custom_training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "custom_training.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ppiont/tensor-flow-state/blob/master/custom_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSuzkeZcM2UE",
        "colab_type": "code",
        "outputId": "9338790f-3408-4708-ae81-f1b539a83130",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/gdrive\", force_remount = True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBC4ZppZNC3c",
        "colab_type": "code",
        "outputId": "4a91494f-48f9-499f-fdc9-8ccf7fa5da0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd \"/gdrive/My Drive/tensor-flow-state/tensor-flow-state\""
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive/tensor-flow-state/tensor-flow-state\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKD3KY8hNEqo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"data/final_data.csv\", index_col = 0, parse_dates = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyqsebC1PLdm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df[df.index > \"2016-06-02\"]\n",
        "df['density'] = (df.flow * 60) / df.speed"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzVAi95GfcBW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cols = [\"speed\", \"flow\", \"density\", \"speed_limit\", \"holiday\", \"weekend\"]\n",
        "continuous_cols = [\"speed\", \"flow\", \"density\"]\n",
        "discrete_cols = [\"speed_limit\", \"holiday\", \"weekend\"]\n",
        "df = df[cols]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uh4evfBugwJa",
        "colab_type": "text"
      },
      "source": [
        "### First resample to elmininate some noise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVkBrcJDhVxA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "def resample_df(df, freq = \"15T\"):\n",
        "    r_df = df.copy()\n",
        "    r_df = r_df.resample(freq).agg({\n",
        "           \"speed\": np.mean, \"flow\": np.sum, \"density\": np.mean, \"speed_limit\": np.median, \"holiday\": np.median, \"weekend\": np.median})\n",
        "    return r_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJyIniCBhWIc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "r_df = resample_df(df, freq = \"15T\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_M66yh3ektP7",
        "colab_type": "text"
      },
      "source": [
        "### Train, val, test split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSeGBQtNq2GC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_split(split_df):\n",
        "    train = split_df[split_df.index.year < 2019].copy()\n",
        "    val = split_df[len(train): -len(split_df[split_df.index > \"2019-06\"]) - 1].copy()\n",
        "    test = r_df[len(train) + len(val):].copy()\n",
        "    return train, val, test\n",
        "\n",
        "train, val, test = train_split(r_df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFm8wSGx5dm-",
        "colab_type": "text"
      },
      "source": [
        "### MinMax Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRSHkyO85jw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(train)\n",
        "X_train = scaler.transform(train)\n",
        "y_train = X_train[:, 0]\n",
        "X_val = scaler.transform(val)\n",
        "y_val = X_val[:, 0]\n",
        "X_test = scaler.transform(test)\n",
        "y_test = X_test[:, 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVzDdHiTRIXA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Shifty! 30 min prediction\n",
        "\n",
        "# from scipy.ndimage.interpolation import shift\n",
        "\n",
        "# y_train = shift(y_train, -1, mode = \"nearest\")\n",
        "# y_val = shift(y_val, -1, mode = \"nearest\")\n",
        "# y_test = shift(y_test, -1, mode = \"nearest\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODFfW7yFcwlM",
        "colab_type": "code",
        "outputId": "9ce57d20-faa1-49a6-dc5f-0745d11c16f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
        "\n",
        "lookback = int(24 * (60 / 15))\n",
        "batch_size = 512\n",
        "\n",
        "train_gen = TimeseriesGenerator(data = X_train, targets = y_train, length = lookback, batch_size = batch_size)\n",
        "val_gen = TimeseriesGenerator(data = X_val, targets = y_val, length = lookback, batch_size = batch_size)\n",
        "test_gen = TimeseriesGenerator(data = X_test, targets = y_test, length = lookback, batch_size = batch_size)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqJorVCYE9vJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ML\n",
        "import tensorflow as tf\n",
        "import pdb\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime, os\n",
        "\n",
        "tf.keras.backend.set_floatx('float64')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPmUXqP0N3rl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "############## Define Neural Network Class ##############\n",
        "class neural_net(tf.keras.Model):\n",
        "    def __init__(self, lookback = 24 * 4, dropout = 0.5, r_dropout = 0.5):\n",
        "        super(neural_net, self).__init__()\n",
        "\n",
        "        # Define lookback\n",
        "        self.lookback = lookback\n",
        "        # Define dropout\n",
        "        self.dropout = dropout\n",
        "        # Define r_dropout\n",
        "        self.r_dropout = r_dropout\n",
        "\n",
        "        # Define discrete feature layers\n",
        "        self.discrete_flatten = tf.keras.layers.Flatten()\n",
        "        self.discrete_prelu1 = tf.keras.layers.PReLU()\n",
        "        self.discrete_bnorm1 = tf.keras.layers.BatchNormalization()\n",
        "        self.discrete_dense1 = tf.keras.layers.Dense(32)\n",
        "        self.discrete_prelu2 = tf.keras.layers.PReLU()\n",
        "\n",
        "        # Define time feature layers\n",
        "        # Time 1\n",
        "        self.time1_conv1 = tf.keras.layers.Conv1D(filters = 64, kernel_size = (1))\n",
        "        self.time1_prelu1 = tf.keras.layers.PReLU()\n",
        "        self.time1_lstm1 = tf.keras.layers.LSTM(units = 32, dropout = self.dropout, recurrent_dropout = self.r_dropout, input_shape = (self.lookback, 64))\n",
        "        self.time1_prelu2 = tf.keras.layers.PReLU()\n",
        "\n",
        "        # Time 2\n",
        "        self.time2_conv1 = tf.keras.layers.Conv1D(filters = 64, kernel_size = (1))\n",
        "        self.time2_prelu1 = tf.keras.layers.PReLU()\n",
        "        self.time2_lstm1 = tf.keras.layers.LSTM(units = 32, dropout = self.dropout, recurrent_dropout = self.r_dropout, input_shape = (self.lookback, 64))\n",
        "        self.time2_prelu2 = tf.keras.layers.PReLU()\n",
        "\n",
        "        # Define Merged layers\n",
        "        self.bnorm1 = tf.keras.layers.BatchNormalization()\n",
        "        self.dense1 = tf.keras.layers.Dense(16)\n",
        "        self.prelu1 = tf.keras.layers.PReLU()\n",
        "        self.bnorm2 = tf.keras.layers.BatchNormalization()\n",
        "        self.dense2 = tf.keras.layers.Dense(1, activation = 'linear')\n",
        "\n",
        "    # Define the forward propagation\n",
        "    def call(self, inputs):\n",
        "        \n",
        "        # Split time and discrete inputs\n",
        "        x_time = inputs[:, :, :-3]\n",
        "        x_discrete = inputs[:, :, -3:]\n",
        "\n",
        "        # Run discrete layers\n",
        "        x_discrete = self.discrete_flatten(x_discrete)\n",
        "        x_discrete = self.discrete_prelu1(x_discrete)\n",
        "        x_discrete = self.discrete_bnorm1(x_discrete)\n",
        "        x_discrete = self.discrete_dense1(x_discrete)\n",
        "        x_discrete = self.discrete_prelu2(x_discrete)\n",
        "\n",
        "        # Run time layers\n",
        "        x_time1 = self.time1_conv1(x_time)\n",
        "        x_time1 = self.time1_prelu1(x_time1)\n",
        "        x_time1 = self.time1_lstm1(x_time1)\n",
        "        x_time1 = self.time1_prelu2(x_time1)\n",
        "        \n",
        "        x_time2 = self.time2_conv1(x_time)\n",
        "        x_time2 = self.time2_prelu1(x_time2)\n",
        "        x_time2 = self.time2_lstm1(x_time2)\n",
        "        x_time2 = self.time2_prelu2(x_time2)\n",
        "\n",
        "        # Concat layers\n",
        "        x = tf.concat([x_time1, x_time2, x_discrete], axis = 1)\n",
        "        x = self.bnorm1(x)\n",
        "        x = self.dense1(x)\n",
        "        x = self.prelu1(x)\n",
        "        x = self.bnorm2(x)\n",
        "        x = self.dense2(x)\n",
        "        \n",
        "        # Return output\n",
        "        return x\n",
        "\n",
        "# Create an instance of neural network model\n",
        "model = neural_net(lookback = lookback, dropout = 0.5, r_dropout = 0.5)\n",
        "\n",
        "# Define optimizer\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate = 1e-3)\n",
        "\n",
        "# Define loss function\n",
        "loss_object = tf.keras.losses.MeanSquaredError()\n",
        "\n",
        "# # Compile model\n",
        "# model.compile(optimizer = optimizer, loss = loss_fn)\n",
        "\n",
        "# # Define callbacks\n",
        "# early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 6, restore_best_weights = True)\n",
        "# learning_rate_reduce = tf.keras.callbacks.ReduceLROnPlateau(monitor = 'val_loss', factor = 0.1, patience = 4, verbose = 0, mode='auto', min_delta = 0.0001, cooldown = 0, min_lr = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9A4twuLTbU7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Metrics\n",
        "train_loss = tf.keras.metrics.Mean(name = 'train_loss')\n",
        "val_loss = tf.keras.metrics.Mean(name = 'val_loss')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDC_hjVBOcTf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training step\n",
        "@tf.function\n",
        "def train_step(x, y):\n",
        "    with tf.GradientTape() as tape:\n",
        "        predictions = model(x, training=True)\n",
        "        loss = loss_object(y, predictions)\n",
        "    gradients = tape.gradient(loss, model.trainable_variables)\n",
        "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "\n",
        "    train_loss(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdKMVW2FV9Xc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@tf.function\n",
        "def val_step(x, y):\n",
        "    predictions = model(x, training=False)\n",
        "    v_loss = loss_object(y, predictions)\n",
        "\n",
        "    val_loss(v_loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oo8aeCiKV9cq",
        "colab_type": "code",
        "outputId": "e3662b3a-cae8-4d9f-be8e-bcb04bd802f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "EPOCHS = 15\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    # Reset the metrics at the start of the next epoch\n",
        "    train_loss.reset_states()\n",
        "    val_loss.reset_states()\n",
        "\n",
        "    for x, y in train_gen:\n",
        "        train_step(x, y)\n",
        "\n",
        "    for val_x, val_y in val_gen:\n",
        "        val_step(val_x, val_y)\n",
        "\n",
        "    template = 'Epoch {}, Loss: {}, Val Loss: {}'\n",
        "    print(template.format(epoch + 1,\n",
        "                            train_loss.result(),\n",
        "                            val_loss.result()))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1, Loss: 0.3584413028579984, Test Loss: 0.02485100995620777\n",
            "Epoch 2, Loss: 0.01873169033288282, Test Loss: 0.01163943234734751\n",
            "Epoch 3, Loss: 0.008207170563713137, Test Loss: 0.010907685183438248\n",
            "Epoch 4, Loss: 0.0054424279040557015, Test Loss: 0.010045538929387414\n",
            "Epoch 5, Loss: 0.004256657597024815, Test Loss: 0.00830104140211539\n",
            "Epoch 6, Loss: 0.0034948922996277695, Test Loss: 0.006299812105837567\n",
            "Epoch 7, Loss: 0.0029949341548112154, Test Loss: 0.005839173625833516\n",
            "Epoch 8, Loss: 0.002702051920337487, Test Loss: 0.005246795701858555\n",
            "Epoch 9, Loss: 0.002489409128290806, Test Loss: 0.005058728150445326\n",
            "Epoch 10, Loss: 0.0023487353582976986, Test Loss: 0.00466464555436938\n",
            "Epoch 11, Loss: 0.002228512222836541, Test Loss: 0.004522596176810049\n",
            "Epoch 12, Loss: 0.002109530331872109, Test Loss: 0.004426234694807951\n",
            "Epoch 13, Loss: 0.002048015861514177, Test Loss: 0.004173836887589303\n",
            "Epoch 14, Loss: 0.00198584847467278, Test Loss: 0.004032251099927415\n",
            "Epoch 15, Loss: 0.0019433292187684568, Test Loss: 0.003936134680206406\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atlb4h3q4Ejv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLahr8Pw4Eaf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u51JBteL4EP8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnrqLVEP4Dup",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_YjgA0ZD59Q",
        "colab_type": "code",
        "outputId": "166456f3-f909-4c1d-ad50-f7c4030d7d0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train model\n",
        "history = model.fit(train_gen, validation_data = val_gen, epochs = 50, callbacks = [early_stopping, learning_rate_reduce]) #, tensorboard_callback])\n",
        "train_loss, val_loss = history.history['loss'], history.history['val_loss']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 177 steps, validate for 29 steps\n",
            "Epoch 1/50\n",
            "177/177 [==============================] - 73s 411ms/step - loss: 0.0690 - val_loss: 0.0060\n",
            "Epoch 2/50\n",
            "177/177 [==============================] - 61s 346ms/step - loss: 0.0034 - val_loss: 0.0050\n",
            "Epoch 3/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0028 - val_loss: 0.0065\n",
            "Epoch 4/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0022 - val_loss: 0.0037\n",
            "Epoch 5/50\n",
            "177/177 [==============================] - 61s 346ms/step - loss: 0.0021 - val_loss: 0.0032\n",
            "Epoch 6/50\n",
            "177/177 [==============================] - 61s 346ms/step - loss: 0.0019 - val_loss: 0.0025\n",
            "Epoch 7/50\n",
            "177/177 [==============================] - 61s 347ms/step - loss: 0.0018 - val_loss: 0.0044\n",
            "Epoch 8/50\n",
            "177/177 [==============================] - 61s 343ms/step - loss: 0.0018 - val_loss: 0.0032\n",
            "Epoch 9/50\n",
            "177/177 [==============================] - 61s 344ms/step - loss: 0.0017 - val_loss: 0.0021\n",
            "Epoch 10/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0017 - val_loss: 0.0023\n",
            "Epoch 11/50\n",
            "177/177 [==============================] - 61s 347ms/step - loss: 0.0017 - val_loss: 0.0022\n",
            "Epoch 12/50\n",
            "177/177 [==============================] - 61s 347ms/step - loss: 0.0016 - val_loss: 0.0022\n",
            "Epoch 13/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0016 - val_loss: 0.0019\n",
            "Epoch 14/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0016 - val_loss: 0.0018\n",
            "Epoch 15/50\n",
            "177/177 [==============================] - 61s 344ms/step - loss: 0.0016 - val_loss: 0.0021\n",
            "Epoch 16/50\n",
            "177/177 [==============================] - 61s 342ms/step - loss: 0.0015 - val_loss: 0.0018\n",
            "Epoch 17/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0016 - val_loss: 0.0020\n",
            "Epoch 18/50\n",
            "177/177 [==============================] - 61s 345ms/step - loss: 0.0017 - val_loss: 0.0019\n",
            "Epoch 19/50\n",
            "177/177 [==============================] - 60s 342ms/step - loss: 0.0016 - val_loss: 0.0024\n",
            "Epoch 20/50\n",
            "177/177 [==============================] - 61s 344ms/step - loss: 0.0016 - val_loss: 0.0017\n",
            "Epoch 21/50\n",
            "177/177 [==============================] - 61s 342ms/step - loss: 0.0016 - val_loss: 0.0018\n",
            "Epoch 22/50\n",
            "177/177 [==============================] - 61s 344ms/step - loss: 0.0017 - val_loss: 0.0018\n",
            "Epoch 23/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0016 - val_loss: 0.0019\n",
            "Epoch 24/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0015 - val_loss: 0.0019\n",
            "Epoch 25/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 26/50\n",
            "177/177 [==============================] - 61s 342ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 27/50\n",
            "177/177 [==============================] - 61s 343ms/step - loss: 0.0013 - val_loss: 0.0017\n",
            "Epoch 28/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 29/50\n",
            "177/177 [==============================] - 61s 342ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 30/50\n",
            "177/177 [==============================] - 61s 344ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 31/50\n",
            "177/177 [==============================] - 61s 343ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 32/50\n",
            "177/177 [==============================] - 60s 342ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 33/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 34/50\n",
            "177/177 [==============================] - 60s 340ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 35/50\n",
            "177/177 [==============================] - 60s 339ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 36/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 37/50\n",
