Run_Experiment_MLP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "3gin-IoGscIF",
        "0bYdUHJ7odgE",
        "2lOH6Rd5pSKg",
        "tjv0oesKBU50",
        "0v_h4ju4qzCS",
        "Onfq5C5Eq6T0",
        "XMrcsqdGsmUc",
        "5k5PWwytDUhm"
      ],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import json\n",
        "import math\n",
        "from typing import Any, Callable, Dict, Generator, List, Set, Tuple\n",
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, recall_score, recall_score, accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "Gl07q11fiyP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Device"
      ],
      "metadata": {
        "id": "3gin-IoGscIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# getting device for training MLP\n",
        "device = torch.device(\n",
        "    'cuda' if torch.cuda.is_available() else 'cpu'\n",
        ")\n",
        "print(f\"Device: \\\"{device}\\\".\")"
      ],
      "metadata": {
        "id": "7JZyJMP4sdHl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2089d6fa-0d4f-4f39-be54-260af5dd1a07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: \"cuda\".\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Helper Functions"
      ],
      "metadata": {
        "id": "JpNtrOzwsazP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Make MLP"
      ],
      "metadata": {
        "id": "0bYdUHJ7odgE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wa4fAd96iu-b"
      },
      "outputs": [],
      "source": [
        "def make_mlp(\n",
        "    input_dim: int,\n",
        "    hidden_dims: List[int],\n",
        "    output_dim: int,\n",
        "    hidden_activation: nn.Module = nn.ReLU,\n",
        "    dropout: float = 0.0\n",
        ") -> nn.Sequential:\n",
        "    num_hidden_layers = len(hidden_dims)\n",
        "    seq_list = []\n",
        "    if num_hidden_layers > 0:\n",
        "        seq_list.append(nn.Linear(input_dim, hidden_dims[0]))\n",
        "        seq_list.append(hidden_activation())\n",
        "\n",
        "        if dropout > 0.0:\n",
        "            seq_list.append(nn.Dropout(dropout))\n",
        "\n",
        "        for i in range(1, num_hidden_layers):\n",
        "            seq_list.append(nn.Linear(hidden_dims[i - 1], hidden_dims[i]))\n",
        "            seq_list.append(hidden_activation())\n",
        "\n",
        "            if dropout > 0.0:\n",
        "                seq_list.append(nn.Dropout(dropout))\n",
        "\n",
        "        seq_list.append(nn.Linear(hidden_dims[-1], output_dim))\n",
        "    else:\n",
        "        seq_list.append(nn.Linear(input_dim, output_dim))\n",
        "\n",
        "    return nn.Sequential(*seq_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Obtain Train-Val-Test"
      ],
      "metadata": {
        "id": "2lOH6Rd5pSKg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# random split\n",
        "def get_train_val_test_arrays(\n",
        "    X: Dict,\n",
        "    y: Dict,\n",
        "    test_size: float = 0.2,\n",
        "    val_size: float = 0.2,\n",
        "    random_state: int = 42\n",
        ") -> Tuple[\n",
        "    List[Any],  # X_train\n",
        "    List[Any],  # X_val\n",
        "    List[Any],  # X_test\n",
        "    List[Any],  # y_train\n",
        "    List[Any],  # y_val\n",
        "    List[Any],  # y_test\n",
        "]:\n",
        "    \"\"\"\n",
        "    Purpose: Split the data into training, validation, and test sets.\n",
        "\n",
        "    :param X: Features matrix.\n",
        "    :param y: Labels vector.\n",
        "    test_size (optional): Proportion of the dataset to include in the test split. Default is 0.2.\n",
        "    val_size (optional): Proportion of the dataset to include in the validation split. Default is 0.2.\n",
        "    random_state (optional): Random state for reproducibility. Default is 42.\n",
        "    :return: Tuple containing the training, validation, and testing sets.\n",
        "    \"\"\"\n",
        "    # ensure the keys are sorted so they are aligned in both dictionaries\n",
        "    keys = sorted(set(X.keys()) & set(y.keys()))\n",
        "    X_values = [\n",
        "        X[k] for k in keys\n",
        "    ]\n",
        "    y_values = [\n",
        "        y[k] for k in keys\n",
        "    ]\n",
        "\n",
        "    # convert to numpy arrays\n",
        "    X_values = np.array(X_values)\n",
        "    y_values = np.array(y_values)\n",
        "\n",
        "    # split the data into training and the rest\n",
        "    X_train, X_temp, y_train, y_temp = train_test_split(\n",
        "        X_values,\n",
        "        y_values,\n",
        "        test_size = (test_size + val_size),\n",
        "        random_state = random_state,\n",
        "        stratify = y_values\n",
        "    )\n",
        "\n",
        "    # split the rest into validation and test\n",
        "    X_val, X_test, y_val, y_test = train_test_split(\n",
        "        X_temp,\n",
        "        y_temp,\n",
        "        test_size = (val_size / (test_size + val_size)),\n",
        "        random_state = random_state,\n",
        "        stratify = y_temp\n",
        "    )\n",
        "\n",
        "    return (X_train, X_val, X_test, y_train, y_val, y_test)"
      ],
      "metadata": {
        "id": "rg-MZSGwo5oB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Convert Dictionary to Tensor"
      ],
      "metadata": {
        "id": "tjv0oesKBU50"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_dict_to_tensor(data_dict):\n",
        "    # sorting dictionary by keys to maintain association\n",
        "    sorted_items = sorted(data_dict.items())\n",
        "    values = [value for _, value in sorted_items]\n",
        "    return torch.Tensor(values)"
      ],
      "metadata": {
        "id": "78MZdeOvBVEO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Obtain `Dataset`"
      ],
      "metadata": {
        "id": "0v_h4ju4qzCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_tensordataset(\n",
        "    X,\n",
        "    y\n",
        ") -> TensorDataset:\n",
        "    dataset = TensorDataset(\n",
        "            X,\n",
        "            y\n",
        "        )\n",
        "    return dataset"
      ],
      "metadata": {
        "id": "_2Jjuaesn5et"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Obtain `DataLoader`"
      ],
      "metadata": {
        "id": "Onfq5C5Eq6T0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_dataloader(\n",
        "    dataset: TensorDataset,\n",
        "    batch_size: int = 64,\n",
        "    shuffle: bool = True\n",
        ") -> DataLoader:\n",
        "    dataloader = DataLoader(\n",
        "        dataset,\n",
        "        batch_size = batch_size,\n",
        "        shuffle = shuffle\n",
        "    )\n",
        "    return dataloader"
      ],
      "metadata": {
        "id": "UzKf2Fffn5g7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Train, Validate, and Test"
      ],
      "metadata": {
        "id": "XMrcsqdGsmUc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_validate_test(\n",
        "    model: nn.Module,\n",
        "    train_loader: DataLoader,\n",
        "    val_loader: DataLoader,\n",
        "    test_loader: DataLoader,\n",
        "    loss_fn: nn.modules.loss._Loss,\n",
        "    optimizer: optim.Optimizer,\n",
        "    device: torch.device = torch.device('cpu'),\n",
        "    epochs: int = 100\n",
        ") -> Tuple:\n",
        "    # move model to device\n",
        "    model = model.to(device)\n",
        "\n",
        "    # instantiate train loss\n",
        "    best_total_train_loss = float('inf')\n",
        "\n",
        "    # define the best epoch to be the epoch with the best validation AUC\n",
        "    best_val_auc = float('-inf')\n",
        "    best_val_auc_idx = None\n",
        "    best_model_state = None\n",
        "    best_optim_state = None\n",
        "\n",
        "    # define lists to keep track of best validation epoch (best AUC) and associated test true/preds\n",
        "    best_val_y_true_list = []\n",
        "    best_val_y_hat_list = []\n",
        "    associated_test_y_true_list = []\n",
        "    associated_test_y_hat_list = []\n",
        "\n",
        "    # loop through epochs\n",
        "    for epoch in tqdm(range(epochs), desc=\"Epochs\"):\n",
        "        epoch_idx = epoch + 1\n",
        "\n",
        "        # 1. BEGIN TRAINING\n",
        "\n",
        "        # set model to train mode\n",
        "        model.train()\n",
        "\n",
        "        # define best train loss, val AUC for this epoch\n",
        "        total_train_loss = 0\n",
        "        val_auc = 0\n",
        "\n",
        "        # loop through data batches\n",
        "        for X_train_batch, y_train_batch in train_loader:\n",
        "\n",
        "            # move tensors to device\n",
        "            X_train_batch = X_train_batch.to(device)\n",
        "            y_train_batch = y_train_batch.to(device)\n",
        "\n",
        "            # reset gradients to 0 before starting backpropagation\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # compute model predictions for the batch\n",
        "            y_train_hat = model(\n",
        "                X_train_batch\n",
        "            )\n",
        "\n",
        "            # compute the loss for trainbatch\n",
        "            train_batch_loss = loss_fn(\n",
        "                y_train_hat,\n",
        "                y_train_batch\n",
        "            )\n",
        "\n",
        "            # backpropagate the batch loss and compute gradients\n",
        "            train_batch_loss.backward()\n",
        "\n",
        "            # perform single optimization step by updating model parameters based on gradients\n",
        "            optimizer.step()\n",
        "\n",
        "            # accumulate epoch's total train loss\n",
        "            total_train_loss += train_batch_loss.item()\n",
        "\n",
        "        # update best train loss if necessary\n",
        "        if total_train_loss < best_total_train_loss:\n",
        "            best_total_train_loss = total_train_loss\n",
        "\n",
        "        # ------------------------------------------------------------------------------------------\n",
        "        # 2. BEGIN VALIDATION\n",
        "\n",
        "        # switch model to evaluation mode\n",
        "        model.eval()\n",
        "\n",
        "        # keep track of evaluation metrics\n",
        "        val_y_true_list = []\n",
        "        val_y_hat_list = []\n",
        "\n",
        "        # disable computing gradients (we are just evaluating)\n",
        "        with torch.no_grad():\n",
        "\n",
        "            # loop through validation batches\n",
        "            for X_val_batch, y_val_batch in val_loader:\n",
        "\n",
        "                # move tensors to device\n",
        "                X_val_batch = X_val_batch.to(device)\n",
        "                y_val_batch = y_val_batch.to(device)\n",
        "\n",
        "                # get predictions from trained model\n",
        "                y_val_hat = model(\n",
        "                    X_val_batch\n",
        "                )\n",
        "\n",
        "                # append y and y_hat to lists\n",
        "                val_y_true_list.append(y_val_batch.cpu())\n",
        "                val_y_hat_list.append(y_val_hat.cpu())\n",
        "\n",
        "            # flatten lists of batch outputs into single array\n",
        "            val_y_true_list = torch.cat(val_y_true_list).numpy()\n",
        "            val_y_hat_list = torch.cat(val_y_hat_list).numpy()\n",
        "\n",
