh8dp///2H//77D8uXL8fQoUOxZMkSWFmZ9v8nBjVERG+ZvCFnLjZWb0xQYyeTbsO6osLuDYsB5HjD3hAVG5IOs+XwM7N98sknWLFihcF8y5YtQ3Z2NiIjI00qn0ENEREREVExVKZMGfj5+cHPzw/+/v4YPnw47t+/b1ZZFSpUwJEjR3S+bmdnZ24zsWPHDo2Apnfv3hg/fjwEQcDMmTOxceNG5WtRUVHo06cPOnbsaHQdDGqIiIiIiIwht8CSzmYOP/P29sajR49EaaNGjTK7GdbW1vDx8TH7en3Cw8NF5++88w7Wrl0La+vXocjatWtx5swZ0ZyguXPnmhTUcElnIiIiIqJiRurVKx89eoR3330Xjo6OsLe3R8WKFfHBBx9g1apVyM7ONrvc5ORkREdHi9K6d++uDGgAwMbGBt27dxflOXDgAJ48eWJ0PQxqiIiIiIiMkTenRuqjCMjIyMCVK1fw8uVLZGZm4t69e9i2bRsGDBgAf39/3Lx506xyz5w5A0EQRGm1a9fWyPfuu++KzgVBMGm1NQY1RERERESFLC0tTXRkZmYWdpOUYmJi0L59e6Smppp87a1btzTSPDw8jErTdq0uDGqIiIiIiIyRt6Sz1AcALy8vuLq6Ko8ZM2YUwNuRo2XLlpgzZw4OHjyIa9eu4eDBg/juu+9gb28vyvvff/9h3rx5JtehLRBycHDQSCtRooRG2rNnz4yuhwsFEBEREREVssTERLi4/N/y9PlZbcxYhw8fRoUKFURp1apVQ8uWLdGsWTN07txZ9Nq6deswderUfNerPhxNV5opGNQQERERERnDgvvUuLi4iIKagqAe0Kjq1KkTGjRogHPnzinT4uPj8fLlS629Krq4urpqpGVkZBiVVrJkSaPr4fAzIiIiIiJjWMleL+ks6VE0FgrQpnLlyhpppgwJAwBfX1+NNPWlqAHg4cOHRl2rC4MaIiIiIiLSoG3FMzc3N5PK8PPz01h++sqVKxr5Ll26JDqXyWTw8/Mzuh4GNURERERExpDLLHMUgtmzZ+P3339HTk6O1td37dolGnoGALVq1dKY5B8aGgqZTKY8goKCRK+XLl1aI23Lli2ivW+ysrKwdetWUZ5WrVrB3d3d6PfDOTVERERERMVQQkKC6Fw9QElPTxflsbe3h6enJwDg/v37GDduHKZMmYKQkBC0bt0aXl5eePToEfbs2YPw8HCN+gYNGmRWO7/55hscOHBAeX7z5k30798fEyZMgCAImDFjBm7fvq1xjSkY1BARERERGSNvHozUZZrJ0JyTTZs2YdOmTcrzwMBAREdHi/LcvXsXM2bMMLiEdKNGjTBq1Ciz2tm5c2cMHDgQK1asUKZt3LgRGzdu1Jp/4MCB6NSpk0l1cPgZEREREdFbxtnZ2ei8Xbt2xa5du/K1zHRERASGDBliMN+QIUMQERFhcvnsqSEiIiIiMobMAnNgZIUzp2batGno3bs3du7ciaNHjyIuLg4PHjzAq1ev4OjoCC8vLwQEBCAkJAQtW7bMd33W1taIiIjAoEGDsHz5chw8eBBJSUkAgHLlyqFly5YYMmQImjVrZl75+W4hEREREREVuPxuWFmnTh3UqVMnX2VERUUhKirK6PzNmjUzO3DRh8PPiN4At27dEq08IpPJsHLlSq15P/vsM428V69e1ZrX399flC84OFhrvnr16mmUKZPJcPnyZYNt9/HxEV3j4+Nj9PsGoFGn+goreW7fvo0qVapo5J85c6Yyj/oKLjKZTGMSZlRUlEYeuVyO8+fPa603LCxMI78h6enpiIiIQEhICGrVqoUyZcrAxsYGJUqUgKenJwICAjB06FCsWrUKKSkpRn9WRESUT5LvUWOBOTpvKX6KRG8AX19fVKxYUZR26NAhrXkPHz5sVFp6errGjbq27ucLFy4gJiZGa12mPLmxpJs3byIwMBA3btwQpc+fPx/jx4/Pd/mCIGDChAn5LicnJwfTpk1DxYoVMXToUKxevRqxsbFITk5GTk4OMjIy8PDhQ5w4cQIREREYMGAAPD09sW/fvnzXTURERniDlnR+0zCoIXpDtGjRQnSuLah5+vSp1l4ZbUHN0aNHkZubq7cOQH/gsmbNGp3r3xeUa9euoWXLlqKlImUyGRYtWoQvv/xSsnr27NmjsaKMKdLS0hAUFITJkycjNTXV6OuysrLYW0NERG89BjVEbwj1gCM+Pl45AS/PkSNHtI6/1RbUqAdFzs7OqFu3rigtOzsba9eu1dmmpKQk7N6922DbLeXKlSsIDAzEvXv3lGlyuRzLli3D559/Lnl95vb65Obmonv37jh69Kgo3cbGBiEhIdi4cSNiYmJw/fp1nDx5EpGRkRgyZIhJm5IREZEEOPysyOKnSPSG0DY0TD1YUT1XHa52584d3LlzR++1TZs2hZWVlShtx44dePz4sfLczs4O77//vihPYQ1Bu3DhAoKCgvDw4UNlmpWVFaKiooxaUtIcJ0+exObNm02+LiIiQrQpGQB4eHjg2LFjWLlyJYKDg1GnTh1UrVoVjRo1QmhoKCIiIpCUlISVK1dqDD0kIiJ62zCoIXpD1KpVS+PJvXpvi+r5sGHD4OrqqvW1zMxMnDp1SnStMUPPunbtihEjRojStm/fjqdPnxr3JiRy+vRptG7dGsnJyco0a2trrF27FiEhIZLXZ29vr/x54sSJGsP2DPnpp5800tauXQs/Pz+911lbWyMkJARNmjQxqT4iIjIT59QUWQxqiN4QMpkMzZs3F6WpBiovXrzAuXPnlOdBQUGiJRVVe2ZOnjyJzMxMUVnqQc3jx4+xc+dOUVpISAg6duwoCq6ysrL0DlGT2s2bN9G2bVvRPBNbW1v8+eef6NOnj0XqVB3KFhsbK9ox2ZCYmBiNXrLGjRujTZs2krWPiIjoTceghugNoh54XL58Gc+ePQMAnDhxQjlp387ODo0aNRINWVMNatR7eGxtbdGoUSNR2po1a5Cdna08d3d3R8eOHWFjY6MRPBTkELTExESkpaUpz+3s7PDXX3+he/fuFqvz008/ha+vr/I8LCxMIyjU5cKFCxppbdu21UhLT09HQkKCzkOfzMxMpKWliQ4iIjID59QUWfwUid4g6vNqFAqFMlhRDVr8/f1hZ2cnyh8XF6ccrqUe1Pj7+4uGWAGagUrfvn1hY2MDAPj4449Fr509e9aoPWssYdq0aejcubNF67C1tcW0adOU54mJiVi4cKFR1z569EgjrUKFChppGzduhK+vr85DnxkzZsDV1VV5eHl5GdU2IiKi4oJBDdEbpEGDBnBychKlaQtq8np0/Pz8UKJECQCv91o5cuQIcnNzcfz4cVEZ6j1A2vamUZ2r0rRpU1SuXFn0emEtGPDDDz9oXd1Nav379xetDjdjxgyze0SM2aDTFBMmTEBqaqrySExMlLR8IqK3BufUFFkMaojeIFZWVggICBClHTp0CNnZ2Thx4oQyLS9IsbGxEU0yP3z4MM6dO4f09HRRGeo9QOoBStWqVdG4cWNR2kcffSQ6L6g9a9QDgvT0dHTs2FFjdTFL1Ks64f/JkyeYPXu2wevKlCmjkaa6BLUU7Ozs4OLiIjqIiMgMcgsMPZPzdlwK/BSJ3jDqAci5c+dw+PBhvHz5EsDrfVqaNm2qNf+hQ4c0hp6p59e2N018fDxkMpnoUB2OBRTcnjXNmjXTWOHsxYsX6Ny5M/755x+L1t2pUyfR57lgwQLRktLa1K9fXyNt//79GmmhoaEQBAGCIGDKlCn5bywREdEbhEEN0RtGfahYdnY2Zs2apTyvU6eOaCln1Zvw8+fPa6xopp5ffW8aUxTEELS8vWgGDx4sSs/IyEC3bt003p/UZs6cqfz5xYsXBt9z3bp1Nea4HDt2DEeOHLFE84iIKD84/KzIYlBD9IZp3LgxbG1tRWmqPRTqQU+TJk2U+XNzczV6CdTz5ycwKag9a+RyOSIiIvDpp5+K0jMzM9GjRw9s3brVYnUHBASIVlp79eqVwWvGjx+vkdavXz9cvXpVyqYRERG9sRjUEL1h7O3t4e/vr/N19SDFwcFB7yaPqvm17U0THh6OW7duaT1iY2OVK6IBxu1Zk5OTo3fp4oSEBCgUCr1lAK/nuCxZsgQjR44UpWdlZaF3797YuHGjwTLMNX36dFhZWRmdf9iwYRrDBu/evQs/Pz+MGDECu3btQmxsLOLi4rBv3z7R/CgiIipAcrllDso368JuABFJr2XLljh69KjW19SDmrz8x44dM5hffW8amUyG/v37w9PTU2dbWrRoIer9iYqK0gg0VN27d8/gEsUpKSkoWbKk3jx57fv1119hY2OD+fPnK9Ozs7PRr18/5X+lVqtWLQwYMACRkZFG5be2tsaWLVvQvn17nD59WpmekZGBxYsXY/HixZK3kYiI6E3C0JDoDaQtcAGAKlWqaA1A1HsJdOVXH3rm5+enN6ABgC5duojOC2PPmnnz5mHcuHGitJycHISEhGDVqlUWqXPq1Kmws7MzOr+bmxuOHDmC8ePHK5fZNoajoyMGDBhgThOJiMhUVgCsZBIfhf2m3gwMaojeQM2aNdM6/ElXsGNMfm1706gHLNpo2/iyMPasmTlzJiZNmiRKy83NRWhoKH7//XfJ6/Py8tLbI6WNra0tZsyYgcTERPzyyy/o2bMnKleuDFdXV1hZWcHJyQmVKlVCmzZt8OWXX2Lr1q1ISkrCihUrJG8/ERFRcSITBEEo7EYQEVHBSUtLg6urK1I7V4WLzZvxiPDbA2/eogr26YbzFCfyXK7wRAUrE2mYCVekpqbme38u5b+bq/vCpYSt4QtMKftlFlw/Xi9JO99m7KkhIiIiIqJijQsFEBEREREZwxL7ynCfGkkwqCEiIiIiMkbe5H6py6R84/AzIiIiIiIq1thTQ0RERERkDEtslsnNNyXBT5GIiIiIiIo19tQQERERERlBIZNBIfHEfoWMc2qkwJ4aIiIiIiIq1thTQ0RERERkBIVcDoXEc2CkLu9txU+RiIiIiIiKNfbUEBEREREZQSG3wJwabr4pCQY1RERERERGyLWSI9dK2oFOUpf3tuKnSERERERExRp7aoiIiIiIjMDhZ0UXgxoiorfUjB1nYAeXwm6GJGYPalDYTZBcWOS5wm4CEVGxwaCGiIiIiMgIglwOQeIlmKUu723FT5GIiIiIiIo19tQQERERERmBc2qKLvbUEBERERFRscaeGiIiIiIiI7CnpuhiUENEREREZITXQY20A50Y1EiDw8+IiIiIiKhYY08NEREREZERBJn0w88EGXtqpMCeGiIiIiIiKtbYU0NEREREZIRcmRy5Mmn7BKQu723FT5GIiIiIiIo1BjVEREREREbIW9JZ6sMcOTk5iI6Oxpw5c9CnTx/4+vpCJpOJjtDQUN3vRaFATEwMfv31V/Tp0wfvvvsuSpYsCWtrazg5OaFKlSro27cvtm7dCkEQzPzEXktISNBom75jzJgxJtfB4WdERERERMXM3bt30apVK7OvX7BgAb755hutr7148QI3btzAjRs3sGHDBrRo0QKbN2+Gu7u72fVZGntqiIiIiIiMUJR6avL9XhQKo/MePnwY3bp1y3ePjSWxp4aIiIiIyAiCXA5B4s0381NemTJl4OfnBz8/P/j7+2P48OG4f/++yWWEhISgXbt28PHxwaNHj7B69WpERESIgphjx45h9+7d6Nixo9ntzRMcHIy5c+fqfN3V1dXkMhnUEBEREREVM97e3nj06JEobdSoUUZf7+7ujgULFmD48OGws7NTpteoUQMtW7aEg4MDfvnlF9E10dHRkgQ1Tk5O8PHxyXc5qjj8jIiIiIjICEVp+Jksn5t2Dho0CKNHjxYFNKo+/vhjjbS0tLR81Zlnz5498PX1hZ2dHZydnVGtWjUMHDgQBw4cMLtMBjVERERERCSibf6Mr6+vJGUnJSUhISEBWVlZSE9PR3x8PFauXInWrVujb9++yMjIMLlMBjUF7NatWxrL1q1cuVJr3s8++0wj79WrV7Xm9ff3F+ULDg7Wmq9evXpal867fPmywbb7+PiIrjG121C9zqCgIK35bt++jSpVqmjknzlzpjJPaGioxusJCQmicqKiojTyyOVynD9/Xmu9YWFhGvkNSU9PR0REBEJCQlCrVi2UKVMGNjY2KFGiBDw9PREQEIChQ4di1apVSElJMfqz0kfb+4qKijI7rzHLLDo5OaFy5cro2bMnVq1ahaysLL1tPHjwIAYPHozatWvD1dUVNjY2KF26NKpVq4bmzZtj+PDhWLJkicbvwpTlHrUd2r5TCxYsMHu5SG3fiejoaFGe6OhovW2ys7NDmTJl0LhxY3z55Zc6v3/qtm/fjv79+6NatWpwdnaGra0tPDw8UKNGDQQFBWHUqFFYvnw5rl27ZlR5RESUfwq53CIH8LoXRPXIzMws1Peqfr8gl8vRu3dvi9e7YcMGDBw40OTrGNQUMF9fX1SsWFGUdujQIa15Dx8+bFRaenq6xo1Sy5YtNfJduHABMTExWuvSdVNc0G7evInAwEDcuHFDlD5//nyMHz8+3+ULgoAJEybku5ycnBxMmzYNFStWxNChQ7F69WrExsYiOTkZOTk5yMjIwMOHD3HixAlERERgwIAB8PT0xL59+/Jdd2F48eIFbt26hc2bN2PAgAF47733cOXKFY182dnZ+PjjjxEUFITIyEhcvXoVaWlpyMnJwZMnTxAfH4+jR4/if//7Hz7//HMMHTrU4m3X9d1es2YNcnJyLF5/VlYWkpOTcerUKfz8889o2LCh3u9yWloa2rZti27dumHdunWIj49Heno6srOz8fjxY1y7dg0HDx7EwoUL8cknn2DixIkWfw9ERGR5Xl5ecHV1VR4zZswotLZs2rQJS5cuFaWNHDkyXz01dnZ2CA4OxrJly3Dq1CnExcVh9+7d+PDDDzXy/vnnnyYPRWNQUwhatGghOtcW1Dx9+lRrr4y2oObo0aPIzc3VWwegP3ApqBs8fa5du4aWLVvi9u3byjSZTIZFixbhyy+/lKyePXv2aDxtN0VaWhqCgoIwefJkpKamGn1dVlaWZL01he369eto06YNnj59KkofM2YM1qxZU0it0qQvkE9KSsLu3bsLuEWvA+tZs2bhr7/+0vp6SEhIsQ1+iYjedAqZzCIHACQmJiI1NVV5SPEQ1hyRkZHo27evaPhZx44dER4ebnaZnp6euHfvHjZu3IhPPvkE/v7+qF69Otq3b4/169fj22+/1bhm3bp1JtXBoKYQqAcc8fHxSEpKEqUdOXJE61hGbUGNelDk7OyMunXritKys7Oxdu1anW0qrBu8PFeuXEFgYCDu3bunTJPL5Vi2bBk+//xzyeszt9cnNzcX3bt3x9GjR0XpNjY2CAkJwcaNGxETE4Pr16/j5MmTiIyMxJAhQ4r0ZlXaBAcH49atW7h16xYuX76M33//HeXKlRPlefjwoWhVlKdPn2Lx4sWiPIGBgfjrr78QExODa9eu4dixY1i2bBk+/vhjlC5dWqPevDrVD3WNGzfWmm/9+vWifIZ6IC3VQzl69GjcunUL8fHx2Lt3L1q3bm1U3ZcuXcK2bdtEaT169MDOnTtx+fJlxMXF4dChQ1i0aBGCg4Ph7OxskfYTEVHBc3FxER26JvBbUlhYGAYPHix60N2tWzds3rwZ1tbmL5psb2+v915I26iDCxcumFQHl3QuBNqGhh0+fFg0TlE1eKlYsSLu3r0LALhz5w7u3LmDSpUqac0LAE2bNoWVlZUobceOHXj8+LHy3M7ODoGBgfjnn3+UaVFRUejSpYuZ78p8Fy5cQLt27ZCcnKxMs7KyQmRkJEJCQixS58mTJ7F582b06NHDpOsiIiI0ukM9PDywY8cO+Pn5aeRv1KgRQkNDsXTpUqxbt05j6GFRpb7UYu3atVGpUiW0bdtWlG/v3r0ICwsDAJw4cUL0j6CDgwN2794Ne3t70TUBAQH45JNPkJ2djdOnT4teM3aelr29vcG82gL5bt26iYKG7du34+nTpyhVqpRR9RqrZMmSyvZVqVIFDRs2hLu7u2ijs//++0/jOvW/5cqVK2PTpk2i+V3Vq1dHixYt8Pnnn+Ply5dahwESEZFlWGKzzMLafFNVdnY2hg0bpvHAbfjw4Vi4cKHGfaXUXFxcULp0adG94LNnz0wqgz01haBWrVoa0ap6b4vq+bBhw0SbEKm+lpmZiVOnTomuNWboWdeuXTFixAhRWt4NXkE6ffo0WrduLfoSW1tbY+3atRYJaFRvsCdOnKgxbM+Qn376SSNt7dq1WgMaVdbW1ggJCUGTJk1Mqq8o0dZ21UBZ/R8fhUKB9PR0neXZ2NigadOmkrVPnXogb2tri2XLlsHDw0OZlpWVpbcHUyolS5bU6JnS1sui/hlmZWXpnShaokQJ+Pv7S9JGIiIyTLDAIgFSb+ZpqrS0NHTq1El0ryiTyTBr1iwsWbLE4gFNXhtU7wUBmPzAkUFNIZDJZGjevLkoTTVQefHiBc6dO6c8DwoKQrNmzZTnqk9zT548qXHTox7UPH78GDt37hSlhYSEoGPHjqLgqqBu8PLcvHkTbdu2Fc0zsbW1xZ9//ok+ffpYpE7VoWyxsbFYsWKF0dfGxMTgzp07orTGjRujTZs2krWvKNPWI+Dm5qb82dPTU/RaZmYmGjRogLCwMOzbt8/kJy75pR7Id+7cGR4eHhoTEgtikYwnT55o/GNdr149jXzqn+Hdu3fRoEEDzJw5E0eOHNEbJBIREZnq7t27aNGiBfbu3atMs7e31znPRRv1FWm1rUT6ySef6J3PPH36dI20hg0bGlV/HgY1hUQ98Lh8+bLypk91GI+dnR0aNWokGrKmGtSo9/DY2tqiUaNGorQ1a9YgOztbee7u7o6OHTvCxsZGI3goyFXQEhMTRZs42dnZ4a+//kL37t0tVuenn34qWrkjLCzM6CUTtY3tVB+OBbxejS4hIUHnUdy8fPkSBw4cQGhoqMZrqt/jpk2bokyZMqLXExMTMXXqVLRt2xZubm6oUqUKBg4ciL/++svgstD5oSuQBzQ3Ezt79qxRS5qb4tmzZ0hISMB///2HvXv3olevXqKhZ3Z2dlqXlO7YsaPGGOrY2FhMmDABLVq0gKurK2rXro3hw4djz549ojL1yczM1FgqlIiITJcLGXJlEh8wf/iZ+j2G+qJP6vckqnO4L1++jCZNmuDixYvKNHt7eyxfvhyNGjXSeg+jPgfcWGfOnEGrVq3g5+eH8PBwHDt2DNeuXcM///yD/v37Y/bs2aL8MplM632HPgxqCon6vBqFQqEMVlSDFn9/f9jZ2Ynyx8XFKZ/6qgc1/v7+GnMY1AOVvn37wsbGBkDB3OAZa9q0aejcubNF67C1tcW0adOU54mJiVi4cKFR1z569EgjrUKFChppGzduhK+vr86jOFixYoXyiYujoyNat26N2NhYUR4nJyeMHj1aeW5vb4///e9/eicS3rhxAytXrkRwcDCqVq0qmtMlJfVAvlSpUsrvVqNGjVCtWjVRfqmD+Z9//hm+vr6oWrUq2rVrJ3o6Va5cOfz777+oWrWqxnXlypXDnDlzdJarUChw9epV/O9//0OHDh1Qp04dnD171mB7ZsyYIVom1MvLy6z3RURERYv6PYbqgkvA66WZVV/v27ev8rWNGzdq5H/16hU++ugjnfcwqteb4+zZsxgzZgyaNWuGGjVqoH379lpXOfviiy9MHl7NoKaQNGjQAE5OTqI0bUFN3pNwPz8/lChRAsDrJWGPHDmC3NxcHD9+XFSGeg+QtiVtVeeqNG3aFJUrVxa9Xlh71vzwww9aV3eTWv/+/UWrw82YMcPsJ9fGbND5JnJ3d8f27ds1Fj7o0aMHoqOjjZo7dOfOHXTp0gUnTpyQvH3q3+E+ffrA1tZWef7RRx+JXi+oJc3r1q2LkydPap33lmfUqFHYtm0batWqZbC8K1euoE2bNlpXiFM1YcIE0TKhiYmJJrediIjyFgqQel7Nm38vYexqnVZWVvjuu+8wf/58k+tgUFNIrKysEBAQIEo7dOgQsrOzRTd5eTc/NjY2ohvFw4cP49y5cxpj7NV7gNRv7qpWrYrGjRuL0grrBk89IEhPT0fHjh1N3mzJnHpVJ/w/efJEo9tTG/WhVQA0nnAUBHk+JxSae72NjQ3q1q2LsLAwxMXFaR0zCwDNmjXD8ePHceXKFSxYsAC9evXS2TOQnZ2NH3/80az26GIokAc0eygLaknzmJgY+Pv7G1yxrGvXrrhy5QpOnz6NmTNnolu3bihbtqzWvKmpqQb3DrCzs9NYKpSIiKigREdHY+/evRg3bhxatWqFihUrwt7eHtbW1nB3d0eTJk0wbtw4xMbGYvr06WY9NGZQU4jUA5Bz587h8OHDePnyJYDXN5+qq0Op5j906JDG0DP1/NqWtI2PjxdN5pLJZKLhWEDB3eA1a9ZM42bzxYsX6Ny5s8WGJeXp1KmT6PNcsGABHj58qPea+vXra6Tt379fIy00NBSCIEAQBEyZMiX/jVWj7Yb01atXWvPmfZdUqa6kp4vqPjUJCQl49OgRXr58iQsXLmDKlCla95hRV6tWLYwePRp//vkn7ty5g1u3bmHy5MkaQdWxY8cMlmUKbT2NzZo1E33n33nnHaOuM9eUKVMgCAKSkpIwdepU0WsPHz5EcHCwUXOK/Pz8MG7cOGzduhVJSUm4evWq1n2bpP4MiYhIO0Ems8hhdnv+//2GsYfqcOiwsLB8XZ8nKirKYB4rKyu0adMGM2fOxP79+5GYmIiMjAxkZ2cjOTkZx48fx8yZM7UOzTYWg5pCpD4EJTs7G7NmzVKe16lTR3QDqnoTfv78eY2J0Or51Ze0NUVBDEGzsrJCVFQUBg8eLErPyMhAt27dNN6f1GbOnKn8+cWLFwbfc926dTV6HI4dO4YjR45Yonk6advr5saNG1rz3rx506jr1eXtU+Pj4wNvb2+UKVMmX5tuAa/3oJk6dapoPybg9WcvFUObzOpjiSXNy5Yti8mTJ+Prr78WpV+7dk20camxatasiUWLFmn0tkr5GRIRERVHDGoKUePGjUXj/AGIeijUg54mTZoo8+fm5mr0Eqjnz09gUlB71sjlckRERODTTz8VpWdmZqJHjx7YunWrxeoOCAgQrbSmq7dD1fjx4zXS+vXrh6tXr0rZNL3q1q0LR0dHUdrmzZtFE+OB1zf4mzdvFqU5OTmJ5hNJ6fz58xg6dKjGggLq1G/AdQ2rMkd+AnlLLmk+ZcoU0fLXADBr1iyNz2LHjh0YM2aM3jkvgiBo9MBJ+RkSEZFueZtvSn1Q/jGoKUT29vZ6V3ZQD1IcHBz0bvKoml/bkrbh4eHKIUXqR2xsrHJFNMC4G7ycnBy9SxcnJCQYteSsTCbDkiVLMHLkSFF6VlYWevfujY0bNxosw1zTp083aVOpYcOGaQwbvHv3Lvz8/DBixAjs2rULsbGxiIuLw759+ywyCd7GxgYDBgwQpd24cQNBQUHYsWMHrl69ih07diAoKEijpyY0NDTfPS66ZGZmIiIiArVq1ULDhg0xZcoU7NixAxcvXsT169cRHR2NoUOH4u+//xZd16pVK8naoB7IBwQE6PzO37p1S2P5cEv1ULq4uGDUqFGitOTkZCxatEiU9vz5c4SHh8Pb2xstW7bETz/9hH/++QdXrlxBXFwc9uzZg+DgYFy6dEl0nZSfIRERUXFkmbsbMlrLli1x9OhRra9pWyGpZcuWOsfPq+ZXX9JWJpOhf//+Gpv7qV+v2vsTFRWlEWiounfvnsElilNSUlCyZEm9efLa9+uvv8LGxka04kV2djb69eun/K/UatWqhQEDBiAyMtKo/NbW1tiyZQvat2+P06dPK9MzMjKwePFiLF68WPI2ajNlyhRs27ZNtFDBsWPH0KVLF53XeHl54fvvvy+I5uHcuXOiDWR1sbW1NXpzL0O0BfLdu3eHj4+Pzmt69uyJLVu2KM/zljR/9913JWmTqi+++ALh4eGi3pnw8HCMHDlSubJhHkEQcPjwYaNWAyxZsiRGjBgheXuJiEiTQiaHQiZtn4DU5b2t+CkWMl1Lu1apUkVrAKLeS6Arv/oTZz8/P70BDQCNG+LC2LNm3rx5GDdunCgtJycHISEhWLVqlUXqnDp1qsaGh/q4ubnhyJEjGD9+vMbNqD6Ojo4aPSzmKlu2LPbu3Yv33nvPqPx169bF3r174eHhIUn92pQoUcKkz7FkyZL4888/Ubt2bUnqVw/kAc3vtLqOHTtqLFxgqd4ad3d3DBs2TJT26NEjLF26VHnu4uJiUk9a+fLlsWvXLov+XomI6P9w+FnRxaCmkDVr1kzr8CddwY4x+bUtaWvo5g6A1o0vC2PPmpkzZ2LSpEmitNzcXISGhuL333+XvD4vLy+9PVLa2NraYsaMGUhMTMQvv/yCnj17onLlynB1dYWVlRWcnJxQqVIltGnTBl9++aVy9aoVK1ZI1u4aNWrg3Llz2LhxI/r3749q1arBxcUFVlZWcHFxQbVq1dC/f39s2rQJZ8+e1dhwUmp16tTBkydPsG3bNnz77bdo164d3nnnHTg7O8PKygoODg6oUKEC2rVrh9mzZ+P69evo1q2bZPWrf1crV65scK+X0qVLa0y6t+SS5t98843GPLo5c+YgIyMDwOtV+R49eoQNGzZg9OjRCAoKgre3NxwdHSGXy1GiRAl4e3ujc+fOWLx4MeLi4ozaE4iIiOhNJxMEQSjsRhARUcFJS0uDq6srxiMVdngz9qwJG9SgsJsgubBIw0M4iUi3TKRhJlyRmpqa7/258v7dPBA7GU7O9hK18LX056/QquYPkrTzbcaeGiIiIiIiKta4UAARERERkREUcjly5RIvFCBxeW8rBjVEhSA7O1vnhpm6lCtXTrS5KhERERG9xqCGqBDcu3cPNWvWNOmayMhIhIaGWqZBREREZJBCJoNCJu1qZVKX97ZifxcRERERERVr7KkhKgQ+Pj7gwoNERETFC3tqii4GNURERERERhDkcggST+yXury3lVFBzQ8//CBZhZMnT5asLCIiIiIiIqOCmrCwMMgk6hpjUENERERExRGHnxVdRgU1LVu2lCyoISIiIiIikpJRQU10dLSFm0FEREREVLSxp6bo4swkIiIiIiIq1rj6GRERERGRERSwQE8N2FMjBUmCmuTkZBw4cAC3b9/Gy5cvuRgAEREREREVmHwFNTk5ORg3bhwWL16MrKwsZbpqUJOSkoLKlSsjIyMDcXFx8PHxyU+VRERERESFQiGTQyGTdvaG1OW9rfIV1PTu3Rvbtm0DANSuXRvXrl1DTk6OKI+bmxv69++PJUuW4I8//sC3336bnyqJiEgiCrkAhUwo7GZI4tu/zhZ2EyQ3e0toYTdBUmHdowq7CUT5ppBJP7FfwdFnkjA7NFy/fj22bt0KDw8PnDlzBhcvXkSpUqW05u3duzcA4MCBA+ZWR0REREREpJXZPTWRkZGQyWSYM2cO6tevrzdvo0aNIJPJcPXqVXOrIyIiIiIqVAq5DLlyiXtqJC7vbWV2T8358+cBAMHBwQbzlihRAq6urnj06JG51REREREREWlldk9NamoqXF1d4eDgYFR+hUIBGTcXIiIiIqJiigsFFF1mf4pubm5ITU3Fq1evDOZ98OAB0tLSULZsWXOrIyIiIiIi0srsoKZBgwYAjJv8//vvvwMAAgICzK2OiIiIiKhQCTKZRQ7KP7ODmo8++giCIOD7779Henq6zny7d+/GtGnTIJPJMHDgQHOrIyIiIiIi0srsOTX9+/fHb7/9hsOHD6NJkyYYPny4cgPOf//9FwkJCdi+fTt27twJhUKBrl27on379pI1nIiIiIioICkggwISr34mcXlvK7ODGplMhi1btqBHjx44dOgQRo8erXytQ4cOyp8FQUDbtm2xZs2a/LWUiIiIiKgQKWQyC2y+yaBGCvlabsHNzQ379+/HihUr0KJFC9ja2kIQBAiCACsrKwQEBCAqKgq7d++Gk5OTVG0mIiIiIiJSMrunJo9cLkdISAhCQkKgUCjw9OlT5Obmwt3dHdbW+S6eiIiIiKhI4JLORZekUYdcLkfp0qWlLJKIiIiIiEgvSYOa3NxcPH36FABQqlQpWFlZSVk8EREREVGh4Zyaoivf/V0vXrxAeHg4/P39UaJECXh6esLT0xMlSpSAv78/wsPD9S75TERERERElB/56qm5cOECevTogTt37kAQBNFr2dnZOHv2LM6dO4eFCxfir7/+Qv369fPVWCIiIiKiwpIrkyFX4p4Vqct7W5kd1Dx48ABt27bF06dPYWtri169eqF169aoUKECAODevXs4cOAANm7ciNu3b6Ndu3a4ePEiypcvL1njiYiIiIiIzA5qfvjhBzx9+hTe3t7YtWsXatSooZFn8ODBmDRpEjp06IA7d+5g2rRpWLJkSb4aTERERERUGDinpugye07Nzp07IZPJsGzZMq0BTZ7q1atj2bJlEAQBO3bsMLmeW7duQSaTiY6VK1dqzfvZZ59p5L169arWvP7+/qJ8wcHBWvPVq1dPo0yZTIbLly8bbLuPj4/oGh8fH6PfNwCNOoOCgrTmu337NqpUqaKRf+bMmco8oaGhGq8nJCSIyomKitLII5fLcf78ea31hoWFaeQ3JD09HREREQgJCUGtWrVQpkwZ2NjYKOdjBQQEYOjQoVi1ahVSUlKM/qz00fa+ZDIZbG1t4eTkhPLly6NevXoIDg7GzJkzcefOHZPKFwQBO3fuxGeffYa6devCw8MDtra2cHNzQ7Vq1dCvXz/8/vvvyMjI0FmGt7e3qG2DBw/Wmm/Dhg0a72Px4sVa844dO1aUr0yZMqJhourfT5lMhu7du+tso3resLAwnXm3b9+O/v37o1q1anB2doatrS08PDxQo0YNBAUFYdSoUVi+fDmuXbumvCYhIUHr78mUIzQ0VKMtX375pda8Cxcu1Nn+POb+3ageDg4O8PT0RPPmzfHdd98hPj7eYL05OTlYu3YtevToAV9fXzg6OsLOzg6enp6oXbs22rVrh2+++QarVq3C7du3DZZHRETSUEBukYPyz+xP8eHDh3BwcEDbtm0N5m3bti1KlCiBx48fm1yPr68vKlasKEo7dOiQ1ryHDx82Ki09PV3jRr1ly5Ya+S5cuICYmBitdUVFRelqcoG6efMmAgMDcePGDVH6/PnzMX78+HyXLwgCJkyYkO9ycnJyMG3aNFSsWBFDhw7F6tWrERsbi+TkZOTk5CAjIwMPHz7EiRMnEBERgQEDBsDT0xP79u3Ld926ZGdn48WLF3jw4AFiYmLw119/YcKECahcuTJ69+6NR48eGSzj9OnTqFOnDjp37oylS5fi4sWLePz4MbKzs/Hs2TPEx8dj/fr1GDJkCHx8fLBixQqt5bRo0UJ0nt/vuLYymjdvbjDw3Lp1K06cOKE3jz5paWlo27YtunXrhnXr1iE+Ph7p6enIzs7G48ePce3aNRw8eBALFy7EJ598gokTJ5pdlzGys7Oxdu1ara8V1N/wq1ev8PDhQxw9ehQzZsxArVq19AZU9+7dg7+/Pz766CNs2bIFCQkJePnyJbKysvDw4UNcvXoVe/fuxbx58zBgwAD8+uuvBfI+iIiIijKzg5oyZcqYtGSzXC5HmTJlzKrLmBu+p0+fau2V0XbDd/ToUeTm5uqtA9B/07NmzRrk5OTofL0gXLt2DS1bthQ9qZXJZFi0aBG+/PJLyerZs2cPoqOjzb4+LS0NQUFBmDx5MlJTU42+LisrS7LeGlPk5uZi48aNqFevHs6cOaMz38aNG9G8eXOjeu0A4NGjRwgNDcU333yj8Zp6UH3jxg3cv39fI5+xQc2LFy9w7tw5UZq277g2+QmGQ0JCLBqImmrHjh06H6acPXvW6N+dlHJycjB69Git363c3Fx07doVFy5cKPB2ERGREWQyCBIf4PAzSZgd1LRp0wbp6ek4e/aswbxnzpxBeno62rRpY1Zd6jdj8fHxSEpKEqUdOXJEYwU2QPsNn3pQ5OzsjLp164rS9D3hBYCkpCTs3r3bYNst5cqVKwgMDMS9e/eUaXK5HMuWLcPnn38ueX3m3ujm5uaie/fuOHr0qCjdxsYGISEh2LhxI2JiYnD9+nWcPHkSkZGRGDJkCNzd3aVotk5z5szBrVu3EBsbi71792LSpEnw8PAQ5Xnw4AG6dOmidTja+fPnMWDAAGRlZYnS+/Tpgz179uDatWs4duwYxo4dC1tbW1GeefPmYenSpaI0bQGH+vf02bNnWm/C7927h1u3bonSjh07phF0GxvUHDx4ELt27TIqr6pLly5h27ZtorQePXpg586duHz5MuLi4nDo0CEsWrQIwcHBcHZ2FuWtWLEibt26pXFo+xsODg7Wmnfu3LmifIZ6YyzVW5P3/bp27Rq2b9+usfKjQqHQOox2586dGr3In3zyCfbu3YvY2FhcuXIF+/btw7x589CpUyfY29tbpP1ERETFjdlBzaRJk+Do6IihQ4fiyZMnOvM9ffoUw4YNg4uLi9lDTbQNDVO/0VE9Vx2udufOHY2bUvVrmzZtqtHrpP6E187ODu+//74oT2ENQbtw4QKCgoLw8OFDZZqVlRWioqIwZMgQi9R58uRJbN682eTrIiIicODAAVGah4cHjh07hpUrVyI4OBh16tRB1apV0ahRI4SGhiIiIgJJSUlYuXKlxtBDqZQuXRo+Pj6oUaMG2rRpg2nTpiEuLk5j3tLDhw+19qx8/fXXGnNkfvzxR2zYsAHvv/8+qlWrhoCAAMyePRs7d+7U+H5NmDBB1GtVs2ZNjZ5M9e/p0aNHoVAoAACOjo5wc3PTmVf93NHR0aQl1b/77jutDwn0Ua+zcuXK2LRpEzp27IjatWujevXqaNGiBT7//HNs3LgRSUlJGDdunDK/tbU1fHx8NA5t3wEnJyeteUuXLq3M8/jxY+zcuVN0Xbdu3UTnlupxzft+VatWDV26dNE67PC///7TSFP/DIOCgrBs2TK0adMGNWrUQK1atdC6dWt89dVX2LFjBx48eIABAwZI3n4iItIub6EAqQ9z5OTkIDo6GnPmzEGfPn3g6+tr1FxTbW7fvo1vv/0W9erVQ6lSpWBvbw8vLy/06NED69atM/meQJ/Lly9j1KhRqFWrFlxdXeHg4AAfHx/0799f4//bpjAqqMkLDFQPW1tbRERE4NatW6hZsyamTJmC6OhoxMfHIz4+HtHR0ZgyZQpq1qyJhIQELFu2TOOJtbFq1aql8eRe/Sm26vmwYcPg6uqq9bXMzEycOnVKdK0xQ8+6du2KESNGiNK2b9+Op0+fGvcmJHL69Gm0bt0aycnJyjRra2usXbsWISEhkten+iR44sSJGsP2DPnpp5800tauXQs/Pz+911lbWyMkJARNmjQxqb78cHNzw19//aXRY7Np0ybR5O4LFy5oDMerW7euzrlHbdq0waeffipKe/bsmcZ3rHnz5qJzfd/xgIAANGvWzKi8efmtrfUvdqj6u75w4QLWrVunN7+6Z8+eic6zsrKQmZmpM3/eBr2WsmbNGmRnZyvPPT098dtvv8HGxkaZVlA9rlWqVNFIU++pAjQ/wxcvXigDWW1KliyJOnXq5Lt9RERU/Ny9exetWrXCt99+iz///FNjIRtjLV26FDVq1MCcOXMQExODlJQUZGZm4u7du9iyZQv69++Ppk2bGjXX2JDJkyejXr16WLhwIWJjY5GWloZXr17h9u3bWLduHTp37oxu3brhxYsXJpdtVFDj6+ur9ejXrx/S0tLw5MkT/Pjjj8qniXlPv3/88UckJycjNTUVffv2ReXKlU1uIPB6noi+Gz71+QNBQUGiGz7Vp58nT57UuNFSD2q0PeENCQlBx44dRcFVVlaW3iFqUrt58ybatm0rmmdia2uLP//8E3369LFInapD2WJjY3VOdNcmJiZGo5escePGZg9DLAhubm4YNmyYKC1vdbM8//zzj8Z1AwcOhFyu+89J22pm6uWo90heuXJFFDSrfo9btmwpyq/6WlZWFk6ePCkqy5ihZx988IFynyng9T88qkGBIZ6enqLzu3fvokGDBpg5cyaOHDmC9PR0o8uSgnrQ2K9fP5QtWxYdOnTQm88S4uLiNNLq1aunkab+GZ4+fRoBAQFYsGABTp8+jVevXlmqiUREZAQFZBY5CktUVBQ+++wzg/9/OXHiBFq1aqV3JVdDwsLCMG3aNIMPyLdv345u3bqZ3DtkVFAjCIIkh74njoao35RdvnxZ+VTzxIkTyiEkdnZ2aNSokc4bPvUn2La2tmjUqJEoTf0Jr7u7Ozp27AgbGxuN4KEgh6AlJiYiLS1NeW5nZ4e//vpL7zK8+fXpp5/C19dXeR4WFqb36bsqbZOdta2Wl56ejoSEBJ1HQVMfggZANM/hypUrGq8b6nGoW7euRk+l+vwY9e+4IAjK725GRoZo/lqLFi1E3/Hr168rn6CcOnVK4x8nY4Iae3t70TLNN27cwLJlywxel6djx46ws7MTpcXGxmLChAlo0aIFXF1dUbt2bQwfPhx79uzJ178HhmhbuTCvJ/Pjjz8WpVuixzU5ORkJCQm4fv06/v77b43uf3d3dwwfPlzjug8++EAj7dSpU/jqq6/QqFEjODs7o0GDBvjqq6805qkREdHbp0yZMujYsSO+//57bNu2zaRN7h88eIDRo0eL0urUqYO9e/fi6tWryq078uSlmSMmJgbTp08XpbVq1QpHjx7FxYsXNeaD79+/X2P+sSFGBTXaJuSae5hL/Sm2QqFQ3vCpBi3+/v6ws7MT5Y+Li1MO11IPavz9/TUm26oHKn379lUOWVG/ISqsFZQAYNq0aejcubNF67C1tcW0adOU54mJiUbt7wFAazelak9Ano0bN+rsDVQNqAqKtjaqzq/StpqW+pA1ddbW1ihVqpQoTXUIIfD6yb2Li4soLe/7evLkSeWiBDY2NmjcuDEaNmwIR0dHZd68vwP173hefmMMGjRItO/UtGnTjO4CLleuHObMmaPzdYVCgatXr+J///sfOnTogDp16hi10Ig51P+Ga9eurZxT1K1bN9HnbIke17Fjx8LX1xfVq1dH165dcfHiReVr1apVw6FDh0RzovI0bNgQX3/9tc5yc3JycP78eSxYsADNmzdH8+bNNZZz1yYzMxNpaWmig4iITPd6Doxc4sO8nhpvb288evQIO3fuxA8//ICuXbuKhlgbsmzZMtH/D2QyGf766y+0adNGObWkX79+omuWLl1q1tCwn3/+WTSH1cXFBZs3b0bTpk3x3nvvYdGiRWjatKnomnnz5pnUW2NUUOPt7S3ZYa4GDRrAyclJlKYtqMl7Iu3n54cSJUoAeP3E+8iRI8jNzcXx48dFZag/wdb3hBd4vaiA+jC6wlow4IcfftC5R4mU+vfvL1odbsaMGWbfFBmzQWdh0/YHVBDttrKy0viD1vYd9/Pzg4ODA6ytrREQEKA3L/D6Rjnvb8GYNqg+SUlKSsKCBQuMfg+jRo3Ctm3bUKtWLYN5r1y5gjZt2uTrYYc22lYuVP0btre319hst6D+hlu3bo2TJ0/q/XzCw8Px+++/G/Xv5dGjR9GqVSuNuTjqZsyYAVdXV+Xh5eVlatOJiAhFa/hZfu9N/vzzT9F5gwYN8M4774jS1EcopaWlYc+ePSbVo1AosGnTJlFau3btRPPftdX133//6dwAXptis4WplZWV6AYOeP1EOjs7W7RZYF6QYmNjI5pkfvjwYZw7d05jXL96D5D6zU3VqlU1nnJ/9NFHovOC2rNG/cubnp6Ojh07aqwuZol6VSf8P3nyBLNnzzZ4nbZ9iVSXoC6qtLVR9b1oe1+GJs/l5ORoDHFSXakrj3qQff78eaSnp2sN3AFoDLPMzc3FsWPHRGVoWz1Qn549e4qGZM6ZM8ek4Vldu3bFlStXcPr0acycORPdunVD2bJlteZNTU1FeHi4Se0zRH3lQrlcrvE3W1g9rvv370ejRo3w4MEDvfkGDRqEmzdv4tChQwgLC0P79u219uwAr3tPIyIi9JaXt9pe3pGYmGj2eyAiouLvxYsXGvs71q5dWyPfu+++q5F2+vRpk+q6fv26xsNwS9RVbIIaQPPm7Ny5czh8+DBevnwJ4PXNi+qTbtX8hw4d0hiWo55f2xPe+Ph4jeXxVIdjAQW3glKzZs00Vjh78eIFOnfurHXyupQ6deok+jwXLFggWlJaG21LCO/fv18jLTQ0VDnvasqUKflvbD5pCxJV34u2P0RDf3QxMTEae9po++NV/47n5OTg0KFDoh5GXUFNTEwMDh06pPEPh7H706iaOXOm8ufU1FStq9gZ4ufnh3HjxmHr1q1ISkrC1atXte6hpB6E5Zf6gwmFQgEvLy/R37C2xSqk7K2JjIyEQqHA7du38dlnn4lei4+PR//+/Q2WIZfL0aJFC0yZMgW7d+/GkydPcObMGfTu3Vsjr6HP0M7ODi4uLqKDiIhMZ8klndWHCRs7h9kcd+7c0Zjbqm0ovbY0U0dYaMtvibokCWqysrJw6tQp/PXXX1i1ahVWrlyp88gP9Zuz7OxszJo1S3lep04dUVeW6g3f+fPnNVY0U8+vb/dxQwpi+EreXjTqK2llZGSgW7du+Vrb2xiqN7ovXrww+J7r1q2rMczl2LFjOHLkiCWaJ4mUlBSNyfEymQwdO3ZUnqvvVwQAq1at0jvuU9tnpa0cbXO8fv75Z2UPo0wmE63s17hxY+Xk/NzcXNHvSFt+Y7Vq1Qrt27dXni9atMjkMtTVrFkTixYt0uj5NGdsri7aVi40ltQ9rjKZDJUqVcLixYvRs2dP0WvR0dHYuHGjyeU1bNgQ69at05gIKuVnSEREhcPLy0s0VHjGjBkWq0t1r7w8Dg4OGmnahq8bGvJcWHXlK6jJzMzEmDFjULp0aQQEBKB3794IDQ3FoEGDtB7alrU1RePGjTVWkFLtoVAPepo0aaLMn5ubq9FLoJ4/P4FJQe1ZI5fLERERobHvSWZmJnr06IGtW7darO6AgADRSmvGLC87fvx4jbR+/fppdHkWBSkpKejZs6dGYBscHIyqVasqz+vVq6fRo3L+/HlRgK0qOjpaYwWPkiVLYuDAgRp581bvU6X6HX/33XdFw5Ds7e1FK6+p99ip5zfFjBkzlEMejfld79ixA2PGjNE7tEkQBGXPah5dQ9PMob5yoSks2eM6d+5cjX2CpkyZovGUbPny5Zg2bZrGIhKqcnNzNX4fUn6GRESkWy5kFjmA18OJVYcK69r/zlK0PZyVctNNS9dldlCTk5OD9u3bY/78+UhPT0eZMmUgCAJkMhkqVKgAOzs75ZAiR0dHVKpUKd+TU9Vv4NSpBykODg56N3lUza/tCW94eLjOVdxiY2NFK0wYs4JSTk6O3qWLExISjFrmViaTYcmSJRg5cqQoPSsrC7179zb5CbAppk+fDisrK6PzDxs2TCMAuHv3Lvz8/DBixAjs2rULsbGxiIuLw759+0Tzoywpb8nduLg47N+/H99//z2qV6+usalm2bJltc75mD9/vkaPyoQJE9CvXz/8+++/iI+Px4kTJzB+/Hh06NBBowfgp59+QsmSJbW2Td9wMW2v6ZszY87Qszz169fHhx9+aHT+58+fIzw8HN7e3mjZsiV++ukn/PPPP7hy5Qri4uKwZ88eBAcH49KlS6LrWrVqZXYb1ak/mOjdu7fe1RjV/32wVI+rr6+vxpCzq1evavytPn78GJMnT0a5cuXQsWNHhIeH48CBA4iNjcXVq1exbds2tG/fXuMBipSfIRERFQ71YcLq2yRISX2SPgCte9BoS9N1/1LYdenfYlyP5cuX49ChQ6hQoQK2bt2KBg0aQC6Xw8PDQzlO7/Dhw5g4cSLOnTuHH3/8UWOyrjlatmypc38GXTd8usabq+ZXf8Irk8nQv39/jc3w1K9X7f2JiorSCDRU3bt3z+ASxSkpKUb9AmUyGX799VfY2Nhg/vz5yvTs7Gz069dP+V+p1apVCwMGDEBkZKRR+a2trbFlyxa0b99eNO8kIyMDixcvxuLFiyVvozHGjh2LsWPH6s1Tvnx5bN26FZUqVdJ4rUGDBli5ciU+/vhj0VyZ9evXY/369XrL/eqrrzTmWahq2bKlxlrueXR9x3XNeclPUAMAP/74IzZt2mRS70fe/jrGrMxXsmRJjBgxIj9NVNK2cmGvXr3g4+Oj85pu3brhzJkzyvO8Hlf15belMH78eI1hij/++CN69+6tsQhITk4Odu/ebVTPUeXKlY2ao0NERPmnOgdGyjILWqVKlSCXy0UP07UteqRt/rSp221oy2+JuszuqVm3bh1kMhmmT5+OBg0aaBYslyMwMBAHDx5E8+bNMXjwYJw7d87c6pR03aRVqVJFawCi6ym2en71J7R+fn56AxoA6NKli+i8MPasmTdvHsaNGydKy8nJQUhICFatWmWROqdOnWrS0wM3NzccOXIE48ePN3ppYQBwdHTEgAEDzGlivlhZWeHDDz/EhQsX9Pb09e7dG4cPH9a6cIA2ZcqUQVRUFObNm6c3X9OmTXX2hmn7/jdr1syk/KZ45513MHToUKPyuri4aAyx0qd8+fLYtWuXwT1+jKX+N2xjYyOaF6SN+t+wJfasyVOzZk2NjXIvXbqEzZs3K89Llixp0hKd1atXx+7duy36NI+IiN48jo6OGtsLaNtcXH10BWB4w3F11apV01igxhJ1mR3U5N289+rVS5Sem5srOreyssK8efOQnZ2NuXPnmludkq4bOF03b8bk1/aEV/1mRxttG18Wxp41M2fOxKRJk0Rpubm5CA0Nxe+//y55fV5eXnp7pLSxtbXFjBkzkJiYiF9++QU9e/ZE5cqV4erqCisrKzg5OaFSpUpo06YNvvzyS+WKWStWrJC8/XmsrKxQokQJlCtXDvXq1UPPnj0xa9YsJCQkYP369VqXblbXqFEjXLp0CTt27MCnn36K9957D+7u7rC2toarqyuqVKmCDz/8EBEREbh9+7bWeTTqnJyctK4c5+vrq3VjUFPzm+r7778XbfKpS6dOnfDo0SNs2LABo0ePRlBQELy9veHo6Ai5XI4SJUrA29sbnTt3xuLFixEXFydadj0/tK1c2Lx5c61d3qrq16+vMenekn/D2sZHT5s2Tdl7M3z4cNy/fx8rV67E8OHD0axZM1SoUAElSpSAXC6Hk5MT3nnnHfTs2RMrV67ExYsXRfO9iIjIsgTILHIUBvXVNM+fP4///vtPlPbHH3+Izl1cXDQeGIaFhYlWGFUfISGXyzX2h/v33381FgFQ3zenSpUqWu9vdJEJZs7KsbOzg6Ojo2hst729PaysrLSuxOPq6goXFxfuj0BEVMjS0tLg6uqKb+XPYCd7M5Z3fuVkOE9xM3vFoMJugqTCukcVdhPoLZOJNMyEK1JTU/O9lH3ev5uzUyLh4GL8qBNjZKS9xLdug8xqZ0JCgui8efPmov32goODRZ0K9vb2ypFIDx48QPXq1fH8+XPl63Xq1MH8+fNRrlw5/PHHH5g6dapo2PTYsWM19ioMCwvD1KlTlefe3t4a7YqJiUHDhg1FnR9BQUGYPn06nJ2dsWTJEixZskR0zaJFi7RuBaGL2XNqPDw8NPbDcHd3R1JSEh49eiQaUiIIArKyssxeLpmIiIiIqLAJMjkUMmm3eRTyUZ6hOSebNm3Cpk2blOeBgYHKRZHKlSuHn3/+WbQ68cWLF7Xu5Qa8HkYdFhZmVjvr1q2LSZMmiYKf6OhondtOtGrVCsOHDzepDrM/xYoVKyI9PV3UdZS3maD65Nbo6GhkZmYaHApCRERERFRUKSCzyFFYBg0ahCVLlmis6KqucePGiI6ONmlutLqwsDBMmjTJ4Cq6Xbp0wfbt2yGXmxammN1T4+/vj1OnTuHYsWPo1KkTAKBHjx74999/MWbMGDg4OKBevXqIiYnB119/DZlMhtatW5tbHb3lsrOzcePGDZOuKVeuHANpIiIiIj2GDx+ODh06YPHixdizZw9u376NjIwMlClTBg0bNkTfvn3Rt29fkxay0WXatGno06cPli5div379+Pu3bvIycmBh4cHAgICMGDAAGVcYSqz59Ts378fbdu2xccff4yVK1cCeH3j2bBhQ1y+fFn0xgVBgJOTE06dOoUaNWqY1VB6uyUkJJi8hGBkZCRCQ0Mt0yCiYoxzaooHzqkhyh9LzKmZ/mwV7CWeU/Mq7SUmlgyRpJ1vM7OHn7Vq1Qq3bt3CjBkzlGk2NjbYt28f+vXrp9x8E3g9aSk6OpoBDRERERERSc7s4WcymQze3t4a6WXKlMGaNWuQk5ODx48fw8XFxajlYIn08fHxgZmdikRERESSsMQcmMKcU/MmMTuoMViwtTXKlSsH4PXws7wNderUqWOpKomIiIiI6C1ksaBG1dOnT1GvXj3I5XLk5OQURJVERERERJLKlcmQK8GEefUyKf+kXWjbAA4fIiIiIiIiqRVITw0RERERUXHHOTVFF4MaIiIiIiIjKCCHQuKBTlKX97bip0hERERERMUae2qIiIiIiIwgQAZB4uFiUpf3tmJPDRERERERFWvsqSEiIiIiMgIXCii6jA5qVq5caXYl6enpZl9LRESWIVfIIH9D/mcqz33ztgwI6x5V2E2QVNhfAwq7CZLrsvL3wm6CpKodtSrsJkgqUyEATwq7FVRQjA5qQkNDIePmQERERET0lhIs0FPDOTXSMGn4GTfPJCIiIiKiosbooObWrVuWbAcRERERUZHGOTVFl9FBjbe3tyXbQURERERUpOUCyJU4CMmVtLS3F5d0JiIiIiKiYo1LOhMRERERGYGbbxZd7KkhIiIiIqJijT01RERERERG4EIBRRd7aoiIiIiIqFhjTw0RERERkRFyBRlyBYlXP5O4vLcVe2qIiIiIiKhYY08NEREREZEROKem6GJQQ0RERERkBC7pXHRx+BkRERERERVr+Q5q7t69i6+//hq1a9eGk5MTrK3FnT8pKSn46aefMGPGDOTk5OS3OiIiIiKiQqGA3CIH5V++hp/9+++/6NOnD9LS0iAIAgBAJhN3obm5uWHLli04e/YsateujW7duuWnSiIiIiIiIhGzQ8PExET06tULqamp6Nq1KzZu3Ag3NzeteQcPHgxBELBjxw6zG0pEREREVJgEQQaFxIfAJZ0lYXZQEx4ejufPn6NPnz7YsmULevbsCVtbW61527dvDwA4ffq0udURERERERFpZXZQs2fPHshkMkybNs1gXl9fX9jZ2eHWrVvmVkdEheT8+fMYOXIk6tevDzc3N9jY2KBUqVKoUqUKmjRpgsGDB+Pnn3/GkSNHNK4NCgqCTCYTHXlCQ0M1XjP1SEhIyHcZQUFByjb5+PiIXvPx8dF4T9raXaFCBWRkZGj9/NQ/A9X6dElMTMTs2bPxwQcf4J133oGbmxusra3h7OwMb29vtG7dGmPGjMGOHTuQlZVlsDwiIpJGLmQWOSj/zJ5Tc+fOHTg4OKBq1apG5XdyckJqaqq51RFRIRg7dizCw8OVc+bypKSkICUlBTdu3MDJkycBAO7u7khOTi6MZha6+/fv4+eff8b48ePzVc6TJ0/w1VdfYd26dVoXVklPT0d6ejru3LmDAwcOIDw8HG5ubrh16xZcXV3zVTcREVFxZnZPjVwuh0KhMCpvTk4O0tLS4OLiYm51RFTA5s+fj7lz52oENKTdrFmzkJKSYvb1ly5dQp06dbBq1SqTVopMSUlBdna22fUSEZHxhP8/B0bqg/LP7J4ab29vxMbG4s6dO6hUqZLevIcOHUJ2drbRvTpEVLgUCgVmzJghSqtbty7GjRuHmjVrwtHRESkpKYiLi8ORI0ewa9cuncOvdJk7dy7CwsI00seMGYNNmzaJ0g4fPoyKFStq5K1YsaLOYa2+vr6i88aNG2P9+vUa+ezt7U1otW7Pnj3DzJkzMWvWLJOvvXv3Ljp06ID79++L0t3c3DB06FC0bdtW+f6Tk5Nx4cIF7Nu3D3v27MGrV68kaT8RERmmgAwKiYeLSV3e28rsoKZt27aIjY3F0qVL8dNPP+nMl52djYkTJ0Imk6Fjx47mVkdEBSguLg6PHz8WpW3duhXe3t6itEaNGmHAgAFQKBQ4evSoSXWULl0apUuX1kh3cnLSSKtYsaLW+S0AdKars7e3NzqvuX799VeMHj0a5cuXN+m6sWPHagQ0TZo0wdatW+Hh4aGRv0WLFhg1ahRSUlKwePFinYu0EBERvS3MHn721VdfwdbWFuHh4Vi+fLnWPOfOnUPbtm1x8uRJODs74/PPPze7oURUcJ49e6aR9vz5c5355XI5WrRoYcEWFV12dnbKnzMyMrT2Pulz48YNbNiwQZRWqlQpnQGNKjc3N0ycOJFDe4mICkiuILPIQflndlDj7e2NiIgI5ObmYtiwYShbtqxyPHnTpk1RoUIF+Pv74/Dhw7C2tsbKlSu1PpUloqLH09NTI61Vq1b49ttvsXPnTjx69KgQWlU0eXp6omfPnsrzyMhIXL9+3ejr//77b415S0OHDjUY0BAREdH/MTuoAYCPPvoIu3btwjvvvIPHjx8jKysLgiDgxIkTePDgAQRBQJUqVbB7925069ZNqjYTkYVVrlwZ7733nigtOTkZc+bMQefOnVG2bFl4eXnhww8/xKpVq/DixYtCamnRMH36dFhZWQF4vTDKpEmTjL72woULGmlt27bVSEtOTkZCQoLWIykpSW8dmZmZSEtLEx1ERGS6vDk1Uh+Uf2bPqcnTrl07XLt2DYcOHcLRo0dx//595ObmwtPTE82aNUOrVq2U/7MnouLjt99+Q9u2bXUGLHfv3sUff/yBP/74A19//TXmz5+Pjz/+uIBbWTTUqFEDgwYNQkREBABg48aNOHPmDPz8/Axeq63Xq0KFChppY8aMwYoVK7SWERgYiOjoaJ11zJgxA1OnTjXYFiIiouIq30ENAMhkMgQGBiIwMFCK4oioCGjSpAlOnjyJb775Bv/884/epZ2Tk5MREhICBwcHBAcHF2Ari46wsDCsWbMGGRkZEAQBEyZMwL///mtWWaqblEphwoQJ+Prrr5XnaWlp8PLykrQOIqK3gSWWYOaSztLI1/AzInqz1a5dG7t378bNmzexZMkSfPTRR6hSpYrO/FOmTCnA1hUtFSpUwMiRI5Xne/fuxd69ew1eV6ZMGY20e/fuSdo2Ozs7uLi4iA4iIqI3CYMaIjLIx8cHw4cPx+rVqxEfH48HDx5g/vz5cHBwEOW7cuXKWz1fY8KECShZsqTo3NDmpfXr19dI279/v0ZaVFQUBEGAIAjsFSciKiScU1N0mT38rHXr1iZfI5PJsG/fPnOrJKIiwtPTE19++SWSkpI0Npt88eLFW9sT4ObmhnHjxmHChAkAgDNnzhjc3LNz58746quvRMHPsmXL8M0336BUqVIWbS8REdGbwuygRt+kVFV5Y8MFQZB8nDgRWcbjx48xcuRIjBkzBv7+/jrzqS8iYGVlBXd3d0s3r0gbPXo0fv31V+Vmmq9evdKbv0qVKujVqxf+/PNPZdrjx4/Rq1cvbN68Ga6urhZtLxERGU8hyKCQeA6M1OW9rcwOagyNnU9NTcXJkydx/PhxuLu747PPPuMqaETFRG5urnJlsxo1aqBbt25o0qQJfH194ejoiMePH2Pr1q1YvHix6LpmzZq99bvbOzg4YPLkyRg+fLjR18ydOxeHDh3Cw4cPlWkHDhxAzZo18dlnnyEoKAienp549eoVbty4gTt37lii6UREZIDCAptlMqiRhsWCmjz79+9Hz549cfXqVWzcuNHc6oiokMTFxSEuLs5gPplMhokTJxZAi4q+IUOGIDw8HPHx8Ublr1SpEnbu3IlOnTqJApsHDx5g8uTJlmomEREVU1FRURg0aJDJ10VGRiI0NNSka0JDQ3VuKaDN8+fP4eTkZGLL8s/iCwW0bt0aP//8MzZv3qzcw4GIijYbGxuT/kGyt7fH0qVL8f7771uwVcWHtbU1pk+fbtI1DRo0QExMDHr27GnSUN3y5cujR48epjaRiIjMIAAQIJP4IClIsk+NIR9++CGGDh2KiIgIfPLJJwVRJRHlg7u7O548eYKDBw/i8OHDOHv2LG7cuIEHDx7gxYsXsLa2RsmSJVG9enW0bt0agwYNQqVKlQq72UVKr1694OfnhzNnzhh9TdmyZbFp0yZcv34d69atw+HDh3Ht2jWkpKQgMzMTTk5O8PDwQLVq1dCwYUO0adMGTZs25dBeIiIyioeHR2E3wWJkgqH1RiXi5uYGhUKB1NTUgqiOiIh0SEtLg6urK8YjFXZ4M1aqe+X05j3rtE9/s8bZh/01oLCbILkuK38v7CZIqtrRN+sBSaYiDYuflERqamq+V+XM+3ez58P9sHGRdmhVdlo6/irb2qR2pqenIzk5WW+eDz74ABcvXlSeV6lSBdeuXYNcbtpALfXhZ4cPH0bFihV15vf29i6UxcEKpKfm3r17SE1NLZTxdUREREREbxInJye999UnT54UBTQA8PXXX5sc0GhTsWJF+Pj45LscqVl8Tk1GRgY+//xzAMB7771n6eqIiIiIiCwi9/+vfib1IbU5c+aIzkuXLm3yAgG6fPDBByhZsiRsbW3h4eGBoKAgzJw5E0+fPpWkfHOZ3VPzww8/6H391atXSExMxJ49e/DkyRPIZDKMGDHC3OqIiIiIiMiAmzdvYvPmzaK0ESNGwMHBQZLyVXuAHj9+jIMHD+LgwYMIDw/HmjVrCm3RILODmrCwMKPGywmCALlcjkmTJqF///7mVkdEREREVKgUwutD6jKB1/N2VNnZ2cHOzs7k8ubNmweFQqE8d3BwKJCOheTkZHzwwQc4cuQIGjZsaPH61Jkd1LRs2VJvUGNtbQ03NzfUrVsXffr0QdWqVc2tioiIiIio0AmCDILEw8XyyvPy8hKlT5kyBWFhYSaV9fTpU0RGRorSBg4ciDJlyuSrjdWrV0fPnj3RokUL+Pr64vnz5zhz5gymTZuGBw8eKPO9evUKX3/9NQ4ePJiv+sxhdlATHR0tYTOIiIiIiN5eiYmJotXPzOmlWbJkCV6+fKk8l8vl+Prrr/PVrunTp6NChQoa6f7+/ujWrRvee+89pKSkKNMPHTqE+/fvo3z58vmq11QWXyiAiIiIiOhNoBBkFjkAwMXFRXSYGtRkZmZi4cKForQPPvgg36OltAU0qq8NGjRII/3ChQv5qtMcZgc1crkc1tbW+O+//6RsDxERERERmWj16tVISkoSpY0dO9bi9VauXFkj7dmzZxavV53Zw88cHBxgY2ODKlWqSNkeIiIiIqIiSQEZFJB2To0U5QmCgHnz5onSmjVrhoCAgHyXbcjNmzc10kqVKmXxetWZ3VNTsWJFZGdnS9kWIiIiIiIy0c6dO3H16lVR2pgxY/Rek5CQAJlMJjrU58zv2bMH3333HVJTU7WWcf/+fY2FCWQyGRo0aGD6m8gns4Oazp0749WrV4WyugERERERUUErqptvzp07V3RerVo1dOvWLd/lZmRkYMaMGahYsSKGDh2Kv/76C1euXMHZs2exdOlS+Pv7ixYJAIBOnTrBw8Mj33WbyuzhZxMmTMDatWvx2WefYd++fShXrpyU7SIiIiIiIgPOnj2r0cPy9ddfQy6Xbj2w9PR0REREICIiQm8+d3d3LFiwQLJ6TWF2UBMbG4vp06fjq6++Qq1atRASEoJmzZrBw8MDVlZWOq9r2bKluVUSERERERUaS+5TYy71XhoPDw8MHDgwX2XmcXR0hFwuF23mqUutWrWwbt26Qptvb3RQs3LlSjg4OKB3794AgKCgINHmm4sWLcKiRYv0liGTyZCTk2NmU4mIiIiICo+gkEGhkDioyUd5d+7cwcaNG0VpI