text_prediction_TV_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AntonThai2022/ML/blob/main/text_prediction_TV_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2DHaEHDtEUtk"
      },
      "outputs": [],
      "source": [
        "# Import all required modules\n",
        "import numpy as np\n",
        "import csv\n",
        "import requests\n",
        "import random\n",
        "import pickle\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Input, Dense, SimpleRNN, LSTM\n",
        "from tensorflow.keras import utils\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "q-UMb8mOYLqv",
        "outputId": "feab0e6a-a273-4b02-bdd2-f37bfb5a63f8"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-08e2d535b55a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Mount google drive in filesystem to persist store files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mDIR_PATH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/content/drive/MyDrive/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    122\u001b[0m       'TBE_EPHEM_CREDS_ADDR'] if ephemeral else _os.environ['TBE_CREDS_ADDR']\n\u001b[1;32m    123\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    125\u001b[0m         'request_auth', request={'authType': 'dfs_ephemeral'}, timeout_sec=None)\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   request_id = send_request(\n\u001b[1;32m    170\u001b[0m       request_type, request, parent=parent, expect_reply=True)\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    100\u001b[0m         reply.get('colab_msg_id') == message_id):\n\u001b[1;32m    101\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ],
      "source": [
        "# Mount google drive in filesystem to persist store files\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "DIR_PATH = '/content/drive/MyDrive/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KXiIsVK-zfbC"
      },
      "outputs": [],
      "source": [
        "def get_data_set_from_csv(file_name):\n",
        "    \"\"\"Function for loading raw data from google drive,\n",
        "    data stares in csv format\"\"\"\n",
        "    with open(file_name, encoding='utf-8', errors='ignore') as csv_file:\n",
        "        data_X = []\n",
        "        data_Y = []\n",
        "        csv_reader = csv.reader(csv_file, delimiter=';')\n",
        "        for row in csv_reader:\n",
        "            data_X.append(row[2])\n",
        "            data_Y.append(row[3])\n",
        "        return data_X, data_Y"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_data_raw, y_data_raw = get_data_set_from_csv(DIR_PATH + \"tv.csv\") # Load raw data"
      ],
      "metadata": {
        "id": "gELJ3pYNwAbE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ghmf10GbqG0l"
      },
      "outputs": [],
      "source": [
        "# Use random.shuffle to shuffle input data, shuffle parallel to lists\n",
        "c = list(zip(x_data_raw, y_data_raw))\n",
        "random.shuffle(c)\n",
        "x_data_raw, y_data_raw = zip(*c)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define list of symbols can be used in input data\n",
        "PRINTABLE = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~ \\t\\n\\r\\x0b\\x0c'\n",
        "RUSSIAN = 'АаБбВвГгДдЕеЁёЖжЗзИиЙйКкЛлМмНнОоПпРрСсТтУуФфХхЦцЧчШшЩщЪъЫыЬьЭэЮюЯя'\n",
        "SPECIAL = '–'  # !symbol U+2013\n",
        "SYMBOLS = PRINTABLE + RUSSIAN + SPECIAL"
      ],
      "metadata": {
        "id": "DgDHBW0wu12i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "glbXuCWiO4nT"
      },
      "outputs": [],
      "source": [
        "# Build dictionary to map input symbols to vectors\n",
        "token_index = {}; i = 0\n",
        "for line in SYMBOLS:\n",
        "    for char in line:\n",
        "        if char not in token_index:\n",
        "            token_index[char] = i\n",
        "            i = i + 1\n",
        "\n",
        "quantity_tokens = len(token_index)\n",
        "# Reverse-lookup token index to decode sequence back to something readable\n",
        "reverse_input_char_index = { i: char for (char, i) in token_index.items()  }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SjUCrKtbNIT"
      },
      "outputs": [],
      "source": [
        "NUM_CLASSES = 82  # 82 MAGIC NUMBER !!! Maximum position number plus one of Model name substring in data set.\n",
        "# You have to change this in production system depend on assumption about input data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "beTy9iuKArh8"
      },
      "outputs": [],
      "source": [
        "# Save dictionaty\n",
        "with open(DIR_PATH + 'token_index.pkl', 'wb') as f:\n",
        "    pickle.dump(token_index, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hxOqlro9Bj1X"
      },
      "outputs": [],
      "source": [
        "# Restore dictionary\n",
        "with open(DIR_PATH + 'token_index.pkl', 'rb') as f:\n",
        "    token_index = pickle.load(f)\n",
        "quantity_tokens = len(token_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gd2zOu1GJY_S"
      },
      "outputs": [],
      "source": [
        "# Split the data into train, validation and test date\n",
        "x_train_raw = x_data_raw[:20000]\n",
        "y_train_raw = y_data_raw[:20000]\n",
        "\n",
        "x_val_raw = x_data_raw[20000:22000]\n",
        "y_val_raw = y_data_raw[20000:22000]\n",
        "\n",
        "x_test_raw = x_data_raw[22000:]\n",
        "y_test_raw = y_data_raw[22000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--qwq0_MGDrz"
      },
      "source": [
        "\n",
        "\n",
        "Get max length of input data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bEyEMSxQhDXR"
      },
      "outputs": [],
      "source": [
        "def build_data_for_model(x_data, y_data, token2index_dict):\n",
        "    \"\"\"Build numpy tensors from input data, return input tensor for lstm and labels for output data\"\"\"\n",
        "    input_texts_numerical_representation = []\n",
        "\n",
        "    # Build numerical representation of string\n",
        "    for text in x_data:\n",
        "        input_texts_numerical_representation.append( [token2index_dict[token] for token in text] )\n",
        "\n",
        "    # Complement the sequence to the equal length\n",
        "    input_texts_numerical_representation = pad_sequences( input_texts_numerical_representation, maxlen=130, padding='post', value=token2index_dict[' '] )\n",
        "\n",
        "    x_data_np = np.zeros( (len(x_data), len(input_texts_numerical_representation[0]), len(token2index_dict) ), dtype='float32')\n",
        "\n",
        "    # Build one-hot encoding tensor from the input test\n",
        "    for i, input_text in enumerate(input_texts_numerical_representation):\n",
        "        for t, char_code in enumerate(input_text):\n",
        "            x_data_np[i, t, char_code] = 1.\n",
        "        # encoder_input_data[i, t + 1:, input_token_index[' ']] = 1.\n",
        "\n",
        "    labels_start = [ x.find(y) for x, y in zip(x_data, y_data) ]   # Get the position of the first and last symbols of substrings\n",
        "    labels_stop = [first_symbol + len(substring) for first_symbol, substring in zip(labels_start, y_data)] # Traditionally, we point the character following the last\n",
        "\n",
        "    # Create one-hot encodings vectors for labels\n",
        "    categorical_labels_start = to_categorical(labels_start, num_classes=NUM_CLASSES)\n",
        "    categorical_labels_stop = to_categorical(labels_stop, num_classes=NUM_CLASSES)\n",
        "\n",
        "    return x_data_np, categorical_labels_start, categorical_labels_stop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0aiqQ8wO5c3l"
      },
      "outputs": [],
      "source": [
        "# Build train, validation and test date in tensor form\n",
        "x_train, y_start_train, y_stop_train, = build_data_for_model(x_train_raw, y_train_raw, token_index)\n",
        "x_val, y_start_val, y_stop_val = build_data_for_model(x_val_raw, y_val_raw, token_index)\n",
        "x_test, y_start_test, y_stop_test = build_data_for_model(x_test_raw, y_test_raw, token_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ABhX0O4GxwWU"
      },
      "outputs": [],
      "source": [
        "np.save(DIR_PATH + 'x_train.npy', x_train)\n",
        "np.save(DIR_PATH + 'y_start_train.npy', y_start_train)\n",
        "np.save(DIR_PATH + 'y_stop_train.npy', y_stop_train)\n",
        "\n",
        "np.save(DIR_PATH + 'x_val.npy', x_val)\n",
        "np.save(DIR_PATH + 'y_start_val.npy', y_start_val)\n",
        "np.save(DIR_PATH + 'y_stop_val.npy', y_stop_val)\n",
        "\n",
        "np.save(DIR_PATH + 'x_test.npy', x_test)\n",
        "np.save(DIR_PATH + 'y_start_test.npy', y_start_test)\n",
        "np.save(DIR_PATH + 'y_stop_test.npy', y_stop_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7CLF8L1PzTti"
      },
      "outputs": [],
      "source": [
        "x_train = np.load(DIR_PATH + 'x_train.npy')\n",
        "y_start_train = np.load(DIR_PATH + 'y_start_train.npy')\n",
        "y_stop_train = np.load(DIR_PATH + 'y_stop_train.npy')\n",
        "\n",
        "x_val = np.load(DIR_PATH + 'x_val.npy')\n",
        "y_start_val = np.load(DIR_PATH + 'y_start_val.npy')\n",
        "y_stop_val = np.load(DIR_PATH + 'y_stop_val.npy')\n",
        "\n",
        "x_test = np.load(DIR_PATH + 'x_test.npy')\n",
        "y_start_test = np.load(DIR_PATH + 'y_start_test.npy')\n",
        "y_stop_test = np.load(DIR_PATH + 'y_stop_test.npy')\n",
        "\n",
        "quantity_tokens = x_test.shape[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V62BNHKGGPao"
      },
      "source": [
        "Filling input data with spaces"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OG-bugUGO65a"
      },
      "outputs": [],
      "source": [
        "batch_size =  128 # define the batch size for training\n",
        "epochs = 200 # define the number of epoch for training\n",
        "latent_dim = 256 #define the dimensionality of the LSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "80LKYBw2ZAb5"
      },
      "outputs": [],
      "source": [
        "# Define the model\n",
        "inputs = Input(shape=(None, quantity_tokens), name='input')\n",
        "lstm_1 = LSTM(latent_dim, return_state=True, name='lstm_1')\n",
        "batch_norm = tf.keras.layers.BatchNormalization()\n",
        "\n",
        "start_output = Dense(NUM_CLASSES, activation='softmax', name='start_output')\n",
        "stop_output = Dense(NUM_CLASSES, activation='softmax', name='stop_output')\n",
        "\n",
        "lstm_outputs, _, _ = lstm_1(inputs)\n",
        "batch_norm_outputs = batch_norm(lstm_outputs)\n",
        "\n",
        "outputs_1 = start_output(batch_norm_outputs)\n",
        "outputs_2 = stop_output(batch_norm_outputs)\n",
        "\n",
        "model = Model(inputs, [outputs_1, outputs_2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OsrxMxeTFsSW"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "SpWC6COVS8Bd",
        "outputId": "3a50618e-9af4-4158-f7c9-16c7debf67c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes=True, show_dtype=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PXEbup_Ar225"
      },
      "outputs": [],
      "source": [
        "checkpoint_filepath = '/content/drive/MyDrive/model_checkpoint'\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    save_weights_only=True,\n",
        "    monitor='val_stop_output_accuracy',\n",
        "    mode='max',\n",
        "    save_best_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBmDkW3QXzu_",
        "outputId": "7dc38bff-3727-4c5c-dbc4-9b2d6ba88dfe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 130, 167)\n",
            "(20000, 82)\n",
            "(20000, 82)\n",
            "(2000, 130, 167)\n",
            "(2000, 82)\n",
            "(2000, 82)\n",
            "(7999, 130, 167)\n",
            "(7999, 82)\n",
            "(7999, 82)\n"
          ]
        }
      ],
      "source": [
        "print(x_train.shape)\n",
        "print(y_start_train.shape)\n",
        "print(y_stop_train.shape)\n",
        "\n",
        "print(x_val.shape)\n",
        "print(y_start_val.shape)\n",
        "print(y_stop_val.shape)\n",
        "\n",
        "print(x_test.shape)\n",
        "print(y_start_test.shape)\n",
        "print(y_stop_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_e0JF0zZvFv",
        "outputId": "f26dd5fb-4172-431e-ab34-81aff297335c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/600\n",
            "10/10 [==============================] - 12s 384ms/step - loss: 8.6038 - start_output_loss: 4.2783 - stop_output_loss: 4.3255 - start_output_accuracy: 0.3302 - stop_output_accuracy: 0.1266 - val_loss: 7.8587 - val_start_output_loss: 3.8221 - val_stop_output_loss: 4.0367 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1410\n",
            "Epoch 2/600\n",
            "10/10 [==============================] - 3s 300ms/step - loss: 8.0462 - start_output_loss: 3.9634 - stop_output_loss: 4.0828 - start_output_accuracy: 0.3617 - stop_output_accuracy: 0.1370 - val_loss: 6.5450 - val_start_output_loss: 2.9710 - val_stop_output_loss: 3.5740 - val_start_output_accuracy: 0.1165 - val_stop_output_accuracy: 0.0995\n",
            "Epoch 3/600\n",
            "10/10 [==============================] - 3s 304ms/step - loss: 7.5986 - start_output_loss: 3.6629 - stop_output_loss: 3.9356 - start_output_accuracy: 0.2011 - stop_output_accuracy: 0.1103 - val_loss: 6.3459 - val_start_output_loss: 2.8798 - val_stop_output_loss: 3.4661 - val_start_output_accuracy: 0.2065 - val_stop_output_accuracy: 0.1255\n",
            "Epoch 4/600\n",
            "10/10 [==============================] - 3s 312ms/step - loss: 6.4863 - start_output_loss: 3.0610 - stop_output_loss: 3.4254 - start_output_accuracy: 0.1964 - stop_output_accuracy: 0.1364 - val_loss: 6.7118 - val_start_output_loss: 3.0996 - val_stop_output_loss: 3.6123 - val_start_output_accuracy: 0.1675 - val_stop_output_accuracy: 0.1215\n",
            "Epoch 5/600\n",
            "10/10 [==============================] - 3s 303ms/step - loss: 6.3151 - start_output_loss: 2.9621 - stop_output_loss: 3.3530 - start_output_accuracy: 0.1671 - stop_output_accuracy: 0.1268 - val_loss: 6.8661 - val_start_output_loss: 3.1275 - val_stop_output_loss: 3.7385 - val_start_output_accuracy: 0.1900 - val_stop_output_accuracy: 0.1255\n",
            "Epoch 6/600\n",
            "10/10 [==============================] - 3s 305ms/step - loss: 6.4455 - start_output_loss: 3.0244 - stop_output_loss: 3.4210 - start_output_accuracy: 0.3523 - stop_output_accuracy: 0.1329 - val_loss: 7.0033 - val_start_output_loss: 3.2550 - val_stop_output_loss: 3.7483 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1085\n",
            "Epoch 7/600\n",
            "10/10 [==============================] - 3s 307ms/step - loss: 5.9646 - start_output_loss: 2.7496 - stop_output_loss: 3.2150 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.7901 - val_start_output_loss: 3.1147 - val_stop_output_loss: 3.6754 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1085\n",
            "Epoch 8/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 5.4973 - start_output_loss: 2.4875 - stop_output_loss: 3.0098 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.6179 - val_start_output_loss: 2.9980 - val_stop_output_loss: 3.6199 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 9/600\n",
            "10/10 [==============================] - 3s 309ms/step - loss: 5.1527 - start_output_loss: 2.2989 - stop_output_loss: 2.8539 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.4439 - val_start_output_loss: 2.8866 - val_stop_output_loss: 3.5573 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 10/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 4.9316 - start_output_loss: 2.1808 - stop_output_loss: 2.7508 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.2569 - val_start_output_loss: 2.7874 - val_stop_output_loss: 3.4695 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 11/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 4.8020 - start_output_loss: 2.1143 - stop_output_loss: 2.6877 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.0975 - val_start_output_loss: 2.7207 - val_stop_output_loss: 3.3768 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 12/600\n",
            "10/10 [==============================] - 3s 309ms/step - loss: 4.7288 - start_output_loss: 2.0779 - stop_output_loss: 2.6509 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.0370 - val_start_output_loss: 2.7646 - val_stop_output_loss: 3.2724 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1085\n",
            "Epoch 13/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 4.6869 - start_output_loss: 2.0578 - stop_output_loss: 2.6291 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 5.8394 - val_start_output_loss: 2.6470 - val_stop_output_loss: 3.1924 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 14/600\n",
            "10/10 [==============================] - 3s 309ms/step - loss: 4.6624 - start_output_loss: 2.0458 - stop_output_loss: 2.6166 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 6.0617 - val_start_output_loss: 2.7356 - val_stop_output_loss: 3.3261 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1085\n",
            "Epoch 15/600\n",
            "10/10 [==============================] - 3s 307ms/step - loss: 4.6760 - start_output_loss: 2.0600 - stop_output_loss: 2.6160 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1326 - val_loss: 5.6383 - val_start_output_loss: 2.7200 - val_stop_output_loss: 2.9183 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 16/600\n",
            "10/10 [==============================] - 3s 309ms/step - loss: 4.6367 - start_output_loss: 2.0336 - stop_output_loss: 2.6031 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 5.4880 - val_start_output_loss: 2.6384 - val_stop_output_loss: 2.8496 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 17/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 4.6933 - start_output_loss: 2.0591 - stop_output_loss: 2.6342 - start_output_accuracy: 0.3722 - stop_output_accuracy: 0.1330 - val_loss: 10.4156 - val_start_output_loss: 5.1365 - val_stop_output_loss: 5.2791 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1055\n",
            "Epoch 18/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 4.6268 - start_output_loss: 2.0289 - stop_output_loss: 2.5979 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1340 - val_loss: 7.6269 - val_start_output_loss: 3.6253 - val_stop_output_loss: 4.0016 - val_start_output_accuracy: 0.3685 - val_stop_output_accuracy: 0.1055\n",
            "Epoch 19/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 4.6704 - start_output_loss: 2.0484 - stop_output_loss: 2.6220 - start_output_accuracy: 0.3731 - stop_output_accuracy: 0.1325 - val_loss: 9.3050 - val_start_output_loss: 4.4467 - val_stop_output_loss: 4.8583 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 5.0000e-04\n",
            "Epoch 20/600\n",
            "10/10 [==============================] - 3s 312ms/step - loss: 4.6213 - start_output_loss: 2.0261 - stop_output_loss: 2.5952 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1323 - val_loss: 5.3974 - val_start_output_loss: 2.3652 - val_stop_output_loss: 3.0322 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 21/600\n",
            "10/10 [==============================] - 3s 312ms/step - loss: 4.6214 - start_output_loss: 2.0261 - stop_output_loss: 2.5953 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1321 - val_loss: 5.5747 - val_start_output_loss: 2.4988 - val_stop_output_loss: 3.0759 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 22/600\n",
            "10/10 [==============================] - 3s 313ms/step - loss: 4.6155 - start_output_loss: 2.0224 - stop_output_loss: 2.5931 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1311 - val_loss: 5.2246 - val_start_output_loss: 2.1956 - val_stop_output_loss: 3.0290 - val_start_output_accuracy: 0.3580 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 23/600\n",
            "10/10 [==============================] - 3s 315ms/step - loss: 4.6022 - start_output_loss: 2.0143 - stop_output_loss: 2.5879 - start_output_accuracy: 0.3720 - stop_output_accuracy: 0.1376 - val_loss: 5.6712 - val_start_output_loss: 2.5680 - val_stop_output_loss: 3.1033 - val_start_output_accuracy: 0.0700 - val_stop_output_accuracy: 0.1110\n",
            "Epoch 24/600\n",
            "10/10 [==============================] - 3s 312ms/step - loss: 4.6472 - start_output_loss: 2.0394 - stop_output_loss: 2.6078 - start_output_accuracy: 0.3718 - stop_output_accuracy: 0.1303 - val_loss: 6.1736 - val_start_output_loss: 2.9115 - val_stop_output_loss: 3.2622 - val_start_output_accuracy: 0.3620 - val_stop_output_accuracy: 0.1410\n",
            "Epoch 25/600\n",
            "10/10 [==============================] - 3s 312ms/step - loss: 4.6501 - start_output_loss: 2.0173 - stop_output_loss: 2.6328 - start_output_accuracy: 0.3758 - stop_output_accuracy: 0.1343 - val_loss: 7.0081 - val_start_output_loss: 3.3702 - val_stop_output_loss: 3.6379 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1060\n",
            "Epoch 26/600\n",
            "10/10 [==============================] - 3s 313ms/step - loss: 4.6319 - start_output_loss: 2.0166 - stop_output_loss: 2.6154 - start_output_accuracy: 0.3762 - stop_output_accuracy: 0.1343 - val_loss: 6.9260 - val_start_output_loss: 3.2834 - val_stop_output_loss: 3.6425 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1165\n",
            "Epoch 27/600\n",
            "10/10 [==============================] - 3s 311ms/step - loss: 4.6264 - start_output_loss: 2.0163 - stop_output_loss: 2.6101 - start_output_accuracy: 0.3762 - stop_output_accuracy: 0.1343 - val_loss: 6.6982 - val_start_output_loss: 3.2322 - val_stop_output_loss: 3.4659 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1060\n",
            "Epoch 28/600\n",
            "10/10 [==============================] - 3s 310ms/step - loss: 4.6295 - start_output_loss: 2.0160 - stop_output_loss: 2.6135 - start_output_accuracy: 0.3762 - stop_output_accuracy: 0.1339 - val_loss: 6.5176 - val_start_output_loss: 3.0874 - val_stop_output_loss: 3.4302 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1410\n",
            "Epoch 29/600\n",
            "10/10 [==============================] - 3s 313ms/step - loss: 4.6214 - start_output_loss: 2.0163 - stop_output_loss: 2.6051 - start_output_accuracy: 0.3762 - stop_output_accuracy: 0.1340 - val_loss: 6.5192 - val_start_output_loss: 3.0005 - val_stop_output_loss: 3.5187 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1410\n",
            "Epoch 30/600\n",
            "10/10 [==============================] - 3s 321ms/step - loss: 4.6186 - start_output_loss: 2.0159 - stop_output_loss: 2.6027 - start_output_accuracy: 0.3763 - stop_output_accuracy: 0.1343 - val_loss: 6.7800 - val_start_output_loss: 3.1204 - val_stop_output_loss: 3.6596 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1420\n",
            "Epoch 31/600\n",