        "            # compute validation AUC\n",
        "            val_auc = roc_auc_score(\n",
        "                y_true = val_y_true_list,\n",
        "                y_score = val_y_hat_list\n",
        "            )\n",
        "\n",
        "            # check if we have found a new best validation AUC\n",
        "            if best_val_auc < val_auc:\n",
        "                best_val_auc = val_auc\n",
        "                best_val_auc_idx = epoch_idx\n",
        "\n",
        "                # keep track of best val true and preds\n",
        "                best_val_y_true_list = val_y_true_list\n",
        "                best_val_y_hat_list = val_y_hat_list\n",
        "\n",
        "                # keep track of model weights\n",
        "                best_model_state  = model.state_dict()\n",
        "                best_optim_state = optimizer.state_dict()\n",
        "\n",
        "    # ------------------------------------------------------------------------------------------\n",
        "    # 3. BEGIN TESTING\n",
        "\n",
        "    # switch model to evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    # disable computing gradients (we are just evaluating)\n",
        "    with torch.no_grad():\n",
        "\n",
        "        # load in model weights from best val AUC epoch\n",
        "        model.load_state_dict(\n",
        "            best_model_state\n",
        "        )\n",
        "        optimizer.load_state_dict(\n",
        "            best_optim_state\n",
        "        )\n",
        "\n",
        "        # loop through test batches\n",
        "        for X_test_batch, y_test_batch in test_loader:\n",
        "\n",
        "            # move tensors to device\n",
        "            X_test_batch = X_test_batch.to(device)\n",
        "            y_test_batch = y_test_batch.to(device)\n",
        "\n",
        "            # get predictions from trained model\n",
        "            y_test_hat = model(\n",
        "                X_test_batch\n",
        "            )\n",
        "\n",
        "            # convert tensors to 2D list and move to cpu\n",
        "            associated_test_y_true_list.append(y_test_batch.cpu())\n",
        "            associated_test_y_hat_list.append(y_test_hat.cpu())\n",
        "\n",
        "        # flatten lists of batch outputs into single array\n",
        "        associated_test_y_true_list = torch.cat(associated_test_y_true_list).numpy()\n",
        "        associated_test_y_hat_list = torch.cat(associated_test_y_hat_list).numpy()\n",
        "\n",
        "    return (\n",
        "        best_val_auc_idx,\n",
        "        best_val_y_true_list, best_val_y_hat_list,\n",
        "        associated_test_y_true_list, associated_test_y_hat_list\n",
        "    )"
      ],
      "metadata": {
        "id": "Qpl6GPJ_smeR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Display Confusion Matrix"
      ],
      "metadata": {
        "id": "smhj3GR9Q_4G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def display_val_test_confusion_matrices(\n",
        "    best_val_data_confusion_matrix,\n",
        "    associated_test_data_confusion_matrix,\n",
        "    val_auc: float,\n",
        "    test_auc: float,\n",
        "    target_metric_substring: str\n",
        ") -> None:\n",
        "    # plotting both confusion matrices side by side\n",
        "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
        "\n",
        "    # val confusion matrix plot\n",
        "    sns.heatmap(\n",
        "        best_val_data_confusion_matrix,\n",
        "        annot = True,\n",
        "        fmt = \"d\",\n",
        "        linewidths = 0.5,\n",
        "        square = True,\n",
        "        cmap = 'Blues',\n",
        "        xticklabels = ['Negative', 'Positive'],\n",
        "        yticklabels = ['Negative', 'Positive'],\n",
        "        ax = axes[0]\n",
        "    )\n",
        "    axes[0].set_ylabel('Actual Label')\n",
        "    axes[0].set_xlabel('Predicted Label')\n",
        "    axes[0].set_title(\n",
        "        f'Val Data Confusion Matrix, {target_metric_substring} (AUC: {val_auc:.2f})',\n",
        "        size = 10\n",
        "    )\n",
        "\n",
        "    # test confusion matrix plot\n",
        "    sns.heatmap(\n",
        "        associated_test_data_confusion_matrix,\n",
        "        annot = True,\n",
        "        fmt = \"d\",\n",
        "        linewidths = 0.5,\n",
        "        square = True,\n",
        "        cmap = 'Blues',\n",
        "        xticklabels = ['Negative', 'Positive'],\n",
        "        yticklabels = ['Negative', 'Positive'],\n",
        "        ax = axes[1]\n",
        "    )\n",
        "    axes[1].set_ylabel('Actual Label')\n",
        "    axes[1].set_xlabel('Predicted Label')\n",
        "    axes[1].set_title(\n",
        "        f'Test Data Confusion Matrix, {target_metric_substring} (AUC: {test_auc:.2f})',\n",
        "        size = 10\n",
        "    )\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "pCIrzI4uRK2Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Functions to Find and Visualize Thresholds"
      ],
      "metadata": {
        "id": "1L8_ydrGRmb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_threshold(\n",
        "    metric_values: np.ndarray,\n",
        "    targets: List[float],\n",
        "    thresholds_val: np.ndarray\n",
        ") -> List[float]:\n",
        "    \"\"\"\n",
        "    Purpose: Finds thresholds corresponding to the closest values in `metric_values` for each\n",
        "        target in `targets`.\n",
        "    :param metric_values (np.ndarray): Array of metric values (either TPR or FPR from ROC).\n",
        "    :param targets (List[float]): List of target values for which to find the closest thresholds.\n",
        "    :param thresholds_val (np.ndarray): Array of thresholds corresponding to the metric values.\n",
        "    :return: List[float] containing thresholds closest to each target metric value.\n",
        "    \"\"\"\n",
        "    closest_thresholds = [\n",
        "        thresholds_val[np.argmin(np.abs(metric_values - target))] for target in targets\n",
        "    ]\n",
        "    return closest_thresholds"
      ],
      "metadata": {
        "id": "x1FHAI1sRnD9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_thresholds(\n",
        "    y_true_val,\n",
        "    y_pred_val,\n",
        "    y_true_test,\n",
        "    y_pred_test,\n",
        "    target_tprs: List[float] = [0.8],\n",
        "    target_fprs: List[float] = [0.05]\n",
        ") -> None:\n",
        "    # get ROC AUC for both val and test\n",
        "    val_auc = roc_auc_score(\n",
        "        y_true = y_true_val,\n",
        "        y_score = y_pred_val\n",
        "    )\n",
        "    test_auc = roc_auc_score(\n",
        "        y_true = y_true_test,\n",
        "        y_score = y_pred_test\n",
        "    )\n",
        "\n",
        "    # compute ROC curve for validation set;\n",
        "    # we will use val threshold on test, too\n",
        "    fpr_val, tpr_val, thresholds_val = roc_curve(\n",
        "        y_true_val,\n",
        "        y_pred_val\n",
        "    )\n",
        "\n",
        "    # process each target TPR\n",
        "    if target_tprs:\n",
        "        thresholds_tpr = find_threshold(\n",
        "            metric_values = tpr_val,\n",
        "            targets = target_tprs,\n",
        "            thresholds_val = thresholds_val\n",
        "        )\n",
        "\n",
        "        for i, threshold in enumerate(thresholds_tpr):\n",
        "            # apply threshold to both validation and test data\n",
        "            pred_val = [\n",
        "                1 if x >= threshold else 0 for x in y_pred_val\n",
        "            ]\n",
        "            pred_test = [\n",
        "                1 if x >= threshold else 0 for x in y_pred_test\n",
        "            ]\n",
        "\n",
        "            # calculate metrics for validation set\n",
        "            val_recall = recall_score(y_true_val, pred_val)\n",
        "            val_accuracy = accuracy_score(y_true_val, pred_val)\n",
        "            val_conf_matrix = confusion_matrix(y_true_val, pred_val)\n",
        "\n",
        "            # calculate metrics for test set\n",
        "            test_recall = recall_score(y_true_test, pred_test)\n",
        "            test_accuracy = accuracy_score(y_true_test, pred_test)\n",
        "            test_conf_matrix = confusion_matrix(y_true_test, pred_test)\n",
        "\n",
        "            # print results\n",
        "            print(f\"\\t- TPR Target: {target_tprs[i]}\")\n",
        "            print(f\"\\t\\t- Validation Accuracy: {val_accuracy}\")\n",
        "            print(f\"\\t\\t- Validation Recall: {val_recall}\")\n",
        "            print(f\"\\t\\t- Test Accuracy: {test_accuracy}\")\n",
        "            print(f\"\\t\\t- Test Recall: {test_recall}\")\n",
        "\n",
        "            # print confusion matrices\n",
        "            display_val_test_confusion_matrices(\n",
        "                best_val_data_confusion_matrix = val_conf_matrix,\n",
        "                associated_test_data_confusion_matrix = test_conf_matrix,\n",
        "                val_auc = val_auc,\n",
        "                test_auc = test_auc,\n",
        "                target_metric_substring = f\"TPR={target_tprs[i]}\"\n",
        "            )\n",
        "\n",
        "    # process each target FPR\n",
        "    if target_fprs:\n",
        "        thresholds_fpr = find_threshold(\n",
        "            fpr_val,\n",
        "            target_fprs,\n",
        "            thresholds_val\n",
        "        )\n",
        "\n",
        "        for i, threshold in enumerate(thresholds_fpr):\n",
        "\n",
        "            # apply threshold to both validation and test data\n",
        "            pred_val = [\n",
        "                1 if x >= threshold else 0 for x in y_pred_val\n",
        "            ]\n",
        "            pred_test = [\n",
        "                1 if x >= threshold else 0 for x in y_pred_test\n",
        "            ]\n",
        "\n",
        "            # calculate metrics for validation and test sets\n",
        "            val_accuracy = accuracy_score(y_true_val, pred_val)\n",
        "            val_conf_matrix = confusion_matrix(y_true_val, pred_val)\n",
        "            val_specificity = val_conf_matrix[0, 0] / (val_conf_matrix[0, 0] + val_conf_matrix[0, 1])\n",
        "\n",
        "            test_accuracy = accuracy_score(y_true_test, pred_test)\n",
        "            test_conf_matrix = confusion_matrix(y_true_test, pred_test)\n",
        "            test_specificity = test_conf_matrix[0, 0] / (test_conf_matrix[0, 0] + test_conf_matrix[0, 1])\n",
        "\n",
        "            # print results for validation and test sets\n",
        "            print(f\"\\t- FPR Target: {target_fprs[i]}\")\n",
        "            print(f\"\\t\\t- Validation Accuracy: {val_accuracy}\")\n",
        "            print(f\"\\t\\t- Validation Specificity: {val_specificity}\")\n",
        "            print(f\"\\t\\t- Test Accuracy: {test_accuracy}\")\n",
        "            print(f\"\\t\\t- Test Specificity: {test_specificity}\")\n",
        "\n",
        "            # print confusion matrices\n",
        "            display_val_test_confusion_matrices(\n",
        "                best_val_data_confusion_matrix = val_conf_matrix,\n",
        "                associated_test_data_confusion_matrix = test_conf_matrix,\n",
        "                val_auc = val_auc,\n",
        "                test_auc = test_auc,\n",
        "                target_metric_substring = f\"FPR={target_fprs[i]}\"\n",