0aMgL29fX6bBQBo164dEhMTsW3bNhw6dAiXL19GYmIinj9/Djs7O5QtWxYNGzZEz5490atXL9jY2EhSrzmMDmpCQ0NRrlw5ZVADvF5pwRSm5iciIiIiIu0qVapk9sJdPj4+Rt2bly9fHsOHD8fw4cPNqqegmDT8TPWNG9MNRUREVBDs06V9ckrSK/9A/2iO4uh+z48KuwmSCtuyobCbIKlMiZdeBl4vFCCTePiZFAsFUD5WPyMiIiIiIioKzF4ogIiIiIjobaIQZFBI3LMidXlvK/bUEBERERFRscaeGiIiIiIiIwiwwJLOFpj78zYyKah5+PCh3j1oDOGSzkREREREJDWTe2q4LDMRERERvY04p6boMimocXR0xDfffGOpthARERERFVkK4fUhdZmUfyYFNU5OTpgyZYql2kJERERERGQyLhRARERERGSEXIUMMoXEm29KXN7biks6ExERERFRscaeGiIiIiIiIwiCBZZ05kIBkmBPDRERERERFWvsqSEiIiIiMgKXdC66jA5qFAqFJdtBRERERERkFvbUEBEREREZQaGQSb5amYKrn0mCQQ0RERERkREUggwyDj8rkrhQABERERERFWsMaoiKuPPnz2PkyJGoX78+3NzcYGNjg1KlSqFKlSpo0qQJBg8ejJ9//hlHjhxRXuPj4wOZTGb24ePjo9GOLVu2aM3bq1cvg+8hKipK4zq5XI7z589rzR8WFqaR35g8MpkMtra2cHZ2RsWKFeHn54d+/frhl19+wePHjw22U/1z0/Y5hIaGatRZoUIFZGRkaC0zKChIlDcoKMhgOxITEzF79mx88MEHeOedd+Dm5gZra2s4OzvD29sbrVu3xpgxY7Bjxw5kZWUZLI+IiKQhKCxzUP5x+BlRETZ27FiEh4dDEARRekpKClJSUnDjxg2cPHkSAODu7o7k5GSLtSUqKkpr+vbt2/H06VOUKlXKpPIEQcCECROwe/duCVr3f7Kzs5GdnY309HTcu3cPZ8+exfr16/Htt99i4MCBCA8Ph5OTk6R13r9/Hz///DPGjx+fr3KePHmCr776CuvWrUNOTo7G6+np6UhPT8edO3dw4MABhIeHw83NDbdu3YKrq2u+6iYiIirO2FNDVETNnz8fc+fO1QhoCsPjx4+xc+dOra9lZWVh7dq1ZpW7Z88eREdH56NlxsvMzMRvv/0GPz8/3Lx5U/LyZ82ahZSUFLOvv3TpEurUqYNVq1ZpDWh0SUlJQXZ2ttn1EhGR8RTC/y3rLN1R2O/qzcCeGqIiSKFQYMaMGaK0unXrYty4cahZsyYcHR2RkpKCuLg4HDlyBLt27RINfzpy5IjWG+PmzZvj3r17yvMKFSqIhq3lsbYW/9OwZs0avTfOUVFRGDlypNHvT9X48eNx4sQJs65Vt27dOjRp0gQvXrzAnTt38O+//2L58uVIS0tT5rl27Ro6d+6MEydOSNq78ezZM8ycOROzZs0y+dq7d++iQ4cOuH//vijdzc0NQ4cORdu2bVGxYkUAQHJyMi5cuIB9+/Zhz549ePXqlSTtJyIiKs4Y1BAVQXFxcRpzQLZu3Qpvb29RWqNGjTBgwAAoFAocPXpUmZ53A6xOPVixtrbWOm9EnfrQs27dumHbtm3K87Nnz+Ly5ct49913DZal7uTJk9i8eTN69Ohh8rXqPD09le+ndu3a6NixI7766it06tQJly9fVuaLi4vDjz/+iDlz5uS7TlW//vorRo8ejfLly5t03dixYzUCmiZNmmDr1q3w8PDQyN+iRQuMGjUKKSkpWLx4MWxtbfPVbiIiMo5CIYOMSzoXSRx+RlQEPXv2TCPt+fPnOvPL5XK0aNHCIm25cOECYmJiRGk//fQT6tevL0rTNedGF3t7e+XPEydORG5urtlt1MfLywtbt27VuPFftGiR3s/UWHZ2dsqfMzIyEBYWZtL1N27cwIYNG0RppUqV0hnQqHJzc8PEiRPh4uJiUp1ERERvGgY1REWQp6enRlqrVq3w7bffYufOnXj06FGBtUU9WKlfvz5q166Njz/+WJS+Zs0ak+aCfP7558qfY2NjsWLFiny1U5/KlSujT58+orSMjAzs378/32V7enqiZ8+eyvPIyEhcv37d6Ov//vtvjXlTQ4cONRjQEBFRwcsVZBY5KP8Y1BAVQZUrV8Z7770nSktOTsacOXPQuXNnlC1bFl5eXvjwww+xatUqvHjxwiLtyM7O1lgEICQkBADQr18/WFlZKdOTkpJMWsns008/ha+vr/I8LCwMmZmZ+WyxbtqWUta1pLSppk+frvwscnJyMGnSJKOvvXDhgkZa27ZtNdKSk5ORkJCg9UhKStJbR2ZmJtLS0kQHERHRm4RBDVER9dtvv8HR0VHn63fv3sUff/yBAQMGwMfHB6tXr5a8DTt27BDN7bGyskK/fv0AAOXKlUPr1q1F+U0ZgmZra4tp06YpzxMTE7Fw4cL8NViPChUqaKQZs3eNMWrUqIFBgwYpzzdu3IgzZ84Yda22XjdtbR0zZgx8fX21Hn379tVbx4wZM+Dq6qo8vLy8jGobERGJCQqZRQ7KPwY1REVUkyZNcPLkSbRv317r5pOqkpOTERISgk2bNknaBvUgpV27dqKhcepD0PL2rDFW//79UbduXeX5jBkzLNaLoG1pbEOfqynCwsLg4OCgrGvChAlmlyVluwBgwoQJSE1NVR6JiYmSlk9E9LZQIG9ZZwmPwn5TbwgGNURFWO3atbF7927cvHkTS5YswUcffYQqVarozD9lyhTJ6ta2N03e0LM8PXv2RIkSJZTnpu5ZI5PJ8NNPPynPnzx5gtmzZ5vZYv1Ul7LOU6ZMGcnKr1ChgmhZ671792Lv3r0Gr9PWBm1tzQ87Ozu4uLiIDiIiojcJgxqiYsDHxwfDhw/H6tWrER8fjwcPHmD+/PnKnoE8V65ckaynQ9veNB999BFkMpnycHZ2xsuXL0V5TF0FrVOnTmjZsqXyfMGCBXj48KHZ7dblwIEDGmnqK7jl14QJE1CyZEnRuaHNU7W1QdsCBlFRURAEAYIgIDAwMN9tJSIi0ykUMuRKfHBJZ2kwqCEqhjw9PfHll1/iiy++0HhNqkUDTA1O8uTtWWOKmTNnKn9+8eKF2XXrcuPGDWzcuFGU5uDggFatWklaj5ubG8aNG6c8P3PmDE6dOqX3ms6dO2sMN1u2bJlJw/iIiIjedgxqiIqgx48f48MPP8Tp06f15lMPYKysrODu7p7v+rXtTWMKU4OSgIAAdO/eXXn+6tUrs+tWl5iYiO7duyMrK0uUPmLECDg7O0tWTx71zTcNvZcqVaqgV69eorTHjx+jV69eSE1Nlbx9RERkPsX/71mR+qD8szachYgKWm5uLv744w/88ccfqFGjBrp164YmTZrA19cXjo6OePz4MbZu3YrFixeLrmvWrJkku8urByVeXl44dOiQzvzh4eGilcvWrFmDmTNnwtra+H9ipk+fju3bt+drE86kpCQkJCTg5cuXuHPnDvbs2YPly5drbLJZo0YNk5ZdNoWDgwMmT56M4cOHG33N3LlzcejQIdGwuwMHDqBmzZr47LPPEBQUBE9PT7x69Qo3btzAnTt3LNF0IiKiYotBDVERFxcXh7i4OIP5ZDIZJk6cmO/6tO1N06VLF/j4+Oi8pk+fPqKgJm/Pmi5duhhdb61atTBgwABERkaa3OY8ectN61OjRg3s2LEDrq6uZtdjyJAhQxAeHo74+Hij8leqVAk7d+5Ep06dRIHNgwcPMHnyZEs1k4iITCQIMggSb5YpdXlvKw4/IyqCbGxs4OTkZHR+e3t7LF26FO+//36+61bfmwaAweCkadOmcHNzE6WZMy9m6tSpsLOzM/k6Y9jb2+Ozzz7DmTNnULlyZYvUkcfa2hrTp0836ZoGDRogJiYGPXv2NGlJ5/Lly6NHjx6mNpGIiOiNwp4aoiLI3d0dT548wcGDB3H48GGcPXsWN27cwIMHD/DixQtYW1ujZMmSqF69Olq3bo1BgwahUqVKktStHoyUKFFCY5NNdVZWVujQoQPWrVunTMvbs6ZUqVJG1+3l5YWRI0ciPDzcpDarsra2hp2dHUqWLAlPT09UrVoVTZs2Rf/+/SWZb2SsXr16wc/Pz+hNOAGgbNmy2LRpE65fv45169bh8OHDuHbtGlJSUpCZmQknJyd4eHigWrVqaNiwIdq0aYOmTZvCysrKgu+EiIjyKBSQfGMZBTeqkYRMMLTeKBERvVHS0tLg6uqK8UiFHbhnDRWM3xY9N5ypmLnv/ElhN0FSYQM2FHYTJJWJNMyEK1JTU/O9P1fev5sVjl6B3EnaRWYU6c9xr1ltSdr5NuPwMyIiIiIiKtY4/IyIiIiIyAi5ChkEiZdg5pLO0mBPDRERERERFWvsqSEiIiIiMoJCIQPYU1MksaeGiIiIiIiKNfbUEBEREREZQVC8PqQuk/KPPTVERERERFSsMaghIiIiIjJCriCzyGGOqKgoyGQyo4+///47X+89PT0d4eHhCAwMRNmyZWFra4uyZcsiMDAQ4eHhePHiRb7Kzy8OPyMiIiIiMoIgyCSf2C+YGdQUpOPHjyM4OBgPHjwQpT969AiPHj3CoUOHMG/ePGzatAlNmjQplDayp4aIiIiIiLS6fPky3n//fY2ARt39+/fRrl07XL58uYBaJsaeGiIiIiIiIygUgKyILhQwZ84c9OrVS+frZcuWNavcwYMHIz09XXnu5uaGhQsXokGDBjh79ixGjhyJZ8+eAXg9RG3IkCE4efKkWXXlB4MaIiIiIqJirnTp0vDx8ZG0zEOHDuH06dOitPDwcPTv3x8AUKNGDbx8+RLDhg1Tvn7q1CkcPnwYLVq0kLQthnD4GRERERGREQSFzCKHFH766SeULVsWNjY2cHNzQ/369fH111/j+vXrZpf5559/is5lMhmCg4NFab1794ZMJn4P6tcVBAY1RERERETFXHx8PB49eoScnBw8e/YMFy5cwPz581G7dm3Mnj3brDLVe2m8vLzg4uIiSitZsiQqVKig97qCwOFnRERERERGUChkkEm9+tn/Ly8tLU2UbmdnBzs7u3yXn5OTg3HjxsHJyQmff/65SdfeunVLdO7h4aE1n4eHB+7evavzuoLAoIaIiIgsbtgI58JuguTCsKGwmyCpMBT9pYVNkQZgZmE3wgReXl6i8ylTpiAsLEzvNWXKlEGPHj3Qpk0b1KhRA3K5HHFxcZg1axbOnDkjyvvdd9+hf//+KFmypNFtSk1NFZ07ODhozVeiRAnRed7CAQWJQQ0RERERkRFyLbj6WWJiomhol6Fems6dO6N///6wtbUVpb/77rvo0aMHWrRogePHjyvTU1NTsXPnTuUkf7PaKggmpRckzqkhIiIiIjKCQiGzyAEALi4uosNQUFOmTBmNgCaPlZUVJkyYoJF+4cIFk96vq6ur6DwjI0NrPvV0U3qDpMKghoiIiIjoDVO5cmWNNFOHhfn6+orOHz16pDXfw4cP9V5XEBjUEBEREREZQciVWeSwhJs3b2qklSpVyqQy/P39Red3797VmGeTkpKCe/fu6b2uIDCoISIiIiIqRlJTU9GnTx/cuHFD6+u5ubmYOVNzmYSGDRsqf05ISIBMJhMd0dHRovy9e/cWnQuCgI0bN4rS/vjjD4161K8rCFwogIiIiIjICJZcKMCkawQBf/75JzZt2oTOnTuje/fuqF+/PmxtbREbG4vZs2dr7BVTtmxZdOzY0aR6WrZsCX9/f1FZY8eOhb29Pfz8/HDmzBmMGzdOdE2jRo3QokUL099UPjGoISIiIiIqhhQKBbZv347t27frzSeXy7F48WI4OTmZXMfvv/+OJk2a4MWLFwBeDzf7+OOPteZ1cnLC8uXLTa5DChx+RkRERERkBEuufmYKKysrjb1hdHFzc8P69evRs2dPk+sBXi8R/e+//6JcuXJ685UrVw7//PMP3n33XbPqyS/21BARERERFSPOzs54+PAhdu7ciQMHDuDcuXO4desWnj17BrlcjlKlSuG9995D+/btERoaavICAeoCAgJw/fp1LF26FNu2bUNsbCyePXuGkiVLombNmujWrRuGDx9uVk+QVGRCUdgth4iICkxaWhpcXV0xHqmwg4vhC4jorRAGy6zCVVjSALji9aR61U0tzSrr//+7iT9vAiWcJWmf0svnQO/KkrTzbcaeGiIiIiIiYyhkkJkxXEwfQeLy3lacU0NERERERMUae2qIiIiIiIxglQvIJN4sU8gFciUt8e3EnhoiIiIiIirW2FNDRERERGQEuYU232RPTf6xp4aIiIiIiIo19tQQERERERlBztXPiiz21BARERERUbHGoIaI8i0oKAgymUx0mGvBggUaZclkMowZM0aULzk5GR4eHqI8dnZ2uHr1qs6yL126BFtbW9E1ZcuWRXJyMgAgNDRUo96EhARRGVFRURp55HI5zp8/r7XOsLAwkz+b9PR0REREICQkBLVq1UKZMmVgY2ODEiVKwNPTEwEBARg6dChWrVqFlJQUg+UREZE0ZLmWOSj/GNQQUZESFRWlNX3NmjXIyclRnpcuXRqLFy8W5cnKysKQIUOgUGjO4szNzcXgwYORnZ0tSl+8eDFKly6drzYLgoAJEybkqwwAyMnJwbRp01CxYkUMHToUq1evRmxsLJKTk5GTk4OMjAw8fPgQJ06cQEREBAYMGABPT0/s27cv33UTEZFhVgqZRQ7KPwY1RFRkXLhwATExMVpfS0pKwu7du0VpvXr1Qp8+fURpJ06cwIIFCzSunzdvHs6cOSNK+/DDDxEcHJy/Rv9/e/bsQXR0tNnXp6WlISgoCJMnT0ZqaqrR12VlZbG3hoiI3noMaoioyNDVS6Pv9UWLFsHDw0OU9v333+PGjRvK8/j4eEyZMkWUx+P/tXfncVGV7f/AP2dAQFFRUdzAPcsVF9xlJLVccjfNLbQsLfUxW9Ws1MytrEfLpdInwa9LlopbbrmCmpoLuOGSK1KoqICC7Of3B79zmsMMMDPMcmbm83695uWZs3HdgnKuue/7uv38sHjxYrNjNWTKlClmXZeTk4N+/frhyJEjiv0lSpTAq6++ig0bNiAmJgZXrlzB8ePHsXLlSowePRq+vr6WCJuIiIykybXOi4qPSQ0RqUJWVhbWrl2r2NenTx/F+23btuHhw4eKfRUrVsSSJUsU+9LS0vDGG29AFEWIoog333wTT58+VZxjiWFn+R0/fhwREREmX7dixQocOHBAsc/Pzw9Hjx7FqlWrMHDgQDRt2hTPPPMMWrdujVGjRmHFihVISEjAqlWr4O/vb6kmEBEROSQmNUSkCr/99hvu378vv/fw8MDy5csVvTCZmZl6iQ9geBjawYMH8cMPP+D777/HoUOHFMcsOewMALy8vOTtadOmISfHtFmfc+bM0du3du1aBAUFFXqdu7s7Xn31VbRt29akr0dERObR5ArQ5Fj4xTk1FsGkhohUIf/Qspdeegl+fn545ZVXCj1PYmgY2kcffYTJkycr9llj2Nm4cePk7djYWISHhxt9bUxMDG7fvq3Y16ZNG3Tp0sVi8RERETk7JjVEZHf379/Hjh07FPteffVVAMCIESMU+0+dOoXz58/r3cNQNbTHjx/j8ePHin3Lli2z+LCzsWPHonbt2vL7GTNmICMjw6hro6Oj9fZ17dpVb9+TJ09w8+bNAl+FycjIQEpKiuJFRESmE/7/4puWflHxMakhIrtbs2aNotRyhQoV8NJLLwEAWrdujfr16yvOL6i3ZuDAgXo9O7qGDBmCAQMGFD/gfDw8PDBr1iz5fVxcnNG9Qffu3dPbV716db19GzZsQO3atQt8FWbu3Lnw8fGRXwEBAUbFRkRE5CiY1BCR3eVPUgYPHgwPDw/5/fDhwxXH869Zo2vx4sV6w9AA6ww70zVs2DAEBgbK7+fOnWt2j0hxFi81ZOrUqUhOTpZfcXFxFr0/EZGr0OQAbhZ+abj4pkUwqSEiuzK0No009EySfwiaoTVrJBUrVsTHH3+st//jjz+2aglkQRAUE/4fPHiAL7/8ssjrKlWqpLcvPj7eorF5enqibNmyihcREZEzYVJDRHZlaChZhw4dIAiC/Kpbt65R10l8fHyM2mdpPXv2hFarld8vXLgQd+/eLfSa5s2b6+3bv3+/3r5Ro0bJJarzr7lDRES2ockVrPKi4mNSQ0R2Y2htGmMZWrNGDebNmydvp6amFrmgaGBgoN4cl6NHj+Lw4cPWCI+IiIpBk2OdFxUfkxoispv8a9OYoqA1a+ytXbt26Nevn/w+PT29yGumTJmit2/o0KG4ePGiJUMjIiJyWu72DoCInFNRZYarVaum14vRrl27QhOVd999F5s3b5bfh4WFYcKECcWI0jpmz56Nbdu2Gb0I55gxY7B+/XpERkbK++7cuYOgoCC89tpr6NWrF2rVqgVBEBAfH49jx45ZK3QiIiqENUows6SzZTCpISKrKKrM8IkTJ/TWpunXrx9q1apV4DUDBgxQJDXSmjWNGzcuTqgW17BhQ4SGhmLlypVGne/u7o7NmzejW7du+PPPP+X9T58+xdKlS/XW3yEiIiIlDj8jIrs4cuSIYm0aAOjVq1eh1/To0QMajfK/raLmrNjLzJkz4enpafT55cuXx+HDhzFlyhSUKlXK6Ou8vb0RGhpqTohERGQiS5dzll5UfExqiMguZsyYoXhfp04dNGzYsNBrKlasiDZt2ij2FbZmjT0FBASYPDTOw8MDc+fORVxcHL799lsMGDAAderUgY+PD9zc3FC6dGnUqFEDXbp0waRJk7BlyxYkJCQgPDzcSq0gIiJyDIIoiqK9gyAiIttJSUmBj48PpiAZnuCaNUSUZwaca25HCgAfAMnJycVen0v6f9N/fjw0Xpb9fzM3PQV3Jle3SJyujD01RERERETk0FgogIiIiIjICJocAZocC/doWfp+LopJDRERERGREYTcvJel70nFx+FnRERERETk0NhTQ0RERERkBDcrDD8TOPzMIthTQ0REREREDo09NURERERERhByAI2FF8sUufimRbCnhoiIiIiIHBp7aoiIiIiIjKDJFaDJtewcGNHC93NV7KkhIiIiIiKHxp4aIiIiIiIjCDl5L0vfk4qPSQ0RERERkRHccgW4WboEM4efWQSHnxERERERkUNjTw0RERERkRE0LOmsWkxqiIhcjCiKAIAMpNg5EiJSE2f7H0Fqj/R/Hjk3JjVERC7m8ePHAID/IsDOkRCRmsyzdwBW8vjxY/j4+FjkXprcvJcliRa+n6tiUkNE5GKqVauGuLg4lClTBoJgvQmqKSkpCAgIQFxcHMqWLWu1r2NLztYmtkf9nK1NtmyPKIp4/PgxqlWrZtWvQ+rApIaIyMVoNBr4+/vb7OuVLVvWKR7GdDlbm9ge9XO2NtmqPZbqoZEIOQIEC1c/s/T9XBWrnxEREREROZBHjx5h69at+OCDD6DValG9enWULFkSJUqUQKVKldCxY0dMnz4dd+7cKfbXGjVqFARBMPr15MkTC7TQdOypISIiIiIygltO3suizLhfzZo15fmR+SUmJiIxMRFHjhzBggULsHjxYrz22mvFDFL9mNQQEZFVeHp6Yvr06fD09LR3KBbjbG1ie9TP2drk6O1RS0nn3FzjqgukpaVh9OjRqFq1Krp37276F3Iggsg6d0REREREBUpJSYGPjw9avn0Pbp6WnQuUk5GCU8v8kJycbPQ8o9KlSyM1NRVdunTByy+/jFatWsHT0xOnT5/GjBkzcOPGDcX5rVu3xvHjx82Kb9SoUQgPD5ffR0VFFTovs2bNmlYtQlMQ9tQQERERERlByBWgsfDE/txc0+/Xo0cPfPbZZ2jSpIlif+PGjRESEoLGjRsrhqedOHECaWlpKFWqVLHj9ff3R61atYp9H0tjoQAiIiIiIgfy66+/6iU0kho1aiA4OFhvf0FzcEzVt29flCtXDh4