            "10/10 [==============================] - 3s 313ms/step - loss: 4.6202 - start_output_loss: 2.0155 - stop_output_loss: 2.6047 - start_output_accuracy: 0.3766 - stop_output_accuracy: 0.1342 - val_loss: 6.8620 - val_start_output_loss: 3.0606 - val_stop_output_loss: 3.8014 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1405\n",
            "Epoch 32/600\n",
            "10/10 [==============================] - 3s 314ms/step - loss: 4.6183 - start_output_loss: 2.0151 - stop_output_loss: 2.6032 - start_output_accuracy: 0.3766 - stop_output_accuracy: 0.1344 - val_loss: 6.1248 - val_start_output_loss: 2.8191 - val_stop_output_loss: 3.3057 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1165\n",
            "Epoch 33/600\n",
            "10/10 [==============================] - 3s 311ms/step - loss: 4.6180 - start_output_loss: 2.0157 - stop_output_loss: 2.6023 - start_output_accuracy: 0.3766 - stop_output_accuracy: 0.1341 - val_loss: 6.4445 - val_start_output_loss: 2.9043 - val_stop_output_loss: 3.5402 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1410\n",
            "Epoch 34/600\n",
            "10/10 [==============================] - 3s 313ms/step - loss: 4.6164 - start_output_loss: 2.0155 - stop_output_loss: 2.6009 - start_output_accuracy: 0.3767 - stop_output_accuracy: 0.1343 - val_loss: 6.1845 - val_start_output_loss: 2.7878 - val_stop_output_loss: 3.3967 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1410\n",
            "Epoch 35/600\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 4.6181 - start_output_loss: 2.0152 - stop_output_loss: 2.6029 - start_output_accuracy: 0.3767 - stop_output_accuracy: 0.1341 - val_loss: 6.1069 - val_start_output_loss: 2.8048 - val_stop_output_loss: 3.3021 - val_start_output_accuracy: 0.3630 - val_stop_output_accuracy: 0.1405\n",
            "Epoch 36/600\n",
            "10/10 [==============================] - 3s 321ms/step - loss: 4.6451 - start_output_loss: 2.0223 - stop_output_loss: 2.6228 - start_output_accuracy: 0.3865 - stop_output_accuracy: 0.1416 - val_loss: 5.8343 - val_start_output_loss: 2.5176 - val_stop_output_loss: 3.3167 - val_start_output_accuracy: 0.4120 - val_stop_output_accuracy: 0.1815\n",
            "Epoch 37/600\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 4.1494 - start_output_loss: 1.7195 - stop_output_loss: 2.4299 - start_output_accuracy: 0.4721 - stop_output_accuracy: 0.1969 - val_loss: 5.5890 - val_start_output_loss: 2.2260 - val_stop_output_loss: 3.3629 - val_start_output_accuracy: 0.4645 - val_stop_output_accuracy: 0.2175\n",
            "Epoch 38/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 4.0010 - start_output_loss: 1.6057 - stop_output_loss: 2.3953 - start_output_accuracy: 0.4857 - stop_output_accuracy: 0.2072 - val_loss: 5.7802 - val_start_output_loss: 2.5760 - val_stop_output_loss: 3.2042 - val_start_output_accuracy: 0.2400 - val_stop_output_accuracy: 0.1195\n",
            "Epoch 39/600\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 3.8259 - start_output_loss: 1.4620 - stop_output_loss: 2.3639 - start_output_accuracy: 0.5102 - stop_output_accuracy: 0.2188 - val_loss: 5.3498 - val_start_output_loss: 2.0546 - val_stop_output_loss: 3.2952 - val_start_output_accuracy: 0.2590 - val_stop_output_accuracy: 0.0805\n",
            "Epoch 40/600\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 3.6676 - start_output_loss: 1.3496 - stop_output_loss: 2.3180 - start_output_accuracy: 0.5408 - stop_output_accuracy: 0.2314 - val_loss: 5.2326 - val_start_output_loss: 2.0153 - val_stop_output_loss: 3.2172 - val_start_output_accuracy: 0.2400 - val_stop_output_accuracy: 0.0805\n",
            "Epoch 41/600\n",
            "10/10 [==============================] - 3s 318ms/step - loss: 3.6566 - start_output_loss: 1.3499 - stop_output_loss: 2.3067 - start_output_accuracy: 0.5462 - stop_output_accuracy: 0.2185 - val_loss: 4.7881 - val_start_output_loss: 1.7174 - val_stop_output_loss: 3.0707 - val_start_output_accuracy: 0.4660 - val_stop_output_accuracy: 0.1160\n",
            "Epoch 42/600\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 3.8052 - start_output_loss: 1.4258 - stop_output_loss: 2.3794 - start_output_accuracy: 0.4946 - stop_output_accuracy: 0.1831 - val_loss: 4.6330 - val_start_output_loss: 1.7692 - val_stop_output_loss: 2.8639 - val_start_output_accuracy: 0.3930 - val_stop_output_accuracy: 0.1405\n",
            "Epoch 43/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 3.4215 - start_output_loss: 1.2211 - stop_output_loss: 2.2004 - start_output_accuracy: 0.5778 - stop_output_accuracy: 0.2414 - val_loss: 6.4242 - val_start_output_loss: 2.8322 - val_stop_output_loss: 3.5920 - val_start_output_accuracy: 0.0725 - val_stop_output_accuracy: 0.0805\n",
            "Epoch 44/600\n",
            "10/10 [==============================] - 3s 321ms/step - loss: 3.4773 - start_output_loss: 1.2210 - stop_output_loss: 2.2562 - start_output_accuracy: 0.5990 - stop_output_accuracy: 0.2355 - val_loss: 4.5889 - val_start_output_loss: 1.6986 - val_stop_output_loss: 2.8903 - val_start_output_accuracy: 0.5525 - val_stop_output_accuracy: 0.1530\n",
            "Epoch 45/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 3.2784 - start_output_loss: 1.1108 - stop_output_loss: 2.1675 - start_output_accuracy: 0.6184 - stop_output_accuracy: 0.2697 - val_loss: 4.7117 - val_start_output_loss: 1.8306 - val_stop_output_loss: 2.8812 - val_start_output_accuracy: 0.3780 - val_stop_output_accuracy: 0.2010\n",
            "Epoch 46/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 3.0923 - start_output_loss: 1.0166 - stop_output_loss: 2.0757 - start_output_accuracy: 0.6436 - stop_output_accuracy: 0.2880 - val_loss: 3.5846 - val_start_output_loss: 1.1013 - val_stop_output_loss: 2.4832 - val_start_output_accuracy: 0.6550 - val_stop_output_accuracy: 0.2300\n",
            "Epoch 47/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 3.0332 - start_output_loss: 0.9697 - stop_output_loss: 2.0635 - start_output_accuracy: 0.6601 - stop_output_accuracy: 0.2864 - val_loss: 4.6223 - val_start_output_loss: 1.9252 - val_stop_output_loss: 2.6970 - val_start_output_accuracy: 0.4420 - val_stop_output_accuracy: 0.1940\n",
            "Epoch 48/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 3.0770 - start_output_loss: 0.9847 - stop_output_loss: 2.0923 - start_output_accuracy: 0.6478 - stop_output_accuracy: 0.2692 - val_loss: 3.6441 - val_start_output_loss: 1.2879 - val_stop_output_loss: 2.3562 - val_start_output_accuracy: 0.5485 - val_stop_output_accuracy: 0.2395\n",
            "Epoch 49/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 3.0909 - start_output_loss: 1.0260 - stop_output_loss: 2.0649 - start_output_accuracy: 0.6449 - stop_output_accuracy: 0.2734 - val_loss: 4.4054 - val_start_output_loss: 1.8817 - val_stop_output_loss: 2.5237 - val_start_output_accuracy: 0.2760 - val_stop_output_accuracy: 0.1750\n",
            "Epoch 50/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 3.0343 - start_output_loss: 0.9798 - stop_output_loss: 2.0544 - start_output_accuracy: 0.6758 - stop_output_accuracy: 0.2718 - val_loss: 4.4144 - val_start_output_loss: 1.8094 - val_stop_output_loss: 2.6051 - val_start_output_accuracy: 0.3915 - val_stop_output_accuracy: 0.1115\n",
            "Epoch 51/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 2.9837 - start_output_loss: 0.9360 - stop_output_loss: 2.0477 - start_output_accuracy: 0.6740 - stop_output_accuracy: 0.2822 - val_loss: 4.9942 - val_start_output_loss: 2.4389 - val_stop_output_loss: 2.5553 - val_start_output_accuracy: 0.1995 - val_stop_output_accuracy: 0.1610\n",
            "Epoch 52/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.8639 - start_output_loss: 0.8753 - stop_output_loss: 1.9886 - start_output_accuracy: 0.6890 - stop_output_accuracy: 0.2973 - val_loss: 8.6205 - val_start_output_loss: 5.3879 - val_stop_output_loss: 3.2326 - val_start_output_accuracy: 0.1165 - val_stop_output_accuracy: 0.1245\n",
            "Epoch 53/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 2.8557 - start_output_loss: 0.8840 - stop_output_loss: 1.9717 - start_output_accuracy: 0.6960 - stop_output_accuracy: 0.2883 - val_loss: 6.8426 - val_start_output_loss: 3.1585 - val_stop_output_loss: 3.6841 - val_start_output_accuracy: 0.1410 - val_stop_output_accuracy: 0.1085\n",
            "Epoch 54/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 2.8535 - start_output_loss: 0.8808 - stop_output_loss: 1.9726 - start_output_accuracy: 0.7087 - stop_output_accuracy: 0.2948 - val_loss: 5.7728 - val_start_output_loss: 2.5757 - val_stop_output_loss: 3.1971 - val_start_output_accuracy: 0.2155 - val_stop_output_accuracy: 0.1940\n",
            "Epoch 55/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 2.7628 - start_output_loss: 0.8009 - stop_output_loss: 1.9619 - start_output_accuracy: 0.7502 - stop_output_accuracy: 0.2937 - val_loss: 3.4188 - val_start_output_loss: 1.2780 - val_stop_output_loss: 2.1408 - val_start_output_accuracy: 0.5160 - val_stop_output_accuracy: 0.2670\n",
            "Epoch 56/600\n",
            "10/10 [==============================] - 3s 321ms/step - loss: 2.7150 - start_output_loss: 0.7940 - stop_output_loss: 1.9210 - start_output_accuracy: 0.7582 - stop_output_accuracy: 0.3082 - val_loss: 3.7162 - val_start_output_loss: 1.4191 - val_stop_output_loss: 2.2971 - val_start_output_accuracy: 0.5115 - val_stop_output_accuracy: 0.2155\n",
            "Epoch 57/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.7041 - start_output_loss: 0.7873 - stop_output_loss: 1.9169 - start_output_accuracy: 0.7605 - stop_output_accuracy: 0.3027 - val_loss: 3.7404 - val_start_output_loss: 1.4206 - val_stop_output_loss: 2.3198 - val_start_output_accuracy: 0.5590 - val_stop_output_accuracy: 0.2130\n",
            "Epoch 58/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 2.6299 - start_output_loss: 0.7343 - stop_output_loss: 1.8957 - start_output_accuracy: 0.7908 - stop_output_accuracy: 0.3049 - val_loss: 3.9319 - val_start_output_loss: 1.8183 - val_stop_output_loss: 2.1135 - val_start_output_accuracy: 0.4270 - val_stop_output_accuracy: 0.2910\n",
            "Epoch 59/600\n",
            "10/10 [==============================] - 3s 321ms/step - loss: 2.6418 - start_output_loss: 0.7562 - stop_output_loss: 1.8855 - start_output_accuracy: 0.7694 - stop_output_accuracy: 0.3167 - val_loss: 4.9821 - val_start_output_loss: 1.9450 - val_stop_output_loss: 3.0371 - val_start_output_accuracy: 0.3465 - val_stop_output_accuracy: 0.1950\n",
            "Epoch 60/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 2.5616 - start_output_loss: 0.7139 - stop_output_loss: 1.8478 - start_output_accuracy: 0.7848 - stop_output_accuracy: 0.3174 - val_loss: 3.5602 - val_start_output_loss: 1.4848 - val_stop_output_loss: 2.0754 - val_start_output_accuracy: 0.4270 - val_stop_output_accuracy: 0.2295\n",
            "Epoch 61/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 2.5524 - start_output_loss: 0.7003 - stop_output_loss: 1.8521 - start_output_accuracy: 0.7896 - stop_output_accuracy: 0.3140 - val_loss: 2.9834 - val_start_output_loss: 0.9588 - val_stop_output_loss: 2.0246 - val_start_output_accuracy: 0.6720 - val_stop_output_accuracy: 0.2735\n",
            "Epoch 62/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.4731 - start_output_loss: 0.6684 - stop_output_loss: 1.8046 - start_output_accuracy: 0.7968 - stop_output_accuracy: 0.3248 - val_loss: 3.2866 - val_start_output_loss: 1.1140 - val_stop_output_loss: 2.1726 - val_start_output_accuracy: 0.6365 - val_stop_output_accuracy: 0.2400\n",
            "Epoch 63/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 2.4270 - start_output_loss: 0.6363 - stop_output_loss: 1.7907 - start_output_accuracy: 0.8074 - stop_output_accuracy: 0.3248 - val_loss: 2.7420 - val_start_output_loss: 0.9001 - val_stop_output_loss: 1.8420 - val_start_output_accuracy: 0.6925 - val_stop_output_accuracy: 0.3145\n",
            "Epoch 64/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 2.4051 - start_output_loss: 0.6284 - stop_output_loss: 1.7767 - start_output_accuracy: 0.8152 - stop_output_accuracy: 0.3196 - val_loss: 2.8760 - val_start_output_loss: 0.9274 - val_stop_output_loss: 1.9486 - val_start_output_accuracy: 0.6910 - val_stop_output_accuracy: 0.2900\n",
            "Epoch 65/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 2.4051 - start_output_loss: 0.6244 - stop_output_loss: 1.7807 - start_output_accuracy: 0.8209 - stop_output_accuracy: 0.3373 - val_loss: 2.6951 - val_start_output_loss: 0.8409 - val_stop_output_loss: 1.8543 - val_start_output_accuracy: 0.7315 - val_stop_output_accuracy: 0.3135\n",
            "Epoch 66/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 2.2830 - start_output_loss: 0.5639 - stop_output_loss: 1.7190 - start_output_accuracy: 0.8370 - stop_output_accuracy: 0.3544 - val_loss: 5.9589 - val_start_output_loss: 2.7298 - val_stop_output_loss: 3.2291 - val_start_output_accuracy: 0.2890 - val_stop_output_accuracy: 0.2020\n",
            "Epoch 67/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 2.3045 - start_output_loss: 0.5618 - stop_output_loss: 1.7427 - start_output_accuracy: 0.8440 - stop_output_accuracy: 0.3521 - val_loss: 2.7042 - val_start_output_loss: 0.8402 - val_stop_output_loss: 1.8640 - val_start_output_accuracy: 0.7385 - val_stop_output_accuracy: 0.2985\n",
            "Epoch 68/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.2955 - start_output_loss: 0.5501 - stop_output_loss: 1.7454 - start_output_accuracy: 0.8435 - stop_output_accuracy: 0.3429 - val_loss: 3.0411 - val_start_output_loss: 1.1365 - val_stop_output_loss: 1.9046 - val_start_output_accuracy: 0.5790 - val_stop_output_accuracy: 0.2525\n",
            "Epoch 69/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 2.2376 - start_output_loss: 0.5503 - stop_output_loss: 1.6873 - start_output_accuracy: 0.8445 - stop_output_accuracy: 0.3663 - val_loss: 4.4521 - val_start_output_loss: 1.5995 - val_stop_output_loss: 2.8527 - val_start_output_accuracy: 0.4985 - val_stop_output_accuracy: 0.2290\n",
            "Epoch 70/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 2.2332 - start_output_loss: 0.5458 - stop_output_loss: 1.6874 - start_output_accuracy: 0.8503 - stop_output_accuracy: 0.3659 - val_loss: 2.8784 - val_start_output_loss: 0.7579 - val_stop_output_loss: 2.1205 - val_start_output_accuracy: 0.7870 - val_stop_output_accuracy: 0.2430\n",
            "Epoch 71/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 2.2351 - start_output_loss: 0.5408 - stop_output_loss: 1.6943 - start_output_accuracy: 0.8533 - stop_output_accuracy: 0.3645 - val_loss: 3.6064 - val_start_output_loss: 1.0962 - val_stop_output_loss: 2.5102 - val_start_output_accuracy: 0.6780 - val_stop_output_accuracy: 0.2320\n",
            "Epoch 72/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 2.2065 - start_output_loss: 0.5022 - stop_output_loss: 1.7043 - start_output_accuracy: 0.8677 - stop_output_accuracy: 0.3591 - val_loss: 5.0506 - val_start_output_loss: 1.8577 - val_stop_output_loss: 3.1929 - val_start_output_accuracy: 0.4595 - val_stop_output_accuracy: 0.1350\n",
            "Epoch 73/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 2.1598 - start_output_loss: 0.5064 - stop_output_loss: 1.6534 - start_output_accuracy: 0.8675 - stop_output_accuracy: 0.3799 - val_loss: 3.6811 - val_start_output_loss: 1.7927 - val_stop_output_loss: 1.8884 - val_start_output_accuracy: 0.5850 - val_stop_output_accuracy: 0.3570\n",
            "Epoch 74/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 2.2395 - start_output_loss: 0.5689 - stop_output_loss: 1.6706 - start_output_accuracy: 0.8249 - stop_output_accuracy: 0.3713 - val_loss: 3.1842 - val_start_output_loss: 0.9158 - val_stop_output_loss: 2.2684 - val_start_output_accuracy: 0.6740 - val_stop_output_accuracy: 0.2445\n",
            "Epoch 75/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 2.0784 - start_output_loss: 0.4713 - stop_output_loss: 1.6071 - start_output_accuracy: 0.8654 - stop_output_accuracy: 0.3999 - val_loss: 3.0008 - val_start_output_loss: 1.1443 - val_stop_output_loss: 1.8564 - val_start_output_accuracy: 0.6240 - val_stop_output_accuracy: 0.3295\n",
            "Epoch 76/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 2.1199 - start_output_loss: 0.4976 - stop_output_loss: 1.6223 - start_output_accuracy: 0.8576 - stop_output_accuracy: 0.3923 - val_loss: 2.3404 - val_start_output_loss: 0.6088 - val_stop_output_loss: 1.7316 - val_start_output_accuracy: 0.8380 - val_stop_output_accuracy: 0.3745\n",
            "Epoch 77/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 2.1865 - start_output_loss: 0.5129 - stop_output_loss: 1.6736 - start_output_accuracy: 0.8547 - stop_output_accuracy: 0.3823 - val_loss: 2.6400 - val_start_output_loss: 0.7427 - val_stop_output_loss: 1.8973 - val_start_output_accuracy: 0.7885 - val_stop_output_accuracy: 0.2720\n",
            "Epoch 78/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 2.0232 - start_output_loss: 0.4432 - stop_output_loss: 1.5800 - start_output_accuracy: 0.8784 - stop_output_accuracy: 0.4006 - val_loss: 2.3408 - val_start_output_loss: 0.6403 - val_stop_output_loss: 1.7006 - val_start_output_accuracy: 0.7985 - val_stop_output_accuracy: 0.3610\n",
            "Epoch 79/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 2.0400 - start_output_loss: 0.4575 - stop_output_loss: 1.5824 - start_output_accuracy: 0.8704 - stop_output_accuracy: 0.4053 - val_loss: 4.6899 - val_start_output_loss: 2.7049 - val_stop_output_loss: 1.9851 - val_start_output_accuracy: 0.4090 - val_stop_output_accuracy: 0.2155\n",
            "Epoch 80/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 2.1110 - start_output_loss: 0.5011 - stop_output_loss: 1.6099 - start_output_accuracy: 0.8629 - stop_output_accuracy: 0.4046 - val_loss: 3.5049 - val_start_output_loss: 1.1838 - val_stop_output_loss: 2.3211 - val_start_output_accuracy: 0.6195 - val_stop_output_accuracy: 0.2245\n",
            "Epoch 81/600\n",
            "10/10 [==============================] - 3s 344ms/step - loss: 1.9690 - start_output_loss: 0.4229 - stop_output_loss: 1.5461 - start_output_accuracy: 0.8870 - stop_output_accuracy: 0.4122 - val_loss: 2.2880 - val_start_output_loss: 0.5891 - val_stop_output_loss: 1.6988 - val_start_output_accuracy: 0.8495 - val_stop_output_accuracy: 0.3615\n",
            "Epoch 82/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 2.0232 - start_output_loss: 0.4581 - stop_output_loss: 1.5651 - start_output_accuracy: 0.8691 - stop_output_accuracy: 0.4200 - val_loss: 3.8643 - val_start_output_loss: 1.6833 - val_stop_output_loss: 2.1810 - val_start_output_accuracy: 0.5055 - val_stop_output_accuracy: 0.2160\n",
            "Epoch 83/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 1.9966 - start_output_loss: 0.4240 - stop_output_loss: 1.5726 - start_output_accuracy: 0.8828 - stop_output_accuracy: 0.4076 - val_loss: 3.5385 - val_start_output_loss: 1.6809 - val_stop_output_loss: 1.8575 - val_start_output_accuracy: 0.4765 - val_stop_output_accuracy: 0.2720\n",
            "Epoch 84/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 1.9513 - start_output_loss: 0.4369 - stop_output_loss: 1.5144 - start_output_accuracy: 0.8811 - stop_output_accuracy: 0.4378 - val_loss: 2.5899 - val_start_output_loss: 0.6600 - val_stop_output_loss: 1.9300 - val_start_output_accuracy: 0.8135 - val_stop_output_accuracy: 0.2665\n",
            "Epoch 85/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 1.9994 - start_output_loss: 0.4366 - stop_output_loss: 1.5628 - start_output_accuracy: 0.8747 - stop_output_accuracy: 0.4094 - val_loss: 2.2019 - val_start_output_loss: 0.6176 - val_stop_output_loss: 1.5843 - val_start_output_accuracy: 0.7815 - val_stop_output_accuracy: 0.4285\n",
            "Epoch 86/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 1.9985 - start_output_loss: 0.4585 - stop_output_loss: 1.5399 - start_output_accuracy: 0.8785 - stop_output_accuracy: 0.4347 - val_loss: 3.4367 - val_start_output_loss: 1.3083 - val_stop_output_loss: 2.1284 - val_start_output_accuracy: 0.5950 - val_stop_output_accuracy: 0.3615\n",
            "Epoch 87/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 2.3415 - start_output_loss: 0.6707 - stop_output_loss: 1.6707 - start_output_accuracy: 0.7811 - stop_output_accuracy: 0.4152 - val_loss: 3.3908 - val_start_output_loss: 1.2580 - val_stop_output_loss: 2.1328 - val_start_output_accuracy: 0.6805 - val_stop_output_accuracy: 0.2890\n",
            "Epoch 88/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.1995 - start_output_loss: 0.5976 - stop_output_loss: 1.6019 - start_output_accuracy: 0.7996 - stop_output_accuracy: 0.4372 - val_loss: 3.5378 - val_start_output_loss: 1.0771 - val_stop_output_loss: 2.4608 - val_start_output_accuracy: 0.6665 - val_stop_output_accuracy: 0.2205\n",
            "Epoch 89/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 2.0097 - start_output_loss: 0.5033 - stop_output_loss: 1.5064 - start_output_accuracy: 0.8522 - stop_output_accuracy: 0.4581 - val_loss: 7.5004 - val_start_output_loss: 3.6523 - val_stop_output_loss: 3.8481 - val_start_output_accuracy: 0.2900 - val_stop_output_accuracy: 0.1505\n",
            "Epoch 90/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.0079 - start_output_loss: 0.4788 - stop_output_loss: 1.5291 - start_output_accuracy: 0.8608 - stop_output_accuracy: 0.4391 - val_loss: 4.0707 - val_start_output_loss: 1.3035 - val_stop_output_loss: 2.7672 - val_start_output_accuracy: 0.6245 - val_stop_output_accuracy: 0.1750\n",
            "Epoch 91/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 2.0250 - start_output_loss: 0.4929 - stop_output_loss: 1.5322 - start_output_accuracy: 0.8432 - stop_output_accuracy: 0.4362 - val_loss: 2.6326 - val_start_output_loss: 0.9524 - val_stop_output_loss: 1.6802 - val_start_output_accuracy: 0.6715 - val_stop_output_accuracy: 0.3920\n",
            "Epoch 92/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 2.0368 - start_output_loss: 0.5246 - stop_output_loss: 1.5122 - start_output_accuracy: 0.8504 - stop_output_accuracy: 0.4570 - val_loss: 3.7892 - val_start_output_loss: 1.1437 - val_stop_output_loss: 2.6455 - val_start_output_accuracy: 0.6945 - val_stop_output_accuracy: 0.2185\n",
            "Epoch 93/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 2.0055 - start_output_loss: 0.4945 - stop_output_loss: 1.5111 - start_output_accuracy: 0.8551 - stop_output_accuracy: 0.4493 - val_loss: 2.0231 - val_start_output_loss: 0.5368 - val_stop_output_loss: 1.4863 - val_start_output_accuracy: 0.8420 - val_stop_output_accuracy: 0.4345\n",
            "Epoch 94/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 2.0795 - start_output_loss: 0.5168 - stop_output_loss: 1.5626 - start_output_accuracy: 0.8460 - stop_output_accuracy: 0.4403 - val_loss: 4.5081 - val_start_output_loss: 1.3880 - val_stop_output_loss: 3.1200 - val_start_output_accuracy: 0.5345 - val_stop_output_accuracy: 0.1475\n",
            "Epoch 95/600\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 1.9555 - start_output_loss: 0.4554 - stop_output_loss: 1.5001 - start_output_accuracy: 0.8634 - stop_output_accuracy: 0.4500 - val_loss: 2.7427 - val_start_output_loss: 0.7597 - val_stop_output_loss: 1.9830 - val_start_output_accuracy: 0.7615 - val_stop_output_accuracy: 0.2755\n",