        "            )"
      ],
      "metadata": {
        "id": "XcGtTQs7RqYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run Experiments"
      ],
      "metadata": {
        "id": "UkslZi91sXTI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Function to Load Feature and Target Data"
      ],
      "metadata": {
        "id": "5k5PWwytDUhm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "id": "wPCppJn9DgqO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "494e613b-2392-417f-977d-8756a8330b21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_DIR = '/content/drive/My Drive/CS6140_final_project/Data/'"
      ],
      "metadata": {
        "id": "ZspLDKPnDSoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_data(\n",
        "        data_dir: str = '/content/drive/My Drive/CS6140_final_project/Data/',\n",
        "        features_file: str = None,\n",
        "        targets_file: str = None\n",
        "    ) -> tuple:\n",
        "    \"\"\"\n",
        "    Load features and labels from JSON files.\n",
        "\n",
        "    Parameters:\n",
        "    data_dir (str): Directory containing the data files.\n",
        "    features_file (str): Name of the features file.\n",
        "    targets_file (str): Name of the targets file.\n",
        "\n",
        "    Returns:\n",
        "    Tuple: (X, y)\n",
        "        X (list): List of feature data.\n",
        "        y (list): List of target labels.\n",
        "    \"\"\"\n",
        "\n",
        "    features_loc = os.path.join(data_dir, 'features_data', features_file)\n",
        "    targets_loc = os.path.join(data_dir, 'targets_data', targets_file)\n",
        "\n",
        "    with open(features_loc, mode='r') as json_f:\n",
        "        X = json.load(json_f)\n",
        "    print(f\"- {format(len(X), ',')} total rows in features dict.\")\n",
        "\n",
        "    with open(targets_loc, mode='r') as json_f:\n",
        "        y = json.load(json_f)\n",
        "    print(f\"- {format(len(y), ',')} total rows in labels dict.\")\n",
        "\n",
        "    return X, y"
      ],
      "metadata": {
        "id": "X4QywGmFF1i3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Experiment Hyperparameters"
      ],
      "metadata": {
        "id": "Bk1yF1YroEmw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_EPOCHS = 10\n",
        "WEIGHT_DECAY = 1.00E-06\n",
        "NUM_FEATURES = 45"
      ],
      "metadata": {
        "id": "qJSt5OL-fg_L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hyperparameters_dict = {\n",
        "    0: {\n",
        "        'features_file': '12_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_12hrfeaturewindow_6hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    1: {\n",
        "        'features_file': '12_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_12hrfeaturewindow_24hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    2: {\n",
        "        'features_file': '24_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_24hrfeaturewindow_6hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    3: {\n",
        "        'features_file': '24_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_24hrfeaturewindow_24hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    4: {\n",
        "        'features_file': '48_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_48hrfeaturewindow_6hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    5: {\n",
        "        'features_file': '48_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_48hrfeaturewindow_24hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    6: {\n",
        "        'features_file': '72_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_72hrfeaturewindow_24hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "    7: {\n",
        "        'features_file': '72_MLP_features_dict.json',\n",
        "        'targets_file': 'targets_dict_anyfailure_72hrfeaturewindow_24hrlookahead.json',\n",
        "        'learning_rate': 0.001,\n",
        "        'batch_size': 64,\n",
        "        'mlp_dropout': 0.0,\n",
        "        'mlp_layers': [16],\n",
        "    },\n",
        "}"
      ],
      "metadata": {
        "id": "t5VYoY4hoIyf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Run Experiments"
      ],
      "metadata": {
        "id": "ZcY0lv12Cn55"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for experiment_idx, experiment_dict in hyperparameters_dict.items():\n",
        "    print(\"-\" * 50)\n",
        "    print(f\"*experiment_idx = {experiment_idx}\")\n",
        "\n",
        "    # get hyperparams from experiment_dict\n",
        "    learning_rate = experiment_dict['learning_rate']\n",
        "\n",
        "    # load data from file paths\n",
        "    X_dict, y_dict = load_data(DATA_DIR, experiment_dict['features_file'], experiment_dict['targets_file'])\n",
        "\n",
        "    # get overlap of keys of dictionaries\n",
        "    keys_set = set(X_dict.keys()).intersection(set(y_dict.keys()))\n",
        "    X_dict = {\n",
        "        k: v for k, v in X_dict.items()\n",
        "        if k in keys_set\n",
        "    }\n",
        "    y_dict = {\n",
        "        k: v for k, v in y_dict.items()\n",
        "        if k in keys_set\n",
        "    }\n",
        "\n",
        "    # convert each value in y_dict to a list\n",
        "    y_dict = {\n",
        "        k: [v] for k, v in y_dict.items()\n",
        "    }\n",
        "\n",
        "    # make MLP model\n",
        "    model = make_mlp(\n",
        "        input_dim = 45,\n",
        "        hidden_dims = experiment_dict['mlp_layers'],\n",
        "        output_dim = 1,\n",
        "        hidden_activation = nn.ReLU,\n",
        "        dropout = 0.0\n",
        "    )\n",
        "\n",
        "    # instantiate loss function\n",
        "    loss_fn = torch.nn.BCEWithLogitsLoss()\n",
        "\n",
        "    # instantiate optimizer\n",
        "    optimizer = optim.Adam(\n",
        "        params = model.parameters(),\n",
        "        lr = learning_rate,\n",
        "        weight_decay = WEIGHT_DECAY\n",
        "    )\n",
        "\n",
        "    # split dictionaries into train-val-test arrays\n",
        "    (\n",
        "        X_train, X_val, X_test,\n",
        "        y_train, y_val, y_test\n",
        "    ) = get_train_val_test_arrays(\n",
        "        X = X_dict,\n",
        "        y = y_dict\n",
        "    )\n",
        "\n",
        "    # convert arrays to tensors\n",
        "    X_train = torch.tensor(X_train, dtype = torch.float32)\n",
        "    X_val = torch.tensor(X_val, dtype = torch.float32)\n",
        "    X_test = torch.tensor(X_test, dtype = torch.float32)\n",
        "    y_train = torch.tensor(y_train, dtype = torch.float32)\n",
        "    y_val = torch.tensor(y_val, dtype = torch.float32)\n",
        "    y_test = torch.tensor(y_test, dtype = torch.float32)\n",
        "\n",
        "    # make Datasets\n",
        "    train_dataset = get_tensordataset(\n",
        "        X = X_train,\n",
        "        y = y_train\n",
        "    )\n",
        "    val_dataset = get_tensordataset(\n",
        "        X = X_val,\n",
        "        y = y_val\n",
        "    )\n",
        "    test_dataset = get_tensordataset(\n",
        "        X = X_test,\n",
        "        y = y_test\n",
        "    )\n",
        "\n",
        "    # make DataLoaders\n",
        "    train_loader = get_dataloader(\n",
        "        dataset = train_dataset,\n",
        "        shuffle = True\n",
        "    )\n",
        "    val_loader = get_dataloader(\n",
        "        dataset = val_dataset,\n",
        "        shuffle = False\n",
        "    )\n",
        "    test_loader = get_dataloader(\n",
        "        dataset = test_dataset,\n",
        "        shuffle = False\n",
        "    )\n",
        "\n",
        "    # train-val-test\n",
        "    (\n",
        "        best_val_auc_idx,\n",
        "        best_val_y_true_list, best_val_y_hat_list,\n",
        "        associated_test_y_true_list, associated_test_y_hat_list\n",
        "    ) = train_validate_test(\n",
        "            model = model,\n",
        "            train_loader = train_loader,\n",
        "            val_loader = val_loader,\n",
        "            test_loader = test_loader,\n",
        "            loss_fn = loss_fn,\n",
        "            optimizer = optimizer,\n",
        "            device = torch.device('cpu'),\n",
        "            epochs = 200\n",
        "    )\n",
        "\n",
        "    # compute metrics of interest\n",
        "    best_val_auc = roc_auc_score(\n",
        "        y_true = best_val_y_true_list,\n",
        "        y_score = best_val_y_hat_list\n",
        "    )\n",
        "    best_test_auc = roc_auc_score(\n",
        "        y_true = associated_test_y_true_list,\n",
        "        y_score = associated_test_y_hat_list\n",
        "    )\n",
        "\n",
        "    # print metrics of interest\n",
        "    print(f\"\\t- Best Val AUC Epoch: {best_val_auc_idx}\")\n",
        "    print(f\"\\t- Best Val AUC: {best_val_auc}\")\n",
        "    print(f\"\\t- Best Test AUC: {best_test_auc}\")\n",
        "\n",
        "    # evaluate thresholds\n",
        "    evaluate_thresholds(\n",
        "    y_true_val = best_val_y_true_list,\n",
        "    y_pred_val = best_val_y_hat_list,\n",
        "    y_true_test = associated_test_y_true_list,\n",
        "    y_pred_test = associated_test_y_hat_list,\n",
        "    target_tprs = [0.8],\n",
        "    target_fprs = [0.05]\n",
        ")"
      ],
      "metadata": {
        "id": "tZhdbaNMixHa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc7ad792-f90f-450c-a451-4a8b70db1261"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 0\n",
            "- 73,000 total rows in features dict.\n",
            "- 73,000 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [04:11<00:00,  1.26s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 183\n",
            "\t- Best Val AUC: 0.8769672536604671\n",
            "\t- Best Test AUC: 0.8885108330035616\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.8296296296296296\n",
            "\t\t- Validation Recall: 0.8\n",
            "\t\t- Test Accuracy: 0.7603566529492456\n",
            "\t\t- Test Recall: 0.8714285714285714\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.9455418381344307\n",
            "\t\t- Validation Specificity: 0.949584487534626\n",
            "\t\t- Test Accuracy: 0.9460219478737997\n",
            "\t\t- Test Specificity: 0.9510387811634349\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 1\n",
            "- 73,000 total rows in features dict.\n",
            "- 73,000 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [04:10<00:00,  1.25s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 184\n",
            "\t- Best Val AUC: 0.9638852983598288\n",
            "\t- Best Test AUC: 0.9560870587147814\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.9325102880658436\n",
            "\t\t- Validation Recall: 0.7992957746478874\n",
            "\t\t- Test Accuracy: 0.9755829903978052\n",