            "177/177 [==============================] - 61s 342ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 38/50\n",
            "177/177 [==============================] - 60s 341ms/step - loss: 0.0013 - val_loss: 0.0016\n",
            "Epoch 39/50\n",
            "177/177 [==============================] - 61s 342ms/step - loss: 0.0013 - val_loss: 0.0016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOvUVaS5LVzd",
        "colab_type": "code",
        "outputId": "2f2444a0-1ee4-4049-e4e5-acb4b66bf94c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        }
      },
      "source": [
        "# Plot test and validation loss\n",
        "plt.rc('font', family='serif')\n",
        "plt.rc('xtick', labelsize='x-small')\n",
        "plt.rc('ytick', labelsize='x-small')\n",
        "plt.rc('legend', fontsize='x-small')\n",
        "plt.rcParams[\"figure.dpi\"] = 300\n",
        "plt.rcParams[\"figure.figsize\"] = (4, 3)\n",
        "plt.figure()\n",
        "plt.semilogy(train_loss, 'k-', lw = 0.5)\n",
        "plt.semilogy(val_loss, 'k--', lw = 0.5)\n",
        "plt.xlabel(\"Epochs\", fontsize = 'x-small')\n",
        "plt.ylabel(\"Loss\", fontsize = 'x-small')\n",
        "plt.legend(['Train loss', 'Val loss'])\n",
        "plt.grid(True)\n",
        "plt.title('Train Loss', fontsize = 'x-small')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "# plt.savefig(\"plots/train_loss_3.pdf\", format = \"pdf\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hw4dn4sSJOfzww/O5z32uxyBo9uzZueWWW3LjjTfmvvvuyyWXXJKFCxdm1KhR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\ni0GSJEmSJEmSejFIkiRJkiRJUi//D1CrwnT3ySbHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x900 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gg96cQGwN3vl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "val_pred = model.predict(val_gen)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ro9ZbLYMN3zV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# predictions = pd.DataFrame(data = np.exp(val_log.iloc[lookback:, 0].values), index = val_log[lookback:].index, columns = ['True speed'])\n",
        "# predictions[\"Predicted speed\"] = np.exp(val_pred)\n",
        "# predictions[\"Speed limit\"] =  np.where(val.iloc[lookback:, val.columns.get_loc('speed_limit')] > 0.5, 130, 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnskeH-k2V-c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = pd.DataFrame(data = r_df.iloc[len(train) + lookback : -len(r_df[r_df.index > \"2019-06\"]) - 1, 0].values, index = val[lookback:].index, columns = ['True speed'])\n",
        "# predictions = pd.DataFrame(data = val.iloc[lookback:, 0].values, index = val[lookback:].index, columns = ['True speed'])\n",
        "fudge = val.copy()\n",
        "fudge.iloc[lookback:, 0] = val_pred\n",
        "predictions[\"Predicted speed\"] = scaler.inverse_transform(fudge)[lookback:, 0]\n",
        "predictions[\"Speed limit\"] =  np.where(val.iloc[lookback:, val.columns.get_loc('speed_limit')] > 0.5, 130, 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndpr0s0WN33G",
        "colab_type": "code",
        "outputId": "f1699328-42bc-458d-80c8-f4b1c2e2f09d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "source": [
        "import matplotlib as mpl\n",
        "start = 1104\n",
        "end = 1296\n",
        "y1 = predictions[\"Speed limit\"]\n",
        "y2 = predictions[\"True speed\"]\n",
        "y3 = predictions[\"Predicted speed\"]\n",
        "fig, ax = plt.subplots(figsize = (6, 3), sharey = True)\n",
        "plt.rc('font', family='serif')\n",
        "plt.rc('xtick', labelsize='x-small')\n",
        "plt.rc('ytick', labelsize='x-small')\n",
        "plt.rc('legend', fontsize='x-small')\n",
        "y1[start:end].plot(ax = ax, style = 'k:', lw = 0.4, label = \"Speed limit\")\n",
        "y2[start:end].plot(ax = ax, style = 'k--', lw = 0.4, label = \"True speed\")\n",
        "y3[start:end].plot(ax = ax, style = 'k-', lw = 0.4, label = \"Predicted speed\")\n",
        "_, labels = ax.get_legend_handles_labels()\n",
        "plt.legend(labels)\n",
        "plt.title(\"Speed prediction on validation set\", fontsize = 'x-small')\n",
        "ax.set_xlabel('Time (15m)', fontsize = 'x-small')\n",
        "ax.set_ylabel('Speed (kph)', fontsize = 'x-small')\n",
        "plt.tight_layout()\n",
        "# plt.savefig(\"plots/prediction_example_3.pdf\", format = \"pdf\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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ttZY0VlIjSbMkMX4TgGCmTp2qdu3aqUOHDuratat69uyp\nfv36ac8991RhYaEkadGiRWrTpo323HNPXXDBBfr73/+uBQsWyN1rvf0xY8Zo//3318KFC7Vw4UId\ncsghmjFjRq3brY6HHnpIRUVFat26tR588EFNnTp1s24fQNU98cQTuu+++0KHAQBAtey3336aNGmS\n9t9//9ChAADSOOOMM/T++++rX79sbzEC0BBYXTzwbMjM7GxJbeLFnpLOjeffkjQ+UXS8u3+WUne0\npF9Wc5ML3L1blnjyJf1DUltJMxQlAltKOlnS9pIKJR3j7p9Uc7t1ysz6x7FIkgoLC9W/f/+AEQHh\nPPTQQ1q1apUuvvji0KFsVhMmTNDkyZPVrFkzNWvWTM2bN1ezZs00ZMgQde3aVR999JGuvvpqTZ8+\nfaNhILp37665c+eqUaNGNdpuYWGh8vLy1K5dO73zzjuaMmWKTjvtNLm7LrroIt10001q3bp11jbK\n/u00sxrF8N1336lbt25yd02dOlVPP/20rrjiCjVu3LhG7QHYtL788kuZGT0SAACopdWrV2vrrbcO\nHQYAAMBGioqKNGDAgORHA9y9aHNtn0RdLZnZfEk7VaHoQe4+NaXuU5KOreYmsybq4na3l3SRpGPi\n2NYp6rH3D0kPuPvaam6zzpGoAyrk5eWpuLhYCxYsCB3KJrds2TKVlpaqffv2Va7j7vrkk0/02muv\nafr06VqzZo0efvhhSdLrr7+uXr16qUOHDlVqa9WqVfrhD3+ojz76SK+88ooGDRokSXrvvfd0zjnn\n6I033tD222+vu+++W8cdd9xGibgNGzbo9ddf14QJEzRhwgSNHz9ee+21l9asWaOSkhK1adMm02bT\nKigo0Ndff63BgwdvtK9SzROAAAAAQJkXXnhBU6ZM0WWXXaYuXSoboGfTGzVqlM4//3zddtttuuyy\ny0KHAwDBrV+/XitWrFDLli0b3OtQgFwTOlHH0Je15O7d3N2qME1NU/e4KtZNTt2qENMX7n6Nu/dz\n963d/XvuPtDd78qFJB2Ajf3jH//QpEmTQoexWVxyySXq16+fPv300yrXMTP16NFDp556qu6///7y\nJN3nn3+uww47TIcccogWL15caTvurnPPPVcffvih/vCHP5Qn6SRpt9120/Tp0/XAAw9o1apV+slP\nfqKXXnpJkvTyyy/rnHPOUZcuXbTffvvpjjvu0Lp167Ro0SItWbJEe+21l4YNG1btITkHDhz4/+zd\neViN+fvA8fdpVSlKjKUYlCgio+y7rNNg7GJsM0zDYDDDWIbGbiwzY29MxmAwmMgIEYWxlL2oZBtU\ntCfa6/z+6Nf5fvtmSdsJ9+u6zqWe5+nzuZ8uXOc59+dz33mSdPHx8QwcOJBffvnltcYRQpS82NhY\nbt68qfbelkIIIcTrOH36NMuXLycxMVHdobB06VLGjRtHVlYWderUUXc4QghRJhw8eBBTU1P27t2r\n7lCEEGomiTohhFCzunXr/u+KjSIpqzulvby82Lp1K/b29pibmxd5PDMzM2bPnk1gYCCdOnV6ZbJu\n+/btbN++HScnJ6ZOnZrvvIaGBuPGjSMkJISFCxfSqVMnIKc0qZubGyYmJsycOZOAgADu37/Phx9+\niImJCQ0aNODPP//k999/L1Dcc+bM4e+//853XKFQcP78eb766ivCwsIKNJYQonTMnj0bKysrEhIS\n1B2KEEIIUWBff/01d+/epW7dumqLQalUMmPGDGbMmEGjRo2IiIigd+/XLSxUcAEBAXTp0oWTJ0+W\n2BzqFhQURM+ePdHT02PQoEGcOnWqzD4DCiFerm7duowbN06t/08LIcoGKX0p1EJKXwrxHzExMejr\n66Ovr1/ksW7evEnPnj2ZM2cOI0aMKIboiseTJ0+wsbEhMTGR69evF0uiLteSJUv49ttvsbW1xcfH\n54VlNRMSEpg5cyYLFizAxMSkwOOHhIQAUL9+/eeej4uLw9bWlsTERC5fvoyFhcULxwoICMDBwYEe\nPXrg5eWV77yvry+dOnXC3t6e06dPo62tXeA4hRAlZ//+/fj7+zN9+nSMjIzUHY4QQgjxxsh9r96y\nZUsOHjyIsbExkPO+2NTUlNq1axfbXEqlklatWnHu3Dm0tLRYt24dn332WbGNX1YcPHiQDz/8kMaN\nG3P16lUAGjVqxPz58wucBH327BlXr16lTp060oNXCCGEQEpfCiHEO6927dp8/PHHRR7n0aNHdOvW\njdu3b9O1a9diiKz4fPPNNzx8+JDly5cXa5IOYMaMGSxevJhr167RuXNnnj17lud87oKUihUrsm7d\nutdK0kFOgu5FSToAExMTfv/9d549e4azszMZGRnPvU6pVDJlyhQ0NTVZvnz5c6/p0KED06ZNw9/f\nn4ULF75WnEKIktO7d28WLlwoSTohhBBvlMjISP7991+17rYaNWoUn332Gd7e3qokXXBwMA4ODsyY\nMaNY51IoFHh4ePDzzz9jbm7O2LFj+fLLL1/4/vxN8fDhQ8aOHcvZs2cB6NmzJyEhIVy5coUbN24w\nYcIE7t27R3x8fJ6fdUCVLAAAIABJREFUyZWens6FCxfw8/NTHRs2bBitW7emZ8+eshtPCCGEKANk\nR51QC9lRJ8R/TJw4EUtLS7788stCj5GUlET79u25fPkymzZtYsyYMSiVSgIDA7G1tX2tsbKysggM\nDOTJkyc8e/aM5ORkOnbsiImJCQkJCfz0009kZ2czbNgwLC0tXzmev78/zZs3p2PHjvj4+KBQKAp7\nmy+1ZMkSEhMTWbRokWoOpVLJ8OHDsbS0ZM6cOWholNz6lG+++YYffviBVatWMXny5Hzn//rrL/r1\n68fnn3/O+vXrXzhOWloazZs3JygoiNOnT9OiRYsSi1kIIYQQQry9Bg4cyO7du8nKyirR98H/Kzk5\nmbNnz9K5c+cXXtO7d288PT05d+4czZs3L/KcmZmZaGlpqb6PiYlhwIAB+Pr6vvD9eVGkpqYSExND\ndHQ0FSpUUPXdW7NmDUFBQSQmJlKjRg0sLS2pV68ejRs3fu0FgzExMSxevJi1a9eSlpbGV199xcqV\nK597bVJSEjo6Oujq6hIcHIyNjQ3t27dX7ZxLT0+nadOmXLx4Ech5Nlm9ejW+vr74+fnRrl27ov1C\nhBCF8vjxYyZNmkSvXr0YPny4usMR4p2m7h11kqgTaiGJOiGKT3p6Ok5OTnh7e+Pq6sp3330H5PRC\nW7JkCV5eXjg6OhZorMjISAYMGMA///yT5/g///xDq1atCA8Px8zMDABdXV1mzZrFN998g66u7gvH\nzM7OZv369XTv3r1Y666npqYSHR1NXFwcsbGxxMXFqb6Ojo4mKiqKhw8f4ufnR8uWLdmyZQu1a9fO\n8wD/35RKJeHh4YSEhBAaGkpoaCgPHjzA1NSUmjVrYm5unuelp6eX5+fT09P56aef+PLLLylXrly+\nc9bW1kRFRXHr1i2qVKny0nsLCgpiypQpuLm58f777xfp9ySEKLq///6bn3/+mR9++IHGjRurOxwh\nhBCiQLZt20ZQUBBLliwptTkTExNxcnLizJkz+Pv707Rp0+deFxwcTMOGDWnTpg2+vr5FWsyXlJRE\n8+bNGT9+POPHj1cdz8jIYO3atXzxxRfo6OgUevz/Hs/Dw4O1a9fm6YE3fvx41qxZA+RUyPjvnWu5\nfvnlFz799FMgZ5dh1apV0dfXJy0tjdTUVNLS0pg7dy6mpqbcunWLadOmcfz4cZKSkrCzs2PRokV0\n69atQL+nkJAQ5syZg4eHB8bGxtjb22Nvb0/Lli3p3r276rrQ0FDq169Pv3792LNnT1F/PUKIQnj4\n8CHm5uZMmTKFFStWqDscId5pkqgT7yRJ1AlRfFauXMnUqVMZO3YsGzZsUD28Xb9+nTZt2pCVlcXJ\nkydp0qTJK8dydHTk2LFjjBkzhsaNG6Ovr4+BgQGdO3emcuXKZGRkEBISQmRkJF999RU3btzAysqK\njRs30r59+xK9z/j4eE6fPs3Jkyfx8/Pj0qVLZGVlvdYYWlpa1K5dG0tLSywtLTEyMiIsLIzQ0FBu\n3ryZr2ymQqF4YSmYSpUqYW5ujrGxMXp6eujp6aGvr6/6U1dXl/Lly1OrVi2MjIwYOHAgrq6uxV7i\nRwhR8jZv3szkyZPZt28fHTt2VHc4QgghRJny7Nkzbt26xd27d5k/fz6XLl1i8uTJrFix4qU7+caN\nG4ebmxuenp44OTkVev7PP/+cjRs3snr1aiZMmPDca5RKJdOmTaNjx458+OGHhZrH1dWVefPmoaOj\nQ8+ePalZsyaVK1emRYsWdOnSBYDw8HB0dXUxMjLiwYMH3Lx5k7CwMLp37069evV48uQJxsbGZGdn\n5xs/JCQEKysrrly5QrNmzahXrx5z585lwIABhdoRmZaWho6OzkuTez169MDb25u7d+9Ss2bN155D\nCFE0SqWSzMxM6U8vRBkgiTrxTpJEnRA5YmJicHFxoW/fvgwdOrRQY2RkZLB+/Xq++OKLfLvF/Pz8\n6Nq1KyYmJpw7d45q1aoRGBiIhoYGjRs3zvfAd/PmTa5cucLAgQNfOW96ejrLly9n/vz5zJ49m1mz\nZuU5f+7cORYtWsS6detUu/CysrKIjo4mIiKCp0+fqsqz6Orq5vlaV1eX5ORkzpw5g5+fHydPnuTa\ntWuqpJmJiQlt27aldu3amJiYUKlSJUxMTFRf79y5k+XLl6OlpcXPP/+Mpqam6iE5LCyM27dvk56e\nrorV3NwcKyurfC8zMzPi4+N58OBBvtf9+/d5+PAhT548ITk5mdTU1Jf+vmxtbenVqxdOTk44ODig\nqan5yt+xUqlk8+bNBAYGMn36dGn0LoQQQggh1CYiIoJ9+/Zx9+5dHj16xNatW4GcBS2jR49WXefq\n6sqcOXNeufsrMjISCwsL3n//fdUzyuvy9vamW7dudOzYkWPHjr1wjPDwcGxtbYmPj2fx4sV88803\nL41PqVRy8uRJNm3axLp16zA0NOTevXvs2LGDMWPGvLJCxsukpaVx9+5d0tPT8zz/VK5cGS0tLZRK\nZYm1C/hfhw4dYuLEifz222+0bt26VOYUQgghyiJJ1Il3kiTqhMgRFhZGvXr1mDlzJgsXLnytn71w\n4QIffPDBCx/ilEolt27d4ueff2bNmjWUK1cOpVJJWloakLMjrH379oSHhzNixAhcXFxeOWdSUhKh\noaHcvn2btLQ0srOzefToERUrVkRDQ4MnT54QFBSEra0tixcvJi4ujvbt25OUlERERASPHz9+7V1w\nAFWrVqV9+/a0a9eOdu3aYW1t/coH+Z07d1KxYsU85V1yZWVl8eDBA548eULdunUxMDB47Zj+V3Z2\nNmlpaTx79gxnZ2e8vb2ZP38+1atX5+jRoxw5ckTV4N3ExIRu3brRo0cPWrVqRXJycp7ynbmv8PBw\n/v77bxISEtDW1sbZ2Znvv/8ec3PzIscrhBBCCCHebp9++ina2tov7Y9cUCkpKVhbW3Pv3j0gp/JE\nfHw8FSpUIDg4mF27dlG7dm0aNWr0wnKXz7Nt2zasra1f62dyJSYm0rBhQxISEggMDHxlyfg7d+7w\n0Ucfcf36dTQ1NTEyMmLo0KGqspVLly4lKCgIIyMj/Pz8uH4957M5Dw8P+vTp89rxvQlyd/aVZg9D\nIURe//zzDwYGBgWqgiSEKDmSqBPvJEnUCfEf2dnZZGdnv7B32vPs3buXAQMGMH/+fGbNmkV2djZ3\n7tzh6tWrXL58mQsXLuDv769KDEHOw3SLFi1wdHQkLS0NLy8vAgMDVedr165Nly5d6NKlC7a2tty9\ne1fVqy23b1tkZORr35+2tjZVq1alevXqVKtWTfWnkZER6enppKenk5aWpnrlfq+hoYGDgwPt27fH\nwsKi1FaVFoeYmBhsbW1JSkrCyMiINWvW4OTkhL+/P15eXhw6dIhLly4VenwzMzMGDx5My5Yt+eCD\nD6hZs6Zafz/x8fFoa2tTvnx5tcUgREmKjY3l9OnT2NjYYGFhoe5whBBCiAJp2rQpurq6nD17tshj\nzZs3D1dXV2bOnMmoUaOoWbNmsfR9K4rRo0ezefNm3Nzc+Oyzzwr0M0lJSXz//ffcunWLhIQEunTp\noqoM0qNHDw4fPgxAhQoVGDlyJC4uLlhZWQE5i/18fX3ZsWMH586dIzMzk6ysLLKzs8nKysrztUKh\noF69etjZ2dGkSRPs7Oxo0KCB2n9nL5ORkSHl94RQA0NDQ1q3bq36/0cIoR6SqBPvJEnUCVF4x44d\no2fPnujp6dG7d29Vgu7p06eqa8qVK0fTpk1xcHBQvQwMDFSlE728vHB2diYhIYHBgwdjZGSEj48P\nt2/ffu6choaGWFlZUb9+faysrLC0tERfXx8NDQ3VS1NTk6dPn7Jp0yYOHjyIlZUVx48fp2rVqu/k\nCs2jR4/StWtXIGeFXKtWrfKcf/ToEYcPH+bq1atUrFgxT+nO3K9NTEwwMDAgODiYCxcu4OHhgZ+f\nH8nJyXl651lZWfHpp5/yySefFKkMz+tSKpWMHTuWLVu20LZtW3x8fEptbiFK0+nTp2nbti0//vgj\nkyZNUnc4QgghRKl6/PgxtWrVwsLCgsuXLxd7MkepVOLu7s6lS5cwNTWlatWqDBgwAFNT05f+3IED\nB9i7dy+bN28ulkVrSqWS1NRUEhMTqVixoqoiib+/P3/88Qd//vknjx49AnIWORoYGKCpqYmmpqbq\neSj368zMTG7cuMGTJ09U4+vo6GBjY6NK3tWqVUv1nt/Y2BgTExN0dXVfO+anT58SHR1NVFQU0dHR\nqq8TEhJo1qwZ3bt3f+WCutGjR3Pt2jUCAgLeqAWSQrwNNm7cSPXq1YvUq1MIUXSSqBPvJEnUCZEj\nJiaGoKAgrK2tC5Rg2bp1K5988kmeY8bGxjRp0oQmTZrQuHFjGjdujI2NzQsfoI8cOUL37t0xNjZm\nx44ddOvWTXXu7t27+Pj4cPPmTerUqaNKzlWtWvW1HthCQkKoUqUKJiYmBf6Zt9Gvv/5KQkICU6dO\nLbYxlUolERERxMTEcPHiRRYtWkRkZCTJycloa2vTu3dvPv30U7p06VKgPnivKyMjg1u3btGgQQMA\nxowZw65du0hLSyMxMRF9ff1in1MIdYuOjubw4cPY29tTv359dYcjhBBClLojR45gaGiYb/FZYTx9\n+pRLly7h7++Pv78/58+f5/79+3mu0dLSokePHgwbNgwnJyf09PSKPO/ruH79Ojt27GDHjh3cuXMH\nAAsLC4YOHcqQIUMK9H4gOzubu3fvcuXKFS5fvszly5e5cuUKERERL/wZPT09VeJOX18/3069/36l\npaURExOjam3wIuXKlaNr16707dsXJycnKlWqlO+aiRMnsnr1avz8/GjXrt0r762gzpw5Q8OGDTEy\nMiq2MYUQQoiSIIk68U6SRJ0QOfbv30+fPn3YunUrw4YNe+m1gYGBODg4kJqayqBBgxg6dChNmjTB\n3Nz8tZJow4cP586dO2zbto3atWsX9RaEGmVkZGBjY8Pdu3dZsWIFBw4c4NixYwDUrFmT0aNHM3r0\n6GLpaRcdHY2bmxvr1q0D4N69e2hra5ORkcHKlSuZMWNGsT/YCyGEEEKIwlEqlXh4ePD+++8Xqv/b\n68rIyODkyZOcO3eOpKQkkpOTefbsGc+ePcvzdWJiIjdv3lT1RlMoFFhbW/Pee+9x/Phx+vfvT58+\nfdi2bRve3t5kZ2djaGhIv379GDZsGB06dGD+/Pn06dOnWPo5PXnyhODgYG7cuKF6Xb9+nX///ReA\n6tWrM3jwYIYMGfLS/uCv4/Hjx1y9epXIyEji4+OJj48nLi4u358pKSmqXXr/u2tPU1MTbW1tTE1N\nqVKlCpUrV1a9cr/X19fnxIkT/PXXX5w8eZKsrCw0NTVp3749H3/8MX369KFGjRoA3Lx5EysrK/r1\n68eePXuKfI/wn2fdVq1acfr0admpJ4QQokyTRJ14J0miTogcd+7cwcvLC0dHR1Xvgxc5e/Ysbdu2\nxdjYmKioKHnQEQD4+fnRoUMHBg0axM6dO7l79y7u7u64u7sTERGBQqGgf//+uLu7F6qHXEZGBpMn\nT+bXX38lLS2NGjVqMH78eCZNmqTaPXfq1CnatWvH4sWLmTFjRnHfohBCCCGEeE3p6eno6uoydOhQ\ntm/fXqgx0tLSGDZsGFOmTKFly5b5zsfFxXHo0CEOHDjA4cOHSUxMfO442traGBgYYGBgQPny5bGx\nsaF58+Y4ODjwwQcfYGhoiFKppE2bNpw/f56AgABsbGyIi4tj27ZtbNiwQVWi38DAgGfPnmFlZcXY\nsWPzlIzPLR9paGjIkydP8iXBcl+xsbGEhYVx48YNHj58mCdWHR0d6tevT4sWLRgyZAht27YtkSoV\npS0mJoYDBw7w119/cfToUdUuPDMzMypXroypqSlBQUFERkYyZcoULCwsMDU1pVGjRoWuJhAfH6+q\nsOLp6Sll/YR4AWdnZ8LDw/H19VV3KEK80yRRJ95JkqgTouCys7PR0NDg33//5f3332fMmDFs2rRJ\n3WGJMuSjjz7iwIEDnD9/HgcHBwAyMzM5cuQIa9eu5dChQzRv3hwvL6/XLke6efNmRo8eTbNmzZg2\nbRoff/xxvrKqKSkpVKlSBRcXF5YtW1Zs9yVEWZGQkEDDhg0ZNGgQK1asUHc4QgghxCtlZGSwZ88e\nzMzMaNu2baHGWLx4MTNnzmTJkiVMnz4dgLCwMDw9PTlw4ACnT58mKysLAAcHB5ycnHB0dKRSpUoY\nGBigr6+Pvr5+gXvanTlzhtatWwM5/Z67dOmSJ9lTnPT09GjQoAHW1tZ5XrVr10ZLS6vY5ytLkpKS\nOHToEPv37ycsLIyYmBiio6Pz9DzPpampya+//sqIESMKNVdERAT16tWjRo0aBAYGoqOjU9TwhXjr\nDBs2jPDwcE6cOKHuUIR4p6k7Ufd2v/sQQog33PXr13F2dmbnzp2cPHkSIE9POSEAli5dipeXF9Om\nTcPPzw+FQoGWlha9evWiZ8+eqg9YOnTogLe3N1WrVi3w2OXKlaNRo0b4+Pi8sLeEnp4ecXFxBf4Q\nRog3Tbly5TA3N8fU1FTdoQghhBAFoq2tzZAhQwr98/fv32fBggVYWlrSrFkzvv76aw4cOEBoaCiQ\n8/6vV69eODk50atXL6pVq1bkmFu1asXKlSsJCgqicuXKAFSoUIEdO3ZgbGyMsbExFStWREdHh9TU\nVOLi4p77SkpKwsjISLXLLvf1399XrVoVDQ2NIsf8JjI0NGTgwIEMHDgwz/Hk5GQaNmxI7dq1mTlz\nJo8fP2bmzJmMHDmS2NhYpkyZUqDxV69eTXx8PDNnzqR69erMmDGDOXPmsG7dOiZPnlwStyTEG23b\ntm3qDkEIUQbIjjqhFrKjTogcGzduZPXq1ezevZsGDRrkOXfjxg06duxIfHw8f/31F+7u7uzfv5+Y\nmBiMjY3VFLEoqz7//HOuXr2Kl5fXc/9+LFmyhG+//RYLCwuOHTtGrVq1Cjy2UqmUUqtCCCGEEO+I\nxMREunXrxvnz5zE0NCQpKQmAatWq4eTkhJOTE507d0ZPT0/NkYrilpCQQMWKFVXfh4eH07VrV27c\nuMHMmTNZsGDBS58LAgMDsbe3x9zcnMuXL1O+fHlSUlKoX78+3bp1w83NrTRuQwghhHht6t5R924u\nHxJCiDJCqVSSlZWVr7zKfyfpdu/eTbdu3fDx8cHBwUGSdOK5Vq1axZkzZ17492PGjBmsW7eO27dv\n07ZtW9Vq6BfJysri/v37AAVK0j169AhXV1eOHj36+sELIYQQQohiFRQURLVq1VizZk2Bf2bPnj04\nOjpiamrK+fPnAbCwsOC7774jICCAhw8fsnHjRj788ENJ0r2l/jtJB1CjRg1OnjxJ8+bNWbRoEZ9/\n/rmq3On/Sk1NxdnZmczMTLZt26bqj62np8eVK1ckSSfEC/j6+vLTTz+Rnp6u7lCEEGokiTohhFCj\nzz//nODgYCwtLVXHgoOD6dSpE3FxcezevZvevXtz/vx5njx5ImUvxQvp6empEmoRERHPvcbFxYXf\nf/+diIgI2rZty5UrV1443p9//kndunXZt29fgebPzMxk3rx57N69+/WDF+INsHr1an799Vd1hyGE\nEEIUiIaGBnXq1MmXeHmR27dvM2DAAE6ePEndunXR1dXlzJkzXLp0CVdXV5o1a/bOlop811y4cIFW\nrVrh5+cHQKVKlTh27BiOjo64ubkxZMgQ0tLS8v3czJkzCQwMZO7cuTRv3jzPudzFhJmZmcTGxpb8\nTQjxBtm5cyeTJ09W7V4WQryb5F2WEEKUMevXryc2NlaVpAM4cuQIIP3pxKuNGDECOzu7F77JHzZs\nGHv37iUxMZEOHTpw5syZfNdkZ2czf/58ypUrR7t27Qo0r5mZGTVr1nzueEK8DVasWMGGDRvUHYYQ\nQghRINbW1vzzzz8MGzasQNf/8ccfAOzbt4+QkBDCw8Np2bJlSYYoyigjIyPOnj3L6tWrVcfKly/P\ngQMHGDBgALt378bJyYmnT5+qzh87doxVq1bRqlUrvv322+eOm5ycTNOmTfnkk09K/B6EeJN89dVX\nnDp1CkNDQ3WHIoRQI0nUCSGEGh0+fJgNGzaQmZmpOrZy5UpOnz5Nnz59VMeOHDlCxYoVsbe3V0eY\n4g3Spk0boqKiWLZs2Quv6d27N15eXmRmZuLo6MixY8fynN+7dy/BwcF8+eWXmJiYFHjuVq1acf36\ndRISEgodvxBl1cGDB9m5c6e6wxBCCCGKnVKpZPv27VSuXJnWrVsDObuoxLupXr169OjRAw8PD1Up\nfABdXV127NjBuHHjOHr0KJ07d1btjitXrhw2NjZs3bo1X1uHXPr6+jRs2BAvLy+8vb1L5V6EeBNY\nWVnRpk0bdHR01B2KEEKNJFEnhBBq5O7ujouLS54yMlpaWnlKhcTExHDhwgW6dOnywoceIXKNGjUK\na2trVqxYQXh4+Auv69y5M0ePHkVHR4devXqxYMEC0tLSVLvpDAwMmDJlymvN3apVKwDOnTtXpHsQ\noiyysbGhbt266g5DCCGEKJBr166xcOFCwsLCXnntxYsXCQ0NxdjYmEaNGhEVFVUKEYqybOLEiWRn\nZ7Nu3bo8xzU1NVm/fj0zZ87E39+fdu3aERoaSps2bbh27Rp16tR56bhLliyhXLlyTJkyJc9iVSHe\ndZmZmWRnZ6s7DCGEGkmiTggh1MjV1RUfHx9Vom7atGlMnjw5zzXHjh1DqVRK2UtRIFpaWixdupSU\nlBS+++67l17bsmVL/Pz8qFOnDnPmzKFx48YsWLCAwMBAXFxcMDU1feV8MTExLFmyhE8//ZQTJ04A\nMHv2bL744gsmTJjAxIkTmTRpEj/++CNKpbJY7lEIdUhOTiY6OlrdYQghhBAFcuHCBWbPns2tW7de\nee327dsBuHnzJo0aNaJKlSolHZ4o47p27YqlpSUrVqxg1apVquO+vr6EhoYyb948VqxYQXBwMLa2\ntixbtqxASYaaNWsybdo0rl+/zqZNm0ryFoRQG39/f37//XceP35coOtXr16Ntra2tJEQ4h2nkA/N\nhDooFAobICj3+6CgIGxsbNQYkRBlg5WVFbq6uly7dk11bNSoUfz222/cv38fc3NzNUYn3hRKpZJO\nnTrh5+fH1atXadSo0UuvT09PZ8WKFXz//fekpqZSpUoVfHx8aNiw4Qt/JjQ0lB9//JEtW7aQkpJS\noLgOHTpE9+7dX+tehCgrevTowalTp/L0YxFCCCHKqvj4eP79919q165NhQoVXnhdZmYmZmZmJCQk\nkJmZyY0bN6hXr14pRirKqrNnz+Lq6srYsWP5+OOPUSqVVKpUifj4eLS1tbG0tOTOnTtkZmaSmZmJ\nvb09mzdvfuVnO0+fPqVevXpkZGQQFhZGxYoVS+mOhCh5MTEx1KtXj/j4eAAGDRr0yvL53t7euLu7\nM3v27Jc+gwshStb169f/999gQ6VSeb205pcddUIIoUYZGRl5dhnFxsbm6QmmVCrx9vamQYMGkqQT\nBaZQKFi+fDn9+/cvUENqHR0dvv32W27cuEHPnj2JioqiTZs2rFu3jqysLNV1SqWSEydO4OTkRP36\n9dmwYQM2Njbs2LGDuLg44uLiiI2NJTo6mqioKB49ekRkZCRXr15FR0cHV1dX2VUnyoz79++/Vj/F\nXr16MWrUqBKMSAghhCg+xsbGNGnS5KVJOoDjx4/z+PFj0tLSGD9+vCTphErLli05fPgwH3/8MQDZ\n2dmsWrWK6dOn0717d9LS0tDW1sbT05NvvvmGixcvYmdnx4IFC8jIyHjhuOXLl2fp0qUMHz48TwsI\nId4Gs2fPJj4+nqlTpzJo0CCsrKxU5yZOnMjYsWPx9PTk2bNnquNdu3Zl586dkqQT4h0nO+qEWsiO\nOiFyNGrUiKysLG7cuEF2djba2tr07t2bv/76C4DAwEBsbW2ZPHlynpIjQhQ3pVLJ5cuXsbOzw8PD\ng0mTJvHw4UPs7e1ZvXo1YWFhrFy5ksuXL6NQKOjduzdTpkyhTZs2KBSKfGNlZ2ejqampOubi4sKG\nDRs4evQoXbp0Ke3bE0Ll6dOnfPvtt6xZswYAa2tr2rRpQ6tWrfjoo48wNjZWc4RCCCFE0T19+pSs\nrCwMDQ1fmgwZNmwY27dvx9DQkLt371KpUqVSjFK8Tfz9/Rk1ahQ3btzAzs4Od3d3mjRpku+6lJQU\nQkJCCAwM5Pr169jY2DB8+PB8zxRCvGmuXbtGkyZNaNeuHSdOnMjzd1qpVNKyZUvOnz8P5CymuHXr\nVp6F2kII9ZIddUII8Q5zdHSkR48eADx58oTs7Ow8D8dHjhwBkP50otAyMzPZsGEDJ06ceOlutsOH\nD/PBBx+wZs0aPv74Y4KDg5k6dSqXLl2iRYsWDB8+nNDQUMaPH8/Nmzfx8PCgbdu2+R6oL168iLm5\nORs3bsxzfMaMGWhpacmuOqE2t27d4quvvsLMzEyVpAO4d+8ebm5ujBw5UtWDLjIykuXLl3Pnzh11\nhSuEEEIUiaurKxUrVuT+/fsvvCY5OZn9+/djZmbG0qVLJUknisTBwYFLly4xe/Zsrl27hr29Pd99\n9x179+5l3rx59O/fn/r161O+fHmaNm3KiBEjWLZsGSNGjODLL7/MU8lDiDdRgwYNWLVqFT///HO+\n52SFQsG5c+cICwtj0KBBxMfHq541QkNDGTdunKrnuxDi3SSJOiGEUKOVK1eyYsUKIKfsJZBnRdWR\nI0fQ1dWlXbt2aolPvPm2b9/OhAkT6NSpE23atMHLyytfokypVOLq6oqOjg59+/YFckrSLF++nEuX\nLjFixAgWLVrEgwcPWLNmDRYWFi+cr1atWoSHh+drhF2rVi1GjhzJ6dOn8fX1Lfb7FOJ5srOzOXLk\nCL169aJevXr8+OOPqkSdr68vH330EcnJydjZ2bFx40YsLS0B8PPz4+uvv2bAgAGqsf744w8GDBhA\nVFSUum5HCCHEY2fHAAAgAElEQVSEKDAHBwfGjBnz0v5fnp6ePH36lG+++QYXF5dSjE68rXR1dZk/\nfz4BAQHY2Ngwf/58+vfvj6urKx4eHgD07duXuXPnsnv3btV7rbVr1zJ48GBSU1PVGb4QRaKtrc2k\nSZOwtbV94TUWFhZ06tQJgLi4OACio6Nxc3Pj2rVrpRKnEKJsktKXQi2k9KUQ+UVHR/PLL7/QokUL\nOnXqxLNnzzAxMaF9+/Z4e3urOzzxBrtz5w5Lly5l8+bNZGRkYGdnx9y5c+nduzcAR48epWvXrri4\nuLBu3boiz1e/fn3S09Pz7Ua6e/culpaWtG3bVlYLihKVlpbGL7/8wurVq7l58yYaGhr06tULGxsb\nFi5cqCoBlp2dzaxZs1iyZAlmZmbs37+fpk2b8uzZM9q2bUtkZCSRkZFATr+JhQsXEhISkqfXhBBC\nCPGmatq0KdeuXSMiIoIqVaqoOxzxlklPT+ePP/5AU1OThg0bUr9+ffT09PJcExcXh62tLY8ePSIr\nK4t27dqxb98+KUUu3ihJSUl88803zJo1CzMzs1def+zYMRYvXsz3339P69atSU9PJykpCUNDQ3R0\ndEohYiHE80jpSyGEeEcplUrGjh3Lpk2bAKhcuTIzZ85Ura7y8/MjPT1dyl6KIqtTpw4bN27kzp07\nTJo0iZCQEFXyN3c3nba2NjNmzCiW+Vq1asXdu3dVCY5ctWvX5pNPPsHX15eTJ08Wy1zi3RMeHs6q\nVatYunTpc8uo3r9/n7Zt2/Lll18SHR3N119/TVhYGDo6OixZsoS///5bda2GhgaLFy9m69atREdH\n06ZNG3bv3o2BgQE1atQgLi5ONce8efPIysqSJJ0QQoi3wpkzZ7h8+TLGxsaSpBMlQkdHh5EjRzJ8\n+HDs7OzyJekgp5rM1q1bycrK4r333uPkyZO0bduWhw8fqiFiIQpn4cKFbNiwAS8vrwJd36VLF3x8\nfGjdujWQ82+lUqVKkqQT4h0niTohhFCTjIwMfvnllxfuLMrtT9e9e/fSDEu8xczMzPjxxx+5d+8e\nc+fOBXL6c/3zzz+MHDmSmjVrFss8rVq1AuDs2bP5zs2cORMNDQ2+//77YplLvBuio6NZv3497du3\nx9zcnClTpnD//v18vR+8vb1p2rQpAQEBzJgxg4cPH7Js2TLc3NzYu3cvgwYN4sMPP8w3/rBhw/D1\n9cXIyIiBAwfi6uqKsbEx6enpJCcnA6ClpaXaiSeEEEKUdYsXL2bMmDEvPD927FgAJkyYUFohCfFc\nHTt2ZPr06Tx+/JhOnTpx/fp1WrZsSVBQ0Kt/WAg1u3nzJitXrqRp06aq/3MDAgLYs2ePqr3Jq2Rm\nZhIYGPjSnqJCiLeffNoghBBqoq2tTXx8PGvXrgXA3d2devXqcenSJSAnUVejRg2sra3VGaZ4C1Wp\nUkW1cjo6Opr27dsze/bsYhv/ZYk6CwsLnJ2d8fHx4Z9//im2OcXb6+DBg1SrVo0vvvgCf39/+vfv\nz+7du1m4cCEAz549Y+TIkUyZMoXu3buTmZnJvn37WLx4Mfr6+mzatImlS5fSokULNm/e/MJkW4sW\nLQgICMDOzo558+Zx7tw5AOLj4wGIiorC19eXmJiY0rlxIYQQogh8fHzYt2/fc88dO3aM69evo6mp\nydSpU0s5MiHy+/777/nggw9ISEjgt99+4/Hjx7Rt21aqcIgyTalUMnnyZDIyMvj5559JSEhg9OjR\nODg4MGDAACpXroyDgwNz5szh9OnTZGZmApCQkMDnn3/Otm3bAEhMTMTW1pZFixap83aEEGqmpe4A\nhBDiXaVQKPI0d3/w4AFhYWFoa2vz77//EhoayujRo/PtGBGiODk4OODr61usY9avXx9/f3+aNGny\n3POzZs1i+/btzJ8/n8OHDxfr3OLtMHHiRCZOnIiFhQUtW7akZ8+eDBo0iI8++ghDQ8M81+7cuZMt\nW7YA8N5773H8+HHVAgcfHx9cXFx4//332b9//3NLLv03c3NzTp06xahRo9i9ezcODg5UqFAByOnl\nOGzYMDw8POjTp08J3LUQQghRfI4ePUp6enq+41lZWapddB9++CHly5cv7dCEyEdHRwdPT08qVaqE\nrq4uNWrUoG/fvnTt2pVt27bRv39/dYcoRD4HDx7k0KFDODs7c/v2bfr06UNMTAyOjo707dsXHx8f\njh07RkBAAAsWLMDIyIjOnTvTrl07Nm7cSEZGBsOGDcPQ0BBXV1eaNWum7lsSQqiR7KgTQgg1SUlJ\n4cqVK0RHRwM5jbQhp05/btlL6U8n3kQaGhrY29ujra393PNWVlYMHjyYI0eOcP78+VKOTpR1ycnJ\nrFmzhhUrVgA5/yd6enri7OycL0l3+fJl1c46IyMjHj9+TL9+/VTJ5+zsbKpXr87BgwcL3H/HwMCA\nXbt2YWtrS0REhGpOe3t7Vq9eTaNGjYrpToUQQoiSo1Ao0NXVzXfcw8OD0NBQAMaNG1faYQnxQtWr\nV1f9nS1fvjx+fn5UrFiRgQMHcuDAATVHJ0R+Hh4e6Ovrc/fuXUaMGIGGhgZ//PEHR44cwcXFhT17\n9hATE8OZM2eYO3cu1tbW7N+/n6+++gqAiIgIICdR/d1339GzZ0913o4QQs0kUSeEEGpy8+ZN7Ozs\n2LhxI5A/UaehoUGXLl3UGaIQhZaUlISXlxf37t177vlZs2ahUCikV53I58aNGyiVSqpWrfrS69zd\n3WnZsiXh4eFs2LCBqKgoFixYwL179+jYsSOenp44OjoSFhb22iWEFQoF1atXJzY2lqysLADq1avH\nhAkTqFu3bqHvTQghhCgtZ86c4dq1a/mO9+3bl2rVqmFqaoqjo6MaIhPi5TZu3KjqUXf69GlMTEwY\nMmQIly9fVndoQqikpaVhbm5ORkYGZ86cYezYsYSEhDBkyJA8VZG0tLRo2bIl8+bN4+zZs0RHR6sW\nSTx+/Fhd4QshyiBJ1AkhhJpUqVKF+fPn06FDBwBiY2PR09NDW1sbHx8f7O3tMTExUW+QQhRSQEAA\nvXr1Ys+ePc89b21tzYABA/Dy8uLChQulHJ0oywIDAwFeunNt6dKljBkzhvfee4/Tp08zbtw4dHV1\nmTVrFkFBQXz++ed0794dyFmhWhgZGRmkpKSoFlMIIYQQb5L+/fvzxRdf5Dt+5coVIiMjGTx4MFpa\n0g1FlD39+/enevXqjB8/HoVCwb59+8jIyMDJyYnw8HB1hycEhw8fpnHjxri6ulKvXj1Onz7Nxo0b\nMTY2fuXPmpiY0Lx5c+A/i7UB+vTpI7uchXjHSaJOCCHUpFq1asyePZs2bdoAOW/SKlWqxPnz50lM\nTJSyl+KN5uDggKamJmfOnHnhNXPmzAGQXXUij1cl6tLT01m+fDm1a9fm4sWL2Nvb5zlft25d1q9f\nX+gEXa5KlSoB8PDhQwBCQkJo2LAhGzZsKNK4QgghRGlYsGABkyZNUn0fGRlJv379WL16NQDDhg1T\nV2hCvFSlSpXYsmULT58+xdnZmebNm+Pu7k54eDhOTk48ffpU3SGKd5i3tzc9e/YkLCyMuXPncunS\nJVq3bv1aY+Q+Zzx58kR17MGDB6pSmEKId5MsnxJCiDJi1apVPHnyRPrTibdC+fLlady4MWfOnEGp\nVOYp/5GrYcOG9OvXj71793L58mXs7OzUEKkoawIDA9HX16dOnTrPPX/w4EFiYmKYOnUqpqamJRZH\nbunNR48eAaCtrU1WVhbZ2dklNqcQQghRXEaPHp3n+1OnTvHXX39RoUIF6tati4ODg5oiE+LVunTp\nwrRp01i+fDkLFizA1dWVsLAwXF1dcXZ25q+//kJTU1PdYYp3THBwMH369EGpVNK+fXvmzZtXqHFy\nn2H+e8HhxYsXiyNEIcQbTHbUCSGEmhw5coQ2bdpw6tQpAFq2bEm3bt04cuQIFSpUkIdn8cZr1aoV\njx8/5u7duy+8Zvbs2QDMnz+/tMISZZylpSVOTk4v/PDF3d0dDQ0NPvnkkxKNo0aNGgBERUUBOTv1\ngoODn1tGTAghhCjrcnshJSYm4uzs/NxFVEKUJQsWLKBJkyYsWbKER48eMXfuXIYOHYqnpyfTp09X\nd3jiHRMbG4ujoyMpKSlUqVKFXbt2FXqs3B11zZo1K67whBBvAUnUCSGEmjx58oTbt2+TmpqKUqkk\nPj6e6OhoAgIC6NKli/SMEG+8Vq1aAby0/GWTJk3o3bs3Hh4efP3115w8eZLMzMzSClGUQevWrWPn\nzp3PPRcZGcmhQ4fo3r071atXL9E4zM3NAYiJiSnReYQQQoji9uTJExo0aJBnIVTuwhMAZ2dndYQl\nxGvR1dVl165d3Lx5k6pVq6JQKPj1119p3bo1K1askD7CotSkp6fTs2dPwsPD0dHR4ejRo1SpUqXQ\n4+XuqPvv5wxfX98XPgMJId4NkqgTQgg1GTBgAJGRkTg6OhIfH4+JiQmDBw9GqVRK2UvxVmjVqhVN\nmjR5ZdJ50aJFmJmZsXz5ctq3b0/lypUZPHgwv//+O48ePWLv3r107dqVNm3a0KdPH1JTUwH4999/\n2bJlCwcPHsTf35/Y2NjSuC2hRlu3biUrK4tRo0aV+FyVK1cGID4+HoCsrCw2bNjA4cOHS3xuIYQQ\noijS09PR1NREqVSqjuX2PrKzs6NevXrqCk2I11KvXj1q1aql+r5cuXJ4eHhQp04dxo8fz9GjR9UY\nnXgXKJVKXFxc8Pf3B2D37t3Y2toWacyKFSuiUCjYtWsXYWFhACxdupRPP/20yPEKId5csl1DCCHK\ngLi4uDx/du3aVZ3hCFEsatWqxeXLl195nbW1Nf/++y+XLl3i4MGDHDx4kF27duUpJ6KpqYmxsTGX\nLl1CV1cXyNmpN3LkyDzXdOjQAWdn51JJ5Ijit2vXLry8vFiwYIFqR1supVKJu7s7lSpVwsnJqcRj\nyS1J89FHHwGgUChwcXGhf//+dO/evcTnF0IIIQrL1NSUoKCgPMeCg4MB2U0n3jzp6els3LgRLS0t\nXFxcqFy5MgcPHqRFixb079+fs2fPYm1tre4wxVtq5cqVuLu707lzZ0aPHq16NigKTU1N9PT0SExM\nJCoqCktLS2bPnk1CQkIxRCyEeFNJok4IIdTk8uXLhIaG0qtXL9VOoJSUFMqVK0fNmjXVHJ0QpUtD\nQ4NmzZrxwQcfMHfuXCIiImjSpAmJiYkoFArS0tJISUlh8eLFZGdnq5Jynp6exMTEEB0dzT///MOR\nI0cwMDBQJeoCAgIwNzenatWqar5DURBHjx7l999/54cffsh37ty5c4SGhjJx4kRVsrYk5Sbqckux\namho4OPjQ7Vq1Up8biGEEKK41alTh7Nnz9KwYUN1hyLEa9HQ0GDFihUkJyczcuRI9PT0qF+/Pnv3\n7qV79+706tULf39/VTUEIYrLgQMH+Prrr7G2tmbv3r1UqFCh2MY2MjIiOTlZtVi7devWxTa2EOLN\nJKUvhRBCTf744w+GDBlCbGys6s1ZSkqKqv6+EG+DCxcu0LdvX06fPv3Ca5RKJRcuXGDo0KEMHz4c\ngOrVq3PixAkSEhJISkpi3759VK5cmcmTJ9O6dWsCAwOpVq0aTk5OjBo1im+++Yb9+/cTFRXFTz/9\npBp75MiRVK9enfbt2+Pm5panBJQoewIDA6lSpcpzez64u7sDMHr06FKJJTdRl1uOBqBTp040aNCg\nVOYXQgghCisiIoK1a9fm2VWXW+5SFpyIN42WlhaTJ08mOjqabdu2qY537tyZ9evXc+/ePaZOnarG\nCMXb6Nq1awwcOBClUsm4ceOKNUkHqMbLLbMvhBCSqBNCCDUZOXIke/bs4b333lPtqEtKSpKHZ/FW\nyczMZN++fRw/fjzP8fT0dI4dO8bkyZOxtLTE3t6eHTt2EB0dTVZWFgA2Njbo6emhra1N7969CQoK\nYsqUKQQEBNC0aVNmz56t6leXy8jIiPfffx/ISQB+9913fPzxxwQEBDBu3DiuXr1aKvctXl92djbX\nr1+nUaNG+c49e/aMXbt20bRpUxo3blwq8ejr66OhocHBgwfJyMgAcv5O5X4thBBClFUhISFMmDCB\nU6dOqY5FRkYCSJUB8UYaM2YMFSpUYOXKlWRnZ+c53r17d7Zu3cqZM2fUGKF4mzx+/JiuXbuSmppK\nlSpVGDRoULHPYWpqCqD6LGjOnDkYGBjw8OHDYp9LCPFmkESdEEKoiY2NDf369UNPT0/15iwxMVEe\nnsVbxc7ODl1dXc6cOUNUVBRJSUkAPHz4EEdHR3766ScyMjL44osvuHLlCkeOHEFTU/O5YxkYGLBi\nxQrOnz+PjY0NCxcupHHjxpw8efK51ysUCgYNGsSePXvYvHkzADdu3CiZGxVFdvfuXZ49e/bcRN3e\nvXtJSkoq1d6DCoUCPT09AFW/iEaNGmFnZ1dqMQghhBCFYWdnx4kTJ1Q9XVNSUtixYwcaGhqqD4eF\neJMYGhoybtw4QkJCOHTokOq4QqHgxx9/REtLi4kTJ6oW/AlRWKmpqfTo0YPHjx+jo6ODt7c37733\nXrHPk1uq9dGjR0BOf/d27dqhoSEf1QvxrpJ//UIIUQaMGzeOgIAAsrOzZUedeKvo6urSrFkzjh49\nStWqVfnzzz+BnD4p69at49q1a9y7d4+1a9cWeKdUs2bNCAgIYMmSJdy/f5/27dszbty4lzbftrKy\nAiA0NLToNyVKRGBgIMBzE3WbN29GR0eHoUOHlmpMBgYGAKryxN27d8fR0bFUYxBCCCFel7GxMR06\ndMDMzAyA6OhoEhMT0dPTkw+BxRvryy+/REtLiw0bNuQ5bmVlxeTJk7l48aKqVLoQhbVw4UIuX74M\nwK5du0qsmkfNmjUBaN++PQCffvophw4donr16iUynxCi7NNSdwBCCPGu6tevH2fPniUiIoJy5cqp\ndhFJok68bQYPHkxoaCgdO3akTp06quMuLi6FHlNbW5vp06fTr18/xo4di5ubG+7u7jRq1Ah7e3vV\ny8bGBi0tLerXr4+XlxdNmjQpjlsSJaBhw4YsW7ZM9bCa6/bt2/j6+jJw4EBMTExKNSZDQ0OioqJU\nibrly5eX6vxCCCFEYWRlZaGhoaHqe/348WOAYu+xJERpMjMzw9PTkzZt2uQ7N2fOHLZt28bMmTPp\n378/xsbGaohQvAnS09P5559/CAkJITQ0lNDQUEJCQjh+/DjVq1fHzc0NgAULFtCnT58SiyP3c5/n\n9eYWQrybZClVMVIoFK0VCsVNhUKhVCgUvxVyDBOFQrHt/8dQFnKM6gqFYpFCobiuUCieKhSKWIVC\ncU6hUExSKBTlCjOmEKL41a9fn2bNmgE5jYp9fX0B6Rsh3j4TJkwgOjqaP//8k44dOxbr2BYWFvj4\n+LBlyxZ69OhBZGQkbm5ufPbZZzRp0gQjIyNat27NjBkzePr0qarEiCh7LCws+Prrr6lbt26e47/9\n9hsAo0ePLvWYcj/kiY6OLvW5hRBCiMLasGEDGhoa+Pn5Af9J1EnZS/Gm69GjB4aGhvmOGxkZsXTp\nUmJiYpg3b17pBybeGNOmTaNTp0588cUX/PTTT/j6+mJoaEhCQgLbt28nKiqKyZMnM2vWrBKNI/f/\n49z/ny9dusT06dMJCQkp0XmFEGWXJOqKgUKhKKdQKFYAJwHLIozjBAQBzkUYowtwDfgWSAFWAZsB\nc+BH4LxCoXi/sOMLIYrPwoUL8fT0BGD69OnMmDEDkB11QrwuhULBJ598gqenJxERETx48AAPDw9m\nzpxJmzZtCA4O5qeffmLgwIFYWlqyZ88elMpCrYURJSg9PT3fsaysLLZs2YKZmRldunQp9ZgqVaoE\n/OcB+tdff+Wzzz6Tvz9CCCHKtNq1a9O/f3/VAsB79+4BlEifJSFKW0REBNOnT+f+/ft5jg8bNowW\nLVqwdu1agoKC1BSdKOvKly9PixYtOHz4sKpH9rVr12jcuDHLli3DyMioVJK9uc8ZkyZNAiA4OJhl\ny5ZJqwYh3mGSqCsihULRArgCTAECCzlGRYVCsQXwBNKAJ4UcpxHgAVQC1iuVymZKpXKOUqmcBtQH\n/AFb4IBCoci/BEkIoTaxsbHo6ekBkqgToigUCgVmZmb06dOHhQsX4u3tTWxsLGFhYdjb23Pv3j0G\nDBhAy5YtVavMhfqlpKRgZGTExIkT8xz38fHhwYMHjBgxQlUeuDTZ2dkB0LZtWwBOnDjBpk2bnptU\nFEIIIcqKnj17snv3blWP3jt37gBgbm6uzrCEKBZXr15l2bJl/Pzzz3mOa2hosHr1arKzs5k4caIs\nrBLPtWjRIs6ePUu3bt14//33VX07PT09CQ0NxcXFpVTKBOcm6hITEwFwcnLi9u3b0g9biHeYJOqK\nQKFQ9AVOA6bAEGByIcbQI2cX3SeAG9AIiC9kSG5AeeDO/8aiVCqTgJFAFtAQmFnIOYQQxWTx4sWs\nX78egLi4OLS0ctqGSulLIYqXQqHAwsKC4cOHA9CnTx8uXbpEhw4d6NWrF9euXVNzhG+WzMzMYk9U\nBQcHk5aWlq8H3ebNmwEYOXJksc5XULmlUmNjYwFYs2YN8fHx6OjoqCUeIYQQojAaNmwI5JTeF+JN\n1717d6ytrXFzc1MlOXI1a9aMMWPGcOLECfbu3aumCMWbRqlUsnTpUnR1dVU73EpabunLZ8+eATnl\nW+vUqYO+vn6pzC+EKHskUVc0tYCDQEOlUrmzkGPoAkqgm1KpHKdUKp8WZhCFQtENaPH/3/6iVCrz\nfYKmVCqDAZ///3aiQqGQ7rpCqNHq1avZvn07kPMhcG7Dd2kmLETJyF1V3qVLF0JCQhg6dCheXl40\nadKETz75RFUWSrzckCFDsLKyKtZVyoGBOUUJbG1tVcfi4+Px8PCgXbt2WFhYFNtcryN3Ne2pU6cA\nqFixIhUrVkShUKglHiGEEKIgtm3bxvjx43n6NOfjhXLlclrV/28fWCHeRAqFgilTppCUlMSmTZvy\nnV+4cCEVKlRg6tSpJCcnqyFCUVbNnTuXkSNHkpqamuf4qVOnOHfuHCNGjCi1Cke5O+pSU1NRKpVk\nZGTw4MED4uMLu3dDCPGmk0Rd0WxTKpW9lUrloyKM8ZScRJ93EWMZ/F9fH3zJdbnn9AGnIs4phCiC\nc+fOsX37djIzM0lISCA7O5sqVaqodtYJIYpXbqIuNDSUOnXqsH37di5duoSjoyNbt27FysqK48eP\nqznKsq9q1arcu3ePq1evPvf8o0ePWLt2LUlJSQUeM3dXY6NGjVTHduzYQVpaGqNHjy5awEVgbJyz\npmn//v0AREdHc+XKFVJSUtQWkxBCCPEqx48fZ926dapFNeHh4YBU7hBvD2dnZ9577z1++uknMjIy\n8pyrUqUKX375Jffv32fZsmWEhYWxbds2NUUqypKdO3fi5+enWryQa+nSpSgUCqZNm1ZqseRWElEq\nlSQlJXHt2jVq1qzJxo0bSy0GIUTZIom6IlAqlTHFMEamUqlMfPWVr9Tj//9MBW685LqLz/kZIYQa\n1KxZk1q1avH06VOqVq1KRkaGPDwLUYLMzc3R09PL06Dbzs6OI0eO4O3tTWZmJmvWrFFjhG+Gvn37\nAvD333/nO6dUKhk5ciQTJkygYcOGeHsXbB1SYGAgenp61KlTR3XM3d2d8uXL079//+IJvBByV9Tm\nrmx1c3PDzs5OmrwLIYQo09asWcOjR48oX748AD/88AMgiTrx9ihXrhwTJkzgwYMHqgVVAGfOnKFf\nv37/x96dh1VVdQ8c/x5GFVERZ5xABRwwcQ4qp58zgUqpOZcjlkNqaaFZ5pCpqamZpWmKqWUqSGj2\nOoeKAw6goYnzgDIIKjJzfn/QvUlMF0UO4vo8j09wzr77rHN7873nrr3XYubMmRgbGzN37lzGjx/P\noEGDuHv3roYRC63dunWLCxcu0LZt20zHz5w5Q0BAAJ6entSrV6/Q4jE1NcXc3BzIaIVSrVo13n//\nfZo3b15oMQghihZJ1BUDiqJUAir/8+tVVVXTchl++bGfnXIcJYR4ptLT07ly5QpxcXGUK1eOW7du\nkZKSUmhlFoR4ERkZGWFvb6/vN/a4jh078uqrr7Jjxw59mSiR1dq1a0lNTaVs2bJs3749y/mAgAB+\n//13mjdvTkxMDJ07d2bYsGHExsbmOm9ISAgNGzbE2NgYyHhgPnHiBH369MHCwuKZ3IshdCVp7t+/\nD0Dbtm2ZOXOmlCgWQghRpJUqVYrKlSvrSzXrdrlLok4UJ15eXvzyyy+4u7vz66+/4uLigqurK1u2\nbKF79+7MnTuXxMRE7t69i6qq/PZbbsWnRHG3f/9+gCyJui+//BKAyZMnF3ZIVKlSBQcHB6pVq0bV\nqlX56quv+L//+79Cj0MIUTRIoq54sH/s51t5jL0DpP/zcz1FmqwIoYkHDx5ga2vLxIkTgYwvgRMS\nEiRRJ8QzdvjwYY4fP57tuTfeeIPExER27NhRyFEZ7uzZs9nuZCsMKSkpjBs3Dm9vb7p06cLRo0eJ\niPi3+ndycjLvv/8+pUqVYtu2bYSGhtKxY0dWrVpFo0aNcv1yJDQ0NFNJolWrVgHw9ttvP7sbMoAu\nUadr8u7q6oq3tzfVqlXTMiwhhBAiVxcuXODChQtAxgLBpKQkTExMKFWqlMaRCVFwrK2t9ZUXxo0b\nR3BwMMOGDePcuXP4+/szceJEPD09OX78OIqi4OPjIwvyirjk5GR9b+iCtm/fPgDatGmjP3blyhU2\nbtxI+/btNdnJVrlyZeLj4zEzMyv0awshih5J1BUP5R/7OddPHf/sttM1VjEDnnqZuqIolRRFaZif\nP4B0sRYvNGNjYyZNmkTnzp35+++/pRyNEIWkZMmSOZ7TlXT89ddfCyucfJs8eTKenp6kpqYW+rUP\nHjxIbGws7u7uuLm5AWRKvi1ZsoS///6bjz/+GBsbG2rVqsXvv//OypUrefDgAW5ubgwaNIiYmJgs\nc6ekpNukXk8AACAASURBVBAcHMzw4cOpU6cOX3/9Nfb29ri4uBTa/WWnXLlyAFkazgshhBBF2eDB\ng2nfvj2QUb5ZVVVNd6gL8SyZmZmxceNGrl69yvfff0/9+vX15+bPn4+FhQWqqrJnzx4sLS2pWrUq\nr7zyCkOGDGHmzJls3LiRW7fyWvMuCoO3tzevvfYaW7duLfC59+/fT+3ataldu7b+2FdffUVaWpom\nu+kgox92ZGQkUVFRJCcn4+Hhod/hJ4R48ZhoHYAoEKUf+znJgPGJ/JugsySP5J4BRgPTn3IOIV4o\npUuX1ifn1q1bx6xZswBkR50Qz9idO3fYvn07LVq04KWXXsp0zsbGhpdffpnffvuNhISEXJN6Wtix\nYwfGxsYkJydz5coV6tatW6jX9/PzA8Dd3Z2aNWvi7+9Pu3btALh79y4zZsygdu3aTJgwQf8aRVEY\nOnQonTt3ZuTIkaxbt45du3axePFizMzM2LNnDzt27CA8PFz/mrp16zJixAjGjRuH1hv/jY2NKVGi\nBObm5qiqSmBgIFOmTGHq1Kl06dJF09iEEEKInAwfPlxftlnXl6ts2bJahiTEM/XKK69ke7x27dqc\nPXuWKVOmsHHjRtq1a0dCQgLnz58nMDBQP87KyoqwsDApb66xnTt3ArB79279IsqCsnLlykwLBqOi\noli5ciXOzs507NixQK9lqNTUVJKSkti4cSOjR49m586d8ne1EC8wSdS9mFStAxBC/OvxD4uyo06I\nZ+vatWsMHz6czz//PEuiDsDT05PDhw+za9cuPDw8NIgwe6mpqQwaNIioqCgAwsLCCjVRp6oqfn5+\n1KpVi8aNG6MoCt27d9efnzp1Kvfv3+eHH37INsFZvXp1/P398fHxYdy4cfTt21d/TleGa/HixfTo\n0YOaNWs++xvKh5o1a2JmZoaiKCQlJREeHk5cXJzWYQkhhBA5euedd/Q/37hxA4AKFSpoFY4QmqpV\nqxafffYZjx494sMPP8TV1RWA2NhYwsPD2bVrFx9//DHe3t58//33Gkf7YtPt/B06dGiBz/3fZO7S\npUtJSEhg8uTJmi0OrFixIgA3b97EyMiIpCRD9l4IIYorKX1ZPDy+I87cgPGPf4P2oIBjEUIY4Ny5\nc7i5ubF9+3aio6P1x2VHnRDPloODAwDnz5/P9rynpydQ9Mpf7tmzh6ioKP1qz5zif1bOnj3L5cuX\ncXd31z/IpqWlcfjwYbZu3crKlStp27YtvXr1ynEORVEYOHAgZ8+eZdq0aSxfvpzz58/TqFEjKlas\nyNixY4tckg4y+p/o/p7u0KEDt2/fpk+fPhpHJYQQQhimVq1aAJr0XxKiqLC3t8fX11efpIOMEufN\nmjVjypQptGvXjlWrVnHixAkNoxSXL1+mdevWODs7F+i858+f59GjR/rf4+PjWbJkCXZ2dvrnPy3o\n+l7fvn1bsxiEEEWHJOqKh3uP/Vw6x1GAoijG/JuoSwHiC+D63wCN8vmn6GxTEEIDUVFR7N69mxs3\nbkiiTohCVKZMGapUqZJjoqt27do0a9YMPz8/kpOTCzm6nG3atAmA6dMzKk2HhYVlO27RokU4OTnp\ny10VlBIlSvDuu+9mSlCdP38eFxcX3n33XRRFYdGiRQatRq1atSozZsxg1KhR1K1bl7Nnz+Lk5FSg\n8RYkRVG4c+cOly5d0joUIYQQwiAdO3bkgw8+ADJ2DQHUqSNt4oWAjMVmj1MUhcWLF6MoCuPGjUNV\npQiVFlJTU+nbty9vvvkmCQkJxMcXxNeVGXr27Enjxo31v69atYqYmBgmTZqEiYl2xeZsbGyAf0sU\nHzx4kH379mkWjxBCW5KoKx4uPPZztTzGVuLff+9/qwXwCURV1buqqp7Nzx8gPM+JhSjGXnvtNRIS\nEvDy8pLSl0IUMgcHB86fP5/jQ7inpydxcXHs3r27kCPLXlJSElu2bMHZ2RkXFxcaNmyoLwvzuAsX\nLjB58mRCQ0PZuHFjgcZQt25dli5dmmkVcv369alYsSK3b99m2LBh2ZYSzcuVK1eIj48v0om69PR0\n0tPTCQ0N5cGDB2zcuJHg4GCtwxJCCCFyFB4ezq1btwA4deoU8G+JNSFeVFevXsXJyYmZM2dmOefk\n5ISXlxeBgYFs2LBBg+iEiYkJixcvpmXLlpQtW5Yff/yxQOa9c+cOf/31Fy4uLgCkpKSwYMECKlWq\nxJAhQwrkGk+qevXqAPrF2yNGjOC9997TMiQhhIYkUVcMqKp6B7j7z681/9k1lxO7x34OeXZRCSEM\n5eDgQPny5bG0tMz2y3chRMFycHDg/v373LlzJ9vzRa385a5du4iNjaVPnz4oikJoaCiLFi3KNEZV\nVby8vEhOTsbc3JyVK1cW2PWTkpJITU3NcjwxMZHExEQA+vXr90Rzh4RkfBQpyok6a2trIKO/YUxM\nDG+99RY//fSTxlEJIYQQObt06RLr168HwMfHB4DSpXMtviNEsWdjY8PNmzfx8/PL9vyMGTMoX748\nH374YYHu5hL506BBA1JSUggMDCyQ+fbv3w9A27ZtgYy/E69du8bYsWOz7a1dmHQLKB4+zOhoNGvW\nLGbNmqVlSEIIDUmirvjY+c8/SwINchnX7LGfdzy7cIQQubly5Qo7duwgOjqaTz/9lKpVq0rZSyEK\niaurK6+//nqmPgWPs7e3x8nJiW3btmWboCps1tbWuLu707t37xzHrF+/nj179jBs2DAGDBjAsWPH\nOH36dIFcf+3atVSuXJnDhw9nOj5//nwePMhodXvo0KEnmjsxMRFbW9tMpWiKmkqVKgEZTd4rVarE\n5s2befvttzWOSgghhDBMZGQkkPH5RogXmYmJCd26dSM4OJgbN25kOV++fHk+//xzbt68yZw5czSI\n8MX25Zdf0qFDB1JTU6lfv362ibqEhASGDh3Kli1bDJ5XV0qybdu2BAQE4OXlhbW1NaNHjy6o0J9Y\nhQoVAPTPeb169cLDQzoFCfGikkRd8fF4javuuYzTnUsAtj+7cIQQufntt9/o1q2bfjfJ7du3peyl\nEIVk0KBB+Pn5YWdnl+MYT09PoqOj9SswteTi4oKvry+2trZARgmr8ePHc/bsWQBiYmKYMGECFStW\nZO7cuQwbNgzI6L1QEHx9fbl//z7169fXH7t+/Tpz5szBwcEBCwsLtm9/so8Uffr04dKlS7Ro0aJA\nYn0WdIsoIiIiKFmyJJ6enjRs2FDjqIQQQojsxcfH4+Pjo1+wc+9eRkv7atXy6pIhRPH3+uuvA+Dv\n75/t+REjRuDk5MT8+fOlP3EhO3z4MPv376dcuXK4urpy9epVbt68mWmMv78/P/zwA56ensyfP9+g\nfoL79u2jRo0anDlzhh49elC6dGn+97//YWVl9axuxWC6yh1RUVEaRyKEKAokUVdMqKq6Azj6z6/D\nFUUx++8YRVHqAx3++XWJqqox/x0jhCgc7du3Z9WqVTg6OvL2228TExMjO+qEKEKKSvnLR48eZXkA\nvXbtGosXLyYoKAiAKVOmEBkZyYIFCyhfvjytWrWiYcOGrFu3joSEhKe6fnx8PP/73/9o06YN5cqV\n0x+fMmUKCQkJLFq0iIEDB9KgQQPS09Of6lpF1X+bvAshhBBFWUREBAMHDtT32dKVVNN9ISzEi6xL\nly6YmJjkuMhM1yctKSmJSZMmFXJ0L7awsDDq1KmDmZmZvi/2f3fVbdu2DcjYIfzBBx8wduxY0tLS\ncpxT9yxla2vLm2++Sbly5di7dy9NmjR5djeSD7q/l48fPw7Au+++S40aNQxKQAohih9J1BUvI4B4\nMvrQZWpeoyiKJbAGMAb+AqTosRAaql+/Pu+88w7W1tasWbMGQBJ1QhSid999lylTpuR4vmHDhjg4\nOLB169ZcH/7yIykpiYMHD+brwWvIkCE4OTllSrg5ODgAGQ+zgYGBfP/997Rv354BAwYAoCgKw4cP\nJzY2lq1btz5VzH/88QdJSUm4u7vrj/3555/89NNPdOvWjS5durB8+XJWrlyJkVH+PlYmJiYyYsQI\nAgICnirGZ6127drAvyUwq1evTq9evTSMSAghhMhZ5cqV2bZtG/379wcySsWZm5vn+/+nhSiOypYt\nS5s2bQgMDCQ5OTnbMe3atcPT05OtW7eye/fuQo7wxZSSksLFixdxdHQEMiqKAPoKIroxAQEBNG/e\nnKCgINq1a8fSpUvp1atXji0NSpUqxfTp0wkMDKRChQrs27evSPXGNjc3x8TEhCNHjpCSkkLZsmWp\nXr16kWi/IIQofPJJ7SkpijJcUZRJiqJMAvo8dqqh7vg/f2rkMofL42OBMo+de3yOLrnFoqrqaaAX\nEAN4KYpyXFGUGYqizCcjOdcSCAW6q6p6/8nvWghRUHSlaAApfSlEIdq7d2+uu+UURcHT05OIiIgn\n7r/2OF2y67XXXmPevHkGvebhw4f4+/tTvnz5TI3O7ezsMDEx4dy5c4waNQozMzO++eYbFEXRjxkw\nYABmZmasXLnyqeL28/MDwMLCgokTJ+Ls7Myrr76KiYkJX3311VPN/ddff/H9999z5MiRp5rnWatR\nI+MjnC5h17x5c32yVAghhChqSpcujYeHB05OTiQnJ6OqKhYWFlqHJUSR8d1333Hjxg3MzLIUotKb\nP38+JUqUYNy4cZI0KQSXL18mNTVV/xm7Xr163Lp1i88++0w/5sCBA8TGxuLh4UG5cuXYuXMnAwYM\nwM/Pj3bt2mVb/WLdunX079+fKlWqsH//fho0aFBo92SoEiVKABAbG8vs2bM5fPgwpqamGkclhNCC\nJOqenjcw758/ox473vyx4/OAOrnM0ek/Yx8vlPz48b55BaOq6i6gMTAXKAVMBIYCN4H3gRaqql42\n4L6EEM+Qt7c3dnZ2hIeH64/JjjohCo+DgwOXL1/OcSUtFFz5y5SUFHr37s2uXbswNzdn2rRpmVaH\n5mT79u0kJCTQp0+fTMdNTU2pW7cuQUFBhIaG8tFHH2VJHFlbW9OrVy/27t3LxYsX8xVvYmIie/bs\nYerUqWzatAmAYcOG8dVXX3Hz5k369OmDv79/pmvOnz+f+vXr5/p+/peuR2fjxo3zFV9h05WkiY6O\nBjJK7syZM0fLkIQQQgiDmJqaYmJiwssvv6x1KEIUGXZ2dpQuXTrXMbVr1+aDDz7g7NmzLF++vJAi\ne3GFhYUB6HfUKYqS5fsRXdlLDw8PAMzMzFi7di3e3t4cPXqU1q1bc/78ef34H374gUGDBmFlZcX+\n/fuxt7cvjFvJN93/FmNipDuREC86SdQ9JVVVa6uqqhjwZ18uc3xq4BxDDIzppqqqU1RVbaCqqoWq\nqlaqqrZSVXWRqqqJBXXvQognZ2FhQbly5YiPj9cfk0SdEIXHwcGBtLS0TMny/3J2dqZ27dps2bLl\nqfoE/PTTT/j5+dG/f3/27NlDzZo1efDgQZ6v27RpE0ZGRrzxxhtZztWoUYOoqCjq1q2bYwnPYcOG\nAbBq1Sr9sfT0dH05y+zcuXMHe3t7OnTowKxZszA1NaVz584sXryYkJAQ7ty5w8aNG+ncuXOm16Wk\npBAWFsaBAwfyvC+dM2fOABSp8jPZ0SXqcuplIoQQQhQlO3bsoGzZsmzatIl79+6RkpJCtWrVtA5L\niCIlNDSUWbNm5dpjefLkyVSvXp1PPvmEqKioQozuxdOiRQvWr19Phw4d9Mfu3LnDihUrCA0NRVVV\nfH19sbOzo1GjRvoxiqIwc+ZMVqxYwbVr13BxcSEwMJAVK1YwdOhQAF599VXq1Mlt74S2ypTJKKp2\n7949Dh06xJw5c4iMjNQ4KiGEFiRRJ4QQGvj4448JDg7O1HdKSl8KUXh0u8EeX3X5X4qi8MYbb3D9\n+nWOHTv2xNcaNGgQPj4+rFmzBhcXF/766y9at26d62vi4uLYsWMH7dq1o3LlypnOqaqqL+2ycOFC\nfbmU/2rXrh22trasWbOGlJQUICPZ1KlTJ1xcXEhMzLx2R1VVRo4cyfXr15kyZQpHjx4lOjqanTt3\nMnbsWBo1apSpvObj3NzcAPD398/7DflHSEgIJUqUoG7duga/Rgu6vj63b98G4Ntvv2X27NkaRyWE\nEEJkr0yZMrRs2ZKKFSsSFBQEkKmEthACNmzYwNSpUzl69GiOYywsLJg3bx6xsbFMmzatEKN78VSt\nWpV+/fpRq1Yt/bHw8HBGjRrF1q1bOXXqFNevX8fDwyPb55ERI0awfft2kpKSaN++PaNGjdI/Q3Xv\n3r3Q7uNJWFllFFWLiIhg7969fPzxx9y8eVPjqIQQWpBEnRBCaKh27dr6sm+yo06IwmNIog7+LX+5\nefPmfM2vqqo+QaYoCv3798fExARA/89Dhw7pd5Wpqsrvv/+Op6cnXl5eLFq0CCMjI/r2zVr1+tdf\nf+X06dMMGDBAnyDLjpGREUOHDiUiIoKAgAAADh48CMCVK1ey9Nvw8fHB19eXvn37MmfOHH799Vd2\n795t0P02atSIWrVqsX37doN3H4aEhNCgQQOMjY0NGq8lMzMzfVnP9evXs2TJEo0jEkIIIbLn6urK\nH3/8Qfv27dm7dy9AvkpTC/EieP3114F/+zHnpE+fPrzyyit89913XLhwoTBCeyFFRkZmeYZo1qwZ\n5ubmBAYGZil7mZ2uXbty4MABrK2tcXR0pEuXLgC0bdv2mcVdEHQLthMTExkyZAgnTpwosmU6hRDP\nliTqhBBCAz4+PixfvhwnJydq1aqFqakp5cuX1zosIV4YDRs2ZObMmZnKq2SnZcuW2NjY8Ouvv+aZ\ngDp37hxbt27l3r17TJw4kbfffpu5c+dmOzYiIoL27dvTv39/fvnlF1q2bEmXLl3YsmUL3377LZ9+\n+imJiYksWrSI8ePH4+fnR2xsLPfv32fcuHFYWVmxYMGCPO9zyJAhGBkZsXLlSgCCgoIoXbo0V69e\n1fdDSE9P5+bNm4wZM4YqVaqwdOlSLly4wNy5cw3uz6coCm5ubly6dIm//vorz/Hp6emMHj1aX56z\nqCtZsiSqqpKQkMD69ev1OxSEEEKIouzatWsA2NraahyJEEVLy5YtqVSpUp6lzRVFYdq0aaSnp/Pz\nzz8XUnQvHkdHRzp27JjpmLm5Oc2bN+fw4cP4+vpibW2Nq6trrvM0bdqUS5cucebMGY4fP46NjU2R\nLnsJGTEDNGjQABsbG5o2bUqpUqU0jkoIoQVJ1AkhhAaWLVvGrFmzALh9+zaVK1fGyEj+ShaisJQt\nWxZvb2+aN2+e6zgjIyN69erFpUuXOH36dLZjYmNjGTt2LE5OTvTq1YsKFSqwcOFC6tatS79+/bJ9\nTYUKFejevTuhoaH07t2b0NBQ3nvvPa5evUp4eDjff/89/fr1Izo6msWLF+Ph4YG1tTUNGjTg1q1b\nzJw5E3d3d8aOHZtr/DY2NnTr1o2AgACuXLnCiRMnaN26tT5Jt2TJEjw8PHjnnXeIi4vju+++w9ra\nWr+62N3dPa+3Uk+3MnnHjh15jjUyMmLq1Kl4eXkZPL+WdO/XvXv3qFmzJjVr1tQ4IiGEECJ7e/fu\nxdvbm1u3bunLNhf1MtNCFDYjIyPc3NwIDQ3l8uXLuY5t164dVlZWBi9gE/kTFRVFTEwMtWvXznLO\n1dWV+/fvc/r0adzc3PSVSXJTokQJYmNjOXv2LG3atMmxdH9RoeuHHRUVRVpaGg8ePMixn7gQoniT\nb4WFEEIDa9aswc/Pj88++4wzZ85QqVIlrUMS4oWUlpaW55g33ngDyFr+Mj09ndWrV2Nvb8+SJUto\n2bIlbm5upKenoygKFy9epG7durz66qssXLiQK1eukJyczMqVK3F0dGTLli36BP2WLVtYsmQJNWvW\nJDg4mEuXLrF48WJu3brFuXPnWLZsGT179iQxMZGOHTsyatQorly5kmtfDZ1hw4aRnp7O0qVLsbKy\nolWrVkBGuc0TJ07g7+/Prl27GDBgQKYyQKVKlcpzx+Hj2rZty/79+xk3bpzBr3le6Jq83717l7i4\nOK5cuUJ6errGUQkhhBBZHTx4kNmzZxMdHU1kZCSQsVtFCJGZ7nNvXrvqTE1N8fDw4NSpU4SHhxdG\naC+UsLAw4N/WBI97fAddbmUv/8vY2JgFCxYwePDgpw/wGdMtCNy+fTu+vr6UKVOGLVu2aByVEEIL\nkqgTQggNODg40LRpU86ePUtycrL0pxNCA9OmTcPCwoLo6Ohcx7m6ulKpUiX9KlpVVdmzZw9NmjTh\nnXfeAWD16tV4e3vj7+9P48aNCQ8PZ/369fTq1Yvg4GAmTJiAra0tFStWZPjw4cTExDB9+nQOHDiA\nmZkZEyZMICEhAYDly5ezYMECTE1NURSF+vXrM3r0aDZv3kxUVBS7du3CyMgIBwcHwsLC8izJ2a1b\nN6pUqcKWLVu4fv0606dPBzJK+XzyySeYmpoCGas4k5KSiIqKIjAwkM6dO1OiRAmD309zc3Nee+01\ng1a6jh07FmdnZx48eGDw/FqqX78+kNFfcNKkSdja2nL//n2NoxJCCCGyGjNmDGFhYdjb23Pv3j0A\n7OzsNI5KiKKnY8eODBo0iJdeeinPsbq+1S/yrrqkpCRWr16d797dedH1DM9uQYGLiwvW1taYmZnR\nqVMng+csX748EyZMyNdrtKLbUXf48GHs7Ox4++23qVWrlsZRCSG0IIk6IYTQQFxcHMnJydy5cwfI\nKE8nhChcpUuXJikpSf9wmBNjY2N69uxJWFgYtra2mJmZ0aFDB0JCQgD4888/GTJkCBUqVODll1/m\njz/+wNbWln79+umTa9u2bWPw4MHY2dkxd+5crl69yqeffoqrqyufffYZFy5cYO/evURERLBv3z66\ndetG2bJlc43L0dGRuLg47t69m+s4U1NThgwZwuXLl9m7d68+MZeens6IESNISUmhffv27Ny5kzff\nfJNt27aRnp6er7KXOsnJyfz+++957vQ7duwYN27c0K8gLep0iTpFUejcuTOTJk3C2NhY46iEEEKI\nrKysrHBwcMDc3BxjY2NMTEzytfBGiBeFhYUFP/74I23atMlzbMeOHbG0tHyhE3WqqjJhwgQ+//zz\nXMfkV2476oyMjIiNjaVz585YWFgYPOfztKCuWrVqQEY7hSZNmvDDDz/g4uKicVRCCC1Iok4IITRQ\nq1Ytunfvri9HIzvqhCh8uofBvBJ1gL4E5JUrV0hNTaVy5cp4eXmxZs0a/X+/rVu35tChQ1lK2ZYs\nWRIPDw/WrFnDyZMn+fDDD7G0tNSfnzRpEidOnKBbt25s3ryZ9PR0+vTpk2dMulWnuofb3AwdOhRA\nv5sO4Ntvv2X37t0MHz6cXbt20b9/f3bs2IGqqnz00Ud07949z3n/KyYmhi5durBgwYIcx6SnpxMa\nGoqTk1OR7xmhU6FCBQCio6N54403mDdvXqZ/h0IIIURRERkZye3bt1FVlTJlykh/OiHyoKpqnlUe\nzM3NcXNz4+jRo1y/fr2QIitazMzM6N27N2fOnMm2d3diYiIvvfQSr7/+er6qZly8eBFTU1NsbW2z\nnAsICCAtLS1fZS+joqKwsrJiypQpBr9GSxUrVgSer+SiEOLZkESdEEJooF+/fnTp0kVfcq9KlSoa\nRyTEiyc/iboePXpQs2ZNKlWqhI+PD7dv3+abb75h8ODBT52wMTExoUmTJgB89913ALi5ueX5Ol38\nhiTqdImmw4cPEx0dTXh4OB988AG1atViwYIFGBsb8+OPP3Lw4EGGDx/O7Nmz9Q+N+VGlShVatmzJ\nzp07mTFjBh9++CErVqzQn58zZw4uLi48fPgQJyenfM+vFd2XDWvWrNE2ECGEECIPY8eOpVq1aqSm\nphIRESHPGULkIj4+HltbW7y8vPIcqyt/+aL2D3NyciIgIACAdevWZTm/du1aQkJC8Pf3p02bNkRE\nRBg07+bNm/XJuv/atm0biqLw9ddfG/y+HzhwgPT09Oem5K+u9GV8fDwREREMHjyYTZs2aRyVEEIL\nkqgTQggNfPPNN0ycOFG/akp21AlR+OrUqYOxsbFBiTpTU1NCQkK4du0a/fv3fyY7wS5fvkxISAht\n2rQxqCRkq1at8PX1NWiF6bFjx4CM3Wxr167l7bff5tGjR6xevVqfaDQ2NqZ169ZPdxPAG2+8wf37\n95k+fTrz5s3LVCLo9u3bXLt2jQYNGtC7d++nvlZhqVy5MgB37tzB398fNzc3zp49q3FUQgghRFZd\nu3Zl3Lhx3Lt3j3v37pGWlqZ1SEIUWRYWFlhbW+urSuSmS5culCxZ8oUsf3nv3j3OnTvHa6+9hp2d\nHevXryc1NVV/Pi0tjXnz5lGuXDk+/vhjTp48iYuLCxcuXMhzbmNjY2rWrJnleGJiIjt37sTZ2Zkz\nZ85w4MABg2Ldt28fAG3btjVovNZKlSqFkZERiYmJJCUlsXbtWv2zmxDixSKJOiGE0JBuVZ4k6oQo\nfGZmZtja2uaZqNuwYQOjRo3i0aNHmJubP7N4atWqxTfffMPq1asNGm9tbY27u7tBK+WPHDkCgKWl\nJR999BEHDx7kvffeo127dk8Vc3bef/99jh8/zpkzZ7h06RIbN27Un/v666+5desWZ8+exdXVtcCv\n/azovjyIjo7m5s2b7N69m6ioKI2jEkIIIbIaNGgQixYt0vfSTUpK0jgiIYo2V1dXYmJiuHLlSq7j\nLCws6Nq1K3/++afBu8WKi+DgYACaN2/OwIEDiYiIYPfu3frzW7du5eLFi7z77rvMmjWL77//nmvX\nruHq6kpQUFCO8969e5dff/2V27dvZzm3Z88e4uPj6dOnDxUqVCAwMDDPOJOTk9mxYwdVq1alXr16\nT3Cn2rC0tMTKyooaNWoQHx/PvHnztA5JCKEBSdQJIUQhu3fvHv3792fDhg36cnRSkkYIbWzevJld\nu3blOmbRokWsW7eOkiVLPtNYjIyM8PLyyrY/Q05SUlIIDw/Pc1xQUBCmpqYMGDCApKQk6tatyxdf\nI1a+swAAIABJREFUfPE04ebIxMSEZs2a4eTkhK2tLeXLl38m1ylMtWvXBiAuLo6RI0eSkJBAmzZt\ntA1KCCGEyIVuJ4s8ZwiRO2dnZwBOnjyZ51hPT09UVWXbtm0Gz5+amsqrr76Kq6urQTvMiqITJ04A\n0KxZMwYPHsyXX36pL92vqipz586lRIkSjB07FoBhw4axbds24uPjad++Pb/99lu28wYGBvLGG29k\n+zyme4979OiBi4sLJ0+eJD4+Ptc4p02bxsWLFxk+fPhz0wsbwNbWFgsLC4yMjChVqtRzFbsQouBI\nok4IIQrZvXv3+Omnnzh58qR+5Zg8QAuhjZdeeonq1avneP7UqVMcPXqUvn37UrZs2UKMzDADBgyg\nXr16JCQk5DhGVVWOHDmCs7MzEydO5OWXX8bHxwcLC4tCjPT5pit9KU3ehRBCFHXvvvsu7733Hpcv\nXwbI9XOOECJ/iTo3NzfMzMzyVf5y7dq1/Pnnnxw6dAhnZ2e+++67PMtsFjXHjx9HURScnZ2xtbXl\ngw8+0H8+3rdvH8ePH+ftt9+mUqVK+te4ubmxZ88eSpYsiYeHBz/88EOWeXW9tnW9t3XS09PZvn07\njo6O2Nvb4+rqSlpaGkePHs0xRlVVSUxMpFWrVkydOrUgbrvQWFtb66t1HD16VErsC/GCkkSdEEIU\nMltbW5KSkujcuTM///wzFhYWmJmZaR2WEC+kxMREgoODuXXrVrbnv/vuOwBGjhxZmGEZzN7eHlVV\n+fvvv3Mck5yczJgxY3jnnXeoU6cOhw4dolWrVoUY5fOvTJkyQEaT95iYGAICAvIsjySEEEJoITAw\nkMDAQK5duwb8uytcCJG9Bg0aYGpqyunTp/McW6ZMGTp27MjevXuJjo4GYMeOHTRu3Ji9e/dmGZ+Y\nmMj06dMpX74827dvp0KFCowcOZIePXoQGRlZ4PfyrJw4cQJ7e3t9b2vI6N189uxZ5s6di5GREZMm\nTcryutatWxMYGEiNGjUYOnQoM2fOzJSk1LUg+G+i7ujRo0RERNCjRw8Afcn83MpfKorC4sWL2bdv\nH6ampk9+sxpISEjg/v37nDt3jjZt2jB58mStQxJCaEASdUIIUcgURcHMzExftqFcuXIaRyTEiyso\nKIhmzZpl6qOm8/DhQ3x8fGjSpAktWrTQILq8OTo6AuTaZ8/c3Jzp06cX2WTj80BRFKysrLCzsyMk\nJITu3bvj7++vdVhCCCFEFqdOnSI4OFhfuaNu3boaRyRE0WZmZkZISIjBu+Q8PT1JS0vDz88PyOgF\nHRISgqWlpb43pM6yZcu4ceMG3t7euLm5cfr0afr164efnx9OTk4EBAQU+P08CVVVOXXqFNu3b892\nt9/Bgwf56aef9L8/evSIOnXqMGTIEH7//XfefPNN7Ozssp3bwcGBw4cP4+zszLRp0xgxYgSPHj0C\nMnbUVapUCSsrq0yv0ZW99PDwADJ64/n6+uLl5ZVt7CtWrNBXGClRosQTvAPaMjLK+Hr+/PnzzJ07\nl2HDhmkckRBCC5KoE0KIQhYTE8OhQ4e4evUqABUrVtQ4IiFeXLrVm9kluvz8/Hjw4AEjRowosn0C\ndIk6XdmY7DxvpXWKqipVqpCQkICjoyOrVq2iQ4cOWockhBBCZEtRFH1v3caNG2scjRBFn4ODg8G7\nsNzd3TE2NtYn9o4dO4aZmRnHjh2jcePG+uNxcXHMnj2bGjVqMHr0aCBjke769etZv349iYmJdO/e\nnffee0+fuCpMqqpy9OhRJk+eTN26dXF2dsbd3Z3FixdnGVutWjWaNm2q/71UqVJ07dqV48ePA+S5\nA6xKlSrs27ePjh07snLlSpo0acKhQ4cICwvTP888ztfXlypVqtCyZUsgY+Ghu7s71tbWWcauWLGC\nUaNG8dlnn+Xr/ouSChUqAHDt2jXGjh2r30kohHixSKJOCCEK2eHDh3F1dWX//v2A9KcTQkuVK1em\nTJky2Sbq3nrrLfbu3Uv//v01iMww9vb2QO6Junbt2uHu7l5YIRVb5cqV4+7du1SsWJF33nmH+vXr\nax2SEEIIkcVvv/3GmTNnMDMzw9TUFFtbW61DEqLIi4uLw9/fP9dy8jrW1ta0a9eOP/74g7i4OI4e\nPUqTJk3o1asX1tbWeHl5ERkZybx584iJieGzzz7LssurX79+nD59mtdee41ly5bRvHlzg3rkPa30\n9HQCAwOZMGECtWvXplWrVnz55ZckJSUxduxYHBwcmDhxIv/73//0rwkJCeH06dOkpaVlmqtTp04A\n+iRfXsqUKcOOHTuYN28e165d49VXX6V06dI0a9Ys07jz588TFhaGu7u7fqcZQGpqKsHBwdy9e1d/\n7Ny5c7z//vvUrl2bjz766Inek6JA1+8vp3YMQogXgyTqhBCikDk6OrJgwQJMTEwAsLGx0TgiIV5c\niqLg4OCQbaJOURTatm2r709WFFlaWmJjY8ONGzeyPZ+UlMThw4ezPFiL/Lt9+zYxMTHExMRoHYoQ\nQgiRrdTUVNzc3Jg9ezYRERFUrlw50xfdQojsnTt3jtdff53NmzcbNN7T05Pk5GRWr15NdHQ0LVu2\npHLlyixdupTIyEiGDh3KwoULadCgAYMGDcp2jlq1arFnzx6++OILLl68iKura66L7/Ly22+/8eWX\nXzJjxgw++ugj3n//fby8vBgyZAh9+/bFw8OD6tWr88orr7Bw4UKMjIyYOHEihw8f5tq1ayxevBhf\nX18sLS3p3bs34eHhAHz++ec4Oztn2fWnSywmJSUZXMHD2NiYSZMmcfLkSZo1a8bNmzfZuXMnx44d\n04/x9fUF/i17qRMQEECzZs3YunUrkNH/r1+/fiQnJ7N+/XrKli37ZG9cEaBbvB0REcHAgQPp0qWL\nxhEJIbQgn9iEEKKQ1alThwkTJuhrqNeqVUvjiIR4sTk4OBAREcH9+/f1x77//nt+//3356JsZGho\nKPv27cv23KlTp0hOTqZVq1aFG1QxpEvYnjlzBjs7u+d61a4QQojia82aNYwYMYKQkBCSkpK0DkeI\n50Ljxo1RFIXg4GCDxvfo0QNFUdiwYQOAvp91nz596NWrF9u3b+fRo0fMmjULY2PjHOcxNjZm8uTJ\n7Nq1i6SkJAYMGEBKSkqO49etW8fDhw+zHPfx8cHNzY3Jkyczffp0vvjiCxYtWsS3337Ljz/+yKZN\nmwgICMDS0pIpU6Zw/PhxLl26xPz582ndurU+oe/g4MCGDRuIjY3Fw8ODBw8ecOLECRwcHLC0tNRf\nLzIykjVr1lCpUiWuX7/OiRMnDHrfdOrXr8+hQ4eYNWsWFy9e5OWXX2bq1KkkJyfj6+uLhYUF7du3\nz/Sal19+GYDAwEAAPvroI06fPs306dNxcXHJ1/WLmurVqwMZ72tiYqImpVCFENqTRJ0QQmhEV4qu\nTp06GkcixIvN0dERGxsbbt++DcDDhw+ZMGHCc5OIKVeuXI499I4cOQIgiboCUL58eSBjZ125cuUo\nXbq0xhEJIYQQmZmYmDB48GDatm1LYmLic7HgSIiiwMLCAkdHR4PLT1apUoVXXnmFkJAQfH196dy5\nM5BRkePDDz8EwNTU1OCdUW3btmXKlCmcOHGCzz//PNsxBw8eZNCgQdjb2/Pjjz+Snp4OwIULFxg1\nahRVq1bl4MGDnDlzhgsXLnD9+nUiIyN58OABKSkpbNiwgZEjRzJnzhyaNWuW4/ND165d+eKLLzh7\n9ix9+/bl0qVLWcpTLlmyhISEBMaPH4+rq+sTLQpYu3YtsbGxHDx4kMaNGzNr1iyaNWvG4cOH6dKl\nS5ZyoRUrVsTe3p7AwEASEhLYvXs3rq6ufPzxx/m+dlFTr149IGMR9y+//MKBAwc0jkgIoQVJ1Akh\nRCFbvXo1TZs25eLFi0BGY2YhhHY+/vhjbty4gYODAwAbNmzg4cOHjBw5MscH2KLk9u3brF27Ntue\nGkFBQQD6Ruziyema10dGRhIcHIy3t7fGEQkhhBDZ0y0+ep5LwQlR2JydnQkPDycuLs6g8Z6eniQk\nJJCSkqLvMQawePFiAJYvX54l2ZSb6dOn4+zszKxZs/SL7SCjr9yhQ4do1aoVy5cvJzk5mSFDhtC6\ndWv27dtH7969SUhIYMOGDbzyyis4OTlRr149qlatyqFDhzAxMcHExISLFy8yceJEg+7vgw8+oF+/\nfgQEBABkStQ9fPiQpUuXUqdOHT788EP+/PNPXF1dDb5PnV9//ZWvv/6a5s2bExQUxPTp0wkLC0NV\nVXr06JHta1xdXbl06RJxcXEEBQXx66+/6luKPM90VZYsLCw0jkQIoSVJ1AkhRCFLTEwkNjZW/6V6\n1apVNY5IiBfbf5NxK1aswMLCgrfeekujiPInNDSUwYMHs2vXrizngoKCcHBwwMrKSoPIihdp8i6E\nEKKoCwsLo1atWsyZMwf4d5GJECJvzs7OQEbpeEPoeqj9/PPP+mMnT55kw4YNdO3alaFDhwIYvLPV\nzMwMHx8fTE1NGThwoL7E5ZEjR3B1dWXOnDmMGjWKv//+m/Hjx3Py5EnatWunL//Ypk0bAGJiYpg3\nbx5169bFw8NDH58ucb9z5848Y1EUhZUrV2JjYwOQqdzmypUruXfvHpMmTcpU1jO3kp3ZCQsLo169\nehgbG2Nqasqnn35KUFAQM2fO5M0338z2NbqEYGBgICVLlsyUIH2e6f6ujoqK4uDBgyxfvpzU1FSN\noxJCFDZJ1AkhRCHz8vLi0qVL+g/IusbBQgjtrFixgtWrV3PixAlOnDhBv3799D3JijpHR0eAbJvP\nHzhwAB8fn8IOqVjSLaqIiIhg+fLlrFu3TuOIhBBCiMxUVaVSpUrExsYC8pwhRH60aNGC5s2bG5xw\n0iWvtm3bpi/9qCvDOHv2bAACAgJo3Lgxd+7cMWjOBg0aMHfuXC5evMikSZMA2Lp1KwDdunUDwMrK\nioULF7JgwQIgo8S9t7c3d+/eZdiwYVSvXp0PP/yQhIQEPvnkEzp27Aj823rD19fXoFhKlizJgAED\nMDIyYu7cuZw5c4aUlBS++uorKlWqxODBgwFITk6mTZs29O/f36B5IWPx8uXLl/XPMTpNmzbF29sb\nc3PzbF/Xrl07oPgtnCtdujSKorB9+3bWrl3L6NGjs+1FKIQo3p7//cFCCPGcSkxMxMjI6LlJBghR\nnM2ZM4eyZcvSunVrAEaOHKlxRIazsbHBwsIi20SdjY2NfiWseDq6FbzOzs7Mnj0bGxsbBg4cqHFU\nQgghxL/q16/PsWPH9F/wV69eXeOIhHh+tGnThmPHjhk8/ujRo0BGouqPP/6gdOnS7Ny5k379+tGk\nSRMgI5kXGhrK6NGj2bx5s0Fl9ceMGcP27dtZsWIFbm5ubN26FRsbG5o3b64fc/nyZT755BMqVqzI\n1q1bMTY2ZseOHaxatYqWLVsyZswY3nzzzUwJL90cAQEBpKSkYGpqmmcsX3zxBW5ubrRv3x4PDw/G\njh3L9evXmTVrFiVLlgQydgIaGxvj6+vLvXv3DKrkcfHiRVRV1bceMJSdnR2XL1+mRo0a+XpdUaco\nCsbGxsTHxzN+/HjeeustSpUqpXVYQohCJjvqhBCikO3evZsVK1aQlpZGyZIln4seWEIUdw4ODvz9\n99+MGTOGL774IkvD9KLMyMgIBwcHzp8/n+l4cHAwoaGh+kbz4unoSuvExMTg5+fHjz/+qHFEQggh\nRPZ0X87rdp8IIQre40m9zZs3M2XKFExMTPj888/1x3v37s0bb7zBli1b2LRpk0HzGhkZsWbNGsqV\nK8eQIUMIDw+nR48e+u8NkpOT6du3L3Fxcaxbt05f9cHGxoYjR44QFBTEgAEDst2V5uHhQVxcHPv3\n7zf4Pl955RWWLVvGlStXmDBhAqVLl8bLyyvTmEGDBpGcnJypDGhudAsM/7ujzhC1a9fOVHKzuDA3\nNyclJYWGDRvSvn17zMzMtA5JCFHIJFEnhBCFbOXKlYwaNQoAS0tLjaMRQkBGoi4hIQFLS0smT56s\ndTj55uDgwPXr1zOVSJkyZQotWrQgLS1Nw8iKj/LlywNw9uxZnJ2d870CWAghhHjWzp07x7x587h9\n+zaQUcpPCGG4n3/+mZ49e5KQkJDn2KNHj1KlShVeeuklfHx8CAoKYuTIkdjZ2WUa980332Btbc2U\nKVMM/lxevXp1vvnmG6KjowHo0aOH/tzHH3/M0aNHmTJlCp07d9Yf/7//+z9atWqV67w9evSgQ4cO\nBu2mO378OGPGjOHs2bMMHz6c0aNHAxmVR/67a87T05OSJUsaXBq+Tp06TJw4Uf6OekypUqVIT08n\nLS0NVVUN7m0ohCg+JFEnhBCFzNvbm2XLlgFQrlw5jaMRQgD6pIuhzeOLGhcXFzp37kxcXBwA6enp\nBAUF0axZM4MexEXedIm6I0eOkJycrH+vhRBCiKLi2LFjfPjhh1y8eBH4dze4EMIwYWFhbNu2jZCQ\nkFzHJSYmcubMGVq2bImnpydpaWlYWFgwbdq0LGMrVqzI6NGjuXr1KgEBAQbH8tZbb9G4cWMArl69\nCoC/vz8LFizAxcWFGTNm5OPOMjRq1Ij//e9/tGnTJs+xe/bsYenSpfpk4aJFi/Dz82PWrFlZxlpa\nWtKrVy8CAwMJDw/Pc25nZ2fmz58vC98eU7p0aQDmzp2LiYkJR44c0TgiIURhk0SdEEIUskaNGulr\nqlesWFHjaIQQALa2tkBGr7rn0XvvvcfOnTv1/ejOnz/P/fv39T33xNMzMzNDURQePXqEm5sbNWvW\n1DokIYQQIhM3NzeCgoL05bCl/LUQ+ePs7AzAyZMncx13/fp1qlWrRosWLejbty9mZmZMmTIlx+T4\nyJEjqVq1KjExMfmKZ+/evdjY2PD+++9z8OBBBg8ejJWVFRs2bHjqxXh57dg6ceIEiqLo3xNTU1Ne\nf/31bEtqAgwcOBBjY2MOHz78VHG9qMqUKQNAiRIl6NGjh/53IcSLw0TrAIQQ4kWjqirVqlUDoFOn\nThpHI4QA6NKlC0uWLOGtt97SOpQCERQUBJBn+RuRPyYmJiQmJtKlSxfq1KmjdThCCCFEJtbW1lhb\nWxMfH4+iKJQqVUrrkIR4rjRt2hTIO1FXr149rl69SlpaGsbGxkRERORaLcfGxobr16/nu7da+fLl\nWb16NZ06daJNmzaoqoqvr+9TLRi7ceMGnp6edO/enU8++STHccePH8fBwcHgdh3/93//x40bN6hS\npUqu41RVxd7eHjc3NxYuXJiv2IszFxcXTp8+Tfv27ZkwYYLW4QghNCA76oQQopA1bdqUXr16AegT\ndkIIbRkZGfHee+9hbW2tdShPRFVVxowZw8yZMwH0pVJkR13B0jV5nzBhAsuXL9c6HCGEECKThIQE\nEhISSExMzHHXixAiZ9WqVaNixYp5Jup0dIk3KysrFEUxaGxqaqpBc/fp04fx48fTsWNHxo4di6qq\njB8/Hnd3d4Nen5MqVapw8eJFtm3bluOYe/fucenSJZo1a2bwvMbGxvokXVRUFCkpKdmOu337Nhcv\nXiQpKSl/gRdz9erVAyA+Pl7jSIQQWpFEnRBCFLLXXnuNSpUqARi8Ok0IIXKjKAr+/v5s3rwZyEgo\nOTk5Ub16dY0jK15KlixJenq6NHcXQghRJM2YMYNSpUrp+2UJIfJHV+rxzJkzuSbUvL29+eWXX/I9\n/5dffknt2rV5+PBhruMiIyPZvHkzV65cAWD+/Pns3r2befPm5fua/2ViYkL37t05efIk165dy3ZM\ncHAwQL4SdTp//fUXjRo14rPPPsv2fFhYGACOjo75nrs4K1u2LJCx4HLMmDH8+eefGkckhChskqgT\nQohCtnjxYkqWLAnw1HXlhRBCx9HRkfPnz5Oens7ixYs5c+ZMnit7Rf6UL18egFWrVtG/f/989xkR\nQgghniVnZ2d69OgB/PulrxAif0aNGsXChQtzTNTFxsYye/Zstm7dmu+5y5Qpw82bN/Hx8cl1nL+/\nP+np6fTs2RPI+N6gffv2mJgUTAcjDw8PAPz8/LI9X6NGDby9venQoUO+565Tpw42NjbMmTMn22ST\nroemg4NDvucuzu7cuQPA77//ztKlSzl16pTGEQkhCpsk6oQQQgPR0dEA2NvbaxyJEKK4cHBwIDEx\nMceVseLpde7cGYBTp07x008/SaJOCCFEkdK7d2+8vLwAqFChgsbRCPF86tmzJ6NGjaJEiRLZnj9+\n/DgALVq0yPfcAwYMoEyZMixbtizXCg1bt27F2NiY119/Pd/XMESnTp0wMzPD19c32/P29vbMnDmT\nxo0b53tuMzMz1q9fj7m5OQMHDuT+/fuZzsuOuuzZ2NgAGS0Nbt26xbBhwzSOSAhR2CRRJ4QQhWzs\n2LHcunULgLp162ocjRCiuNA97E6fPp1x48YRGRmpcUTFj66H4aBBg0hKSqJOnToaRySEEEJkpvtS\nvF+/fhpHIsTzLaceaseOHQOgZcuW+Z6zdOnSDBkyhNDQUA4cOJDtmIcPH7Jr1y7atGmjr+ZQ0Cwt\nLenQoQN//vknjx49ynI+PT39qeZ3dHRk/vz5XLlyhbFjx2Y6FxYWRsmSJalRo8ZTXaO4qVmzJgBx\ncXFUrVo1x0SxEKL4kkSdEEIUotTUVJYsWcLDhw9RFAUzMzOtQxJCFBO6RN3atWtZunSpvsSuKDi6\nL2z++usvzMzMpLSoEEKIImX+/PksWrQIgEaNGmkcjRDPJ1VVadSoEV26dMn2/NGjRzE2NsbZ2fmJ\n5h89ejQAy5Yty/b8rl27SEpK0pexfVYWL17MzZs3KVWqVKbj9+7do0yZMjn2mDOUl5cXXbt25ccf\nf2T37t2Zrrt161aMjOQr6cfZ2dkBGaVVz58/r1/cLYR4cRRMceMCpiiKOdAUqAfYAmUAC0AF4oE4\nIBy4AJxUVTVNo1CFECJfjI2NuXPnDtWrV5f+dEKIAuXk5MQnn3zCjBkzaNiwIaVLl9Y6pGLnwYMH\nAAQFBVGvXj0cHR2f2UpnIYQQIr927dql3+0jPeqEeDKKomBlZcXJkydRVTXLwqxjx47RsGHDLAku\nQzk4ONCpUyf++usvkpOTsyze9fDw4MCBA8+8h1u9evWyPR4cHEx8fDxlypR5qvkVReGHH37g559/\npl27dvrjjo6OUvYyG9WrVwcydtQ5OjoyevToHJO5QojiqcgsX1AUxVxRlMGKovwB3AP+BFYDnwDj\ngeHACOB94FNgHRAE3FMUZbuiKL0URSky9yOEENlRFIWKFSuSlpYmfSOEEAXK2tqaESNGANC6dWuN\noymeqlatCsDJkydxdXXl0KFDGkckhBBC/CsgIICmTZsCcP36dY2jEeL51bRpU+Li4rh8+XKm46mp\nqYwaNYrhw4c/1fwbNmzgzJkz2VbYMTY25tVXX6VSpUpPdQ1DhIeH8+WXX5KcnKw/duLECQCaNWv2\n1PNXqVKFsWPHYmRkhKqq+vf0aUtrFkdGRkYoikJiYiLe3t507dpV65CEEIWsSOyoUxRlGDAL0H1r\nnZ86QqWBbv/8uaYoykRVVbcUcIhCCFEgkpKSCA4OJj09PcdSGkII8aSCgoIAaNWqlcaRFE+6XhrG\nxsYsWLBAVgMLIYQoUkxMTIiJiQGQPqpCPAVdWcuTJ0/qSxJCxn9jU6dOfer5c6rIEBISwo0bN+jQ\noUOhtMnYtGkT3t7eNGnShE6dOgEZiTpFUZ64tGd2oqOjGTx4MA8ePODAgQP89NNPvPXWWwU2f3FR\nr149FEVh5syZWocihNCApjvQFEWxVBRlJ7ACqPjP4VBgPRk76UYCnkB34P+Ajv/8/CbgBXwGbALO\nk5HcqwX8oijKOkVRpKacEKLI+fvvv3FxcQH+3ZkhhBAFRfdQV5AP1uJftWrVAjIWXUyYMIG6detq\nHJEQQgjxr+PHjxMZGQlA5cqVNY5GiOeXbmfqyZMnMx1XVbXArnHp0iV69uzJzz//rD+2bNkyunXr\nRnh4eIFdJzceHh4A+Pn56Y+dOHECBwcHLC0tC+w6JUqU4MKFCxw4cABAFrvloHLlyvrFFkKIF49m\nO+oURSkNHABeAv4GvgY2qaoa9YTzVQMGAO8B/YHKiqJ0lf51QoiixNramt69e/Pzzz8THR2tdThC\niGLGx8eHP/74QxJ1z4itrS2Q0TtCCCGEKGp69uxJbGwsgJTZF+Ip1K9fH3Nzc06dOpXpuLu7Ow8e\nPGDv3r1ZetflV5kyZdixYweRkZH07t2b9PR0fH19cXBwoH79+k81t6EaNGhAnTp18PPzY8mSJcTG\nxhIeHk7//v0L9DoWFhb4+Pjg4uJCWloa9vb2BTp/cWFqakp0dDS9evWievXqfP3111qHJIQoRFru\nqFsPNCaj31wjVVWXPWmSDkBV1Vuqqn4JOALLyNiBt/D/2bvzuKiq94HjnzvsoiIoCgKKAmIKqLnh\nvmRKaplrimka9Su3sjKXsqxv0qJpat/6VpaaJrlRuVYa5lYqauaO4oKiLCoo+zrz/P5Ab+KSoMCg\nnvfrdV/MnXvuuc+ZGZ2597nnnJIIVFEUpaS4urrqF9BVok5RlJLWoEEDXn755bu+cKDcnKurqz7H\nxsMPP8w333xj7pAURVEURffmm28iIlhbW2NhYWHucBTlnmVlZcWePXtYvny5/pyIsG3bNvLy8krk\nt3a1atUYOHAgf/zxB3///Tc7d+4kISGB3r1733XdRaVpGr169SI2Npa9e/dSpUoVTp06xTvvvFPi\nx2rRogVfffUVEyZMwN7evsTrvx+cPn0ak8nEwYMHOXnypLnDURSljJklUadpWj+gJ/C0iPxHRPJK\nqm4RyRSRMcAkYJSmac1Lqm5FUZSScObMGQDc3NzMHImiKIpSHBYWFri4uGBvb09KSgo5OTnmDklR\nFEVRdH379gVQF8EVpQQ0bNgQOzs7ff348eNcvnyZ5s1L7jLj6NGjgYIhL3/88UcAnnzyyRJqhAMi\nAAAgAElEQVSrvyiuDn+5cuVKNE3D09Oz1IZ3f/bZZ/nwww9Lpe77gYODAwDz589nzZo1Zo5GUZSy\nZq6hL2OBjiKytbQOICIfaZq2F8gqrWMoiqIU18aNG/Ux6D08PMwcjaIoilJcVatWJTMzs8zmDlEU\nRVGUooqPjwf+ufivKMqdS09P588//8TLywsvLy927doFFPQMKynNmjWjRYsWLF68GHt7e2rWrFmi\nicCiaN26NU8//TTNmzdn69atODs7qznkzMTR0REo6FnXpk0bM0ejKEpZM0uPOhHZWZpJumuOs15E\nDpb2cRRFUYoqOTlZnzeibt26Zo5GURRFKa60tDROnz5t7jAURVEUpZCMjAy9J44auUNR7t7Bgwfp\n1q0bS5YsASAyMhKgxBNpo0ePJisrCysrK0aMGIHBULaXai0tLVm0aBE9e/Zk2LBhZd6jT/mHs7Mz\nAFu3btV7WCqK8uAw5xx1iqIoD5x+/frh6ekJFMx1pCiKotxbsrKyyM3N5euvvyYiIsLc4SiKoigK\nADk5OaSlpemPFUW5OwEBARgMBvbu3QvArl27qFKlSokPCzlgwAAOHDhAXFwckydPLtG6i+PSpUuc\nPHmSpk2bmi2GB12NGjUAWLt2LcOHDzdzNIqilLX7JlGnadpzmqapmTYVRSn3cnNzcXBwUENfKoqi\n3IMqVqwIwPPPP8/cuXPNHI2iKIqiFHByciI4OBiAI0eOmDkaRbn3VahQgfr16/PXX38BEB4ezrp1\n69A0rUSPY2Njg5+fX4nWWVwmkwknJycAlagzo9q1awPg7+/Pt99+a+ZoFEUpa/dNog5wAGqbMwBN\n09pomnZM0zTRNG3BHezfSdO05ZqmndE0LfvK3+WapnUuZj31NE379EosmZqmJWqa9rumacM1Tbuf\n3nNFueccOHCA5ORkAgICqFmzprnDURRFUYqpSpUqAHz88cdmvetZURRFUa535swZ4J+LvYqi3J0m\nTZpw6tQpLl++jIuLC61atTJ3SKXi2uE2GzVqZMZIHmzdu3cHCt6DXr16mTkaRVHKmqW5A/g3mqY1\nA/yBaoAt8G+3rZhtlk1N02yBUGAsd5D81Apux/kEeBnIB5YDUUB9oD/QT9O0OcBYEZHb1DUY+Aqo\nAGwCwih4/YKBecAzmqb1FpFLxY1TUZS7t3DhQjIyMnBwcDB3KIqiKModuDrJu6urq9nvflYURVGU\nqxISEti3bx9AiQ/NpygPqiZNmrB48WKWLFlCYGAgfn5+WFqW60upd+zPP/9k7ty5tGvXztyhPLCq\nVasGQFJSkpkjURTFHMrlt4umaT2ATzFzD7mi0DQtEFgA+AL7gDu59eQdCpJ02UCQiGy+pv4vgV+A\nl4DLwJR/ieVR4FvAApgoIh9dsy0U2AZ0AJZrmhYkIvl3EKuiKHfhkUce4eOPP1bD0SiKotyjrk7y\nHhsbi4iU+PBHiqIoinInoqKiOHXqFADu7u5mjkZR7g/NmjUjICCA0NBQzp49S1xc3H0713yrVq3u\n2x6D9wpbW1s0TWPt2rU4Ojpy+PDh+/bzpijKjcrdMIiapnUAfqIgSacVcynrWHtTkPyqBgyioEdd\ncetoAFwdN2nGtUk6gCvrM6+sTtY07aFb1GMNfE1Bku6Pa5N0V+qJB168svoIEFLcWBVFuXtXh0y7\nfPmymSNRFEVR7kTz5s0BmDVrFk2aNDFzNIqiKIpSoFGjRvo8qtWrVzdzNIpyf+jQoQP79u3DyckJ\nNzc3lTRRSpWlpSUiQmpqKg8//LC5w1EUpYyVu0QdMImCZNMR4BWgE9AQqAvU+ZflAzPEWhtYC/iJ\nyJI7rOMtCt4HE/D5Lcp8DsiVcm/dosxwoNaVx/+9WQER2QAcu7I6Wc1XpyhlLz4+HkANfakoinKP\nCgwMBMDLy4v27dubORpFURRFKeDo6Eh+fj6VKlVSiTpFKUEZGRkcOnSIFi1amDsU5T5nY2ODpmmI\nCBERESoxrCgPmPI49GUL4CjQVERyirqTpmnJlH2vuu9EZNad7qxpmh3wxJXVv0Uk7mblROScpml/\nA02AJzRNsxORrOuKDbxaHPj5Xw67FqgHuANtgS13Gr+iKMX3/vvvA1C1alUzR6IoiqLciav/f7dv\n317/P11RFEVRzC0jI4Ps7Gz69u2r96xTFOXuTZkyBaPRqOYmVsqElZUVOTlFvhyuKMp9pDz2qLIB\nwouTpLtiKQW978qMiFy8yyo6ABWuPP7rNmX3XPlrDxS6fVvTtIrA1dleT4hIShHqAXisiHEqilJC\nLCwsADUcjaIoyr0qIyMDgIiICDNHoiiKoij/mDmzYMaM/Hw1Fb2ilKS4uIJ76gMCAswcifIgsLGx\nIS8vj8mTJxMdHW3ucBRFKUPlMVF3Csgu7k4icvb6+d3uAdd+y5+4TdlT1zz2v25bAwqGC73behRF\nKWV169YFwM3NzcyRKIqiKHfi6o0Wp06dYvTo0WaORlEURVEK2NraApCUlGTmSBTl/vLVV1+xbNky\n+vbta+5QlAdAhQoF/TlCQ0M5evSomaNRFKUslcdE3ffAo8XdSdO0VpqmvV0K8ZSmetc8vumwl7fY\n7ltK9SiKUsqSk5OxtLRkyJAh5g5FURRFuQPVqlUDIDU1lc8++4y8vDwzR6QoiqIoUKdOHUAl6hSl\npFWsWJH+/fujaWU9247yIPLy8gJg/fr1dOzY0bzBKIpSpspjou5jwEbTtHe04n0LtgamlFJMpcXp\nmsfptyl77XbHUqrnjmiaVl3TtIbFWQCvm9WVkpKiDykFkJWVRUpKCiaTCQCTyURKSgpZWf9M0ZeR\nkUFKyj+jfebl5ZGSkkJubq7+XGpqKunp/zQ9JyeHlJQUfVgQESElJYXMzEy9TGZmJikpKYgIUDCE\nSEpKSqGxotPT00lNTdXXc3NzSUlJKXTRTrVJtenaNh08eJDKlSvTtm1bFEVRlHvP1UneLS0tSUxM\n1L9T/u17KC0tjbS0NH39+u83uP33kNFoJCUlhezsfwaeuP77rSjfQ9nZ2aSkpGA0GoGbf78V5btV\ntUm1SbVJtUm1qXy16ciRIwDUqFEDRVEU5d70+OOPAwUJYqPRWOi74HbfQ0W5ZlaU76GiXDMryndr\nUa6ZqTapNpWnNl27bhYiYpaFgnnWbrV0B/YBR4H3gaeAR26zz+eA0VztudKmjoBcWRYUofz6a8r3\nuE3ZnteU/fW6bW9cs236beqpeE3ZnBJq9zvX1HlHy8GDB0VExN7eXoKCguSq8ePHCyAnT54UEZGY\nmBgB5LXXXtPL9OjRQ2xtbfX1devWCSDz5s3Tn/P09JSmTZvq69OnTxdAduzYISIimZmZAsjQoUP1\nMsOGDRNA0tLSRERk165dAsiHH36ol2nRooXUqlVLX1+wYIEAsnr1av051aaiteno0aPi6up6X7VJ\npPD7FBERIQaDQSpVqiSKoijKvcva2lqsrKxERGTgwIECSH5+voiIbNmyRQCZPXu2Xt7f31/q1aun\nr3/xxRcCyG+//aY/ZzAYpHfv3vr6mDFjBJC4uDgREYmKihJAJk+erJfp3LmzODo66uvh4eECSFhY\nmP6ci4uLtG3bVl//z3/+I4Ds27dPRESSkpIEkP/7v//Ty6g2qTapNqk2qTbdm20CZNiwYaIoiqLc\nm+bOnSuAhIaGCiBffvmlvs3Hx0caNWqkr3/yyScCyLZt20REJDc3VwAZNGiQXub5558XQC5duiQi\nInv37hVApk6dqpdp3bq1uLq66uuLFy8WQH788Uf9OQcHB+nSpYu+/sYbbwggR48eFRGRc+fOCSAv\nv/yyXqZXr15iaWmpr69fv161SbWp3LfpuqWhlGFuyRLz2XSlwbczoZTjuFcU5bUqy3pKxTPPPIOP\nj4++3rx5c0JCQqhYsSJQcEdJSEgILVq00Mt07doVDw8Pfd3d3Z2QkJBC9Tz11FPY29vr6/7+/oSE\nhODs7AyAhYUFISEhtGrVSi/Tvn17LCwssLQs+GdSrVo1QkJCCk0g/MQTTxTK0Pv4+BASElIoHtWm\n27fpu+++4/nnnyc7OxsLCwvCwsLo16/fPd2mq66+T99//z3PPPMMlpaW5OXlsXnzZjp06ICiKIpy\n77GxsSEtLQ0RoVOnTtjb2+vDIdWoUYOQkBAaNGigl+/Tp0+huwLr169PSEgINWvW1J979tlnady4\nsb7eqlUrMjMzsbOzA8DBwYGQkBCaNm2ql3nsscdo2LChvu7p6UlISIg+HypAcHCwPq8eQOPGjQkJ\nCcHR0VFvS0hICG3atNHLqDapNqk2qTapNt07bTIajXzwwQeF4lIURVHuTadOnQIKpk0JCQnB1/ef\nWYv69++PtbW1vt6wYUNCQkL0ntSapt1w3apt27aYTCZ9PycnJ0JCQgp9V/Xs2ZOLFy/q615eXoSE\nhFC7dm39uSFDhuhDLAM0bdqUkJAQKleuDBTMrRcSEkJgYKBepkuXLoV6edesWVO1SbWpXLepT58+\n/PDDD5iLJmKevI2maaZSqFZExKIU6i0STdM6Ar9fWf1WRIbdpvwPQO8rq31F5JafBE3T+gHLr6yG\ni0i/a7a9BMy+svqpiLz0L/VUAy5cWU0SkWr/FmNRaJr2Dnc57OjBgwcLnZwoD4b8/HwmTJjAzJkz\ncXNz49FHH2XJkiVkZ2fj4uLCiy++yAsvvICLi4u5Q71jIsKMGTN4/fXXcXNzo3///syaNYvIyEia\nN29u7vAURVGUOxAUFMSvv/5KWlqafkOHoiiKopS1xMREgoOD2bhxI15eXpw4cYLFixcTHBxs7tAU\nRVGUO/Daa68xc+ZMRo4cyWeffWbucBTlgXLo0CH8/PyufcpPRA6V1fHN2aMOYAnwawnVFQQMKKG6\nysqlax7f7irPtdsvl1I9d+pz/kkiFpUXsPLqypo1a9i/fz9Go1EfYzYxMZGMjAzy8/NvuVSuXJl2\n7drRoUMHqlatWkLNKUggRUREsHTpUlJSUmjYsCENGzbEz8+PevXqYWVlVWLHup28vDyMRiO2traY\nTCa+/vprEhMTCy1ZWVnMnTuXxo0bYzKZyM3NxdbWtsxivBNJSUkMHDiQ3377jcaNGzN69GiqV6/O\nxIkT+emnn/j888955513CA0N5amnnmLMmDGF7sy4FxiNRl599VXmzJlDw4YN+fnnn/n000+BgjtJ\nFEVRlHtTw4YN+fXXX0lKSlKJOkVRFMUstm3bxlNPPUVcXBwjRoxgyJAhhIWFXX+BSVEURbmHuLq6\nApCQkGDmSBRFKWvmTtTtFpFvS6KiKz3F7rVE3bFrHte8ZakCrtc8PlpK9dwRETkPnC/OPleHFLlq\n4sSJd3z8WbNmARAQEEDHjh3p2LEj7du3L3bizmQysX37dsLCwli+fDkXLhR0PDQYDIW6vVpZWVGv\nXj38/Pxo2LAhDz30EO7u7ri5ueHi4nJXSTyTycTevXuJiIhg+/btHDhwgJiYGIKDg3nnnXeoU6cO\nY8eOLTRBZtWqVbGystKHhzx48CBNmzbF39+fZs2a0bx5c5o2bUrDhg2xsbEpVjyZmZns2rWLP//8\nk7y8PJ5//nn9R0NxnTp1in379tGzZ08OHz5MUFAQ8fHxAPz9998899xzQEEC6+LFi7z22mt88cUX\nfPvtt3z33Xd89913NGjQAHd3d6pUqUKVKlVwdHTUH1epUoWqVasSEBBQohOoiwhnzpxh586d7Ny5\nk9jYWLp27UqfPn3+NdmWnZ3N008/TXh4OB06dOCnn36iSpUqJCcn6+1UFEVR7k1X/w8/f/58oWE5\nFEVRFKW0iQiffPIJ48ePx8bGhu+++47g4GByc3MLTRGgKIqi3HuuTq+SlJRk5kgURSlr5kzUnQZS\nbluq6GKBLSVYX1nYf83jurcsdeP2A9dtOwwYAYu7rMdsAgICcHJywsrKChcXF/r370+NGjV46623\nWL9+PVAw99ijjz5K9+7d6dSpE5UqVSI+Pp5Nmzbpy5w5c5gzZw6aphEQEEDbtm1xd3enevXqODs7\nF1oqVaoEwJNPPsn+/fs5f/48mZmZ+rFGjRrFyy+/jIeHB2vWrOHChQucOXOGQ4cOcfDgQZYuXXpD\nOzRNo3r16tSsWRMnJyccHR31Oxrj4uKIjY0lPz8fo9Go9wq0srLCysqKc+fOER0dTX5+/g31Llq0\niEWLFvHQQw/Rq1cvunTpQteuXXF1ddXnZrvKZDLx2GOPsWvXLubOncvcuXOBgvnTYmNjAdizZw8n\nT57Ez88Pb29vrKysEBGMRiOWlpYYjUbatm3L7t27C8Xz/vvv8/fffxd53gOTycT69ev55JNP9Pdx\nzpw5TJw4kby8PGrXrk3r1q3x8fHBx8eHjIwMMjIy0DQNS0tLoqOj2b17N3Z2dlSpUoWzZ89y8uRJ\nsrOz//W4np6eBAYG0rJlSwIDA2nSpEmRk5SXL19m165dREZGsnPnTiIjI0lMTCxUZvny5YwcOZJu\n3boxcOBAnnjiCf3zBAVjiT/55JNs3bqVAQMGsHDhQv34ycnJGAwGHBwcihSPoiiKUv5s3rwZgCNH\njqhhjBVFUUpBbm4uBw4cYNeuXezfv58WLVowZMgQLCzMNtNFuZCamsqzzz5LeHg43t7evPDCC0RE\nRDB+/HhefPFF3nrrLXOHqCiKotyFWrVqAeg3eSuK8uAw2xx196M7mKOuAnARsAP+EpGm/1L2L6AJ\nkAlUE5Gs67ZvBtoDAlQRkdRb1DMTeOXKakcR2XybZpUKTdMaAgevrt9qjjqTycTOnTtZtWoVK1eu\n5MiRIwD897//ZdSoUQCkpaVRqVIlRIRjx47pSbvff//9hgTLtWxsbKhUqVKhSSuvtWzZMvr374/R\naMTGxkYfftLDwwMPDw9cXFwIDg7m8uXL7N69m9WrV5OcnEx6enqhScaLolKlSvok5AaDAR8fHxo1\nakRAQABubm4kJiaydu1a1qxZo3d/d3R0JCgoiJ49e9KtW7eb9iCMi4tj9+7d7N69G4PBwDvvvAPA\nyy+/zJw5c4CCHoK+vr5cunSJ0aNH670bH3vsMaytrWndujWtW7cmKSmJ1atX8/XXX6NpGlFRUdjZ\n2d2yJ8G8efOYPn06UVFRGAwG+vTpQ3Z2NmvWrKFGjRqsWLGCtm3b/uvrsm3bNn7++Wf+/PNPIiMj\n9UTqxIkTGTduHJcuXSI0NJT8/HyqV6+OnZ0dMTEx/PXXX/pnBcDa2prGjRvTsmVLKleuTEpKCqmp\nqaSkpBRaUlNTC30eLC0tadSoES1btqRly5a0aNGCGjVqsHLlSr7//nsiIiIwGo3Y2dnRs2dPBg4c\nSMOGDenduzdHjhzhlVde4eOPP8ZgMOh1dujQgUOHDt3yc6coiqKUf4MHDyYsLIzQ0FDeeOMNc4ej\nKIpyT8vPz+fIkSPs2rVLP3fZt28fubm5hcr5+fkxbdo0goKCbhih5X4mIly4cIH9+/czcuRIoqOj\ncXJyKnQR96GHHmLUqFH6ObKiKIpybzpx4gTe3t7UqlWL06dPmzscRXmgmHuOOpWoK0HFTdRd2Wcp\nBUN2mgAPEYm7SRk3CnoMasBSERl4kzIjKJgrDmCQiCy5xfGOAvWAOKCWiBhvF2NpKGqi7nrR0dGs\nWrWKgQMH4ubmxtmzZ6lbty6dOnWib9++PPnkk1SvXh34Z8jCxMREEhIS+Pnnn/nxxx9JTEzEzs6O\nTp06kZyczMMPP8ygQYMIDAwkIyOD1NRUUlNTcXNzo0qVKmRlZTFjxgzOnDlDbGysvqSmphIXF4er\nqyu7d++mZcuWuLq66ok8Dw8PnJycePzxxzEYDCQkJBATE4OVlRWWlpb6XxcXF1q3bl2k1+3q0Jhr\n1qxhzZo17N69W99WtWpVPD09qV27Np6enjc8rly5sl722LFjREZG6r0DDx06RMWKFRkxYgQjRowo\nUiyPPvoomzZtYtiwYUyaNIm6deuSm5uLtbU1AIMGDWLt2rV06dKFevXqsWPHDjZv3kyzZs348ccf\ncXd3L9JxrsrPzycqKopDhw7x0EMPERAQgIjg5OTE5cuFp1u0srLixx9/xNrams2bNzN//nySk5Nv\n6IlnYWGBg4MDdnZ2ZGZm6u/HsGHD9J54dnZ2t4zp/PnzhIeHs2TJErZu3cq1/5/OnDmTV155RV8X\nEfLy8oiJieHSpUu0bNmyWO1XFEVRyo+rk7yPGjWK//73v+YOR1EUxWySkpI4ffo01tbWhRYbGxv9\ncV5enn4Ode051dXHZ86cKfQ73dnZmebNm+vD+NevX5/58+czc+ZMsrOz6dy5M9OmTaNp01ve56pL\nTk7ml19+4fTp07z66qvFngqgLCUnJ3P48GGio6M5fvw4x48f59ixYxw7dky/YdHS0pKBAweyatUq\nHn30Ubp160a3bt30HhiKoijKvS09PZ1atWrh6OjIiRMnzB2OojxQVKLuNq4kqR4B/IGrkzolUzBs\n40YROWuu2K53h4k6P2AfYABCRWTyTcqEAm9QkMwLuNkHRNM0GyAa8AC2iUi7m5R5FFh/ZXWkiPyv\nCM0qFXeaqLve4cOHGT9+POvXrycvLw+DwUD79u0ZOHAgL7zwAkajkaVLlzJ16lSOHDmCvb09o0eP\n5tVXX9UTencqJSWFypUro2kaRqMRk8l0V/PT3Yn4+HjWrVvHpk2biImJ4fTp05w7dw6TyXRDWXt7\ne2rUqIGLi4v+99rHtWvXpl69elSoUKFIx16/fj3vvfce27Ztw2Aw4OfnR3x8PE888YSeAExPTy+0\nz7Bhw/j888//NflVXFlZWXoC78iRIyQmJnL+/HlmzJiBj48Px48fx8fH56b7pqamUqlSJdatW0eP\nHj3058eMGcPs2bOLdafu2bNnWb58OevWrSMoKAh7e3tOnDjByZMn9aV///58/fXXd91mRVEUxbxm\nzJjBuHHj6NevH8uXLzd3OIqiKGUuPz+fOXPm8Pbbb5ORkXFHdVSqVAkPDw9q165N48aN9cScu7v7\nTX+Hnz17lilTprBgwQJMJhPBwcFMnTqVOnXq6GVEhIMHD7JmzRrWrl3L9u3b9XOj3r17s2zZshum\nDjC33NxcPvjgA0JDQ28YmcVgMOjxjxs3jiFDhlC/fn0MBkO5a4eiKIpSMlq2bMm5c+c4e7bcXPJW\nlAeCStTdgqZpnsAnQE8Kklg3YwJWA6+JyKmyiezW7iRRd2W/qcCbQDYQdO1wlJqmtQd+BWy5RSLv\nmrLdgLUUzFU3UUQ+umabK7AV8AI2AY+KyI2ToZWRkkrUXZWSksKaNWtYsWIFv/zyC23atOG3334j\nNzcXHx8fLl26xJgxY3jllVeoVq1aSTSh3MrNzeXs2bOcPn1aT97FxMSQkJBAQkKCnsi6WTIPCsbD\n9vX1pX79+vj6+uqPK1eurN/RefToUX2Jioq6oadatWrV8Pf3x8/PT18aNGhAlSpVyuIluEF2djYX\nLlzgwoULmEwmbGxssLGxwcvLCwsLC/Lz88nNzUVE6NmzJ5s2bWLKlCn6UKHFFRoayuTJBf9UDQYD\nHh4eeHl50b17d1577bUSbJmiKIpiDkuXLmXgwIF07NiR33///fY7KIqimIGIkJycrJ8HxMfH64+v\nLtnZ2QQHBzNs2LAi37C3Y8cOXnzxRfbt20fdunUZPnw4RqOR3NxcfcnJydEfW1hY4O7uTq1atfDw\n8MDd3V0fPv/FF1/kxIkT1K9fn1atWtGqVSv8/Pz+NQl14MABJk6cyLp167C2tmbUqFF07NiRn3/+\nmbVr1+rzctvb2/Poo4/So0cPIiIiWLJkCUOHDmX+/PmFhqY3p507dxISEqJfGBo6dCgfffQRSUlJ\nADRq1Ih+/frRr1+/Is8TriiKotzbevTowcaNG8nMzHyghnpW7kxcXByRkZFcvnyZnj173vfXvUuT\nStTdhKZpnYCVgD0Fwz3+GwEygCdEZFMph3YDTdOeBxyurHoBL155vBtYek3RpSISe4s6NGAOMBrI\nB5YDR4CHgP6AJfAZMEZu84ZpmjYE+JKCee9+BzYD1YDBgCOwBegjIknFamgJK+lE3bXS09M5f/48\ndevWBSAyMhJvb2+cnJxus+eDw2g0cvHiRT1xFx8fz8mTJwsl4LKysm5bj4eHB76+vtSrV486derw\n8MMP4+fnd9e9Fc0pNTWVRx55hJiYGA4fPoyzs/Nt9xERPv74YwYNGoS7uzvR0dGcOnWKunXrUqtW\nLX04UEVRFOX+sHPnTgIDAwkICGDfvn3mDke5R+Tk5GBlZVVuEgTK/evPP/9k3rx5hIeH3zBE/LUc\nHBwwGo2kp6fj5OTEyJEjGT16NDVq1NDLxMXF4eDggL29PZcuXWLSpEl89dVXWFpaMmHCBN54441b\njpYhIpw+fZo9e/awd+9e9u7dy99//83s2bPp168fJpOJKlWqUKFChUJzi9vb2xMVFYW7uzsZGRlk\nZ2ffdD7u33//nddff509e/boz3l5edGzZ0969OhB+/bt9aEu8/Ly6NOnD2vWrGH06NHMmTPHrBc/\nMzIymDhxIp999hmapumJRmtrayZPnoy9vT39+vW75cggiqIoyv3L3d2dc+fOkZ6ejr29vbnDUcqR\n1NRUdu/eTWRkpL6cO3dO325paUn37t0ZMmQIPXv2xNbW1ozR3ntUou46mqZ5UJDAqXTlqf3ANuAk\nkHblucpAHaAtEHDluTQKXrybJsNKi6ZpMUDtIhTtdLtEoqZpjwAjgEDAGbgA7AD+JyIRxYipHvAy\n0A1wA9KBw8BCYIG55qW7Vmkm6pS7ZzKZOHv2LFFRUXriLjU1FR8fHz0x5+Pjc9/+YLh48SJJSUn4\n+vretmxeXh4vvPAC8+fPZ+jQoXz77bdlEKGiKIpiTrm5uTg6OtK6dWs2bNhg7nCUciIzM5NTp07p\nIxpcXa6uJyQk4OHhwcsvv8xzzz2Hg4PD7StVylR6ejrvvvsuo0ePpnbt2hw7dgyDwW+rWXwAACAA\nSURBVIC3t7e5Q7upyMhIIiIi2LdvH4mJiXoP38mTJxMaGgr8M3Sig4MDQ4cOJTg4WB/+3s7OjoyM\nDBYsWMAnn3zCiRMnsLa2Jjg4mOrVqxMREcGePXvQNI2aNWuSlJREdnY2HTp04Isvvrihh1dCQgKp\nqanUq1cPKOgRsG7dOn27lZUVfn5+TJ48mT59+gAFw9jb2dlx4cIFduzYwfbt2zl48CArV65E0zS+\n//57goOD8ff3JygoiG7dutG2bVs9AWcymfjpp584e/Ys3bp1o169erdMwGVlZdGjRw9+//133nzz\nTaZOnVqyb0gRZGZm8uGHHzJz5sxCQ4b279+fZcuWlXk8iqIoSvnj4uJCYmIip0+fVnOQKiQlJfHR\nRx+xZs0aoqKiuJrLMRgM+Pv706JFC1q0aIG1tTVhYWFs2LBBvxmqf//+DBkyhDZt2tzVzYKZmZn8\n8ccfODg40KJFi5Jqmtnl5ORw5swZ4uLiiIuLY+/evUyfPv3aImWaqENEytUC/I+CIS33A82KUL75\nlbJG4HNzx6+WIr/PDSnoDSmAHDx4UBSlPNq1a5esWrXqptvS0tIkKChIAOnevbukp6eXcXSKoiiK\nudSqVUuaNWtm7jCUciA2NlZGjRol1tbWcu3v26uLi4uLtGzZUvr27Ss1atQQQCpVqiSvvPKKxMTE\nmDv8ciUnJ0eio6Nl3759YjKZyvTYP//8s9SuXVsAef311yUzM1M8PDzEzc1NoqOjyzSW27l48aI8\n88wzhT5nXl5ekp6eLgsXLhRbW1uxtraWb775Ri5cuCChoaHi6uoq8+fP1+s4ffp0oTqTkpJk7ty5\n0rp1a71OS0tLadeunTg7O+vPffXVV2IymSQiIkK8vb1lwIAB0rt3b3F3dxdAgoKC9DpnzZolo0aN\nkvnz58vff/8tOTk5xW7rli1bJDg4WKpXr67HUKFCBRk3btwt9zEajXLhwgXJzs4WEZGUlBT5+uuv\nJSIiQg4fPiwtWrQQQKZNm1bseIrLZDJJVFSU5OTkSFJSkjzyyCN6Ozw9PWX69Onq/wFFURSlEG9v\nbwFkz5495g5FMaOsrCyZPn26ODg4CCC1atWSp556SmbMmCFbt2695TXIuLg4+fjjj6VRo0aFfnNM\nmjRJ5s2bJytXrpRt27ZJVFSUXLhwQfLz82+oIyEhQVasWCGpqakiIrJjxw69rsGDB0tiYmKptr00\nJCYmyhdffCHdu3eXrKwsERFZs2bNTc/frlkaSlnmS8ryYEUKCE4B8YBTMfapBiQAp8wdv1qK/J6p\nRJ1S7uXl5Ym3t7dYW1tLREREoW0JCQnStGlTASQkJETy8vLMFKWiKIpiDr6+vuLm5mbuMBQzuj5B\n17x5c5k8ebLMnTtX1q9fL8eOHdNPAq/Kzs6WefPmiZ+fnwBiYWEhAwYMkJ07d5qpFWUvJydHf10u\nXLggzz33nHTu3Flq164tBoNBANE0Tfbv318m8Zw/f14GDx4sgFSsWFE+/fRT/YJFWFiYGAwGcXNz\nk2PHjhW5zn379kmLFi2kSpUq0rVrV5kyZYrs3bu3UJnc3Fw5d+5csePNzc2VOnXqCCDdunWTLVu2\nSGpqquTm5srLL78sgLi5ud3wmcrJyZHc3FwRETl8+LBomibdunWTL7/8Uvr27Ss2Njby3HPPiYjI\nn3/+KV26dNHfD0Cee+45OXXqlF7fqlWrxNfXVzRNEwsLCwkICJCQkBBZtGhRsdtUFEajUf766y95\n//33pUOHDvLee+/p24YPHy5du3aVxo0bi6urq1haWgogGzduFJGCxOa15562trZiY2MjgMycOVNE\nRFJTUyUmJkZ/je4mzoMHD8pnn30mAwYMEBcXFwHkww8/1BP19evXl+3bt9/VcRRFUZT719VrTevW\nrTN3KIoZGI1GCQsL028gq127toSFhYnRaCx2Xfv375fXX39datasectklKZpUrFiRXF2dhY3Nzdx\ndHTUt61cuVJECn5/zpgxQ3r06CGAODk5yTfffFOiN9ZlZGTI4sWLZdeuXSVWZ1xcnPz3v/+Vjh07\n6r9rra2tJTY2VkRETp48KePHj5fQ0FCZNm2avPLKK2ZN1JXHoS+zgS9F5OVi7jcHeF5Ebj5AvlKu\nqKEvlXvF33//TceOHTEajUREROhdvEeNGsXnn3/OlClTmDJliprgV1EU5QFjbW2NyWQiPz/f3KEo\npSwzMxNra2ssLS3Jyclhw4YNzJ8/n9WrV5OXl0f9+vUZPHgwEyZMwMrKqkh1iggbNmxgxowZrF+/\nHoC2bdsydOhQKlSogMFguOlia2tLu3btqFChQmk2+ZYyMjI4fvw4DRo0KHJbr7V582ZCQkL44osv\n6NKlC2lpaVSuXJlKlSrh5eVF3bp19WXEiBEAnDt3DldX1yIN15Odnc23337LsGHDsLGx4cKFC7z/\n/vs0bdqUZs2aUa9evUL1xMbG0qRJE5KSkujRowf/+9//8PDwKFTnkiVLGDx4MC4uLmzatOm2c4aF\nhYUxbNgwTCYTDz/8MPv37ycnJ4dZs2YxcuRINm7cyIQJE4iOjiYzM5MmTZowePBgBg0aRM2aNf+1\nbVfnGZk3bx52dnYMHDgQTdO4cOECAwYMYNOmTbRr147ly5cXmmfuekePHuWtt94iPDwck8mkz5H2\n3HPPERwcrJc7efIky5cvp127drRu3fqmdaWnp2MwGMz2mTQajQQEBBATE0ONGjUKLaNHj8bf3x8R\nYe3atZw8eZLo6Giio6M5cuQIZ86cQdM0Fi9ejK2tLX369MFgMODq6oqHhwceHh40atSIN998E4AD\nBw4QFRVFVlYWWVlZZGZmkpWVRUBAAD179gSgadOm/PXXXwBomqaf4x48eBBHR0dmz57N008/rc4d\nFEVRlFvq1q0b69ev53//+x8vvviiucNRytDmzZsZN24cu3fvpkqVKrz55puMGjWK3NxcsrOzsbOz\nw87O7pa/w41GI7GxsWRlZfHQQw8BMH36dMLCwjh+/Djp6el62cDAQBo1asTFixf59ddfC227ytLS\nEl9fX/z9/QkICMDNzY3IyEi+++47UlJSGDhwII888ggigslkwtbWlkaNGtGwYcNinSuICAEBARw8\neBBbW1tWrFhBjx49ivnq/fMaWFhYYDKZcHd3Jz4+HltbWx577DG6detGcnIyJ0+eJDY2ljNnznDm\nzJlCw5Ff54Ef+jIBmHQH+00C4swdv1qK/H6pHnXKPWPbtm1iZ2cnTk5OcujQIREpuNMjPDzczJEp\niqIo5lKpUiUB7rr3hVK+PP3009K5c2dp1KiRuLu7i62trQASGRkpZ86ckaFDh97ybtSUlBQRETl6\n9KiEhobK/v37i3SX6YEDB2T48OG3HDrz+qVixYryzDPPyG+//XbToWpK2urVq2XkyJHSpEkTsbCw\nEEB8fX1ly5YtRa4jLS1NRo0aJYDY2dnJ77//rm+7cOHCLV+njIwM8fLykvbt28vRo0dvWX9WVpZ8\n+umn+t3CX3zxhYiIrF279obXrl27djJhwgQRKRiW8Nlnn5UlS5b863u1aNEiMRgMUq1aNQkLC5Ol\nS5fK559/LosXL5Y//vhDzp07p9/lfPz4cWnRooVERkaKiEh6errMnj1bBg0aJE5OTje9i/nq306d\nOsmCBQv0YYZECu5g/uijj8TFxUXOnj17Q2y7d+8WDw8PAWT06NHF+j8pJiZGFi1apN9V/KA5duyY\nuLm5iaWlpcyaNUvGjh0rffv2lebNm+s94QIDA/XyN7nLWgAZPny4Xubdd9+VV199VVauXCkbNmwQ\nHx8fAaRLly43ff8URVEU5XpDhgwRQB5//PEyHwpcKXtGo1HWrVsnrVq1EkCsrKxk7NixsmXLFqlf\nv75UrFix0O8OS0tLfd/w8HCpXbu21K9fX7y9vcXKykoAadeunV5m3LhxUqtWLenQoYP06dNHhg8f\nLmPHji00zc+WLVtkw4YNEhMTI0eOHJFly5bJW2+9Jb169ZK6desW6Rzl2sXW1lYCAwNlzJgxsnDh\nQjl8+HChHoGpqany1VdfydSpU/XnvvrqKwkNDZWqVauKpaWlLFu2rFivY15enkyaNEleeOEF/bm5\nc+fKkiVLZP/+/TJmzBipUKFCoaHU69evL127dpWQkBB59913ZerUqapH3bU0TVsPnBOR4cXcbz5Q\nTUQeL53IlJKketQp95pffvmF7t2706BBAw4ePHj7HRRFUZT7mqurKwkJCWzcuJFOnTqZOxylhNSs\nWZPMzEyqVq1KtWrVqFq1KlWrVqVTp06MGDGC3NxcPDw8CAoKomHDhlhaWmJpaYmFhQVDhgzBxsaG\nDz/8kEmTJgHg6elJz549efzxx+nQoQM2Nja3PHZCQgKRkZGYTKZCi4gQExPD77//zsMPP0xERASR\nkZEAWFlZUbVqVWrUqEGVKlWwsrLim2++oVatWpw5c0bv5XftYm1tzYwZMwA4dOgQK1euLLQ9LS0N\nGxsbXnnlFQCeeeYZFi5cSI0aNWjVqhVubm58++23/Pbbb7Rs2fK2r+nGjRsJCQkhJiaGdu3aMW/e\nPLy9vW9ZPjU1lW3btnH+/Hni4+MJDw9nz549GAwGPD09sbe35/Lly1hZWVGhQgXS09OJi4sjNzcX\nW1tbGjduTNOmTfH29qZFixY4ODhw4MAB9uzZw+7du/nrr7/w9vZmz549Nxw7KSmJLVu2sGnTJiIj\nI0lMTOTixYukpaXdtp2WlpZ4enri6elJ7dq18fT05PTp0/z4448kJSUB0KhRI/r370/79u1JTExk\n27ZtbNy4kQMHDujvZ15eHra2tvj5+fHYY48RFhbGiRMncHV1Zdy4cbi7u5OTk0NOTg7x8fG8//77\niAhffPEFw4YNu22cSmFRUVG0a9eOtLQ0xo0bR//+/QkICEDTNHJyckhJSaF69eoA/PXXX0RHR+t3\ns9vZ2VGhQgWqV6+Ou7u7Xmd+fj4ffPAB7777LpaWlnz44Ye89NJLReoVqiiKoij79+9n2LBh7N27\nl4kTJ/LBBx+YO6QHVm5uLr169eLkyZMcPnwYCwsL9u/fz9q1a3FxccHFxQVXV1dcXFxwdnbGwsKi\nSPXm5+czbtw4fvnlF44fP47RaNS3rVq1iscff5yYmBiCgoLw8PDA3d2dChUqkJWVhYgwf/58AFav\nXs27775LVlYWmqZRp04dvLy8aNKkCc888wxQ0Enrbnvyp6WlcejQIRITEzEYDGiaVujvvHnz2L59\nO8888wyJiYkcOnSIQ4cOkZKSotdhb2+vj0wRFRVFdnY21apV4+zZs4XOkQ4fPkyXLl1ITExk8eLF\nDBw48LbxnT9/nkGDBrFx40a6du3Kr7/+ChT8dps+fTrLli3DZDLRpEkTXn/9dbp164ajo+MNr8uh\nQ4fw8/O79qky7VFXHhN1wcCXQBMROV7EfbyAv4G+IrL+um2vAR+JiGWJB6vcMZWoU+5F4eHhzJw5\nk59++glnZ2dzh6MoiqKYUbt27di2bRuVK1dm48aNNG3a1NwhKXchMzMTW1tbROSmJ9j/+c9/mDJl\nCgsXLrztsHW5ubls2bKFVatWsXr1amJiYgAIDg5m8eLFQEFSrkaNGv9aT0pKCkuWLOGbb75h165d\nAEybNo3XX3+dAwcO0LFjR1JTU/XhVy0sLLC0tOSTTz7B2dmZ6Oho3njjjRvqNRgM/PDDD7i6urJ9\n+3bGjh17QxkfHx+OHTsGwPHjx7GwsMDT01OPNyUlBQcHB6DgAkFYWBghISEcOHCAiIgIqlSpQo8e\nPahfvz7NmjXD1taWjz76iJEjR940WZGdnc3PP/9MWFgYq1evJicn55avi7W1Nd7e3lhbW3P27Fku\nXryIpmlomobJZLpp+SZNmtCyZUsCAwNp0aIFlStXxtnZmQsXLuiJuc2bN+sJM4Bq1arh4eFBtWrV\nblicnJwwGAwkJCQwc+ZMTp8+jYODA+7u7sTExBQaPsff358BAwbQv39/fH19b9qmpKQk9u3bR+PG\njVmxYgUffPCB/rm5HTc3N3788UeaN29epPLKjfbu3csTTzzB2bNngYLPf79+/ejfvz+NGzcu1sWt\nEydOMGTIELZv346/vz+LFy/G39+/tEJXFEVR7lMZGRl06dKFHTt2MH36dMaNG2fukB5Ib7/9Nu+9\n9x6NGzdm7969AHzxxRf6EO3XatmyJTt27AAKhinfunWrnsiztbVlz549BAQEkJKSwooVK/T6LCws\n8Pb2plevXnTo0IE2bdrov7PvFR999BFTpky54Tf8l19+iYODAzt27GDWrFmFthkMBh5++GE6d+58\nQ7tPnDjBgAEDGDt2LBkZGURFRelLeno6Pj4+1KtXT/9tPXv2bM6fP8+IESOYOXMmmzdvZvr06URE\nRADQtWtXxo8fT+fOnf/1d51K1N2EpmnfAZ2BsUC4iBhvUc4CeBL4BPhcRD68SZnXgGkiUrSUtlIm\nVKJOURRFUZR72dChQ1m0aBG2trbY29uzZcsWGjRoUKLHuHjxIqmpqdStW7dE61Vu5O3tTaVKlfQT\n5uu99NJLfPrpp5w9exY3N7ci1ysiHDp0iNWrV9OkSROCgoL046WlpdGpUyc6d+5Mp06d8Pb21k8c\nZ8+ezaRJk8jKysLe3p6nnnqK5557jsDAwEInlyaTiW3btrFo0SKWLVtGamrqHbW/cuXKVK1aFUdH\nR6pXr07Tpk1p2LAhPj4++Pj43PRiQWpqKhEREbz99tuFRhu42isMCk7A69SpQ+/evRkyZAj+/v56\n/EajkU2bNhEWFkZ4eDgpKSlomkaHDh3o27cvtWvXxsnJSe/VaGdnx4cffqj3WHzvvfdITU3lm2++\n4f/+7/+oUKECubm5ZGRkkJ6eztGjR9mxY4e+JCcn6zFWr14dJycnoqKi9OdcXV3p0KEDHTt2pEOH\nDvj6+t7yRH7SpEl88803pKenk52dzcsvv0xoaCgVKlRAREhOTiYmJobKlSvfdk67m0lKSmLFihWs\nWrUKPz8/PD09sbGxwdraGhsbG32xtramadOmVK5cudjHUAozGo388ccfLF++nPDwcOLj4wHw8vLS\nk3b+/v5YWFjod5BfS0RYsGABL730Eunp6bz66quEhobq8woqiqIoSnFkZWWRnJxMt27dOHToEPPm\nzWP48GINPqfcpd27dxMYGIi/vz87d+7E2toagMuXL3PixAkSEhKIj48nISGBhIQE3N3dmThxIgAh\nISHMmzfvlnVXqVKFTp068fTTT9O9e/f74vdCdHQ0y5YtIz8/X7+JbsCAAfj6+pKfn8+0adMAqFWr\nFomJiWzZsoWtW7dy6dIloOC8oXHjxlhZWREVFVWoNx6Ara0tvr6+VKpUiWPHjnH+/PlC2zVNw9PT\nEysrK44dO4aFhQUDBw7k9ddfp1GjRkVqg0rUXUfTtHmABvQF7IFUCnrLxQPZV4rZAi5AY8ABiAb+\nvEWVfkBTlagrX1SiTlEURVGUe9n48eNZu3Ytb7/9Nk8//TTOzs5s3boVLy+vEqk/NjaWVq1akZyc\nzK5du9TvpFLm7OxMgwYN2Lx58023Dx48mLCwMLKysu76RDovL4+JEyeyceNG/v77b/15d3d3NmzY\nQP369fnhhx/4+OOPCQkJYcCAAVSqVOm29WZlZfHbb7+RmpqKpaUlVlZWN/2bmZl5w4WFq4/j4+Nv\nmuxzdnbWk3ZXe+L98ccfem8+Nzc3cnNzuXDhAvb29jg5OTF+/HgiIiLYsGGD3sPMw8OD7t27Y2dn\nx9KlS/VkSJMmTRg8eDBPPfVUoSEEb2b//v34+vr+6zCi1xMRjh8/zs6dO/XE3cWLF2nTpo2enPPx\n8Slyz6nw8HAGDhyIh4cHCxYsoH379kWORSn/TCYTf/75J8uXL2fFihXExcXdtNzVpN3VxF12drY+\nNOwjjzxSxlEriqIo94udO3cSGBhI7969mTNnDm3btiU2Npbw8HCefPJJc4f3QMjJyeHhhx8mOjqa\nXbt2FSnRk5mZyd69e9m1axe7du0iMjKS48f/GSzQycmJvn370rdvXzp16qQn/h5kJpOJAwcOsHnz\nZj1xB/DQQw9Rv3596tevj8FgYOLEibz00kt88MEH+u/1y5cv8+OPP/LWW2/x2GOPcenSJY4ePcqF\nCxcYNGgQY8eOpXbt2sWKRyXqrqNpmomCyfpKrEpAVKKufFGJOkVRFEVR7mUXL17EwcEBKysrli9f\nzsCBA6lVqxZbt269baLhdi5fvkzbtm05dOgQBoOB+vXrExkZib29fQlFr1xLRLCxsSEoKIhVq1bd\ntExQUBDbtm0jPT29RI+dlJTE5s2b2bhxI3/88Qfbt2/Xh+C827kk7lRqairHjx8nOjr6huXixYsA\nVKhQgc6dOxMUFES3bt3w9vbGZDKxePFi3nnnHerVq8f8+fNxcXEhJyeHLVu2sHbtWtauXatfsPDy\n8iI4OJhBgwbx0EMPmaWtdyM2NpZq1aphZ2dn7lCUUmQymdi+fTs//PAD8fHxheaPNBqNhdY9PT15\n7733cHJyMnfYiqIoyj3MZDLRu3dvVq1axdSpUxkwYABt2rQhNTWVX375hY4dO5o7xPveZ599xujR\no3nvvfeYPHnyv5ZdsWIF//nPfzh06FChYdi9vb1p1qwZzZs3p2XLlrRs2RJLSzUzV3FdunSJ7t27\ns2PHDkaNGsWoUaNISUkhMDAQKPj3UlLzAKtE3XWuJOpKmkrUlTMqUacoiqIoyv3i/PnzrF27lmef\nfRZfX1+2bNlC9erV76iunJwcgoKC2LRpE9OmTcNoNDJp0iSGDx/+r8OnKHcuMzMTe3t7hgwZwsKF\nC29apnnz5pw/f57Tp0+XcXTly6VLlzh79iw+Pj533LMwOjqajIwMGjVqZLZkpKIoiqIoSnmWnp5O\n27Zt2bdvH8uXL8fLy4uOHTsiIvz+++9qfuxSZjQaWbhwIUOGDPnX5JqI4Ovry7lz5+jWrRvNmzen\nWbNmNGvWDEdHxzKM+