            "Epoch 96/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.9825 - start_output_loss: 0.5004 - stop_output_loss: 1.4821 - start_output_accuracy: 0.8502 - stop_output_accuracy: 0.4529 - val_loss: 2.3647 - val_start_output_loss: 0.7821 - val_stop_output_loss: 1.5826 - val_start_output_accuracy: 0.7675 - val_stop_output_accuracy: 0.4265\n",
            "Epoch 97/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 1.9816 - start_output_loss: 0.4813 - stop_output_loss: 1.5003 - start_output_accuracy: 0.8510 - stop_output_accuracy: 0.4290 - val_loss: 3.1504 - val_start_output_loss: 1.2352 - val_stop_output_loss: 1.9152 - val_start_output_accuracy: 0.6085 - val_stop_output_accuracy: 0.3115\n",
            "Epoch 98/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.9647 - start_output_loss: 0.4863 - stop_output_loss: 1.4784 - start_output_accuracy: 0.8425 - stop_output_accuracy: 0.4493 - val_loss: 6.3164 - val_start_output_loss: 2.4925 - val_stop_output_loss: 3.8239 - val_start_output_accuracy: 0.4300 - val_stop_output_accuracy: 0.1920\n",
            "Epoch 99/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.9792 - start_output_loss: 0.4947 - stop_output_loss: 1.4845 - start_output_accuracy: 0.8536 - stop_output_accuracy: 0.4533 - val_loss: 2.1784 - val_start_output_loss: 0.6219 - val_stop_output_loss: 1.5564 - val_start_output_accuracy: 0.8225 - val_stop_output_accuracy: 0.4290\n",
            "Epoch 100/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.9176 - start_output_loss: 0.4638 - stop_output_loss: 1.4539 - start_output_accuracy: 0.8587 - stop_output_accuracy: 0.4586 - val_loss: 2.7701 - val_start_output_loss: 1.0396 - val_stop_output_loss: 1.7304 - val_start_output_accuracy: 0.5820 - val_stop_output_accuracy: 0.3495\n",
            "Epoch 101/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 2.2903 - start_output_loss: 0.6757 - stop_output_loss: 1.6145 - start_output_accuracy: 0.8050 - stop_output_accuracy: 0.4108 - val_loss: 2.9329 - val_start_output_loss: 0.9646 - val_stop_output_loss: 1.9684 - val_start_output_accuracy: 0.6605 - val_stop_output_accuracy: 0.2650\n",
            "Epoch 102/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 1.8224 - start_output_loss: 0.4292 - stop_output_loss: 1.3933 - start_output_accuracy: 0.8708 - stop_output_accuracy: 0.5066 - val_loss: 2.0360 - val_start_output_loss: 0.6025 - val_stop_output_loss: 1.4335 - val_start_output_accuracy: 0.8240 - val_stop_output_accuracy: 0.5005\n",
            "Epoch 103/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.9062 - start_output_loss: 0.4752 - stop_output_loss: 1.4310 - start_output_accuracy: 0.8557 - stop_output_accuracy: 0.4640 - val_loss: 2.0739 - val_start_output_loss: 0.5381 - val_stop_output_loss: 1.5358 - val_start_output_accuracy: 0.8130 - val_stop_output_accuracy: 0.3925\n",
            "Epoch 104/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 1.8944 - start_output_loss: 0.4581 - stop_output_loss: 1.4363 - start_output_accuracy: 0.8561 - stop_output_accuracy: 0.4728 - val_loss: 2.0160 - val_start_output_loss: 0.4988 - val_stop_output_loss: 1.5172 - val_start_output_accuracy: 0.8480 - val_stop_output_accuracy: 0.4450\n",
            "Epoch 105/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 1.8637 - start_output_loss: 0.4655 - stop_output_loss: 1.3983 - start_output_accuracy: 0.8569 - stop_output_accuracy: 0.4809 - val_loss: 1.8969 - val_start_output_loss: 0.5014 - val_stop_output_loss: 1.3955 - val_start_output_accuracy: 0.8535 - val_stop_output_accuracy: 0.5245\n",
            "Epoch 106/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.8662 - start_output_loss: 0.4708 - stop_output_loss: 1.3954 - start_output_accuracy: 0.8585 - stop_output_accuracy: 0.4872 - val_loss: 2.2638 - val_start_output_loss: 0.7610 - val_stop_output_loss: 1.5028 - val_start_output_accuracy: 0.7450 - val_stop_output_accuracy: 0.4670\n",
            "Epoch 107/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.7745 - start_output_loss: 0.3951 - stop_output_loss: 1.3794 - start_output_accuracy: 0.8896 - stop_output_accuracy: 0.4972 - val_loss: 1.9575 - val_start_output_loss: 0.4239 - val_stop_output_loss: 1.5336 - val_start_output_accuracy: 0.8935 - val_stop_output_accuracy: 0.4140\n",
            "Epoch 108/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.7816 - start_output_loss: 0.4148 - stop_output_loss: 1.3668 - start_output_accuracy: 0.8865 - stop_output_accuracy: 0.4841 - val_loss: 2.4644 - val_start_output_loss: 0.5885 - val_stop_output_loss: 1.8758 - val_start_output_accuracy: 0.8120 - val_stop_output_accuracy: 0.2925\n",
            "Epoch 109/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.7560 - start_output_loss: 0.4103 - stop_output_loss: 1.3458 - start_output_accuracy: 0.8853 - stop_output_accuracy: 0.5105 - val_loss: 2.2647 - val_start_output_loss: 0.7600 - val_stop_output_loss: 1.5047 - val_start_output_accuracy: 0.7515 - val_stop_output_accuracy: 0.4830\n",
            "Epoch 110/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.7919 - start_output_loss: 0.4198 - stop_output_loss: 1.3721 - start_output_accuracy: 0.8819 - stop_output_accuracy: 0.4924 - val_loss: 4.9106 - val_start_output_loss: 1.6026 - val_stop_output_loss: 3.3080 - val_start_output_accuracy: 0.5465 - val_stop_output_accuracy: 0.1940\n",
            "Epoch 111/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 1.8025 - start_output_loss: 0.4479 - stop_output_loss: 1.3546 - start_output_accuracy: 0.8751 - stop_output_accuracy: 0.5013 - val_loss: 9.1569 - val_start_output_loss: 5.0194 - val_stop_output_loss: 4.1375 - val_start_output_accuracy: 0.2200 - val_stop_output_accuracy: 0.1475\n",
            "Epoch 112/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 1.7468 - start_output_loss: 0.4011 - stop_output_loss: 1.3457 - start_output_accuracy: 0.8930 - stop_output_accuracy: 0.5256 - val_loss: 1.9783 - val_start_output_loss: 0.4991 - val_stop_output_loss: 1.4791 - val_start_output_accuracy: 0.8545 - val_stop_output_accuracy: 0.4125\n",
            "Epoch 113/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.7222 - start_output_loss: 0.4565 - stop_output_loss: 1.2657 - start_output_accuracy: 0.8802 - stop_output_accuracy: 0.5535 - val_loss: 2.4108 - val_start_output_loss: 0.6753 - val_stop_output_loss: 1.7354 - val_start_output_accuracy: 0.8140 - val_stop_output_accuracy: 0.2795\n",
            "Epoch 114/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.6193 - start_output_loss: 0.3988 - stop_output_loss: 1.2205 - start_output_accuracy: 0.8964 - stop_output_accuracy: 0.5527 - val_loss: 2.0745 - val_start_output_loss: 0.5278 - val_stop_output_loss: 1.5467 - val_start_output_accuracy: 0.8450 - val_stop_output_accuracy: 0.3240\n",
            "Epoch 115/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 1.5946 - start_output_loss: 0.3815 - stop_output_loss: 1.2131 - start_output_accuracy: 0.9024 - stop_output_accuracy: 0.5561 - val_loss: 4.1997 - val_start_output_loss: 1.3221 - val_stop_output_loss: 2.8776 - val_start_output_accuracy: 0.6075 - val_stop_output_accuracy: 0.1275\n",
            "Epoch 116/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.5782 - start_output_loss: 0.3747 - stop_output_loss: 1.2035 - start_output_accuracy: 0.9024 - stop_output_accuracy: 0.5619 - val_loss: 3.5878 - val_start_output_loss: 2.0598 - val_stop_output_loss: 1.5280 - val_start_output_accuracy: 0.4265 - val_stop_output_accuracy: 0.4415\n",
            "Epoch 117/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.6747 - start_output_loss: 0.4177 - stop_output_loss: 1.2570 - start_output_accuracy: 0.8820 - stop_output_accuracy: 0.5390 - val_loss: 1.7460 - val_start_output_loss: 0.4283 - val_stop_output_loss: 1.3177 - val_start_output_accuracy: 0.8880 - val_stop_output_accuracy: 0.5105\n",
            "Epoch 118/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.5758 - start_output_loss: 0.3782 - stop_output_loss: 1.1976 - start_output_accuracy: 0.8948 - stop_output_accuracy: 0.5570 - val_loss: 1.7084 - val_start_output_loss: 0.4208 - val_stop_output_loss: 1.2876 - val_start_output_accuracy: 0.8910 - val_stop_output_accuracy: 0.5070\n",
            "Epoch 119/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.8525 - start_output_loss: 0.5056 - stop_output_loss: 1.3470 - start_output_accuracy: 0.8640 - stop_output_accuracy: 0.4927 - val_loss: 3.2287 - val_start_output_loss: 1.3606 - val_stop_output_loss: 1.8681 - val_start_output_accuracy: 0.6510 - val_stop_output_accuracy: 0.3725\n",
            "Epoch 120/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.9190 - start_output_loss: 0.6212 - stop_output_loss: 1.2978 - start_output_accuracy: 0.8036 - stop_output_accuracy: 0.5413 - val_loss: 7.9303 - val_start_output_loss: 3.8403 - val_stop_output_loss: 4.0899 - val_start_output_accuracy: 0.2125 - val_stop_output_accuracy: 0.1715\n",
            "Epoch 121/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 1.8420 - start_output_loss: 0.5423 - stop_output_loss: 1.2997 - start_output_accuracy: 0.8191 - stop_output_accuracy: 0.5234 - val_loss: 2.3798 - val_start_output_loss: 0.9802 - val_stop_output_loss: 1.3997 - val_start_output_accuracy: 0.6725 - val_stop_output_accuracy: 0.5425\n",
            "Epoch 122/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.6685 - start_output_loss: 0.4669 - stop_output_loss: 1.2016 - start_output_accuracy: 0.8555 - stop_output_accuracy: 0.5641 - val_loss: 2.0103 - val_start_output_loss: 0.6791 - val_stop_output_loss: 1.3312 - val_start_output_accuracy: 0.7640 - val_stop_output_accuracy: 0.5020\n",
            "Epoch 123/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.6837 - start_output_loss: 0.4695 - stop_output_loss: 1.2143 - start_output_accuracy: 0.8608 - stop_output_accuracy: 0.5505 - val_loss: 2.2257 - val_start_output_loss: 0.6453 - val_stop_output_loss: 1.5805 - val_start_output_accuracy: 0.7800 - val_stop_output_accuracy: 0.3315\n",
            "Epoch 124/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 1.6697 - start_output_loss: 0.4565 - stop_output_loss: 1.2132 - start_output_accuracy: 0.8597 - stop_output_accuracy: 0.5508 - val_loss: 1.8685 - val_start_output_loss: 0.5942 - val_stop_output_loss: 1.2743 - val_start_output_accuracy: 0.8275 - val_stop_output_accuracy: 0.5450\n",
            "Epoch 125/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.5784 - start_output_loss: 0.4006 - stop_output_loss: 1.1778 - start_output_accuracy: 0.8840 - stop_output_accuracy: 0.5724 - val_loss: 1.8425 - val_start_output_loss: 0.5227 - val_stop_output_loss: 1.3199 - val_start_output_accuracy: 0.8320 - val_stop_output_accuracy: 0.4690\n",
            "Epoch 126/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.5247 - start_output_loss: 0.3776 - stop_output_loss: 1.1472 - start_output_accuracy: 0.8935 - stop_output_accuracy: 0.5854 - val_loss: 2.7405 - val_start_output_loss: 1.2470 - val_stop_output_loss: 1.4935 - val_start_output_accuracy: 0.7050 - val_stop_output_accuracy: 0.4605\n",
            "Epoch 127/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.7632 - start_output_loss: 0.5295 - stop_output_loss: 1.2338 - start_output_accuracy: 0.8394 - stop_output_accuracy: 0.5530 - val_loss: 13.9716 - val_start_output_loss: 9.0006 - val_stop_output_loss: 4.9711 - val_start_output_accuracy: 0.2685 - val_stop_output_accuracy: 0.1040\n",
            "Epoch 128/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 2.4671 - start_output_loss: 1.0202 - stop_output_loss: 1.4468 - start_output_accuracy: 0.7716 - stop_output_accuracy: 0.5502 - val_loss: 1.8644 - val_start_output_loss: 0.6008 - val_stop_output_loss: 1.2635 - val_start_output_accuracy: 0.8450 - val_stop_output_accuracy: 0.5430\n",
            "Epoch 129/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.4987 - start_output_loss: 0.4223 - stop_output_loss: 1.0764 - start_output_accuracy: 0.8826 - stop_output_accuracy: 0.6214 - val_loss: 1.8365 - val_start_output_loss: 0.5210 - val_stop_output_loss: 1.3155 - val_start_output_accuracy: 0.8615 - val_stop_output_accuracy: 0.4760\n",
            "Epoch 130/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.4727 - start_output_loss: 0.3891 - stop_output_loss: 1.0836 - start_output_accuracy: 0.8998 - stop_output_accuracy: 0.6152 - val_loss: 2.4532 - val_start_output_loss: 0.7283 - val_stop_output_loss: 1.7249 - val_start_output_accuracy: 0.7920 - val_stop_output_accuracy: 0.3160\n",
            "Epoch 131/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.4608 - start_output_loss: 0.3751 - stop_output_loss: 1.0857 - start_output_accuracy: 0.9004 - stop_output_accuracy: 0.6134 - val_loss: 1.7317 - val_start_output_loss: 0.4929 - val_stop_output_loss: 1.2389 - val_start_output_accuracy: 0.8735 - val_stop_output_accuracy: 0.5285\n",
            "Epoch 132/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 1.6173 - start_output_loss: 0.4966 - stop_output_loss: 1.1207 - start_output_accuracy: 0.8613 - stop_output_accuracy: 0.5926 - val_loss: 1.8480 - val_start_output_loss: 0.5500 - val_stop_output_loss: 1.2980 - val_start_output_accuracy: 0.8765 - val_stop_output_accuracy: 0.5105\n",
            "Epoch 133/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.5853 - start_output_loss: 0.4320 - stop_output_loss: 1.1533 - start_output_accuracy: 0.8867 - stop_output_accuracy: 0.5830 - val_loss: 1.7709 - val_start_output_loss: 0.4684 - val_stop_output_loss: 1.3024 - val_start_output_accuracy: 0.8875 - val_stop_output_accuracy: 0.4775\n",
            "Epoch 134/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 1.5016 - start_output_loss: 0.4202 - stop_output_loss: 1.0815 - start_output_accuracy: 0.8766 - stop_output_accuracy: 0.6156 - val_loss: 1.6425 - val_start_output_loss: 0.5016 - val_stop_output_loss: 1.1409 - val_start_output_accuracy: 0.8505 - val_stop_output_accuracy: 0.6120\n",
            "Epoch 135/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 2.1010 - start_output_loss: 0.8208 - stop_output_loss: 1.2802 - start_output_accuracy: 0.8123 - stop_output_accuracy: 0.5753 - val_loss: 1.7365 - val_start_output_loss: 0.5490 - val_stop_output_loss: 1.1876 - val_start_output_accuracy: 0.8750 - val_stop_output_accuracy: 0.5755\n",
            "Epoch 136/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 1.4215 - start_output_loss: 0.3994 - stop_output_loss: 1.0221 - start_output_accuracy: 0.8986 - stop_output_accuracy: 0.6353 - val_loss: 1.7320 - val_start_output_loss: 0.5550 - val_stop_output_loss: 1.1769 - val_start_output_accuracy: 0.8510 - val_stop_output_accuracy: 0.5955\n",
            "Epoch 137/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.4221 - start_output_loss: 0.3982 - stop_output_loss: 1.0240 - start_output_accuracy: 0.9006 - stop_output_accuracy: 0.6408 - val_loss: 1.7056 - val_start_output_loss: 0.5677 - val_stop_output_loss: 1.1379 - val_start_output_accuracy: 0.8685 - val_stop_output_accuracy: 0.6070\n",
            "Epoch 138/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.5923 - start_output_loss: 0.4712 - stop_output_loss: 1.1211 - start_output_accuracy: 0.8676 - stop_output_accuracy: 0.5847 - val_loss: 1.7919 - val_start_output_loss: 0.5676 - val_stop_output_loss: 1.2243 - val_start_output_accuracy: 0.8550 - val_stop_output_accuracy: 0.5395\n",
            "Epoch 139/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.4175 - start_output_loss: 0.3913 - stop_output_loss: 1.0262 - start_output_accuracy: 0.8981 - stop_output_accuracy: 0.6354 - val_loss: 1.7106 - val_start_output_loss: 0.5628 - val_stop_output_loss: 1.1478 - val_start_output_accuracy: 0.8555 - val_stop_output_accuracy: 0.5675\n",
            "Epoch 140/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.5051 - start_output_loss: 0.4313 - stop_output_loss: 1.0738 - start_output_accuracy: 0.8753 - stop_output_accuracy: 0.6040 - val_loss: 6.9293 - val_start_output_loss: 2.1803 - val_stop_output_loss: 4.7490 - val_start_output_accuracy: 0.5620 - val_stop_output_accuracy: 0.0555\n",
            "Epoch 141/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.4684 - start_output_loss: 0.4182 - stop_output_loss: 1.0502 - start_output_accuracy: 0.8802 - stop_output_accuracy: 0.6270 - val_loss: 2.6460 - val_start_output_loss: 0.6718 - val_stop_output_loss: 1.9741 - val_start_output_accuracy: 0.7885 - val_stop_output_accuracy: 0.2780\n",
            "Epoch 142/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.4080 - start_output_loss: 0.3852 - stop_output_loss: 1.0228 - start_output_accuracy: 0.8924 - stop_output_accuracy: 0.6368 - val_loss: 2.1328 - val_start_output_loss: 0.5269 - val_stop_output_loss: 1.6059 - val_start_output_accuracy: 0.8445 - val_stop_output_accuracy: 0.3830\n",
            "Epoch 143/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.6213 - start_output_loss: 0.5237 - stop_output_loss: 1.0976 - start_output_accuracy: 0.8341 - stop_output_accuracy: 0.6115 - val_loss: 3.7173 - val_start_output_loss: 0.8942 - val_stop_output_loss: 2.8231 - val_start_output_accuracy: 0.7450 - val_stop_output_accuracy: 0.1175\n",
            "Epoch 144/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.4741 - start_output_loss: 0.4235 - stop_output_loss: 1.0506 - start_output_accuracy: 0.8715 - stop_output_accuracy: 0.5945 - val_loss: 3.5836 - val_start_output_loss: 1.3103 - val_stop_output_loss: 2.2733 - val_start_output_accuracy: 0.6970 - val_stop_output_accuracy: 0.2700\n",
            "Epoch 145/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.4257 - start_output_loss: 0.4355 - stop_output_loss: 0.9903 - start_output_accuracy: 0.8600 - stop_output_accuracy: 0.6405 - val_loss: 2.5740 - val_start_output_loss: 1.3487 - val_stop_output_loss: 1.2253 - val_start_output_accuracy: 0.5405 - val_stop_output_accuracy: 0.5265\n",
            "Epoch 146/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.5639 - start_output_loss: 0.4689 - stop_output_loss: 1.0951 - start_output_accuracy: 0.8551 - stop_output_accuracy: 0.5838 - val_loss: 2.9058 - val_start_output_loss: 0.9384 - val_stop_output_loss: 1.9674 - val_start_output_accuracy: 0.7340 - val_stop_output_accuracy: 0.2395\n",
            "Epoch 147/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.3509 - start_output_loss: 0.3896 - stop_output_loss: 0.9613 - start_output_accuracy: 0.8899 - stop_output_accuracy: 0.6493 - val_loss: 2.1108 - val_start_output_loss: 0.5729 - val_stop_output_loss: 1.5379 - val_start_output_accuracy: 0.8210 - val_stop_output_accuracy: 0.3225\n",
            "Epoch 148/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.3923 - start_output_loss: 0.4188 - stop_output_loss: 0.9734 - start_output_accuracy: 0.8683 - stop_output_accuracy: 0.6377 - val_loss: 1.6729 - val_start_output_loss: 0.5279 - val_stop_output_loss: 1.1451 - val_start_output_accuracy: 0.8390 - val_stop_output_accuracy: 0.5685\n",
            "Epoch 149/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.4337 - start_output_loss: 0.4249 - stop_output_loss: 1.0088 - start_output_accuracy: 0.8727 - stop_output_accuracy: 0.6305 - val_loss: 2.0004 - val_start_output_loss: 0.7393 - val_stop_output_loss: 1.2611 - val_start_output_accuracy: 0.7070 - val_stop_output_accuracy: 0.5910\n",
            "Epoch 150/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.3461 - start_output_loss: 0.3948 - stop_output_loss: 0.9513 - start_output_accuracy: 0.8827 - stop_output_accuracy: 0.6478 - val_loss: 1.9764 - val_start_output_loss: 0.7082 - val_stop_output_loss: 1.2682 - val_start_output_accuracy: 0.7615 - val_stop_output_accuracy: 0.4935\n",
            "Epoch 151/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.3658 - start_output_loss: 0.4183 - stop_output_loss: 0.9475 - start_output_accuracy: 0.8766 - stop_output_accuracy: 0.6634 - val_loss: 1.9922 - val_start_output_loss: 0.5192 - val_stop_output_loss: 1.4731 - val_start_output_accuracy: 0.8490 - val_stop_output_accuracy: 0.3425\n",
            "Epoch 152/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.3006 - start_output_loss: 0.3798 - stop_output_loss: 0.9209 - start_output_accuracy: 0.8920 - stop_output_accuracy: 0.6723 - val_loss: 1.7349 - val_start_output_loss: 0.5636 - val_stop_output_loss: 1.1713 - val_start_output_accuracy: 0.8405 - val_stop_output_accuracy: 0.5160\n",
            "Epoch 153/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 1.3862 - start_output_loss: 0.4383 - stop_output_loss: 0.9479 - start_output_accuracy: 0.8651 - stop_output_accuracy: 0.6585 - val_loss: 1.4670 - val_start_output_loss: 0.4889 - val_stop_output_loss: 0.9781 - val_start_output_accuracy: 0.8690 - val_stop_output_accuracy: 0.6870\n",
            "Epoch 154/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.2668 - start_output_loss: 0.3601 - stop_output_loss: 0.9067 - start_output_accuracy: 0.9028 - stop_output_accuracy: 0.6787 - val_loss: 2.5568 - val_start_output_loss: 1.3007 - val_stop_output_loss: 1.2561 - val_start_output_accuracy: 0.6485 - val_stop_output_accuracy: 0.5900\n",
            "Epoch 155/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 1.2919 - start_output_loss: 0.4055 - stop_output_loss: 0.8863 - start_output_accuracy: 0.8798 - stop_output_accuracy: 0.6891 - val_loss: 1.3405 - val_start_output_loss: 0.4636 - val_stop_output_loss: 0.8769 - val_start_output_accuracy: 0.8570 - val_stop_output_accuracy: 0.7180\n",
            "Epoch 156/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.3203 - start_output_loss: 0.4293 - stop_output_loss: 0.8910 - start_output_accuracy: 0.8689 - stop_output_accuracy: 0.6820 - val_loss: 2.0440 - val_start_output_loss: 0.9814 - val_stop_output_loss: 1.0626 - val_start_output_accuracy: 0.6360 - val_stop_output_accuracy: 0.6190\n",
            "Epoch 157/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.2756 - start_output_loss: 0.3728 - stop_output_loss: 0.9028 - start_output_accuracy: 0.8946 - stop_output_accuracy: 0.6567 - val_loss: 1.4924 - val_start_output_loss: 0.5026 - val_stop_output_loss: 0.9898 - val_start_output_accuracy: 0.8500 - val_stop_output_accuracy: 0.6655\n",
            "Epoch 158/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.3058 - start_output_loss: 0.4043 - stop_output_loss: 0.9016 - start_output_accuracy: 0.8809 - stop_output_accuracy: 0.6765 - val_loss: 2.0878 - val_start_output_loss: 1.0311 - val_stop_output_loss: 1.0567 - val_start_output_accuracy: 0.6620 - val_stop_output_accuracy: 0.6370\n",
            "Epoch 159/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.1358 - start_output_loss: 0.3571 - stop_output_loss: 0.7787 - start_output_accuracy: 0.9027 - stop_output_accuracy: 0.7498 - val_loss: 1.8698 - val_start_output_loss: 0.4836 - val_stop_output_loss: 1.3862 - val_start_output_accuracy: 0.8690 - val_stop_output_accuracy: 0.3735\n",
            "Epoch 160/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.3088 - start_output_loss: 0.4353 - stop_output_loss: 0.8735 - start_output_accuracy: 0.8717 - stop_output_accuracy: 0.6779 - val_loss: 9.5409 - val_start_output_loss: 2.4455 - val_stop_output_loss: 7.0954 - val_start_output_accuracy: 0.4740 - val_stop_output_accuracy: 0.0030\n",
            "Epoch 161/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 1.1960 - start_output_loss: 0.3625 - stop_output_loss: 0.8335 - start_output_accuracy: 0.8993 - stop_output_accuracy: 0.7231 - val_loss: 1.1706 - val_start_output_loss: 0.4013 - val_stop_output_loss: 0.7693 - val_start_output_accuracy: 0.8880 - val_stop_output_accuracy: 0.7315\n",