            "\t\t- Test Recall: 0.5335689045936396\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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qaqplb8CTwOBg02X8+PHy9PQ0W8aPH2+VS7lx44aGDBmiTp06ycPDQ5IUGxub6fePo6OjvL29FRsba2pTtGhRszYZr+/XJmM7coeMz4nz589r2rRpprwmY/nggw9sEseiRYt0/vx5HTp0SLNmzVJiYqLq1q2b6TPaloYPH6433nhDderU0U8//aSDBw9q8uTJ2r9/v77++murnuvEiRN68cUX9dRTT1mlMsnaeca9lChRIlMn2blz57R582YVK1bskZzz5s2bkmS13FO61Ul3+/e+v7+/evbsabauRIkSkv6XK50+fVqff/655s6dmykHyMjDwsPD5evrq5dffvm+HUT3MmDAAP3www9auXKlfv/9d/3999969dVX77nPwIEDtXHjRn3zzTeKjo5W//791adPH61bt+6u+xiNRn3xxRcKDg7OtO3atWtasWKFBg8erIULFz7wtdy8eVOBgYH67LPPFBISoh07dmjXrl3q3bu3Zs6cqUOHDmX7WN9++60GDhyojz76SHv37lX16tUVGBioixcv3nWfESNG6KuvvtLMmTN1+PBhvfPOO2rXrp327dtnanP58mU1aNBATk5O+umnn3T48GFNnjxZBQoUMLVp0aKFrl69qp9++unB3ojHkY1zqCeaETkmJSXFWLRoUePHH39stv7q1atGd3d34+zZs43//POPsWPHjkZfX1+jm5ubsWrVqsZly5aZtX/++eeN/fr1u+t5Fi1aZPT09My0/sKFC8ZChQoZg4KCTOt++uknY4MGDYyenp5Gb29vY6tWrYzHjx83bZdktjz//PNGo9Fo3LVrlzEgIMBYsGBBo4eHh7Fx48bGyMjIe15/r169jPny5TOeO3cu07arV68aU1JSjEaj0RgXF2d88803jV5eXkY3Nzdj8+bNjUePHs10fRs3bjRWrFjRmC9fPmNgYKDx77//NhqNRuNHH32UKe67vW9t2rQxduvWzfR61qxZxnLlyhldXFyMRYoUMbZv39607c79HzbOrJw6dcooyThixAhj+fLlzbZVqFDBOHLkSKMk46lTp4xGo9GYmppq7NGjh7F06dJGV1dXY4UKFYzTpk0z7ZPVe/Hrr7+azrNixQpj48aNjS4uLsZFixaZfe+kp6cbmzZtamzWrJkxPT3daDQajf/++6+xePHixpEjR971Gu53bfv27cu0rVSpUsapU6eaXqekpBjz5s1rHDp0qOk6qlevbrbPwIEDjQUKFLA4jgxffvmlsUCBAsbk5GTTuiFDhhj9/Pzuus/u3buNkoxnz541rTtw4IBRkvHYsWN33a93797G1157LcttTZo0Mc6ZM8c4e/Zs40svvZRpuyTjmjVrMq3v1q2bsU2bNkaj8dbXqkqVKsZatWoZ09LSMrW9fPnyXWO70+HDh42SjLt37zat++mnn4wGgyHLn12j0Wj8+eefjQ4ODsYrV66Y1sXHxxsNBoMxLCzMaDTe+poWL17cOH/+/HuePzk52eji4mL85Zdfsh3zk8K11UybLjdu3DBeuXLFbLlx40a2Yr3b963RaDTevHnT2Lp1a2PNmjXNvmc+/fRTY4UKFTK1L1y4sPHLL780Go1G40svvWQMCQkx237o0CGjJOPhw4eNRqPR6OTklOlzc9asWcYiRYpkK3bYn6zymnnz5hkrVqxodHFxMfr5+RlnzZpl2pacnGzs3bu30cfHx+ji4mIsWbKkcdy4cUaj8dbnze2fiaVKlbrree/2fdy1a1dj/vz5jXFxcUaj0Xjf3K1bt26ZPotPnTp138/wrERERBgl3bXd7b/vv/zyS2PZsmWNTk5OxgoVKhiXLl2a6frmzZtnbNu2rdHNzc1Yrlw54//93/8Zjcb/fWbfvtyZJ2RYs2aNKdcyGo3GqKgoY5MmTYzu7u7G/PnzG5999lnTZ0pW+z9MnHfz/PPPG3v16mUsWLCgcdu2bab1n376qbF169bG6tWrGz/66CPT+qVLlxpr1apldHd3NxYtWtTYqVMn44ULF+76XmTkjs8//7yxd+/exn79+hkLFixobNKkiSnmjO+dJUuWGPPly2eWI/bq1cvo5+dnTEpKuud13O3asvo7IKtcqWfPnkYfHx+z67g9D8vIYe73ft5NfHy80cnJybhy5UrTuujoaKMk486dO++6X5UqVYxjx441W/fss88ahw8fftd9du/ebXRwcDAmJCRk2rZ48WJjvXr1jPHx8ca8efOa5WlGo3nOdLtff/3VKMn0c/P5558bHRwcjHv37s3U9ubNm8bExMS7xnen5557zti7d2/T67S0NKOvr69x/Pjxd92nWLFixi+++MJs3auvvmr2t9uQIUOMDRs2vO/5u3fvbuzSpUu2433c2TqHepI94d1tOcvR0VFdu3bV4sWLzYaPrVy5UmlpaerUqZNu3LihWrVq6ccff9TBgwcVEhKiN998874lmNlRpEgRBQUFad26daZ5ZJKSkjRw4EDt2bNHmzdvloODg9q1a2eqSrizKiWjIuXq1avq1q2btm3bpj/++EPly5dXy5YtdfXq1SzPnZ6erhUrVigoKEi+vr6Ztru7u8vR0VHSrbLXPXv2aN26ddq5c6eMRqNatmxpVkFy7do1TZo0SV9//bXCw8N19uxZ093ODz74QIsWLZL0vzuk2bFnzx717dtXY8eOVUxMjDZu3KjGjRvftf3Dxnkvr7zyii5fvqxt27ZJulUhc/nyZbVu3dqsXXp6up566imtXLlShw8f1qhRo/Thhx/qu+++M70XHTp0UPPmzU3vxe1lyEOHDlW/fv0UHR2twMBAs2MbDAYtWbJEu3fv1owZMyRJ77zzjooXL65Ro0bd9xoehqOjo5ycnEx3Cu90+vRp/fzzz2bDaqRbd3Tc3d3vulSpUsXUNjtDc+7k5+enggULasGCBbp586auX7+uBQsWqFKlSipduvRdr2fr1q1mw4QynDhxQjt37lSHDh3UoUMHbd26VWfOnLnXW5OlqKgoHTp0SO+//74cHDL/mr/9rnSTJk301ltv3fVY2RnWdKfk5GQZDAazu7murq5ycHAwfQ/v3btX586dk4ODg2rWrKlixYqpRYsWmSZydnZ2Vo0aNR66xP+xZOO7cy4uLvLw8DBbHvaOfUpKijp06KAzZ84oLCzMVN0k3aoIuPPOb2pqquLi4uTj42Nqc+ed+IzX92uTsR25X2hoqEaNGqVPP/1U0dHRGjdunEaOHKklS5ZIkmbMmKF169bpu+++U0xMjEJDQ02/o3fv3i3pf5VLGa8tMWDAAF29elVhYWGSdN/cbfr06ZmqUkqUKHHfz/C7Xbu7u7vefffdLLdn/L5fs2aN+vXrp/fff18HDx7U22+/re7du+vXX381az9mzBh16NBBBw4cUMuWLRUUFKS4uDiVKFFC58+fl4eHh6ZNm6bz58/rjTfeyNb7ExQUpKeeekq7d+9WZGSkhg4dKicnpyzbPmyc9+Ls7KygoCBTTijdGp7Wo0ePTG1TUlL08ccfa//+/Vq7dq1Onz5t+qwsUaKEaT6cmJgYnT9/XtOnTzftu2TJEjk7O2v79u1ZDgvr2rWrKebU1FT9+OOPmj9/vkJDQ5U3b957XsPDcnNzu2sudf36dVOl3u25UMb32L2WjM/oyMhIpaSkmA1jrlixokqWLHnPYcz169fXunXrdO7cORmNRv366686evSomjVrdtd9tm7dqgoVKmQ52mDBggXq0qWLPD091aJFiwce/hkaGqqAgADVrFkz0zYnJyfly5dP0v+GOd7NzZs3FRkZafa+ODg4KCAg4J7vS3JyslxdXc3Wubm5mXIpSVq3bp1q166t119/XUWKFFHNmjU1b968TMd67rnnyKVuR4WTzTjmdABPuh49emjixIn6/fffTfPXLFq0SO3btzcNWbi9Q+K9997Tzz//rO+++07PPffcQ5+/YsWKunr1qv79918VKVIk0xCfhQsXqnDhwjp8+LCqVq2qwoULS5IKFixolqy/+OKLZvvNnTtXXl5e+v333/Xyyy9nOu8///yjy5cvq2LFiveM79ixY1q3bp22b99u6hgJDQ1ViRIltHbtWr3++uuSbiUGc+bM0dNPPy1J6tOnj8aOHSvpVudVRsJlyR8YZ8+eVb58+fTyyy8rf/78KlWqVJYfONaK816cnJzUpUsXLVy4UA0bNtTChQvVpUuXTAmbk5OTxowZY3pdpkwZ7dy5U9999506dOggd3d3ubm5KTk5Ocv3on///vcsey5evLi++uorde3aVbGxsdqwYYP27dtn6hx8FG7evKnJkyfrypUrZt9nf/75p9zd3ZWWlqYbN25IUqa5fubPn6/r16/f9di3v3+xsbEqU6aM2fbbh+bcXpqcIX/+/Prtt9/Utm1b03xe5cuX188//3zP9+TMmTNZdrQuXLhQLVq0MJ0rMDBQixYtsnhukmPHjknSfX++JKlkyZL3HEqQnWFNd6pXr57y5cunIUOGaNy4cTIajRo6dKjS0tJMHb4nT56UdGs+rilTpqh06dKaPHmymjRpoqNHj8rb29t0PF9f3wfqeIN9y+hsOnbsmH799VcVLFjQbLu/v7/i4+MVGRmpWrVqSZK2bNmi9PR01a1b19Rm+PDhSklJMf08h4WFyc/Pz/Rz5O/vr82bN5vNlxMWFiZ/f38bXCVs4aOPPtLkyZNNn19lypTR4cOH9dVXX6lbt246e/asypcvr4YNG8pgMKhUqVKmfTPyGi8vrwfuhMz4XZsxn2Lx4sXvmbt5enrK2dlZefPmNTtnnjx57vkZnpVjx46pbNmyd+3AyTBp0iS99dZbpo6pgQMH6o8//tCkSZP0wgsvmNq99dZb6tSpkyRp3LhxmjFjhnbt2qXmzZvLx8dHBoNBnp6eFudTgwYNMr1P5cuXf6Rx3kuPHj3UqFEjTZ8+XZGRkbpy5YpefvnlTJ+zt3dClS1bVjNmzFCdOnWUmJgod3d302dUkSJFMg0tLF++/H3ngPzqq6/0zDPPqG/fvlq9erVGjx5t+j33qERGRmrZsmWZcvb69evLwcFB165dk9FoVK1atdS0aVPT9ldeecX0O/duMqZ4iI2NlbOzc6b35H7DmGfOnKmQkBA99dRTcnR0lIODg+bNm3fPG713y6WOHTumP/74w3RTvEuXLho4cKBGjBhxz06hrBw7dsz099m9eHp6ys/P767b//nnH6WlpWU5vPvIkSN33S8wMFBTpkxR48aN9fTTT2vz5s1avXq12QNnTp48qdmzZ2vgwIH68MMPtXv3bvXt21fOzs7q1q2bqZ2vr6/++9//Kj09PcubkcCjwndbDqtYsaLq169vGl98/Phxbd261TQeOS0tTR9//LGqVasmb29vubu76+effzY9xedhZVRWZfwCPnbsmDp16qSyZcvKw8PDdAfwfue7cOGCevbsqfLly8vT01MeHh5KTEy8637GbE4IHh0dLUdHR7MPuoIFC8rPz0/R0dGmdXnz5jV14khSsWLF7jkmOjteeukllSpVSmXLltWbb76p0NBQXbt2Lcfi7NGjh1auXKnY2FitXLkyyzty0q0JCWvVqqXChQvL3d1dc+fOzfb3S1ZVN3d6/fXX1a5dO3322WeaNGnSPRPHhzFkyBC5u7srb968+vzzz/XZZ5+pVatWpu1+fn6KiorS7t27NWTIEAUGBuq9994zO0bx4sVVrly5uy63/9HxIK5fv67g4GA1aNBAf/zxh7Zv366qVauqVatW9+zoun79eqY7VmlpaVqyZIm6dOliWtelSxctXrzY4kkes/vzJUlLly612jw8GQoXLqyVK1fqhx9+kLu7uzw9PRUfH69nn33WlORkXNPw4cPVvn171apVS4sWLZLBYMg0waebm9tdf/aeaHb+SN/ExERFRUWZHjJx6tQpRUVF6ezZs0pJSdFrr72mPXv2KDQ0VGlpaYqNjVVsbKzp7nulSpXUvHlz9ezZU7t27dL27dvVp08fdezY0fRHRufOneXs7Kzg4GAdOnRI3377raZPn66BAwea4ujXr582btyoyZMn68iRIxo9erT27NmjPn36PPzXADkuKSlJJ06cUHBwsFnFxSeffGKav/Gtt95SVFSU/Pz81LdvX23atMmqMdyZSz1M7mbpZ7gl+VSDBg3M1jVo0MAsR5FkNndovnz55OHh8dD51MCBA/Wf//xHAQEB+uyzz0xfl5yIs3r16ipfvry+//57LVy4UG+++WaWN4giIyPVunVrlSxZUvnz59fzzz8v6f75sKRsdRwVKFBACxYs0OzZs/X0009r6NCh993nQWTcnHNzc9Nzzz0nf3//TE/o/Pbbb7Vv3z6tWrVK5cqV0+LFi806MPPnz3/PXKpcuXJyc3N7qDhnzpypP/74Q+vWrVNkZKQmT56s3r1765dffrnrPlnlUtKtm3eBgYEqVKiQJKlly5a6cuWKtmzZYnFc2f35ateu3T07jh7U9OnTVb58eVWsWFHOzs7q06ePunfvbtZhlJ6ermeffVbjxo1TzZo1FRISop49e2aqrnNzc1N6erpVH/aRq9l5DvU4ocPJDgQHB2vVqlW6evWqFi1apKefftr0wTZx4kRNnz5dQ4YM0a+//qqoqCgFBgbetRzWUtHR0fLw8DDdWW7durXi4uI0b948RUREmIbM3O983bp1U1RUlKZPn64dO3YoKipKBQsWvOt+hQsXlpeXl9V+Od95Z89gMNz3Q8LBwSFTm9uHv+XPn1979+7V8uXLVaxYMY0aNUrVq1fP9hNJrBVnhmrVqqlixYrq1KmTKlWqpKpVq2Zqs2LFCn3wwQcKDg7Wpk2bFBUVpe7du2f7+yWjNPherl27psjISOXJk8dUSfMoDBo0SFFRUfrrr790+fJlDRkyxGy7s7OzypUrp6pVq+qzzz7LdGdYsmxIXXaG5txp2bJlOn36tBYtWqQ6deqoXr16WrZsmU6dOqX/+7//u+u1FSpUKNMwvZ9//lnnzp3TG2+8IUdHRzk6Oqpjx446c+aMNm/ebGqXP3/+LCfUj4+Pl6enpySpQoUKkmSVn6/sDGvKSrNmzXTixAldvHhR//zzj77++mudO3dOZcuWlSRTVVXlypVN+7i4uKhs2bKZEvq4uDhTFQJyjz179qhmzZqmytCBAweqZs2aGjVqlM6dO6d169bpr7/+Uo0aNVSsWDHTcvvDAUJDQ1WxYkU1bdpULVu2VMOGDc0m8vf09NSmTZt06tQp1apVS++//75GjRplNql9/fr1tWzZMs2dO1fVq1fX999/r7Vr12b5OxS5T8YTt+bNm2fq4IyKitLBgwf1xx9/SJKeffZZnTp1Sh9//LGuX7+uDh066LXXXrNaDBmdIRlVsg+auz3IZ3iFChV08uRJi5+WdTdZ5Sn3uulxv1xKulXJeujQIbVq1UpbtmxR5cqVtWbNGpvGebsePXpo1qxZ+v7777O8eZeUlKTAwEB5eHgoNDRUu3fvNsWbnXwqO7mUJIWHhytPnjw6f/68kpKSsrWPpTJuzkVHR+v69etat25dpiqbEiVKqHz58mrXrp3GjRundu3amXVKWDKkzsfHRzdv3syUK99rGPP169f14YcfasqUKWrdurWeeeYZ9enTR2+88YYmTZp012vLKpfKuHn3448/mnKpvHnzKi4uzmzycA8Pj7vmUnny5DF9DStUqGCVXKpQoULKkyePxcO7CxcurLVr1yopKUlnzpzRkSNH5O7ubsqlpFv51O25lHTrhk1WuVS+fPkeunMQsBQdTnagQ4cOcnBw0LJly7R06VL16NHDdJds+/btatOmjbp06aLq1aurbNmyVnt87MWLF7Vs2TK1bdtWDg4O+vfffxUTE6MRI0aoadOmqlSpUqZf5Bljum8v5cyIs2/fvmrZsqWqVKkiFxcX/fPPP3c9t4ODgzp27KjQ0NAsn5KXmJio1NRUVapUSampqWZzxWTEeecvV0sVLlzYbD6ntLS0TPPHODo6KiAgQBMmTNCBAwd0+vTpLO+QPMo4b9ejRw/99ttvd61uyhjS9+6776pmzZoqV65cpjuJzs7Omb5+lsiYF+inn37SjBkzHuiOUXYUKlRI5cqVM5Xw38+IESM0adIks++n+fPnm/0BcueyYcMGU1t/f3+Fh4ebJcp3Ds2507Vr1+Tg4GAWX8breyW+NWvW1OHDh83WLViwQB07dswUY8eOHbVgwQJTOz8/v0yP4E5LS9P+/ftNHU01atRQ5cqVNXny5CzjsKTT9PZhTRnuHNZ0L4UKFZKXl5e2bNmiixcv6pVXXpF06w6wi4uLYmJiTG1TUlJ0+vTpTJVnBw8evOtw1ieanc8/0KRJExmNxkzL4sWLVbp06Sy3GY1Gs+EL3t7eWrZsma5evaorV65o4cKFcnd3NzvPM888o61bt+rGjRv666+/MnVOS7cqM2NiYpScnKyDBw+qZcuWFl8P7FPRokXl6+urkydPZqq6uH2YtIeHh9544w3NmzdP3377rVatWmWa88fJyemhPhcznpqXMT9LdnK3rD6Ls/MZfqfOnTsrMTFRX375ZZbbM37fV6pUSdu3b890PmvkUlevXjXrMMmoarxdhQoVNGDAAG3atEmvvvqq2TxKt3tUcd6uc+fO+vPPP1W1atUsj3vkyBH9+++/+uyzz9SoUSNVrFgx042Xu+XD2bVjxw59/vnnpkrgR1VxmXFzrnTp0pnmuczKa6+9JkdHR7Pvp1deeeWeuVRUVJSpQr5WrVpycnIyu1EWExOjs2fP3nUYc0pKilJSUjIN88qTJ899c6kjR46YdXhu2LBBV69e1b59+8ziW758uVavXm36efDz89OhQ4cyVfvs3btXZcqUMXVodu7cWb/88ovZU+Fujzu7HYXOzs6qVauW2fuSnp6uzZs3Z2t4t6urq4oXL67U1FStWrVKbdq0MW1r0KCBWS4lSUePHiWXuh87z6EeJ0/21dsJd3d3vfHGGxo2bJjOnz9vNoFv+fLlFRYWph07dig6Olpvv/32Az2q1Gg0KjY2VufPn1d0dLQWLlyo+vXry9PTU5999pmkW+W9BQsW1Ny5c3X8+HFt2bLFbFiCdGusupubmzZu3KgLFy6Y7g6UL19eX3/9taKjoxUREaGgoKD79qB/+umnKlGihOlxwocPH9axY8e0cOFC1axZU4mJiSpfvrzatGmjnj17atu2bdq/f7+6dOmi4sWLm/2yfRAvvviifvzxR/344486cuSIevXqZfaH+Pr16zVjxgxFRUXpzJkzWrp0qdLT07Mco/0o47xdz549denSJf3nP//Jcnv58uW1Z88e/fzzzzp69KhGjhyZaQLU0qVL68CBA4qJidE///xj0V3RH3/8UQsXLlRoaKheeuklDRo0SN26dbvrpNq25O/vr2eeeUbjxo0zrbNkSF12huasWbPGbF6kl156SZcvX1bv3r0VHR2tQ4cOqXv37nJ0dDSba+JOgYGBZhM+Xrp0ST/88IO6deumqlWrmi1du3bV2rVrTX8YDRw4UPPnz9eXX36pY8eOKSoqSiEhIbp8+bLp+8JgMGjRokU6evSoGjVqpA0bNujkyZM6cOCAPv30U7Pvya5du2rYsGF3jTU7w5rOnTunihUrmj3MYNGiRfrjjz904sQJffPNN3r99dc1YMAA08+Ph4eH3nnnHX300UfatGmTYmJi1KtXL0kyzXkm3ZoT5dy5c2YTbQLA7caMGaPx48drxowZOnr0qP78808tWrTINK/flClTtHz5ch05ckRHjx7VypUr5ePjY5pnpnTp0tq8ebNiY2Pv+3kWHx+v2NhY00T3r732mpYtW6bZs2ebjped3K106dKKiIjQ6dOn9c8//yg9PT1bn+F3qlu3rgYPHqz3339fgwcP1s6dO02Vsa+//rpp4vRBgwZp8eLFmj17to4dO6YpU6Zo9erV2Xpwyf3OnzdvXn344Yc6ceKEli1bZjZB8/Xr19WnTx/99ttvOnPmjLZv367du3erUqVKWR7vUcV5uwIFCuj8+fNmf/zfrmTJknJ2dtbMmTN18uRJrVu3zjRPY4ZSpUrJYDBo/fr1unTpkqnSLjuuXr2qN998U3379lWLFi0UGhqqb7/9Vt9///1DXZc1GAwG9e3bV5999plpKLslQ+o8PT0VHBysgQMH6tdff1VkZKS6d+8uf39/1atXz3SeihUrmqrGPDw89Pzzz2vQoEH67bffdOrUKS1evFhLly5Vu3bt7hrrCy+8oMTERB06dMi0bsGCBWrVqpWqV69ulkt16NBBXl5eCg0NlXRrInuDwaCuXbsqMjJSx48f18KFCzVt2jS9//77puP1799fDRo0UNOmTTVr1izt379fJ0+e1Hfffad69eqZKv3vzA+zMnDgQM2bN09LlixRdHS0evXqpaSkJHXv3t3U5s6cLCIiQqtXr9bJkye1detWNW/eXOnp6Ro8eLCpzYABA/THH39o3LhxOn78uKmit3fv3mbn37p16z0nYQceFTqc7ERwcLAuX76swMBAswnwRowYoWeffVaBgYFq0qSJfHx81LZtW4uPn5CQoGLFiql48eLy9/c3TaS5b98+09AWBwcHrVixQpGRkapataoGDBigiRMnmh3H0dFRM2bM0FdffSVfX1/TH64LFizQ5cuX9eyzz5o+RO+caPhO3t7e+uOPP9SlSxd98sknqlmzpho1aqTly5dr4sSJpuFBixYtUq1atfTyyy/L399fRqNRGzZsuO8EmffTo0cPdevWTV27dtXzzz+vsmXLmnUSeHl5afXq1XrxxRdVqVIlzZkzR8uXLzcbhnW7RxXn7RwdHVWoUKG7Tkj99ttv69VXX9Ubb7yhunXr6t9//8305JqePXvKz89PtWvXVuHChTPdSbybS5cuKTg4WKNHj9azzz4r6VaSX7RoUb3zzjumdqVLl7Z4kmtrGTBggObPn6///ve/Fu+bnaE5V65cMbuLVLFiRf3www86cOCA/P391ahRI/3999/auHHjPSfiDgoK0qFDh0zHWrp0qfLly2c2SWeGpk2bys3NTd98840kqVOnTpo/f74WLlyoWrVqqXnz5oqNjVV4eLhZmfxzzz2nPXv2qFy5curZs6cqVaqkV155RYcOHdK0adNM7c6ePXvfJzfeb1hTSkqKYmJizOZZiomJUdu2bVWpUiWNHTtWw4cPz1QaP3HiRHXs2FFvvvmm6tSpozNnzmjLli1mFWXLly9Xs2bNHnq+LQCPr//85z+aP3++Fi1apGrVqun555/X4sWLTRVO+fPn14QJE1S7dm3VqVNHp0+f1oYNG0wVFZMnT1ZYWJhKlChx3wqA7t27q1ixYqpYsaJ69eold3d37dq1S507dza1yU7u9sEHHyhPnjyqXLmyChcurLNnz2brMzwrn3/+uZYtW6aIiAgFBgaqSpUqGjhwoJ555hnTpMFt27bV9OnTNWnSJFWpUkVfffWVFi1alK0Jke/F29tb33zzjTZs2KBq1app+fLlZjlAnjx59O+//6pr166qUKGCOnTooBYtWmQaAp/hUcV5Jy8vr7sOfStcuLAWL16slStXqnLlyqY5K29XvHhxjRkzRkOHDlXRokUtqlDq16+f8uXLZ7pBVq1aNY0bN05vv/22zp07J+nWMMR7Pe32UerWrZtSUlIyzfWUXVOnTtXLL7+s9u3bq3HjxvLx8TFN4J0hJibGbEjbihUrVKdOHQUFBZne808//dQsv7xTwYIF1a5dO1Mn0oULF/Tjjz9megCSJNNTtzMqxr28vLR161alpKTolVdeUY0aNTRjxgxNmTJFb7/9tmk/FxcXhYWFafDgwfrqq69Ur1491alTRzNmzFDfvn1NQ7PvzA+zkjFEcNSoUapRo4aioqK0ceNGs9ztzpzsxo0bGjFihCpXrqx27dqpePHi2rZtm9mk7HXq1NGaNWu0fPlyVa1aVR9//LGmTZumoKAgU5tz585px44dZp1bgK0YjJbMLgsA93Dt2jUVLFhQP/30k9WTw8fNoEGDlJCQoK+++iqnQ7FbN2/eVPny5bVs2bJMk8hCcms79/6NrOj62pD7NwIAPLRu3brJYDCYVYshswMHDuill17SiRMnMg23xv8MGTJEly9fNrtZ+KQjh7IdKpwAWM2vv/6qF198kc6mbBg+fLhKlSpl8RPoniRnz57Vhx9+SGcTAOCJYTQa9dtvv2UaxofMnnnmGX3++ec6depUTodi14oUKcL3E3IMFU4AAORCbu3m2/R819dkPXccAABAbkIOZTtUOAEAAAAAAMCqsp55GAAA2DeDIacjAAAAyH3IoWyGCicAAAAAAABYFRVOAADkQgbuzgEAAFiMHMp2qHACAAAAAACAVVHhBABALsTdOQAAAMuRQ9nOY9vh5FazT06HAORa1/d9oUuJqTkdBpArFXZ/bD9a8YQghwIezPV9X+h6Sk5HAeRebk45HQGsjSF1AAAAAAAAsCpuwwIAkBtRDQ4AAGA5ciibocIJAAAAAAAAVkWHEwAAuZDBYLDpAgAA8Diw5xwqPDxcrVu3lq+vrwwGg9auXWvalpKSoiFDhqhatWrKly+ffH191bVrV/39999mx4iLi1NQUJA8PDzk5eWl4OBgJSYmmrU5cOCAGjVqJFdXV5UoUUITJkzIFMvKlStVsWJFubq6qlq1atqwYYNF1yLR4QQAAAAAAJDjkpKSVL16dc2aNSvTtmvXrmnv3r0aOXKk9u7dq9WrVysmJkavvPKKWbugoCAdOnRIYWFhWr9+vcLDwxUSEmLanpCQoGbNmqlUqVKKjIzUxIkTNXr0aM2dO9fUZseOHerUqZOCg4O1b98+tW3bVm3bttXBgwctuh6D0Wg0Wvge5Ao8YQV4cDylDnhwtnpKXf43ltjkPBmuftvNpudDziGHAh4MT6kDHo6tnlKXW3Iog8GgNWvWqG3btndts3v3bj333HM6c+aMSpYsqejoaFWuXFm7d+9W7dq1JUkbN25Uy5Yt9ddff8nX11ezZ8/W8OHDFRsbK2dnZ0nS0KFDtXbtWh05ckSS9MYbbygpKUnr1683natevXqqUaOG5syZk+1roMIJAAAAAADgEUhOTlZCQoLZkpycbJVjX7lyRQaDQV5eXpKknTt3ysvLy9TZJEkBAQFycHBQRESEqU3jxo1NnU2SFBgYqJiYGF2+fNnUJiAgwOxcgYGB2rlzp0Xx0eEEAEAuZM/zDwAAANgrW+dQ48ePl6enp9kyfvz4h76OGzduaMiQIerUqZM8PDwkSbGxsSpSpIhZO0dHR3l7eys2NtbUpmjRomZtMl7fr03G9uyyTd0/AAAAAADAE2bYsGEaOHCg2ToXF5eHOmZKSoo6dOggo9Go2bNnP9SxHiU6nAAAyIWoOgIAALCcrXMoFxeXh+5gul1GZ9OZM2e0ZcsWU3WTJPn4+OjixYtm7VNTUxUXFycfHx9TmwsXLpi1yXh9vzYZ27OLIXUAAAAAAAB2LqOz6dixY/rll19UsGBBs+3+/v6Kj49XZGSkad2WLVuUnp6uunXrmtqEh4crJeV/TzkICwuTn5+fChQoYGqzefNms2OHhYXJ39/fonjpcAIAAAAAAMhhiYmJioqKUlRUlCTp1KlTioqK0tmzZ5WSkqLXXntNe/bsUWhoqNLS0hQbG6vY2FjdvHlTklSpUiU1b95cPXv21K5du7R9+3b16dNHHTt2lK+vrySpc+fOcnZ2VnBwsA4dOqRvv/1W06dPNxv2169fP23cuFGTJ0/WkSNHNHr0aO3Zs0d9+lj2JFuG1AEAkBsxog4AAMBydpxD7dmzRy+88ILpdUYnULdu3TR69GitW7dOklSjRg2z/X799Vc1adJEkhQaGqo+ffqoadOmcnBwUPv27TVjxgxTW09PT23atEm9e/dWrVq1VKhQIY0aNUohISGmNvXr19eyZcs0YsQIffjhhypfvrzWrl2rqlWrWnQ9dDgBAAAAAADksCZNmshoNN51+722ZfD29tayZcvu2eaZZ57R1q1b79nm9ddf1+uvv37f890LHU4AAORCTBoOAABgOXIo22EOJwAAAAAAAFgVFU4AAORC3J0DAACwHDmU7VDhBAAAAAAAAKuiwgkAgFyIu3MAAACWI4eyHSqcAAAAAAAAYFV0OAEAAAAAAMCqGFIHAEAuRDk4AACA5cihbIcKJwAAAAAAAFgVFU4AAORG3JwDAACwHDmUzVDhBAAAAAAAAKuiwgkAgFyI+QcAAAAsRw5lO1Q4AQAAAAAAwKqocAIAIBfi7hwAAIDlyKFshwonAAAAAAAAWBUVTgAA5ELcnQMAALAcOZTtUOEEAAAAAAAAq6LDCQAAAAAAAFbFkDoAAHIjqsEBAAAsRw5lM1Q4AQAAAAAAwKqocAIAIBdiwksAAADLkUPZDhVOAAAAAAAAsCoqnAAAyIW4OwcAAGA5cijbocIJAAAAAAAAVkWFEwAAuRB35wAAACxHDmU7VDgBAAAAAADAqqhwAgAgF+LuHAAAgOXIoWyHCicAAAAAAABYFR1OAAAAAAAAsCqG1AEAkBtRDQ4AAGA5ciibocIJAAAAAAAAVkWFEwAAuRATXgIAAFiOHMp2qHACAAAAAACAVVHhBABALsTdOQAAAMuRQ9kOFU4AAAAAAACwKiqcAADIhbg7BwAAYDlyKNuhwgkAAAAAAABWRYcTAAAAAAAArIohdQAA5EZUgwMAAFiOHMpmqHACAAAAAACAVVHhBABALsSElwAAAJYjh7IdKpwAAAAAAABgVVQ4AQCQC3F3DgAAwHLkULZDhRMAAAAAAACsigonAAByIe7OAQAAWI4cynaocAIAAAAAAIBVUeEEAEAuxN05AAAAy5FD2Q4VTgAAAAAAALAqOpwAAAAAAABgVXQ4AQCQGxlsvFgoPDxcrVu3lq+vrwwGg9auXWu23Wg0atSoUSpWrJjc3NwUEBCgY8eOmbWJi4tTUFCQPDw85OXlpeDgYCUmJpq1OXDggBo1aiRXV1eVKFFCEyZMyBTLypUrVbFiRbm6uqpatWrasGGD5RcEAAAeD3aeQz1O6HACAABWl5SUpOrVq2vWrFlZbp8wYYJmzJihOXPmKCIiQvny5VNgYKBu3LhhahMUFKRDhw4pLCxM69evV3h4uEJCQkzbExIS1KxZM5UqVUqRkZGaOHGiRo8erblz55ra7NixQ506dVJwcLD27duntm3bqm3btjp48OCju3gAAADIYDQajTkdxKPgVrNPTocA5FrX932hS4mpOR0GkCsVdrfN8zjKDrRtlc7JKS0feF+DwaA1a9aobdu2km5VN/n6+ur999/XBx98IEm6cuWKihYtqsWLF6tjx46Kjo5W5cqVtXv3btWuXVuStHHjRrVs2VJ//fWXfH19NXv2bA0fPlyxsbFydnaWJA0dOlRr167VkSNHJElvvPGGkpKStH79elM89erVU40aNTRnzpwHvqbHGTkU8GCu7/tC11NyOgog93Jzss15clMOldtR4QQAAO4rOTlZCQkJZktycvIDHevUqVOKjY1VQECAaZ2np6fq1q2rnTt3SpJ27twpLy8vU2eTJAUEBMjBwUERERGmNo0bNzZ1NklSYGCgYmJidPnyZVOb28+T0SbjPAAAAHg06HACACAXMhgMNl3Gjx8vT09Ps2X8+PEPFHtsbKwkqWjRombrixYtatoWGxurIkWKmG13dHSUt7e3WZusjnH7Oe7WJmM7AAB4stg6h3qS2abuHwAA5GrDhg3TwIEDzda5uLjkUDQAAACwd3Q4AQCQC9n6hpmLi4vVOph8fHwkSRcuXFCxYsVM6y9cuKAaNWqY2ly8eNFsv9TUVMXFxZn29/Hx0YULF8zaZLy+X5uM7QAA4MnyhBcd2RRD6gAAgE2VKVNGPj4+2rx5s2ldQkKCIiIi5O/vL0ny9/dXfHy8IiMjTW22bNmi9PR01a1b19QmPDxcKSn/m6U3LCxMfn5+KlCggKnN7efJaJNxHgAAADwadDgBAJAL2fv8A4mJiYqKilJUVJSkWxOFR0VF6ezZszIYDOrfv78++eQTrVu3Tn/++ae6du0qX19f05PsKlWqpObNm6tnz57atWuXtm/frj59+qhjx47y9fWVJHXu3FnOzs4KDg7WoUOH9O2332r69OlmQ//69eunjRs3avLkyTpy5IhGjx6tPXv2qE8fnsQGAMCTyN5zqMcJQ+oAAIDV7dmzRy+88ILpdUYnULdu3bR48WINHjxYSUlJCgkJUXx8vBo2bKiNGzfK1dXVtE9oaKj69Omjpk2bysHBQe3bt9eMGTNM2z09PbVp0yb17t1btWrVUqFChTRq1CiFhISY2tSvX1/Lli3TiBEj9OGHH6p8+fJau3atqlataoN3AQAA4MllMBqNxpwO4lFwq8mdS+BBXd/3hS4lpuZ0GECuVNjdNvdyyg/aaJPzZDg2sblNz4ecQw4FPJjr+77Q9ZT7twOQNTcn25yHHMp2qHACACAXesIrtAEAAB4IOZTt2M0cTlu3blWXLl3k7++vc+fOSZK+/vprbdu2LYcjAwAAsE/kTwAAwF7ZRYfTqlWrFBgYKDc3N+3bt0/JycmSpCtXrmjcuHE5HB0AAPaHCS9B/gQAgOXIoWzHLjqcPvnkE82ZM0fz5s2Tk9P/Bm42aNBAe/fuzcHIAAAA7BP5EwAAsGd2MYdTTEyMGjdunGm9p6en4uPjbR8QAAB27gm/YQaRPwEA8CDIoWzHLiqcfHx8dPz48Uzrt23bprJly+ZARAAAAPaN/AkAANgzu6hw6tmzp/r166eFCxfKYDDo77//1s6dO/XBBx9o5MiROR0eAAB2x8GB23NPOvInAAAsRw5lO3bR4TR06FClp6eradOmunbtmho3biwXFxd98MEHeu+993I6PAAAALtD/gQAAOyZXXQ4GQwGDR8+XIMGDdLx48eVmJioypUry93dPadDAwAAsEvkTwAAwJ7ZRYfTN998o1dffVV58+ZV5cqVczocAADsHhNegvwJAADLkUPZjl1MGj5gwAAVKVJEnTt31oYNG5SWlpbTIQEAANg18icAAGDP7KLD6fz581qxYoUMBoM6dOigYsWKqXfv3tqxY0dOhwYAgF0yGAw2XWB/yJ8AALCcPedQ4eHhat26tXx9fWUwGLR27Vqz7UajUaNGjVKxYsXk5uamgIAAHTt2zKxNXFycgoKC5OHhIS8vLwUHBysxMdGszYEDB9SoUSO5urqqRIkSmjBhQqZYVq5cqYoVK8rV1VXVqlXThg0bLLoWyU46nBwdHfXyyy8rNDRUFy9e1NSpU3X69Gm98MILevrpp3M6PAAAALtD/gQAwOMlKSlJ1atX16xZs7LcPmHCBM2YMUNz5sxRRESE8uXLp8DAQN24ccPUJigoSIcOHVJYWJjWr1+v8PBwhYSEmLYnJCSoWbNmKlWqlCIjIzVx4kSNHj1ac+fONbXZsWOHOnXqpODgYO3bt09t27ZV27ZtdfDgQYuuxy7mcLpd3rx5FRgYqMuXL+vMmTOKjo7O6ZAAALA7FB3hduRPAABkjz3nUC1atFCLFi2y3GY0GjVt2jSNGDFCbdq0kSQtXbpURYsW1dq1a9WxY0dFR0dr48aN2r17t2rXri1Jmjlzplq2bKlJkybJ19dXoaGhunnzphYuXChnZ2dVqVJFUVFRmjJliqljavr06WrevLkGDRokSfr4448VFhamL774QnPmzMn29dhFhZMkXbt2TaGhoWrZsqWKFy+uadOmqV27djp06FBOhwYAAGCXyJ8AALBvycnJSkhIMFuSk5MtPs6pU6cUGxurgIAA0zpPT0/VrVtXO3fulCTt3LlTXl5eps4mSQoICJCDg4MiIiJMbRo3bixnZ2dTm8DAQMXExOjy5cumNrefJ6NNxnmyyy46nDp27KgiRYpowIABKlu2rH777TcdP35cH3/8sSpWrJjT4QEAYHfsef4B2Ab5EwAAlrN1DjV+/Hh5enqaLePHj7c47tjYWElS0aJFzdYXLVrUtC02NlZFihQx2+7o6Chvb2+zNlkd4/Zz3K1NxvbssoshdXny5NF3332nwMBA5cmTJ6fDAQAAsHvkTwAA2L9hw4Zp4MCBZutcXFxyKBrbsosOp9DQ0JwOAQCAXIWqI5A/AQBgOVvnUC4uLlbpYPLx8ZEkXbhwQcWKFTOtv3DhgmrUqGFqc/HiRbP9UlNTFRcXZ9rfx8dHFy5cMGuT8fp+bTK2Z1eOdTjNmDFDISEhcnV11YwZM+7Ztm/fvjaKCgAAwH6RPwEA8GQqU6aMfHx8tHnzZlMHU0JCgiIiItSrVy9Jkr+/v+Lj4xUZGalatWpJkrZs2aL09HTVrVvX1Gb48OFKSUmRk5OTJCksLEx+fn4qUKCAqc3mzZvVv39/0/nDwsLk7+9vUcw51uE0depUBQUFydXVVVOnTr1rO4PBQMIEAAAg8icAAB5niYmJOn78uOn1qVOnFBUVJW9vb5UsWVL9+/fXJ598ovLly6tMmTIaOXKkfH191bZtW0lSpUqV1Lx5c/Xs2VNz5sxRSkqK+vTpo44dO8rX11eS1LlzZ40ZM0bBwcEaMmSIDh48qOnTp5vlFf369dPzzz+vyZMnq1WrVlqxYoX27NmjuXPnWnQ9OdbhdOrUqSz/DwAA7o8RdU8m8icAAB6OPedQe/bs0QsvvGB6nTH3U7du3bR48WINHjxYSUlJCgkJUXx8vBo2bKiNGzfK1dXVtE9oaKj69Omjpk2bysHBQe3btzerivb09NSmTZvUu3dv1apVS4UKFdKoUaMUEhJialO/fn0tW7ZMI0aM0Icffqjy5ctr7dq1qlq1qkXXYzAajcYHfTOsZezYsfrggw+UN29es/XXr1/XxIkTNWrUKIuP6Vazj7XCA5441/d9oUuJqTkdBpArFXa3zb2cGqM32+Q8GaJGN7Xp+XB/jyJ/ksihgAd1fd8Xup6S01EAuZebk23OQw5lOw45HYAkjRkzRomJiZnWX7t2TWPGjMmBiAAAsG+2fqQv7A/5EwAAliOHsh27eEqd0WjM8guxf/9+eXt750BEuF2DZ5/WgK4BerZySRUr7KkOA+bqh98OmLYPf7ulXg98Vk/5FNDNlDTtiz6r0V/8oN0Hz5jarJz2tqpXKK7C3vl1OeGafo2I0YgZ/6fzl66Ynav/m03Vo30DlSxWQP/GJ+mr77ZqwoKfTdvf7tBY77zRWKV8vfXf2Mv6fMHPWrZ+16N/E4BH7FpSkubNnqHwXzfr8uU4VfCrpH4fDFWlKtWUmpKiubNn6I9tW/X3ub+Uz91dtev6q9d7A1SocBFJ0t49u9T37e5ZHnve0hWqVKWaLS8HgA2QP9m/++VQt5sxvKN6vtZQgyZ+ry+W/WZaX8Ajr6YMeV0tG1dVutGotZuj9MGE75V0/aakW3nYiHdaZjpe0vVkFar/viSpzYvVNSg4UE+XKCQnxzw6fvaSpn+9Wct/3G39iwZsZMG8r7T5l006feqkXFxdVb1GTfUf8IFKlykrSbpyJV6zZ83Uzh3bFHv+vAoU8NYLLwbo3ff6KX/+/JmOFx9/WR3at9HFCxcUvmO3PDw8bH1JwGMnRzucChQoYOr1q1ChglnSlJaWpsTERL3zzjs5GCEkKZ+bi/48ek5L/2+nvp0Skmn78TMXNeDzlTr11z9yc3HSe11e1A9f9lHVNmP0z+Vbd17Ddx/VxAU/K/afK/It4qXxA9pp2cRgvfDWFNNxJg9+TU3rVdSwqWt08Njf8vbMqwIe+Uzbe77eUGPfa63eHy/XnkNnVKdqac0a2UnxCde0Ifzgo38jgEfos49H6eSJYxr58WcqVLiwft6wXv17/UfffL9Obm55dfRItLr95x2Vr+CnhKsJmj5xvIYM6KMF33wnSapWvYb+7+ffzI45f/ZM7dkdoYqVLRtrjdzhCb9h9kQjf8o97pdDZXjlhWf0XLXS+vtifKZti8Z1k08hT73c6ws5OebRV2O6aNbIznrrw8WSpGlLf9H877ea7bPhq76KPPS/G39xV65pwvyNijl9QTdT0tSyUVXNHd1Fl+IS9cvOaKtcK2BrkXt26Y1OQapStZrSUtM0c/oU9QoJ1ur/+1FuefPq0sWLunTxogZ+MERly5bT+fPn9MnY0bp06aImTc38lM/Ro4arfAU/XbzjUfB4/JBD2U6OdjhNmzZNRqNRPXr00JgxY+Tp6Wna5uzsrNKlS1v82D1Y36bth7Vp++G7bv924x6z10Mmr1b3dvVVtbyvftt1VJI0M/RX0/az5y9r0qIwfTelpxwdHZSami6/MkXV87VGqvX6pzp25qIk6czf/5odt3Or57Rg1XZ9v2mvJOn0uX9Vq0pJvf/WS3Q4IVdLvnFDv28J0/jJM1Xj2dqSpOC3e2t7+G9a8/0KhbzbT9O+nG+2z8Ahw9Wza0fFnv9bPsV85eTkrIKFCpu2p6akaOvvv+q1Nzo/8aW8wOOG/Cn3uF8OJUm+hT01Zcjrav3uLK2Z2ctsm1+ZogpsUEUNgiZo7+GzkqSBn6/U2pm9NGzqGp2/dEVJ12+aqp0kqVqF4qr8dDH1/XSFad3WyGNmx521/DcFta6r+jXL0uGEXOvLrxaYvR776Wd6sbG/Dh8+pFq166hc+QqaPG2maXuJkiXVp29/DR86SKmpqXJ0/N+fwt+tWKarCVf1dq93tX1ruM2uAXjc5WiHU7du3SRJZcqUUf369eXkZKNZwvDIODnmUfCrDRR/9Zr+PHouyzYFPPKqY4va+mP/KaWmpkuSWjWuplPn/lHLxlX1zhuNZTAYtCUiRsOnrdXlhGuSJGcnR924aT4T4/UbKapdtZSp4wrIjdLS0pSWliZnFxez9S4uLjoQtS/LfRITE2UwGJQ/f9bl3tvCf1XClXi1fKWd1eOFfaAj8clF/vT4MBgMWvBJV01dslnRJ2Mzba/7TBldTrhm6mySpC0RMUpPN6pO1VJa92vm4Xnd29XX0dMXtH3fibuet8lzFVShdBGNmH73NkBuk5h4VZLMOuEztbmaKHd3d7POphMnjmvunC/19fLv9Nd///vI40TOI4eyHbuYw+n55583/f/GjRu6efOm2fZ7jZ9NTk5WcnKy2TqXO/5ow6PXolFVLf2su/K6Oin2nwS9/M4X+jc+yazNJ33b6J2OjZXPzUURB07p1b5zTNtKP1VIJYt569WAmvrPyK/l4OCgCR+8qmUTg9Xi7Vt3Jn7ZGa232tbXD78e0L7o/+rZyiX1Vrv6cnZyVCEvd8X+k2DTawasJW++fKr6TA0tnj9HpcuUVQHvgvrl5w069Od+FS9RMlP75ORkzZ4xRQGBLZXP3T3LY67/v9V6zr+BihT1edThA8ghD5M/SeRQ9uD97i8pNS1ds5b/luX2ogU9dCnuqtm6tLR0xSVcU9FCmb++Ls6OeqNFbU1eFJZpm4e7q078/KlcnByVlp6ufuO/1ZaII1a5DiCnpaena+Jn41Sj5rMqV75Clm0uX47TvK++1KuvvWFad/PmTQ0bNFAD3h+kYsV86XACrMwunlJ37do19enTR0WKFFG+fPlUoEABs+Vexo8fL09PT7Nl/PjxNoocGX7ffVR1O47XC29N0aYdh/XNhB4qXMD8D+GpS39RvY6fq9U7XygtLV3zP37TtM3BYJCri5OCR36t7ftOaGvkMfUaE6omz/mpfKlbkyKPn7dRm7Yf1u9LPtDV3dO1cmqIQn+IkCSlpxttd7HAIzBy7HjJaFTb5i/oRf+a+n7FNwoIbCkHg/mv6dSUFI0aOlAyGvXBsKwfeX7xQqx27dyul9u8aovQkUMMBtsusD8Pkz9J5FA5rWalEurdqYlCPvrGasds82J15c/rqm/+f350u6tJyarbcbwadpmg0bN+0Ofvv6pGtcpb7dxAThr/yRgdP35Mn0+cmuX2xMREvffu2yr79NN6590+pvUzpk1WmbJPq1XrNrYKFXaAHMp27KLDadCgQdqyZYtmz54tFxcXzZ8/X2PGjJGvr6+WLl16z32HDRumK1eumC3Dhg2zUeTIcO3GTZ387z/a9edp9RqzTKlp6erWrr5Zm3/jk3T87EVtiTiirkMXqUWjqqr7TBlJUuw/V5SSkqbjZy+a2h85dWvCvhI+t560cyM5Re+MCZV3/QGq2OojlW8xUmfO/6uExOu6dDnzY6GB3KR4iZL6Yt4ShW3brVU/bta8pd8qNTVVvsWfMrVJTUnRyKHvK/b835r65fy7VjdtWLdGHp5eatj4BVuFDyAHPEz+JJFD5bQGNZ9WEW93Hd0wVld3T9fV3dNVyregPhv4qo78OEaSdOHfBBX2Nn+aVp48DvL2yKsLWVR2v9W2vn7aelAX76iKkm491fDkf//RgaPnNP3rLVrzS5QG9Wj2aC4OsKHxn45V+O+/af7CJSrqk7myOykpUe++/R/ly5dPU6bPMhuGvCviD4Vt2qha1SurVvXKevs/b0mSXmhUT19+kXlicQCWsYshdT/88IOWLl2qJk2aqHv37mrUqJHKlSunUqVKKTQ0VEFBQXfd18XFhfJvO+RgMMjF6e7fXg4Ot7p6nf9/m51RJ+XklEdlniqkU3/9I0mmyqaz5+PM9k1NTde5//8Ul9cDa+mnrYdkNFLhhMeDm1teubnlVULCFe3auV29+g2U9L/Opr/+e0YzvlokTy+vLPc3Go368Ye1at7qFTkyrwvwWHuY/Ekih8ppy37crS0RMWbrfviyt5b9uEtL/+8PSVLEgVMq4JFXNSuV0L7oW0N9mtSpIAcHg3YfPGO2bynfgnq+Tnm91n9uts7vYDDIxdku/hQAHojRaNRn4z7Wls1hmr/oaxV/qkSmNomJiXr37WA5OTlr2szZmX7nTZ46U8nJN0yvDx78U6NHfqiFS0JVIotpDQBYxi4+ZeLi4lS2bFlJt+YbiIu71cHQsGFD9erV6167wgbyuTnr6RL/e/pV6eIF9UyF4rqccE3/xidpyH8C9ePvfyr2nysq6OWutzs0lm8RL60Ou/U0uTpVS6lWlVLase+E4q9eU5mnCuujd1vpxNlLijhwStKtCTD3Hj6rr0YHadDEVXJwMGja0A76ZWe0qeqpXMkiql21lHYfPK0C+fOq75svqvLTvvrPyK9t/6YAVhaxY5uMMqpkqTI699+zmjV9kkqWLqNWrdspNSVFI4YM0NEj0fp82iylp6Xp338uSZI8PD3l5ORsOk7k7gidP/eXWrdtn1OXAhthwkuQP9m/e+VQ/429rLgr5vNdpqSm6cI/CaYn9sacuqCftx/SrJGd1ffTFXJyzKOpQzto5c97df7SFbN9u7Wtp9h/EvTz9kOZ4vigRzPtPXRWJ/+6JBdnRzVvWEWdWz2nvuNXZGoL5BbjPhmjnzas17QZXypfvnz65//nRu7u+eXq6qrExET1CumhG9ev69PpE5WUlKikpFujIgoU8FaePHlUoqR5p9Lly5clSWXKPn3fefCQe5FD2Y5ddDiVLVtWp06dUsmSJVWxYkV99913eu655/TDDz/I6y538WE7z1YupU3z+5leT/jg1h+yX6/7Q+99ukJ+pYuqS+u6KuiVT3FXrmnPoTMK6DHV9LSVazdS1ObF6hrxTivlc3NW7D9XtGlHtD6ft1A3U1Il3bpD8Vr/rzRlyOsKW9BfSddvatP2wxo6ZbXpvHnyGNTvzRdVoVRRpaSmKXzPUb3w1uRMFVBAbpSYmKivvpimSxdj5eHhqeebvqSQd/vJ0clJ5/8+p22//ypJ6t7JvCNpxleL9Gzt50yv169dpWrVa6hUmbI2jR+A7ZE/2b975VDZnbup+4dLNHVoB2346j2lpxu1dnOU3p+w0qyNwWDQm63r6et1EVnOa5nP1VnTP+yg4kW8dD05RUdPX1CPEUv0/aa9D3F1QM5a+e1ySdJ/ur9ptn7MJ+PVpu2rij58SH8e2C9Jat3yJbM2P/68WcVvm7YAwKNhMNrBWKSpU6cqT5486tu3r3755Re1bt1aRqNRKSkpmjJlivr163f/g9zBrWaf+zcCkKXr+77QpcTUnA4DyJUKu9vmXs5z436zyXky7PqwiU3Ph/t7FPmTRA4FPKjr+77Q9ZScjgLIvdxsNBsEOZTt2EWF04ABA0z/DwgI0JEjRxQZGaly5crpmWeeycHIAAAA7BP5EwAAsGd20eF0p1KlSqlUqVI5HQYAAHaL+QdwJ/InAADujxzKduyiw2nGjKwfOWkwGOTq6qpy5cqpcePGypMnj40jAwAAsE/kTwAAwJ7ZRYfT1KlTdenSJV27dk0FChSQdOsJAXnz5pW7u7suXryosmXL6tdff1WJEpkfdwkAwJOGm3MgfwIAwHLkULbjkNMBSNK4ceNUp04dHTt2TP/++6/+/fdfHT16VHXr1tX06dN19uxZ+fj4mM1VAAAA8CQjfwIAAPbMLiqcRowYoVWrVunpp582rStXrpwmTZqk9u3b6+TJk5owYYLat29/j6MAAAA8OcifAACAPbOLDqfz588rNTXzI9hTU1MVGxsrSfL19dXVq1dtHRoAAHaJCS9B/gQAgOXIoWzHLobUvfDCC3r77be1b98+07