P6Wnp5Or1692LhxIwaDAVdXV44fP17iQ4aaO1FXMunGkjdORAwlsQDjzd0Y\nRVEURVEU5f40btw4fH196dGjB3PmzOHo0aN07dpVH2u/OEwmE8OHD2fTpk2MGjWKcePGMX78eIKC\ngpg/fz6LFi0qhRYoV+c/+LdJ25OSkqhatWpZhVRuOTo64u/vf1cnxT4+PjRu3Fgl6RRFURRFUW6h\nYsWKrF69GhcXF4YOHYrRaGTVqlXk5eURFBTE0aNHSz2Gq0OcP0jy8/MRESwsLBg+fPhte8Dt37+f\n6Ohonn32WX744QcmTZrEo48+qpJ0JaxixYqsXbuWPn364OLios8Vf78pr4m6klS+ugwqiqIoiqIo\n943AwEAuX77MG2+8wZgxYwgNDWXfvn1079692MMkvvHGG3z//ff06tWL2bNno2kaBoOBhQsXUrNm\nTUaMGEFUVFQpteTBdfnyZaBgUvdbuXjxokrUKYqiKIqiKGXGw8ODlStXIiL8+uuvdOjQgaVLl3Lp\n0iW6du1KbGxsqRz38uXLTJgwgUqVKvHSSy9R3kbjK00TJkygb9++N52z+WaWLVsGQP/+/UszLAWw\ntbUlPDyc2NhYOnXqZO5wSkV5TNS9C/xZgvX9CfynBOtTFEVRFEVRFAD69u3LI488wjfffENkZCST\nJk1iwoQJ7Nixg169epGcnFykej777DM++ugjAgMDCQsLw8Lin1HbnZ2d+f7778nKymLAgAFkZWWV\nVnMeSN7e3pw6dYqXXnrppttzc3NJS0tTiTpFURRFURSlTLVo0YLDhw/z5ptvAvDEE08wb948zpw5\nQ8OGDXnrrbfuaCSPS5cusWzZMjIzM/XncnJymDVrFl5eXkybNg0LCws+/fRT3nvvvRJrT3m2detW\nPvnkE+Lj44s0N7iIsHz5clxcXGjTpk0ZRKgAJTYfXXlU7lomIu+KyI7i7qdp2gBN0zbepL7tIvJu\nyUSnKIqiKIqiKP/QNI05c+ZgaWnJqFGjEBE++OADRo4cycaNG3Fzc+PZZ59l165dt6zjp59+YsyY\nMfj4+LB69WoqVKhwQ5n27dvz7rvvcuDAAcaOHVuaTXrgWFlZ4enpibOz8023X022VqtWrSzDUhRF\nURRFURTq1KkDQHZ2NnPmzOHpp58mPDwcV1dXpk6diqenJ++8844+SsTtbN68mYCAAJ599lksLS0R\nEaZNm4a7uzuvvPIK9vb2fPvtt5w9e5YmTZowZcoUvvzyy9JsotllZGQwfPhwbGxsWLBgQaGbJm/l\n6rCX/fr1K1J5Rbmdcpeo0/6fvTsPj7K63z/+/mQhAskkxIBsk8gmm0L9IlWLiiiuKC1SV4prFetG\n3UWrWLVW/VWt1gUQq1B3FBe0AiIiuIviCgHZiSCQyUJYAlnO74/JpEMyIdtkZgL367rmyjPPOc9z\nPoPClZl7zjlmK83skgZc6gUGh7seEREREZE96dOnD2PHjmXhwoX8+9//xsz417/+xXPPPcehhx7K\nM888w69//WsGDhzIv//9792+ufrpp59y7rnnkpGRwbvvvrvHMGjcuHEMHTqUSZMm8dJLL0Xipe0T\nNm7cyMKFCykqKgrZ7vP5ADSjTkRERESi5oEHHmDs2LHcdtttnHHGGfz4449MnTqVdu3a8de//pUu\nXbpw1113Ve6/XFVJSQm33norQ4YMIT8/n7vvvpvPPvuMww8/nJtvvpnc3FzAv2/2+++/z5tvvsmr\nr75Kt27duOKKK5g+fXokX25E3XLLLaxYsYK///3v9OzZs07XTJs2DdCylxI+FmvrzJpZOXCDc+6h\nelyTCPwHONM5pwi7GTCzvsAPgec//PADffv2jWJFIiIiIg23ZcsWLr30Um6//XYOPvjg3dq++eYb\nnnzySZ577jm2b99OWloaF154ISeffDKjRo1ix44dzJs3j4EDB9Y6zsaNG/nVN2OFCwAAIABJREFU\nr37F1q1b+frrr+nRo0dTvaR9xqRJkxgzZgyzZ8/mhBNOqNY+f/58Bg8ezCOPPFLj8pgiIiIiIk1p\nx44dDBkyhM8//5xnnnmGCy+8EIDS0lKef/557r77blasWEGbNm247rrruPDCC9m5cyeFhYUsXryY\nO++8kxUrVpCZmcnIkSNZtmwZ77zzDgkJCVx88cUcccQRLFy4kA8++IAlS5YAMHr0aMaPH8+gQYMo\nKCio3CsvVrz55pu8/fbbdOjQgY4dO9KxY8fK4w4dOtRpmcS5c+dy/PHHc/TRRzNv3rw6XeOco2fP\nnhQVFZGTk6MZdXuJH3/8sep7+YOdcz9GavxYDerudM7VaV85MxsIPAP0BlBQ1zwoqBMREZF9TWFh\nIVOnTuXJJ5+sfPMbFxfHm2++yWmnnVbn+8ydO5ehQ4fSv39/Pv30U/bbb7+mKnmf8MADD3DzzTfz\nxRdfhAxLX3/9dc444wyee+45Ro0aFYUKRURERETgl19+4fDDD2fDhg1MmjSJYcOGVS7fXlJSwnPP\nPcfdd9/NqlWr6nS/M888k3vvvZfu3bvvdn7Dhg1MmTKF888/n44dO7Jo0SIGDx6MmTF//nz69+8f\n9tfWEAsWLGDs2LEsWrSoWtu2bdto1aoVc+bMYdy4cbRo0YLExERatGhBixYt6NKlC//6179Ys2YN\nV199NQ8//DDdunWr07jffvstv/rVr7jyyit57LHHwv2yJEqiHdQlRGqgerrVzBY552bU1MHMWgB3\nA9cC8YABsZU6ioiIiMg+xTnHyy+/TO/evau9gU1NTeXqq6/mqquu4sMPP2TKlCmceOKJ9QrpAI47\n7jjuuOMO/vrXv3L99dfz+OOPh/Ml7HMC+3mkpqaGbA8sA6SlL0VEREQkmtq3b8+MGTM46qijuOii\ni3jooYe49tprAf/sst/85jdkZ2fz/PPPs2DBAuLj4+nSpQsej4fi4mJ69OhBamoqHo+HAw44gE6d\nOoUcp0OHDtxyyy2Vz7Ozs/nLX/7C7bffzsknn8wnn3xSuXdeNB111FG8//77rF+/ng0bNrB+/XrW\nr1+Pz+er3Pd7x44dbNq0iZKSEnbt2sWuXbsoKSmpXOIyKyuLt956q17jBpa9POuss8L7gmSfFqsz\n6rYBicDZzrk3Q/Q5Avg30BN/QFcGLAH6akZd86AZdSIiIrI3Wrp0KX369OHII49kwYIFmFm1PmVl\nZXz77bfMnz+f5ORkBgwYQP/+/eu0zErwPYYOHcq8efPIzs6u814KUt2VV17JE088wcaNG2nXrl21\n9vvuu49x48bx5Zdfcthhh0WhQhERERGR/8nJyWHmzJkce+yxdO/enfz8fPbff3+cc7Rt25ZBgwax\ncOFCkpOTWbRoUaNW4Ni+fTvdunVj8+bNXHLJJTz11FN069aNjz/+OOTvzpGya9cunHMkJSVFdFwt\ne7n3ivaMurp/GhBZ9wJzgVfMbETgpJklmdmDwAL+F9ItBY4CpkajUBERERGRgJ49e/KnP/2Jjz/+\nmOeff363tnXr1jFixAgyMjIYMGAA1157LZdeeilHHnkkZWVlAHz33Xc8/fTTfPvtt5SUlNQ4Tnx8\nfOXG5evXr2+6F7QPqG1Gnc/nAzSjTkRERERiQ+fOnfnjH/9YuWRlUlIS06ZN489//jNZWVnMmDGD\n/Px8brrppkYHWa1atWLevHlkZWUxadIkTjjhBJYvX86pp55KUVFROF5Og/z3v/+ldevWvPrqqxEd\n97vvvuOnn35i5MiRCukkrGJx6cs1wCbgd8B04CUzOw/4Bf8suu74A7py4GHgNufcTjNLQ2GdiIiI\niETZXXfdxcsvv8wNN9zAjBkzuOGGGxg4cCDp6enMmjWLfv36cdxxx3HssceyY8cO1q9fT2JiIuDf\nD+3OO+8E/G+4R40axdNPPx1yHI/HAxDVN8h7A+ccHo+nxg8xFNSJiIiISCxr1aoVI0eOZOTIkQBs\n3boVM6N169ZhuX/Pnj359NNPGT58OLNnz6Zv37589dVXXHbZZbz44othGaO+Fi9eTFlZGV6vN6Lj\nBpa9DHxpUiRcYi6oc85VLnBbMZvuNeBF/LP/AjMAlwMXOec+DrpuFjArgqWKiIiIiFSTnp7Offfd\nxx//+EemTZvGkCFDGDhwIK1bt8bn89GyZcsarx0zZgz9+/dn4cKFTJkyheeff57JkyeHXEIzJSUF\ngC1btjTZa9kXvPDCC3ts9/l8JCQkVP55i4iIiIjEsuTk5LDfs127dnzwwQf84Q9/YPr06RxyyCG8\n/vrr7NixY4/vb5rKkiVLAOjVq1fExnTOMW3aNNq3b89RRx0VsXFl3xCrS18C4JwrAUYC/8Vfaznw\nKNA/OKQDMLNOZnZM5KsUEREREdndxRdfzBdffMHmzZu5/PLLK8/X9ia2ffv2/O53v+Oee+5hyJAh\n7Ny5kx07doTsG5hRp6CuaeXm5rL//vuHDEtFRERERPYVLVu25JVXXmHOnDlccMEF7Ny5k/nz50el\nliVLltCpU6cal69vCt999x3Lli3TspfSJGJuRl1VzrkSM/s98ApwKjDfORfq04pzgAcA/S0RERER\nkagyMwYOHNioewwfPpysrCzKy8tDtiuoC49nn32WDh06cNJJJ4Vs9/l8WvZSRERERAT/XtnHH388\n7dq1A2D27Nk1/h7dVMrLy8nOzubII4+M6Lha9lKaUkzPqAtwzpUCZwEz8O9ZNzrKJYmIiIiINKnf\n//733H333TUuXaOgLjyuueYaHnzwwRrbfT4fGRkZEaxIRERERCS2Pf3005gZM2fOjPjYOTk5bNu2\njd69e0dszMCylwcccICWvZQmEbUZdWZW1tBLgWfN7NkwliMiIiIi0qwoqGu8srIyioqKSEtLC9le\nXl5OXl6eZtSJiIiIiAQxM5xzLF68mJ9//plOnTpFbOzMzEw2b95MaWlpxMb8/vvvWbZsGVdeeaWW\nvZQmEc0ZddbAx56uFRERERHZK8yZM4f+/fvX+C1VBXWNF/izq2lvi8LCQsrLyxXUiYiIiIgE6dy5\nc+Xxe++9F/HxMzIyaN++fcTGe+WVVwAteylNJ9p71H0JLA7TvfoCA8J0LxERERGRqCotLeW7774j\nJycnZPt+++1HQkKCgrpGKCgoAGoO6nw+H4CCOhERERGRIF6vt/J41qxZXHjhhREb+6OPPiIuLo4j\njzwSs6afu6NlLyUSoh3UveyceygcNzKz61FQJyIiIiJ7ifT0dADy8vJCtpsZHo9HQV0jFBYWAtS4\n9GVubi6goE5EREREJFggqOvUqRPvvfce5eXlxMVFZvG+W265hcWLF1d+qa6padlLiYRoLn0Zblr+\nUkRERET2GrUFdQApKSkUFRVFqqS9TkJCAoMGDaJbt24h2zWjTkRERESkusDSlx06dMDn8/H1119H\nZNzAvnh9+vSJyGw6gGnTpgFa9lKaVjRn1HUBav7UoZ6cc/8A/hGu+4mIiIiIRFNdgjrNqGucgw8+\nmI8++qjG9kBQl5GREamSRERERERiXseOHXnvvffIy8vj7LPPZvbs2Rx22GFNPu6mTZvIz8+nd+/e\nTT4W+IPBV155RcteSpOL2ow659wa55y+/isiIiIiEkJqaipmpqAuijSjTkRERESkuvj4eIYOHcrv\nfvc7WrduzaxZsyIy7pIlSwAiFtQFlr0cOXKklr2UJrU3LX0pIiIiIrLXiI+P5/nnn+f666+vsY+C\nusZ57733GDt2LGvWrAnZrqBORERERCS0rVu3smrVKoYMGcInn3wSkSX5Ix3UBZa9POussyIynuy7\nohLUmdntZrbMzDo24RiHmNnPZnZBU40hIiIiItKUzj33XI488sga2z0eD9u3b6e0tDSCVe09Pvvs\nMx599FEKCgpCtiuoExEREREJ7eKLL6ZXr14cd9xxlJaW8sEHHzT5mHFxcXTt2pU+ffo0+Vjl5eW8\n/PLLWvZSIiJaM+oeAZKBuWbWPdw3N7OBwCwgF/hPuO8vIiIiIhIpO3furLHN4/EAROTbq3ujQECX\nmpoasj03NxeANm3aRKwmEREREZHmwOv1AtCvXz8AZs+e3eRjjhkzhhUrVpCVldXkY7366qv89NNP\nXHDBBVr2UppcVII659wWYCSQBSwys1vNLPS743ows7Zmdj+wAEgCRjrnyht7XxERERGRaBg+fDjp\n6ek1tgeCOi1/2TCFhYUApKWlhWz3+XykpaWRkJAQybJERERERGJeIKhLSEggKysrYvvURUJpaSl3\n3HEHycnJ3HjjjdEuR/YBUdujzjn3KTACKAfuBn42s+lmdpWZDTazDmZWY1RtZglm1tnMjjez68zs\nbWAdcAOQD5zknFseidciIiIiItIUkpOT2b59O8XFxSHbFdQ1TiCoS0lJCdnu8/nIyMiIZEkiIiIi\nIs1C586dAfj555858cQTWb58OStXrmyy8bZs2cKYMWMiEgi+8MILLF26lGuvvVbvByQiohbUATjn\nZgJHAp8BrYDf4l8Wcy6QA+wys3wzW29mq81sjZltMLMCYCewBpgN/D/gVKAFMBP4tXNuYeRfkYiI\niIhI+AT2RsvPzw/ZrqCucQoKCvB4PDUuZePz+bQ/nYiIiIhICIEZdevWreOkk04Cmnb5yyVLljBp\n0iS++uqrJhsDoKSkhDvvvJO0tDSuu+66Jh1LJCCqQR2Ac26xc24QcDbwMWBVHqlAe8ALdAYOADxV\n+gC8C5zinBvmnFsX0RchIiIiItIEAste5uXlhWwPzARTUNcwp512GpdeemnINuecgjoRERERkRoE\ngrpNmzZx3HHHERcX1+RBHUDv3r2bbAyAZ555hlWrVnHjjTfWuES+SLjFzGYLzrlpwDQz6wycgH+m\n3UFAF/zBXGvAAduAAmAlsBR/uDfHObcpGnWLiIiIiDSV2oK6wIy6oqKiiNW0Nxk7dmyNbYElRxXU\niYiIiIhU1759e4qKikhOTgbg8MMP5/3336e0tLRJ9niORFBXXFzM3XffTUZGBtdcc02TjSNSVcwE\ndQHOuRzgmYqHiIiIiMg+q65BnWbUhZ/P5wNQUCciIiIiEkJcXFxlSAdw4okn8umnn/L5558zaNCg\nsI+3ZMkSEhMT6datW9jvHTBp0iRycnJ48MEHd3ttIk0t6ktfioiIiIhIaKeddhrZ2dmccMIJIdsV\n1DXOMcccw/jx40O2BYI6bR4vIiIiIhLaF198wX/+8x+AJt+nbvHixfTo0YPExMQmuf+2bdu49957\n6dChA3/605+aZAyRmiioExERERGJUW3atKFnz560atUqZLuCuoYrLi5mwYIFrFy5MmS7ZtSJiIiI\niOzZww8/zPnnn09xcTEDBw4kNTWVWbNmNclYF198cY37S4fD448/zsaNG/nLX/5Cy5Ytm2wckVBi\nbulLERERERHxKykpITs7m+TkZLp06VKtXUFdwxUUFACQmpoasl1BnYiIiIjInnXu3BmAnJwcunfv\nzvHHH88bb7xBXl5e5TL+4XLrrbeG9X7BtmzZwv33309mZiaXXHJJk40jUhPNqBMRERERiVEFBQX0\n69ePBx54IGS7grqGKywsBCAtLS1ku4I6EREREZE983q9AKxbtw7wL39ZXl7O+++/H82y6u2f//wn\neXl53HHHHSQlJUW7HNkHKagTEREREYlRbdq0ASAvLy9ke2CDcwV19VfbjLrc3FxAQZ2IiIiISE2q\nBnUnnngiEP596u677z769+/PmjVrwnpf8L/XevDBB+nevTvnn39+2O8vUhcK6kREREREYlRCQgIe\nj6fGoC4+Pp7k5GQFdQ2gGXUiIiIiIo0TCOpycnIAOPDAAznooIOYNWsWzrmwjbNo0SK+//572rZt\nW+dr1q1bx1133cWCBQsoLy+vsd+DDz7Ili1buPPOO0lMTAxHuSL1pqAuxpjZoWY20cyyzazIzLaZ\n2TIze8LMDqnHfQaY2TNmttLMis1svZm9Y2ZnNGX9IiIiIhJe6enpNQZ1ACkpKQrqGqBXr15MmDCB\no48+OmS7gjoRERERkT3LzMzkkEMOqVySH/yz6tatW8fSpUvDNs6SJUs48MADadWqVZ2v+de//sX4\n8eM55phj6Ny5M1dffTXz58+nrKysss+mTZt45JFH6NOnD+ecc07Y6hWpLwV1McLMEszsEeBr4DJg\nPfAg8A9gI/An4Cszu74O97oR+By4APgBuBf4LzAEeM3MXjEzLbYrIiIi0gzUFtR5PB6KiooiWNHe\nITMzkzFjxtCrV6+Q7T6fj1atWtGyZcsIVyYiIiIi0jy0a9eO7777jquuuqry3EknnQTArFmzwjJG\naWkpS5cupXfv3vW6bsmSJSQmJjJu3DiSk5N57LHHGDx4MJ07d+aqq67iww8/5N5772Xbtm3cdddd\nxMfHh6VekYZQUBc7ngCuARxwgXPuOOfcnc658c65o4E/A4nAP8zsjzXdxMwuAh7A/9/2POfccOfc\nXc65PwKHAT7gTGBSE78eEREREQmD/fffn9LS0hrbPR6PZtQ1AZ/Pp9l0IiIiIiL1dOyxx5KYmBi2\nfepWrVrFrl276h3UZWdn06NHD+69916WLl3KN998w2233YbH4+Hxxx/n2GOP5ZFHHuHQQw9lxIgR\nYalVpKEU1MUAMxsKXFrx9Dnn3NSqfZxzjwBvVzx9yMwOCHGfDOCRiqcvOOdeqnKPxcC4iqfnm9lJ\n4ahfRERERJrOzJkzKzdnD0VBXcOMHz+e/fffnxUrVoRsV1AnIiIiIlK7KVOmcPXVV1c+T05O5je/\n+Q3z5s1j165djb7/kiVLAOoV1O3cuZOVK1dWrp5hZvTv35977rmH7Oxsvv32W/7yl78waNAgHn30\nUeLiFJNIdOn/wNgwNuh48h76Taz4mVLlmoDrK9oAHqvhHlOBwCc54+taoIiIiIhER21vGgNBXTg3\na98X+Hw+8vLyaN26dcj23NxcBXUiIiIiIrV47733eOyxx9i+fXvluV//+tds376dNWvWNPr+Rx99\nNLNmzeLkk0+u8zXLly+nvLw85DL3Zka/fv24++67+eijjzjqqKMaXaNIY+01QZ2ZjTazr6NdR32Z\nmQHHVjwtBxbuofunQcehdrcMnPM55z4LdQPn3E7g/YqnR5pZZt2rFREREZFI+/HHH5k8eTK5ubkh\n2z0eD+Xl5bu9MZbaFRYWApCamlqtraSkhC1btiioExERERGphdfrBdhtFZDMzMxq5xqqTZs2nHji\niXTq1KnO12RnZwPUuB+1SKzZa4I6oB3QP9pFNEA6kFxxvMU5V+MnLM45H1Bc8bSLmXkDbWbWCziw\n4umiWsb8Kuj4lHpVKyIiIiIRNXPmTC699FJ++umnkO0ejwdAy1/WU0FBAYmJiey3337V2vLy8gDI\nyMiIdFkiIiIiIs1K586dAcjJyak8Fyq8a6hly5axY8eOel0TCOp69uzZ6PFFIiEhWgOb2flhvuVh\nYb5fpLQIOi6rQ/+dQODThEOAwL92/YL6hN5o439WBR0fUocxRURERCRK0tPTgf+FR1UFB3UdOnSI\nWF3NXWFhIWlpafgXuNidz+cD0Iw6EREREZFahArlwhXUOecYMGAAAwcOZO7cuXW+bunSpYCCOmk+\nohbUAc8C2kgD8vD/ORjgMbM451x5qI5mFsf/Zt8BeIOODwo6Xl/LmMHt+tdKREREJIbVJ6iTuiss\nLAy57CUoqBMRERERqaumDOpycnLYunUrvXv3rtd12dnZdOjQocbf90ViTTSDOvCHU+HU7II/59xO\nM1sM9AUS8QdnS2rofhAQH/Q8Jeg4Peh4ay3DBre3qWOpNTKzdkDbel7WrbHjioiIiOwLAkFdIDyq\nKiXF/yuhgrr6eeGFF2pcQiewH6CCOhERERGRPTvwwAO57LLL+L//+7/KcxkZGey3336sXbu2Ufde\nssT/MXl9gjrnHNnZ2Rx2WHNdgE/2RdEO6p4EXglxPhn4f0B74FXgM2ADENi/rSXQAfg18Hv8Adc1\nwJomrrepvIo/qAM4A/hbDf1GVHmeFHQcPNNuZy3jFQcdp9TYq+6uAMaH4T4iIiIiUoVm1DWNvn37\n1timGXUiIiIiInXTpk0bJk6cuNs5M8Pr9TZ6Rl1DgroNGzZQVFREr169GjW2SCRFO6hb4Zz7MPiE\nmSUC84Fs4DfOuYI9XP+Mmd0E/Bu4h+a739q/gMuBA4DrzewF51zwPnKYWRZwQ5Xraps5V5NmN/NQ\nREREZF/Vtm1bBg4cyAEHHBCyPRDUFRUVRbKsZs05x4oVK2jbtm3I5XAU1ImIiIiINI7X62XhwoWN\nukcgqOvTp0+dr8nOzgZQUCfNSlwUx34HWBHi/DX4l1E8p5aQDgDnXBEwCtgF3BTWCiPEOecDRgIF\n+Jei/MTMrjCzg82sr5ldDnyCP2B7N+jS4K9NB4d2wTPtQmkZdKxPdERERERiWLt27fjiiy+48sor\nQ7ZrRl39bdmyhR49enDdddeFbA8EdRkZGZEsS0RERESkWbr55ps5/PDDdzvn9XrZsmVLo96nbNy4\nkbS0NNq3b1/naxTUSXMUtRl1zrnTa2g6F3jdOberHvfaZWbTgdOB28JRX6Q55z42s4HA34HfAo8H\nNZcAb+GfUTcWOKXi/OqgPvlBx8HLYIYS3F5rGFoHTwDT6nlNN+DNMIwtIiIisk9TUFd/BQX+X4Fr\n2lxeM+pEREREROpuw4YNfPHFFxQVFVXuoe31egFYt27dHped35PXX3+dbdu2YWZ1vkZBnTRH0V76\nMpRuwPQGXFcAZIW5lohyzi0HzjSzZPzLeLYBCoEfnHOFAGYW/GnBt0HHy4KOO9YyVIeg46UNr9jP\nObcJ2FSfa+rzj6uIiIjIvu6+++6jrKyM226r/p00BXX1V1hYCEBaWlrIdp/PR0JCQuWfrYiIiIiI\n1CwQyuXk5FTuJxeOoA6gdevW9eqfnZ1Nq1at6Ny5c4PHFIm0aC59WZMEGrbXXD9i8/XUm3Nuq3Pu\nU+fcf51zHwdCugo9Kn5mO+fygs5/F3TctZYhgtu/b0ytIiIiItL0XnrpJZ555pmQbQrq6i8Q1NU0\noy43N5f09HR9uUxEREREpA6CQ7mq59auXdugey5fvpyJEyfW+/qlS5dy0EEHERe3V0QFso+Ixf9b\nfwJGmNkRdb3AzH4DjKi4dq9lZq2AQyuevhjc5pxbAqypePp/tdxqQNDxzPBUJyIiIiJNJT09nby8\nvJBtCurqL7D05Z5m1GnZSxERERGRugmeUReQmZkJ7B7e1cfcuXO5/PLL+f77us8z2bZtG2vXrtWy\nl9LsxGJQ9xLQAphjZreY2QE1dTSzA8zsVmA2/pl4L0SoxrAzsz+Y2VG1dBsOJAE7gFBfqX654uf+\nNQWdZpYEHF/x9HPn3OoGlCsiIiIiEZSenk5BQQFlZWXV2pKSkkhMTFRQVw+1zahTUCciIiIiUneB\nZSZDzahraFC3ZMkSgMqlNOti2TL/7lAK6qS5icU96v4J/AE4GPgb8DczWwGsBIoq+qTgX76xK2AV\nj++ARyJebfj8B1jO/5a23I2ZtQRur3h6v3Mu1L9wDwJXAMnAVcBnIfqcDwQ22/hrYwoWERERkchI\nT0/HOUdhYSHp6enV2j0ej4K6ehg1ahQjRowgMTGxWptzjry8PDIyMqJQmYiIiIhI89OtWzeeffZZ\nBg4cWHnO4/Hg8XgaFdTtt99+ZGVl1fma7OxsQEGdND8xN6POObcL/4yvL/lfCNcNOAE4o+JxQsW5\nuIr2L4ATnHMl0ag5jLqb2S1VT5pZOjAN6AO8jT/ArMY5twm4tuLpeWZ2TpX79AH+XvH0eefcu+Eq\nXERERESaTiCc29Pylwrq6s7MaN26NS1atKjWVlhYSFlZmWbUiYiIiIjUUXJyMhdccAF9+vTZ7bzX\n621wULd48WJ69uxJfHx8na9RUCfNVSzOqMM5t9nMjgQuA/4EHFJD1++BJ4CnnHPlkaqvif3dzEYA\n84Ct+GfYDcc/C24SMNY5V1rTxc65yWa2P/4w7wUzOxdYCGQB5wKtgOnAH5vyRYiIiIhI+Bx11FFs\n27aNli1bhmz3eDwUFRWFbJPqFi1axIYNGzj++ONJSkrarc3n8wEoqBMRERERqadt27bRunXryude\nr5d58+bhnMPM6nyf6dOns27dOgYPHlyv8bOzszEzevQIuWidSMyKyaAOoCJ4mwBMqNin7hAg8G7Z\nB3zvnNsYrfqawPEVj6OBTOAaYCfwM/5lMac6576sy42cc/eb2fvA1cBg4CQgH5gPTHbOvRb+8kVE\nRESkqZx22mmcdtppNbZ7PB42bNgQwYqatyeffJKnnnqKvLy8akFdbm4uoKBORERERKQ+hg8fzgcf\nfMCWLVsqQzmv10txcTG5ubm0bdu2zvcaMmQIp59+On/9a/12bsrOziYrK4tWrVrV6zqRaIvZoC5Y\nRSC3N4Vy1Tjn5gJzw3i/hcAF4bqfiIiIiMQuLX1ZPwUFBYD/z60qzagTEREREam/jIwMtm7dSmFh\nIWlpaQBkZmYCsG7dulqDus8++4yvv/6aK664gjZt2vDWW2/Va/zy8nKWLVvGscce26D6RaIp5vao\nExERERGR3f3www8ce+yxvPTSSyHbPR4PxcXF7Nq1K8KVNU+FhYWkpKSE3O8iENRlZGREuiwRERER\nkWarc+fOAOTk5FSe83q9AHvcp845xz//+U+OPvpobr75ZjZv3tyg8deuXUtxcbH2p5NmKeaDOjMb\naGa3mNkLZjbLzAYFtV1uZunRrE9EREREpKmVl5fz4YcfsmzZspDtgZlh2qeubgoKCkhNTQ3Zphl1\nIiIiIiL1FyqUqy2oKygoYOTIkVx77bV07dqVjz/+uF5LZAbLzs4GoGfPng26XiSaYjaoM7MBZrYQ\n+Az4G3A2MJT/7VMH8ASwysz+HIUSRUREREQiIj3d/920vLy8kO2BoE6DAs+WAAAgAElEQVTLX9ZN\n8HI8VSmoExERERGpv/oGdYsWLWLAgAG8/vrrnH322SxcuJB+/fo1ePxAUKcZddIcxWRQZ2bDgE+A\nQwELelT1NtAKeNDM7o1chSIiIiIikVNbUJeSkgIoqKur9u3b06NHj5Btubm5gII6EREREZH6CBXK\nBZbDDBXUbdmyhV9++YXHH3+cF198sfI9TUMpqJPmLCHaBVRlZu2AF4BEYDXwMvAN0AJ4Nrivc264\nmfUA3gJuMrPpzrmFES1YRERERKSJtWzZkqSkJM2oC5N58+bV2BaYURcIR0VEREREpHY9evRg6dKl\nleEc+N/HZGRksHbt2mr9Bw8ezOrVqxu81GVV2dnZpKamcsABB4TlfiKRFHNBHXANkAI8CfzZOVcC\nYGZZhJhV55z7ycxOBrKBMYCCOhERERHZq5gZ6enpCuoiwOfzkZqaSkJCLL5VEhERERGJTS1atOCg\ngw6qdj4zM3O3GXXFxcVs2LCBLl26hC2kA39Q16tXL8xCLcwnEtticenLk4HFwFWBkK42zrk1wBvA\nMU1ZmIiIiIhItNx///3ccsstIdsU1NXdli1bGD9+PHPnzg3Z7vP5yMjIiHBVIiIiIiLN3/Lly5k/\nf/5u57xeLz///DNlZWUAvPXWW3Tt2pWXXnopbOMWFBSwceNGLXspzVYsBnVdgRnOOVfP67KBzrX2\nEhERERFphkaPHs3w4cNDtgWCuqKiokiW1Cz98ssv3HXXXcyZMydku8/n0/50IiIiIiINcOWVV3LK\nKacQ/NG+1+ulrKyMX375BYApU6YQHx/PkCFDwjbu0qVLAe1PJ81XLAZ1LYGGfMIQH+5CRERERESa\nA82oq7vCwkIAUlNTQ7YrqBMRERERaRiv18v27dvJz8/f7RzAunXr+OWXX5g1axYnn3xyWPeSy87O\nBhTUSfMVi0HdRuD/GnDd0cCGMNciIiIiIhITrr32Wlq2bFkZNAVTUFd3BQUFQOigbvv27ezYsUNB\nnYiIiIhIAwSHcqHOvfjii5SVlXH++eeHdVwFddLcxWJQ9xHwWzM7vq4XmNkIYDDwYZNVJSIiIiIS\nRfHx8RQXF+/27dQABXV1Fwg609LSqrX5fD4ABXUiIiIiIg3QubN/Z6pQQd3atWuZOnUqqampnH76\n6WEdNzs7m/j4eLp27RrW+4pESiwGdZPwL2P5tpndY2adaupoZm3N7E7gRcABEyNTooiIiIhIZKWn\npwOQl5dXrU1BXd3taelLBXUiIiIiIg0XCOVycnIqz2VmZgKwcuVK2rdvz7nnnkvLli3DOm52djbd\nunWjRYsWYb2vSKQkRLuAqpxz883s38DFwDhgnJktBwJ/u280s0uBLkBP/GGjAU84576IRs0iIiIi\nIk0tEB6FCupat24NKKiri86dOzNixIjKDwyCBYK6jIyMSJclIiIiItLseb1e2rRpw65duyrPdezY\nkbi4OH755RfeffddnHNhHbOkpITly5dz6qmnhvW+IpEUc0FdhTH4awssVtu94uGA3wT1s4qfTwNX\nR6w6EREREZEI29OMuri4OFJSUhTU1cFJJ53ESSedFLJNM+pERERERBqud+/e1d6vJCQk0KFDh8rl\nMM0s1KUNtnLlSkpLS7U/nTRrsbj0Jc65MufchcBvgY8rTluVB/j3szvdOXepC3cULyIiIiISQ/YU\n1IF/+UsFdY2joE5EREREJPxSUlJYtGgRn376adjvnZ2dDaCgTpq1WJ1RB4BzbgYww8zaAIcCgTVo\ncoFFzrn8qBUnIiIiIhJBhx12GJ9++indu3cP2a6grm4eeughvvzyS6ZOnUpiYuJubbm5uYCCOhER\nERGRhpo+fTpr1qzh2muvrTy3fft2SktLSUtLC/t4S5cuBRTUSfMWkzPqqnLO5Tvn5jrnXql4zFVI\nJyIiIiL7ktTUVI444oga90/zeDwUFRVFuKrm58MPP2TatGkkJFT/zqJm1ImIiIiINM7kyZO59dZb\nK/ei27JlC+vXrwcgKSkp7OMFZtT17Nkz7PcWiZRmEdSJiIiIiOzrnHPk5OSQk5MTsl0z6uqmsLCQ\ntLS0kHtjKKgTEREREWmczp07U1xcXLlaxWuvvUZpaSlA5T514ZSdnU27du0qtwoQaY5iPqgzs4Fm\ndouZvWBms8xsUFDb5Wamv4EiIiIisk/o0qULV111Vci2wIy68vLyCFfVvBQWFpKamhqyzefz0bJl\nS1q1ahXhqkRERERE9g5erxeg8guGU6dOrVzNItxBnXOO7OxszaaTZi9mgzozG2BmC4HPgL8BZwND\ngeCvtz4BrDKzP0ehRBERERGRiDEz0tPTycvLC9nu8XhwzrFt27YIV9a8FBQU1Lg3hs/n02w6ERER\nEZFGCAR169atIy8vj88//5xjjz228lw4bd68mfz8fO1PJ81eTAZ1ZjYM+AQ4FLCgR1VvA62AB83s\n3shVKCIiIiISebUFdYCWv6zFnmbU5ebmKqgTEREREWmE4KAuPT2d9evXc++991aeC6fA/nQK6qS5\ni7mgzszaAS8AicAa4H7gXOCCqn2dc8OBPsAy4CYzOyyCpYqIiIiIRNSegrqUlBRAQV1txo0bx0UX\nXRSyTTPqREREREQap1u3bgwbNoxOnToBkJaWxoABA0hMTFRQJ1KDmAvqgGuAFOBJoKdzbpxz7mVg\nPiFm1TnnfgJOBkqAMZEsVEREREQkkgJBnXOuWptm1NXNjTfeyOjRo6udLy0tpbCwUEGdiIiIiEgj\nHHjggbz99ttkZmYyfvx41q9fT1xcHJ07d2bt2rVhHUtBnewtYjGoOxlYDFzlnCupywXOuTXAG8Ax\nTVmYiIiIiEg0paens2vXLnbs2FGtTUFd4wRmKiqoExERERFpvMmTJ3PXXXexceNGADIzM8M+o27p\n0qUkJSWRlZUV1vuKRFosBnVdgRku1NeE9ywb6NwE9YiIiIiIxIQJEyZQUlJCq1atqrUpqKtddnY2\n3bp1Y8KECdXafD4fABkZGZEuS0RERERkr3Lbbbfx5JNPcvDBB/OrX/0K8O9dl5+fz7Zt28I2TnZ2\nNgcddBDx8fFhu6dINMRiUNcSKGrAdfrbKCIiIiJ7tZYtW9b4JjQQ1BUVNeRX6X1Dbm4uK1eupLi4\nuFpbIKjTjDoRERERkca59957ARg9ejRm/t2svF4vQNhm1RUXF7Nq1Soteyl7hVgM6jYC/9eA644G\nNoS5FhERERGRmJGTk8Orr77K+vXrq7VpRl3tCgsLAUhNTa3WlpubCyioExERERFprDFjxgBwzjnn\nVJ4Ld1D3008/4ZxTUCd7hVgM6j4Cfmtmx9f1AjMbAQwGPmyyqkREREREomzBggWceeaZfPbZZ9Xa\nFNTVrqCgAIC0tLRqbZpRJyIiIiISHo888gjr168nMzOz8ly4g7rs7GwAevbsGZb7iURTLAZ1k/Av\nY/m2md1jZp1q6mhmbc3sTuBFwAETI1OiiIiIiEjkpaenA5CXl1etTUFd7fY0o05BnYiIiIhIeCQl\nJdGhQ4fdzgWCurVr14ZljEBQpxl1sjeIuaDOOTcf+DeQBIwD1prZ0opzADea2Qwz+wFYD9wOtACe\ndM59EY2aRUREREQiYU9BXUpKCqCgbk/qMqMuIyMjojWJiIiIiOwLArPrwjGjrri4mM8//xzQjDrZ\nOyREu4AajMFf2/kVz7tXPBzwm6B+VvHzaeDqiFUnIiIiIhIFgaAuECoFU1BXu7POOovevXvTvXv3\nam2aUSciIiIi0nTS0tJo3bp1g4O64uJiZs6cybRp03jrrbfYunUrPXr0IDk5OcyVikReTAZ1zrky\n4EIzew24CX84Z1W74d/P7n7n3DsRLlFEREREJOL2NKMuKSmJpKQkBXV70L1795AhHfiDuvj4+JDL\nYoqIiIiISOOYGV6vt15BXXA4N2PGDIqKigA4/PDDOfPMMznvvPOaqlyRiIrJoC7AOTcDmGFmbYBD\ngcA6NLnAIudcftSKExERERGJsNTUVHr06FEZ2FXl8XgU1O3Btm3bSEpKIiGh+tug3Nxc0tPTMav6\n/UAREREREQkHr9fLJ598gnNuj793O+e4/vrrmTx5crVw7ve//z1ZWVmRKlkkImI6qAuoCOTmRrsO\nEREREZFoiouLY9myZTW2K6jbs5EjR/LRRx+xdevWam0+n0/LXoqIiIiINCGv18u2bdsoKCigTZs2\nNfabN28eDz/8MH379uWiiy5SOCd7vWYR1ImIiIiISO08Hg8FBQXRLiNmFRYW1ri0pc/no0ePHhGu\nSERERERk35GZmQnA2rVr9xjUTZgwAYDXX39dv6PLPiHmgzozOxI4ATgECKzxkwd8D7znnPs0WrWJ\niIiIiETa1KlTWbNmDbfffnu1No/H0+DN2fcFBQUFIYM65xx5eXlkZGSEuEpERERERMLB6/UCsG7d\nOvr37x+yz8aNG5k+fTrHH3+8QjrZZ8RsUGdmRwBPAKH/xsIZwHgz+xa4wjn3WcSKExERERGJkpde\neokPP/ywxqBOS1/WrLCwkAMPPLDa+S1btlBaWqqlL0VEREREmlBwUFeTZ555htLSUi6//PJIlSUS\ndXHRLiAUMxsFzMcf0lktj18B883s3OhUKyIiIiISOenp6Wzfvp3i4uJqbR6Ph127drFz584oVBb7\nalr6Mjc3F0BBnYiIiIhIE6otqCsvL2fixIm0b9+e3/72t5EsTSSqYm5GnZkdDDyNv7ZdwEzgI2Al\nUFTRzQN0AY4CTgZaAM+Y2XfOuR8jXrSIiIiISISkp/tXg8/Pz6dDhw67taWkpAD+GWJt27aNeG2x\nrKSkhO3bt5OWllatzefzAQrqRERERESaUm1B3ezZs1m9ejW33XYbiYmJkSxNJKpiLqgDbsIfvM0B\nLnTOrd9D33+YWSdgCnAccDNwftOXKCIiIiISHYGgLi8vr1pQ5/F4AAV1oSQkJJCTk0NcXPVFRRTU\niYiIiIg0vdatW9OmTZsag7oJEyZgZlx66aURrkwkumIxqDsOWA381jm3o7bOzrmfzex0YHHFtSIi\nIiIie63goK6q4KBOdmdmdOrUKWSbgjoRERERkcjIzMxk7dq11c7n5OQwY8YMTjnlFLKysqJQmUj0\nxOIedRnAq3UJ6QIq+r4K6J21iIiIiOzVDj74YEaNGhVyrzUFdTUrLCzkk08+YfPmzdXaAkFdRkZG\npMsSEREREdmneL1ecnJyKC8v3+38008/TXl5OZdffnmUKhOJnlgM6nxAQQOuywdyw1yLiIiIiEhM\nOe6443juuefo169ftTYFdTVbuHAhgwYNYtq0adXaNKNORERERCQyvF4vJSUlbNq0qfJcaWkpTz31\nFF6vl1NPPTWK1YlERywGdV8C/RtwXT/go6onzSzZzDIbXZWIiIiISIxTUFezwsJCANLS0qq15eb6\nv++noE5EREREpGl5vV6A3fape+edd/j555+59NJLiY+Pj1ZpIlETi0HdRGCEmR1R1wvM7NfA6cDD\nIZrHAKvCVJuIiIiISFRt3LiRESNGMHny5GptgaCuqKgo0mVFTX5+PmeffTbPP/88zrka+wWCulBL\nhga+zRvY/09ERERERJpGqKBuwoQJxMfHc8kll0SrLJGoirmgzjn3LvAPYLaZ3WRm7Wrqa2YZZnY9\n8BYwxjn3RaTqFBERERGJhvj4eN544w2++uqram374oy6OXPm8Morr/CHP/yBoUOHsnTp0pD9Cgr8\nq+sHz6hbvXo1Z511Fq+99hrdu3cnMTExIjWLiIiIiOyrAkHd2rVrAVi1ahWzZs1i+PDhdOzYMZql\niURNzAV1ZjYXCMym+zuwwcxWmdknZja34vGJma0EfgEeAHYAFwe1Vz6AZrP7pPmdaWbTzWyNme0w\ns2IzW2dmb5jZ2WZWp/9mZjbEzKaZ2dqKe6yteH5cU78OEREREWk6gaApLy+vWltwUOec44UXXqjc\nf21vtXr1agCGDh3K3Llz6devH7fffjs7duzYrV/wjLpt27Zx++2306tXL6ZNm8bw4cOZOXNmpEsX\nEREREdnnZGb6d6kKzKh76qmncM5x+eXN5mN8kbCLuaAOOBYYDLQGrOKRBRxecX5wxfGB+Os3IDOo\nreqjWySLbygz2x+YD7wCjADWAo8BDwI/AL8FXgLmV/St6T5mZv8E5gK/w79v370VP38HvG9mj5iZ\nNeHLEREREZEmkpCQQGpqasigLiUlBfAHdZMmTWLUqFHcdNNNkS4xogJB3ZQpU/j444/p1asX99xz\nDwcffPBu4Vt5eTktW7bk/fff56CDDuKee+6he/fuvPfee7z55pt069Ys3jaIiIiIiDRrnTp1wsxY\nt24du3bt4umnn6Zr164MHTo02qWJRE1CtAuoQQEQrvV6PED1HeNjz6vAURXHlznnngpuNLNTgBnA\nIGA6/hAylDuBsUAxcLJz7sOge0wEZgLX4P8zHh/G+kVEREQkQtLT02udUTdp0iTAv4fb3mzNmjW0\naNGC9u3b07FjR7766iseffRR7rjjDk455RTOPPNMHn74YYYNG8bs2bP585//THp6Oo8//jiXXXYZ\nCQmx+pZIRERERGTv06JFCw444ADWrVvHG2+8waZNm7juuuuIi4vFOUUikRGr//f/zTnXJRwP/Mtn\nxjQz+w3+mYQAM6uGdFC5d9/kiqfHmFm1rxiYWR/gLxVPHwwO6Sru8SHwUMXTv5hZ7zCULyIiIiIR\nVlNQ17p1a8yMwsJCsrKyAPbaIMo5x65du1i9ejVZWVmVb+wTEhK47rrrWLJkCWeccQbTpk2jR48e\nHHHEESxcuJBrrrmGn376iSuuuGKv/bMREREREYllXq+XdevWMWHCBBITE7nooouiXZJIVO0L70xd\ntAuog8ODjhfsod8CYEzF8XHAnCrtt+MPX8uBJ2q4xxPAuIp+twPn1bdYEREREYmusWPHVtuDDcDM\n8Hg8FBUVMWfOHLKysvaaMKpXr15s3ryZXbt2VT4AWrZsyaBBg6r193q9vPbaa7zzzjtcf/31tGrV\nivPOO48bbrgh0qWLiIiIiEgQr9fLl19+yc8//8w555xDu3btol2SSFTF4rv2i4Avw3i/d4HcMN6v\nKbQIOt66h35FQccZwQ1m1hIYXvH0G+fc+lA3cM79bGbfAIcCw82spXOu+qc8IiIiIhKzRo8eXWOb\nx+Nhyxb/KvJr1qyJVElNrlu3bnTo0IGkpCRatGhBUlIS5eXlTJ8+nQMPPJDly5fTvXv3atcNGzaM\nYcOGMWDAAJ544gkFdSIiIiIiUeb1eiuPL7/88ihWIhIbYi6oc85NCfP9FgOLw3nPJvBN0PHBe+gX\n3LaxSttgoFXF8de1jPcV/qCuNXAMMKsONYqIiIhIMxAXF8c333zDggULOProo6NdTqOtWLGCuLg4\n3nnnnWptX331FdOnT2flypX06NGDH374gb59+4a8T0FBAampqU1droiIiIiI1CIzMxPwr5pxzDHH\nRLkakeiL1T3q9jXvAV9UHI82s2pfBTazdGBs0KkZVbr0CzpeUct4q4KOD6lrkSIiIiISGx599FE6\nduzIsmXLqrUVFRVRUlJC586d+eCDD7j//vspLS2NQpXhcckll3DYYYeFbFu9ejVA5dKXEydOrPE+\nBQUFpKWlhb0+ERERERGpnx49egD+2XRmFuVqRKKvWQV1Znaomd1mZk+a2UNmdrmZdYp2XY3lnCsH\nTgVeA/YDPjezsWb264rXfDH+WXeBxXofd859UeU2BwUdh1z2sob2no0oXURERESioLS0lA0bNpCb\nu/sK7xs3biQ/P5/4+Hi6dOnCtGnTuOWWW8jLy4tSpY2Xm5tL27ZtQ7YFgroTTjiBvn37MnXqVLZv\n316tn3OOwsJCzagTEREREYkBw4YN47///S9XXXVVtEsRiQlRXfrSzMYBbYJOLXPOTQ7RLwF4GvhD\niNv808zuc87d2TRVRoZzzgf83swGAw8A/wzRbR3wN+dcqK8Kpwcd72mfu6rtbWrsVUdm1g4I/elJ\nzbo1dlwRERGRfVV6uv9Xv6oB3JQpU3DOUVZWRllZWeWm7Js2bWq2G7Rv3ry58hu3VQWCui5dujBm\nzBiuueYaXnnlFS688MLd+m3bto2ysjLNqBMRERERiQFxcXGccsop0S5DJGZELagzs/bA3wAXOAXM\nAaoFdfhDq9E13KoFcLuZ0ZzDOjNrBfwdGAPEA88CHwLF+JenPBp4AXilhlskBx3vrGW44qDjlAaU\nW9UVwPgw3EdERERE6iBUUOecY/LkySQlJbFz5062bt1aORNt06ZNUamzscrLy/H5fBx55JEh29es\nWUNiYiIdOnRg9OjR3HzzzUycOLFaULdr1y6GDBnCwQfvaTtoERERERERkciL5oy6IRU/DfgAeBL4\nuGonM+sP/In/BXqr8Ad3PwEZwHnAKcA4M5vinFtV9R6xrmLG4H+BwfhDtCHOuY+CurxUMWttHvB3\nM7vCOfdiI4Z0tXcRERERkVi1//77A7sHdStWrGDDhg307duXr7/+mi1btlTOotu8eXNU6mysgoIC\nysrK9rj0ZWZmJvHx8aSlpTF69Gjy8vIoLi5mv/32q+yXnp7O3LlzI1W2iIiIiIiISJ1Fc4+6X1f8\nfNQ5d7xz7lXn3IYQ/a7FH+YBLAd+5Zx7zDk3yzn3vHNuGPAIkEjNs+5i3Vj8IR3A/6sS0gHgnNsE\nXAqkAS+Y2e+rdAlezjKplvFaBh0X1bNWEREREYmyUDPqunfvzoYNGzjuuOMAdgvqmuuMuvz8fBIT\nE8nIyKjW5pxj9erVZGVlVZ6bMGEC06ZN2y2kExEREREREYll0ZxR1w/YDNxYUwczSwLOCDp1u3Mu\n1P5rt+AP6QaHaGsOLgs6nlpTJ+fcx2a2EugKPGpmbznndlU05wd1Ta5+9W6C2wvqVWloTwDT6nlN\nN+DNMIwtIiIiss/Jysri3XffpVevXrudT05Opn379oA/qPN6vZxwwgmV55qbbt26sXPnTkpLS6u1\n5efnU1RUxIEHHlh5zsz//b7y8nLKyspITEwE4JtvvmHSpElcfPHFHHbYYRGpXURERERERKQuojmj\nriswyzlXsoc+x/O/UMkHvBaqk3NuJzAL6BWqPZaZWQpwUMXTHc655bVc8n3Fzw5A8GYdy4KOO9Zy\njw5Bx0trLbIWzrlNzrkf6/MAVjR2XBEREZF9VatWrTj55JMrQ6opU6Zw4403kp+fj8fjAfxBXdeu\nXZk9ezZnnnlmFKttHDOrDNyCrVmzBmC3oA7g22+/pWfPnjz77LOV55YsWcKTTz5ZeY2IiIiIiIhI\nrIhmUJeBfynLPTmt4qcD3nDOle2h7zL8y0I2N56g42116B88o7BL0PF3Qcdda7lHcPv3NfYSERER\nkZi1detWNm/ejHOOBx98kKeeeoqkpKTdgrrm7scff+TFF18kNze3Wtvq1auB6kFdly5d2LBhAxMn\nTqw8V1DgX0QiLa05vl0QERERERGRvVk0g7okoLiWPqcHHb9VS98dRHcpz4YKXrIyxcxq+2+SGnQc\n/Of3If4/A4ABtdwj0L694joRERERaWb69u3LsGHD+PLLL/n+++8ZNWoUrVq1qhbU3XzzzYwbNy6a\npTbY22+/zXnnnff/2bvz+KjKs//j3xOyk0TIxpIJCYIkEnaV9sciICq2IkJRy6MoiyhurXu1dX+0\naIt1R/FRBFpEXKoUFEVB0AIq7iwKIpCEPRMIJCGBbPfvj2TGmWSWBLJOPu/Xa16cc+7r3Oea0CaS\na6771s6dO2uMOQp1rnvUSVJMTIwuv/xyff311/r6668lSUeOHJEknXLKKQIAAAAAoDlpykJdoaQ4\nb4OWZQ2SlFR1WizpIz/zxci926xFMMYU6ZdlK8MkZXiLtSo33Rjgcsm5bGXVPEurTvtZluVx+UvL\nspIk9as6XWqMKfYUBwAAgOYtNjZWhw4d0ksvvSRJuuaaayTJWagrKCiQJC1btkz//rfHFeSbPbvd\nLklKSEioMeato06Spk+fLknOrjoKdQAAAACA5qopC3U75b7HWnXXVf1pJC2r2ofOl96S9tZHYk3g\nNZfj6T7iRuuX/ee2S/qu2vjDkipU+fd6g5c5bpBkVcU9XOdMAQAA0CzExsYqKytLr732ms4880z1\n61f5Wazo6GhJv3TUJSYmOgteLY1jycv4+PgaY1lZWQoODlbnzjU/n3bGGWfojDPO0MKFC5Wfn8/S\nlwAAAACAZqspC3WfSRpsWdaw6gOWZfWX9D8ul/7layLLsuIlnadfOtNamickbas6vt6yrCuqB1iW\n1VPS7KrTCkm3GGOMa4wxZpOkR6tOb6/+tbUs62xJt1WdPmqM2VxP+QMAAKCRxcbGqqysTEePHtW0\nadOc16svfZmYmKjDhw+rpKSkSfI8Gbm5uQoLC1Pbtm1rjGVmZio5OVnBwZ5Xv58+fbratGmj77//\nXmeffbamT59ORx0AAAAAoNlpyj3d5qmyu+s/lmU9qMqlLYMlnS3pQUltVNlNt8UYs9TzFJJlWaFV\nc4VJWt+QCTcUY0y+ZVnnS/q3Kpe2XGBZ1nRV7h93XJXdgmMlhapyec/rjTHvepnuPlXuY3eTpBWW\nZb0p6UdJp0u6VJVf41lVcQAAAGihYmNjJUnz5s3TuHHjnNerF+ocy0ba7XYlJSWpJbHb7UpISFDl\nCvDuMjMz1b9/f6/3Xnnllbr88sudRb7/+Z//8RoLAAAAAEBTabJCnTHmK8uy5kuaJOkfXsIq5GEJ\nR8uyolVZ0Ost6WpJ3VRZ1FvdIMk2AmNMpmVZv5I0XpUFtTMl3a7Kv6PDkj5XZTFzjjFmn495jKQ/\nWJa1WNL1qvw6jZdkl/QfSS8YY1Y25HsBAABAw3MU6gYPHuwszkmel76UWmahrmvXrh5zPnz4sI4c\nOeJxfzqH8PDwBswMAAAAAID60ZQddZJ0bVUONZZ6lHRU0nRjzG+jsSwAACAASURBVCcexnpKWqrK\n4pzj47X7jTFfNEiWjcQYUybp9arXyc61UhIFOQAAgAB19913609/+lON5RxDQkIUERHhLNQNGzZM\n999/v+Li4poizZOyaNEij9ezsrIkyWehTpLKysp099136x//+IemTJmiV155pb5TBAAAAADgpDRp\noc4YUyrpSsuynpR0oaQkVS71uEnSv40xh7zcul3SlGrXshssUQAAAKCZ8bXfWkxMjLNQN3ToUA0d\nOrSx0moUmZmZkvwX6oKDg7VmzRpJ0rp16xo4KwAAAAAA6q6pO+okScaYbyR9U4f4XEnzGy4jAAAA\noOVyLdS1VIcOHdJDDz2k3/zmN7rgggvcxhyFupSUFL/zTJ8+XV988YW2bt3aEGkCAAAAAHBSgpo6\nAQAAAAD1y7VQt2fPHvXt21ePPvpoE2dVN7t27dIzzzyj9evX1xir7dKXkvT73/9eknTzzTfXa34A\nAAAAANSHZtFRBwAAAKD+xMTEaMeOHZKkqKgobdiwQWeeeWYTZ1U3ubm5kqSEhIQaY5mZmWrTpo1s\nNpvfeSIjI3X8+HGFhITUe44AAAAAAJwsCnUAAABAgImOjlZBQYGMMYqJiVFISIhycnKaOq06sdvt\nkqT4+PgaY5mZmbLZbAoOrt0/Z0JDQ+s1NwAAAAAA6gtLXwIAAAABJiYmRmVlZTp27Jgsy1JiYqKz\n8NXcGGNUXl5e47qjo85boa42+9MBAAAAANDcUagDAAAAAkxMTIwkOfepS0hIaLYddePGjdMFF1xQ\n47qjsFh96cv8/Hzl5eXVan86AAAAAACaOwp1AAAAQICpXqhLTExstoW6tWvXauXKlTpy5Ijb9X79\n+mny5Mnq1KmT2/WsrCxJolAHAAAAAAgI7FEHAAAABJjqhbp77rlHhYWFMsbIsqymTM3N0aNHnUtc\nfv755xo1apRzbNy4cRo3blyNezIzMyVRqAMAAAAABAYKdQAAAECAqV6oO/vss5syHa8c3XFSZWed\na6HOG0ehjj3qAAAAAACBgKUvAQAAgABTvVAnSUVFRSopKWmqlDxyLdStW7fObWzixImaPn2613vo\nqAMAAAAABAIKdQAAAECAqV6omz9/vtq2bauPPvqoKdOqwdEdFx4ers8//1xlZWXOsY8//lgbN270\neE9QUJBsNltjpQkAAAAAQIOhUAcAAAAEmOqFuri4OEmS3W5vspw8cXTHXXTRRTp69Kg2bNggSTLG\nKDc3VwkJCTXuyczMVFJSkkJDQxs1VwAAAAAAGgKFOgAAACDAREdHS5IKCgokSYmJiZKknJycJsvJ\nE0ehbsKECZIq96mTKguMpaWlio+Pr3FPZmYm+9MBAAAAAAIGhToAAAAgwFTvqHN0pjW3Ql1mZqY6\nduyoESNGSPqlUJebmytJNQp1hYWFOnjwIPvTAQAAAAACBoU6AAAAIMBUL9Q5Ouqa49KXKSkpat++\nvTIyMrRu3TpJv+RZfelLRwcehToAAAAAQKCgUAcAAAAEmOqFurZt2yoiIqJZddQdP35c+/btcxbd\nBg8erF27dmnXrl1KSUnR7Nmzdd5557ndk5mZKYlCHQAAAAAgcFCoAwAAAAJMRESE2rRp4yzUSdLG\njRv12muvucW99dZbzuUmG1t2drYkOfebGzRokKTK5S87deqk6dOnq2/fvm73UKgDAAAAAAQaCnUA\nAABAgLEsSzExMW6Fum7duqldu3bO86+++kqXXXaZbrnllqZI0bmMpaNQN3jwYEnyWTisfg8AAAAA\nAC0dhToAAAAgAFUv1GVmZurjjz+WMUYVFRW66aabZIzRli1bZIxp9Pyqd8d169ZNiYmJWrt2rf78\n5z8rPj7e2XXneo9lWUpOTm7kbAEAAAAAaBgU6gAAAIAAVL1Q97//+78aOXKkCgoKNHfuXH3xxRcK\nDw9XYWGh9u3b1+j5Ve+OsyxLgwcP1vfff689e/bo4MGDOuWUU9zuyczMVOfOnRUWFtbo+QIAAAAA\n0BAo1AEAAAABqHqhLiEhQZL0008/6e6771aHDh30l7/8RZK0ZcuWRs/P0zKWgwYNUkVFhbZt26aQ\nkBDFxMS43ZOZmcn+dAAAAACAgEKhDgAAAAhA0dHRboW6xMRESdKjjz6q3NxczZw5U7/61a8kSVu3\nbm30/DIzMxUXF6eoqCjnNcc+dbt371Z8fLwsy3KOFRUVyW63sz8dAAAAACCgUKgDAAAAAlBMTIyK\ni4tVVlYm6ZeOusWLF2vIkCGaOHGi0tLSJDVdR131otuAAQMUFhamgwcPKj4+vka8JDrqAAAAAAAB\nhUIdAAAAEIAcy0YWFBRIkrPwZYzRc889J8uylJycrIiIiEbvqCsrK9OePXtqFOrCwsJ01llnqbi4\nuEahLjMzUxKFOgAAAABAYKFQBwAAAAQgR6HOsfzl+vXrJUnDhg1T3759JUlBQUHq0aNHo3fU7d69\nW+Xl5R6Lbo7lL2+88Ua363TUAQA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G6PPPP3eLpaMOAAAAANBaBTd1AgAAAAAaX8eOHRUf\nH6/8/HyP4z169JAkbdmy5aSek5mZKamyo86xR+aKFSt09tlnO2MmTJigfv36qU+fPif1LAAAAAAA\nWho66gAAAIBW6M4775Tdblfv3r09jkdFRclms51wR926dev07LPPOve4S0lJ0ZlnnqlTTjlFK1as\ncIvt2LGjhg8frpiYmBN6FgAAAAAALRWFOgAAAAAepaWlaevWrSe0j+WSJUv0xz/+UTt37lRcXJyi\noqIUHBysESNGaP369Tpy5Igzdu/evTp48GB9pg4AAAAAQItAoQ7ACfvyyy910003acCAAWrfvr1C\nQkIUFRWl1NRUnXPOObrlllu0cOFC7du3r6lTbTZGjx4ty7LcXvPmzavzPPPmzasxz/Dhwz3GlpaW\n6uKLL1Z0dLRmzJhxcm+gnsydO1ft2rXT0KFDvS65BgBoWHl5eZo9e7bWrVvnNaZ79+46evSo133s\nfMnJyZEk7d+/XykpKc7r5557rsrLy7V69WrntQkTJig9Pb3OzwAAAAAAoKWjUAegzkpKSjRlyhQN\nHDhQs2bNUmRkpKZNm6aHH35Yd9xxh4YMGaKNGzfq6aef1hVXXKHOnTvrr3/9a1On3SxcffXVmjlz\npq677rqTmuess87SzJkzNXPmTLVv395n7IYNG7RkyRIVFhbqscceO6nn1pcnnnhCR44c0Zo1a2os\nf+bgWox0/WUuAKB+HDlyRNdff73efvttrzE2m02StHv37jrPn5OTo9DQUO3du9etUHfeeefJZrOp\noKDAec1utyshIaHOzwAAAAAAoKULbuoEALQ8V199tRYsWKDo6GgtXrxY55xzTo2Y48eP684779Sz\nzz4rqfJT+5DGjRsnSVq9erVmz559wvNkZGQoIyNDkvTcc8/5/Pr26dNHF154oVavXq3bbrvthJ9Z\nn26++Wbdfvvt6tmzp0aMGNHU6QBAq+QojDk63zxJTk6WJO3atUsDBgyo0/w5OTmKi4vTvn373Ap1\np512mrKzs2VZlvNabm6uTj/99DrNDwAAAABAIKBQB6BOvv76ay1YsECS9MADD3gs0klSWFiYnnnm\nGW3atEmrVq1qzBRRTUhIiN59992mTsPNtGnTNG3atKZOAwBatbZt2yoyMtJnoe5kO+qio6O1b98+\ndenSxXndtUAnSeXl5Tp06BAddQAAAACAVomlLwHUyXvvvec8Pv/88/3GX3/99Q2ZDgAAOAkJCQnK\nzc31Ou7oqDuRQt3gwYPVvXt3t3kcsrKyNGXKFL399tvKy8tTRUWF4uPj6/wMAAAAAABaOgp1AOpk\nz549zuOysjK/8YMHD9bIkSPVo0ePhkwLAACcgLi4OJ+FOkdH3a5du+o896uvvqqhQ4dKkltHnSSF\nh4dr3rx5evvtt53Pp6MOAAAAANAaUagDUCdhYWHO408//dRvfOfOnbVixQpde+21btfnzZsny7Lc\nXsOHD5ckvf/++7r44ovVuXNnhYWFKSkpSVdeeaU2btxYqxw/+eQTTZw4USkpKQoPD1f79u11xhln\n6J577tGBAwcaZY7jx4/r73//u/r376+oqCi1a9dOZ5xxhh5//HGVlJTUKof6UP1rbFmWMjMz3WJS\nU1NrxKxevVrHjh3TX//6V51++umKjIxU165dNW3aNGVnZzvvraio0Msvv6wBAwYoKipK8fHxGjdu\nnDZs2OAxn8mTJ9d41oMPPugxZsqUKc5rI0aM8Ps+AAB1N3ToUP3qV7/yOh4ZGanY2NgT6qiT5PyZ\nUb2jrkOHDurTp49WrFihtLQ0HT16VHffffcJPQMAAAAAgJaMPeoA1EmvXr2cxw8++KCGDh2qAQMG\n1Hmes846SzNnzpQkzZgxQ3l5eZKkP//5z3r88cc1duxY3XDDDTp8+LDeeustLViwQG+88YbmzJmj\niRMnepyzrKxM1113nebMmaOgoCBdfPHFGjBggAoKCrR06VLNmDFDzz77rBYuXKjRo0c32Bw5OTk6\n99xztXHjRoWEhGjs2LHq06eP8vLy9Pzzz+vNN9/UfffdV+ev2YlwfI0//PBDffTRRx5j7rnnHh05\nckRfffWVXn/9dUnS0aNHNWLECMXExOjyyy9XXl6eFixYoDlz5ujdd9/VunXr1LVrV11zzTXauXOn\nxo0bp4KCAr322mtavHixPvroI61Zs0b9+vVze9aECRPUq1cv5eXlacaMGR7zccS45nPdddepW7du\nbnGxsbEn9bUBAEhPPfWU3xibzVbnjroffvhBf/3rX7Vjxw6FhISoQ4cONWLOPfdcPfHEE9q0aZN6\n9+5dp/kBAAAAAAgYxhhevBr9JSlDknG8Nm3aZPwpLS01P/zwg9urtLTU732oX7m5uaZt27bOv7s2\nbdqYiRMnmjVr1piKiooTmjMlJcVIMlFRUaZt27ZmzZo1buOFhYVm5MiRzuetXbvW4zyTJk0ykkxo\naKhZvny521hZWZm57rrrjCQTHBxs1q9f3yBzlJWVmYEDBxpJJiIiosZ7KS4uNhdffLFJT093fg3n\nzp3r70vkk+PrN2zYMK8xDzzwgPN5O3fu9Bgzd+5cZ0zv3r3No48+6ja+c+dOExkZaSSZ0aNHm/nz\n55uHHnrILSYnJ8ckJiYaSeacc87xms/OnTudz3rggQf85rNq1Sqvc6Hu+H4KoC4uvPBCExoaWqef\n8//5z3+MJNOlSxfTtWtXjzHvv/++kWSuu+468+6775q8vLz6ShkAAAAAgFrbtGmTca1XSMowjVgv\nYelLAHUSFxenF198UUFBld8+ysvLtWDBAg0ZMkTJycm6/vrr9d577+nYsWN1nruwsFD33HOPBg8e\n7Ha9bdu2mj9/vkJDQ1VeXq4bbrihxr1Lly7V/PnzJUm33Xabzj//fLfxNm3a6Omnn1ZqaqrKyso0\nffr0BpnjpZde0vr16yXJ43sJDw/X3LlztX//fn9fjiZVUlKiu+66y+1aamqqxo4dK6lyedI5c+bo\n3nvvdYtJSEjQpEmTJEmrVq2S3W5vnIQBACfkww8/1OTJk7Vjxw6vMTabTSUlJXX6np6TkyNJOnTo\nUI1lLx2GDh2qkJAQzZ49W6NHj9b27dvrljwAAAAAAAGAQh2AOrviiiu0YsUKpaenu13fs2eP85dt\njoLNd999V6e5Xfclc5WUlKQLLrhAkvT9999r7dq1buN/+9vfnMeeCnmSFBoaqt///veSpG+//VZf\nfPFFvc/x/PPPO4+nTp3qcY727dvrkksu8TjWXIwfP16WZdW43rdvX0mVBdrBgwc7C7au+vfvL6my\nY7u2+woCAJrGtm3bNH/+fJ/7fjoKbXXZp86xn2thYaG6dOniMaZt27aaPXu2Bg0aJEmKj4+v9fwA\nAAAAAAQKCnVoFTIyMnTrrbc6zx955BFlZGTo0KFDkqSff/5ZGRkZmjVrljNm8uTJGjJkiPN8+fLl\nysjI0PLly53XhgwZosmTJzvPZ82apYyMDP3888+SKj9FnpGRoUceecQZc+uttyojI8N5vn79emVk\nZGjhwoXOa2PGjNGYMWOc5wsXLlRGRoazU8vTe2psI0aMf1fbfwAAIABJREFU0ObNm7V48WKNHz9e\nERERbuOFhYX65z//qQEDBujGG29UaWmp3zm7dOmijh07eh13/ftw3W/t0KFDWrdunSSpe/fuXj+5\nL8ltz7SVK1fW6xz79+93Fqa6deumTp06eZ3j17/+tdex5sDbXkGu+8J5i4mLi3Me01EHAM2b43v2\nwYMHvcbYbDZJqtM+dY6OOkk+f6ZOnTrVuQdpQkJCrecHAAAAACBQBDd1AgBarqCgIF188cW6+OKL\nVVxcrI8++khLlizR4sWLnb/wM8bo+eefV35+vv71r3/5nM9XYUuSTj31VOexa6fWt99+69j7UDab\nzWdXgGvBcMOGDfU6h2tOrrl64u+9NjXXYpur4OBffmx463wIDQ11Hp/IEqgAgMbj+F6em5vrNeZE\nOupqW6iTpOzsbElSZGRkrecHAAAAACBQUKhDq7B582a383vvvddtb63u3bvXiJk3b57b+ahRo2rE\nrFmzxu38xhtv1I033ug8j42NrXHPk08+6XY+cODAGjFLlixxO7/88st1+eWX+3xPTS0iIsLZCfjC\nCy/o3//+t+677z5nd+GCBQt0yy236IwzzvA6R3h4uM9nREdHO48d3ZCS+y8XV69era5du9YqZ9fu\ngfqYw/XYNVdPmvsvI0NCQuolxlH8BAA0Tw3VUTdu3Djl5+dr2bJlXpe+lCp/TnzyySe1nhcAAAAA\ngEBDoQ5AvQsJCdGECRN04YUXql+/ftqxY4ckadmyZT4LdfVh0KBBuvPOO2sV661rrD7mAACgJahN\nR52jUFeXjrrLLrtMmzdv1rJly3x21FmWpfnz56uioqLWcwMAAAAAEEgo1AGoE7vdru3bt6tnz56K\niYnxGRsdHa1bbrlFf/zjHyX5/wWfv2US8/Pznceue6W5LsHYrl07jR071uc8ntTHHK5Fu4KCAp+x\nxcXFdZ4fAID6lpCQoJtvvlnDhw/3GhMZGanY2Ng6ddRJv3Tg+eqok6SrrrqqTvMCAAAAABBIKNQB\nqJP33ntPU6ZM0ZIlS3TRRRf5jT/ttNOcxxERET5j9+/f73N8586dzuPevXs7j/v37y/LsmSMcYup\ni/qYwzUnRxehN/v27TuhZwAAUJ/Cw8P11FNP+Y1LTk6udUddeXm50tLSVFZWpqioKJ1yyiknmyYA\nAAAAAAErqKkTANAybdy4sVZxrktp9ejRw2dsdna2cnJyvI677gl43nnnOY9jY2M1ePBgSdLWrVt1\n4MABn88ZM2aMLMvS888/X69zdOzY0Vms27Fjh89i3Pr1633OD6lNmzZex3bv3u23sAsAqD82m027\nd++u1RKVBw8e1Pbt23X48GElJyfLsqxGyBAAAAAAgJaJQh2AEzJnzhy/S1VK0sKFCyVV7lvnrwPP\nGKP58+d7HNuzZ48++OADSVKfPn2cRTWHu+66S5JUUVGhWbNmeX3Gtm3btGzZMrVt21aXXXZZvc9x\nww03ON/LnDlzPN6fn5+vt956y+v8qOS6tGpRUZHz2PGL35EjRzZFWgAQcKZOner2ARhPkpOTVVJS\n4nMvOwfHh26OHj3qd9lLAAAAAABaOwp1AE7Ijh07NH78eNntdo/jx44d06233qr3339fUmURLDk5\n2eecXbp00cyZM/XFF1+4XS8qKtKkSZNUUlKiNm3auHWxOYwePVpTp06VJD322GN6++23a8RkZWVp\nzJgxKi8v10MPPeS2L119zXHNNddo4MCBkqQZM2Zo7dq1buPHjx/X1VdfrZKSEp9fC7gvJfrDDz84\nj//73/9KkjIyMho9JwAIRLt27fLb6W2z2Zyx/jgKdWVlZX5/9gMAAAAA0NqxRx2AOunSpYs6duyo\n/fv3a9myZerSpYvOO+889e7dWzExMSouLtZPP/2k5cuX69ChQwoODtbdd9+thx9+2O/cXbt21dSp\nUzVixAhddNFF6tOnjw4fPqw333xTWVlZCgkJ0SuvvFKjm87h//7v/xQWFqYXXnhB48eP17BhwzR0\n6FC1adNGW7Zs0eLFi1VSUqI//elPuu222xpkjjZt2mjp0qU699xztXHjRo0YMULjxo1zvpe3335b\nx48f1zPPPKMpU6ZIkj744APl5uYqMTFRV111Va3+HjZv3uwsgubn50uq/OXp448/Lkm69tprFRMT\n4zxft26d896XXnpJ7du316BBgzRo0CC9/vrr2rVrl7766itnzOuvv66vvvrKGfPBBx9o06ZNtYrZ\nvn27M8bx3nr16qULLrjAGZOXl+eMWbdunR5//HGdcsopuuaaa5zXTz31VJ177rlasWKFHnroIR04\ncECWZemll15SUFCQ/vCHP9TqawUA8C0uLk75+fkqKSlRaGioxxhHwW337t0644wzfM7nuow1hToA\nAAAAAPwwxvDi1egvSRmSjOO1adMm409paan54Ycf3F6lpaV+70P9Ky8vN+vWrTMzZswwY8eONT17\n9jTt2rUzwcHBJjw83HTs2NEMHz7c3H///ebnn3/2O19KSoqRZIYNG2aMMeazzz4zl112mencubMJ\nDQ01nTp1MpdffrnZsGFDrfL77LPPzOTJk03Xrl1NeHi4CQ8PN927dzdTpkwxX3zxRaPMcezYMfO3\nv/3N9O3b10RGRpqoqCiTkZFh7rrrLnPo0CGzatUq4/r/AUmmb9++tcrNGGPmzp1b437X186dO40x\nxmfMAw88YIwxZtiwYX5jJk2adFIxkyZN8huTkpJS433m5eWZG264wXTp0sUEBwebqKgoc/bZZ5sP\nPvig1l8r1MT3UwCubrzxRiPJ7Nu3z2vMypUrjSTz7LPP+p3v6aefdn5vnzt3bj1mCgAAAABA/du0\naVP131VmmEasl1imsmgCNCrLsjIkbXKcb9q0ye8ydmVlZdq2bZvbtdNOO03BwTSGtnSpqanKysrS\nsGHDtHr16qZOBwh4fD8F4OrBBx/UQw89pI0bN6pXr14eY7Zt26YePXrorrvu0mOPPeZzvszMTD3y\nyCOaM2eOVqxYwZ6iAAAAAIBmbfPmzdX/PdzLGLO5sZ7PHnUAAABAKxYXFydJOnjwoNeYpKQkSbXb\noy41NdU5Z5cuXeohQwAAAAAAAheFOgAAAKAVGzVqlF577TWlp6d7jYmMjFRcXJx2797td778/Hxl\nZ2dLkmw2W73lCQAAAABAIGKNKwAAAKAV69Gjh3r06OE3zmaz1aqj7oILLtDXX3+t+Ph4RURE1EeK\nAAAAAAAELAp1AJrE5s2b9f7770uq/OS9VLmc1uOPPy5JuvbaaxUTE9Nk+QEA0JoYY1RaWqrQ0FCv\nMcnJyfrwww9VUVGhoCDvC3Pk5OTIGMOylwAAAAAA1AKFOgBN4ssvv9Sdd97pdm3Hjh3Oa5dccgmF\nOgAAGsHu3bvVtWtX/eEPf9ATTzzhNc5ms6mkpER2u10dOnTwGnfgwAGVlZUpOTm5IdIFAAAAACCg\nsEcdgCYxefJkGWO8vlJTU5s6RQAAWoX27durrKxMBw8e9BnnKLz52qeuuLhYhYWFMsZQqAMAAAAA\noBYo1AEAAACtWGRkpMLDw5Wbm+szzmazSZLPfersdrvzmKUvAQAAAADwj0IdAAAA0IpZlqW4uLh6\n6ag7cOBAjXgAAAAAAOAdhToAAACglYuPj6+Xjrr+/fvrvvvuk0RHHQAAAAAAtUGhDgAAAGjlatNR\nl5SUJMl3R11wcLCOHDkiiY46AAAAAABqI7ipEwAAAADQtF566SUFBfn+DF9kZKTi4uJ8dtRt27ZN\n3333nYKCgtSpU6f6ThMAAAAAgIBDoQ4AAABo5U499dRaxdlsNp8ddbNnz9ann36qzp07KziYf2oA\nAAAAAOAPS18CAAAArdyhQ4f01VdfqbCw0GdccnKydu/erYqKCo/jOTk5ktifDgAAAACA2qJQBwAA\nALRy//rXv3TWWWfp22+/9Rlns9lUWloqu93ucXzfvn2SpJSUlHrPEQAAAACAQEShDgAAAGjl4uPj\nJUm5ubk+45KTkyXJ6z51e/fulURHHQAAAAAAtUWhDgAAAGjl4uLiJPkv1NlsNknyuk+dY+lLR0EP\nAAAAAAD4RqEOAAAAaOUcHXUHDx70GecowHkq1BljFBRU+c8LOuoAAAAAAKgdCnUAAABAK1fbpS8d\nHXWelr60LEt//OMfJdFRBwAAAABAbVGoAwAAAFq5uLg4tWnTRsePH/cZ52/py+zsbEkU6gAAAAAA\nqK3gpk4AAAAAQNOKiopSaWmpLMvyGRcREaG4uDiPHXU7duzQf//7X4WFhTk79AAAAAAAgG901AGo\nleHDh8uyrHp5Ab706tWrxv9mVq9e3dRpAUBAq8vP6OTkZI8ddd988422bNmi9u3b8/MeAAAAAIBa\noqOuGbIsK1bSVZLGSeomKUHSYUn7JH0j6WNJy4wxh3zMMULSDZJ+JSlRUo6kLyS9YIz5uEHfAALa\nzJkzPV6fMWOG8vLyfMa88MIL2rFjR4PlhsBwxx13KDc3V1999ZVef/31pk4HAFqN5cuXy263a+LE\niT7jbDabNm/erIqKCgUF/fK5v5ycHElS586dGzRPAAAAAAACCYW6ZsayrN9JekFSe0krJM2XdEzS\nqZLGS+oraYqk6yXN9nC/JelJSTdLKpP0pqQtktIlXSrpEsuynpF0izHGNPT7QeC54447PF5/7rnn\nnIU6bzHvvvsuhTr4NXnyZEnSvHnzKNQBQCN67LHH9P333/st1CUnJ6u0tFR2u10dOnRwXmd/OgAA\nAAAA6o5CXTNiWdYESa+qsrA2zBizpdr4nyT9V1Kaj2keVGWR7pikC4wxn7jc/6KkDyT9UZUdeg/U\nZ/4AAABoueLj45WXl6eysjIFB3v/Z4LNZpMk7dq1y61Qt3PnTklSt27dGjZRAAAAAAACCHvUNROW\nZZ2myu65IlUW2LZUjzHG2CU9LOmopFIPc/SUdG/V6T9ci3RV938i6Ymq03styzq9/t4BAl1ERITa\ntm3b5HMAAICGERcXJ0nODnlvHB1z1fepc5ynpfn6TBkAAAAAAHBFoa75eEJSqKRZxphd3oKMMa8a\nY6KMMXM8DN+nyr/TCknPe5nieUmmKu6+k0sZrcn777+vwsLCJp8DAAA0jPj4eElSbm6uzzjXjjpX\n7du3lySlpKQ0QHYAAAAAAAQmCnXNgGVZqZIurDp96wTniJA0pur0O2PMXk9xxpg9kr6rOh1TdR/Q\n6IYPHy7Lstxe8+bNkzFG8+bN09lnn634+Hi38e+++67GPcOHD6/V3JmZmT7zOXDggP7yl7+oX79+\nOuWUUxQZGamuXbvqyiuv1CeffOLz3tr6+uuvNWXKFHXr1k0RERGKiIhQt27ddOmll+qVV17R4cOH\n3eIffPDBGu/DsX/bwoULdd5556lDhw4KCwtTamqqrrvuOuf+QP785z//0e9+9zslJSUpLCxM8fHx\nGjRokGbMmKH8/PxGmePIkSO699571bNnT0VGRio+Pl5DhgzRSy+9JLbQBIDG5+ioO3jwoM84bx11\nffv2dRsHAAAAAAD+sUdd8/B7SZakMknfOy5alhUsKV5SkTHG32+9h0mKrDr+xk/s15L6S2or6WxJ\ny08gZ+CkXH/99Ro9erS2b9+u2bNnS5IqKip02WWX6fPPP9f48eN17rnnavny5Vq3bp0kKSkpSTNn\nzpQkzZgxw+vSXI65P/zwQ3300Ud+c1myZIkmTpyogoIC9ezZUzfccIMiIyP19ddfa+HChVqwYIGm\nTZum2bNnq02bNif0fp9++mnddtttMsZo1KhR+v3vf6/Q0FD9+OOPeuedd/TWW2/ppptu0po1azRg\nwABJ0vnnn6+oqChJ0p133ilJMsboiiuu0OLFi3XJJZdo+PDh2r9/v9544w29+OKLWrhwoRYvXqxz\nzjnHYx6FhYWaMGGC3nvvPYWFhemSSy7R6aefrtzcXL3zzju655579Mwzz2jJkiUaOHBgg83x008/\naeTIkdq9e7ciIyN12WWX6bTTTtP+/ft13333aenSpbr44otP6GsNADgx3bp106BBg3zuTydV/jyW\nanbUOc4p1AEAAAAAUAfGGF5N/JK0VJXLUe6sOh8t6WNVFu5M1Wu/pBcldfEyx59cYu/287y/uMTe\n0UTvOcMlB7Np0ybjT2lpqfnhhx/cXqWlpX7vQ+NISUlx/n3WxapVq5z39e3b14wcOdIUFhY6x8vL\ny02/fv1qzOt43rBhw7zO/cADDzjn3rlzp8eYjz76yLRp08ZIMldffbUpKytzG1+xYoUJDw83kszN\nN99cp/fmsGHDBhMUFGQkmTfeeKPG+I8//mjat29vJJlVq1Z5nMPxPqKiokyHDh3Mli1b3Mbtdrvp\n1auXkWTatm1rtm/fXmOO8vJyM2LECCPJtGvXznz77bdu48XFxeaiiy4ykkxMTIzJzMxskDkKCgpM\namqqkWQSEhLMjz/+6Dael5dnfvWrX5n09HTn+/b2dcGJ4fspgJMVHx9vhg4d6jw/fvy4SUhIMJGR\nkU2YFQAAAAAAdbdp0ybjWq+QlGEasV7C0pfNQ9+qPwsty3pOlYW7CEm3SJog6QFV7jt3raQfLMv6\njYc5ergce1z20st42gllDDSArVu36p///Kfatm3rvBYUFKTx48c3yH43x44d06RJk1ReXq7U1FTN\nmjWrRsfcyJEjdeutt0qSnnnmGX3//feepvLpzTffVEVFhU455RRdeumlNcbT09N111131WquwsJC\nPfnkk0pLc/+/bnx8vF555RVJ0tGjR505u5o1a5ZWrVolSXrsscfUr18/t/Hw8HDNnTtXUVFRys/P\nb7A5HnnkEedSpE8++aTS09Pdxtu1a6cFCxbo559/9vWlAAA0IZvN5tZRZ7fbZbfbFRHBquoAAAAA\nANQFhbomZllWkCTH+kC9JN0o6WVJg4wxzxljXjfG/K+kPpJ+UuVylf+2LKtntaliXY4L/TzWdbz9\nCSdfxbKsRMuyMuryktTtZJ+LwHP++eerc+fONa7fe++9fveYOxELFizQ3r2VdeupU6cqLCzMY9xV\nV10lqbID2bFMZ13Y7XZJUlFRkQoKCjzGjB49WjfffLNsNpvPuWJiYnTJJZd4HDvrrLPUp08fSdK7\n777r9gvUiooKPf7445KkqKgo53uqLi4uThdeWLll5pIlS5xfn/qao6SkRC+//LIkKTo6WpdddpnH\nObp37+5x/0EAQMM5fPiwbr31Vi1atMhvbHJysvbs2aOKigpJ0v79+yX9ss8dAAAAAACoHQp1TS+m\n2vkBSTcbU7k+pIMxJlfSdVWnEZKeqHZflMvxcT/PPOZyHF3LPH25QdKmOr7+Uw/PrbWMjAyPL0fH\nzs8//+w1xmH58uUex8eMGeOMmTVrlscY166iW2+91WPMrFmznDFjxozxGLN8+S/bCXrLsyU788wz\nG/V5S5cudR5729NNktLS0pwdAitXrqzzcxzdb6WlpZowYYJb4cohIyNDTz31lLp37+5zrn79+ikk\nJMTr+JAhQyRVFtVcc/3++++VnZ0tSfr1r3/ts+PB0SVXXl6u1atX1+sc33zzjQ4ePChJGjhwoM/3\n8utf/9rrGACg/lmWpaeeekoffvih31ibzabS0lLl5ORIqtx7VJI6duzYoDkCAAAAABBofO8Uj8YQ\nVe3838aYIk+BxphVlmVlSkqVNMqyrGRjzC5PsX4Y/yFA4+vQoUOjPu+bb75xHoeHh/vs2ouOjlZx\ncbF+/vlnFRcX12lpr6lTp2rmzJnau3evli1bptTUVF144YUaP368fvvb3yo2Ntb/JFU6derkc/zU\nU091Hm/cuNF57PpeO3bs6PO9un5OYMOGDbr88svrbQ7XnFxz9cTfewUA1K+YmBgFBwc7P1DhS3Jy\n5YIQu3fvVseOHZ2Fui5dujRojgAAAAAABBoKdU2vpNr5l37i16uyUCdJgyU51iZyXc7S8/p9v3Ct\nMHhehy/AbN682ed49+7d/caMGjXKb8yNN96oG2+80WfMk08+6XNcqlwu0B9/ubREoaGhjfq83Nxc\n53Ftu/mMMTp06JCSkpJq/ZyYmBh9+umnmj59ulauXKnS0lItXrxYixcvVps2bTRixAhNnz5d48eP\nl2VZPucKDw/3OR4d/UuT7KFDh5zHru91wYIFWrBgQa1yd/1lbX3M4XrsmqsnkZGRtZofAFA/LMtS\nfHy82/d7bxxLNe/atUtnnnmmdu7cKcn/hzAAAAAAAIA7CnVNr3qhbL+feNcOuhSX4zyX4+pdetW5\njh/2E1sbz0t6s473dFMjL38J+LJo0SKve9RV165duzrP361bN61YsUIbNmzQq6++qkWLFik7O1vl\n5eVasWKFVqxYoeHDh+udd945ofnrYuzYsZo0aVKtYh0dEw0xBwCg+YmLi6tzR530y4cv+vbt23DJ\nAQAAAAAQgCjUNTFjTLFlWYckOda+K/VzS7HLcVuX459cjjv7mcN1PbmtfmL9MsbkSMqpyz3+uoYA\nfxz/G6q2naObY8eOeR2TpPj4eOcvGM855xwlJCTUX4Je9OnTR3369NFjjz2mdevW6eWXX9arr76q\n0tJSrV69WrfffrvmzJnj9X5/7yk/P9957LqkZnx8vPO4c+fOGjt2bJ1zr4854uLinMcFBb4beouL\ni32OAwDqX3x8vDZt2uQ3zrWjTvqlK75///4NlxwAAAAAAAEoqKkTgCRpo8ux77Xg3Itzrt1wG1yO\n/a055Dq+0WsU0Iw5fiF4/PhxrzF2u93nHAMGDHAeO5bsaiyWZWnw4MGaO3euvvrqK2cnwsKFC1VR\nUeH1vv37fTfdur6P3r17O4/r473WxxyuOe3YscNn7L59+07oGQCAE3fJJZdo6tSpfuMchTrHB16y\ns7NlWVadloYGAAAAAAAU6pqL1S7H/opsXV2OXbvhPtEv3XZn+JnDMV5UdR/Q4ji6uw4cOOA15rvv\nvvM5x5gxY5zHn376qc/Yt99+W5ZlaeDAgXXIstL999+vIUOGeB3v06ePrrjiCkmVHXO+Cozfffed\nSku9N96uWbNGkhQUFKRzzjnHeb1v377q0qWLJOnzzz/3OYcj3rIsLVu2rF7nGDBggLOrbv369T7n\nWL9+vc/5AQD176abbtLf//53v3Hh4eGKj493dtStWLFCoaGhjb7fLAAAAAAALR2FuubhDZfjs70F\nWZYVJOn/VZ0WS/qvY8wYUyRpadVpP8uyPC5/aVlWkqR+VadLjTGsLYcWybEHTlZWlvLy8mqM//jj\nj/rmm298znHFFVc4P/n/4osvqqSkxGOcMUbPPvusJNWqy6C67OxsrV27VpmZmV5jHM+OiopyWx6y\nuiNHjmjx4sUex7788ktt2FDZXDt69GhnUU2qLNzdeeedkqS8vDz961//8vqMVatWacOGDUpOTtZ5\n551Xr3OEhoZq2rRpkiqXvnz99dc93p+ZmamPP/7/7N15eBRF/sfx9zcJhyHcATkFFVgFRERRPJEF\nFQ/AAxURRbxFVxdWXdcL2VW8WH6irsd6AAseiMAqCniDB4rigSAociqy3EhAQAip3x/dM/Qkc+SY\nZCD5vJ6nnnRPVX+7ZpKp7kp1V78fM76IiKRe06ZNw3fUbdmyhYwMzaovIiIiIiIiUlQaqNsLOOcW\nAJP81dPNrFmMoucC+/vLzzjncvLl/wPIw/u9DowRYyBgfrl/FLvSIil23nnnAd4g2siRIyPyfvvt\nN66++mratWsXN0bVqlUZO3YsGRkZLF68mMsvv7zAM+B27tzJoEGDmDFjBh06dCjWQF3Itddey9at\nWwu8Pn/+fCZOnAhA//794/6js2XLlgwaNIjFixdHvL5+/XquuOIKAKpVq8aIESMKbDtw4EC6desG\nwKBBg/jkk08KlJk3bx4XXXQRACNGjKBSpUpJj3HnnXfSvHlzAAYPHswPP0Q+KjMnJ4dLLrmEqlWr\nRv8QRESk1Pz3v//l2GOPTXixC3jTX/7yyy/s2LGD3bt3h6dxFhEREREREZHC02Wve49BwAlAfeAF\nMzvTObc5lGlmhwCP+6s/AHfmD+Ccm29m9wN3AH8xs3ecczMDMU4CBvur9zvnviudtyIVxaxZs5g1\naxbgDa6EDB8+HPCutL/wwgujbjt9+nTmz5/PkiVLIl5bv349AKeffjpt2rSJue+uXbtywQUX8Mor\nrzB06FC+/PJLOnXqxObNm5kwYQI9e/akdu3a4TvMnnnmGWrXrs2RRx5Jly5dwnG6dOnC66+/Tt++\nfXnhhRf48MMPOfvss2nQoAErV65k+vTpLFu2jPbt2zNlypRiTekVumvvrbfeonnz5vTs2ZNmzZqx\na9cuFi5cyJQpU9i1axennXYaDz74YNxYxx13HK1ataJ9+/b07t2bVq1asXr1asaPH8/atWvJysri\nv//9LwcffHCBbdPS0njttdfo168fkydP5qSTTqJ79+507NiR3Nxc5s2bxxtvvEF6ejojR46kd+/e\npRIjKyuLt956i27duvHzzz/ToUMHLrjgAlq2bMnq1auZMGEC2dnZ/P3vf2fQoEEAjB8/njlz5tCy\nZUt69epV5N+BiIgUTk5ODp999hkrV66MeDZpNE2bNmXXrl3hizbi3REuIiIiIiIiItFpoG4v4Zz7\nycxOAyYDxwPfm9kEYDXwB+B8YD/gc+Bs59yWGKHuAmoCNwDv+jEWAof6MTKAf/nlRErk7bffZujQ\noQVeD02P2Llz55gDdS+//DJjxoyJeG38+PHhqRCzs7PjDtQBjBs3jo4dOzJ27Fjee+89PvroIw47\n7DDuv/9++vTpwz333BMuO2zYMABuuummiIE68AYFFy9ezGOPPcabb77J2LFj2bp1K3Xq1OHwww/n\njjvuSHinWzz33Xcfffv2ZcKECcycOZNp06axceNGAOrXr88ZZ5zBxRdfTO/evTGzhPFuv/12OnTo\nwGOPPcb06dPZtGkTDRo04Oqrr+b222+nWbNYN+WZpqVWAAAgAElEQVRCZmYmkyZNYvr06YwePZpZ\ns2bx7rvvkpGRQfPmzbnuuuu4/vrrOfTQQ0s1RqtWrZg3bx4PP/wwEydO5OWXX6ZKlSocfPDB3HDD\nDQwePDhiWsynnnoKgF69emmgTkSkFIUG2zZs2JCwbJMmTQB47733ANh///3jFRcRERERERGRKMw5\nl+o6SICZVQOuA84DWgI1gPXAHOAlYLxzLq8Qcbr6cToB9YB1wGfAk86590qn9oVnZm2A+aH1+fPn\nJxyUyc3N5ccff4x4rWXLlnoeilQIoQG8/v37M3r06NRWRvZ5ak9FJJbZs2fTqVMnHnroofCFN7GM\nGzeOSy65hJNPPpkZM2Zw6aWXFrgIR0RERERERGRv991339G2bdvgS23LckZC/UduL+Oc+w0Y7qeS\nxHkPSPmAnIiIiIjsO4pzR93ChQsB6NGjR+lVTERERERERKScSkt1BUREREREZO+QnZ0NEH5mbDxN\nmzYFYM2aNQAcf/zxpVcxERERERERkXJKd9SJiIiIiAgANWvW5IknnqBdu3YJyzZu3Di8XKlSJT2j\nTkRERERERKQYNFAnIrKXmjVrFrNmzYp47bvvvmP4cG9m3JtvvjkV1RIRkXLMzLjuuusKVbZq1arU\nq1ePdevWsWvXLlavXk2jRo1KuYYiIiIiIiIi5YsG6kRE9lJvv/02Q4cOjXhtzpw5zJkzB9BAnYiI\nlB7nHGaWsFyTJk1Yt24dsGfaTBEREREREREpPD2jTkRkL3XPPffgnIuZRERESkOPHj0KfWdc6Dl1\nlStXpnLlyqVZLREREREREZFySQN1IiIiIiISlpGRwdq1a8nLy0tYtkmTJgBkZWWVdrVERERERERE\nyiUN1ImIiIiISFh2djZ5eXls3rw5YdnQHXW1a9cu7WqJiIiIiIiIlEsaqBMRERERkbDQs+bWr1+f\nsOxhhx0G7BmwExEREREREZGiyUh1BUREREREZO9Rt25dwBuoa9myZdyyp59+Ol9//TX169cvi6qJ\niIiIiIiIlDsaqBMRERERkbDQHXUbNmxIWDYtLY327duXdpVEREREREREyi0N1ImIiIiISNiZZ57J\nV199RYsWLRKWXbt2LYsWLaJNmzZ6Tp2IiIiIiIhIMegZdSIiIiIiElavXj2OOOIIqlevnrDs22+/\nzYknnsj7779fBjUTERERERERKX80UCciIiIiImG7d+9myZIlrFy5MmHZzz77DIB27dqVdrVERERE\nREREyiUN1ImIiIiISNjmzZtp0aIFQ4cOTVh25syZNGrUqFDTZIqIiIiIiIhIQRqoExERERGRsFq1\napGWlsaGDRvillu/fj3z58+nc+fOmFkZ1U5ERERERESkfNFAnYiIiIiIhKWlpVGnTh3Wr18ft9xH\nH30EQOfOncuiWiIiIiIiIiLlkgbqREREREQkQt26dRPeUbdz505atmypgToRERERERGREtBAnYiI\niIiIRMjOzk54R92FF17IokWLOOSQQ8qoViIiIiIiIiLljwbqRKTQRo8ejZklTOnp6dSpU4dOnTox\nZMgQVq9eneqqF1nbtm0LvK8ZM2ZELTtt2jTq16/PYYcdxk8//VS2FU2RG264ocDnc88996S6WhXC\nWWedVeCzHz16dKqrJSLlTN26ddm6dSvOuVRXRURERERERKRcy0h1BURk39GxY0cefvhhAJ588kmW\nLl0KwO23307t2rXD5XJycvj88895++23mT17No888gijRo3i3HPPTUm9i+Pmm29m/fr1zJkzh/Hj\nx8ct+8QTT7Bu3TrWrVvHq6++yuDBg5Nen+XLl3PggQcCMGTIkJQPip1zzjk0b96cTZs2MWzYsJTW\npaK54oorOPnkk1myZAlPPfVUqqsjIuXUK6+8QuXKlTGzqPnvvvsuzz33HHfeeSdt2rQp49qJiIiI\niIiIlB8aqBORQmvTpk34n3FvvPFGeKDuqquuonnz5gXKf/TRR5x55pnk5ORw4YUX8vbbb9OlS5ey\nrHKxXXbZZYB3F2Gigbprr72Wzz77jHr16nHOOeeUQe1Sr2vXrnTt2pXly5droK6Mhf7GZsyYoYE6\nESk1VapUiZs/depUXn75Ze66664yqpGIiIiIiIhI+aSpL0Wk1Jx44on84x//ACA3N5cbb7wxxTUq\nHWeeeSbr1q1jwYIF4bveRERE9mXLli3jxRdf5H//+1/U/JkzZ1KvXj0OPfTQMq6ZiIiIiIiISPmi\ngToRKVX9+vULL8+fP5+FCxemsDYiIiJSGB988AEXX3wxX3/9dYG8zZs3880333DSSSfFnBpTRERE\nRERERApHA3UiUqrq1q1LdnZ2eP37779PYW1ERESkMOrWrQvA+vXrC+R9/PHH5OXl0blz57KuloiI\niIiIiEi5o4E6ESl1lSpVCi/v3LkT8J4BZ2YR6Z577gHgtddeo3v37jRo0IC0tLRw/vLlywvEfu21\n1zj33HNp3LgxVapUITs7m+OOO45hw4aRk5OTsG6bN2/mzjvvpHXr1mRmZpKdnc0JJ5zAM888g3Mu\n7rb33HNPgfcQerZdLBs3bmTo0KF07NiROnXqULlyZerVq8eJJ57Ibbfdxueffx5RfvTo0ZhZxJSa\nQ4cOLbDf0aNHR93fN998w9VXX02LFi3IzMykZs2atG3blj//+c/hZwzGk5eXx9NPP82xxx5LzZo1\nqV69Om3btmXIkCGF+nyLYtGiRVx//fUceuihVKtWjSpVqtC0aVN69uzJ448/zurVqyPKhz6bYDr5\n5JMBmDZtGr169aJRo0ZUqVKFxo0bc8kllzBv3rxC1WXmzJn069ePZs2aUbVqVWrXrs2RRx7JHXfc\nwZo1a8okxu+//85DDz3EEUccQVZWFrVq1eLII49k+PDh4e+RiEhpCV1ks2HDhgJ5M2fOBNBAnYiI\niIiIiEgSZKS6AiJSvv3+++8RV+M3adIEgD59+tC2bVs2bdrEsGHDwvl/+ctfGDNmDOeffz7HH388\nn376KdOmTSsQd+vWrfTp04c333yTKlWq0Lt3bw499FDWr1/P5MmTueOOO3j00Ud5/fXXOfroo6PW\nbdGiRXTt2pWVK1eSmZnJBRdcQMuWLVm9ejV33XUXU6ZMoVevXjHf26mnnkpWVhYAt9xyS8LPYtq0\nafTt25dff/2VFi1acPnll1OjRg2WLVvGa6+9xscff8yDDz7IiSeeyIcffghAx44defjhhyM+p1NO\nOYVTTz01InbHjh0L7O/uu+/mvvvuIy8vj27dunHppZeyc+dO3nnnHUaOHMmTTz7J448/zlVXXRW1\nvtu2beOss87igw8+wMw4/fTTOeaYY9i+fTuTJk3ipZdeijlAWFQTJ07k4osv5vfff+eEE06gR48e\nZGVlsWTJEiZOnMiUKVMYNGgQkyZNokePHhGfDcCwYcPYtGkTAH/7298YPnw4Z599NgMHDuTXX3/l\n1VdfZdy4cbzyyis899xzEVOyBuXm5nLttdfy3HPPkZaWRq9evejQoQNbtmxhypQpDBs2jMcee4wX\nX3yRs846q9RirF27lm7dujFv3jwqVarE2WefTbt27di0aRNPPPEEEyZM4K677irpxy4iElNooC7a\nHXWXXnopDRs2pG3btmVdLREREREREZHyxzmnpFTmCWgDuFCaP3++S2TXrl1uwYIFEWnXrl0Jt5PS\n0blz5/Dvb9myZTHLTZ06NVyudu3abufOnRH5y5YtC+cffvjh7rDDDnNr166NKNOzZ8+I/ezevdt1\n6dLFAa5WrVru66+/jii/fft216NHDwe4GjVquOXLlxeo15YtW1zz5s0d4OrVq+cWLlwYkb9p0yZ3\nzDHHuEMOOSRcvw8++CDm+wyV6d+/f9T8999/32VkZITL5P8cNmzY4P74xz86wNWsWbPA9sHPaciQ\nITHrETJkyJBw+VGjRhXIv++++8L5kyZNihrjvPPOc4BLS0tzEyZMiMjLzc11AwcOjPh8ClOvaNas\nWeOysrIc4B566KEC+atWrXIHHXRQzPfinHPNmjVzgMvKynLVqlVzH3/8cUT+1q1bXdeuXR3g0tPT\n3SeffBI1Tv/+/R3gKleu7N56660C7/naa691gMvIyHCff/55qcTIzc11Rx99tAPcfvvtV+C9bN++\n3fXq1Svis4/1ucSi9lREElm3bp0D3DXXXJPqqoiIiIiIiIiUqvnz54f/z+anNq4Mx0s09aWIlJot\nW7Zw2223hdfvuOOOiGkw85s7dy5jxoyhXr16Ea+fc845NGvWjIwM7ybgf/3rX3zwwQcAPPDAA7Rv\n3z6ifNWqVRk1ahRZWVnk5OQwaNCgAvu69957w1Np/t///R+HHHJIRH6tWrUYN24cixcvLvwbjmHH\njh3069eP3NxcmjVrxr///e8Cn0OdOnUYM2ZM+D2WxDfffMO9994LeHcuRpuO8/bbb+e4444DYODA\ngQWmUpw+fToTJ04E4PLLL6d3794R+enp6TzyyCPk5uaWuL5vvPEGW7duBeDKK68skN+wYcOIuy7j\n2bp1K3fccQfHH398xOvVqlVjzJgxVK5cmd27dzNw4MAC206ZMoUxY8YAMHjw4AJ3LaanpzNy5Eia\nN29Obm4u11xzTanEeOaZZ8JToEZ7L6G/7/xTgYqIJFPt2rVp06ZNgWPyunXr2Lx5c4pqJSIiIiIi\nIlL+aKBOREps5cqVLF++PJzmzZvHc889R8eOHfn2228BGDBgAIMHD44bp127dhxxxBEFXr/ssstY\nvnw5TZo0IS8vj+HDhwOQlZXFpZdeGjVW3bp1OfPMMwF4/fXXWbVqVThv586dPPvsswBUr16dCy64\nIGqMFi1ahJ95VhLjxo0L7/+KK66gcuXKUcs1adKEE088scT7Gz58OLt37waIOiAVcskllwCwevVq\nJk+eHJH3xBNPhJevuOKKqNtXqlQp4TP5CmPdunXh5VjPbjvppJO46aabaN26dcJ4AwYMiPp648aN\n6d69O+ANCn/yyScR+Q8++GB4OdbnVrlyZS688EIAvv76a2bPnp30GMHP/vLLL48ao3bt2gUGT0VE\nkik9PZ358+fzj3/8I+L1ESNGUKdOHb7//vsU1UxERERERESkfNEz6qTc69mzJ0uWLEl1NUrdwQcf\nzOuvv56SfccbXGrXrh233norF198ccI4Rx11VMIyc+fO5aeffgKgU6dO7LfffjHLtm/fnvHjx7N7\n925mzJhB3759Afjqq6/YsGEDAEcffXTcu/w6derEu+++m7Be8UyZMiW8fNJJJ8Ut+9RTT7F27dpi\n7ysvL48333wTgP32249jjz02ZtngnYjvvfdeePBo9+7dvP/++4A3qBTt+XchnTp1KnZdQ/7whz+E\nl/v378/YsWNp1apVRJmGDRvyyCOPJIx1wAEH0KBBg5j5J5xwQvh78s4774TvVtu4cSOzZs0CvAHa\npk2bxoyR/3M75phjkhZj9erVzJs3D/C+0w0bNowZo1OnTuEBZxGRsjJz5kyqV69Oy5YtU10VERER\nERERkXJBA3UiUmJPP/009evXD6+npaVRs2ZNWrVqFXegIb/9998/YZmvvvoqvNygQYPw9JXROO95\niAB8++234YG60EAIwEEHHRR3f0WpfyzBOrdo0SJu2VatWhUYpCqKZcuW8euvvwLeHWQrV66MWXb7\n9u3h5dCdjwBLly7lt99+A6BZs2akp6fHjJGMz+ess87i8MMPZ+7cuXz++ecceuihdO3ald69e9Oj\nR48i7SNR2eDvO/h38PXXX4f/Xpo0aRL372rXrl3h5eDnlowYZf23KSISz+jRo/nxxx+57777APjt\nt9/44osv6N69e9xjg4iIiIiIiIgUngbqpNxL1V1mFcmpp55K8+bNSxwn1pSQQevXrw8vjxs3jnHj\nxhUqdugOuvzL1atXj7tdZmZmoeLHE6xzjRo1ShyvsPtavHgxBx54YKG2S+Xnk5GRwdtvv83111/P\nxIkTycvL45133uGdd97h2muvpVOnTlx55ZVccsklce9+BO/5bfEE38/GjRvDy8HPbcaMGcX63JIR\no6w/exGReCZNmsT06dO59957MTNmzZpFbm5uUqaFFhERERERERGPBupEZJ919tln079//0KVjTcN\nYXnVokULHn744UKVjTeFaFmoX78+EyZMYPHixbz44ou89NJLfP/99zjn+PTTT/n0008ZOXIkU6ZM\n4YADDijVuhx33HHccssthSpbt27dUoshIpJq2dnZ7Nq1iy1btlCjRg1mzpwJQOfOnVNcMxERERER\nEZHyQwN1IrJPyc7ODi83atSIs88+u8gxggMjW7ZsiVs2OD1kcWVnZ4enoMzJyUl4p1RJ9xVSuXLl\nfeLzCWrRogV33303d999N9988w2jRo3iueee47fffuPbb79lwIABvPfeezG337FjR9z4OTk54eU6\ndeqEl4OfW61atYr1uSUjRio/exGR/EJt0oYNG6hRowarVq2iZs2aEc/ZFBEREREREZGSSUt1BURE\niqJDhw7h5WXLlhUrxmGHHRZeXrp0adyy//vf/4q1j6BgnZcsWVLiePEceOCB1KpVC4AVK1ZEPKev\nsA466CCqVasWjrF79+6YZZPx+cTSvn17Ro4cycKFC2nSpAkA77//PqtWrYq5zerVq+PGDP7NBP8O\njjjiCMysQJmiSEaMsv7bFBGJJ3QBQmhq3+eff55ffvmFjAxd6yciIiIiIiKSLBqoE5F9yuGHHx6e\n+vCzzz5j165dCcubGVOnTg2/1qFDh/BdAp9//nncGJ9//nmJ69yzZ8/w8ocffhi3bPv27cnIyGD6\n9OkRr6enp8fcZt26daxcuZLdu3eTlpbGWWedBcBvv/3Gl19+GXd/N954I2bGrbfeGrGvP/7xjwDs\n3LmTL774Iub2yfh8nnrqKdq3bx9xt1tQ06ZNueGGG8LrP//8c8xYP/30E2vXro2Z//HHH4eXTznl\nlPBynTp1OP744wH44YcfWLNmTdw69+zZEzPjiSeeSGqMBg0ahAfrli5dGncwLhmfvYhIPME76kJC\nF3KIiIiIiIiISHJooE5E9ilpaWnhZ39t2rSJsWPHxiz7wQcf8O2339K0adOIQZnKlStz5ZVXAt70\nguPHj4+6/fLly3n//fdLXOeLL76Yxo0bA97dCLEGBr/44gvmzp1LvXr16NKlS0RejRo1wsvbtm2L\nyDvllFNo2rQpGzduBODmm28OD+w9+uijMeu1YcMGXnjhBdLS0rjssssi8gYOHBhefvbZZ6Nuv3v3\nbsaMGRMzfmGtXr2auXPn8tVXX8Uss3PnTgDMLO4z6pxzMev0yy+/hAdA27VrFx5UC/nrX/8KQF5e\nHv/6179i7uPHH39k6tSpVKtWjQsuuCDpMUKfvXOO5557Lur2OTk5vPrqqzHji4gkQ9u2bRkwYAD1\n69fn5ZdfZsSIEQWOQSIiIiIiIiJSMhqoE5F9zsCBA+nWrRsAgwYN4pNPPilQZt68eVx00UUAjBgx\ngkqVKkXk33nnnTRv3hyAwYMH88MPP0Tk5+TkcMkll1C1atUS17dq1aqMHTuWjIwMli1bxnXXXUdu\nbm5EmV9++YV+/foBcO+991KlSpWI/Jo1a9K0aVMAFixYEH593bp1/PDDD9SvX5969eoB3l2EQ4YM\nAWDs2LE8/vjjBeq0fv16zj33XDZu3MjAgQNp3bp1RH737t0577zzABg1ahQTJ06MyM/Ly+Mvf/kL\nv/zyS5E/j1j+/Oc/h6dXC1q1alV4sPCMM86gYcOGMWMccMABPPzww8yePTvi9W3bttG/f3927txJ\nenp6xF1sIWeddRaXX345AA888ACTJk0qUGbFihX07NmT3bt3M3To0Ijn0iUrxlVXXcXRRx8NwLBh\nwwr8ff/+++9cccUV4cFLEZHS0qlTJ55//nk6dOjAU089xd13313geCoiIiIiIiIiJaMHTIhIoX33\n3XdMmzYNiJx+8JlnnqF27doAnH766bRp0yZhrFmzZjFr1iw2bdoU8drw4cMBOO644zjuuOOibpuW\nlsZrr71Gv379mDx5MieddBLdu3enY8eO5ObmMm/ePN544w3S09MZOXIkvXv3LhAjKyuLt956i27d\nuvHzzz/ToUMHLrjgAlq2bMnq1auZMGEC2dnZ/P3vf2fQoEEAjB8/njlz5tCyZUt69eoVfg/5P6PQ\ne7j55pvDr3fp0oXXX3+dvn378txzz/HRRx/Rs2dPqlevzuLFi5k4cSLbtm3jr3/9K1dccUXU933N\nNddw5513MnXqVPr370/z5s2ZMGECO3bs4K677oooe9ddd2Fm3HPPPfzpT39i1KhRnHrqqWRmZrJ4\n8WImT57Mli1b6NevHyNGjIi6v//85z9s2rSJ999/n/PPP58zzjiDY445hu3bt/Pmm2/y008/8fzz\nz3P++edH/P6qVq0aMVVlIg0bNiQtLY25c+fSvHlzzj77bA466CDMjCVLljB58mS2bdtGhw4deP75\n5+PGOvDAA7n88svp0qULPXr0oF27dvz6669MmDCBFStWUKlSJZ5//vkCd9OF/Pvf/6ZKlSo8+eST\nnHfeeXTu3JkTTzyR9PR0vv/+e/773/+yc+dObr31VgYPHlwqMdLT05kyZQrdunVj3rx5dOnShXPO\nOSf8XiZNmsTvv//Oo48+yoABAwCYPn0669evp379+lx66aWF/uxFRApjx44dfPbZZ3Tu3FkDdSIi\nIiIiIiLJ5pxTUirzBLQBXCjNnz/fJbJr1y63YMGCiLRr166E20nyjBo1ygV/b9HSqFGjChVryJAh\nceMMGTKkUHGmTZvmLrzwQte0aVNXuXJll5mZ6Vq3bu2uv/56t2DBgoTb//rrr+6OO+5whxxyiKta\ntaqrWbOm69Chg7v33nvdtm3bor7nXr16Feo9RLN+/Xo3ZMgQd9RRR7latWq5SpUquUaNGrnzzz/f\nzZgxI25dd+/e7R5++GHXpk0bV6VKFVelShXXtm1bN3LkSJeXlxd1mwULFriBAwe6P/zhDy4zM9NV\nqVLFNWvWzF144YXu7bffTvj57N692z399NOuU6dOrnr16i4zM9O1aNHCXX/99e7nn392y5YtK/C+\na9asmTBufsuWLXMPPvigO/XUU12TJk1clSpVXKVKlVyDBg3caaed5p599tm43/dmzZo5wHXu3Nk5\n59ynn37qLrjgAteoUSNXuXJl17BhQ9e3b1/37bffFqo+n376qbvsssvcgQce6KpWreqqVq3qWrRo\n4QYMGOBmz55dJjF27NjhHnzwQXf44Ye7zMxMl5WV5dq0aeP++te/uo0bN7oPPvigwGd/+OGHF6pu\nak9FpDBycnLc6aef7s4880wHuPvuuy/VVRIRERERERFJuvnz5+f/P1sbV4bjJea8QRORMmVmbYD5\nofX58+cnvAsrNzeXH3/8MeK1li1bkpGhG0NFKrrmzZuzYsUKOnfuzIwZM1Jdnb2e2lMRKYzc3NyI\nO+g+/vjjmHcki4iIiIiIiOyrvvvuO9q2bRt8qa1z7ruy2r+eUSciIiIiIgVkZGSEp7beb7/96Nix\nY4prJCIiIiIiIlL+aKBORERERESiqlu3LgC33XYblStXTnFtRERERERERMofzXElIiIiIiJRZWdn\ns23bNu6+++5UV0VERERERESkXNJAnYiI7JO+++47pk2bBkBOTg4AP//8M8OHDwfg6quvpkaNGimr\nn4hIeVC9enU2bNjgPdzaLNXVERERERERESl3NFAnIiL7pC+++IJbbrkl4rWlS5eGX+vdu7cG6kRE\nSqhdu3YsXbpUA3UiIiIiIiIipUTPqNsLmNloM3OFTE8VIt6RZjbKzJaa2Q4zW2Vmb5rZuWXxfkR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qOZfeq3l1v99vN+M2tchDjn+G32Kr8NX+q36UeWUr2Tsr9kHXuk3ChUG+ycywV+9lcbmFnd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\nYII=\n",
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGw0tlmWN3-2",
        "colab_type": "code",
        "outputId": "f847a775-55ad-4673-a0ff-b941152700e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "speed = predictions['True speed'].values\n",
        "print(f\"Naive MSE: {np.mean((speed[1:] - speed[:-1])**2)}\")\n",
        "print(f\"Model MSE: {np.mean((predictions['True speed'] - predictions['Predicted speed'])**2)}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model R^2: 20.027088882785808\n",
            "Naive R^2: 24.448094035079595\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7-i4cN5pNl9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Evaluate on test set\n",
        "test_loss = model.evaluate(test_gen)\n",
        "print('Test Loss: ' + str(test_loss))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5j3YoP6JW2tU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "    \\hline\n",
        "    Model run & Val $R^2$ & Test $R^2$\\\\\n",
        "    \\hline\n",
        "    \\#1 & $22.04$ & $14.81$ \\\\\n",
        "    \\hline\n",
        "    \\#2 & $20.03$ & $14.90$ \\\\\n",
        "    \\hline\n",
        "    \\#3 & $20.97$ & $14.63$ \\\\\n",
        "    \\hline\n",
        "    Average & $21.01$ & $14.78$ \\\\\n",
        "    \\hline\n",
        "    Naive & $24.45$ & $22.22$ \\\\\n",
        "    \\hline\n",
        "    \\end{tabular}\n",
        "    \\caption{Validation and test results.}\n",
        "    \\label{tab:R2}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlbN7BhqXIlM",
        "colab_type": "code",
        "outputId": "4a6b5e4d-774d-4b8e-d80b-6e8c298a5c67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "import numpy as np\n",
        "nums = [24.45, 22.22]\n",
        "for num in nums:\n",
        "    print(np.sqrt(num))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.944694126030447\n",
            "4.71380949975707\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcyCCqzmXk_s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "$24.45$ & $22.22$"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}