            "Epoch 162/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.1750 - start_output_loss: 0.3747 - stop_output_loss: 0.8004 - start_output_accuracy: 0.8896 - stop_output_accuracy: 0.7377 - val_loss: 1.6671 - val_start_output_loss: 0.7642 - val_stop_output_loss: 0.9029 - val_start_output_accuracy: 0.7285 - val_stop_output_accuracy: 0.6665\n",
            "Epoch 163/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.1970 - start_output_loss: 0.3870 - stop_output_loss: 0.8101 - start_output_accuracy: 0.8853 - stop_output_accuracy: 0.7226 - val_loss: 1.6540 - val_start_output_loss: 0.4655 - val_stop_output_loss: 1.1885 - val_start_output_accuracy: 0.8735 - val_stop_output_accuracy: 0.5955\n",
            "Epoch 164/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.2962 - start_output_loss: 0.3893 - stop_output_loss: 0.9068 - start_output_accuracy: 0.8848 - stop_output_accuracy: 0.6577 - val_loss: 1.5217 - val_start_output_loss: 0.4325 - val_stop_output_loss: 1.0892 - val_start_output_accuracy: 0.8830 - val_stop_output_accuracy: 0.6430\n",
            "Epoch 165/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 1.0573 - start_output_loss: 0.3280 - stop_output_loss: 0.7293 - start_output_accuracy: 0.9141 - stop_output_accuracy: 0.7682 - val_loss: 3.6772 - val_start_output_loss: 1.1812 - val_stop_output_loss: 2.4961 - val_start_output_accuracy: 0.7455 - val_stop_output_accuracy: 0.3125\n",
            "Epoch 166/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.2717 - start_output_loss: 0.4337 - stop_output_loss: 0.8380 - start_output_accuracy: 0.8685 - stop_output_accuracy: 0.6995 - val_loss: 2.9893 - val_start_output_loss: 0.7063 - val_stop_output_loss: 2.2831 - val_start_output_accuracy: 0.8290 - val_stop_output_accuracy: 0.0665\n",
            "Epoch 167/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 1.0889 - start_output_loss: 0.3289 - stop_output_loss: 0.7600 - start_output_accuracy: 0.9141 - stop_output_accuracy: 0.7424 - val_loss: 1.7470 - val_start_output_loss: 0.4640 - val_stop_output_loss: 1.2830 - val_start_output_accuracy: 0.8850 - val_stop_output_accuracy: 0.4455\n",
            "Epoch 168/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 1.2055 - start_output_loss: 0.3871 - stop_output_loss: 0.8184 - start_output_accuracy: 0.8899 - stop_output_accuracy: 0.7105 - val_loss: 2.2482 - val_start_output_loss: 0.6230 - val_stop_output_loss: 1.6253 - val_start_output_accuracy: 0.7925 - val_stop_output_accuracy: 0.3010\n",
            "Epoch 169/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 1.0640 - start_output_loss: 0.3407 - stop_output_loss: 0.7233 - start_output_accuracy: 0.9065 - stop_output_accuracy: 0.7728 - val_loss: 1.7827 - val_start_output_loss: 0.5840 - val_stop_output_loss: 1.1988 - val_start_output_accuracy: 0.8415 - val_stop_output_accuracy: 0.5880\n",
            "Epoch 170/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 1.1846 - start_output_loss: 0.3712 - stop_output_loss: 0.8134 - start_output_accuracy: 0.8946 - stop_output_accuracy: 0.7023 - val_loss: 1.1592 - val_start_output_loss: 0.3859 - val_stop_output_loss: 0.7734 - val_start_output_accuracy: 0.9110 - val_stop_output_accuracy: 0.7775\n",
            "Epoch 171/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.0642 - start_output_loss: 0.3488 - stop_output_loss: 0.7154 - start_output_accuracy: 0.8980 - stop_output_accuracy: 0.7702 - val_loss: 3.5301 - val_start_output_loss: 0.8718 - val_stop_output_loss: 2.6583 - val_start_output_accuracy: 0.7140 - val_stop_output_accuracy: 0.0905\n",
            "Epoch 172/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 1.0785 - start_output_loss: 0.3504 - stop_output_loss: 0.7281 - start_output_accuracy: 0.9014 - stop_output_accuracy: 0.7511 - val_loss: 1.7005 - val_start_output_loss: 0.4317 - val_stop_output_loss: 1.2688 - val_start_output_accuracy: 0.8980 - val_stop_output_accuracy: 0.5490\n",
            "Epoch 173/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 1.1625 - start_output_loss: 0.3760 - stop_output_loss: 0.7865 - start_output_accuracy: 0.8872 - stop_output_accuracy: 0.7257 - val_loss: 1.1563 - val_start_output_loss: 0.4117 - val_stop_output_loss: 0.7446 - val_start_output_accuracy: 0.8935 - val_stop_output_accuracy: 0.7765\n",
            "Epoch 174/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.0494 - start_output_loss: 0.3458 - stop_output_loss: 0.7036 - start_output_accuracy: 0.9061 - stop_output_accuracy: 0.7703 - val_loss: 1.4758 - val_start_output_loss: 0.4285 - val_stop_output_loss: 1.0472 - val_start_output_accuracy: 0.8640 - val_stop_output_accuracy: 0.5450\n",
            "Epoch 175/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.0267 - start_output_loss: 0.3216 - stop_output_loss: 0.7051 - start_output_accuracy: 0.9112 - stop_output_accuracy: 0.7727 - val_loss: 2.2630 - val_start_output_loss: 0.9196 - val_stop_output_loss: 1.3434 - val_start_output_accuracy: 0.6730 - val_stop_output_accuracy: 0.3925\n",
            "Epoch 176/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 1.2768 - start_output_loss: 0.3886 - stop_output_loss: 0.8883 - start_output_accuracy: 0.8800 - stop_output_accuracy: 0.6812 - val_loss: 1.7745 - val_start_output_loss: 0.5541 - val_stop_output_loss: 1.2204 - val_start_output_accuracy: 0.8455 - val_stop_output_accuracy: 0.4220\n",
            "Epoch 177/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.9213 - start_output_loss: 0.2830 - stop_output_loss: 0.6383 - start_output_accuracy: 0.9311 - stop_output_accuracy: 0.8194 - val_loss: 1.6018 - val_start_output_loss: 0.4515 - val_stop_output_loss: 1.1502 - val_start_output_accuracy: 0.8905 - val_stop_output_accuracy: 0.4965\n",
            "Epoch 178/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.9710 - start_output_loss: 0.3430 - stop_output_loss: 0.6280 - start_output_accuracy: 0.9047 - stop_output_accuracy: 0.8121 - val_loss: 1.4479 - val_start_output_loss: 0.4126 - val_stop_output_loss: 1.0353 - val_start_output_accuracy: 0.8955 - val_stop_output_accuracy: 0.6200\n",
            "Epoch 179/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 1.0867 - start_output_loss: 0.3593 - stop_output_loss: 0.7275 - start_output_accuracy: 0.8924 - stop_output_accuracy: 0.7483 - val_loss: 1.0995 - val_start_output_loss: 0.4351 - val_stop_output_loss: 0.6645 - val_start_output_accuracy: 0.8620 - val_stop_output_accuracy: 0.7970\n",
            "Epoch 180/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 1.0342 - start_output_loss: 0.3280 - stop_output_loss: 0.7062 - start_output_accuracy: 0.9097 - stop_output_accuracy: 0.7589 - val_loss: 1.2413 - val_start_output_loss: 0.4055 - val_stop_output_loss: 0.8357 - val_start_output_accuracy: 0.8805 - val_stop_output_accuracy: 0.6650\n",
            "Epoch 181/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.9942 - start_output_loss: 0.3318 - stop_output_loss: 0.6623 - start_output_accuracy: 0.9036 - stop_output_accuracy: 0.7858 - val_loss: 1.7976 - val_start_output_loss: 0.4119 - val_stop_output_loss: 1.3856 - val_start_output_accuracy: 0.8940 - val_stop_output_accuracy: 0.4590\n",
            "Epoch 182/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.9928 - start_output_loss: 0.3085 - stop_output_loss: 0.6843 - start_output_accuracy: 0.9186 - stop_output_accuracy: 0.7640 - val_loss: 2.0897 - val_start_output_loss: 0.6801 - val_stop_output_loss: 1.4097 - val_start_output_accuracy: 0.7815 - val_stop_output_accuracy: 0.4530\n",
            "Epoch 183/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.9724 - start_output_loss: 0.3136 - stop_output_loss: 0.6588 - start_output_accuracy: 0.9148 - stop_output_accuracy: 0.7836 - val_loss: 3.9295 - val_start_output_loss: 2.4907 - val_stop_output_loss: 1.4388 - val_start_output_accuracy: 0.2935 - val_stop_output_accuracy: 0.3445\n",
            "Epoch 184/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 1.0195 - start_output_loss: 0.3446 - stop_output_loss: 0.6748 - start_output_accuracy: 0.9029 - stop_output_accuracy: 0.7717 - val_loss: 1.2366 - val_start_output_loss: 0.4899 - val_stop_output_loss: 0.7467 - val_start_output_accuracy: 0.8490 - val_stop_output_accuracy: 0.8155\n",
            "Epoch 185/600\n",
            "10/10 [==============================] - 3s 347ms/step - loss: 0.9220 - start_output_loss: 0.2759 - stop_output_loss: 0.6461 - start_output_accuracy: 0.9300 - stop_output_accuracy: 0.7904 - val_loss: 2.0982 - val_start_output_loss: 0.6378 - val_stop_output_loss: 1.4604 - val_start_output_accuracy: 0.8100 - val_stop_output_accuracy: 0.4165\n",
            "Epoch 186/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 1.0848 - start_output_loss: 0.3548 - stop_output_loss: 0.7300 - start_output_accuracy: 0.8945 - stop_output_accuracy: 0.7243 - val_loss: 0.9479 - val_start_output_loss: 0.3388 - val_stop_output_loss: 0.6091 - val_start_output_accuracy: 0.9100 - val_stop_output_accuracy: 0.8375\n",
            "Epoch 187/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.9077 - start_output_loss: 0.3279 - stop_output_loss: 0.5798 - start_output_accuracy: 0.9086 - stop_output_accuracy: 0.8379 - val_loss: 1.3419 - val_start_output_loss: 0.3858 - val_stop_output_loss: 0.9561 - val_start_output_accuracy: 0.8955 - val_stop_output_accuracy: 0.5755\n",
            "Epoch 188/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 1.0452 - start_output_loss: 0.3129 - stop_output_loss: 0.7323 - start_output_accuracy: 0.9136 - stop_output_accuracy: 0.7407 - val_loss: 0.9068 - val_start_output_loss: 0.3376 - val_stop_output_loss: 0.5693 - val_start_output_accuracy: 0.9185 - val_stop_output_accuracy: 0.8400\n",
            "Epoch 189/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.8595 - start_output_loss: 0.2795 - stop_output_loss: 0.5800 - start_output_accuracy: 0.9266 - stop_output_accuracy: 0.8304 - val_loss: 3.3154 - val_start_output_loss: 2.4114 - val_stop_output_loss: 0.9040 - val_start_output_accuracy: 0.2890 - val_stop_output_accuracy: 0.6440\n",
            "Epoch 190/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.0862 - start_output_loss: 0.3851 - stop_output_loss: 0.7011 - start_output_accuracy: 0.8777 - stop_output_accuracy: 0.7476 - val_loss: 1.1961 - val_start_output_loss: 0.4417 - val_stop_output_loss: 0.7543 - val_start_output_accuracy: 0.8675 - val_stop_output_accuracy: 0.7525\n",
            "Epoch 191/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.8688 - start_output_loss: 0.2630 - stop_output_loss: 0.6058 - start_output_accuracy: 0.9341 - stop_output_accuracy: 0.8101 - val_loss: 1.4859 - val_start_output_loss: 0.7563 - val_stop_output_loss: 0.7296 - val_start_output_accuracy: 0.7450 - val_stop_output_accuracy: 0.7580\n",
            "Epoch 192/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.9600 - start_output_loss: 0.3405 - stop_output_loss: 0.6195 - start_output_accuracy: 0.8954 - stop_output_accuracy: 0.7947 - val_loss: 2.0080 - val_start_output_loss: 0.5861 - val_stop_output_loss: 1.4219 - val_start_output_accuracy: 0.8235 - val_stop_output_accuracy: 0.3900\n",
            "Epoch 193/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.8470 - start_output_loss: 0.2652 - stop_output_loss: 0.5817 - start_output_accuracy: 0.9296 - stop_output_accuracy: 0.8235 - val_loss: 1.2915 - val_start_output_loss: 0.3870 - val_stop_output_loss: 0.9045 - val_start_output_accuracy: 0.9095 - val_stop_output_accuracy: 0.6710\n",
            "Epoch 194/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.8943 - start_output_loss: 0.3038 - stop_output_loss: 0.5905 - start_output_accuracy: 0.9143 - stop_output_accuracy: 0.8109 - val_loss: 8.8910 - val_start_output_loss: 4.6201 - val_stop_output_loss: 4.2709 - val_start_output_accuracy: 0.3270 - val_stop_output_accuracy: 0.0260\n",
            "Epoch 195/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.9144 - start_output_loss: 0.3253 - stop_output_loss: 0.5891 - start_output_accuracy: 0.9110 - stop_output_accuracy: 0.8221 - val_loss: 2.8774 - val_start_output_loss: 0.6385 - val_stop_output_loss: 2.2389 - val_start_output_accuracy: 0.8115 - val_stop_output_accuracy: 0.1680\n",
            "Epoch 196/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.9077 - start_output_loss: 0.3227 - stop_output_loss: 0.5850 - start_output_accuracy: 0.9063 - stop_output_accuracy: 0.8116 - val_loss: 1.7577 - val_start_output_loss: 0.3816 - val_stop_output_loss: 1.3762 - val_start_output_accuracy: 0.8895 - val_stop_output_accuracy: 0.4075\n",
            "Epoch 197/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.9515 - start_output_loss: 0.2941 - stop_output_loss: 0.6573 - start_output_accuracy: 0.9211 - stop_output_accuracy: 0.7631 - val_loss: 0.9422 - val_start_output_loss: 0.3110 - val_stop_output_loss: 0.6311 - val_start_output_accuracy: 0.9320 - val_stop_output_accuracy: 0.7945\n",
            "Epoch 198/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.7889 - start_output_loss: 0.2615 - stop_output_loss: 0.5274 - start_output_accuracy: 0.9301 - stop_output_accuracy: 0.8542 - val_loss: 3.1169 - val_start_output_loss: 0.8348 - val_stop_output_loss: 2.2821 - val_start_output_accuracy: 0.6950 - val_stop_output_accuracy: 0.2115\n",
            "Epoch 199/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 1.0111 - start_output_loss: 0.3294 - stop_output_loss: 0.6817 - start_output_accuracy: 0.9053 - stop_output_accuracy: 0.7673 - val_loss: 0.8685 - val_start_output_loss: 0.2818 - val_stop_output_loss: 0.5867 - val_start_output_accuracy: 0.9415 - val_stop_output_accuracy: 0.8340\n",
            "Epoch 200/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.8393 - start_output_loss: 0.2425 - stop_output_loss: 0.5968 - start_output_accuracy: 0.9394 - stop_output_accuracy: 0.8130 - val_loss: 0.8725 - val_start_output_loss: 0.3291 - val_stop_output_loss: 0.5434 - val_start_output_accuracy: 0.9275 - val_stop_output_accuracy: 0.8590\n",
            "Epoch 201/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.8651 - start_output_loss: 0.3581 - stop_output_loss: 0.5070 - start_output_accuracy: 0.8877 - stop_output_accuracy: 0.8557 - val_loss: 1.0297 - val_start_output_loss: 0.3171 - val_stop_output_loss: 0.7126 - val_start_output_accuracy: 0.9360 - val_stop_output_accuracy: 0.7515\n",
            "Epoch 202/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.8399 - start_output_loss: 0.2552 - stop_output_loss: 0.5847 - start_output_accuracy: 0.9331 - stop_output_accuracy: 0.8090 - val_loss: 0.9403 - val_start_output_loss: 0.2981 - val_stop_output_loss: 0.6422 - val_start_output_accuracy: 0.9370 - val_stop_output_accuracy: 0.7760\n",
            "Epoch 203/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.8161 - start_output_loss: 0.2787 - stop_output_loss: 0.5374 - start_output_accuracy: 0.9220 - stop_output_accuracy: 0.8321 - val_loss: 1.0675 - val_start_output_loss: 0.3130 - val_stop_output_loss: 0.7545 - val_start_output_accuracy: 0.9335 - val_stop_output_accuracy: 0.7135\n",
            "Epoch 204/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.8855 - start_output_loss: 0.2705 - stop_output_loss: 0.6150 - start_output_accuracy: 0.9251 - stop_output_accuracy: 0.7817 - val_loss: 1.0652 - val_start_output_loss: 0.4356 - val_stop_output_loss: 0.6296 - val_start_output_accuracy: 0.8755 - val_stop_output_accuracy: 0.7985\n",
            "Epoch 205/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 0.8593 - start_output_loss: 0.3049 - stop_output_loss: 0.5543 - start_output_accuracy: 0.9097 - stop_output_accuracy: 0.8310 - val_loss: 0.8289 - val_start_output_loss: 0.3180 - val_stop_output_loss: 0.5109 - val_start_output_accuracy: 0.9300 - val_stop_output_accuracy: 0.8445\n",
            "Epoch 206/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.7935 - start_output_loss: 0.2393 - stop_output_loss: 0.5542 - start_output_accuracy: 0.9370 - stop_output_accuracy: 0.8177 - val_loss: 1.2961 - val_start_output_loss: 0.5523 - val_stop_output_loss: 0.7438 - val_start_output_accuracy: 0.8245 - val_stop_output_accuracy: 0.7030\n",
            "Epoch 207/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.9061 - start_output_loss: 0.3502 - stop_output_loss: 0.5558 - start_output_accuracy: 0.8972 - stop_output_accuracy: 0.8068 - val_loss: 7.1296 - val_start_output_loss: 4.7941 - val_stop_output_loss: 2.3356 - val_start_output_accuracy: 0.2955 - val_stop_output_accuracy: 0.3510\n",
            "Epoch 208/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.7588 - start_output_loss: 0.2524 - stop_output_loss: 0.5064 - start_output_accuracy: 0.9315 - stop_output_accuracy: 0.8573 - val_loss: 0.8906 - val_start_output_loss: 0.2937 - val_stop_output_loss: 0.5969 - val_start_output_accuracy: 0.9300 - val_stop_output_accuracy: 0.8090\n",
            "Epoch 209/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.8479 - start_output_loss: 0.2641 - stop_output_loss: 0.5838 - start_output_accuracy: 0.9250 - stop_output_accuracy: 0.8085 - val_loss: 1.1316 - val_start_output_loss: 0.4080 - val_stop_output_loss: 0.7237 - val_start_output_accuracy: 0.8870 - val_stop_output_accuracy: 0.6865\n",
            "Epoch 210/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.8408 - start_output_loss: 0.2972 - stop_output_loss: 0.5436 - start_output_accuracy: 0.9154 - stop_output_accuracy: 0.8234 - val_loss: 1.6104 - val_start_output_loss: 0.7031 - val_stop_output_loss: 0.9073 - val_start_output_accuracy: 0.7960 - val_stop_output_accuracy: 0.5960\n",
            "Epoch 211/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.8054 - start_output_loss: 0.2598 - stop_output_loss: 0.5456 - start_output_accuracy: 0.9298 - stop_output_accuracy: 0.8259 - val_loss: 1.6389 - val_start_output_loss: 0.7318 - val_stop_output_loss: 0.9071 - val_start_output_accuracy: 0.7505 - val_stop_output_accuracy: 0.5110\n",
            "Epoch 212/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.6931 - start_output_loss: 0.2202 - stop_output_loss: 0.4729 - start_output_accuracy: 0.9438 - stop_output_accuracy: 0.8663 - val_loss: 2.2319 - val_start_output_loss: 0.6129 - val_stop_output_loss: 1.6190 - val_start_output_accuracy: 0.8630 - val_stop_output_accuracy: 0.3405\n",
            "Epoch 213/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.8725 - start_output_loss: 0.3243 - stop_output_loss: 0.5481 - start_output_accuracy: 0.9041 - stop_output_accuracy: 0.8228 - val_loss: 1.3264 - val_start_output_loss: 0.3468 - val_stop_output_loss: 0.9796 - val_start_output_accuracy: 0.9215 - val_stop_output_accuracy: 0.6455\n",
            "Epoch 214/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.8076 - start_output_loss: 0.2524 - stop_output_loss: 0.5552 - start_output_accuracy: 0.9332 - stop_output_accuracy: 0.8043 - val_loss: 2.8124 - val_start_output_loss: 0.7576 - val_stop_output_loss: 2.0548 - val_start_output_accuracy: 0.7655 - val_stop_output_accuracy: 0.2965\n",
            "Epoch 215/600\n",
            "10/10 [==============================] - 3s 344ms/step - loss: 0.7728 - start_output_loss: 0.2570 - stop_output_loss: 0.5158 - start_output_accuracy: 0.9336 - stop_output_accuracy: 0.8392 - val_loss: 1.0239 - val_start_output_loss: 0.3130 - val_stop_output_loss: 0.7108 - val_start_output_accuracy: 0.9100 - val_stop_output_accuracy: 0.7590\n",
            "Epoch 216/600\n",
            "10/10 [==============================] - 3s 344ms/step - loss: 0.8061 - start_output_loss: 0.2977 - stop_output_loss: 0.5084 - start_output_accuracy: 0.9097 - stop_output_accuracy: 0.8364 - val_loss: 1.1952 - val_start_output_loss: 0.6081 - val_stop_output_loss: 0.5871 - val_start_output_accuracy: 0.7910 - val_stop_output_accuracy: 0.8305\n",
            "Epoch 217/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.6656 - start_output_loss: 0.2221 - stop_output_loss: 0.4436 - start_output_accuracy: 0.9437 - stop_output_accuracy: 0.8867 - val_loss: 1.0447 - val_start_output_loss: 0.3207 - val_stop_output_loss: 0.7240 - val_start_output_accuracy: 0.9275 - val_stop_output_accuracy: 0.6970\n",
            "Epoch 218/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.8229 - start_output_loss: 0.2355 - stop_output_loss: 0.5874 - start_output_accuracy: 0.9363 - stop_output_accuracy: 0.8056 - val_loss: 0.8365 - val_start_output_loss: 0.2821 - val_stop_output_loss: 0.5543 - val_start_output_accuracy: 0.9335 - val_stop_output_accuracy: 0.8350\n",
            "Epoch 219/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.8340 - start_output_loss: 0.3247 - stop_output_loss: 0.5093 - start_output_accuracy: 0.8985 - stop_output_accuracy: 0.8437 - val_loss: 1.0944 - val_start_output_loss: 0.3648 - val_stop_output_loss: 0.7296 - val_start_output_accuracy: 0.9245 - val_stop_output_accuracy: 0.7340\n",
            "Epoch 220/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.6965 - start_output_loss: 0.2157 - stop_output_loss: 0.4808 - start_output_accuracy: 0.9482 - stop_output_accuracy: 0.8511 - val_loss: 0.8683 - val_start_output_loss: 0.2677 - val_stop_output_loss: 0.6006 - val_start_output_accuracy: 0.9520 - val_stop_output_accuracy: 0.8130\n",
            "Epoch 221/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.6821 - start_output_loss: 0.2309 - stop_output_loss: 0.4513 - start_output_accuracy: 0.9368 - stop_output_accuracy: 0.8723 - val_loss: 2.1793 - val_start_output_loss: 0.3634 - val_stop_output_loss: 1.8159 - val_start_output_accuracy: 0.8965 - val_stop_output_accuracy: 0.1785\n",
            "Epoch 222/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.7441 - start_output_loss: 0.2374 - stop_output_loss: 0.5068 - start_output_accuracy: 0.9322 - stop_output_accuracy: 0.8500 - val_loss: 0.8286 - val_start_output_loss: 0.3697 - val_stop_output_loss: 0.4590 - val_start_output_accuracy: 0.8930 - val_stop_output_accuracy: 0.8765\n",
            "Epoch 223/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.8671 - start_output_loss: 0.3102 - stop_output_loss: 0.5569 - start_output_accuracy: 0.9014 - stop_output_accuracy: 0.8168 - val_loss: 1.2211 - val_start_output_loss: 0.2823 - val_stop_output_loss: 0.9388 - val_start_output_accuracy: 0.9345 - val_stop_output_accuracy: 0.5830\n",
            "Epoch 224/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.6140 - start_output_loss: 0.1951 - stop_output_loss: 0.4190 - start_output_accuracy: 0.9545 - stop_output_accuracy: 0.8972 - val_loss: 1.0994 - val_start_output_loss: 0.2754 - val_stop_output_loss: 0.8240 - val_start_output_accuracy: 0.9380 - val_stop_output_accuracy: 0.6510\n",
            "Epoch 225/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.7273 - start_output_loss: 0.2609 - stop_output_loss: 0.4664 - start_output_accuracy: 0.9250 - stop_output_accuracy: 0.8517 - val_loss: 1.5449 - val_start_output_loss: 0.3503 - val_stop_output_loss: 1.1945 - val_start_output_accuracy: 0.9160 - val_stop_output_accuracy: 0.3995\n",
            "Epoch 226/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.7478 - start_output_loss: 0.2466 - stop_output_loss: 0.5013 - start_output_accuracy: 0.9348 - stop_output_accuracy: 0.8475 - val_loss: 1.3832 - val_start_output_loss: 0.3113 - val_stop_output_loss: 1.0719 - val_start_output_accuracy: 0.9245 - val_stop_output_accuracy: 0.3640\n",