p9+/apV69eevHFFyVJf/75p8qUKZNTIQIAANgV8icAAGDP7KLDacGCBfL29latWrXk4uIiFxcX1a5dW97e3lqwYIEkyd3dXZMnT87hSAEAsA8Gg20X2B/yJwAALEcOZTt2MaTOx8dHYWFhOnLkiI4ePSpJ8vPzk5+fn6nNCy+8kFPhAQAA2B3yJwAAYM/sosMpQ9myZWUwGPT000/L0dGuQgMAwK4w/wAykD8BAJB95FC2YxdD6q5du6bg4GDlzZtXVapU0dmzZyVJ7733nj777LMcjg4AAMD+kD8BAAB7ZhcdTsOGDdP+/fv122+/ydXV1bQ+ICBA3377bQ5GBgCAfWL+AZA/AQBgOXIo27GLuuu1a9fq22+/Vb169czK26pUqaITJ07kYGQAAAD2ifwJAADYM7vocLp06ZKKFCmSaX1SUhLjKwEAyAKfjyB/AgDAcnxG2o5dDKmrXbu2fvzxR9PrjG+A+fPny9/fP6fCAgAAsFvkTwAAwJ7ZRYXTuHHj1KJFCx0+fFipqamaPn26Dh8+rB07duj333/P6fAAAADsDvkTAACwZ3ZR4dSwYUNFRUUpNTVV1apV06ZNm1SkSBHt3LlTtWrVyunwAACwOwaDwaYL7A/5EwAAliOHsh27qHCSpKefflrz5s3L6TAAAAByDfInAABgr3K0w8nBweG+PX4Gg0Gpqak2iggAgNzhCb9h9kQjfwIA4MGRQ9lOjnY4rVmz5q7bdu7cqRkzZig9Pd2GEQEAANg38icAAJAb5GiHU5s2bTKti4mJ0dChQ/XDDz8oKChIY8eOzYHIAACwb0/6nABPMvInAAAeHDmU7djFpOGS9Pfff6tnz56qVq2aUlNTFRUVpSVLlqhUqVI5HRoAAIBdIn8CAAD2Ksc7nK5cuaIhQ4aoXLlyOnTokDZv3qwffvhBVatWzenQAACwWwaDbRfYF/InAAAeDDmU7eTokLoJEybo888/l4+Pj5YvX55liTgAAAD+h/wJAADkBjna4TR06FC5ubmpXLlyWrJkiZYsWZJlu9WrV9s4MgAA7BvzDzy5yJ8AAHhw5FC2k6MdTl27duWLDQAAYAHyJwAAkBvkaIfT4sWLc/L0AAAAuQ75EwAAyA1ytMMJAAA8GApcAAAALEcOZTs5/pQ6AAAAAAAAPF6ocAIAIBdy4PYcAACAxcihbIcKJwAAAAAAAFgVFU4AAORC3JwDAACwHDmU7VDhBAAAAAAAAKuiwgkAgFzIwO05AAAAi5FD2Q4VTgAAAAAAALAqOpwAAAAAAABgVQypAwAgF3KgGhwAAMBi5FC2Q4UTAAAAAAAArIoKJwAAciEmvAQAALAcOZTtUOEEAACsKi0tTSNHjlSZMmXk5uamp59+Wh9//LGMRqOpjdFo1KhRo1SsWDG5ubkpICBAx44dMztOXFycgoKC5OHhIS8vLwUHBysxMdGszYEDB9SoUSO5urqqRIkSmjBhgk2uEQAAAPdGhxMAALmQwWDbxRKff/65Zs+erS+++ELR0dH6/PPPNWHCBM2cOdPUZsKECZoxY4bmzJmjiIgI5cuXT4GBgbpx44apTVBQkA4dOqSwsDCtX79e4eHhCgkJMW1PSEhQs2bNVKpUKUVGRmrixIkaPXq05s6d+9DvLwAAeDzZcw71uGFIHQAAsKodO3aoTZs2atWqlSSpdOnSWr58uXbt2iXpVnXTtGnTNGLECLVp00aStHTpUhUtWlRr165Vx44dFR0drY0bN2r37t2qXbu2JGnmzJlq2bKlJk2aJF9fX4WGhurmzZtauHChnJ2dVaVKFUVFRWnKlClmHVMAAACwPSqcAADIhQw2/pecnKyEhASzJTk5OcvY6tevr82bN+vo0aOSpP3792vbtm1q0aKFJOnUqVOKjY1VQECAaR9PT0/VrVtXO3fulCTt3LlTXl5eps4mSQoICJCDg4MiIiJMbRo3bixnZ2dTm8DAQMXExOjy5cvWfcMBAMBjwdY51JOMDicAAHBf48ePl6enp9kyfvz4LNsOHTpUHTt2VMWKFeXk5KSaNWuqf//+CgoKkiTFxsZKkooWLWq2X9GiRU3bYmNjVaRIEbPtjo6O8vb2NmuT1TFuPwcAAAByBkPqAADIhRxsfMNs2LBhGjhwoNk6FxeXLNt+9913Cg0N1bJly0zD3Pr37y9fX19169bNFuECAABkydY51JOMDicAAHBfLi4ud+1gutOgQYNMVU6SVK1aNZ05c0bjx49Xt27d5OPjI0m6cOGCihUrZtrvwoULqlGjhiTJx8dHFy9eNDtuamqq4uLiTPv7+PjowoULZm0yXme0AQAAQM5gSB0AALCqa9euycHBPMXIkyeP0tPTJUllypSRj4+PNm/ebNqekJCgiIgI+fv7S5L8/f0VHx+vyMhIU5stW7YoPT1ddevWNbUJDw9XSkqKqU1YWJj8/PxUoECBR3Z9AAAAuD86nAAAyIUMBoNNF0u0bt1an376qX788UedPn1aa9as0ZQpU9SuXTtT7P3799cnn3yidevW6c8//1TXrl3l6+urtm3bSpIqVaqk5s2bq2fPntq1a5e2b9+uPn36qGPHjvL19ZUkde7cWc7OzgoODtahQ4f07bffavr06ZmG/gEAAGSw5xzqccOQOgAAYFUzZ87UyJEj9e677+rixYvy9fXV22+/rVGjRpnaDB48WElJSQoJCVF8fLwaNmyojRs3ytXV1dQmNDRUffr0UdOmTeXg4KD27dtrxowZpu2enp7atGmTevfurVq1aqlQoUIaNWqUQkJCbHq9AAAAyMxgNBqNOR3Eo+BWs09OhwDkWtf3faFLiak5HQaQKxV2t829nLbz99jkPBnW/qe2Tc+HnEMOBTyY6/u+0PWU+7cDkDU3J9uchxzKdhhSBwAAAAAAAKtiSB0AALmQwxM+JwAAAMCDIIeyHSqcAAAAAAAAYFVUOAEAkAtxcw4AAMBy5FC2Q4UTAAAAAAAArIoKJwAAciEDt+cAAAAsRg5lO1Q4AQAAAAAAwKqyVeG0bt26bB/wlVdeeeBgAAAAHhfkTwAAwBJpaWkaPXq0vvnmG8XGxsrX11dvvfWWRowYYarMMhqN+uijjzRv3jzFx8erQYMGmj17tsqXL286TlxcnN577z398MMPcnBwUPv27TV9+nS5u7ub2hw4cEC9e/fW7t27VbhwYb333nsaPHiwVa8nWx1Obdu2zdbBDAaD0tLSHiYeAACQDVSD2z/yJwAA7I8951Cff/65Zs+erSVLlqhKlSras2ePunfvLk9PT/Xt21eSNGHCBM2YMUNLlixRmTJlNHLkSAUGBurw4cNydXWVJAUFBen8+fMKCwtTSkqKunfvrpCQEC1btkySlJCQoGbNmikgIEBz5szRn3/+qR49esjLy0shISFWu55sdTilp6db7YQAAABPAvInAABgiR07dqhNmzZq1aqVJKl06dJavny5du3aJelWddO0adM0YsQItWnTRpK0dOlSFS1aVGvXrlXHjh0VHR2tjRs3avfu3apdu7YkaebMmWrZsqUmTZokX19fhYaG6ubNm1q4cKGcnZ1VpUoVRUVFacqUKVbtcHqoOZxu3LhhrTgAAIAFHAwGmy6wHvInAAByjq1zqOTkZCUkJJgtycnJWcZWv359bd68WUePHpUk7d+/X9u2bVOLFi0kSadOnVJsbKwCAgJM+3h6eqpu3brauXOnJGnnzp3y8vIydTZJUkBAgBwcHBQREWFq07hxYzk7O5vaBAYGKiYmRpcvX7bee23pDmlpafr4449VvHhxubu76+TJk5KkkSNHasGCBVYLDAAA4HFB/gQAwJNp/Pjx8vT0NFvGjx+fZduhQ4eqY8eOqlixopycnFSzZk31799fQUFBkqTY2FhJUtGiRc32K1q0qGlbbGysihQpYrbd0dFR3t7eZm2yOsbt57AGizucPv30Uy1evFgTJkww6w2rWrWq5s+fb7XAAADA3RlsvODhkD8BAGAfbJ1DDRs2TFeuXDFbhg0blmVs3333nUJDQ7Vs2TLt3btXS5Ys0aRJk7RkyZJH8E48ehZ3OC1dulRz585VUFCQ8uTJY1pfvXp1HTlyxKrBAQAAPA7InwAAeDK5uLjIw8PDbHFxccmy7aBBg0xVTtWqVdObb76pAQMGmCqifHx8JEkXLlww2+/ChQumbT4+Prp48aLZ9tTUVMXFxZm1yeoYt5/DGizucDp37pzKlSuXaX16erpSUlKsEhQAALg3g8Fg0wUPh/wJAAD7YM851LVr1+TgYN5NkydPHtODSMqUKSMfHx9t3rzZtD0hIUERERHy9/eXJPn7+ys+Pl6RkZGmNlu2bFF6errq1q1rahMeHm6Wg4SFhcnPz08FChSw7A29B4s7nCpXrqytW7dmWv/999+rZs2aVgkKAADgcUL+BAAA7qd169b69NNP9eOPP+r06dNas2aNpkyZonbt2km61VnWv39/ffLJJ1q3bp3+/PNPde3aVb6+vmrbtq0kqVKlSmrevLl69uypXbt2afv27erTp486duwoX19fSVLnzp3l7Oys4OBgHTp0SN9++62mT5+ugQMHWvV6HC3dYdSoUerWrZvOnTun9PR0rV69WjExMVq6dKnWr19v1eAAAAAeB+RPAADgfmbOnKmRI0fq3Xff1cWLF+Xr66u3335bo0aNMrUZPHiwkpKSFBISovj4eDVs2FAbN26Uq6urqU1oaKj69Omjpk2bysHBQe3bt9eMGTNM2z09PbVp0yb17t1btWrVUqFChTRq1CiFhIRY9XoMRqPRaOlOW7du1dixY7V//34lJibq2Wef1ahRo9SsWTOrBvcw3Gr2yekQgFzr+r4vdCkxNafDAHKlwu4W38t5IEFfR9nkPBlC36xh0/M9jnJD/iSRQwEP6vq+L3SdEbLAA3Nzss15yKFs54Gy4kaNGiksLMzasQAAADy2yJ8AAMCT5IFvw+7Zs0fR0dGSbs1LUKtWLasFBQAA7o2JvHMn8icAAHIWOZTtWNzh9Ndff6lTp07avn27vLy8JEnx8fGqX7++VqxYoaeeesraMQIAAORq5E8AAOBJY/FT6v7zn/8oJSVF0dHRiouLU1xcnKKjo5Wenq7//Oc/jyJGAABwB4PBtgseDvkTAAD2gRzKdiyucPr999+1Y8cO+fn5mdb5+flp5syZatSokVWDAwAAeByQPwEAgCeNxR1OJUqUUEpK5scvpKWlydfX1ypBAQCAe2P+gdyF/AkAAPtADmU7Fg+pmzhxot577z3t2bPHtG7Pnj3q16+fJk2aZNXgAAAAHgfkTwAA4EmTrQqnAgUKmPUCJiUlqW7dunJ0vLV7amqqHB0d1aNHD7Vt2/aRBAoAAP7HgZtzdo/8CQAA+0MOZTvZ6nCaNm3aIw4DAADg8UL+BAAAnmTZ6nDq1q3bo44DAADgsUL+BAAAnmQWTxp+uxs3bujmzZtm6zw8PB4qIAAAcH9MeJl7kT8BAJBzyKFsx+JJw5OSktSnTx8VKVJE+fLlU4ECBcwWAAAAmCN/AgAATxqLO5wGDx6sLVu2aPbs2XJxcdH8+fM1ZswY+fr6aunSpY8iRgAAcAeDjRc8HPInAADsAzmU7Vg8pO6HH37Q0qVL1aRJE3Xv3l2NGjVSuXLlVKpUKYWGhiooKOhRxAkAAJBrkT8BAIAnjcUVTnFxcSpbtqykW/MNxMXFSZIaNmyo8PBw60YHAACy5GAw2HTBwyF/AgDAPpBD2Y7FHU5ly5bVqVOnJEkVK1bUd999J+nWnTsvLy+rBgcAAPA4IH8CAABPGos7nLp37679+/dLkoYOHapZs2bJ1dVVAwYM0KBBg6weIAAAyMxgsO2Ch0P+BACAfSCHsh2L53AaMGCA6f8BAQE6cuSIIiMjVa5cOT3zzDNWDQ4AAOBxQP4EAACeNBZXON2pVKlSevXVV+Xt7a2QkBBrxAQAAO7DYDDYdIF1kT8BAJAzyKFs56E7nDL8+++/WrBggbUOBwAA8NgjfwIAAI8rq3U4AQAAAAAAANIDzOEEAABy3hNeoQ0AAPBAyKFshwonAAAAAAAAWFW2K5xeffXVe26Pj49/2FgAAEA2OXB7LlcgfwIAwL6QQ9lOtjucPD0977u9a9euDx0QAADA44L8CQAAPKmy3eG0aNGiRxkHAACwADfncgfyJwAA7As5lO0whxMAAAAAAACsiqfUAQCQCxm4PQcAAGAxcijbocIJAAAAAAAAVkWHEwAAAAAAAKzqsR1Sd33fFzkdApCrFXZ/bH89AI8F7hjhUSGHAh6cm1NORwDgfsihbCdbf1GuW7cu2wd85ZVXHjgYa7qRmtMRALmXq6N07aYxp8MAcqW8zswLgFtyY/4kkUMBD8rVkZ8f4GG4cr/7sZOtL2nbtm2zdTCDwaC0tLSHiQcAAGQDE17aP/InAADsDzmU7WSrwyk9Pf1RxwEAAPBYIX8CAABPMorWAADIhRy4OQcAAGAxcijbeaAOp6SkJP3+++86e/asbt68abatb9++VgkMAADgcUL+BAAAniQWdzjt27dPLVu21LVr15SUlCRvb2/9888/yps3r4oUKULCBACADXB3LnchfwIAwD6QQ9mOxU8EHDBggFq3bq3Lly/Lzc1Nf/zxh86cOaNatWpp0qRJjyJGAACAXI38CQAAPGks7nCKiorS+++/LwcHB+XJk0fJyckqUaKEJkyYoA8//PBRxAgAAO5gMBhsuuDhkD8BAGAfyKFsx+IOJycnJzk43NqtSJEiOnv2rCTJ09NT//3vf60bHQAAwGOA/AkAADxpLJ7DqWbNmtq9e7fKly+v559/XqNGjdI///yjr7/+WlWrVn0UMQIAAORq5E8AAOBJY3GF07hx41SsWDFJ0qeffqoCBQqoV69eunTpkubOnWv1AAEAQGYOBtsueDjkTwAA2AdyKNuxuMKpdu3apv8XKVJEGzdutGpAAAAAjxvyJwAA8KSxuMMJAADkvCd8DkoAAIAHQg5lOxZ3OJUpU+aeM62fPHnyoQICAAB43JA/AQCAJ43FHU79+/c3e52SkqJ9+/Zp48aNGjRokLXiAgAA9+DA7blchfwJAAD7QA5lOxZ3OPXr1y/L9bNmzdKePXseOiAAAIDHDfkTAAB40lj8lLq7adGihVatWmWtwwEAgHtwsPGCR4P8CQAA2yKHsh2rXf/3338vb29vax0OAADgsUf+BAAAHlcWD6mrWbOm2aSXRqNRsbGxunTpkr788kurBgcAALLG9AO5C/kTAAD2gRzKdizucGrTpo1ZwuTg4KDChQurSZMmqlixolWDAwAAeByQPwEAgCeNxR1Oo0ePfgRhAAAAPL7InwAAwJPG4jmc8uTJo4sXL2Za/++//ypPnjxWCQoAANybg8Fg0wUPh/wJAAD7QA5lOxZ3OBmNxizXJycny9nZ+aEDAgAAeNyQPwEAgCdNtofUzZgxQ5JkMBg0f/58ubu7m7alpaUpPDycOQgAALCRJ/yGWa5B/gQAgH0hh7KdbHc4TZ06VdKtO3Rz5swxK/92dnZW6dKlNWfOHOtHCAAAkEuRPwEAgCdVtofUnTp1SqdOndLzzz+v/fv3m16fOnVKMTEx+vnnn1W3bt1HGSsAAPj/HAy2XSx17tw5denSRQULFpSbm5uqVaumPXv2mLYbjUaNGjVKxYoVk5ubmwICAnTs2DGzY8TFxSkoKEgeHh7y8vJScHCwEhMTzdocOHBAjRo1kqurq0qUKKEJEyY80Pv5qJA/AQBgX+w9h3qcWDyH06+//qoCBQo8ilgAAMBj4PLly2rQoIGcnJz0008/6fDhw5o8ebJZ/jBhwgTNmDFDc+bMUUREhPLly6fAwEDduHHD1CYoKEiHDh1SWFiY1q9fr/DwcIWEhJi2JyQkqFmzZipVqpQiIyM1ceJEjR49WnPnzrXp9WYH+RMAAHjSZHtIXYb27dvrueee05AhQ8zWT5gwQbt379bKlSutFhwAAMiaPT/15PPPP1eJEiW0aNEi07oyZcqY/m80GjVt2jSNGDFCbdq0kSQtXbpURYsW1dq1a9WxY0dFR0dr48aN2r17t2rXri1Jmjlzplq2bKlJkybJ19dXoaGhunnzphYuXChnZ2dVqVJFUVFRmjJlilnHlD0gfwIAwD7Ycw71uLG4wik8PFwtW7bMtL5FixYKDw+3SlAAAMC+JCcnKyEhwWxJTk7Osu26detUu3Ztvf766ypSpIhq1qypefPmmbafOnVKsbGxCggIMK3z9PRU3bp1tXPnTknSzp075eXlZepskqSAgAA5ODgoIiLC1KZx48ZmT3kLDAxUTEyMLl++bNXrf1jkTwAA4EljcYdTYmJilo/vdXJyUkJCglWCAgAA9mX8+PHy9PQ0W8aPH59l25MnT2r27NkqX768fv75Z/Xq1Ut9+/bVkiVLJEmxsbGSpKJFi5rtV7RoUdO22NhYFSlSxGy7o6OjvL29zdpkdYzbz2EvyJ8AAMCTxuIOp2rVqunbb7/NtH7FihWqXLmyVYICAAD3ZjDYdhk2bJiuXLlitgwbNizL2NLT0/Xss89q3LhxqlmzpkJCQtSzZ88n+mls5E8AANgHW+dQTzKL53AaOXKkXn31VZ04cUIvvviiJGnz5s1avnw58w8AAPCYcnFxkYuLS7baFitWLFMnSqVKlbRq1SpJko+PjyTpwoULKlasmKnNhQsXVKNGDVObixcvmh0jNTVVcXFxpv19fHx04cIFszYZrzPa2AvyJwAA8KSxuMKpdevWWrt2rY4fP653331X77//vv766y/98ssvatu27SMIEQAA3MmeH+nboEEDxcTEmK07evSoSpUqJenWBOI+Pj7avHmzaXtCQoIiIiLk7+8vSfL391d8fLwiIyNNbbZs2aL09HTVrVvX1CY8PFwpKSmmNmFhYfLz87O7J8KRPwEAYB/sOYd63BiMRqPRWgc7ePCgqlataq3DPZQbqTkdAZB7uTpK125a7VcD8ETJ62ybzOLTzcdtcp4Mw5uWy3bb3bt3q379+hozZow6dOigXbt2qWfPnpo7d66CgoIk3XqS3WeffaYlS5aoTJkyGjlypA4cOKDDhw/L1dVV0q0JtS9cuKA5c+YoJSVF3bt3V+3atbVs2TJJ0pUrV+Tn56dmzZppyJAhOnjwoHr06KGpU6fa3VPq7sWe8ieJHAp4UK6O/PwAD8PV4vFXD8aec6jHzUN/Sa9evarly5dr/vz5ioyMVFpamjXiAgAA92CQ/d4yq1OnjtasWaNhw4Zp7NixKlOmjKZNm2bqbJKkwYMHKykpSSEhIYqPj1fDhg21ceNGU2eTJIWGhqpPnz5q2rSpHBwc1L59e82YMcO03dPTU5s2bVLv3r1Vq1YtFSpUSKNGjcoVnU3kTwAA5Ax7zqEeNw9c4RQeHq758+dr9erV8vX11auvvqr27durTp061o7xgXB3AXhwVDgBD85WFU7jNp+wyXkyfNj0aZue73Fl7/mTRA4FPCgqnICHY6sKJ3Io27HoSxobG6vFixdrwYIFSkhIUIcOHZScnKy1a9fyhBUAAGzoSZ8TIDchfwIAwH6QQ9lOticNb926tfz8/HTgwAFNmzZNf//9t2bOnPkoYwMAAMjVyJ8AAMCTKtsVTj/99JP69u2rXr16qXz58o8yJgAAcB/cncsdyJ8AALAv5FC2k+0Kp23btunq1auqVauW6tatqy+++EL//PPPo4wNAAAgVyN/AgAAljh37py6dOmiggULys3NTdWqVdOePXtM241Go0aNGqVixYrJzc1NAQEBOnbsmNkx4uLiFBQUJA8PD3l5eSk4OFiJiYlmbQ4cOKBGjRrJ1dVVJUqU0IQJE6x+LdnucKpXr57mzZun8+fP6+2339aKFSvk6+ur9PR0hYWF6erVq1YPDgAAIDcjfwIAANl1+fJlNWjQQE5OTvrpp590+PBhTZ48WQUKFDC1mTBhgmbMmKE5c+YoIiJC+fLlU2BgoG7cuGFqExQUpEOHDiksLEzr169XeHi42VN8ExIS1KxZM5UqVUqRkZGaOHGiRo8erblz51r1eh74KXWSFBMTowULFujrr79WfHy8XnrpJa1bt86a8T0wnhABPDieUgc8OFs9pW7ibydtcp4Mg5qUten5Hmf2nD9J5FDAg+IpdcDDsdVT6uw5hxo6dKi2b9+urVu3ZrndaDTK19dX77//vj744ANJ0pUrV1S0aFEtXrxYHTt2VHR0tCpXrqzdu3erdu3akqSNGzeqZcuW+uuvv+Tr66vZs2dr+PDhio2NlbOzs+nca9eu1ZEjRx7yiv8n2xVOWfHz89OECRP0119/afny5daKCQAA4LFF/gQAwJMjOTlZCQkJZktycnKWbdetW6fatWvr9ddfV5EiRVSzZk3NmzfPtP3UqVOKjY1VQECAaZ2np6fq1q2rnTt3SpJ27twpLy8vU2eTJAUEBMjBwUERERGmNo0bNzZ1NklSYGCgYmJidPnyZatd+0N1OGXIkyeP2rZta1d35wAAeJw5GGy7wPrInwAAsD1b51Djx4+Xp6en2TJ+/PgsYzt58qRmz56t8uXL6+eff1avXr3Ut29fLVmyRJIUGxsrSSpatKjZfkWLFjVti42NVZEiRcy2Ozo6ytvb26xNVse4/RzWYKOiNQAAAAAAgCfLsGHDNHDgQLN1Li4uWbZNT09X7dq1NW7cOElSzZo1dfDgQc2ZM0fdunV75LFam1UqnAAAgG0ZDLZdAAAAHge2zqFcXFzk4eFhttytw6lYsWKqXLmy2bpKlSrp7NmzkiQfHx9J0oULF8zaXLhwwbTNx8dHFy9eNNuempqquLg4szZZHeP2c1gDHU4AAAAAAAA5rEGDBoqJiTFbd/ToUZUqVUqSVKZMGfn4+Gjz5s2m7QkJCYqIiJC/v78kyd/fX/Hx8YqMjDS12bJli9LT01W3bl1Tm/DwcKWkpJjahIWFyc/Pz+yJeA+LDicAAHIhB4PBpgsAAMDjwJ5zqAEDBuiPP/7QuHHjdPz4cS1btkxz585V7969JUkGg0H9+/fXJ598onXr1unPP/9U165d5evrq7Zt20q6VRHVvHlz9ezZU7t27dL27dvVp08fdezYUb6+vpKkzp07y9nZWcHBwTp06JC+/fZbTZ8+PdPQv4fFHE4AAAAAAAA5rE6dOlqzZo2GDRumsWPHqkyZMpo2bZqCgoJMbQYPHqykpCSFhIQoPj5eDRs21MaNG+Xq6mpqExoaqj59+qhp06ZycHBQ+/btNWPGDNN2T09Pbdq0Sb1791atWrVUqFAhjRo1SiEhIVa9HoPRaDRa9Yh24kZqTkcA5F6ujtK1m4/lrwbgkcvrbJtqoBnbTtnkPBn6Nixj0/Mh55BDAQ/G1ZGfH+BhuNqoHIYcynYYUgcAAAAAAACrosMJAAAAAAAAVsUcTgAA5ELM4w0AAGA5cijbocIJAAAAAAAAVkWFEwAAuZCDuD0HAABgKXIo26HCCQAAAAAAAFZFhRMAALkQ8w8AAABYjhzKdqhwAgAAAAAAgFVR4QQAQC7kwN05AAAAi5FD2Q4VTgAAAAAAALAqOpwAAAAAAABgVQypAwAgF3JgxksAAACLkUPZDhVOAAAAAAAAsCoqnAAAyIW4OQcAAGA5cijbocIJAAAAAAAAVkWFEwAAuRDzDwAAAFiOHMp2qHACAAAAAACAVVHhBABALsTNOQAAAMuRQ9kOFU4AAAAAAACwKiqcAADIhbhjBAAAYDlyKNvhvQYAAAAAAIBV0eEEAAAAAAAAq2JIHQAAuZCBGS8BAAAsRg5lO1Q4AQAAAAAAwKqocAIAIBfi3hwAAIDlyKFshwonAAAAAAAAWBUVTgAA5EIOzD8AAABgMXIo26HCCQAAAAAAAFZFhRMAALkQ9+YAAAAsRw5lO1Q4AQAAAAAAwKrspsNp69at6tKli/z9/XXu3DlJ0tdff61t27blcGQAANgfg8G2C+wT+RMAAJYhh7Idu+hwWrVqlQIDA+Xm5qZ9+/YpOTlZknTlyhWNGzcuh6MDAACwP+RPAADAntlFh9Mnn3yiOXPmaN68eXJycjKtb9Cggfbu3ZuDkQEAANgn8icAAGDP7GLS8JiYGDVu3DjTek9PT8XHx9s+IAAA7JzhSa/RBvkTAAAPgBzKduyiwsnHx0fHjx/PtH7btm0qW7ZsDkQEAABg38ifAACAPbOLDqeePXuqX79+ioiIkMFg0N9//63Q0FB98MEH6tWrV06HBwCA3XGw8QL7Q/4EAIDlyKFsxy6G1A0dOlTp6elq2rSprl27psaNG8vFxUUffPCB3nvvvZwODwAAwO6QPwEAAHtmMBqNxpwOIsPNmzd1/PhxJSYmqnLlynJ3d3/gY91ItWJgwBPG1VG6dtNufjUAuUpeZ9vMC/Bd1N82OU+GDjV8bXo+ZJ818yeJHAp4UK6O/PwAD8PVRuUw5FC2YxcVXt98842uXbsmZ2dnVa5cWc8999xDJ0sAAACPM/InAABgz+yiw2nAgAEqUqSIOnfurA0bNigtLS2nQwIAwK4ZbLzA/pA/AQBgOXIo27GLDqfz589rxYoVMhgM6tChg4oVK6bevXtrx44dOR0aAACAXSJ/AgAA9syu5nCSpGvXrmnNmjVatmyZfvnlFz311FM6ceKExcdh/DTw4JjDCXhwtprDaaWN5x94/QmefyA3sFb+JJFDAQ+KOZyAh2OrOZzIoWzHLp5Sd7u8efMqMDBQly9f1pkzZxQdHZ3TIQEAYHcMhie9SBu3I38CACB7yKFsxy6G1Em37syFhoaqZcuWKl68uKZNm6Z27drp0KFDOR0aAACAXSJ/AgAA9souKpw6duyo9evXK2/evOrQoYNGjhwpf3//nA4LAAC7ZTd3jJBjyJ8AALAcOZTt2EWHU548efTdd98pMDBQefLkyelwAAAA7B75EwAAsGd20eEUGhqa0yEAAJCrMP8AyJ8AALAcOZTt5FiH04wZMxQSEiJXV1fNmDHjnm379u1ro6hgLbNnzdScL78wW1e6TBn93/qNOnfuL7Vs1jTL/SZOmaZmgS1sESJgNxbM/0pbfgnT6VMn5eLqqurVa6rfgPdVukxZSdKVK/GaPWum/ti5XbHnz6tAAW81ebGp3u3TT/nz55ckrVu7Wh+N/DDL42/+bbu8Cxa02fUAeHTInx4vkXt2a/HCBYo+fFCXLl3S1Bmz9GLTANP22bNmauNPPyo2NlZOTk6qXLmK+vQboGeeqS5JOnfuL82d86V2Rfyhf//5R4WLFFGrl19Rz5B35OTsnFOXBdjM/X6GRn44VOv+b43ZPvUbNNTsuQtMr+d9NVtbw39XzJFoOTk5adsfe2wWP/C4y7EOp6lTpyooKEiurq6aOnXqXdsZDAYSplzq6XLlNXf+ItPrPI63yv19fIpp82/bzNp+v/JbLVm0QA0bNrZpjIA92Ltnt97o2FlVqlZTalqavpg+Vb3e/o9Wr10vt7x5deniRV26dFED3h+ssk+X0/m//9anH3+kS5cuatKUW39wNmveUvUbNjI77kcjhik5OZnOpscU9+aeTORPj5fr16/Jz89PbV9tr4H9+mTaXqpUaQ0bPkpPPVVCN5Jv6Juli9WrZw/98FOYvL29dfrkSaWnGzXyo7EqWbKUjh87qjGjR+r69et6f9CQHLgiwLbu9zMkSQ0aNtLYT8abXjvf0RmbkpKil5o11zPVa2jt6u8fabywD+RQtpNjHU6nTp3K8v94fDjmyaNChQtnWp8ni/VbNv+iZs1bKG++fLYKD7Abs+bMN3s95pPxavp8fR0+fEi1atdRufIVNHnqTNP2EiVKqs97AzR82CClpqbK0dFRrq6ucnV1NbWJi4vTrogIfTT2E5tdB4BHj/zp8dKw0fNq2Oj5u25v+XJrs9cfDB6mNau+17GjMapbz18NGjVWg0b/u1n3VIkSOn36lL77djkdTngi3O9nSLrVwZTV3yQZ3u1zq3P+/9astmpsAOxkgvaxY8fq2rVrmdZfv35dY8eOzYGIYA1nzp5RQJOGahnYVMMGv6/zf/+dZbvDhw4q5ki02r36mo0jBOxTYuJVSZKnp+dd21xNvKp87u5ydMz6vsH6H9bK1c1VAS8FPpIYkfMMBtsusD/kT0+WlJs3tWrlt8qfP78q+PndtV3i1av3/PwAnjR7du9Sk0b+eqVVoD4Z+5Hi4y/ndEjIYeRQtmMXHU5jxoxRYmJipvXXrl3TmDFjciAiPKxqzzyjjz8dry+/mq/hI0fr3Llz6t41SElJmb/Oa1Z9r7Jln1aNms/mQKSAfUlPT9ekz8epRs1nVa58hSzbXL58WfO+mq32r3W463HWrl6lFi1fNqt6AvB4IX96Mvz+26+qV7um6jz7jL5eulhz5i1UgQLeWbY9e+aMli/7Rq+93tHGUQL2qX7DRvpk3Oeat2Cx+g8cpMjdu/Xu2z2VlpaW06EBTwS7eEqd0WjMcqb4/fv3y9s76w/UDMnJyUpOTjZb5+LiIuVxsWqMsMztpa0V/Cqq2jPV1eKlF/Tzxp/0avvXTdtu3LihnzasV8933s2JMAG7M/7TsTp+/JgWLVmW5fbExET17f22ypZ9Wm/3ynqugv1R+3Tq5Al9Mu7zRxkqgBz2MPmTRA6VW9R5rq6+W7VW8fGXter77zTo/f76ZvlKFbxjfr4LFy7o3bf/o5cCm6v963e/IQE8SVq0bGX6f/kKfqpQwU+tmgdoz+5dqlvPPwcjA54MOVrhVKBAAXl7e8tgMKhChQry9vY2LZ6ennrppZfUocO9PzDHjx8vT09Ps2X8+PH33Ae25+HhoVKlSuu/Z8+arQ/btFHXr99Q61fa5kxggB357NOx2vr7b5q3YOn/a+/e46qq8v+Pv4/iOdxVSCAVlcdQCkkmzfwSLcsiccTUpG83M03MMMyQUob5lallaI2jlbdm+o7od3RKLS3Fy5AXysQyTVNTrMbCQtD6Cgwq9/37ox9nPHk9eNxw8PX0sR+Pztprr732ebhPH9f67LUVHBJy1v6TJ8uUnDRK3t4++vNrc9SiRYtztrPyvRXq3CVCkTd0vdJdRgNqJoupGxoPV8RPEjGUu/D29laHjh11Y7ebNOXFl+XR3OOshY2PHSvSqMceVbfu3TVp8osN1FOg8WsfGqrWrVsrP//7hu4KGhAxlHkaNMNp9uzZMgxDI0eO1JQpUxyeN7darerUqZNiYi488pyenq7U1FSHMpvNJuOK9Bj1derkSR05ckTxAx0X7Fv13ru6o8+dlzQTCzRVhmFoxssvatOmD/XXvy1Wu/btz6pTVlamJ59IlNVq1ew35v2ShXAOp06dVPaGdXrq6dRz7gfg/lwRP0nEUO6q1qhVZWWl/XNR0S+DTZGRN2jqSxlq1qxRrJgBNEpFhYUqLi5Wm2vOv4g4ANdp0AGn4cOHS5LCwsLUs2fP887WX4jNZjvnP7zKqy+7e7gMM1+dodvv6KNr27bV8WPHNH/uG2revJl+33+AvU7+999r5+c7NHf+Xxqwp0DDy5g2VevWrtGs1+bKx8dHP/10XJLk6+snT09P+2BT+enTmjb9VZ08WWZfD6116wA1b97c3taG9etUU1Oj+AEDG+RaYJ6rfRHKq5kr4ieJGKoxOHXypPLPyP7+8YcfdPDAgV8yzlq10lt/WaA7+typa9q0UfGJE3r7H0t0rKhId8f1k/T/B5tGDNO1bdsqdUKaTvzv/9rbutBbuYCm4oL3UMuWWjB/jmLvjlPgNdfohyNHNGvmqwrt0FE9b73NfszRggKVlJTo6NEC1dTU6OCBA5KkDh068AbtJooYyjwNNuBUWloqf39/SVL37t11+vRpnT59+px16+rBfRQVFeoPE1JVXFys1gEB6h59s/5n6TKHTKZVK99VcHCIYnrd2oA9BRre8nf+IUl6fOSjDuVTXnxZAwcP0cED+7X3yz2SpIH9+zrUyVr/odq2+09G1Kr3VujOu+6WH7+bQJNE/NS07N+/T6Me+89v/59e+eWRxoGD7tVzL0zR4cP/0gfvr1TxiRNq1aqVbugapYWLlyg8/DpJ0vZtnyg//3vl53+vvnf2dmh7z/488y4EaCAXuof+76TJOpR3SB+8v0r/Lv23goKCFNOzl5KfelpWq9V+zLw5r+uD91faPz9w32BJ0lsLF+t3/+cWcy4EaKIshmE0SOZ08+bNdfToUQUFBalZs2bnXPSybjHM+rxFgNk5oP48PaRTlTxUAdSHt9WcabOsfcdMOU+d+K5Bpp4P53al4yeJGAqoL08P7h/gcnialA5DDGWeBstw2rRpkz3bZfPmzQ3VDQAAALdB/AQAANxFg2U4XWnMLgD1R4YTUH9mZTit3W/u7Fz/G67e2bmrDTEUUD9kOAGXx6wMJ2Io8zSK11isX79eW7dutX+eO3eubrrpJj388MM6ceJEA/YMAABcrunTp8tisSglJcVeVl5eruTkZAUGBsrX11cJCQkqKipyOC4/P1/x8fHy9vZWUFCQJkyYoOpqx3/NbdmyRdHR0bLZbAoPD1dmZqYJV9Q4ED8BAIDGrFEMOE2YMEGlpaWSpL179yo1NVX9+/fX4cOHz3pdLwAAkJrJYupWXzt27NCbb76pG2+80aF8/PjxWr16tZYvX66cnBwVFBRoyJAh9v01NTWKj49XZWWltm3bpkWLFikzM1OTJk2y1zl8+LDi4+PVp08f7d69WykpKRo1apQ2bNhQ7/66E+InAACc5y4xVFPQKB6p8/X11b59+9SpUydNnjxZ+/bt04oVK7Rr1y71799fhYWFTrdJOitQfzxSB9SfWY/Urd9/3JTz1Ol3g/OvWC8rK1N0dLTmzZunl156STfddJNmz56tkpIStWnTRkuXLtV9990nSTp48KAiIiKUm5urHj16aN26dRowYIAKCgoUHBwsSVqwYIHS0tJ0/PhxWa1WpaWlKSsrS/v27bOf88EHH1RxcbHWr1/vmgtvxK5E/CQRQwH1xSN1wOUx65E6d4ihmopGkeFktVp16tQpSdKHH36ovn1/ee13QECAfeYOAAA0nIqKCpWWljpsFRUVFzwmOTlZ8fHxio2NdSjfuXOnqqqqHMq7dOmiDh06KDc3V5KUm5urqKgo+2CTJMXFxam0tFT79++31/l123FxcfY2mjriJwAA0Jg12FvqznTrrbcqNTVVvXr10meffaZ33nlHknTo0CG1b9++gXsHAEDjYzE5QzsjI0NTpkxxKHvhhRc0efLkc9Z/++23tWvXLu3YseOsfYWFhbJarWrVqpVDeXBwsD0rp7Cw0GGwqW5/3b4L1SktLdXp06fl5eV1ydfnjoifAABwntkx1NWsUWQ4zZkzRx4eHlqxYoXmz5+vdu3aSZLWrVunfv36NXDvAABAenq6SkpKHLb09PRz1j1y5IiefvppLVmyRJ6enib39OpB/AQAABqzRpHh1KFDB61Zs+as8lmzZjVAbwAAaPzMnp2z2Wyy2WyXVHfnzp06duyYoqOj7WU1NTX66KOPNGfOHG3YsEGVlZUqLi52yHIqKipSSEiIJCkkJESfffaZQ7t1b7E7s86v32xXVFQkf3//Jp/dJBE/AQBQH2Q4madRDDhJvwSiq1at0oEDByRJN9xwgwYOHKjmzZs3cM8AAIAz7rrrLu3du9eh7LHHHlOXLl2Ulpam0NBQtWjRQhs3blRCQoIkKS8vT/n5+YqJiZEkxcTEaNq0aTp27JiCgoIkSdnZ2fL391dkZKS9ztq1ax3Ok52dbW/jakD8BAAAGqtGMeD0zTffqH///vrxxx/VuXNnSb+sFREaGqqsrCz95je/aeAeAgDQuFga8Wt2/fz81LVrV4cyHx8fBQYG2ssTExOVmpqqgIAA+fv766mnnlJMTIx69OghSerbt68iIyM1bNgwvfLKKyosLNRzzz2n5ORke6ZVUlKS5syZo4kTJ2rkyJHatGmTli1bpqysLHMvuIEQPwEA4LzGHEM1NY1iDadx48bpN7/5jY4cOaJdu3Zp165dys/PV1hYmMaNG9fQ3QMAAC42a9YsDRgwQAkJCerdu7dCQkL03nvv2fc3b95ca9asUfPmzRUTE6NHHnlEjz76qKZOnWqvExYWpqysLGVnZ6tbt26aOXOm3nrrLcXFxTXEJZmO+AkAADRmFsMwjIbuhI+Pj7Zv366oqCiH8j179qhXr14qKytzus3yalf1Drj6eHpIpyob/KcBcEveVnNmzTYe/MmU89S5q8s1pp4PF3cl4ieJGAqoL08P7h/gcnia9PwVMZR5GkWGk81m07///e+zysvKymS1WhugRwAAAI0b8RMAAE3X9OnTZbFYlJKSYi8rLy9XcnKyAgMD5evrq4SEhLNeoJKfn6/4+Hh5e3srKChIEyZMUHW142j4li1bFB0dLZvNpvDwcGVmZl6Ra2gUA04DBgzQ6NGj9emnn8owDBmGoe3btyspKUkDBw5s6O4BAAA0OsRPAAA0TTt27NCbb76pG2+80aF8/PjxWr16tZYvX66cnBwVFBRoyJAh9v01NTWKj49XZWWltm3bpkWLFikzM1OTJk2y1zl8+LDi4+PVp08f7d69WykpKRo1apQ2bNjg8utoFI/UFRcXa8SIEVq9erU8PH7Jo6uurtbAgQOVmZmpli1bOt0m6axA/fFIHVB/Zj1St+ngz6acp86dXQJNPR8u7krETxIxFFBfPFIHXB6zHqlr7DFUWVmZoqOjNW/ePL300ku66aabNHv2bJWUlKhNmzZaunSp7rvvPknSwYMHFRERodzcXPXo0UPr1q3TgAEDVFBQoODgYEnSggULlJaWpuPHj8tqtSotLU1ZWVnat2+f/ZwPPvigiouLtX79etdduBo4w6m2tlYzZsxQfHy8fvzxRw0ePFjLly/XihUrlJeXp5UrV9Y7WAIAAGiKiJ8AAHAfFRUVKi0tddgqKirOWz85OVnx8fGKjY11KN+5c6eqqqocyrt06aIOHTooNzdXkpSbm6uoqCj7YJMkxcXFqbS0VPv377fX+XXbcXFx9jZcqUEHnKZNm6Y//vGP8vX1Vbt27bR27VqtWrVK99xzj8LDwxuyawAANGoWi7kbGg/iJwAA6s/sGCojI0MtW7Z02DIyMs7Zt7ffflu7du065/7CwkJZrVa1atXKoTw4OFiFhYX2OmcONtXtr9t3oTqlpaU6ffp0vb7T8zEpae3cFi9erHnz5umJJ56QJH344YeKj4/XW2+9pWbNGsXyUgAAAI0K8RMAAO4jPT1dqampDmU2m+2sekeOHNHTTz+t7OxseXp6mtW9K6pBo5L8/Hz179/f/jk2NlYWi0UFBQUN2CsAABo/i8l/0HgQPwEAUH9mx1A2m03+/v4O27kGnHbu3Kljx44pOjpaHh4e8vDwUE5Ojl5//XV5eHgoODhYlZWVKi4udjiuqKhIISEhkqSQkJCz3lpX9/lidfz9/eXl5eWqr1lSAw84VVdXnzVy16JFC1VVVTVQjwAAABo34icAAJqeu+66S3v37tXu3bvt229/+1sNHTrU/t8tWrTQxo0b7cfk5eUpPz9fMTExkqSYmBjt3btXx44ds9fJzs6Wv7+/IiMj7XXObKOuTl0brtSgj9QZhqERI0Y4jO6Vl5crKSlJPj4+9rL33nuvIboHAECj1Yyko6sW8RMAAPXXWGMoPz8/de3a1aHMx8dHgYGB9vLExESlpqYqICBA/v7+euqppxQTE6MePXpIkvr27avIyEgNGzZMr7zyigoLC/Xcc88pOTnZHjckJSVpzpw5mjhxokaOHKlNmzZp2bJlysrKcvk1NeiA0/Dhw88qe+SRRxqgJwAAAO6B+AkAgKvTrFmz1KxZMyUkJKiiokJxcXGaN2+efX/z5s21Zs0ajRkzRjExMfLx8dHw4cM1depUe52wsDBlZWVp/Pjxeu2119S+fXu99dZbiouLc3l/LYZhGC5vtREor27oHgDuy9NDOlXZJH8agCvO22rOtNnHh06Ycp46t13f2tTzoeEQQwH14+nB/QNcDk+T0mGIoczDq0wAAAAAAADgUgw4AQAAAAAAwKUadA0nAABQP5ZGuuAlAABAY0YMZR4ynAAAAAAAAOBSZDgBAOCGmJwDAABwHjGUechwAgAAAAAAgEuR4QQAgBtqxgIEAAAATiOGMg8ZTgAAAAAAAHApMpwAAHBDzM0BAAA4jxjKPGQ4AQAAAAAAwKXIcAIAwB0xPQcAAOA8YijTkOEEAAAAAAAAl2LACQAAAAAAAC7FI3UAALghC/ngAAAATiOGMg8ZTgAAAAAAAHApMpwAAHBDFibnAAAAnEYMZR4ynAAAAAAAAOBSZDgBAOCGmJwDAABwHjGUechwAgAAAAAAgEuR4QQAgDtieg4AAMB5xFCmIcMJAAAAAAAALsWAEwAAAAAAAFyKR+oAAHBDFvLBAQAAnEYMZR4ynAAAAAAAAOBSZDgBAOCGLEzOAQAAOI0YyjxkOAEAAAAAAMClyHACAMANMTkHAADgPGIo85DhBAAAAAAAAJciwwkAAHfE9BwAAIDziKFMQ4YTAAAAAAAAXIoMJwAA3JCF6TkAAACnEUOZhwwnAAAAAAAAuBQDTgAAAAAAAHApHqkDAMANWcgGBwAAcBoxlHnIcAIAAAAAAIBLkeEEAIAbYnIOAADAecRQ5iHDCQAAAAAAAC5FhhMAAO6I6TkAAADnEUOZhgwnAAAAAAAAuBQZTgAAuCEL03MAAABOI4YyDxlOAAAAAAAAcCkynAAAcEMWJucAAACcRgxlHjKcAAAAAAAA4FIMOAEAAAAAAMCleKQOAAA3RDY4AACA84ihzEOGEwAAAAAAAFyKDCcAANwR03MAAADOI4YyDRlOAAAAAAAAcCkynAAAcEMWpucAAACcRgxlHjKcAAAAAAAA4FJkOAEA4IYsTM4BAAA4jRjKPGQ4AQAAAAAAwKUYcAIAAAAAAIBL8UgdAABuiGxwAAAA5xFDmYcMJwAAAAAAALgUGU4AALgjpucAAACcRwxlGjKcAAAAAAAA4FJkOAEA4IYsTM8BAAA4jRjKPGQ4AQAAAAAAwKXIcAIAwA1ZmJwDAABwGjGUechwAgAAAAAAgEuR4QQAgBticg4AAMB5xFDmIcMJAAAAAAAALsWAEwAAcKmMjAz97ne/k5+fn4KCgjR48GDl5eU51CkvL1dycrICAwPl6+urhIQEFRUVOdTJz89XfHy8vL29FRQUpAkTJqi6utqhzpYtWxQdHS2bzabw8HBlZmZe6csDAADAJWDACQAAd2QxeXNCTk6OkpOTtX37dmVnZ6uqqkp9+/bVyZMn7XXGjx+v1atXa/ny5crJyVFBQYGGDBli319TU6P4+HhVVlZq27ZtWrRokTIzMzVp0iR7ncOHDys+Pl59+vTR7t27lZKSolGjRmnDhg3OdRgAAFw9GnEM1dRYDMMwGroTV0J59cXrADg3Tw/pVGWT/GkArjhvqzmRxaGiU6acp871wd71Pvb48eMKCgpSTk6OevfurZKSErVp00ZLly7VfffdJ0k6ePCgIiIilJubqx49emjdunUaMGCACgoKFBwcLElasGCB0tLSdPz4cVmtVqWlpSkrK0v79u2zn+vBBx9UcXGx1q9ff3kXfBUjhgLqx9OD+we4HJ4mrTDtTjGUuyPDCQAAN2Qx+U9FRYVKS0sdtoqKikvqa0lJiSQpICBAkrRz505VVVUpNjbWXqdLly7q0KGDcnNzJUm5ubmKioqyDzZJUlxcnEpLS7V//357nTPbqKtT1wYAAMCvmR1DXc0YcAIAABeVkZGhli1bOmwZGRkXPa62tlYpKSnq1auXunbtKkkqLCyU1WpVq1atHOoGBwersLDQXufMwaa6/XX7LlSntLRUp0+frtd1AgAAwDVMSloDAACuZDF5wiw9PV2pqakOZTab7aLHJScna9++fdq6deuV6hoAAMAlMzuGupox4AQAAC7KZrNd0gDTmcaOHas1a9boo48+Uvv27e3lISEhqqysVHFxsUOWU1FRkUJCQux1PvvsM4f26t5id2adX7/ZrqioSP7+/vLy8nKqrwAAAHAtHqkDAMANNeYXrBiGobFjx2rlypXatGmTwsLCHPbffPPNatGihTZu3Ggvy8vLU35+vmJiYiRJMTEx2rt3r44dO2avk52dLX9/f0VGRtrrnNlGXZ26NgAAAH6tMcdQGRkZ+t3vfic/Pz8FBQVp8ODBysvLc6hTXl6u5ORkBQYGytfXVwkJCWdNwOXn5ys+Pl7e3t4KCgrShAkTVF3t+FaDLVu2KDo6WjabTeHh4crMzHSytxfHgBMAAHCp5ORk/f3vf9fSpUvl5+enwsJCFRYW2tdVatmypRITE5WamqrNmzdr586deuyxxxQTE6MePXpIkvr27avIyEgNGzZMe/bs0YYNG/Tcc88pOTnZnmmVlJSkf/3rX5o4caIOHjyoefPmadmyZRo/fnyDXTsAAEB95eTkKDk5Wdu3b1d2draqqqrUt29fnTx50l5n/PjxWr16tZYvX66cnBwVFBRoyJAh9v01NTWKj49XZWWltm3bpkWLFikzM1OTJk2y1zl8+LDi4+PVp08f7d69WykpKRo1apQ2bNjg0uuxGIbRJN99zitJgfrz9JBOVTbJnwbgivO2mrMwwLfHzV0U+zdtLv0RNct5FkdYuHChRowYIemX2blnnnlG//jHP1RRUaG4uDjNmzfP/ricJH3//fcaM2aMtmzZIh8fHw0fPlzTp0+Xh8d/VgTYsmWLxo8fr6+++krt27fX888/bz8H6ocYCqgfTw/uH+ByeJq04E9jjqF+7fjx4woKClJOTo569+6tkpIStWnTRkuXLtV9990nSTp48KAiIiKUm5urHj16aN26dRowYIAKCgrsL1dZsGCB0tLSdPz4cVmtVqWlpSkrK0v79u2zn+vBBx9UcXGx1q9ff3kXfAbWcAIAAC51KXNZnp6emjt3rubOnXveOh07dtTatWsv2M4dd9yhL774wuk+AgAAmKGiokIVFRUOZZe6NmZJSYkkKSAgQJK0c+dOVVVVKTY21l6nS5cu6tChg33AKTc3V1FRUQ5v8o2Li9OYMWO0f/9+de/eXbm5uQ5t1NVJSUmp72WeE4/UAQAAAAAAXAEZGRlq2bKlw5aRkXHR42pra5WSkqJevXqpa9eukqTCwkJZrVaHl65IUnBwsAoLC+11zhxsqttft+9CdUpLS+1LILhCk81wMisdD86pqKhQRkaG0tPTnX7bEcxl1mNBcA73EOpYnF6GErg0xFCNE7//7oH7p3Hi/sGZzI6h0tPTlZqa6lB2KX8Pk5OTtW/fPm3duvVKde2KI8MJpqqoqNCUKVPOSikEcGm4hwDg6sTvP1B/3D9oSDabTf7+/g7bxQacxo4dqzVr1mjz5s1q3769vTwkJESVlZUqLi52qF9UVGRfBzMkJOSst9bVfb5YHX9/f3l51X/NqV9jwAkAADdksZi7AQAANAWNOYYyDENjx47VypUrtWnTJoWFhTnsv/nmm9WiRQtt3LjRXpaXl6f8/HzFxMRIkmJiYrR3714dO3bMXic7O1v+/v6KjIy01zmzjbo6dW24CkmfAAAAAAAADSw5OVlLly7V+++/Lz8/P/uaSy1btpSXl5datmypxMREpaamKiAgQP7+/nrqqacUExOjHj16SJL69u2ryMhIDRs2TK+88ooKCwv13HPPKTk52Z5ZlZSUpDlz5mjixIkaOXKkNm3apGXLlikrK8ul18OAEwAAboikIwAAAOc15hhq/vz5kn55C++ZFi5cqBEjRkiSZs2apWbNmikhIUEVFRWKi4vTvHnz7HWbN2+uNWvWaMyYMYqJiZGPj4+GDx+uqVOn2uuEhYUpKytL48eP12uvvab27dvrrbfeUlxcnEuvhwEnmMpms+mFF15gsT6gnriHAODqxO8/UH/cP3AXhmFctI6np6fmzp2ruXPnnrdOx44dtXbt2gu2c8cdd+iLL75wuo/OsBiXckUAAKBR+e7nclPP1ynQ09TzAQAAXAnEUOZh0XAAAAAAAAC4FANOAAAAAAAAcCnWcAIAwA1ZGvWSlwAAAI0TMZR5yHBCo9epUyfNnj27obsBNJgtW7bIYrGouLj4gvW4VwAAdfh/AkAMBTQ0BpyuciNGjJDFYtH06dMdyletWiWLxdyR38zMTLVq1eqs8h07dmj06NGm9gWoj7r7yWKxyGq1Kjw8XFOnTlV1dfVltduzZ08dPXpULVu2lMS9gl9YLOZuAP6D+AlwLWIomIkYyjwMOEGenp6aMWOGTpw40dBdOac2bdrI29u7obsBXJJ+/frp6NGj+vrrr/XMM89o8uTJevXVVy+rTavVqpCQkIv+I4Z7BQDMQ/wEuBYxFND0MOAExcbGKiQkRBkZGeets3XrVt12223y8vJSaGioxo0bp5MnT9r3Hz16VPHx8fLy8lJYWJiWLl16Vmrqn//8Z0VFRcnHx0ehoaF68sknVVZWJumXdNfHHntMJSUl9tmNyZMnS3JMcX344Yf1wAMPOPStqqpK11xzjRYvXixJqq2tVUZGhsLCwuTl5aVu3bppxYoVLvimgIuz2WwKCQlRx44dNWbMGMXGxuqDDz7QiRMn9Oijj6p169by9vbW73//e3399df2477//nvdc889at26tXx8fHTDDTdo7dq1khzTwblXUMdi8gbAEfET4FrEUDALMZR5GHCCmjdvrpdffllvvPGGfvjhh7P2f/vtt+rXr58SEhL05Zdf6p133tHWrVs1duxYe51HH31UBQUF2rJli95991395S9/0bFjxxzaadasmV5//XXt379fixYt0qZNmzRx4kRJv6S7zp49W/7+/jp69KiOHj2qZ5999qy+DB06VKtXr7YHWpK0YcMGnTp1Svfee68kKSMjQ4sXL9aCBQu0f/9+jR8/Xo888ohycnJc8n0BzvDy8lJlZaVGjBihzz//XB988IFyc3NlGIb69++vqqoqSVJycrIqKir00Ucfae/evZoxY4Z8fX3Pao97BQAaB+In4MoihgKaAANXteHDhxuDBg0yDMMwevToYYwcOdIwDMNYuXKlUffXIzEx0Rg9erTDcR9//LHRrFkz4/Tp08aBAwcMScaOHTvs+7/++mtDkjFr1qzznnv58uVGYGCg/fPChQuNli1bnlWvY8eO9naqqqqMa665xli8eLF9/0MPPWQ88MADhmEYRnl5ueHt7W1s27bNoY3ExETjoYceuvCXAVymM++n2tpaIzs727DZbMbgwYMNScYnn3xir/vTTz8ZXl5exrJlywzDMIyoqChj8uTJ52x38+bNhiTjxIkThmFwr+AXR/633NQNwH8QPwGuRQwFMxFDmcejoQa60PjMmDFDd95551kj/Xv27NGXX36pJUuW2MsMw1Btba0OHz6sQ4cOycPDQ9HR0fb94eHhat26tUM7H374oTIyMnTw4EGVlpaqurpa5eXlOnXq1CU/M+3h4aH7779fS5Ys0bBhw3Ty5Em9//77evvttyVJ33zzjU6dOqW7777b4bjKykp1797dqe8DqI81a9bI19dXVVVVqq2t1cMPP6whQ4ZozZo1uuWWW+z1AgMD1blzZx04cECSNG7cOI0ZM0b//Oc/FRsbq4SEBN1444317gf3CgCYg/gJcA1iKKDpYcAJdr1791ZcXJzS09M1YsQIe3lZWZmeeOIJjRs37qxjOnTooEOHDl207e+++04DBgzQmDFjNG3aNAUEBGjr1q1KTExUZWWlU4v0DR06VLfffruOHTum7OxseXl5qV+/fva+SlJWVpbatWvncJzNZrvkcwD11adPH82fP19Wq1Vt27aVh4eHPvjgg4seN2rUKMXFxSkrK0v//Oc/lZGRoZkzZ+qpp56qd1+4V5q6q31VAKBxIH4CXIMYCuYhhjILA05wMH36dN10003q3LmzvSw6OlpfffWVwsPDz3lM586dVV1drS+++EI333yzpF9G/s98a8vOnTtVW1urmTNnqlmzX5YOW7ZsmUM7VqtVNTU1F+1jz549FRoaqnfeeUfr1q3Tf/3Xf6lFixaSpMjISNlsNuXn5+v222937uIBF/Dx8TnrXomIiFB1dbU+/fRT9ezZU5L0888/Ky8vT5GRkfZ6oaGhSkpKUlJSktLT0/XXv/71nMES9woANC7ET8DlI4YCmh4GnOAgKipKQ4cO1euvv24vS0tLU48ePTR27FiNGjVKPj4++uqrr5Sdna05c+aoS5cuio2N1ejRozV//ny1aNFCzzzzjLy8vOyvIA0PD1dVVZXeeOMN3XPPPfrkk0+0YMECh3N36tRJZWVl2rhxo7p16yZvb+/zztw9/PDDWrBggQ4dOqTNmzfby/38/PTss89q/Pjxqq2t1a233qqSkhJ98skn8vf31/Dhw6/AtwZc2HXXXadBgwbp8ccf15tvvik/Pz/94Q9/ULt27TRo0CBJUkpKin7/+9/r+uuv14kTJ7R582ZFREScsz3uFQBoXIifgCuDGApwb7ylDmeZOnWqamtr7Z9vvPFG5eTk6NChQ7rtttvUvXt3TZo0SW3btrXXWbx4sYKDg9W7d2/de++9evzxx+Xn5ydPT09JUrdu3fTnP/9ZM2bMUNeuXbVkyZKzXiPcs2dPJSUl6YEHHlCbNm30yiuvnLePQ4cO1VdffaV27dqpV69eDvtefPFFPf/888rIyFBERIT69eunrKwshYWFueLrAepl4cKFuvnmmzVgwADFxMTIMAytXbvWPltWU1Oj5ORk+9/Z66+/XvPmzTtnW9wrkCSLxdwNwIURPwFXBjEUXI0YyjwWwzCMhu4Emp4ffvhBoaGh+vDDD3XXXXc1dHcAoMn5sbjS1PO1a2U19XzA1Yj4CQCuPGIo8/BIHVxi06ZNKisrU1RUlI4ePaqJEyeqU6dO6t27d0N3DQCapKt8wgxoEoifAMB8xFDmYcAJLlFVVaU//vGP+te//iU/Pz/17NlTS5Yssae6AgAAwBHxEwCgKeOROgAA3NDREnPTwa9tefWmgwMAgKaDGMo8LBoOAAAAAAAAl+KROgAA3JCFFQgAAACcRgxlHjKcAAAAAAAA4FJkOAEA4I6YnAMAAHAeMZRpyHACAAAAAACASzHgBLiRESNGaPDgwfbPd9xxh1JSUkzvx5YtW2SxWFRcXHzFzvHra60PM/oJAAAaN+In5xA/AXAVBpyAyzRixAhZLBZZLBZZrVaFh4dr6tSpqq6uvuLnfu+99/Tiiy9eUl2zg4dOnTpp9uzZppwLuBpZTN4AwJWIn86N+Am48oihzMMaToAL9OvXTwsXLlRFRYXWrl2r5ORktWjRQunp6WfVrayslNVqdcl5AwICXNIOAACA2YifAKBpI8MJcAGbzaaQkBB17NhRY8aMUWxsrD744ANJ/0ltnjZtmtq2bavOnTtLko4cOaL7779frVq1UkBAgAYNGqTvvvvO3mZNTY1SU1PVqlUrBQYGauLEiTIMw+G8v04Jr6ioUFpamkJDQ2Wz2RQeHq7//u//1nfffac+ffpIklq3bi2LxaIRI0ZIkmpra5WRkaGwsDB5eXmpW7duWrFihcN51q5dq+uvv15eXl7q06ePQz/ro6amRomJifZzdu7cWa+99to5606ZMkVt2rSRv7+/kpKSVFlZad93KX0HmiqLxdwNAFyN+Mk5xE+AaxBDmYcMJ+AK8PLy0s8//2z/vHHjRvn7+ys7O1uSVFVVpbi4OMXExOjjjz+Wh4eHXnrpJfXr109ffvmlrFarZs6cqczMTP3tb39TRESEZs6cqZUrV+rOO+8873kfffRR5ebm6vXXX1e3bt10+PBh/fTTTwoNDdW7776rhIQE5eXlyd/fX15eXpKkjIwM/f3vf9eCBQt03XXX6aOPPtIjjzyiNm3a6Pbbb9eRI0c0ZMgQJScna/To0fr888/1zDPPXNb3U1tbq/bt22v58uUKDAzUtm3bNHr0aF177bW6//77Hb43T09PbdmyRd99950ee+wxBQYGatq0aZfUdwAA4D6Iny6M+AmA2zEAXJbhw4cbgwYNMgzDMGpra43s7GzDZrMZzz77rH1/cHCwUVFRYT/mf/7nf4zOnTsbtbW19rKKigrDy8vL2LBhg2EYhnHttdcar7zyin1/VVWV0b59e/u5DMMwbr/9duPpp582DMMw8vLyDElGdnb2Ofu5efNmQ5Jx4sQJe1l5ebnh7e1tbNu2zaFuYmKi8dBDDxmGYRjp6elGZGSkw/60tLSz2vq1jh07GrNmzTrv/l9LTk42EhIS7J+HDx9uBAQEGCdPnrSXzZ8/3/D19TVqamouqe/numagqThWWmXqBgCuRPx0bsRPwJVHDGUeMpwAF1izZo18fX1VVVWl2tpaPfzww5o8ebJ9f1RUlMO6A3v27NE333wjPz8/h3bKy8v17bffqqSkREePHtUtt9xi3+fh4aHf/va3Z6WF19m9e7eaN2/u1MzUN998o1OnTunuu+92KK+srFT37t0lSQcOHHDohyTFxMRc8jnOZ+7cufrb3/6m/Px8nT59WpWVlbrpppsc6nTr1k3e3t4O5y0rK9ORI0dUVlZ20b4DAIDGi/jJecRPANwJA06AC/Tp00fz58+X1WpV27Zt5eHheGv5+Pg4fC4rK9PNN9+sJUuWnNVWmzZt6tWHuhRvZ5SVlUmSsrKy1K5dO4d9NputXv24FG+//baeffZZzZw5UzExMfLz89Orr76qTz/99JLbaKi+A43GVb4mAAD3R/zkHOInwEWIoUzDgBPgAj4+PgoPD7/k+tHR0XrnnXcUFBQkf3//c9a59tpr9emnn6p3796SpOrqau3cuVPR0dHnrB8VFaXa2lrl5OQoNjb2rP11M4Q1NTX2ssjISNlsNuXn5593Zi8iIsK+gGed7du3X/wiL+CTTz5Rz5499eSTT9rLvv3227Pq7dmzR6dPn7YHg9u3b5evr69CQ0MVEBBw0b4DAIDGi/jJOcRPANwNb6kDGsDQoUN1zTXXaNCgQfr44491+PBhbdmyRePGjdMPP/wgSXr66ac1ffp0rVq1SgcPHtSTTz6p4uLi87bZqVMnDR8+XCNHjtSqVavsbS5btkyS1LFjR1ksFq1Zs0bHjx9XWVmZ/Pz89Oyzz2r8+PFatGiRvv32W+3atUtvvPGGFi1aJElKSkrS119/rQkTJigvL09Lly5VZmbmJV3njz/+qN27dztsJ06c0HXXXafPP/9cGzZs0KFDh/T8889rx44dZx1fWVmpxMREffXVV1q7dq1eeOEFjR07Vs2aNbukvgMAgKaD+In4CYB7YcAJaADe3t766KOP1KFDBw0ZMkQRERFKTExUeXm5fcbumWee0bBhwzR8+HB72vS99957wXbnz5+v++67T08++aS6dOmixx9/XCdPnpQktWvXTlOmTNEf/vAHBQcHa+zYsZKkF198Uc8//7wyMjIUERGhfv36KSsrS2FhYZKkDh066N1339WqVavUrVs3LViwQC+//PIlXeef/vQnde/e3WHLysrSE088oSFDhuiBBx7QLbfcop9//tlhtq7OXXfdpeuuu069e/fWAw88oIEDBzqs7XCxvgNNmcXkDQAaGvET8RPgCsRQ5rEY51tBDwAANFo/lVWber5rfHkKHwAAuD9iKPNcvVcOAIAbs1ztU2YAAAD1QAxlHh6pAwAAAAAAgEuR4QQAgBuyXPWrAgAAADiPGMo8ZDgBAAAAAADApchwAgDADbH+AAAAgPOIocxDhhMAAAAAAABcigEnAAAAAAAAuBQDTgAAAAAAAHApBpwAAAAAAADgUiwaDgCAG2LBSwAAAOcRQ5mHDCcAAAAAAAC4FBlOAAC4IYuYngMAAHAWMZR5yHACAAAAAACAS5HhBACAG2L9AQAAAOcRQ5mHDCcAAAAAAAC4FBlOAAC4ISbnAAAAnEcMZR4ynAAAAAAAAOBSZDgBAOCOmJ4DAABwHjGUachwAgAAAAAAgEsx4AQAAAAAAACX4pE6AADckIV8cAAAAKcRQ5mHDCcAAAAAAAC4FBlOAAC4IQuTcwAAAE4jhjIPGU4AAAAAAABwKTKcAABwQ0zOAQAAOI8YyjxkOAEAAAAAAMClyHACAMAdMT0HAADgPGIo05DhBAAAAAAAAJdiwAkAAAAAAAAuxSN1AAC4IQv54AAAAE4jhjIPGU4AAAAAAABwKTKcAABwQxYm5wAAAJxGDGUeMpwAAAAAAADgUhbDMIyG7gQAAAAAAACaDjKcAAAAAAAA4FIMOAEAAAAAAMClGHACAAAAAACASzHgBAAAAAAAAJdiwAkAAAAAAAAuxYATAAAAAAAAXIoBJwAAAAAAALgUA04AAAAAAABwKQacAAAAAAAA4FL/D3SqA4SN/uR6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.9457475994513032\n",
            "\t\t- Validation Specificity: 0.9501259093452714\n",
            "\t\t- Test Accuracy: 0.980315500685871\n",
            "\t\t- Test Specificity: 0.990487514863258\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 2\n",
            "- 36,500 total rows in features dict.\n",
            "- 36,500 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [02:05<00:00,  1.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 164\n",
            "\t- Best Val AUC: 0.9984033613445379\n",
            "\t- Best Test AUC: 0.9956692677070829\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.9932692307692308\n",
            "\t\t- Validation Recall: 0.8071428571428572\n",
            "\t\t- Test Accuracy: 0.989010989010989\n",
            "\t\t- Test Recall: 0.6071428571428571\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.9793956043956044\n",
            "\t\t- Validation Specificity: 0.9791316526610644\n",
            "\t\t- Test Accuracy: 0.9567307692307693\n",
            "\t\t- Test Specificity: 0.9561624649859944\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 3\n",
            "- 36,500 total rows in features dict.\n",
            "- 36,500 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [02:06<00:00,  1.58it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 193\n",
            "\t- Best Val AUC: 0.9986054421768708\n",
            "\t- Best Test AUC: 0.9965436174469787\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.9943681318681319\n",
            "\t\t- Validation Recall: 0.8071428571428572\n",
            "\t\t- Test Accuracy: 0.9901098901098901\n",
            "\t\t- Test Recall: 0.6642857142857143\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.9784340659340659\n",
            "\t\t- Validation Specificity: 0.9781512605042016\n",
            "\t\t- Test Accuracy: 0.9758241758241758\n",
            "\t\t- Test Specificity: 0.9756302521008403\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 4\n",
            "- 18,200 total rows in features dict.\n",
            "- 18,200 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [01:08<00:00,  2.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 198\n",
            "\t- Best Val AUC: 0.9982498541545128\n",
            "\t- Best Test AUC: 0.9979919517102616\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.992817679558011\n",
            "\t\t- Validation Recall: 0.8028169014084507\n",
            "\t\t- Test Accuracy: 0.988950276243094\n",
            "\t\t- Test Recall: 0.9571428571428572\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.9883977900552486\n",
            "\t\t- Validation Specificity: 0.9887292194984503\n",
            "\t\t- Test Accuracy: 0.9701657458563536\n",
            "\t\t- Test Specificity: 0.9698591549295774\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 5\n",
            "- 18,200 total rows in features dict.\n",
            "- 18,200 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [01:02<00:00,  3.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 200\n",
            "\t- Best Val AUC: 0.9949281487743027\n",
            "\t- Best Test AUC: 0.9944225352112677\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.9883977900552486\n",
            "\t\t- Validation Recall: 0.8028169014084507\n",
            "\t\t- Test Accuracy: 0.9867403314917127\n",
            "\t\t- Test Recall: 0.8428571428571429\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.9676795580110498\n",
            "\t\t- Validation Specificity: 0.9675965060580445\n",
            "\t\t- Test Accuracy: 0.9599447513812155\n",
            "\t\t- Test Specificity: 0.9594366197183098\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 6\n",
            "- 12,100 total rows in features dict.\n",
            "- 12,100 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [00:44<00:00,  4.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 169\n",
            "\t- Best Val AUC: 0.9819948413592768\n",
            "\t- Best Test AUC: 0.9907446808510638\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.9766666666666667\n",
            "\t\t- Validation Recall: 0.803921568627451\n",
            "\t\t- Test Accuracy: 0.9791666666666666\n",
            "\t\t- Test Recall: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.945\n",
            "\t\t- Validation Specificity: 0.9455087271179226\n",
            "\t\t- Test Accuracy: 0.9858333333333333\n",
            "\t\t- Test Specificity: 0.9927659574468085\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------\n",
            "*experiment_idx = 7\n",
            "- 12,100 total rows in features dict.\n",
            "- 12,100 total rows in labels dict.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epochs: 100%|██████████| 200/200 [00:46<00:00,  4.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- Best Val AUC Epoch: 186\n",
            "\t- Best Val AUC: 0.9819697994140184\n",
            "\t- Best Test AUC: 0.9917872340425532\n",
            "\t- TPR Target: 0.8\n",
            "\t\t- Validation Accuracy: 0.9716666666666667\n",
            "\t\t- Validation Recall: 0.803921568627451\n",
            "\t\t- Test Accuracy: 0.9875\n",
            "\t\t- Test Recall: 0.78\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t- FPR Target: 0.05\n",
            "\t\t- Validation Accuracy: 0.95\n",
            "\t\t- Validation Specificity: 0.9510429970200085\n",
            "\t\t- Test Accuracy: 0.97875\n",
            "\t\t- Test Specificity: 0.9808510638297873\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}