eHvDz80NISAjmzZuHhw8fWuT+5mJSQ0RERERkBCHXOi9Lyz+7pFSpUvDz87PIvc+ePYvk5GRkZWXh/v37OHToEKZOnYpnn30We/bsscjXMAeTGiIiIiIiO0tJSVG8MjIyzLrP7du3sX//fsW+V155xerzXBITE9G3b1+cOnXKql+nIExqiIiIiIiMIJV0tvQLAAICAuDj4yO/5s6da3J8qampGDJkiCIh8vHxwaefflqsdj/77LOYOnUqduzYgdjYWJw4cQJLly5F1apVFeelp6fjvffeK9bXMhernxERERERFUKqftb29ftw97Bs9bPszBQc+6kS4uLiFNXPPD09TSp9nZiYiN69e+PYsWPyPi8vL2zfvh1dunQxO774+HhUr169wGNNmjTBo0eP9PZXq1bN7K9pDvbUEBEREREZQZMjWOUFAGXLllW8TElo/vrrL7Rr106R0JQuXRrbtm0rVkIDoMCERjpmaGHP6OjoYn1Nc7CkMxERmezAgQPyJ3P+/v5o3bo1AODWrVuoU6dOodeOHDkSP/30k9VjtJRbt27h66+/xtGjR5GamorKlSujc+fOGDduHCpWrGjv8MySkpKCFStWKNr0/PPPY9iwYShRooS9wyNSLWssvlnc+/3xxx/o06cPEhMT5X1VqlTB9u3b0bJly2JGVzRD/+cnJSVZ/evmx6SGiIhMkpSUhB49eiArKwsAsGrVKjmpAfSr7uS3YcMGLF682CLrJRTXlStX8Mknn8jvJ0+erHgIOH36NLp27Yrk5GS5XVeuXEFUVBS+/fZb7Ny5E61atbJ53IW5ffs2Fi9eLL8PDQ1F48aN5ffXr19H586dERcXp7ju//7v/zB79mzs2rWryMTUXh4/fiz/3Hl4eKB06dIAgLt372Lo0KGFXjtgwABMmDDB6jGayhnbRLazadMmDB8+HOnp6fK+hg0bYseOHahZs6ZNYrh+/brevgoVKtjka+vinBoiIjLJ6tWrERoaCgCoWLEiEhISoNHkjWa+desWateuXWCVHVEUIQgCtmzZgl69etks5oL89NNPeOONNyAIAsqUKYOEhAR4eXkByIu1adOmuHDhgny+1C7pV6e/vz9iY2Ph7e1t++ALIH1/BEGAl5cX/vnnH8U4/eDgYBw5cqTA6xs0aICYmBi4u6vrc8+srCxUrVpV7iH84osvMHXqVADG/dxVr14dt2/ftstK5wVxxjYBztmTK82p0Q5NtMqcmsh1FZGcnKz4t1qUhQsX4v3330du7r81oTt37oyNGzeiXLlyRV5/8+ZN1K5dW7HvwIEDCAkJkd/v3r0bhw4dwuTJk+Hj46N3j7///huNGzdWzKkRBAEJCQkWKyFtLM6pISIik0RGRgLI+8XVr18/OaHJTxRFvZfkwIEDNom1KLrjvl988UU5oQGA33//HRcuXIAgCPJLaoP0EBkfH4/Vq1fbNOai6Lapc+fOioeko0eP4siRI4o26b4A4NKlS1i/fr2twy7S77//jocPH0IURXh5eWHcuHEGzyvos9q///4bR48etWaIJnPGNkk9uYMGDcKgQYNw9epVxXFD/y/ovjZs2IC0tDQ7Re84Jk2ahHfffVeR0HTp0gXff/89kpKScPPmTb2Xbm+OsZ4+fYq5c+fC398fb775JjZt2oQLFy7g1KlT+P7779GqVSu9IgE9e/a0eUIDcPgZERGZ6Pz58/J2x44dCzxPEAR89tln8vtjx45h9+7dAIBz585ZL0AT6MbRuXNnxbGIiAh5W3qobNOmDdLS0hTX/fbbbxg7dqyVIzXe2bNn5e2uXbsqjm3YsEHeltpUrVo1pKenK1YD37x5M4YPH27lSE0jJcKCIKBbt24GPzWWjusmAbq9GPv27UOHDh2sG6gJnLFN27dvR2ZmJoC8nlxDQ+gK631KTU3F/v37VdGTa4juxH5L3tNUixYt0tu3b98+1K9fv8Br8vfCmOLJkydYsWIFVqxYUeh5vr6+WLhwoVlfo7iY1BARkUlu3Lghbzds2LDQc6dPny5v79+/X05q8n96ay8JCQnydpMmTRTHoqKi5IdJQRAwbtw4fPfddwDy5jJs3rwZoigqkjw1+Pvvv+XtwMBAxbFDhw4p2jRo0CCsXbsW2dnZ6NatGw4dOgRRFBETE2PrsIt0+vRpebuoak6dOnWSt2/fvi3/zNprUcCCOGObTOnJLciBAwdUm9S4Gm9vb2g0GkWPUEEaNmyIdevWoV69ejaITB+TGiIiMklKSoq8Xb58eYPnSA/NuqSyoKIoKnoF7Ek3Dt3hEqmpqbh06ZLiXN0J2W+99RY2b94MALh//751gzTRgwcP5O0qVarI2xkZGXo9ZJMnT4ZGo4GHhwfee+89HDp0CEDeJHW1uXbtmrzdrFkzg+dIP3e6wxs3b96MAQMGAMgr8qAmztgmZ+rJNUSTa4XqZ0XnC3bzwgsvIC4uDlu3bkVkZCTOnz+PuLg4PH78GJ6enqhcuTJatmyJAQMG4OWXX7Zr9UQmNUREZBKpUhOQN95aV8WKFbFy5UqD1+l+Mpv/OnvRLTuanZ0tb0dHRyM3N1dOzCpXroxnn31WPq5bVciccerWpDu+XfdT8nPnziE7O1tuU/ny5dG8eXP5uO6nq2qc06CbgJpSSltqlyiKuHfvnsXjKg5nbJMz9eSqmSXqfNWqVcuo+1SrVg1vvfUW3nrrrWJ/TWtiUkNERCYpV66cvB7C1atXFeWCvb29MXLkSIPX3bx5U96Wytbam6enp5yk3b59Gw0aNAAAHD58WD5HEAS0adNGcV1Ozr8f1ZYsWdIGkRrP3d1dntMQHx8vj7HXrXgmCIKiDLe0T6JbMEEtCkuES5cuXeDPnW5bnjx5YvG4isMZ2+RMPbmGaHKAAkbUFeueVHxMaoiIyCSVKlWSk5qtW7eif//+Rl23fft2edvX19cqsZmqatWq8qfCK1asQLdu3ZCRkYFVq1Yp5p60b99ecV18fDyAvERAbQtw+vn5yQnk+vXr8fzzz0MURaxbtw7Avw+U7dq1U1wnzS8SBKHAh1F7KlOmjNyzdufOHUXPma+vb4E9hLrzpkxZod0WnLFNztSTS46FJZ2JiMgk0uKU0oPymTNnirwmNjYWK1eulEsHFzR/wNaCgoLk7U2bNuGZZ55B/fr1ERsbqzgv/yTukydPytv513mwN+nvVhRFLF++HF26dEG7du1w4sQJxafj+au96U5ar1Wrli1CNYnuYn779u0z+rqDBw/K22pL1pyxTbrro+QfRib15EovXWrsyTVEyPn/vTUWfAnsqbEIJjVERGQSqQqTIAjIzMxEz549FQ9Z+R09ehTdu3fH06dP5U9jzS0ramlDhgyRt0VRxLVr1xAXFyfvEwQBDRs2RIsWLRTX7dq1S97OX2HM3l5++WUA/5YBPnjwIE6cOKE4p2bNmnplgPfu3Stv568EpwbSMEdRFPHjjz8qCiIUJCUlBcuWLZOTad2eEDVwxjZVqlRJ3t66davR16mxJ9cQqaSzpV9UfExqiIjIJEOGDJHX0xAEAXfv3kWXLl3Qvn17fPbZZ/jhhx/w448/Yvr06QgODkZwcDDi4uLkXoJSpUqpZg2U3r17o3PnzvKQLEMLbc6bN09xzZ07d3D48GG5PfmHptnbK6+8gsDAQMW8Bd3FNQVBUEzQBvIqpu3du1e1bQIgJ2GCIODRo0fo27dvoUlAUlISBgwYgH/++Uf+XgYHB9skVmM5Y5ucqSeXHAvn1BARkUm8vb3x3nvvYfr06YoE4Pjx4zh+/Lje+dLDtfTnxIkTFUNU7C0iIgIjRozAtm3bFPs9PT3x1Vdf4aWXXlLsX7RokfxAWaJECbzwwgs2i9UYbm5u+O2339CrVy9ER0frHX///ff1hv4sW7ZMrv6m0WhU1yYAGDp0KKZNmyYXafjjjz/w3HPPYcyYMejSpQuqV68OQRAQHx+P/fv3Y/ny5bh//77iZ8/QQpD25Ixt6tSpE1avXq3oyV23bl2BvbNHjx7F0KFD5Xk0giCopifXEBYKUC9BtERNOCIicik5OTno1KkTjh49Kn+6X9CvE93jrVu3xuHDh+Hurr7P1M6fP4+oqCgkJSWhSpUq6NGjh2KdF8mOHTvw+PFjAEDZsmXRo0cPW4dqlNzcXOzYsUPRpr59++oNpQOAn376SV5vp1y5chg7dqytwzVKaGio/MAs/bwVtjq9RBAEDBgwAL/++qtN4jSFs7UpNTUV/v7+chU0Kflq06YNunbtqpeoHT16VPHBh7e3N+Li4lT1wQeQN+zPx8cHPbo/RIkSZS1676ysFOzcVQHJyckoW9ay93YlTGqIiMgsjx8/Rt++fXHw4MECH8IkoihCq9Vi8+bNqntYIcfx8OFDNGnSRFH9y5hkukqVKoiJiVHM91ALZ2zTrFmz5J5cwHAJZ0n+ntwpU6Zg9uzZtgzXKFJS89IL1klqfvudSU1xcU4NERGZpUyZMti7dy+WLVuGgIAAiKJo8OXv748lS5Zg3759TpnQZGRk2DsEl1GhQgXs2rULVapUUfRqGHoBeQ/MlStXxm+//abKh3/AOdv08ccfo3379or2FPT/g26y06pVK8ycOdNeYZODY08NERFZxNmzZxEdHS2vcF6pUiU0b94cTZs2tXNk1pGeno5ly5ZhwYIF8ro1ziAiIgJffPEFTp06Ze9QChQfH48PPvgAv/zyS6G9Gi+//DIWLFiAgIAAG0doOmdrk7P15Eo9Nb27PEQJdwv31GSnYNs+9tQUF5MaIiIiEzx9+hRLlizB119/LSdw0kRvR7ZhwwZ88cUXOHfuHADHaNP169exfft2g8n0Sy+9hLp169o5QtM5U5tyc3OxfPlyzJ07F7dv3zZ4TkBAAKZMmYIxY8bAzc3NxhEaj0mN+jGpISIil7d582aEhYXhr7/+QpkyZdCsWTN88MEHigfI7OxsfPvtt5g/fz4SExMVQ2vUmACcPn0aq1atUrTprbfekstxSzZt2oTp06fj4sWLAP6d46DGNpHjcvSeXCmp6RvyyCpJzZaD5ZnUFBOTGiIicmlTp07Fl19+CUA5odnT0xN79+5F+/btERsbi4EDB+Ly5ct68wTUmAAsWbIEkyZNQm5urmJ/xYoVERkZiWeffRZ3797FsGHDcPDgQYdoE5E9SUlN/2DrJDURUUxqiotJDRERmcQSQ0QEQZDXRbGn6OhotGzZUq9UrvS+evXqOHbsGIKCgnDv3j29ic2iKMLT01NeY0MN/vrrLzRs2LDAv9/nnnsOx44dQ6tWrfDXX3/plRFmUkOkj0mN+qlvoQAiIlI1Z/osLDw83GC5Wen933//jd69e+Pu3bt6Fai8vb0xZswYvP/++zaPuzArV65Edna2wcnZoiji8uXLGDhwIK5evarXJkEQ0L9/f0ydOtXWYRepc+fOxb6HIAjYt2+fBaKxDGdsk7PT5ACawusemHVPKj4mNUREZLKiqhkVRk1J0R9//CFvi6IIjUaDihUrIjExEbm5uRAEATExMYoH//Lly+M///kPJk6ciAoVKtgr9AJFRkbK24b+rkVRxP79+xXv3d3dMWzYMEyZMgXPPfecTeI0lTFVtApT2Fop9uKMbXKmnlxyLFynhoiITFbQmhNFvdTm+vXr8kNhly5d8ODBAyQkJODu3bsICQmRY5bif++993Dz5k3MmDFDlQkNkDf8TGpTixYtcPbsWaSlpeHUqVN6k7JFUcSAAQNw5coVhIWFqTahMYeaf+7M5QhtMvf/BrX/XyHR5FjnRcXHnhoiIjLJ9OnTjT737t27CAsLU+0ClSkpKfKn3R9//LFcGczX1xfTpk3DwYMHAeR9cjxz5kx88skndozWOMnJyXKbPv/8czRu3BgA0Lx5c3zxxRfo06cPgLw2vf3221i8eLE9wzWZKQ+8auvFKIiztclZenLJsTCpISIikxiT1Dx58gTz58/H6tWrFQmNNLF+woQJ1gzRaJmZmfJ2zZo1Fcdq1aoF4N8hPmPHjrVlaGZLT0+Xt+vXr6849uyzzwL4t01TpkyxaWzFodVqjX5YFkURJ06cUG0yLXHGNgHmJyYOkbBZYU6NwJ4ai2BSQ0REFpOVlYXFixdj7ty5ePDggfzwLIoi3NzcEBoaipkzZ8Lf39/eoepxd3cv9H2lSpVsGY5FeHh4FPpejd+Hgki9ZkXZt28fpk6divT0dL2H5KCgICtEZj5nbJMz9eSSY2FSQ0RExSaKIsLDwzFjxgzExcUpJjCLooh+/fph9uzZaNCggZ0j1SfFuXDhQpQrV07en5SUpDjv888/L/Aen332mTVCM5vUpg0bNqBixYry/sTERMV5q1atKvAeoaGh1gnOSk6fPo2pU6di7969AJQlqp955hnMnj0bL7/8sj1DNJkjtsmZenIN0eQK0ORYtqtGk6v+HipHwHVqiIioWLZs2YJp06YhNjZW0TMD5A2vmTdvHtq2bWvnKA3TaDSKBSfzy79+TUHUtKaLM7apMH/99Rc++eQTbNiwQZ5kLrW/WrVq+OyzzzB69GiLVOWyFWdsE+DYPbnSOjXDApPg4WbZtWQyc1KwNqYc16kpJvbUEBGRWSIjIzFlyhQcP35cb0X6wMBAzJkzBz169LBzlMYr6jO+go6reR6AM7ZJkpCQgBkzZsjr8ugmcT4+Ppg8eTLeeecdeHl52TlS4zljmwDH7sklx8GkhoiITBITE4OpU6di9+7dAJQPxrVq1cKsWbMwbNgwe4VnFnMf4tU82MEZ2wTkVXebN28evvvuOzx9+lTxgOzl5YUJEyZg6tSpiqGEaueMbZIY6skF8n7O1N6Ta4gmx/LrobCks2UwqSEiIpO0aNECgPLht2TJkhg3bhzGjh0LDw8P3L59u8j71KhRw2oxmkLtD/HmcMY2ZWRkYNGiRZg/fz6SkpIUbdRoNHjttdcwY8YMVKtWzY5RmsYZ2yRxtp5cUj8mNUREZBLp01bdT1zT09PxzTff4JtvvjHqHmpZMfzAgQP2DsHiVq5cae8QrKJu3br4559/9OYEDRgwALNnz9YrX+0InLFNztiTq4s9NerFQgFERGQS3Yno5hIEwWEmopM6SD93ujw9PVGlShWj7yEIAq5du2bp0MzmjG2SihcU1pNrDLX05EqkQgGhDaxTKGBVLAsFFBeTGiIiMomhBzFTSD09TGrIFLo/d7rDmXTfF0VtP3fO3iYAink0xlJLT64uKakZVd86SU3YFSY1xcXhZ0REZDJ+Hkb2lP8h2ZiHZrX/zDpbm3RjU3Oc5DyY1BARkUmcac5GZGSkRe6j1Wotch9LMKZIgzHUNvwHcM6HY2dsE+C81fc4p0a9mNQQEZFJRo4cae8QLCYkJKTYa7KobahMrVq1nK5NgHP93EmcsU2A+hOT4tDkABoLN0+Ta9n7uSomNURE5PKc7SHM2doDOFcPoYRtIrIcJjVEROTynG2oTHELORCZy1l7nySaHAEasXg9oXr3zLXs/VwVkxoiIjKJs83ZcMaHeGdsExFRYZjUEBGRSZxpzsb06dPtHYLFOfsn5UT2JFhhTo3AOTUWwXVqiIjIJBpN8Wv/qG1tDVK/OnXqFPsealuo0hnb5Gw9uRJpnZqx1ZLhqbHsWjIZuSn44W8frlNTTOypISIik3HOxr9yc3MtkuhR4W7evAlBEIr181PcHkZLc8Y2OVNPriGsfqZe/F+YiIhMJoqi2S9ncfHiRXz00Ueq+0S5OJKSkrB06VK0bt3a3qEUSBAEs15q5mxtKs7/D872/wTZDntqiIjIJK48ZyMpKQlr165FWFgYTp06Ze9wLCI3Nxe7du1CWFgYtm3bhszMTHuHRA7OmXty2VOjXkxqiIjIJK62DoWhh37dBy81f2JemIsXLyIsLAxr1qxBQkICgH8fKNXYpho1aqgyruJwxjYB6k9MioNJjXoxqSEiIpsLCwvDqFGj7B1GoYx56He0hzdDPU35EzS1tunmzZv2DsHinLFNoaGhTpmokfoxqSEiIpN8+eWX+Oijj8y+fvXq1XjzzTdVmdQY89Cvu0+j0aBt27a2D9QExvY06c5lqFatml1iJccXFhZm7xCsSpNrhZ4adX6O4HCY1BARkUmmTJmCkiVL4j//+Y/J165fvx6vvfYacnPVM97ClOFloihCEAT069cPAwYMQI8ePeDr62uPsItk7PAyqU1BQUHo06cPevXqhWbNmtkj5EJ17twZQF7s69atg5+fn50jIkMePnyIChUq2DsMckFMaoiIyGSTJk2Ch4cHxo4da/Q1GzduRGhoKHJyclQzPOWjjz4y6qE//76FCxeqturZ0qVLDfY06VbMMtSmDRs2qLZNAHDw4EE53vT0dDtHYz2pqanYv38/zpw5g/v370MQBFSqVAnNmzfH888/D29vb3uHWKhGjRph2bJl6Nevn71DsQpNDqCx8H9f7KmxDCY1RERkMlEUMX78eHh6eho1jGzr1q0YNmyY6taeWLBggWIeSf6H/vr162P48OEYPnw46tWrZ7c4TTFhwoRC21S2bFkMHDgQI0aMQJcuXewWpzmkXiVn9PjxY3zxxRdYsmQJnj59avCckiVLYsKECZg2bRrKlClj4wiNc/fuXQwcOBBDhgzBd999x14bKxNFET///DN++eUXnDx5Evfv30epUqVQo0YNvPjiixg/fjxq1qxZ7K+TlZWFn376CZs2bcK5c+fw4MEDlClTBnXr1kXPnj0xfvx4VKxY0QItMp8gqnVGIBERqZJGo5Efmt3c3BAeHo5hw4YVeP7OnTvRv39/ZGZmytcJgoCcnBwbRm2Y1BaJKIrw8/PDK6+8ghEjRqBVq1aKc4G8JOHGjRuq7dUw1KYSJUqge/fuGDFiBPr06QNPT0/5XED9bQIcK1ZT3bhxA7169cKlS5eKLNQgCAIaNGiA7du3o1atWrYJ0AS6P39+fn5O02uTkpICHx8fvO+VDE+hrEXvnSGm4Ot0HyQnJ6NsWePvfe/ePfTp0wfHjx8v8BwvLy8sWrQIY8aMMTu+K1euoHfv3rhy5UqB55QrVw7h4eHo06eP2V+nuLj4JhERmaROnToAICcmo0aNwsaNGw2e+/vvv2PAgAF6Cc0nn3xiy5CLJD1I9u3bF+fPn8eiRYsUCY0j0m3TnTt3sGXLFgwaNEhOaEgdUlNT0bNnT8TGxsr/Pgp7iaKIixcvolevXgX26KiBKIpyr83w4cPx8OFDe4fkVNLS0hASElJoQgPkDdUcO3as2QUcEhISoNVqC01ogLwiKwMHDsTevXvN+jqWwKSGiIhMsm/fPgQEBADIS2yys7MxbNgwbNu2TXHewYMH0a9fP2RkZCgSmsmTJ2PmzJn2CL1A0ifLW7duRUBAAAYNGoQtW7aobricKaS/861bt6JZs2b48MMPERMTY++wKJ/3338fly9f1pvzZOgF/PuzGhsbW6wqhNbi7p43s0E3Cfv555/RqFEjbNmyxc7RFZ8mxzovU02fPh2xsbHye0EQMHPmTFy8eBF79+5FkyZNFOe/88478txBU/znP//B3bt35feenp5YsmQJYmNj5f8vJdnZ2Rg9erTd5rxx+BkREZns+vXr0Gq1+OeffwDkPYR5enoiIiIC3bt3x+HDh9GjRw+kpqYqEpp3330XCxYssHP0/zI0VAv498GxQoUK8lC09u3by8fUPPypqDY1atQIoaGhGDZsGPz9/eVjam4ToGxXmzZt4OXlZfI9BEHAvn37LB2a2RITExEQEIDMzEwAed8rDw8PDB48GCEhIahevTpEUUR8fDwOHDiADRs2ICsrSz63ZMmSuHPnDsqXL2/PZiicO3cOr7/+Ok6dOmWwMMXQoUPx3XffqSpmY0jDzz4sYZ3hZ19lGT/8LDU1FVWrVsXjx4/lfaGhoQgPD5ffX7lyBc8995xiOOPnn3+OTz/91Oi4bt68iTp16iju8dlnnyk+lPr999/x4osvKq5btWoVXn31VaO/jqUwqSEiIrNcvnwZISEhuHfvHoC8BxcvLy/MmjULn3/+OR4/fqxIaCZMmIBFixbZOWqlhQsXIjw8XO7BKKrqmdQWNScAkyZNwrp163D//n0AhhcKFQQBGo0Gubm5DtEmQDmXy5xiAWqayyX57rvv8M477ygSzi1btqB27doGz7927Rr69euHixcvyu359ttvMX78eFuGXaTc3Fx8/fXXmDFjBp4+far4GRQEAX5+fvj444/h4+NT4D1CQ0NtFa5RpKRmssY6Sc38XOOTmo0bN+Lll19W7Nu2bRt69eql2Ne8eXNER0fL75s0aYKzZ88aHdfXX3+NDz74QLHv3LlzaNy4sfxeFEVUrFhRMbywd+/e2Lp1q9Ffx1KY1BARkdkuXLiA559/Hg8ePADw70OL7p8A8NZbb2Hp0qX2DLVQMTExWLlyZaHJgG672rVrh379+qFXr1547rnn7BZ3QbKzs/Hbb78hLCwMO3bsQFZWVoEJm9SmQYMG4eWXX0a3bt1UWVlLN6kxldoKVEiGDRuGn3/+GQDg4+OD8+fPo3r16oVeExcXh8aNG+PJkycAgCFDhmDNmjVWj9UcV69exZtvvonIyMhCy4kboqbvE/BvUjMFyfCCZZOadKRgHoxPaqZMmYL58+cr9l2/fl0vGX711VexevVq+b2bmxtSUlJQqlQpo+IaMmQI1q9fL7/XaDTIyMiQhxhKgoODcfjwYfl9lSpV5F58W+KcGiIiMlujRo2wZ88e+RNXQwnN66+/ruqEBgACAwOxcOFCxMfHIyIiAn379oW7u7ti4jbwb/v++OMPTJ48GY0aNUL9+vXtHL0+d3d39O3bFxEREYiPj8c333yDpk2bynMz8rcJAH799Ve88sorqFSpEl544QV7hl+koibT53+p1dWrVwHktWf06NFFJjQAEBAQgNdff13+9yXdQ42eeeYZHDx4EEuWLEHp0qUBKOfaFDZ3SK0ykIJ0C78ykAIgL3HSfWVkZBiM4caNG3r7DC1Gm39fTk4Obt++bXRb83+dChUq6CU0hr5OQkKCXebVcJ0aIiIqlmbNmmHPnj3o2rWr3pCzV199FStWrLB3iEaTkoG+ffsiMTERq1ev1huelv8T52vXrtktXmNUrFgRkyZNwqRJk4zqkcrMzMT+/fvtFq8x1P7gayzdidumJJLdu3eXh3KaM/nb1qRy3LoKSjbV+r318PBAlSpV8N+EgKJPNkPp0qUVk+6BvGIAM2bM0Ds3OTlZb1/JkiX19hnqkUlKSjI6pvxfx9DXKOzrVKlSxeivZQlMaoiIyCSdO3c2uN/HxwcpKSmKHoAbN24YPF9tE7YNMSYZUOsDWEGkHqkFCxYUODxNzW1ylPk/xtJ9aDSlPdK5oiia9JBqazdu3MAbb7yBgwcPAlAunqrmnzNDvLy8cOPGDbmog6UZmitmSvl1Q3+flv47Luh+avleMqkhIiKTHDx4sNAhPdIvOFEUFeOsdY+rZUiQlHAJgoB169YZHMIBFJ4MqM3rr78uby9YsMDgiu5F9UiRbegOL/L29jb6Ot1PxgsaomRvCxcuxKeffoq0tDTFkEdpgduiCgWokZeXl1lV9yzN0N/b06dP5SF+uvvyK1eunNlfp6B1kYr7dSyFSQ0REZmlqE/nDB1XSzIj0U3QjBkDXlAyoCZhYWFym2bMmGEwqdFVUI8UWZ9uD9mxY8dw8+ZNo67THXKmtrWULl26hNGjR+PYsWN6RQFEUcQrr7yCxYsXw9fX155hOjRD1fHu3bunl9Tori8D5BUKMKVHsHbt2jhx4oT8/tGjR8jOztabV5P/61SpUsUuyR+TGiIiMovaEhRzmdtzpJsMqI25bdLtkSLbEUURQ4cONekatQ5/bNasGbKysvSGmvn5+WHp0qUYMGCAnSN0fK1atdLbd+HCBdSpU0ex79y5c4r3DRs2NLrymfR1dKuf5ebmIjY2VrGwpyiKuHDhQpHx2QKrnxERkckKq1xU1IvUz1CFIzVwlkTaEGf5dyTNOdFNaAYPHowLFy4wobGQ7t2765Vd/+WXXxTvL126pLcmzaBBgxTv81cJDAsLUxwfOHCg3r+5/F9nz549evO68n8dW2FSQ0REJsnNzS32S21rUJBjUPsDvblMLVHtCMmdKIqoVKkSfv31V/z8888mDTfLzc21YmSOz9vbG2PHjlXsW7NmDT7//HPExsZi3759eolF2bJl8eabb5r0dWrVqoWBAwcq9n311VdYsmQJLl++jK1bt+KNN95QHK9Ro4bdkhouvklERC5LKjXrTBW1nLFNAHDr1i0Aee3y9/c3WCbY0ViiDWpbUFRq0+DBg7FkyRKTkpmLFy8iLCwMa9euxZ07d6wVolNIS0tDUFAQYmNjjTp/5cqVGDVqlGJf/uTY0DkJCQlo1qyZ3rwZQ9zd3bFz50507drVqJgsTZ39y0RE5JRSUlLkbWNWzrYF6Rf7kCFDzJrcqsby1FKbPvjgA73Jw8Ze/7///c/SYRWLNF9AEARcv37dKZK16dOn2zsEi6tUqRKWLVtm9FCzpKQkrF27FmFhYTh16pSVo3MepUqVwoEDB9CnTx/FZP78vLy8sHDhQr1kxVhVqlRBZGQkevfujStXrhR4no+PD8LDw+2W0ADsqSEiIhu5deuW4sFUDVWbNBqNYrFQU0nXqe2TcmdrE+C8PVDO5sGDB0X2zuTm5mLXrl0ICwvDtm3bkJmZqRhWqMafP7USRRE///wzfv75Z5w6dQr3799HyZIlUbNmTXTr1g3jx49HzZo1DV5rTE+NJDMzEytXrsTGjRtx9uxZPHz4EGXKlEHdunXRs2dPTJgwARUrVrR080zCpIaIiGzi1q1bcilStTy06CYAptJNHNTQFklx2iRRW5uAf9sFgEmNg5KGl61Zs0YuS22o7LMaf/5I/Tj8jIiICM5ZWcvcnhq1UtPCrWQcQ8PL8vfK6O7TaDRo27at7QMlh8ekhoiICOp+mDeXM7aJ1M+Y4WUSKVHt168fBgwYgB49enBhTjILkxoiInJp0kOVMw1pktoUGRkJf39/e4djcfHx8WZfq6bvsZubW7HvoZb5aZKPPvqo0OFlBe1buHChqr435HiY1BARETmpgIAAp3pQlOYKdezY0ezr1ZQAOGNP2oIFCxRzuvIPL6tfvz6GDx+O4cOHo169enaLk5wPkxoiIiJyKM6UDBRnjpCa/x50kxk/Pz+88sorGDFiBFq1amXnyMhZMakhIiIih+JMBRDMjUvtBROkIZB9+/bF8uXL7V7ul5wfkxoiIjKJtNaMqdQ07EeX2h8OzeGMbdKl1gTFVKYsvnn37l2EhYUhIyPDihFZjvQzuHXrVuzatQu9evXCiBEj8NJLL8HdnY+fZHn8qSIiIpPcvHmz2Gu7qIna4rEEZy59LAgCBgwYgNKlS9s7lGIzJql58uQJ5s+fj9WrVysSGlEU4enpiQkTJlgzxGITRREZGRnYtGkTNm3ahAoVKshD0YgsiYtvEhGRSXQXQTQHF9ezrlu3bsnb/v7+FqmwpQYajQYAnK5SXUGysrKwePFizJ07Fw8ePJD/3YiiCDc3N4SGhmLmzJmqq263cOFChIeHIyYmBkDRVc+csfog2QeTGiIiMgmTGrIHV0lqRFFEeHg4ZsyYgbi4OEUyAwD9+vXD7Nmz0aBBAztHWriYmBisXLkS69atw/379wHoV0KT2iUIAtq1a4d+/fqhV69eeO655+wWNzkuJjVERGSSWrVqWWRo040bNywQjfWdOHECX3zxBY4ePYrU1FRUrlwZnTt3xocffqj6B8uC3Lp1C19//bVem8aNG6faCd2ukNRs2bIF06ZNQ2xsrF4yo9VqMW/ePLRt29bOUZomOzsbv/32G8LCwrBjxw5kZWXp/f+Rf7hk3bp1ceXKFVuHSg6OSQ0REbms6OhojBkzRn7/7bffKh4af//9d/Tu3RtZWVl6K6J7eHhgw4YNeOmll2wac1GuXLmCTz75RH4/efJktGzZUn5/+vRpdO3aFcnJyXrDgcqXL4+dO3eqsuyubg+hsyU1kZGRmDJlCo4fP67XixEYGIg5c+agR48edo6y+BITE7F69WqjhqexJ5dMxaSGiIhc1vfff49x48YBACpVqoS///5bnoOSlZWFZ555Brdv3wZg+OGrfPnyuHLlCnx9fW0cecF++uknvPHGGxAEAWXKlEFCQgK8vLwA5MXdtGlTXLhwQT4//5Agf39/xMbGwtvb2/bBF8IZe2piYmIwdepU7N69G4CyaEXt2rUxa9YsDBs2zF7hWVVBw9M4PJXMxepnRERkUxcvXkRYWBi+/PJLe4ei+LS4e/fuikn127dvx+3btwv8JFkURSQlJSEsLAzvv/++bQMvRHR0tLz94osvygkNkNfzdOHCBYMTtaU2xcfHY/Xq1Rg7dqwtwy7SyJEj5W1nqHwGAC1atACgTGZKliyJcePGYezYsfDw8JCT6sI4YoIXGBiIhQsXYsGCBXrD04jMwaSGiIisLikpCWvXrkVYWBhOnToFAKpIas6fPy9vd+rUSXFsy5Yt8rYoitBoNBg8eDBSU1Oxfft2OTH4/fffVZXUnDt3Tt7u3Lmz4lhERIS8LT1It2nTBmlpaYrrfvvtN9UlNStXrrR3CBanm1BK79PT0/HNN9/gm2++MeoegiCodg0oY7i7u6Nv377o2bMnYmJicOTIEcycOdPeYZEDYlJDRERWkZubi127diEsLAzbtm1DZmYmAHWtoXLv3j15u1GjRopjR44cUQyH+fTTT+V1Rd58803873//A5DX86QmCQkJ8naTJk0Ux6KiohRtGjduHL777jsAwIABA7B582aIoqhI9sj6dHtqXHVWwN9//402bdrYOwxyYBp7B0BERM7l4sWL+OijjxAQEIDevXtj48aNyMjIUOXD2sOHD+Vt3XkxycnJuHbtmuLc1157Td7WXThQ9x5qoBuPn5+fvJ2amopLly4pztVduPGtt96St6U5Dmri5uZW7JdaV7KXemtMfTkbURRV+f8EOQZ1/usmIiKHYmh4Wf5qYfn3qUFycrK8nZ6eLm9LbZAEBAQo5i1Uq1ZN3pZ6oNQiKSlJ3tYdlhQdHY3c3Fz5e1G5cmU8++yz8vGaNWvK27p/F2qhtp8dS3HWdhHZGpMaIiIyi6HhZYYSGSDvwa1EiRIIDg5Gr169VFMGuVSpUkhJSQEAXLt2DY0bNwaQN0xLIgiC3rAY3Yf+UqVK2SBS43l6esqTrW/fvi2vpXP48GH5HENt0q02VbJkSRtEarriLvqqNs44T4jIXpjUEBGRSaTqZWvWrJHnb+Rf70Tapzt/49y5c6hfv75dYi6Iv7+/PCdm0aJF6NmzJ1JSUvDTTz8pYu/YsaPiuri4OAB57dUd4qUGVatWxdWrVwEAK1asQLdu3ZCRkYFVq1Yp2tS+fXvFdfHx8QDy2qTWBTgBdSYn5tKt6EZExcOkhoiITNK4cWPFSueA/vCyOnXqYPjw4Zg1a5Z8jm5pYbVo06aNnNQcOnQI5cuXR25uLtLT0xUJ2gsvvKC47sSJE/J2nTp1bBOskYKCguSkZtOmTXjmmWeQmZmJuLg4RZu6dOmiuO7kyZPydu3atW0TrAm0Wq1TziMprqSkJJQrV87eYRDZHZMaIiIyi24iI4oifH19MXjwYIwYMQLt2rUDAEVSo0YjR46UhwCJooi0tDT5mNSj0bZtWzz33HOK67Zv3y5vN2/e3DbBGmnIkCFYt24dgLw25C94IAgCGjZsKK+RItm1a5e8HRgYaP1ATXTw4EF7h6AaukM/t2/frvi5JXJVTGqIiMgs0kN/t27dMH78eHTv3l211aUKotVqMWzYMKxdu1avF0AURbi7u2PBggWK/ZcuXcLp06fl8/MPTbO33r17o3Pnzti/f7/BNgmCgHnz5in237lzB4cPH5bPzz80jdQh/9BPNZVHJ7I3x/rtQ0REqiENQTt06BDKli0LQRDQvXt3uLm52Ts0k4SFhcHX1xfLli1TVAurVKkSfvjhB7nXSSItiiiKIry8vPQWuFSDiIgIjBgxAtu2bVPs9/T0xFdffaVXqGHRokXy0MESJUroDbdzNE+ePEFqaip8fX0dLtHOr6jKgmpTnH//+Ye1EplCEPnTQ0REJtBoNAYLAgB5a70MGTIEoaGhCAoKgkaTtxyaIAi4ceOGoiyy2jx69AjHjh1DUlISqlSpgvbt28PT01PvvOjoaGRkZADIq3yWf4FLNTl//jyioqLkNvXo0QNVqlTRO2/Hjh14/PgxAKBs2bLo0aOHrUO1iIiICEyfPh0XLlwAkPeA3bFjR0ybNk1vDpGaGVtZUPq3p1u5zt6kf/Pm0C1koaY2kWNgUkNERCZp1qwZzp49C8Dw+jPSvmeeeQZXrlyR96k9qSF1O3LkCPr37w8g7+dp06ZN6NChg3x8zZo1CA0NBaDfk6HRaPDDDz9g9OjRtgvYDIVVFgT0/72VL18e3bt3x5o1a2wfbAHyf+hhKiY1ZC4mNUREZLKYmBisXLkS69atk1efLyjBkR5SvvnmGwwdOlR1JZDJMXz77beYNGkSgLzFUG/duiUfS0tLQ61atZCYmGjwgVoURZQsWRKXL1+Gv7+/rUI2irEL10r7BUHA+PHjMXjwYLRv375YPSPWICU1xXm8ZFJD5mBSQ0REZsvOzsZvv/2GsLAw7NixA1lZWYV+SisIAoKCgtC7d29MmzbNhpEWLikpCd9++y327t2L+/fvo3z58tBqtZg4cSKqVatm7/AIwOjRo7Fy5UoIgoA333wT33//vXxs9erVCA0N1UsAAGViPWPGDHz66ac2j70ggwcPLnJ4GZA3zFGqcKb2Xs+QkBCLFC84cOCABaIhV8KkhoiILCIxMRGrV69GeHg4YmJiAOh/yixR0yexFy9eRNeuXXH37l0AyjlCpUuXxvbt2xEcHGzPEE3y+eefW+Q+n332mUXuYynt2rXD8ePHIQgCwsPDMWLECPnYkCFD8Msvv8gJTMmSJTFx4kSkpqZi6dKl8s+eVqtV1cNy/l4W3R4ONzc3dO7cGSNGjED//v1RtmxZ+Rw1JzVE9sKkhoiILK6o4WlqSWqysrLQpEkTee4PoF+BqVKlSoiNjUWFChXsEaLJijunQaKG74+uZ555BteuXYMgCDh+/DiCgoLkYzVq1EB8fLz8s/Xf//4XEydOBABMnjwZX331FQCgSpUq+Pvvv+0SvyG63yvpZ6558+YYMWIEhg4dqijq4EhFN4jsQV0DMYmIyCkEBgZi4cKFiI+PR0REBPr27Qt3d3fVlWtdv349rly5AkEQ5BcAxXZiYiKWLl1qzzDNIi2Kas5LjR4+fChvly9fXt6+f/8+7ty5ozh3yJAh8na/fv3k7aSkJKvFVxxSMta3b1+sXr0a7777rsEqdURUMCY1RERkkkGDBuG7775DdHR0kQ/A7u7u6Nu3LyIiIhAfH49vvvkGTZs2tVGkRYuIiFC8N/RwL4oiNm3aZI/wikU3UTPlpVZSyWkASE1NlbdPnjypOK9evXqKYhSVKlWSt3XXIVIT6e9969ataNy4MYKCgvDtt9/i3r17do6MyHEwqSEiIpNs3LgRkyZNQsuWLVG+fHn07NkTc+fOxeHDh5GZmVngdRUrVsSkSZMQHR2N06dP2zDigkVHR8vDzapWrYq9e/fiyZMnOHbsGBo0aCA/bF68eBG5ubl2jtZ4ztRDIyldurS8HRsbK28fPHhQ3hYEAW3atFFcp5sAeXt7Wy9AC5C+D6dPn8a7774Lf39/9OrVCz///LO9QyNSPSY1RERkFlEUkZKSgt27d+OTTz5Bp06d4OPjA61Wi2nTpmH37t2KT9d1BQYG2jhaw+7duycP/Zk9ezY6d+6MUqVKoXXr1li8eLH8oJ+VlYVHjx7ZOVrj5ObmGvV68OAB3n//fXh5een10qjx4b9WrVry9vz583Hnzh1cvHhRrogmfa86duyouO7GjRsA8hKeqlWr2ixeY2zatAl9+vSRh2bq9piJoojs7Gzs3LkTw4cPV3x/1NyjRmQvTGqIiMgsug/C0ifMGRkZOHLkCObNm4eePXuiQoUKaNmyJSZNmoSNGzeqbjiN7qf4rVu3Vhxr27at4r1UUtfRPX36FHPnzkXdunXxzTffICMjA0De99Dd3R3jxo3D1atX7RylPt2FNmNiYlCzZk00adIEiYmJ8n5BENC9e3fFdceOHZO369ata/1ATdCvXz+9oZnSv6X8/750BQcHY8KECdi1a1ehvaNEroTVz4iIyCStWrVCdHS0ojpW/k+O8/9q0T1er149aLVaLF++3LqBGqGoilLOVHEqJycHy5cvx6xZs5CQkCA/OEt/vvLKK/jiiy9Qp04de4dqUHR0NFq2bAmg4AVeu3Xrhh07diiua9iwIS5dugRBEPDpp59ixowZtgzbZIVVDgSU6+8Aeb1qKSkptg+USGWY1BARkclSU1Nx5MgRREZGIioqCidOnJA/8QeMS3LUUDLYVZKan3/+GZ999hmuXbumSGYAoFu3bpg7dy6aNWtm3yCN8OGHH+Lrr782+PNVunRpHD9+HA0aNJD3nzx5Eq1bt5bP37NnD7p06WLTmM1laGFbQH9hTrX8WyKyNyY1RERUbJmZmTh+/DgiIyMRGRmJP/74A0+ePJGP5/+kWS0PYrrrhAwcOFAxGR0AwsLCAOTFb+i4dOx///uf1WM1x+7du/Hxxx8brFTXunVrzJs3DyEhIfYJzkz//e9/MW/ePLkXAwCaNGmCH3/8Ua9IgLQoJwCUKVMGDx48gLu7u03jtYSCFrZV078lIntjUkNERBaXm5uL06dPIzIyEjt27MD+/ftVufimlNRIMeWXf5iToeNqaYuuEydOYMqUKTh06BAAZTuee+45zJ49G/3797dXeMUmiiIuXbqEpKQkVKlSBbVr1zZ4XmJiovy9KVGihOoWUJWG+gmCgKioKFSrVq3Ia/IPT1Pjzx+RPTCpISIii3r48CEOHz6MqKgoREZG4syZM4qHLjUlArpJjbnU0hbJgAEDsGXLFgDKZMbf3x8zZszAqFGj5GF1ZF/FGd6YnZ2N7du3Izw8XG+9JSJXxKSGiIiK5c6dO/LcmqioKFy6dMlgkqC7z93dXRVVm3SHn5lDTQmaRDdhkRK2kiVLom/fvvDy8jLqHmoeUudMnGXOFpEaMKkhIiKTXLp0SU5goqKicPv2bcVxQ79WPDw8EBQUBK1WC61Wiw4dOqBMmTK2CrlAluixUGNSY2gyubHUmKg5KyY1RJbjeLPliIjIrho2bFjg+hmSUqVKoW3btnIS07ZtW6N7CWxJWpjRWRkqBWzs+UREjoRJDRERmUW3NLCPjw86dOggJzFBQUEOUWWqZs2a9g7BZpiwqBu/P0TFo/7fOEREpErSQ1hAQADGjBmDkJAQtG7dGiVKlLBzZK6tRo0afEB2INL3qkOHDmZ9ECAIAq5du2bpsIgcDufUEBGRSQqbXO/p6YlWrVohODgYwcHBaN++vSrmzhCpkTNW3yOyFyY1RERkkrfffhuHDx/GxYsXDT6M6SY8Go0GTZs2lZOc4OBg+Pn52TLcQq1atcqo8zQaDXx8fODv74/AwECWRCaLKGqdpKKwqAPRv5jUEBGRWR49eqRYj+b06dPIzs7WOy//w1q9evWg1WqxfPlyW4VaIHNKOpcuXRqvvfYaPvnkE1SsWNFKkZErYFJDZDlMaoiIyCLS0tJw7NgxudTzsWPHkJaWZvBctTyImTv8RxAE1KhRA3v37kXdunWtFJ15KleujODgYHTs2BHBwcFo3rw5e5ZUSjeprlatmtnFNZy9ih+RMZjUEBGRVWRnZyMqKgrz58/Hnj17FGWg1ZbUmEr61dm8eXOcPHlSVRPz87fJ29sb7dq1k4f/tWnTRpXltV0R16khshwmNUREZDEpKSlyT400JC0rKwuAsgS0mpIaU+VPzjZu3Ih+/fpZODLzGep90k1ySpQogZYtW0Kr1SI4OBgdOnSAj4+PPUJ1eUxqiCyHSQ0REZnt3r17cgITGRmJ8+fPIzc3FwAKfKhWU1ITHh5u9LlpaWm4fv06IiIicP36dQB57Ro2bBj+7//+z1ohmqyg3qeCvh+CIKBx48aKYg5Vq1a1SayujkkNkeUwqSEiIpOsXr1aTmKuXr0q78//6yT/g7XucXd3d2RmZlo3UCvJzs5Gp06d8McffwAAnn32WcTGxto5qn99+OGHiIqK0ivcUNj3I//xOnXqKL63ZB1Maogsh0kNERGZpKDJ9YU9NHt4eKBVq1aKIU+OvH7Njh070KtXLwCAj48PHj16ZOeI9KWlpeHo0aNyT9rx48eRnp4uH8/fe6ZLLT1pzk63V41JDVHxMKkhIiKTGDO8qXTp0mjXrp2cxLRp0waenp62DNOqYmNj0ahRIwCO0+uUlZWFEydOIDIyElFRUTh69ChSUlIAqHd4oLOrVauW/Hd/5MgRVKtWzc4RETkuJjVERGQSQz01vr6+cglhrVaLFi1aOHUZYUfoqSlKbm4uIiMjMW/ePNVWp6N/PXnyBKmpqfD19TW79DORM+O/CiIiMln16tXlSeVarRYNGza0d0g2k5OTgzlz5sjvq1SpYsdoTJOYmChXp4uKikJMTAxycnJUVZKalCIiIjB9+nRcuHABAODm5oaOHTti2rRp6NKli52jI1IPJjVERGSS69evo1atWvYOwyJWrVpl9Lnp6em4du0aIiIicO3aNQB5w7aCgoKsFV6x3bp1S55TExUVhStXriiO65bZlri5uaFFixa2DtUlHTlyBP3795ffR0REoEOHDvL7NWvWIDQ0FMC/wzuzs7Nx8OBBREZG4ocffsDo0aNtGzSRSnH4GRERuSxzFt/MXxp5w4YNigdTe/v+++/lnpj4+HjFMUO/8j09PeUiDlqtFh06dIC3t7etwnVp3377LSZNmgQACAgIwK1bt+RjaWlpqFWrFhITEwucw1ayZElcvnwZ/v7+tgqZSLXYU0NERC7PlM/3dB8wmzRpgr59+1ojJLONGzfOYHU6ibe3t1zEQavVOl0RB0cSExMDIO9nqkePHopjmzZt0ktopO+ptC89PR0rV67Ep59+aqOIidSLSQ0REbk8c3prqlevjl9++UW1BRGkNpUvXx4dOnSQ5z+1bNkSbm5udo6OAODixYvydseOHRXHtm/fLm9LvTITJ05Eamoqli5dKh/bv38/kxoiMKkhIiIXZ+oobG9vb4SGhmL69Onw8/OzUlSWIYoiMjMzkZ6ejoyMDKSnpyM7O5tJjUokJibK288995zi2NGjR+UeN0EQMHfuXEycOBEAULJkSXz11VcAgMuXL9suYCIV45waIiJyWeHh4Uadp9FoULZsWfj7+yMwMFDVJXV1e44M9UCVKFECQUFBcvW6Dh06wMfHx5Yh0v/n6+uLR48eQRAEXLlyBXXr1gUA3L9/H5UrV1YkNf/884+cRP/xxx9yQQEvLy+kpaXZrQ1EasGkhoiIyIn88ssvcqGA8+fPIzc3V+8c3WRHEAQ0btxYTnKCg4NRtWpVW4bssjw8PJCdnQ1BEHDmzBk0bdoUALBz50689NJLclLzzDPPKHpk/vrrL9SvXx+A4yz+SmRt6v2oiYiIyAGlpKRg8+bNACCX47WlwYMHY/DgwQCA5ORkHDlyRC7pfOrUKWRmZiqG3ImiiLNnz+LcuXPyXI06derg6tWrNo/d1ZQuXRpJSUkAgNjYWDmpOXjwoHyOIAho06aN4rrU1FR5m5XqiPKwp4aIiMiCLl++jAYNGkCj0SA7O9ve4Sikp6fj+PHjck/OH3/8gSdPnuhVSxMEATk5OXaM1DW0aNEC0dHREAQBgYGB2Lp1K1JSUhASEoIHDx7IQ8+WLVuGMWPGyNdt3rwZAwYMgCAIePbZZxUFB4hcFXtqiIiIrECNnxl6eXmhU6dO6NSpE9LS0nDo0CEsWrQIe/bskYekqTFuZ9WhQwdER0cDyCvvXLNmTQD/LooK5CWY3bt3V1x37NgxeVuah0Pk6pjUEBERuYCkpCRERUXJQ9HOnDmj6EnSfZAm2xg9erQ85C9/T5n0/XjxxRdRo0YNxXVbt26Vt1u2bGmbYIlUjkkNERGRE0pISJATmMjISFy4cEF+cM7fG8Nkxj6aNWuG9957D19//bXB74G3tze+/vprxb6TJ0/i0qVL8vnBwcE2iZVI7ZjUEBEROZE33ngDkZGRuHbtmryvqCRG97ibmxtatGhh3SBJ9tVXX6FatWqYN28e7t+/L+9v0qQJfvzxRzRo0EBx/oIFCwDkfc/KlCmDTp062TReIrVioQAiIiILkgoF2GuyvUajMTjxX5fuMS8vL7Ru3RrBwcHQarVo3749K2rZgSiKuHTpEpKSklClShXUrl3b4HmJiYnyz1WJEiVQoUIFW4ZJpFrsqSEiInJCuomMbhJTtmxZtG/fXk5iWrVqBQ8PD3uESDoEQdDrlTGkYsWKNoiGyPEwqSEiInIyuklMpUqV0LFjR2i1WgQHB6NZs2bQaDR2jI6IyPKY1BARETmRgIAAaLVaOYl57rnn7B0SEZHVMakhIiJyIrdu3bJ3CERENsf+ZyIiIiIicmjsqSEiInJijx8/RlhYGPbs2YPz588jMTERQN6E88aNG+PFF1/EyJEjUbZsWTtHSkRkPpZ0JiIisqDs7GzEx8cDAGrWrGnXWH788UdMnToVSUlJAAper6ZcuXKYO3cuxowZY+sQiYgsgkkNERGRE/rwww/xzTffGL1ejSAIeO+99/DVV1/ZLEYiIkthUkNERC7twIEDePToEQDA398frVu3BpA34b5OnTqFXjty5Ej89NNPVo/RVKtWrcKoUaMA6CcyBRFFEYIgIDw8HCNGjLBidERElsekhoiIXJa0entWVhaAvGRg+PDhAPKSmoJWdZeULl0aCQkJKFWqlNVjNVZ6ejpq1aqFe/fuyQmN9Kvew8NDXoH+4cOHyMzMBADFeX5+frh16xY8PT3tED0RkXlY/YyIiFzW9u3bkZmZCVEU4evri6FDh+qdIwiCwRcApKamYv/+/bYOu1Dr1q2TExpRFKHRaDBu3DicPHkSqamp+Pvvv/H3338jNTUVJ0+exPjx4+Hm5iZff//+faxdu9aOLSAiMh2TGiIiclmRkZEA8hKXfv36QaMx/GtRFEW9l+TAgQM2idVYv//+O4C8mD09PbF7924sXrwYLVq0UCQvbm5uaNGiBb777jvs2bMHHh4ecrIm3YOIyFGwpDMREbms8+fPy9sdO3Ys8DxBEPDZZ5/J748dO4bdu3cDAM6dO2e9AM1w6tQpAHkxv/POO+jcuXOR14SEhOCdd97Bl19+CUEQ5HsQETkKJjVEROSybty4IW83bNiw0HOnT58ub+/fv19Oaq5evWqd4Mx0//59ebt///5GX9e/f398+eWXEEVRcQ8iIkfA4WdEROSyUlJS5O3y5csbPMdQPZ3q1avLxx4+fGid4MyUmpoqb/v6+hp9ne65uvcgInIE7KkhIiKXJVU9A4CnT58qjlWsWBErV640eJ1uopP/OnsrV66c3NNy8+ZN1KtXz6jrbt68KW/7+PhYIzQiIqthUkNERC6rXLlySExMBJA3jKxx48byMW9vb4wcOdLgdboJQOnSpa0ao6mqVq0qJzU//fQTunbtatR10no7giCgWrVqVouPiMgaOPyMiIhcVqVKleTtrVu3Gn3d9u3b5W1ThnjZQvv27QHk9SatX78e8+bNK/KaL7/8Ej///LNc/Uy6BxGRo2BSQ0RELqtly5YA8hKAdevW4cyZM0VeExsbi5UrV8rr1TRr1szKUZqmd+/eACCvUzNt2jS0aNECixYtwtGjR/HXX3/hr7/+wtGjR7Fo0SIEBQVh6tSpAP4dVtenTx+7xU9EZA5BNDQDkoiIyAX873//w5tvviknAJUrV8a6desQEhJi8PyjR49i6NChiIuLA5CXOHz77bcYP368DaMuWpMmTXDx4kUA/yYqUi9MfvmPN2rUCGfPnrVBlERElsOkhoiIXFZqair8/f3lKmiiKEIQBLRp0wZdu3ZF9erVIQgC4uPjsX//fhw9elQ+RxRFeHt7Iy4uDuXKlbNvQ/L5888/ERwcLBdCKOpXvdQeT09PREVFISgoyBZhEhFZDJMaIiJyabNmzcL06dPlngopaTFEN6ERBAFTpkzB7NmzbRmu0TZs2ICRI0fi6dOnBbZHIooivLy8sGrVKrz88ss2ipCIyHKY1BARkUvLyclBp06dcPToUUViY4ju8datW+Pw4cNwd1dvIdEzZ87gvffew6FDhwo9T6vV4r///S+aN29uo8iIiCyLSQ0REbm8x48fo2/fvjh48KBRvRparRabN29W3bCzgsTExGDPnj24cOGCXO65UqVKaNSoEV588UUEBgbaOUIiouJhUkNERAQgNzcXy5cvx9y5c3H79m2D5wQEBGDKlCkYM2YM3NzcbBwhEREVhEkNERFRPmfPnkV0dDTu3bsHIK9Xo3nz5mjatKmdIyMiIkOY1BARETm5a9euYceOHThz5gzu378PQRDkRK1Hjx6oW7euvUMkIioWJjVERERO6s6dO3j//fexcePGQosfvPzyy1iwYAH8/f1tHCERkWUwqSEiInJCp06dQu/evXH37l2j1qmpUqUKtm/fzgpoROSQmNQQEZHLssRkf0EQkJ2dbYFoLOf+/fto1qwZ/vnnHwAwqqIbAPj7++PMmTPw9fW1eoxERJaksXcARERE9iKKokVeajN+/Hj8888/EARBsbZOQbFL58THx2PixIl2i5uIyFzsqSEiIpel0WiK7MUojCiKEAQBOTk5FoyqeOLi4lCnTh3k5uYCyIvRz88PY8aMQUhICKpXrw5RFBEfH4/9+/djxYoVcvEAURTh7u6Omzdvolq1anZuCRGR8ZjUEBGRy9JozB+wICUBaktq5s+fj6lTp8rJWkhICDZu3FjgQqGPHj3CgAEDcOjQIQB57Zo/fz4++OADW4VMRFRs7vYOgIiIyF6mT59u9Ll3795FWFgYMjIyrBhR8f35558A8npoKleujE2bNsHHx6fA88uXL49NmzbhueeeQ2JiIgDg+PHjNomViMhSmNQQEZHLMiapefLkCebPn4/Vq1crEhpRFOHp6YkJEyZYM0STXbt2DUBej8uYMWMKTWgk5cuXx5gxYzB79mwIgoDr169bO0wiIotioQAiIiIDsrKy8N///hd16tTBnDlz8OTJEwB5yYxGo8Frr72Gq1ev4quvvrJzpEr37t2Tt0NCQoy+rnPnzvL23bt3LRkSEZHVsaeGiIhIhyiKCA8Px4wZMxAXFyfPm5GO9evXD7Nnz0aDBg3sHKlhKSkp8nb16tWNvk4qDCCKIpKTky0eFxGRNTGpISIi+v+2bNmCadOmITY2Vi+Z0Wq1mDdvHtq2bWvnKAuXmZkpb3t5eRl9ne65ap83RESUH5MaIiJyeZGRkZgyZQqOHz+uWLtFFEUEBgZizpw56NGjh52jNE5WVpacjG3YsAEVK1Y06jqpSAAAVVVzIyIyBks6ExGRy4qJicHUqVOxe/duAFAspFm7dm3MmjULw4YNs1d4ZpHW3tHtaTKFGstUExEVhUkNERG5LDc3NwDKZKZkyZIYN24cxo4dCw8PD6PuU6NGDavEZw7dpMZUal17h4ioKExqiIjIZUkJgMSc3g1BEJCdnW3p0MyWv02mYlJDRI6Ic2qIiMjl6X6+5wyf9TlDG4iITMGkhoiIXJ65PRtqTB5Wrlxp7xCIiGyOw8+IiMhlaTTFX4OaQ7WIiOyPPTVEROSy2KthnJSUFGzevBkAEBoaat9giIgMYE8NERERFery5cto0KABNBqNqooiEBFJit/vTkRERC6Bn4MSkVoxqSEiIiIiIofGOTVEROSybt++bZH7qGnxTSIiV8SkhoiIXFatWrWKtVAloL7FN4mIXBGTGiIicmmcJ0JE5PiY1BARkUsrTk8NEyIiInVgUkNERC6NiQkRkeNjUkNERC5r5MiR9g6BiIgsgItvEhERUaGkxTcFQUBOTo69wyEi0sN1aoiIiIohLCzM3iEQEbk8JjVEROSyvvzyy2Jdv3r1arz55psWioaIiMzF4WdEROSyNBoNFi1ahP/85z8mX7t+/XqMGDECubm5Tj8kKzs7G/Hx8QCAmjVr2jkaIiJ9TGqIiMhlaTQaCIKApUuXYuzYsUZft3HjRgwbNgxZWVmcZ0JEpAJMaoiIyGVpNBr5zxUrVmDUqFFFXrN161YMGjQI2dnZEEVRlUnNgQMH8OjRIwCAv78/WrduDQC4desW6tSpU+i1I0eOxE8//WT1GImILIlJDRERuSypp0YURbi5uSE8PBzDhg0r8PydO3eif//+yMzMlK9TW1KTlJSEKlWqICsrCwCwatUqDB8+HEBeUlO7du1Cry9dujQSEhJQqlQpq8dKRGQpLBRAREQuS+q1kBKTUaNGYePGjQbP/f333zFgwAC9hOaTTz6xZchF2r59OzIzMyGKInx9fTF06FC9cwRBMPgCgNTUVOzfv9/WYRMRFQuTGiIicln79u1DQEAAgLwH/ezsbAwbNgzbtm1TnHfw4EH069cPGRkZioRm8uTJmDlzpj1CL1BkZCSAvPb069dPHmKXnyiKei/JgQMHbBIrEZGluNs7ACIiInupWbMm9u/fD61Wi3/++QeCICArKwuDBw9GREQEunfvjsOHD6N37954+vSpIqF59913MWfOHHs3Qc/58+fl7Y4dOxZ4niAI+Oyzz+T3x44dw+7duwEA586ds16ARERWwDk1RETk8i5fvoyQkBDcu3cPQF4vhpeXF2bNmoXPP/8cjx8/ViQ0EyZMwKJFi+wctWFVq1bF3bt3IQgCjh8/jqCgIPmYNKdGGmqmOxdo//796Nq1KwRBQI0aNXDjxg2bx05EZC4OPyMiIpf37LPPYu/evfD19QWQ14uRnp6Ojz76SJHQAMDYsWNVm9AAQEpKirxdvnx5g+cY+jyzevXq8rGHDx9aJzgiIivh8DMiIiIAjRo1wp49e9C5c2ckJycremakJOD111/H0qVL7Rxp4aSqZwDw9OlTxbGKFSti5cqVBq/TTXTyX0dEpHZMaoiIiP6/Zs2aYc+ePejatavekLNXX30VK1assHeIRSpXrhwSExMBAFevXkXjxo3lY97e3hg5cqTB627evClvly5d2qoxEhFZGpMaIiJyWZ07dza438fHBykpKfLcE0EQcOPGDYPnC4KAffv2WTVOU1SqVElOarZu3Yr+/fsbdd327dvlbWkYHhGRo2ChACIiclnS4puG6P56LOwctS2+GRoaitWrVwMAPDw88Mcff6B58+aFXhMbG4ugoCCkp6cDAAYMGIBff/3V6rESEVkKCwUQEZHLK2zNFmOOq0mnTp0A5CVimZmZ6NmzJw4ePFjg+UePHkX37t3x9OlTuV0hISE2iJSIyHLYU0NERC6rsJ4aY6ixpyY1NRX+/v5yFTQpxjZt2qBr166oXr06BEFAfHw89u/fj6NHjyoKInh7eyMuLg7lypWzb0OIiEzAOTVEROTSnO2zPW9vb7z33nuYPn06BEGQk5Xjx4/j+PHjeufrJjSCIGDixIlMaIjI4bCnhoiIyMnk5OSgU6dOOHr0qNwTVdCve93jrVu3xuHDh+Huzs88icixcE4NERGRk3Fzc8POnTsREhIiJzNSr03+F5CX0Gi1WuzatYsJDRE5JCY1REREZkhJSZFfalSmTBns3bsXy5YtQ0BAgMFiB6Iowt/fH0uWLMG+ffs47IyIHBaHnxEREZno1q1bqFOnDoC8HpDs7Gw7R1S0s2fPIjo6Gvfu3QOQt55N8+bN0bRpUztHRkRUfExqiIiITHTr1i3Url0bAFRX/YyIyBVx+BkRERERETk0JjVEREREROTQWOKEiIjIibi5uRX7Ho4yT4iISMKkhoiIyIlwqiwRuSImNURERE5GWn/GHEyKiMgRMakhIiJyMuYmJsVJhoiI7IlJDRERuSxprRlTqXm+yfTp040+9+7duwgLC0NGRoYVIyIisj6uU0NERC5Lo9FAEASzejak6xxxnZonT55g/vz5WLRoEVJTU+X9oijC09MTEyZMwFdffWXHCImITMOeGiIicnmuMuwqKysLixcvxty5c/HgwQM5KRNFEW5ubggNDcXMmTPh7+9v71CJiEzCpIaIiMjJiaKI8PBwzJgxA3FxcXIyIx3r168fZs+ejQYNGtg5UiIi8zCpISIil1WjRg2n76XZsmULpk2bhtjYWL1kRqvVYt68eWjbtq2doyQiKh7OqSEiInJCkZGRmDJlCo4fPy7PGZKGmgUGBmLOnDno0aOHnaMkIrIM9tQQERE5kZiYGEydOhW7d+8GoCzvXKtWLcyaNQvDhg2zV3hERFbBnhoiIiIzXbx4EWFhYfjyyy/tHYrMzc0NgDKZKVmyJMaNG4exY8fCw8PDqPvUqFHDKvEREVkDkxoiIiITJCUlYe3atQgLC8OpU6cAQFUlnaUy1RLdeTTGEgRB1WvxEBHlx+FnRERERcjNzcWuXbsQFhaGbdu2ITMzE4B5CYOt6H5myc8vicjZMakhIiIqgDS8bM2aNUhISAAAxaR7NTM3PiZAROSImNQQERHpMDS8TPdBX7ckslqpOTYiImtgUkNERC7P0PAyQ4kMkJcwlChRAsHBwejVqxdeeukle4RcoJUrV9o7BCIim2OhACIiclnGDi+T5s5If8bGxqJ+/fp2iZmIiPSxp4aIiFxW48aN5WRFkn94WZ06dTB8+HDMmjVLPsfLy8u2gRIRUaHYU0NERC7LUPljAPD19cXgwYMxYsQItGvXTj4XyEt6bty4wXVciIhUhD01RETk8qRhZd26dcP48ePRvXt3uLs75q/I27dvW+Q+TNqIyJE45v/YREREFiQNQTt06BDKli0LQRDQvXt3uLm52Ts0k9WqVavY5aa5+CYRORoOPyMiIpdlaPiZ9N7X1xdDhgxBaGgogoKCHGb4mRRncQiCgJycHAtEQ0RkG0xqiIjIZTVr1gxnz54FYHj9GWnfM888gytXrsj71J7UFKenRkrsmNQQkSMp/sc5REREDio6OhpnzpzBxIkTUbFiRUU5Z2lImiiKuHLliiJRiIiIwL179+wVdpGkuM15ERE5IvbUEBERAcjOzsZvv/2GsLAw7NixA1lZWYX2eAiCgKCgIPTu3RvTpk2zYaSFe+211yxyHy7iSUSOhEkNERFRPomJiVi9ejXCw8MRExMDQH8xTgmHahER2R+TGiIiokLExMRg5cqVWLduHe7fvw9AOf/GGZOasLAwjBo1yt5hEBEZjXNqiIiIChEYGIiFCxciPj4eERER6Nu3L9zd3VU7/+TLL78s1vWrV6/Gm2++aaFoiIhsgz01RETksgYNGgStVovg4GAEBgYaXTVMGp4WFhaGc+fOqaqnRqPRYNGiRfjPf/5j8rXr16/HiBEjkJubq6o2EREVhUkNERG5LN3yx2XKlEH79u0RHByM4OBgtG7dGh4eHkXeIyYmBoGBgdYO1WhSm5YuXYqxY8cafd3GjRsxbNgwuUACkxoiciRMaoiIyGVJCYBuKWeJh4cHWrVqheDgYGi1WrRv3x5lypSxV6hGkxbf1Gg0WLFihVFzY7Zu3YpBgwYhOzvbaecJEZFzY1JDREQuy9BClYYW35TObdq0qdyTExwcDD8/P5vFaizdRM3NzQ3h4eEYNmxYgefv3LkT/fv3R2ZmpnwdkxoicjRMaoiIyGW1atUK0dHRigf4wpKc/Mfr1asHrVaL5cuXWzdQE9SrVw83btwAkBe7u7s71q1bh4EDB+qd+/vvv6NPnz7IyMhQJDSffPIJZs6caevQiYjMxqSGiIhcWmpqKo4cOYLIyEhERUXhxIkTyMjIkI8bk+SoqVfj1q1b6NSpE+Li4gDkxVuiRAls2LABvXv3ls87