            "Epoch 227/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.6490 - start_output_loss: 0.2341 - stop_output_loss: 0.4148 - start_output_accuracy: 0.9345 - stop_output_accuracy: 0.8784 - val_loss: 2.7218 - val_start_output_loss: 1.4858 - val_stop_output_loss: 1.2360 - val_start_output_accuracy: 0.6205 - val_stop_output_accuracy: 0.5635\n",
            "Epoch 228/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.7456 - start_output_loss: 0.2375 - stop_output_loss: 0.5081 - start_output_accuracy: 0.9373 - stop_output_accuracy: 0.8277 - val_loss: 1.2642 - val_start_output_loss: 0.5156 - val_stop_output_loss: 0.7486 - val_start_output_accuracy: 0.8830 - val_stop_output_accuracy: 0.7685\n",
            "Epoch 229/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.6638 - start_output_loss: 0.2162 - stop_output_loss: 0.4476 - start_output_accuracy: 0.9437 - stop_output_accuracy: 0.8666 - val_loss: 1.5873 - val_start_output_loss: 0.2668 - val_stop_output_loss: 1.3205 - val_start_output_accuracy: 0.9530 - val_stop_output_accuracy: 0.5395\n",
            "Epoch 230/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.6609 - start_output_loss: 0.2187 - stop_output_loss: 0.4422 - start_output_accuracy: 0.9409 - stop_output_accuracy: 0.8642 - val_loss: 1.0864 - val_start_output_loss: 0.2840 - val_stop_output_loss: 0.8025 - val_start_output_accuracy: 0.9465 - val_stop_output_accuracy: 0.7145\n",
            "Epoch 231/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.7959 - start_output_loss: 0.2785 - stop_output_loss: 0.5174 - start_output_accuracy: 0.9213 - stop_output_accuracy: 0.8237 - val_loss: 1.0799 - val_start_output_loss: 0.3822 - val_stop_output_loss: 0.6977 - val_start_output_accuracy: 0.8980 - val_stop_output_accuracy: 0.7505\n",
            "Epoch 232/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.6284 - start_output_loss: 0.1961 - stop_output_loss: 0.4324 - start_output_accuracy: 0.9510 - stop_output_accuracy: 0.8798 - val_loss: 1.9023 - val_start_output_loss: 0.3004 - val_stop_output_loss: 1.6020 - val_start_output_accuracy: 0.9260 - val_stop_output_accuracy: 0.2305\n",
            "Epoch 233/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.6173 - start_output_loss: 0.2282 - stop_output_loss: 0.3891 - start_output_accuracy: 0.9341 - stop_output_accuracy: 0.8880 - val_loss: 2.7228 - val_start_output_loss: 0.4440 - val_stop_output_loss: 2.2789 - val_start_output_accuracy: 0.8950 - val_stop_output_accuracy: 0.3435\n",
            "Epoch 234/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 1.0652 - start_output_loss: 0.3048 - stop_output_loss: 0.7604 - start_output_accuracy: 0.9089 - stop_output_accuracy: 0.7163 - val_loss: 0.8789 - val_start_output_loss: 0.3024 - val_stop_output_loss: 0.5765 - val_start_output_accuracy: 0.9340 - val_stop_output_accuracy: 0.8470\n",
            "Epoch 235/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.5166 - start_output_loss: 0.1802 - stop_output_loss: 0.3364 - start_output_accuracy: 0.9585 - stop_output_accuracy: 0.9293 - val_loss: 1.3048 - val_start_output_loss: 0.3981 - val_stop_output_loss: 0.9067 - val_start_output_accuracy: 0.8885 - val_stop_output_accuracy: 0.6215\n",
            "Epoch 236/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.8824 - start_output_loss: 0.3241 - stop_output_loss: 0.5583 - start_output_accuracy: 0.9039 - stop_output_accuracy: 0.8105 - val_loss: 0.7830 - val_start_output_loss: 0.3058 - val_stop_output_loss: 0.4772 - val_start_output_accuracy: 0.9415 - val_stop_output_accuracy: 0.8495\n",
            "Epoch 237/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.6590 - start_output_loss: 0.2286 - stop_output_loss: 0.4304 - start_output_accuracy: 0.9392 - stop_output_accuracy: 0.8746 - val_loss: 1.6313 - val_start_output_loss: 0.8210 - val_stop_output_loss: 0.8104 - val_start_output_accuracy: 0.7640 - val_stop_output_accuracy: 0.7245\n",
            "Epoch 238/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.6426 - start_output_loss: 0.2257 - stop_output_loss: 0.4170 - start_output_accuracy: 0.9424 - stop_output_accuracy: 0.8808 - val_loss: 0.9650 - val_start_output_loss: 0.4013 - val_stop_output_loss: 0.5637 - val_start_output_accuracy: 0.8815 - val_stop_output_accuracy: 0.7950\n",
            "Epoch 239/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.5631 - start_output_loss: 0.1842 - stop_output_loss: 0.3789 - start_output_accuracy: 0.9576 - stop_output_accuracy: 0.8895 - val_loss: 1.4543 - val_start_output_loss: 0.6370 - val_stop_output_loss: 0.8172 - val_start_output_accuracy: 0.8065 - val_stop_output_accuracy: 0.7545\n",
            "Epoch 240/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.7279 - start_output_loss: 0.2256 - stop_output_loss: 0.5023 - start_output_accuracy: 0.9383 - stop_output_accuracy: 0.8264 - val_loss: 0.8103 - val_start_output_loss: 0.2324 - val_stop_output_loss: 0.5779 - val_start_output_accuracy: 0.9655 - val_stop_output_accuracy: 0.8130\n",
            "Epoch 241/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.6797 - start_output_loss: 0.2793 - stop_output_loss: 0.4004 - start_output_accuracy: 0.9139 - stop_output_accuracy: 0.8842 - val_loss: 1.8915 - val_start_output_loss: 0.4369 - val_stop_output_loss: 1.4546 - val_start_output_accuracy: 0.8775 - val_stop_output_accuracy: 0.2850\n",
            "Epoch 242/600\n",
            "10/10 [==============================] - 3s 352ms/step - loss: 0.5918 - start_output_loss: 0.1832 - stop_output_loss: 0.4086 - start_output_accuracy: 0.9548 - stop_output_accuracy: 0.8828 - val_loss: 1.2577 - val_start_output_loss: 0.3501 - val_stop_output_loss: 0.9076 - val_start_output_accuracy: 0.9160 - val_stop_output_accuracy: 0.6310\n",
            "Epoch 243/600\n",
            "10/10 [==============================] - 3s 348ms/step - loss: 0.6112 - start_output_loss: 0.1884 - stop_output_loss: 0.4228 - start_output_accuracy: 0.9484 - stop_output_accuracy: 0.8676 - val_loss: 1.1417 - val_start_output_loss: 0.4494 - val_stop_output_loss: 0.6923 - val_start_output_accuracy: 0.8640 - val_stop_output_accuracy: 0.7635\n",
            "Epoch 244/600\n",
            "10/10 [==============================] - 3s 352ms/step - loss: 0.8107 - start_output_loss: 0.2562 - stop_output_loss: 0.5545 - start_output_accuracy: 0.9247 - stop_output_accuracy: 0.7916 - val_loss: 1.6600 - val_start_output_loss: 1.0819 - val_stop_output_loss: 0.5781 - val_start_output_accuracy: 0.6380 - val_stop_output_accuracy: 0.8130\n",
            "Epoch 245/600\n",
            "10/10 [==============================] - 3s 349ms/step - loss: 0.4824 - start_output_loss: 0.1656 - stop_output_loss: 0.3168 - start_output_accuracy: 0.9622 - stop_output_accuracy: 0.9398 - val_loss: 0.7420 - val_start_output_loss: 0.2737 - val_stop_output_loss: 0.4683 - val_start_output_accuracy: 0.9460 - val_stop_output_accuracy: 0.8505\n",
            "Epoch 246/600\n",
            "10/10 [==============================] - 4s 355ms/step - loss: 0.7351 - start_output_loss: 0.2534 - stop_output_loss: 0.4817 - start_output_accuracy: 0.9304 - stop_output_accuracy: 0.8336 - val_loss: 0.6118 - val_start_output_loss: 0.2382 - val_stop_output_loss: 0.3736 - val_start_output_accuracy: 0.9510 - val_stop_output_accuracy: 0.9185\n",
            "Epoch 247/600\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 0.5683 - start_output_loss: 0.1879 - stop_output_loss: 0.3804 - start_output_accuracy: 0.9510 - stop_output_accuracy: 0.9009 - val_loss: 0.8553 - val_start_output_loss: 0.3975 - val_stop_output_loss: 0.4578 - val_start_output_accuracy: 0.8795 - val_stop_output_accuracy: 0.8770\n",
            "Epoch 248/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.5979 - start_output_loss: 0.1857 - stop_output_loss: 0.4122 - start_output_accuracy: 0.9527 - stop_output_accuracy: 0.8751 - val_loss: 0.8308 - val_start_output_loss: 0.4129 - val_stop_output_loss: 0.4180 - val_start_output_accuracy: 0.8520 - val_stop_output_accuracy: 0.8805\n",
            "Epoch 249/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.8417 - start_output_loss: 0.3754 - stop_output_loss: 0.4662 - start_output_accuracy: 0.8790 - stop_output_accuracy: 0.8571 - val_loss: 1.0545 - val_start_output_loss: 0.4630 - val_stop_output_loss: 0.5915 - val_start_output_accuracy: 0.8390 - val_stop_output_accuracy: 0.7995\n",
            "Epoch 250/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.5183 - start_output_loss: 0.1700 - stop_output_loss: 0.3483 - start_output_accuracy: 0.9625 - stop_output_accuracy: 0.9151 - val_loss: 0.9360 - val_start_output_loss: 0.2532 - val_stop_output_loss: 0.6828 - val_start_output_accuracy: 0.9505 - val_stop_output_accuracy: 0.6915\n",
            "Epoch 251/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.5309 - start_output_loss: 0.1630 - stop_output_loss: 0.3679 - start_output_accuracy: 0.9626 - stop_output_accuracy: 0.8913 - val_loss: 2.3144 - val_start_output_loss: 1.6927 - val_stop_output_loss: 0.6217 - val_start_output_accuracy: 0.4870 - val_stop_output_accuracy: 0.7890\n",
            "Epoch 252/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.6276 - start_output_loss: 0.2099 - stop_output_loss: 0.4176 - start_output_accuracy: 0.9413 - stop_output_accuracy: 0.8598 - val_loss: 2.4354 - val_start_output_loss: 1.6233 - val_stop_output_loss: 0.8120 - val_start_output_accuracy: 0.5900 - val_stop_output_accuracy: 0.7790\n",
            "Epoch 253/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.5829 - start_output_loss: 0.2016 - stop_output_loss: 0.3813 - start_output_accuracy: 0.9424 - stop_output_accuracy: 0.8931 - val_loss: 4.6688 - val_start_output_loss: 1.0363 - val_stop_output_loss: 3.6325 - val_start_output_accuracy: 0.6670 - val_stop_output_accuracy: 0.0180\n",
            "Epoch 254/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.6306 - start_output_loss: 0.2363 - stop_output_loss: 0.3942 - start_output_accuracy: 0.9366 - stop_output_accuracy: 0.8798 - val_loss: 0.7465 - val_start_output_loss: 0.3682 - val_stop_output_loss: 0.3783 - val_start_output_accuracy: 0.9215 - val_stop_output_accuracy: 0.9260\n",
            "Epoch 255/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.5869 - start_output_loss: 0.1614 - stop_output_loss: 0.4255 - start_output_accuracy: 0.9635 - stop_output_accuracy: 0.8551 - val_loss: 6.5093 - val_start_output_loss: 2.0048 - val_stop_output_loss: 4.5045 - val_start_output_accuracy: 0.7315 - val_stop_output_accuracy: 0.1850\n",
            "Epoch 256/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.5306 - start_output_loss: 0.2030 - stop_output_loss: 0.3276 - start_output_accuracy: 0.9460 - stop_output_accuracy: 0.9226 - val_loss: 0.6338 - val_start_output_loss: 0.2053 - val_stop_output_loss: 0.4285 - val_start_output_accuracy: 0.9665 - val_stop_output_accuracy: 0.8810\n",
            "Epoch 257/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.5917 - start_output_loss: 0.2043 - stop_output_loss: 0.3875 - start_output_accuracy: 0.9459 - stop_output_accuracy: 0.8827 - val_loss: 1.3543 - val_start_output_loss: 0.3123 - val_stop_output_loss: 1.0420 - val_start_output_accuracy: 0.9340 - val_stop_output_accuracy: 0.4210\n",
            "Epoch 258/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.6326 - start_output_loss: 0.2346 - stop_output_loss: 0.3980 - start_output_accuracy: 0.9297 - stop_output_accuracy: 0.8821 - val_loss: 0.6187 - val_start_output_loss: 0.2076 - val_stop_output_loss: 0.4111 - val_start_output_accuracy: 0.9675 - val_stop_output_accuracy: 0.8975\n",
            "Epoch 259/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.5355 - start_output_loss: 0.1540 - stop_output_loss: 0.3815 - start_output_accuracy: 0.9644 - stop_output_accuracy: 0.8781 - val_loss: 1.7844 - val_start_output_loss: 1.1144 - val_stop_output_loss: 0.6700 - val_start_output_accuracy: 0.7180 - val_stop_output_accuracy: 0.7705\n",
            "Epoch 260/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.6622 - start_output_loss: 0.2036 - stop_output_loss: 0.4586 - start_output_accuracy: 0.9433 - stop_output_accuracy: 0.8296 - val_loss: 1.2035 - val_start_output_loss: 0.6179 - val_stop_output_loss: 0.5856 - val_start_output_accuracy: 0.8120 - val_stop_output_accuracy: 0.7950\n",
            "Epoch 261/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.4290 - start_output_loss: 0.1515 - stop_output_loss: 0.2774 - start_output_accuracy: 0.9638 - stop_output_accuracy: 0.9412 - val_loss: 2.7224 - val_start_output_loss: 0.3199 - val_stop_output_loss: 2.4024 - val_start_output_accuracy: 0.9245 - val_stop_output_accuracy: 0.0760\n",
            "Epoch 262/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.6394 - start_output_loss: 0.2626 - stop_output_loss: 0.3768 - start_output_accuracy: 0.9218 - stop_output_accuracy: 0.8888 - val_loss: 0.5108 - val_start_output_loss: 0.2076 - val_stop_output_loss: 0.3032 - val_start_output_accuracy: 0.9665 - val_stop_output_accuracy: 0.9450\n",
            "Epoch 263/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.5381 - start_output_loss: 0.1772 - stop_output_loss: 0.3609 - start_output_accuracy: 0.9562 - stop_output_accuracy: 0.8943 - val_loss: 0.6143 - val_start_output_loss: 0.2161 - val_stop_output_loss: 0.3983 - val_start_output_accuracy: 0.9635 - val_stop_output_accuracy: 0.9070\n",
            "Epoch 264/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4957 - start_output_loss: 0.1776 - stop_output_loss: 0.3181 - start_output_accuracy: 0.9542 - stop_output_accuracy: 0.9168 - val_loss: 0.6041 - val_start_output_loss: 0.2191 - val_stop_output_loss: 0.3850 - val_start_output_accuracy: 0.9630 - val_stop_output_accuracy: 0.8790\n",
            "Epoch 265/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.5823 - start_output_loss: 0.1733 - stop_output_loss: 0.4090 - start_output_accuracy: 0.9535 - stop_output_accuracy: 0.8601 - val_loss: 2.9735 - val_start_output_loss: 0.8038 - val_stop_output_loss: 2.1697 - val_start_output_accuracy: 0.6760 - val_stop_output_accuracy: 0.2915\n",
            "Epoch 266/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.6683 - start_output_loss: 0.2844 - stop_output_loss: 0.3839 - start_output_accuracy: 0.9233 - stop_output_accuracy: 0.8876 - val_loss: 0.5401 - val_start_output_loss: 0.2231 - val_stop_output_loss: 0.3171 - val_start_output_accuracy: 0.9565 - val_stop_output_accuracy: 0.9450\n",
            "Epoch 267/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.5472 - start_output_loss: 0.1470 - stop_output_loss: 0.4002 - start_output_accuracy: 0.9658 - stop_output_accuracy: 0.8717 - val_loss: 1.0626 - val_start_output_loss: 0.5209 - val_stop_output_loss: 0.5418 - val_start_output_accuracy: 0.8360 - val_stop_output_accuracy: 0.8175\n",
            "Epoch 268/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.4376 - start_output_loss: 0.1704 - stop_output_loss: 0.2671 - start_output_accuracy: 0.9549 - stop_output_accuracy: 0.9427 - val_loss: 5.0433 - val_start_output_loss: 0.6716 - val_stop_output_loss: 4.3717 - val_start_output_accuracy: 0.8980 - val_stop_output_accuracy: 0.1460\n",
            "Epoch 269/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.6061 - start_output_loss: 0.2013 - stop_output_loss: 0.4048 - start_output_accuracy: 0.9420 - stop_output_accuracy: 0.8735 - val_loss: 0.6487 - val_start_output_loss: 0.3024 - val_stop_output_loss: 0.3463 - val_start_output_accuracy: 0.9270 - val_stop_output_accuracy: 0.9380\n",
            "Epoch 270/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.5081 - start_output_loss: 0.1668 - stop_output_loss: 0.3413 - start_output_accuracy: 0.9553 - stop_output_accuracy: 0.9013 - val_loss: 0.6744 - val_start_output_loss: 0.1932 - val_stop_output_loss: 0.4812 - val_start_output_accuracy: 0.9670 - val_stop_output_accuracy: 0.8565\n",
            "Epoch 271/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.4816 - start_output_loss: 0.1520 - stop_output_loss: 0.3296 - start_output_accuracy: 0.9617 - stop_output_accuracy: 0.9036 - val_loss: 1.1992 - val_start_output_loss: 0.6064 - val_stop_output_loss: 0.5928 - val_start_output_accuracy: 0.8040 - val_stop_output_accuracy: 0.7840\n",
            "Epoch 272/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.6867 - start_output_loss: 0.2955 - stop_output_loss: 0.3911 - start_output_accuracy: 0.9033 - stop_output_accuracy: 0.8763 - val_loss: 0.6100 - val_start_output_loss: 0.2037 - val_stop_output_loss: 0.4062 - val_start_output_accuracy: 0.9665 - val_stop_output_accuracy: 0.8770\n",
            "Epoch 273/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.4744 - start_output_loss: 0.1476 - stop_output_loss: 0.3269 - start_output_accuracy: 0.9635 - stop_output_accuracy: 0.9039 - val_loss: 0.6199 - val_start_output_loss: 0.1953 - val_stop_output_loss: 0.4247 - val_start_output_accuracy: 0.9670 - val_stop_output_accuracy: 0.8935\n",
            "Epoch 274/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.4978 - start_output_loss: 0.1612 - stop_output_loss: 0.3366 - start_output_accuracy: 0.9556 - stop_output_accuracy: 0.8960 - val_loss: 0.8099 - val_start_output_loss: 0.2703 - val_stop_output_loss: 0.5397 - val_start_output_accuracy: 0.9460 - val_stop_output_accuracy: 0.8060\n",
            "Epoch 275/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.4948 - start_output_loss: 0.1503 - stop_output_loss: 0.3445 - start_output_accuracy: 0.9643 - stop_output_accuracy: 0.9002 - val_loss: 2.0705 - val_start_output_loss: 0.2738 - val_stop_output_loss: 1.7966 - val_start_output_accuracy: 0.9535 - val_stop_output_accuracy: 0.3520\n",
            "Epoch 276/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.4491 - start_output_loss: 0.1654 - stop_output_loss: 0.2838 - start_output_accuracy: 0.9578 - stop_output_accuracy: 0.9294 - val_loss: 1.7399 - val_start_output_loss: 0.2617 - val_stop_output_loss: 1.4782 - val_start_output_accuracy: 0.9440 - val_stop_output_accuracy: 0.3665\n",
            "Epoch 277/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.4752 - start_output_loss: 0.1560 - stop_output_loss: 0.3192 - start_output_accuracy: 0.9614 - stop_output_accuracy: 0.9103 - val_loss: 1.6186 - val_start_output_loss: 0.3052 - val_stop_output_loss: 1.3135 - val_start_output_accuracy: 0.9290 - val_stop_output_accuracy: 0.4270\n",
            "Epoch 278/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.5891 - start_output_loss: 0.2531 - stop_output_loss: 0.3359 - start_output_accuracy: 0.9244 - stop_output_accuracy: 0.8997 - val_loss: 0.5866 - val_start_output_loss: 0.2959 - val_stop_output_loss: 0.2907 - val_start_output_accuracy: 0.9250 - val_stop_output_accuracy: 0.9540\n",
            "Epoch 279/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.4639 - start_output_loss: 0.1481 - stop_output_loss: 0.3157 - start_output_accuracy: 0.9628 - stop_output_accuracy: 0.9112 - val_loss: 0.5391 - val_start_output_loss: 0.1948 - val_stop_output_loss: 0.3443 - val_start_output_accuracy: 0.9690 - val_stop_output_accuracy: 0.9145\n",
            "Epoch 280/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.5425 - start_output_loss: 0.1514 - stop_output_loss: 0.3911 - start_output_accuracy: 0.9629 - stop_output_accuracy: 0.8689 - val_loss: 0.8369 - val_start_output_loss: 0.4179 - val_stop_output_loss: 0.4190 - val_start_output_accuracy: 0.8725 - val_stop_output_accuracy: 0.8910\n",
            "Epoch 281/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.4496 - start_output_loss: 0.1475 - stop_output_loss: 0.3021 - start_output_accuracy: 0.9637 - stop_output_accuracy: 0.9101 - val_loss: 0.8746 - val_start_output_loss: 0.4323 - val_stop_output_loss: 0.4423 - val_start_output_accuracy: 0.8645 - val_stop_output_accuracy: 0.8775\n",
            "Epoch 282/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.4912 - start_output_loss: 0.1575 - stop_output_loss: 0.3337 - start_output_accuracy: 0.9578 - stop_output_accuracy: 0.8968 - val_loss: 0.8764 - val_start_output_loss: 0.2100 - val_stop_output_loss: 0.6664 - val_start_output_accuracy: 0.9635 - val_stop_output_accuracy: 0.7285\n",
            "Epoch 283/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.5976 - start_output_loss: 0.2767 - stop_output_loss: 0.3209 - start_output_accuracy: 0.9180 - stop_output_accuracy: 0.9103 - val_loss: 0.6371 - val_start_output_loss: 0.2488 - val_stop_output_loss: 0.3883 - val_start_output_accuracy: 0.9570 - val_stop_output_accuracy: 0.8960\n",
            "Epoch 284/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4442 - start_output_loss: 0.1390 - stop_output_loss: 0.3052 - start_output_accuracy: 0.9690 - stop_output_accuracy: 0.9199 - val_loss: 0.7620 - val_start_output_loss: 0.2577 - val_stop_output_loss: 0.5043 - val_start_output_accuracy: 0.9445 - val_stop_output_accuracy: 0.8660\n",
            "Epoch 285/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3402 - start_output_loss: 0.1256 - stop_output_loss: 0.2146 - start_output_accuracy: 0.9696 - stop_output_accuracy: 0.9534 - val_loss: 2.0228 - val_start_output_loss: 1.7074 - val_stop_output_loss: 0.3154 - val_start_output_accuracy: 0.6855 - val_stop_output_accuracy: 0.9250\n",
            "Epoch 286/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.5222 - start_output_loss: 0.1524 - stop_output_loss: 0.3698 - start_output_accuracy: 0.9584 - stop_output_accuracy: 0.8683 - val_loss: 7.0993 - val_start_output_loss: 0.5504 - val_stop_output_loss: 6.5489 - val_start_output_accuracy: 0.8940 - val_stop_output_accuracy: 0.0830\n",
            "Epoch 287/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.6536 - start_output_loss: 0.1960 - stop_output_loss: 0.4577 - start_output_accuracy: 0.9476 - stop_output_accuracy: 0.8630 - val_loss: 0.6579 - val_start_output_loss: 0.2619 - val_stop_output_loss: 0.3960 - val_start_output_accuracy: 0.9365 - val_stop_output_accuracy: 0.9185\n",
            "Epoch 288/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3425 - start_output_loss: 0.1349 - stop_output_loss: 0.2076 - start_output_accuracy: 0.9646 - stop_output_accuracy: 0.9572 - val_loss: 1.3046 - val_start_output_loss: 0.8921 - val_stop_output_loss: 0.4125 - val_start_output_accuracy: 0.7090 - val_stop_output_accuracy: 0.8965\n",
            "Epoch 289/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.7179 - start_output_loss: 0.3429 - stop_output_loss: 0.3750 - start_output_accuracy: 0.8967 - stop_output_accuracy: 0.8906 - val_loss: 0.6287 - val_start_output_loss: 0.2556 - val_stop_output_loss: 0.3731 - val_start_output_accuracy: 0.9570 - val_stop_output_accuracy: 0.9130\n",
            "Epoch 290/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.5435 - start_output_loss: 0.1746 - stop_output_loss: 0.3688 - start_output_accuracy: 0.9594 - stop_output_accuracy: 0.8719 - val_loss: 0.6861 - val_start_output_loss: 0.2831 - val_stop_output_loss: 0.4029 - val_start_output_accuracy: 0.9520 - val_stop_output_accuracy: 0.9145\n",
            "Epoch 291/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.3772 - start_output_loss: 0.1307 - stop_output_loss: 0.2465 - start_output_accuracy: 0.9711 - stop_output_accuracy: 0.9372 - val_loss: 2.1753 - val_start_output_loss: 0.2848 - val_stop_output_loss: 1.8905 - val_start_output_accuracy: 0.9405 - val_stop_output_accuracy: 0.1220\n",
            "Epoch 292/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.5080 - start_output_loss: 0.1715 - stop_output_loss: 0.3366 - start_output_accuracy: 0.9538 - stop_output_accuracy: 0.9037 - val_loss: 1.0326 - val_start_output_loss: 0.2261 - val_stop_output_loss: 0.8065 - val_start_output_accuracy: 0.9620 - val_stop_output_accuracy: 0.6140\n",
            "Epoch 293/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.3910 - start_output_loss: 0.1228 - stop_output_loss: 0.2682 - start_output_accuracy: 0.9731 - stop_output_accuracy: 0.9311 - val_loss: 3.7840 - val_start_output_loss: 0.4767 - val_stop_output_loss: 3.3073 - val_start_output_accuracy: 0.9085 - val_stop_output_accuracy: 0.3130\n",
            "Epoch 294/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.6580 - start_output_loss: 0.1884 - stop_output_loss: 0.4697 - start_output_accuracy: 0.9489 - stop_output_accuracy: 0.8364 - val_loss: 0.6662 - val_start_output_loss: 0.2286 - val_stop_output_loss: 0.4376 - val_start_output_accuracy: 0.9555 - val_stop_output_accuracy: 0.8850\n",
            "Epoch 295/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.3823 - start_output_loss: 0.1633 - stop_output_loss: 0.2191 - start_output_accuracy: 0.9539 - stop_output_accuracy: 0.9525 - val_loss: 1.0392 - val_start_output_loss: 0.2590 - val_stop_output_loss: 0.7801 - val_start_output_accuracy: 0.9485 - val_stop_output_accuracy: 0.7035\n",
            "Epoch 296/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.4902 - start_output_loss: 0.1214 - stop_output_loss: 0.3688 - start_output_accuracy: 0.9708 - stop_output_accuracy: 0.8766 - val_loss: 3.0972 - val_start_output_loss: 0.9178 - val_stop_output_loss: 2.1794 - val_start_output_accuracy: 0.7050 - val_stop_output_accuracy: 0.3070\n",
            "Epoch 297/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.4379 - start_output_loss: 0.1601 - stop_output_loss: 0.2778 - start_output_accuracy: 0.9565 - stop_output_accuracy: 0.9247 - val_loss: 0.5942 - val_start_output_loss: 0.1833 - val_stop_output_loss: 0.4109 - val_start_output_accuracy: 0.9725 - val_stop_output_accuracy: 0.9010\n",
            "Epoch 298/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3208 - start_output_loss: 0.1131 - stop_output_loss: 0.2077 - start_output_accuracy: 0.9743 - stop_output_accuracy: 0.9526 - val_loss: 2.7290 - val_start_output_loss: 0.4345 - val_stop_output_loss: 2.2945 - val_start_output_accuracy: 0.9145 - val_stop_output_accuracy: 0.2880\n",
            "Epoch 299/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.8056 - start_output_loss: 0.3304 - stop_output_loss: 0.4752 - start_output_accuracy: 0.9125 - stop_output_accuracy: 0.8360 - val_loss: 0.4979 - val_start_output_loss: 0.2115 - val_stop_output_loss: 0.2865 - val_start_output_accuracy: 0.9620 - val_stop_output_accuracy: 0.9600\n",
            "Epoch 300/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.2942 - start_output_loss: 0.1041 - stop_output_loss: 0.1901 - start_output_accuracy: 0.9770 - stop_output_accuracy: 0.9630 - val_loss: 0.8626 - val_start_output_loss: 0.2099 - val_stop_output_loss: 0.6527 - val_start_output_accuracy: 0.9635 - val_stop_output_accuracy: 0.7385\n",
            "Epoch 301/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.5885 - start_output_loss: 0.2100 - stop_output_loss: 0.3785 - start_output_accuracy: 0.9353 - stop_output_accuracy: 0.8625 - val_loss: 0.8200 - val_start_output_loss: 0.2147 - val_stop_output_loss: 0.6053 - val_start_output_accuracy: 0.9645 - val_stop_output_accuracy: 0.8065\n",
            "Epoch 302/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3667 - start_output_loss: 0.1150 - stop_output_loss: 0.2517 - start_output_accuracy: 0.9732 - stop_output_accuracy: 0.9356 - val_loss: 1.3367 - val_start_output_loss: 0.6155 - val_stop_output_loss: 0.7212 - val_start_output_accuracy: 0.8250 - val_stop_output_accuracy: 0.6945\n",
            "Epoch 303/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.3908 - start_output_loss: 0.1700 - stop_output_loss: 0.2208 - start_output_accuracy: 0.9514 - stop_output_accuracy: 0.9504 - val_loss: 1.3167 - val_start_output_loss: 0.2014 - val_stop_output_loss: 1.1153 - val_start_output_accuracy: 0.9660 - val_stop_output_accuracy: 0.4235\n",
            "Epoch 304/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.4320 - start_output_loss: 0.1156 - stop_output_loss: 0.3164 - start_output_accuracy: 0.9725 - stop_output_accuracy: 0.8995 - val_loss: 0.4198 - val_start_output_loss: 0.1708 - val_stop_output_loss: 0.2491 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.9570\n",
            "Epoch 305/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4229 - start_output_loss: 0.1722 - stop_output_loss: 0.2507 - start_output_accuracy: 0.9499 - stop_output_accuracy: 0.9254 - val_loss: 1.0484 - val_start_output_loss: 0.6975 - val_stop_output_loss: 0.3509 - val_start_output_accuracy: 0.7245 - val_stop_output_accuracy: 0.9295\n",
            "Epoch 306/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.5220 - start_output_loss: 0.1281 - stop_output_loss: 0.3939 - start_output_accuracy: 0.9689 - stop_output_accuracy: 0.8727 - val_loss: 0.4478 - val_start_output_loss: 0.1880 - val_stop_output_loss: 0.2598 - val_start_output_accuracy: 0.9705 - val_stop_output_accuracy: 0.9575\n",
            "Epoch 307/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3533 - start_output_loss: 0.1105 - stop_output_loss: 0.2429 - start_output_accuracy: 0.9741 - stop_output_accuracy: 0.9313 - val_loss: 1.8116 - val_start_output_loss: 0.5874 - val_stop_output_loss: 1.2242 - val_start_output_accuracy: 0.8710 - val_stop_output_accuracy: 0.4865\n",
            "Epoch 308/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.4506 - start_output_loss: 0.2260 - stop_output_loss: 0.2246 - start_output_accuracy: 0.9319 - stop_output_accuracy: 0.9400 - val_loss: 0.4125 - val_start_output_loss: 0.1683 - val_stop_output_loss: 0.2442 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.9565\n",
            "Epoch 309/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.4596 - start_output_loss: 0.1286 - stop_output_loss: 0.3310 - start_output_accuracy: 0.9684 - stop_output_accuracy: 0.9012 - val_loss: 0.5470 - val_start_output_loss: 0.2567 - val_stop_output_loss: 0.2903 - val_start_output_accuracy: 0.9340 - val_stop_output_accuracy: 0.9560\n",
            "Epoch 310/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3078 - start_output_loss: 0.1060 - stop_output_loss: 0.2019 - start_output_accuracy: 0.9725 - stop_output_accuracy: 0.9477 - val_loss: 13.0534 - val_start_output_loss: 1.1758 - val_stop_output_loss: 11.8777 - val_start_output_accuracy: 0.8510 - val_stop_output_accuracy: 0.0275\n",
            "Epoch 311/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.5804 - start_output_loss: 0.1960 - stop_output_loss: 0.3844 - start_output_accuracy: 0.9485 - stop_output_accuracy: 0.8667 - val_loss: 0.7561 - val_start_output_loss: 0.3155 - val_stop_output_loss: 0.4406 - val_start_output_accuracy: 0.9110 - val_stop_output_accuracy: 0.8740\n",
            "Epoch 312/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.2657 - start_output_loss: 0.0971 - stop_output_loss: 0.1686 - start_output_accuracy: 0.9772 - stop_output_accuracy: 0.9629 - val_loss: 0.9515 - val_start_output_loss: 0.5382 - val_stop_output_loss: 0.4134 - val_start_output_accuracy: 0.8250 - val_stop_output_accuracy: 0.9110\n",
            "Epoch 313/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.5276 - start_output_loss: 0.1863 - stop_output_loss: 0.3413 - start_output_accuracy: 0.9501 - stop_output_accuracy: 0.8944 - val_loss: 0.6966 - val_start_output_loss: 0.2664 - val_stop_output_loss: 0.4302 - val_start_output_accuracy: 0.9400 - val_stop_output_accuracy: 0.8185\n",
            "Epoch 314/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.4113 - start_output_loss: 0.1237 - stop_output_loss: 0.2876 - start_output_accuracy: 0.9669 - stop_output_accuracy: 0.9121 - val_loss: 0.5257 - val_start_output_loss: 0.2117 - val_stop_output_loss: 0.3140 - val_start_output_accuracy: 0.9595 - val_stop_output_accuracy: 0.9400\n",
            "Epoch 315/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4103 - start_output_loss: 0.1020 - stop_output_loss: 0.3083 - start_output_accuracy: 0.9766 - stop_output_accuracy: 0.9057 - val_loss: 0.7413 - val_start_output_loss: 0.2943 - val_stop_output_loss: 0.4470 - val_start_output_accuracy: 0.9385 - val_stop_output_accuracy: 0.8805\n",
            "Epoch 316/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.3807 - start_output_loss: 0.1623 - stop_output_loss: 0.2184 - start_output_accuracy: 0.9539 - stop_output_accuracy: 0.9473 - val_loss: 0.5834 - val_start_output_loss: 0.2118 - val_stop_output_loss: 0.3716 - val_start_output_accuracy: 0.9620 - val_stop_output_accuracy: 0.8870\n",
            "Epoch 317/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.4423 - start_output_loss: 0.1159 - stop_output_loss: 0.3264 - start_output_accuracy: 0.9718 - stop_output_accuracy: 0.8984 - val_loss: 0.6003 - val_start_output_loss: 0.1608 - val_stop_output_loss: 0.4395 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.8415\n",
            "Epoch 318/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.4508 - start_output_loss: 0.2448 - stop_output_loss: 0.2060 - start_output_accuracy: 0.9238 - stop_output_accuracy: 0.9445 - val_loss: 1.6077 - val_start_output_loss: 0.5982 - val_stop_output_loss: 1.0095 - val_start_output_accuracy: 0.8785 - val_stop_output_accuracy: 0.5440\n",
            "Epoch 319/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.4059 - start_output_loss: 0.1850 - stop_output_loss: 0.2209 - start_output_accuracy: 0.9488 - stop_output_accuracy: 0.9402 - val_loss: 2.2952 - val_start_output_loss: 0.3292 - val_stop_output_loss: 1.9661 - val_start_output_accuracy: 0.9150 - val_stop_output_accuracy: 0.4295\n",
            "Epoch 320/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.3715 - start_output_loss: 0.1068 - stop_output_loss: 0.2647 - start_output_accuracy: 0.9758 - stop_output_accuracy: 0.9186 - val_loss: 0.4613 - val_start_output_loss: 0.1615 - val_stop_output_loss: 0.2998 - val_start_output_accuracy: 0.9720 - val_stop_output_accuracy: 0.9505\n",
            "Epoch 321/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.3163 - start_output_loss: 0.0947 - stop_output_loss: 0.2217 - start_output_accuracy: 0.9781 - stop_output_accuracy: 0.9399 - val_loss: 1.7005 - val_start_output_loss: 0.2660 - val_stop_output_loss: 1.4345 - val_start_output_accuracy: 0.9420 - val_stop_output_accuracy: 0.3505\n",
            "Epoch 322/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.4599 - start_output_loss: 0.1802 - stop_output_loss: 0.2798 - start_output_accuracy: 0.9468 - stop_output_accuracy: 0.9215 - val_loss: 1.4496 - val_start_output_loss: 0.7603 - val_stop_output_loss: 0.6893 - val_start_output_accuracy: 0.8285 - val_stop_output_accuracy: 0.7225\n",
            "Epoch 323/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.3361 - start_output_loss: 0.1168 - stop_output_loss: 0.2193 - start_output_accuracy: 0.9707 - stop_output_accuracy: 0.9370 - val_loss: 1.2157 - val_start_output_loss: 0.2027 - val_stop_output_loss: 1.0130 - val_start_output_accuracy: 0.9610 - val_stop_output_accuracy: 0.6160\n",
            "Epoch 324/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.4247 - start_output_loss: 0.1300 - stop_output_loss: 0.2947 - start_output_accuracy: 0.9661 - stop_output_accuracy: 0.8990 - val_loss: 0.4564 - val_start_output_loss: 0.1587 - val_stop_output_loss: 0.2976 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.9515\n",
            "Epoch 325/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.3977 - start_output_loss: 0.1161 - stop_output_loss: 0.2816 - start_output_accuracy: 0.9706 - stop_output_accuracy: 0.8990 - val_loss: 6.7021 - val_start_output_loss: 0.3989 - val_stop_output_loss: 6.3033 - val_start_output_accuracy: 0.9135 - val_stop_output_accuracy: 0.1815\n",
            "Epoch 326/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.3668 - start_output_loss: 0.1073 - stop_output_loss: 0.2596 - start_output_accuracy: 0.9739 - stop_output_accuracy: 0.9208 - val_loss: 0.8400 - val_start_output_loss: 0.2291 - val_stop_output_loss: 0.6108 - val_start_output_accuracy: 0.9460 - val_stop_output_accuracy: 0.7375\n",
            "Epoch 327/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.2891 - start_output_loss: 0.1203 - stop_output_loss: 0.1688 - start_output_accuracy: 0.9658 - stop_output_accuracy: 0.9571 - val_loss: 3.8558 - val_start_output_loss: 0.3939 - val_stop_output_loss: 3.4619 - val_start_output_accuracy: 0.9090 - val_stop_output_accuracy: 0.0550\n",
            "Epoch 328/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.4638 - start_output_loss: 0.1804 - stop_output_loss: 0.2834 - start_output_accuracy: 0.9485 - stop_output_accuracy: 0.9121 - val_loss: 1.1087 - val_start_output_loss: 0.2078 - val_stop_output_loss: 0.9009 - val_start_output_accuracy: 0.9575 - val_stop_output_accuracy: 0.6370\n",
            "Epoch 329/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3112 - start_output_loss: 0.1195 - stop_output_loss: 0.1917 - start_output_accuracy: 0.9705 - stop_output_accuracy: 0.9454 - val_loss: 5.4697 - val_start_output_loss: 0.3392 - val_stop_output_loss: 5.1305 - val_start_output_accuracy: 0.9300 - val_stop_output_accuracy: 0.1620\n",
            "Epoch 330/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.5606 - start_output_loss: 0.1095 - stop_output_loss: 0.4512 - start_output_accuracy: 0.9747 - stop_output_accuracy: 0.8337 - val_loss: 0.4276 - val_start_output_loss: 0.1568 - val_stop_output_loss: 0.2707 - val_start_output_accuracy: 0.9755 - val_stop_output_accuracy: 0.9595\n",
            "Epoch 331/600\n",
            "10/10 [==============================] - 3s 349ms/step - loss: 0.2559 - start_output_loss: 0.1034 - stop_output_loss: 0.1525 - start_output_accuracy: 0.9743 - stop_output_accuracy: 0.9692 - val_loss: 0.5651 - val_start_output_loss: 0.1775 - val_stop_output_loss: 0.3876 - val_start_output_accuracy: 0.9700 - val_stop_output_accuracy: 0.9100\n",
            "Epoch 332/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.4602 - start_output_loss: 0.1054 - stop_output_loss: 0.3548 - start_output_accuracy: 0.9764 - stop_output_accuracy: 0.8677 - val_loss: 0.4959 - val_start_output_loss: 0.2178 - val_stop_output_loss: 0.2782 - val_start_output_accuracy: 0.9595 - val_stop_output_accuracy: 0.9485\n",
            "Epoch 333/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3885 - start_output_loss: 0.2129 - stop_output_loss: 0.1756 - start_output_accuracy: 0.9351 - stop_output_accuracy: 0.9595 - val_loss: 0.5179 - val_start_output_loss: 0.2646 - val_stop_output_loss: 0.2533 - val_start_output_accuracy: 0.9220 - val_stop_output_accuracy: 0.9540\n",
            "Epoch 334/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2327 - start_output_loss: 0.0842 - stop_output_loss: 0.1485 - start_output_accuracy: 0.9815 - stop_output_accuracy: 0.9653 - val_loss: 3.8680 - val_start_output_loss: 0.2900 - val_stop_output_loss: 3.5780 - val_start_output_accuracy: 0.9445 - val_stop_output_accuracy: 0.0360\n",
            "Epoch 335/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4422 - start_output_loss: 0.1071 - stop_output_loss: 0.3351 - start_output_accuracy: 0.9736 - stop_output_accuracy: 0.8771 - val_loss: 0.4592 - val_start_output_loss: 0.1601 - val_stop_output_loss: 0.2991 - val_start_output_accuracy: 0.9750 - val_stop_output_accuracy: 0.9515\n",
            "Epoch 336/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.2864 - start_output_loss: 0.1177 - stop_output_loss: 0.1687 - start_output_accuracy: 0.9686 - stop_output_accuracy: 0.9509 - val_loss: 1.6836 - val_start_output_loss: 0.3870 - val_stop_output_loss: 1.2967 - val_start_output_accuracy: 0.8610 - val_stop_output_accuracy: 0.3535\n",
            "Epoch 337/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.3380 - start_output_loss: 0.1131 - stop_output_loss: 0.2250 - start_output_accuracy: 0.9743 - stop_output_accuracy: 0.9336 - val_loss: 0.6037 - val_start_output_loss: 0.2099 - val_stop_output_loss: 0.3938 - val_start_output_accuracy: 0.9615 - val_stop_output_accuracy: 0.8920\n",
            "Epoch 338/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.6208 - start_output_loss: 0.2126 - stop_output_loss: 0.4082 - start_output_accuracy: 0.9403 - stop_output_accuracy: 0.8485 - val_loss: 0.4820 - val_start_output_loss: 0.2003 - val_stop_output_loss: 0.2816 - val_start_output_accuracy: 0.9640 - val_stop_output_accuracy: 0.9540\n",
            "Epoch 339/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.2282 - start_output_loss: 0.0838 - stop_output_loss: 0.1444 - start_output_accuracy: 0.9818 - stop_output_accuracy: 0.9716 - val_loss: 0.6231 - val_start_output_loss: 0.1882 - val_stop_output_loss: 0.4349 - val_start_output_accuracy: 0.9655 - val_stop_output_accuracy: 0.8650\n",
            "Epoch 340/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.4176 - start_output_loss: 0.1129 - stop_output_loss: 0.3047 - start_output_accuracy: 0.9692 - stop_output_accuracy: 0.8977 - val_loss: 0.4381 - val_start_output_loss: 0.1599 - val_stop_output_loss: 0.2782 - val_start_output_accuracy: 0.9755 - val_stop_output_accuracy: 0.9590\n",
            "Epoch 341/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.2894 - start_output_loss: 0.1337 - stop_output_loss: 0.1557 - start_output_accuracy: 0.9613 - stop_output_accuracy: 0.9688 - val_loss: 0.4309 - val_start_output_loss: 0.1836 - val_stop_output_loss: 0.2473 - val_start_output_accuracy: 0.9685 - val_stop_output_accuracy: 0.9490\n",
            "Epoch 342/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4169 - start_output_loss: 0.0982 - stop_output_loss: 0.3187 - start_output_accuracy: 0.9751 - stop_output_accuracy: 0.8921 - val_loss: 0.8507 - val_start_output_loss: 0.4192 - val_stop_output_loss: 0.4315 - val_start_output_accuracy: 0.8715 - val_stop_output_accuracy: 0.8525\n",
            "Epoch 343/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.2307 - start_output_loss: 0.0823 - stop_output_loss: 0.1484 - start_output_accuracy: 0.9801 - stop_output_accuracy: 0.9651 - val_loss: 0.3927 - val_start_output_loss: 0.1861 - val_stop_output_loss: 0.2066 - val_start_output_accuracy: 0.9595 - val_stop_output_accuracy: 0.9665\n",
            "Epoch 344/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.5404 - start_output_loss: 0.2082 - stop_output_loss: 0.3322 - start_output_accuracy: 0.9386 - stop_output_accuracy: 0.8917 - val_loss: 0.6186 - val_start_output_loss: 0.3354 - val_stop_output_loss: 0.2832 - val_start_output_accuracy: 0.9385 - val_stop_output_accuracy: 0.9490\n",
            "Epoch 345/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.2372 - start_output_loss: 0.0993 - stop_output_loss: 0.1379 - start_output_accuracy: 0.9768 - stop_output_accuracy: 0.9703 - val_loss: 0.4651 - val_start_output_loss: 0.1444 - val_stop_output_loss: 0.3207 - val_start_output_accuracy: 0.9775 - val_stop_output_accuracy: 0.9275\n",
            "Epoch 346/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.4136 - start_output_loss: 0.1349 - stop_output_loss: 0.2787 - start_output_accuracy: 0.9607 - stop_output_accuracy: 0.9106 - val_loss: 0.4431 - val_start_output_loss: 0.1552 - val_stop_output_loss: 0.2879 - val_start_output_accuracy: 0.9810 - val_stop_output_accuracy: 0.9440\n",
            "Epoch 347/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.2341 - start_output_loss: 0.0795 - stop_output_loss: 0.1546 - start_output_accuracy: 0.9814 - stop_output_accuracy: 0.9636 - val_loss: 0.7010 - val_start_output_loss: 0.1659 - val_stop_output_loss: 0.5350 - val_start_output_accuracy: 0.9710 - val_stop_output_accuracy: 0.8105\n",
            "Epoch 348/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.4184 - start_output_loss: 0.1164 - stop_output_loss: 0.3021 - start_output_accuracy: 0.9686 - stop_output_accuracy: 0.9032 - val_loss: 0.3888 - val_start_output_loss: 0.1650 - val_stop_output_loss: 0.2238 - val_start_output_accuracy: 0.9695 - val_stop_output_accuracy: 0.9695\n",
            "Epoch 349/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.2783 - start_output_loss: 0.0853 - stop_output_loss: 0.1929 - start_output_accuracy: 0.9786 - stop_output_accuracy: 0.9423 - val_loss: 3.8835 - val_start_output_loss: 0.3362 - val_stop_output_loss: 3.5474 - val_start_output_accuracy: 0.9295 - val_stop_output_accuracy: 0.0355\n",
            "Epoch 350/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3840 - start_output_loss: 0.1254 - stop_output_loss: 0.2586 - start_output_accuracy: 0.9650 - stop_output_accuracy: 0.9190 - val_loss: 0.3831 - val_start_output_loss: 0.1458 - val_stop_output_loss: 0.2373 - val_start_output_accuracy: 0.9825 - val_stop_output_accuracy: 0.9650\n",
            "Epoch 351/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.2571 - start_output_loss: 0.0970 - stop_output_loss: 0.1601 - start_output_accuracy: 0.9745 - stop_output_accuracy: 0.9599 - val_loss: 0.6026 - val_start_output_loss: 0.3569 - val_stop_output_loss: 0.2456 - val_start_output_accuracy: 0.9090 - val_stop_output_accuracy: 0.9585\n",
            "Epoch 352/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.4698 - start_output_loss: 0.1292 - stop_output_loss: 0.3406 - start_output_accuracy: 0.9668 - stop_output_accuracy: 0.8727 - val_loss: 16.1611 - val_start_output_loss: 2.8387 - val_stop_output_loss: 13.3224 - val_start_output_accuracy: 0.8065 - val_stop_output_accuracy: 0.1555\n",
            "Epoch 353/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.3325 - start_output_loss: 0.1125 - stop_output_loss: 0.2200 - start_output_accuracy: 0.9706 - stop_output_accuracy: 0.9483 - val_loss: 0.6008 - val_start_output_loss: 0.2062 - val_stop_output_loss: 0.3945 - val_start_output_accuracy: 0.9515 - val_stop_output_accuracy: 0.8635\n",
            "Epoch 354/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.2018 - start_output_loss: 0.0716 - stop_output_loss: 0.1302 - start_output_accuracy: 0.9820 - stop_output_accuracy: 0.9732 - val_loss: 0.4957 - val_start_output_loss: 0.2365 - val_stop_output_loss: 0.2592 - val_start_output_accuracy: 0.9485 - val_stop_output_accuracy: 0.9565\n",
            "Epoch 355/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.5772 - start_output_loss: 0.2425 - stop_output_loss: 0.3348 - start_output_accuracy: 0.9262 - stop_output_accuracy: 0.8802 - val_loss: 0.8373 - val_start_output_loss: 0.1667 - val_stop_output_loss: 0.6706 - val_start_output_accuracy: 0.9710 - val_stop_output_accuracy: 0.7865\n",
            "Epoch 356/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.2368 - start_output_loss: 0.0760 - stop_output_loss: 0.1608 - start_output_accuracy: 0.9802 - stop_output_accuracy: 0.9603 - val_loss: 0.3661 - val_start_output_loss: 0.1514 - val_stop_output_loss: 0.2148 - val_start_output_accuracy: 0.9795 - val_stop_output_accuracy: 0.9625\n",
            "Epoch 357/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.4018 - start_output_loss: 0.0870 - stop_output_loss: 0.3147 - start_output_accuracy: 0.9764 - stop_output_accuracy: 0.8842 - val_loss: 1.0959 - val_start_output_loss: 0.2364 - val_stop_output_loss: 0.8595 - val_start_output_accuracy: 0.9540 - val_stop_output_accuracy: 0.6000\n",