ePAgXnrpJTx9+lSR0EyePBlz5syxV/hERGZhUkNERKQjMzMTx48fR2RkJCIjI/HHH3/gyZMn8vH8i3CqLakBgOvXr0Or1eKff/4BkBenp6cnIiIi0L17dxw+fBg9evRAamqqIqF59913sWDBAjtHT0RkOiY1REREhcjNzcXp06cRGRmJHTt2YP/+/Q6x+Obly5cREhKCe/fuAciL1cvLC7NmzcLnn3+Ox48fKxKaCRMmYNGiRXaOmojIPExqiIiICvDw4UMcPnwYUVFRiIyMxJkzZxQJjJqTGgC4cOECnn/+eTx48ADAv/Hq/gkAb731FpYuXWrPUImIioVJDRER0f93584deW5NVFQULl26pDeHBlDOq3F3d0dmZqYtwzRJdHQ0OnfujOTkZADQS2hef/11rFixwp4hEhEVG5MaIiJyWZcuXZITmKioKNy+fVtx3NCvSA8PDwQFBUGr1UKr1aJDhw6qX7/m5MmT6Nq1Kx4/fgzg38Tm1VdfRVhYmH2DIyKyACY1RETksnTXqSno12GpUqXQtm1bOYlp27YtvLy8bBmmSTp37mxw/7Vr1xAXFyf30mg0GnTo0AFubm565wqCgH379lk7VCIii2FSQ0RELkt3oUrpTx8fH3To0EFOYoKCguDu7m7vUI1maEFRSUELi+Y/R83zhIiIDHGc/6WJiIisRHrADwgIwJgxYxASEoLWrVujRIkSdo7MfEV9ZmnoeEGJDhGR2rGnhoiIXFZhvRqenp5o1aoVgoODERwcjPbt26t+7gxQeJuMwZ4aInJETGqIiMhlvf322zh8+DAuXrxYZM+FRqNB06ZN5SQnODgYfn5+tgzXKBqNptj3YFJDRI6GSQ0REbm8R48eKdajOX36NLKzs/XOy98DUq9ePWi1WixfvtxWoRIRkQFMaoiIiPJJS0vDsWPH5FLPx44dQ1pamsFz2atBRGR/TGqIiIiKkJ2djaioKMyfPx979uxRlIF2pqQmJSVF3i5btqwdIyEiMg2rnxERERmQkpIi99RIQ9KysrIUJaCdya1bt1CnTh0Aeb1PhobfERGpFZMaIiIiAPfu3ZMTmMjISJw/fx65ubkA9Nd3cbaERsLBG0TkqJjUEBGRy1q9erWcxFy9elXen//hPn8So3vczc3NukESEVGROKeGiIhclrSmiylJjIeHB1q1agWtVovg4GB06NDBIdavKcqtW7dQu3ZtACx+QESOhz01RETk8gpLYkqXLo127drJSUybNm3g6elp6xCJiKgQTGqIiMjl6SYxvr6+6NixI4KDg6HVatGiRQuLLGhJRETWw6SGiIhcWvXq1REcHCwnMQ0bNrR3SEREZCImNURE5LKuX7+OWrVq2TsMIiIqJiY1RETkspwxoZHWmjEV16UhIkfG6mdEREROpKCKbsbQXViU1c+IyJGwp4aIiMgJOesCoUREhrCcCxEREREROTT21BARETmRGjVqsJeGiFwO59QQEREREZFD4/AzIiIiIiJyaExqiIiISHbx4kV89NFH9g6DiMgkHH5GRETk4pKSkrB27VqEhYXh1KlTAMCSzkTkUFgogIiIyAXl5uZi165dCAsLw7Zt25CZmQkA8jo1RESOhEkNERGRC7l48SLCwsKwZs0aJCQkAIC8UCeTGSJyVExqiIiInJyh4WW6o8+lZIYj0onIUTGpISIickKGhpcZSmSAvGSmRIkSCA4ORq9evfDSSy/ZI2QiIrOxUAAREZETMXZ4mTR3RvozNjYW9evXt0vMRETFxZ4aIiIiJ9K4cWM5WZHkH15Wp04dDB8+HLNmzZLP8fLysm2gREQWxJ4aIiIiJ6LRaPR6ZADA19cXgwcPxogRI9CuXTv5XCAv6blx4wZq1Khh+4CJiCyAPTVEREROSBpW1q1bN4wfPx7du3eHuzt/7RORc+L/bkRERE5IGoJ26NAhlC1bFoIgoHv37nBzc7N3aEREFsfhZ0RERE7E0PAz6b2vry+GDBmC0NBQBAUFcfgZETkNJjVEREROpFmzZjh79iwAw+vPSPueeeYZXLlyRd7HpIaIHJnG3gEQERGR5URHR+PMmTOYOHEiKlasqCjnLA1JE0URV65cUfToRERE4N69e/YKm4ioWNhTQ0RE5KSys7Px22+/ISwsDDt27EBWVpYikclPEAQEBQWhd+/emDZtmg0jJSIqHiY1RERELiAxMRGrV69GeHg4YmJiAOgvxikRBAE5OTk2j5GIyFxMaoiIiFxMTEwMVq5ciXXr1uH+/fsAlPNvmNQQkaNhUkNEROSiDA1PA9hTQ0SOh0kNERGRExk0aBC0Wi2Cg4MRGBhY6BwaXdLwtLCwMJw7d45JDRE5FCY1RERETkR3nZoyZcqgffv2CA4ORnBwMFq3bg0PD48i7xETE4PAwEBrh0pEZDFMaoiIiJyIlNTolnKWeHh4oFWrVggODoZWq0X79u1RpkwZe4VKRGQxTGqIiIiciG5PjcTQ4pvSuU2bNpV7coKDg+Hn52ezWImILIVJDRERkRNp1aoVoqOjFXNiCkty8h+vV68etFotli9fbt1AiYgsiEkNERGRk0lNTcWRI0cQGRmJqKgonDhxAhkZGfJxY5IcFgogIkfCpIaIiMjJZWZm4vjx44iMjERkZCT++OMPPHnyRD6efxFOJjVE5GiY1BAREbmY3NxcnD59GpGRkdixYwf279/PxTeJyKG52zsAIiIisp2HDx/i8OHDiIqKQmRkJM6cOWP0WjZERGrFpIaIiMiJ3blzR55bExUVhUuXLunNoQGU82rc3NxsGSIRUbExqSEiInIily5dkhOYqKgo3L59W3HcUELj4eGBoKAgaLVaaLVadOjQwVbhEhFZBOfUEBERORHddWoK+hVfqlQptG3bVk5i2rZtCy8vL1uGSURkUeypISIickLShH9RFOHj44MOHTrISUxQUBDc3fkIQETOg/+jEREROSGptyYgIABjxoxBSEgIWrdujRIlStg5MiIiy+PwMyIiIieiO/wsP09PT7Rq1QrBwcEIDg5G+/btUaZMGRtHSERkeUxqiIiInMjbb7+Nw4cP4+LFiwbn1OgmPBqNBk2bNpWTnODgYPj5+dkyXCIii2BSQ0RE5IQePXqkWI/m9OnTyM7O1jsvf69OvXr1oNVqsXz5cluFSkRUbExqiIiIXEBaWhqOHTsml3o+duwY0tLSDJ4rCAJycnJsHCERkfmY1BAREbmg7OxsREVFYf78+dizZ4+iDDSTGiJyNKx+RkRE5CJSUlLknhppSFpWVpZc+rmgAgNERGrHpIaIiMhJ3bt3T05gIiMjcf78eeTm5gJQLswpCAITGiJyaExqiIiInMjq1avlJObq1avy/vyjzfMnMbrH3dzcrBskEZGFcU4NERGRE5HWqTElifHw8ECrVq2g1WoRHByMDh06cP0aInIo7KkhIiJyQoUlMaVLl0a7du3kJKZNmzbw9PS0dYhERBbDpIaIiMgJ6SYxvr6+6NixI4KDg6HVatGiRQtoNBo7RkdEZFlMaoiIiJxM9erVERwcLCcxDRs2tHdIRERWxTk1RERETuTmzZuoVauWvcMgIrIpJjVEREREROTQOKCWiIiIiIgcGpMaIiIiIiJyaExqiIiIiIjIoTGpISIiIiIih8akhoiIyMGEhIRAEATMmDFD71itWrUgCALCwsJsHpe1CYIAQRBw8OBBe4diFHvGO2rUKAiCgFGjRtn8axPZA5MaIiJyKTNmzJAfNnVfXl5e8Pf3R58+ffDLL7+AxUHz3Lx5EzNmzDCYQDkS6fvs6O0gIsO4+CYREbmsypUry9vJycmIj49HfHw8tm3bhrCwMERERMDT09OOEZqubt268PLygo+Pj0Xud/PmTcycORMAmBAQkWqxp4aIiFxWQkKC/EpNTcX58+fxwgsvAAB27tyJTz75xM4Rmm7fvn24dOkS+vfvb+9QiIhshkkNERERAI1Gg0aNGmHr1q2oV68eAOCHH35Adna2nSMjIqKiMKkhIiLS4eXlhUGDBgEAHj9+jEuXLgHIG4Ylzcu4efMmrl27hjFjxqB27drw9PRErVq1FPfJzc3FmjVr0LNnT1SuXBkeHh6oVKkSXnzxRaxbt67QOTs5OTn47rvv0KJFC3h7B5bEXgAADMlJREFUe6NChQoICQnBhg0biozfmEIBx48fx2uvvYZ69eqhVKlSKFu2LBo2bIjXX38du3fvVtzr+eefl9/nn4dkaBL648ePMW/ePLRr1w4VKlSAp6cnAgICMGTIEPzxxx+Fxv7o0SN8+OGH8hC6qlWrYtCgQTh16lSR7baWY8eOYfLkyQgODkbNmjXh5eWFcuXKoW3btpg/fz6ePHli1H0SEhIwYcIE1K5dG15eXqhSpQqGDx8u/3wV5rfffsPAgQNRvXp1eHp6onz58tBqtVi2bBkyMzOL20Qi5yASERG5kOnTp4sAxMJ+BS5ZskQ+58iRI6IoiuKNGzfkfWvWrBFLly4tAhBLlSolent7izVr1pSvf/DggajVauXzAYg+Pj6K93369BEzMjL0vnZ6errYrVs3+TyNRiOWK1dOFARBBCBOnjxZ7NSpkwhAnD59ut71NWvWFAGIK1eu1DuWnZ0tTpw4URGHt7e3WL58efn+Pj4+8vlBQUFi+fLl5XMrV66seE2cOFFx/zNnzoj+/v7y+W5ubmKZMmXk94IgiHPmzDH4d37jxg05dgCih4eHWLZsWXl7y5Yt8rEDBw4U+L0riHStob8zY66Tvte6fx8AxIYNG4p3794t9NqffvpJrFKlighALFmypPyzA0D08vISd+7cafD6tLQ08eWXX1Z8vbJly8rfKwBi27ZtxYcPH+pdO3LkSBGAOHLkSJPaS+So2FNDRESUz82bN+XtChUq6B0fO3YsGjVqhD///BOpqal48uQJ9uzZAyCvl2XAgAGIjIxEs2bNsG3bNqSmpiIpKQlPnjxBeHg4/Pz8sHXrVkyePFnv3lOnTsXu3bshCAK++OILPHr0CI8ePUJCQgLefvttzJ8/H9HR0Wa16+OPP8a3334LAHj99ddx+fJlPHnyBA8fPsSjR4+wefNmdO/eXT7/zz//xKZNm+T3unOQEhISsGjRIvnYP//8g27duuHOnTsYMGAATp48iadPnyIlJQV3797Fp59+Cjc3N3z88cfYvHmzIq6cnBwMGjQIt27dQvny5fHLL78gNTUVycnJuHDhAtq0aYORI0ea1ebi6t27N9avX49//vkHqampePjwIdLS0rBp0yY8++yzuHjxIt56661C7/Huu+/Cw8MDe/bsQWpqKh4/fozjx4+jSZMmSE9PxyuvvII7d+7oXTdmzBhs2LABderUwZo1a5CcnIzk5GSkpaVhy5YtqFOnDo4dO4bXX3/dWs0nchz2zqqIiIhsqaiemuTkZLFatWoiALFChQpiTk6OKIrKnpqaNWuKjx8/Nnj9qlWrRADic889JyYlJRk85+TJk6IgCKKHh4fiU/74+HjR3d1dBCB++umnBq8dOnRoob0OBfXUXL58WdRoNCIA8aOPPjJ4b0MOHDhQZM+WKIri66+/LgIQhw0bVuA533zzjQhADAwMVOxfv369/DX27t2rd11qaqpYt25du/TUFObOnTuip6enKAiCeOvWrQK/poeHh3jx4kW943fv3hUrVKggAhDHjRunOBYZGSkCEP38/MTbt28b/PpxcXGit7e3CEA8c+aM4hh7asjVsKeGiIgIQFJSEvbt24fOnTvj77//BgC888470Gj0f1VOmDABpUuXNnif//3vfwCAt99+u8Cyyi1btkSjRo2QmZmJAwcOyPs3bNiA7OxslCxZEh988IHBa80tqxweHo7c3Fz4+vrKJZotJT09HWvXrgUAg71PktDQUABATEwM7t69K+//+eefAQAdOnRAly5d9K4rVaoUPvroI0uGbBHVq1dHYGAgRFHE0aNHCzxv0KBBaNCggd5+Pz8/uZdn/fr1imPSz9Hw4cMREBBg8L7+/v7ynCfduVBErojr1BARkcsSBKHAYyNGjMC0adMMHuvQoYPB/Tk5OTh27BiAvORjzpw5Bd7/4cOHAIBbt27J+06ePAkACAoKQtmyZQ1eV79+fVSvXh3x8fEF3tsQ6aH7hRdegJeXl0nXFuXUqVNIT08HALz44otGXXPr1i15nSCp3Z07dy7w/MKOWVNubi5+/vln/Pzzz4iOjsb9+/fltuoyNHxMUlS75syZgwcPHuDGjRuoXbs2AODIkSMA8pIbKWE0JDk5GYDy54jIFTGpISIil6W7+KanpycqVqyI5s2bY/jw4YqqX/n5+fkZ3P/w4UNkZGQAyKvkZYy0tDR5+969ewDyegAK4+/vb3JSk5CQAACoWbOmSdcZQ+rZAqDogSmMqe329/c3MzrzpaWloVevXoreNA8PD1SoUAElSpQAkPc9z8rKQmpqaoH3Kaxdusfu3bsnJzXS32lKSgpSUlKMipXIlTGpISIilyU96JvKzc3N4P6cnBx5e+fOnYpJ9/ZWWK9Ucem2++nTpxbvCbKX2bNn48CBAyhZsiTmzJmDAQMGICAgQPF3GRwcjMOHDxdaotsc0t/psmXLiixEQERcp4aIiMhifH194e6e93mhOcOBpB6gonphTO2lAYAqVaqYHZex9zb3/sa025w2F5c01+ezzz7DpEmTUKNGDb3k0JjE2Nh26fYAWvP7ReSMmNQQERFZSIkSJdC6dWsAwLZt20y+PigoCEDeHJOCFnW8evVqofM3CtK+fXsAwO+//25wTkhBdAslFNQb0apVK3h4eAAoXrt1h3nlt3//fpPvW1xxcXEAgObNmxs8fvPmTfz1119F3qewdknHKlSoIA89A/6dt7V9+3aj4yVyZUxqiIiILGjMmDEAgB07dmDHjh2FnisVC5AMHDgQbm5uePr0KRYsWGDwms8//9ysuEaNGgU3Nzc8ePAA06dPN/o63YIFSUlJBs/x9vbGsGHDAADz58/H7du3C71n/na/8sorAIDDhw/j4MGDeuc/ffoUX331ldExW4pUvS4mJsbg8SlTphh1n19//RWXL1/W25+YmIgffvgBwL9/BxLp5+j8+fNYtmxZofdPTU1FZmamUbEQOSsmNURERBY0YsQIdO3aFaIoon///vjiiy8UE+lTU1Nx4MABjB8/HnXq1FFcW716dYwfPx4AMGvWLMydOxePHz8GANy/fx8TJkzA6tWrCywVXZh69erhww8/BAB8+eWXeOONN3D16lX5eEpKCtavX4/+/fsrrqtfv77cC7NixYoCe2vmzJmDatWqITExEe3atcP//d//ybFL8W/cuBH9+/fH0KFDFdcOHDgQLVq0kLc3btwozymJjY1Fjx49cP/+fZPbbEhaWhoSExMLfUkJgjQn6osvvsCmTZuQnZ0NALhx4waGDRuGX375BeXLly/ya3p5eaF79+7Yu3ev/Pf3559/omvXrkhMTESZMmX0EqROnTrhtddeAwCMHz8e7777Lq5fvy4fz8jIwLFjx/DRRx+hZs2acrEFIpdlz0VyiIiIbK2oxTcLorv45o0bNwo9Nzk5WezVq5d8PgCxbNmyYrly5URBEOR97u7uetc+ffpU7Nq1q3yOm5ubWL58efm6yZMni506dTJ58U1RFMXs7Gxx/PjxirhKly6tuL+Pj4/edaNHj5bPL1WqlFijRg2xZs2a4vvvv6847+LFi2L9+vXlczUajVihQgV5gUjp1bVrV72vce3aNTEgIEA+x9PTU/Tx8ZEXr9yyZYtFFt805hURESGKoijevHlTrFy5suL7JcUEQJwzZ06h3wvpvJ9++kmsUqWK/PdXunRpRTu3b99uMOaMjAzxjTfeMPj9khZSlV537txRXMvFN8nVsKeGiIjIwsqWLYtt27Zhx44deOWVV1CjRg1kZGQgLS0N1atXx4svvoi5c+caHJLk5eWFnTt3YtGiRWjWrBk8PDwgiiKCg4Pxyy+/YN68eWbH5ebmhsWLF+Pw4cMYPnw4atSogaysLIiiiIYNG2L06NHYuHGj3nVLlizBjBkz0KRJEwDA7du3cevWLSQmJirOa9CgAc6ePYsffvgBL774IipWrIiUlBSIooh69eph0KBB+PHHH/HLL7/ofY06deogOjoa7733HmrXrg1RFOHl5YWXX34ZR48eRZ8+fcxut7lq1qyJkydPYvTo0ahWrRqAvO9Pr169sHv3bkydOtWo+9SuXRtnzpzB+PHjUalSJWRmZsLPzw9Dhw7FmTNn8NJLLxm8zsPDA8uXL8fRo0cxatQo1K1bFzk5OXjy5An8/PwQEhKCzz77DGfPni2yDDiRsxNE0cI1CImIiIiIiGyIPTVEREREROTQmNQQEREREZFDY1JDREREREQOjUkNERERERE5NCY1RERERETk0JjUEBERERGRQ2NSQ0REREREDo1JDREREREROTQmNURERERE5NCY1BARERERkUNjUkNERERERA6NSQ0RERERETk0JjVEREREROTQ/h/0UI1k48xcqgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Test the functions\n",
        "print_classification_metrics(X_test, y_test_oh, model)\n",
        "plot_normalized_confusion_matrix(X_test, y_test_oh, model, LABELS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "scrolled": true,
        "id": "0wF5321sO1cP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1c712ca-6cca-4d59-da8e-8a341406fe15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NbConvertApp] Converting notebook LSTM_new.ipynb to markdown\n",
            "[NbConvertApp] Support files will be in LSTM_new_files/\n",
            "[NbConvertApp] Making directory LSTM_new_files\n",
            "[NbConvertApp] Making directory LSTM_new_files\n",
            "[NbConvertApp] Making directory LSTM_new_files\n",
            "[NbConvertApp] Making directory LSTM_new_files\n",
            "[NbConvertApp] Making directory LSTM_new_files\n",
            "[NbConvertApp] Writing 67338 bytes to LSTM_new.md\n"
          ]
        }
      ],
      "source": [
        "# Let's convert this notebook to a README automatically for the GitHub project's title page:\n",
        "!jupyter nbconvert --to markdown LSTM_new.ipynb\n",
        "!mv LSTM_new.md README.md"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SIN8zM-cjSBu"
      },
      "execution_count": 101,
      "outputs": []
    }
  ],
  "metadata": {
    "hide_input": false,
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}