            "Epoch 358/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.2652 - start_output_loss: 0.1048 - stop_output_loss: 0.1604 - start_output_accuracy: 0.9711 - stop_output_accuracy: 0.9633 - val_loss: 0.4347 - val_start_output_loss: 0.1533 - val_stop_output_loss: 0.2814 - val_start_output_accuracy: 0.9785 - val_stop_output_accuracy: 0.9430\n",
            "Epoch 359/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.2053 - start_output_loss: 0.0781 - stop_output_loss: 0.1271 - start_output_accuracy: 0.9796 - stop_output_accuracy: 0.9682 - val_loss: 1.4167 - val_start_output_loss: 0.6258 - val_stop_output_loss: 0.7909 - val_start_output_accuracy: 0.7940 - val_stop_output_accuracy: 0.7220\n",
            "Epoch 360/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.5183 - start_output_loss: 0.2020 - stop_output_loss: 0.3163 - start_output_accuracy: 0.9421 - stop_output_accuracy: 0.8953 - val_loss: 0.5311 - val_start_output_loss: 0.2079 - val_stop_output_loss: 0.3232 - val_start_output_accuracy: 0.9530 - val_stop_output_accuracy: 0.9160\n",
            "Epoch 361/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.2051 - start_output_loss: 0.0708 - stop_output_loss: 0.1343 - start_output_accuracy: 0.9826 - stop_output_accuracy: 0.9655 - val_loss: 0.6567 - val_start_output_loss: 0.2294 - val_stop_output_loss: 0.4273 - val_start_output_accuracy: 0.9390 - val_stop_output_accuracy: 0.8910\n",
            "Epoch 362/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4202 - start_output_loss: 0.1704 - stop_output_loss: 0.2497 - start_output_accuracy: 0.9492 - stop_output_accuracy: 0.9194 - val_loss: 0.3911 - val_start_output_loss: 0.1420 - val_stop_output_loss: 0.2491 - val_start_output_accuracy: 0.9795 - val_stop_output_accuracy: 0.9520\n",
            "Epoch 363/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.3587 - start_output_loss: 0.0787 - stop_output_loss: 0.2800 - start_output_accuracy: 0.9814 - stop_output_accuracy: 0.8971 - val_loss: 4.1516 - val_start_output_loss: 1.2760 - val_stop_output_loss: 2.8756 - val_start_output_accuracy: 0.8770 - val_stop_output_accuracy: 0.2460\n",
            "Epoch 364/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 0.3106 - start_output_loss: 0.1304 - stop_output_loss: 0.1802 - start_output_accuracy: 0.9642 - stop_output_accuracy: 0.9571 - val_loss: 0.3680 - val_start_output_loss: 0.1262 - val_stop_output_loss: 0.2418 - val_start_output_accuracy: 0.9865 - val_stop_output_accuracy: 0.9585\n",
            "Epoch 365/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1889 - start_output_loss: 0.0671 - stop_output_loss: 0.1218 - start_output_accuracy: 0.9832 - stop_output_accuracy: 0.9717 - val_loss: 1.0151 - val_start_output_loss: 0.5452 - val_stop_output_loss: 0.4699 - val_start_output_accuracy: 0.8400 - val_stop_output_accuracy: 0.8655\n",
            "Epoch 366/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.3680 - start_output_loss: 0.1188 - stop_output_loss: 0.2492 - start_output_accuracy: 0.9661 - stop_output_accuracy: 0.9154 - val_loss: 0.5030 - val_start_output_loss: 0.1973 - val_stop_output_loss: 0.3057 - val_start_output_accuracy: 0.9525 - val_stop_output_accuracy: 0.9320\n",
            "Epoch 367/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2071 - start_output_loss: 0.0699 - stop_output_loss: 0.1372 - start_output_accuracy: 0.9830 - stop_output_accuracy: 0.9673 - val_loss: 4.6098 - val_start_output_loss: 0.4421 - val_stop_output_loss: 4.1677 - val_start_output_accuracy: 0.8990 - val_stop_output_accuracy: 0.0275\n",
            "Epoch 368/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.3502 - start_output_loss: 0.1158 - stop_output_loss: 0.2344 - start_output_accuracy: 0.9682 - stop_output_accuracy: 0.9279 - val_loss: 0.3341 - val_start_output_loss: 0.1285 - val_stop_output_loss: 0.2057 - val_start_output_accuracy: 0.9835 - val_stop_output_accuracy: 0.9625\n",
            "Epoch 369/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.2998 - start_output_loss: 0.0958 - stop_output_loss: 0.2039 - start_output_accuracy: 0.9744 - stop_output_accuracy: 0.9330 - val_loss: 0.5327 - val_start_output_loss: 0.1512 - val_stop_output_loss: 0.3816 - val_start_output_accuracy: 0.9725 - val_stop_output_accuracy: 0.8890\n",
            "Epoch 370/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4036 - start_output_loss: 0.0838 - stop_output_loss: 0.3198 - start_output_accuracy: 0.9779 - stop_output_accuracy: 0.8741 - val_loss: 0.8119 - val_start_output_loss: 0.3658 - val_stop_output_loss: 0.4461 - val_start_output_accuracy: 0.9055 - val_stop_output_accuracy: 0.8720\n",
            "Epoch 371/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.1978 - start_output_loss: 0.0705 - stop_output_loss: 0.1272 - start_output_accuracy: 0.9807 - stop_output_accuracy: 0.9720 - val_loss: 0.4350 - val_start_output_loss: 0.2088 - val_stop_output_loss: 0.2262 - val_start_output_accuracy: 0.9535 - val_stop_output_accuracy: 0.9665\n",
            "Epoch 372/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.2167 - start_output_loss: 0.0990 - stop_output_loss: 0.1178 - start_output_accuracy: 0.9707 - stop_output_accuracy: 0.9706 - val_loss: 0.9295 - val_start_output_loss: 0.1906 - val_stop_output_loss: 0.7389 - val_start_output_accuracy: 0.9660 - val_stop_output_accuracy: 0.7255\n",
            "Epoch 373/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.3527 - start_output_loss: 0.0801 - stop_output_loss: 0.2725 - start_output_accuracy: 0.9787 - stop_output_accuracy: 0.9052 - val_loss: 0.3356 - val_start_output_loss: 0.1281 - val_stop_output_loss: 0.2076 - val_start_output_accuracy: 0.9850 - val_stop_output_accuracy: 0.9640\n",
            "Epoch 374/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.4658 - start_output_loss: 0.1062 - stop_output_loss: 0.3596 - start_output_accuracy: 0.9719 - stop_output_accuracy: 0.8729 - val_loss: 0.5875 - val_start_output_loss: 0.1441 - val_stop_output_loss: 0.4433 - val_start_output_accuracy: 0.9765 - val_stop_output_accuracy: 0.8610\n",
            "Epoch 375/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.1807 - start_output_loss: 0.0566 - stop_output_loss: 0.1241 - start_output_accuracy: 0.9870 - stop_output_accuracy: 0.9739 - val_loss: 0.3457 - val_start_output_loss: 0.1442 - val_stop_output_loss: 0.2014 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.9675\n",
            "Epoch 376/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.3766 - start_output_loss: 0.2069 - stop_output_loss: 0.1698 - start_output_accuracy: 0.9395 - stop_output_accuracy: 0.9529 - val_loss: 0.9993 - val_start_output_loss: 0.1603 - val_stop_output_loss: 0.8390 - val_start_output_accuracy: 0.9785 - val_stop_output_accuracy: 0.6875\n",
            "Epoch 377/600\n",
            "10/10 [==============================] - 3s 348ms/step - loss: 0.2446 - start_output_loss: 0.0609 - stop_output_loss: 0.1838 - start_output_accuracy: 0.9862 - stop_output_accuracy: 0.9467 - val_loss: 0.3159 - val_start_output_loss: 0.1284 - val_stop_output_loss: 0.1874 - val_start_output_accuracy: 0.9835 - val_stop_output_accuracy: 0.9720\n",
            "Epoch 378/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.3570 - start_output_loss: 0.0730 - stop_output_loss: 0.2840 - start_output_accuracy: 0.9818 - stop_output_accuracy: 0.8903 - val_loss: 1.0858 - val_start_output_loss: 0.2585 - val_stop_output_loss: 0.8273 - val_start_output_accuracy: 0.9280 - val_stop_output_accuracy: 0.6955\n",
            "Epoch 379/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.2071 - start_output_loss: 0.0611 - stop_output_loss: 0.1460 - start_output_accuracy: 0.9851 - stop_output_accuracy: 0.9654 - val_loss: 0.3712 - val_start_output_loss: 0.1627 - val_stop_output_loss: 0.2084 - val_start_output_accuracy: 0.9710 - val_stop_output_accuracy: 0.9665\n",
            "Epoch 380/600\n",
            "10/10 [==============================] - 3s 344ms/step - loss: 0.1893 - start_output_loss: 0.0577 - stop_output_loss: 0.1316 - start_output_accuracy: 0.9858 - stop_output_accuracy: 0.9683 - val_loss: 0.9225 - val_start_output_loss: 0.1403 - val_stop_output_loss: 0.7822 - val_start_output_accuracy: 0.9760 - val_stop_output_accuracy: 0.6890\n",
            "Epoch 381/600\n",
            "10/10 [==============================] - 3s 349ms/step - loss: 0.3789 - start_output_loss: 0.0724 - stop_output_loss: 0.3065 - start_output_accuracy: 0.9820 - stop_output_accuracy: 0.8882 - val_loss: 0.5610 - val_start_output_loss: 0.1760 - val_stop_output_loss: 0.3851 - val_start_output_accuracy: 0.9575 - val_stop_output_accuracy: 0.8770\n",
            "Epoch 382/600\n",
            "10/10 [==============================] - 4s 352ms/step - loss: 0.1731 - start_output_loss: 0.0566 - stop_output_loss: 0.1165 - start_output_accuracy: 0.9864 - stop_output_accuracy: 0.9704 - val_loss: 7.6165 - val_start_output_loss: 0.8741 - val_stop_output_loss: 6.7425 - val_start_output_accuracy: 0.8815 - val_stop_output_accuracy: 0.1035\n",
            "Epoch 383/600\n",
            "10/10 [==============================] - 4s 352ms/step - loss: 0.4619 - start_output_loss: 0.1494 - stop_output_loss: 0.3126 - start_output_accuracy: 0.9574 - stop_output_accuracy: 0.8943 - val_loss: 0.3169 - val_start_output_loss: 0.1166 - val_stop_output_loss: 0.2003 - val_start_output_accuracy: 0.9875 - val_stop_output_accuracy: 0.9705\n",
            "Epoch 384/600\n",
            "10/10 [==============================] - 4s 353ms/step - loss: 0.1702 - start_output_loss: 0.0477 - stop_output_loss: 0.1225 - start_output_accuracy: 0.9890 - stop_output_accuracy: 0.9693 - val_loss: 2.0016 - val_start_output_loss: 0.1989 - val_stop_output_loss: 1.8028 - val_start_output_accuracy: 0.9645 - val_stop_output_accuracy: 0.4795\n",
            "Epoch 385/600\n",
            "10/10 [==============================] - 3s 347ms/step - loss: 0.3138 - start_output_loss: 0.1057 - stop_output_loss: 0.2081 - start_output_accuracy: 0.9674 - stop_output_accuracy: 0.9320 - val_loss: 0.3187 - val_start_output_loss: 0.1143 - val_stop_output_loss: 0.2044 - val_start_output_accuracy: 0.9870 - val_stop_output_accuracy: 0.9685\n",
            "Epoch 386/600\n",
            "10/10 [==============================] - 3s 347ms/step - loss: 0.1516 - start_output_loss: 0.0471 - stop_output_loss: 0.1045 - start_output_accuracy: 0.9890 - stop_output_accuracy: 0.9745 - val_loss: 0.4679 - val_start_output_loss: 0.1159 - val_stop_output_loss: 0.3521 - val_start_output_accuracy: 0.9845 - val_stop_output_accuracy: 0.8825\n",
            "Epoch 387/600\n",
            "10/10 [==============================] - 4s 354ms/step - loss: 0.4402 - start_output_loss: 0.1024 - stop_output_loss: 0.3378 - start_output_accuracy: 0.9696 - stop_output_accuracy: 0.8760 - val_loss: 0.3110 - val_start_output_loss: 0.1105 - val_stop_output_loss: 0.2004 - val_start_output_accuracy: 0.9870 - val_stop_output_accuracy: 0.9725\n",
            "Epoch 388/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.1447 - start_output_loss: 0.0429 - stop_output_loss: 0.1019 - start_output_accuracy: 0.9897 - stop_output_accuracy: 0.9781 - val_loss: 0.3189 - val_start_output_loss: 0.1223 - val_stop_output_loss: 0.1966 - val_start_output_accuracy: 0.9840 - val_stop_output_accuracy: 0.9650\n",
            "Epoch 389/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.4130 - start_output_loss: 0.1343 - stop_output_loss: 0.2787 - start_output_accuracy: 0.9596 - stop_output_accuracy: 0.9036 - val_loss: 0.6606 - val_start_output_loss: 0.2277 - val_stop_output_loss: 0.4328 - val_start_output_accuracy: 0.9495 - val_stop_output_accuracy: 0.8640\n",
            "Epoch 390/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.1879 - start_output_loss: 0.0602 - stop_output_loss: 0.1277 - start_output_accuracy: 0.9855 - stop_output_accuracy: 0.9709 - val_loss: 0.4553 - val_start_output_loss: 0.1274 - val_stop_output_loss: 0.3279 - val_start_output_accuracy: 0.9845 - val_stop_output_accuracy: 0.9190\n",
            "Epoch 391/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.1678 - start_output_loss: 0.0467 - stop_output_loss: 0.1211 - start_output_accuracy: 0.9890 - stop_output_accuracy: 0.9672 - val_loss: 0.9362 - val_start_output_loss: 0.1472 - val_stop_output_loss: 0.7890 - val_start_output_accuracy: 0.9760 - val_stop_output_accuracy: 0.6600\n",
            "Epoch 392/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.3287 - start_output_loss: 0.0596 - stop_output_loss: 0.2691 - start_output_accuracy: 0.9856 - stop_output_accuracy: 0.9013 - val_loss: 1.6971 - val_start_output_loss: 0.9596 - val_stop_output_loss: 0.7375 - val_start_output_accuracy: 0.8390 - val_stop_output_accuracy: 0.7650\n",
            "Epoch 393/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.2408 - start_output_loss: 0.0705 - stop_output_loss: 0.1702 - start_output_accuracy: 0.9814 - stop_output_accuracy: 0.9544 - val_loss: 0.3514 - val_start_output_loss: 0.1184 - val_stop_output_loss: 0.2330 - val_start_output_accuracy: 0.9870 - val_stop_output_accuracy: 0.9660\n",
            "Epoch 394/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.1990 - start_output_loss: 0.0947 - stop_output_loss: 0.1042 - start_output_accuracy: 0.9697 - stop_output_accuracy: 0.9718 - val_loss: 0.3154 - val_start_output_loss: 0.1280 - val_stop_output_loss: 0.1874 - val_start_output_accuracy: 0.9810 - val_stop_output_accuracy: 0.9710\n",
            "Epoch 395/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.3020 - start_output_loss: 0.0574 - stop_output_loss: 0.2446 - start_output_accuracy: 0.9851 - stop_output_accuracy: 0.9164 - val_loss: 0.3346 - val_start_output_loss: 0.1067 - val_stop_output_loss: 0.2279 - val_start_output_accuracy: 0.9870 - val_stop_output_accuracy: 0.9605\n",
            "Epoch 396/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.1430 - start_output_loss: 0.0444 - stop_output_loss: 0.0986 - start_output_accuracy: 0.9888 - stop_output_accuracy: 0.9779 - val_loss: 0.4462 - val_start_output_loss: 0.1137 - val_stop_output_loss: 0.3325 - val_start_output_accuracy: 0.9855 - val_stop_output_accuracy: 0.9155\n",
            "Epoch 397/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.4769 - start_output_loss: 0.1670 - stop_output_loss: 0.3099 - start_output_accuracy: 0.9596 - stop_output_accuracy: 0.8946 - val_loss: 2.9406 - val_start_output_loss: 0.6190 - val_stop_output_loss: 2.3216 - val_start_output_accuracy: 0.8650 - val_stop_output_accuracy: 0.3445\n",
            "Epoch 398/600\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 0.2720 - start_output_loss: 0.0695 - stop_output_loss: 0.2025 - start_output_accuracy: 0.9815 - stop_output_accuracy: 0.9477 - val_loss: 0.5598 - val_start_output_loss: 0.1806 - val_stop_output_loss: 0.3792 - val_start_output_accuracy: 0.9645 - val_stop_output_accuracy: 0.8760\n",
            "Epoch 399/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.2146 - start_output_loss: 0.0523 - stop_output_loss: 0.1623 - start_output_accuracy: 0.9880 - stop_output_accuracy: 0.9640 - val_loss: 3.2115 - val_start_output_loss: 0.2556 - val_stop_output_loss: 2.9559 - val_start_output_accuracy: 0.9580 - val_stop_output_accuracy: 0.2400\n",
            "Epoch 400/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.1801 - start_output_loss: 0.0399 - stop_output_loss: 0.1402 - start_output_accuracy: 0.9905 - stop_output_accuracy: 0.9703 - val_loss: 0.3206 - val_start_output_loss: 0.1176 - val_stop_output_loss: 0.2029 - val_start_output_accuracy: 0.9840 - val_stop_output_accuracy: 0.9695\n",
            "Epoch 401/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.3790 - start_output_loss: 0.0817 - stop_output_loss: 0.2972 - start_output_accuracy: 0.9782 - stop_output_accuracy: 0.8843 - val_loss: 1.1923 - val_start_output_loss: 0.3349 - val_stop_output_loss: 0.8573 - val_start_output_accuracy: 0.9000 - val_stop_output_accuracy: 0.6605\n",
            "Epoch 402/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.1748 - start_output_loss: 0.0423 - stop_output_loss: 0.1325 - start_output_accuracy: 0.9898 - stop_output_accuracy: 0.9689 - val_loss: 0.5017 - val_start_output_loss: 0.1637 - val_stop_output_loss: 0.3380 - val_start_output_accuracy: 0.9625 - val_stop_output_accuracy: 0.8935\n",
            "Epoch 403/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.1429 - start_output_loss: 0.0389 - stop_output_loss: 0.1040 - start_output_accuracy: 0.9902 - stop_output_accuracy: 0.9736 - val_loss: 2.9108 - val_start_output_loss: 1.5649 - val_stop_output_loss: 1.3459 - val_start_output_accuracy: 0.5645 - val_stop_output_accuracy: 0.5770\n",
            "Epoch 404/600\n",
            "10/10 [==============================] - 3s 346ms/step - loss: 0.3899 - start_output_loss: 0.1613 - stop_output_loss: 0.2285 - start_output_accuracy: 0.9525 - stop_output_accuracy: 0.9275 - val_loss: 0.2978 - val_start_output_loss: 0.1045 - val_stop_output_loss: 0.1933 - val_start_output_accuracy: 0.9885 - val_stop_output_accuracy: 0.9700\n",
            "Epoch 405/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.1267 - start_output_loss: 0.0374 - stop_output_loss: 0.0893 - start_output_accuracy: 0.9913 - stop_output_accuracy: 0.9789 - val_loss: 1.0224 - val_start_output_loss: 0.1314 - val_stop_output_loss: 0.8910 - val_start_output_accuracy: 0.9740 - val_stop_output_accuracy: 0.6290\n",
            "Epoch 406/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.4719 - start_output_loss: 0.0710 - stop_output_loss: 0.4009 - start_output_accuracy: 0.9821 - stop_output_accuracy: 0.8657 - val_loss: 0.3329 - val_start_output_loss: 0.1074 - val_stop_output_loss: 0.2254 - val_start_output_accuracy: 0.9855 - val_stop_output_accuracy: 0.9675\n",
            "Epoch 407/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.1244 - start_output_loss: 0.0326 - stop_output_loss: 0.0918 - start_output_accuracy: 0.9926 - stop_output_accuracy: 0.9818 - val_loss: 0.3272 - val_start_output_loss: 0.0998 - val_stop_output_loss: 0.2275 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9520\n",
            "Epoch 408/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.2441 - start_output_loss: 0.0664 - stop_output_loss: 0.1776 - start_output_accuracy: 0.9812 - stop_output_accuracy: 0.9445 - val_loss: 1.3694 - val_start_output_loss: 0.4866 - val_stop_output_loss: 0.8827 - val_start_output_accuracy: 0.8715 - val_stop_output_accuracy: 0.7835\n",
            "Epoch 409/600\n",
            "10/10 [==============================] - 4s 355ms/step - loss: 0.1591 - start_output_loss: 0.0441 - stop_output_loss: 0.1150 - start_output_accuracy: 0.9891 - stop_output_accuracy: 0.9659 - val_loss: 0.3025 - val_start_output_loss: 0.1137 - val_stop_output_loss: 0.1888 - val_start_output_accuracy: 0.9835 - val_stop_output_accuracy: 0.9725\n",
            "Epoch 410/600\n",
            "10/10 [==============================] - 3s 348ms/step - loss: 0.4463 - start_output_loss: 0.0747 - stop_output_loss: 0.3716 - start_output_accuracy: 0.9832 - stop_output_accuracy: 0.8572 - val_loss: 0.9260 - val_start_output_loss: 0.1505 - val_stop_output_loss: 0.7755 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.7725\n",
            "Epoch 411/600\n",
            "10/10 [==============================] - 4s 352ms/step - loss: 0.1528 - start_output_loss: 0.0401 - stop_output_loss: 0.1126 - start_output_accuracy: 0.9913 - stop_output_accuracy: 0.9738 - val_loss: 0.3137 - val_start_output_loss: 0.0975 - val_stop_output_loss: 0.2163 - val_start_output_accuracy: 0.9900 - val_stop_output_accuracy: 0.9620\n",
            "Epoch 412/600\n",
            "10/10 [==============================] - 4s 360ms/step - loss: 0.1254 - start_output_loss: 0.0344 - stop_output_loss: 0.0910 - start_output_accuracy: 0.9918 - stop_output_accuracy: 0.9790 - val_loss: 2.0101 - val_start_output_loss: 1.1382 - val_stop_output_loss: 0.8719 - val_start_output_accuracy: 0.7545 - val_stop_output_accuracy: 0.7060\n",
            "Epoch 413/600\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 0.3224 - start_output_loss: 0.0718 - stop_output_loss: 0.2506 - start_output_accuracy: 0.9787 - stop_output_accuracy: 0.9212 - val_loss: 0.3802 - val_start_output_loss: 0.1099 - val_stop_output_loss: 0.2703 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9510\n",
            "Epoch 414/600\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 0.2015 - start_output_loss: 0.0472 - stop_output_loss: 0.1543 - start_output_accuracy: 0.9875 - stop_output_accuracy: 0.9523 - val_loss: 2.3870 - val_start_output_loss: 1.8241 - val_stop_output_loss: 0.5629 - val_start_output_accuracy: 0.7120 - val_stop_output_accuracy: 0.8235\n",
            "Epoch 415/600\n",
            "10/10 [==============================] - 4s 356ms/step - loss: 0.3166 - start_output_loss: 0.0734 - stop_output_loss: 0.2432 - start_output_accuracy: 0.9788 - stop_output_accuracy: 0.9270 - val_loss: 0.3354 - val_start_output_loss: 0.1038 - val_stop_output_loss: 0.2316 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9580\n",
            "Epoch 416/600\n",
            "10/10 [==============================] - 3s 348ms/step - loss: 0.1257 - start_output_loss: 0.0296 - stop_output_loss: 0.0962 - start_output_accuracy: 0.9938 - stop_output_accuracy: 0.9801 - val_loss: 0.4172 - val_start_output_loss: 0.1095 - val_stop_output_loss: 0.3077 - val_start_output_accuracy: 0.9860 - val_stop_output_accuracy: 0.9190\n",
            "Epoch 417/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.4581 - start_output_loss: 0.1362 - stop_output_loss: 0.3219 - start_output_accuracy: 0.9661 - stop_output_accuracy: 0.9012 - val_loss: 0.4530 - val_start_output_loss: 0.1426 - val_stop_output_loss: 0.3104 - val_start_output_accuracy: 0.9740 - val_stop_output_accuracy: 0.9410\n",
            "Epoch 418/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.1378 - start_output_loss: 0.0352 - stop_output_loss: 0.1027 - start_output_accuracy: 0.9916 - stop_output_accuracy: 0.9772 - val_loss: 0.3084 - val_start_output_loss: 0.1113 - val_stop_output_loss: 0.1971 - val_start_output_accuracy: 0.9855 - val_stop_output_accuracy: 0.9650\n",
            "Epoch 419/600\n",
            "10/10 [==============================] - 3s 345ms/step - loss: 0.1482 - start_output_loss: 0.0539 - stop_output_loss: 0.0943 - start_output_accuracy: 0.9846 - stop_output_accuracy: 0.9761 - val_loss: 0.3860 - val_start_output_loss: 0.0980 - val_stop_output_loss: 0.2880 - val_start_output_accuracy: 0.9860 - val_stop_output_accuracy: 0.9410\n",
            "Epoch 420/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.3392 - start_output_loss: 0.0629 - stop_output_loss: 0.2763 - start_output_accuracy: 0.9818 - stop_output_accuracy: 0.8939 - val_loss: 0.3183 - val_start_output_loss: 0.1096 - val_stop_output_loss: 0.2087 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9690\n",
            "Epoch 421/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.1199 - start_output_loss: 0.0282 - stop_output_loss: 0.0917 - start_output_accuracy: 0.9938 - stop_output_accuracy: 0.9776 - val_loss: 0.5602 - val_start_output_loss: 0.1081 - val_stop_output_loss: 0.4522 - val_start_output_accuracy: 0.9830 - val_stop_output_accuracy: 0.8555\n",
            "Epoch 422/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.1096 - start_output_loss: 0.0289 - stop_output_loss: 0.0808 - start_output_accuracy: 0.9924 - stop_output_accuracy: 0.9794 - val_loss: 2.2102 - val_start_output_loss: 0.3534 - val_stop_output_loss: 1.8568 - val_start_output_accuracy: 0.9345 - val_stop_output_accuracy: 0.5515\n",
            "Epoch 423/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.3726 - start_output_loss: 0.0818 - stop_output_loss: 0.2908 - start_output_accuracy: 0.9797 - stop_output_accuracy: 0.9061 - val_loss: 0.2900 - val_start_output_loss: 0.0967 - val_stop_output_loss: 0.1933 - val_start_output_accuracy: 0.9890 - val_stop_output_accuracy: 0.9725\n",
            "Epoch 424/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.1117 - start_output_loss: 0.0290 - stop_output_loss: 0.0827 - start_output_accuracy: 0.9933 - stop_output_accuracy: 0.9815 - val_loss: 0.3277 - val_start_output_loss: 0.1217 - val_stop_output_loss: 0.2060 - val_start_output_accuracy: 0.9810 - val_stop_output_accuracy: 0.9675\n",
            "Epoch 425/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.2971 - start_output_loss: 0.0320 - stop_output_loss: 0.2651 - start_output_accuracy: 0.9916 - stop_output_accuracy: 0.9053 - val_loss: 0.9659 - val_start_output_loss: 0.4069 - val_stop_output_loss: 0.5590 - val_start_output_accuracy: 0.9155 - val_stop_output_accuracy: 0.7755\n",
            "Epoch 426/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.1427 - start_output_loss: 0.0332 - stop_output_loss: 0.1094 - start_output_accuracy: 0.9919 - stop_output_accuracy: 0.9721 - val_loss: 0.3414 - val_start_output_loss: 0.1023 - val_stop_output_loss: 0.2391 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9540\n",
            "Epoch 427/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.2902 - start_output_loss: 0.0704 - stop_output_loss: 0.2199 - start_output_accuracy: 0.9823 - stop_output_accuracy: 0.9311 - val_loss: 0.5886 - val_start_output_loss: 0.1969 - val_stop_output_loss: 0.3917 - val_start_output_accuracy: 0.9660 - val_stop_output_accuracy: 0.8615\n",
            "Epoch 428/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.1361 - start_output_loss: 0.0400 - stop_output_loss: 0.0962 - start_output_accuracy: 0.9910 - stop_output_accuracy: 0.9773 - val_loss: 0.3754 - val_start_output_loss: 0.1066 - val_stop_output_loss: 0.2688 - val_start_output_accuracy: 0.9860 - val_stop_output_accuracy: 0.9460\n",
            "Epoch 429/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.1226 - start_output_loss: 0.0332 - stop_output_loss: 0.0894 - start_output_accuracy: 0.9922 - stop_output_accuracy: 0.9778 - val_loss: 1.8514 - val_start_output_loss: 0.2057 - val_stop_output_loss: 1.6457 - val_start_output_accuracy: 0.9695 - val_stop_output_accuracy: 0.2995\n",
            "Epoch 430/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.4440 - start_output_loss: 0.0768 - stop_output_loss: 0.3671 - start_output_accuracy: 0.9768 - stop_output_accuracy: 0.8584 - val_loss: 0.3051 - val_start_output_loss: 0.1073 - val_stop_output_loss: 0.1978 - val_start_output_accuracy: 0.9865 - val_stop_output_accuracy: 0.9675\n",
            "Epoch 431/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.1022 - start_output_loss: 0.0250 - stop_output_loss: 0.0773 - start_output_accuracy: 0.9947 - stop_output_accuracy: 0.9840 - val_loss: 0.3506 - val_start_output_loss: 0.1476 - val_stop_output_loss: 0.2030 - val_start_output_accuracy: 0.9720 - val_stop_output_accuracy: 0.9695\n",
            "Epoch 432/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.1902 - start_output_loss: 0.0443 - stop_output_loss: 0.1460 - start_output_accuracy: 0.9886 - stop_output_accuracy: 0.9547 - val_loss: 1.6728 - val_start_output_loss: 0.1236 - val_stop_output_loss: 1.5491 - val_start_output_accuracy: 0.9815 - val_stop_output_accuracy: 0.3830\n",
            "Epoch 433/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.2230 - start_output_loss: 0.0403 - stop_output_loss: 0.1827 - start_output_accuracy: 0.9895 - stop_output_accuracy: 0.9423 - val_loss: 0.2830 - val_start_output_loss: 0.0965 - val_stop_output_loss: 0.1865 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9720\n",
            "Epoch 434/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.1885 - start_output_loss: 0.0304 - stop_output_loss: 0.1581 - start_output_accuracy: 0.9926 - stop_output_accuracy: 0.9431 - val_loss: 0.8038 - val_start_output_loss: 0.1923 - val_stop_output_loss: 0.6116 - val_start_output_accuracy: 0.9585 - val_stop_output_accuracy: 0.7940\n",
            "Epoch 435/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.2166 - start_output_loss: 0.0428 - stop_output_loss: 0.1738 - start_output_accuracy: 0.9894 - stop_output_accuracy: 0.9474 - val_loss: 0.3625 - val_start_output_loss: 0.0927 - val_stop_output_loss: 0.2698 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9560\n",
            "Epoch 436/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.1661 - start_output_loss: 0.0673 - stop_output_loss: 0.0988 - start_output_accuracy: 0.9827 - stop_output_accuracy: 0.9721 - val_loss: 0.8841 - val_start_output_loss: 0.5737 - val_stop_output_loss: 0.3104 - val_start_output_accuracy: 0.8740 - val_stop_output_accuracy: 0.9175\n",
            "Epoch 437/600\n",
            "10/10 [==============================] - 3s 346ms/step - loss: 0.2342 - start_output_loss: 0.0594 - stop_output_loss: 0.1748 - start_output_accuracy: 0.9836 - stop_output_accuracy: 0.9417 - val_loss: 0.5200 - val_start_output_loss: 0.1143 - val_stop_output_loss: 0.4057 - val_start_output_accuracy: 0.9790 - val_stop_output_accuracy: 0.8550\n",
            "Epoch 438/600\n",
            "10/10 [==============================] - 3s 348ms/step - loss: 0.1209 - start_output_loss: 0.0286 - stop_output_loss: 0.0923 - start_output_accuracy: 0.9936 - stop_output_accuracy: 0.9736 - val_loss: 0.7425 - val_start_output_loss: 0.1440 - val_stop_output_loss: 0.5985 - val_start_output_accuracy: 0.9800 - val_stop_output_accuracy: 0.8005\n",
            "Epoch 439/600\n",
            "10/10 [==============================] - 3s 345ms/step - loss: 0.1093 - start_output_loss: 0.0297 - stop_output_loss: 0.0795 - start_output_accuracy: 0.9919 - stop_output_accuracy: 0.9786 - val_loss: 0.8110 - val_start_output_loss: 0.5912 - val_stop_output_loss: 0.2198 - val_start_output_accuracy: 0.8585 - val_stop_output_accuracy: 0.9615\n",
            "Epoch 440/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.4494 - start_output_loss: 0.1093 - stop_output_loss: 0.3401 - start_output_accuracy: 0.9656 - stop_output_accuracy: 0.8810 - val_loss: 0.3937 - val_start_output_loss: 0.1278 - val_stop_output_loss: 0.2659 - val_start_output_accuracy: 0.9800 - val_stop_output_accuracy: 0.9440\n",
            "Epoch 441/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.1100 - start_output_loss: 0.0272 - stop_output_loss: 0.0827 - start_output_accuracy: 0.9935 - stop_output_accuracy: 0.9824 - val_loss: 0.2991 - val_start_output_loss: 0.0919 - val_stop_output_loss: 0.2072 - val_start_output_accuracy: 0.9900 - val_stop_output_accuracy: 0.9655\n",
            "Epoch 442/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.1087 - start_output_loss: 0.0251 - stop_output_loss: 0.0837 - start_output_accuracy: 0.9942 - stop_output_accuracy: 0.9791 - val_loss: 0.3020 - val_start_output_loss: 0.0920 - val_stop_output_loss: 0.2099 - val_start_output_accuracy: 0.9900 - val_stop_output_accuracy: 0.9635\n",
            "Epoch 443/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.1678 - start_output_loss: 0.0598 - stop_output_loss: 0.1080 - start_output_accuracy: 0.9814 - stop_output_accuracy: 0.9696 - val_loss: 0.9618 - val_start_output_loss: 0.2009 - val_stop_output_loss: 0.7609 - val_start_output_accuracy: 0.9600 - val_stop_output_accuracy: 0.7020\n",
            "Epoch 444/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.2750 - start_output_loss: 0.0426 - stop_output_loss: 0.2324 - start_output_accuracy: 0.9893 - stop_output_accuracy: 0.9207 - val_loss: 0.3456 - val_start_output_loss: 0.0902 - val_stop_output_loss: 0.2554 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9445\n",
            "Epoch 445/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.1159 - start_output_loss: 0.0252 - stop_output_loss: 0.0907 - start_output_accuracy: 0.9944 - stop_output_accuracy: 0.9758 - val_loss: 1.1694 - val_start_output_loss: 0.3160 - val_stop_output_loss: 0.8533 - val_start_output_accuracy: 0.9315 - val_stop_output_accuracy: 0.7260\n",
            "Epoch 446/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.1227 - start_output_loss: 0.0286 - stop_output_loss: 0.0941 - start_output_accuracy: 0.9936 - stop_output_accuracy: 0.9726 - val_loss: 0.2505 - val_start_output_loss: 0.0899 - val_stop_output_loss: 0.1606 - val_start_output_accuracy: 0.9915 - val_stop_output_accuracy: 0.9755\n",
            "Epoch 447/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3266 - start_output_loss: 0.0703 - stop_output_loss: 0.2563 - start_output_accuracy: 0.9786 - stop_output_accuracy: 0.9182 - val_loss: 0.3044 - val_start_output_loss: 0.0975 - val_stop_output_loss: 0.2070 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9735\n",
            "Epoch 448/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.0890 - start_output_loss: 0.0220 - stop_output_loss: 0.0670 - start_output_accuracy: 0.9952 - stop_output_accuracy: 0.9858 - val_loss: 0.3198 - val_start_output_loss: 0.0838 - val_stop_output_loss: 0.2360 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9655\n",
            "Epoch 449/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.1285 - start_output_loss: 0.0510 - stop_output_loss: 0.0775 - start_output_accuracy: 0.9866 - stop_output_accuracy: 0.9793 - val_loss: 2.9841 - val_start_output_loss: 1.3041 - val_stop_output_loss: 1.6800 - val_start_output_accuracy: 0.8030 - val_stop_output_accuracy: 0.5285\n",
            "Epoch 450/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.2939 - start_output_loss: 0.1386 - stop_output_loss: 0.1553 - start_output_accuracy: 0.9650 - stop_output_accuracy: 0.9508 - val_loss: 0.3174 - val_start_output_loss: 0.0881 - val_stop_output_loss: 0.2294 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9630\n",
            "Epoch 451/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.0850 - start_output_loss: 0.0214 - stop_output_loss: 0.0637 - start_output_accuracy: 0.9956 - stop_output_accuracy: 0.9852 - val_loss: 0.4539 - val_start_output_loss: 0.0995 - val_stop_output_loss: 0.3544 - val_start_output_accuracy: 0.9855 - val_stop_output_accuracy: 0.8870\n",
            "Epoch 452/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.2881 - start_output_loss: 0.0426 - stop_output_loss: 0.2455 - start_output_accuracy: 0.9888 - stop_output_accuracy: 0.9212 - val_loss: 0.3210 - val_start_output_loss: 0.0858 - val_stop_output_loss: 0.2352 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9640\n",
            "Epoch 453/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.0792 - start_output_loss: 0.0181 - stop_output_loss: 0.0612 - start_output_accuracy: 0.9963 - stop_output_accuracy: 0.9872 - val_loss: 0.3950 - val_start_output_loss: 0.0933 - val_stop_output_loss: 0.3017 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9275\n",
            "Epoch 454/600\n",
            "10/10 [==============================] - 3s 348ms/step - loss: 0.1442 - start_output_loss: 0.0551 - stop_output_loss: 0.0891 - start_output_accuracy: 0.9832 - stop_output_accuracy: 0.9725 - val_loss: 0.7028 - val_start_output_loss: 0.3586 - val_stop_output_loss: 0.3441 - val_start_output_accuracy: 0.9045 - val_stop_output_accuracy: 0.9250\n",
            "Epoch 455/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.2808 - start_output_loss: 0.0636 - stop_output_loss: 0.2172 - start_output_accuracy: 0.9824 - stop_output_accuracy: 0.9349 - val_loss: 0.2691 - val_start_output_loss: 0.0917 - val_stop_output_loss: 0.1774 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9675\n",
            "Epoch 456/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.0892 - start_output_loss: 0.0210 - stop_output_loss: 0.0682 - start_output_accuracy: 0.9954 - stop_output_accuracy: 0.9834 - val_loss: 1.8370 - val_start_output_loss: 0.1159 - val_stop_output_loss: 1.7211 - val_start_output_accuracy: 0.9825 - val_stop_output_accuracy: 0.4380\n",
            "Epoch 457/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.3574 - start_output_loss: 0.0426 - stop_output_loss: 0.3148 - start_output_accuracy: 0.9883 - stop_output_accuracy: 0.8751 - val_loss: 0.2651 - val_start_output_loss: 0.0948 - val_stop_output_loss: 0.1703 - val_start_output_accuracy: 0.9875 - val_stop_output_accuracy: 0.9775\n",
            "Epoch 458/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.0844 - start_output_loss: 0.0177 - stop_output_loss: 0.0667 - start_output_accuracy: 0.9963 - stop_output_accuracy: 0.9866 - val_loss: 0.2586 - val_start_output_loss: 0.0991 - val_stop_output_loss: 0.1595 - val_start_output_accuracy: 0.9845 - val_stop_output_accuracy: 0.9785\n",
            "Epoch 459/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1478 - start_output_loss: 0.0516 - stop_output_loss: 0.0961 - start_output_accuracy: 0.9839 - stop_output_accuracy: 0.9690 - val_loss: 1.6354 - val_start_output_loss: 0.1245 - val_stop_output_loss: 1.5109 - val_start_output_accuracy: 0.9835 - val_stop_output_accuracy: 0.4320\n",
            "Epoch 460/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.2412 - start_output_loss: 0.0302 - stop_output_loss: 0.2111 - start_output_accuracy: 0.9923 - stop_output_accuracy: 0.9215 - val_loss: 0.2582 - val_start_output_loss: 0.0832 - val_stop_output_loss: 0.1750 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9765\n",
            "Epoch 461/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.1738 - start_output_loss: 0.0819 - stop_output_loss: 0.0920 - start_output_accuracy: 0.9797 - stop_output_accuracy: 0.9758 - val_loss: 0.5541 - val_start_output_loss: 0.1226 - val_stop_output_loss: 0.4315 - val_start_output_accuracy: 0.9810 - val_stop_output_accuracy: 0.8475\n",
            "Epoch 462/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.1052 - start_output_loss: 0.0251 - stop_output_loss: 0.0801 - start_output_accuracy: 0.9938 - stop_output_accuracy: 0.9773 - val_loss: 0.9283 - val_start_output_loss: 0.1117 - val_stop_output_loss: 0.8166 - val_start_output_accuracy: 0.9815 - val_stop_output_accuracy: 0.6950\n",
            "Epoch 463/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3257 - start_output_loss: 0.0363 - stop_output_loss: 0.2894 - start_output_accuracy: 0.9903 - stop_output_accuracy: 0.8832 - val_loss: 0.2752 - val_start_output_loss: 0.0834 - val_stop_output_loss: 0.1918 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9650\n",
            "Epoch 464/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0780 - start_output_loss: 0.0162 - stop_output_loss: 0.0618 - start_output_accuracy: 0.9966 - stop_output_accuracy: 0.9870 - val_loss: 0.2589 - val_start_output_loss: 0.0943 - val_stop_output_loss: 0.1646 - val_start_output_accuracy: 0.9865 - val_stop_output_accuracy: 0.9775\n",
            "Epoch 465/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.1005 - start_output_loss: 0.0224 - stop_output_loss: 0.0780 - start_output_accuracy: 0.9944 - stop_output_accuracy: 0.9780 - val_loss: 1.7023 - val_start_output_loss: 0.1739 - val_stop_output_loss: 1.5284 - val_start_output_accuracy: 0.9610 - val_stop_output_accuracy: 0.4990\n",
            "Epoch 466/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.1994 - start_output_loss: 0.0412 - stop_output_loss: 0.1582 - start_output_accuracy: 0.9886 - stop_output_accuracy: 0.9454 - val_loss: 0.3030 - val_start_output_loss: 0.0854 - val_stop_output_loss: 0.2177 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9645\n",
            "Epoch 467/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.0741 - start_output_loss: 0.0175 - stop_output_loss: 0.0566 - start_output_accuracy: 0.9962 - stop_output_accuracy: 0.9861 - val_loss: 0.4755 - val_start_output_loss: 0.2818 - val_stop_output_loss: 0.1937 - val_start_output_accuracy: 0.9295 - val_stop_output_accuracy: 0.9715\n",
            "Epoch 468/600\n",
            "10/10 [==============================] - 3s 340ms/step - loss: 0.4749 - start_output_loss: 0.1533 - stop_output_loss: 0.3216 - start_output_accuracy: 0.9606 - stop_output_accuracy: 0.8868 - val_loss: 0.4774 - val_start_output_loss: 0.2771 - val_stop_output_loss: 0.2003 - val_start_output_accuracy: 0.9330 - val_stop_output_accuracy: 0.9700\n",
            "Epoch 469/600\n",
            "10/10 [==============================] - 3s 341ms/step - loss: 0.0893 - start_output_loss: 0.0225 - stop_output_loss: 0.0669 - start_output_accuracy: 0.9952 - stop_output_accuracy: 0.9862 - val_loss: 0.2354 - val_start_output_loss: 0.0895 - val_stop_output_loss: 0.1458 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9865\n",
            "Epoch 470/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.1327 - start_output_loss: 0.0251 - stop_output_loss: 0.1076 - start_output_accuracy: 0.9939 - stop_output_accuracy: 0.9669 - val_loss: 0.2510 - val_start_output_loss: 0.0817 - val_stop_output_loss: 0.1693 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9745\n",
            "Epoch 471/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1214 - start_output_loss: 0.0219 - stop_output_loss: 0.0995 - start_output_accuracy: 0.9942 - stop_output_accuracy: 0.9686 - val_loss: 0.8482 - val_start_output_loss: 0.1735 - val_stop_output_loss: 0.6747 - val_start_output_accuracy: 0.9480 - val_stop_output_accuracy: 0.7345\n",
            "Epoch 472/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.1770 - start_output_loss: 0.0244 - stop_output_loss: 0.1526 - start_output_accuracy: 0.9937 - stop_output_accuracy: 0.9445 - val_loss: 0.2345 - val_start_output_loss: 0.0744 - val_stop_output_loss: 0.1601 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9820\n",
            "Epoch 473/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0749 - start_output_loss: 0.0154 - stop_output_loss: 0.0596 - start_output_accuracy: 0.9967 - stop_output_accuracy: 0.9842 - val_loss: 0.2604 - val_start_output_loss: 0.0953 - val_stop_output_loss: 0.1652 - val_start_output_accuracy: 0.9855 - val_stop_output_accuracy: 0.9810\n",
            "Epoch 474/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.2768 - start_output_loss: 0.0511 - stop_output_loss: 0.2257 - start_output_accuracy: 0.9863 - stop_output_accuracy: 0.9291 - val_loss: 0.2695 - val_start_output_loss: 0.0860 - val_stop_output_loss: 0.1835 - val_start_output_accuracy: 0.9890 - val_stop_output_accuracy: 0.9775\n",
            "Epoch 475/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.0782 - start_output_loss: 0.0157 - stop_output_loss: 0.0626 - start_output_accuracy: 0.9967 - stop_output_accuracy: 0.9840 - val_loss: 0.2536 - val_start_output_loss: 0.0788 - val_stop_output_loss: 0.1748 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9830\n",
            "Epoch 476/600\n",
            "10/10 [==============================] - 3s 346ms/step - loss: 0.2684 - start_output_loss: 0.0392 - stop_output_loss: 0.2291 - start_output_accuracy: 0.9889 - stop_output_accuracy: 0.9161 - val_loss: 0.7261 - val_start_output_loss: 0.2275 - val_stop_output_loss: 0.4986 - val_start_output_accuracy: 0.9400 - val_stop_output_accuracy: 0.8350\n",
            "Epoch 477/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1190 - start_output_loss: 0.0306 - stop_output_loss: 0.0884 - start_output_accuracy: 0.9918 - stop_output_accuracy: 0.9794 - val_loss: 0.3204 - val_start_output_loss: 0.0842 - val_stop_output_loss: 0.2362 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9570\n",
            "Epoch 478/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.0945 - start_output_loss: 0.0171 - stop_output_loss: 0.0774 - start_output_accuracy: 0.9962 - stop_output_accuracy: 0.9786 - val_loss: 0.2794 - val_start_output_loss: 0.0788 - val_stop_output_loss: 0.2005 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9695\n",
            "Epoch 479/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.0964 - start_output_loss: 0.0194 - stop_output_loss: 0.0771 - start_output_accuracy: 0.9955 - stop_output_accuracy: 0.9754 - val_loss: 0.9682 - val_start_output_loss: 0.4755 - val_stop_output_loss: 0.4927 - val_start_output_accuracy: 0.8675 - val_stop_output_accuracy: 0.8260\n",
            "Epoch 480/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.2684 - start_output_loss: 0.0450 - stop_output_loss: 0.2234 - start_output_accuracy: 0.9883 - stop_output_accuracy: 0.9327 - val_loss: 0.2595 - val_start_output_loss: 0.0746 - val_stop_output_loss: 0.1849 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9660\n",
            "Epoch 481/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.0697 - start_output_loss: 0.0152 - stop_output_loss: 0.0544 - start_output_accuracy: 0.9965 - stop_output_accuracy: 0.9872 - val_loss: 0.3174 - val_start_output_loss: 0.0862 - val_stop_output_loss: 0.2312 - val_start_output_accuracy: 0.9885 - val_stop_output_accuracy: 0.9535\n",
            "Epoch 482/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.2058 - start_output_loss: 0.0274 - stop_output_loss: 0.1785 - start_output_accuracy: 0.9934 - stop_output_accuracy: 0.9337 - val_loss: 1.4474 - val_start_output_loss: 0.1991 - val_stop_output_loss: 1.2483 - val_start_output_accuracy: 0.9480 - val_stop_output_accuracy: 0.8140\n",
            "Epoch 483/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.1058 - start_output_loss: 0.0199 - stop_output_loss: 0.0859 - start_output_accuracy: 0.9952 - stop_output_accuracy: 0.9788 - val_loss: 0.3328 - val_start_output_loss: 0.0936 - val_stop_output_loss: 0.2392 - val_start_output_accuracy: 0.9845 - val_stop_output_accuracy: 0.9480\n",
            "Epoch 484/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.3106 - start_output_loss: 0.0390 - stop_output_loss: 0.2716 - start_output_accuracy: 0.9888 - stop_output_accuracy: 0.8921 - val_loss: 1.6994 - val_start_output_loss: 0.6620 - val_stop_output_loss: 1.0373 - val_start_output_accuracy: 0.9230 - val_stop_output_accuracy: 0.6065\n",
            "Epoch 485/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0903 - start_output_loss: 0.0193 - stop_output_loss: 0.0710 - start_output_accuracy: 0.9953 - stop_output_accuracy: 0.9875 - val_loss: 0.2995 - val_start_output_loss: 0.0785 - val_stop_output_loss: 0.2210 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9625\n",
            "Epoch 486/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.0647 - start_output_loss: 0.0127 - stop_output_loss: 0.0520 - start_output_accuracy: 0.9972 - stop_output_accuracy: 0.9891 - val_loss: 0.3019 - val_start_output_loss: 0.0784 - val_stop_output_loss: 0.2235 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9480\n",
            "Epoch 487/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.2017 - start_output_loss: 0.0401 - stop_output_loss: 0.1616 - start_output_accuracy: 0.9884 - stop_output_accuracy: 0.9497 - val_loss: 0.2917 - val_start_output_loss: 0.0861 - val_stop_output_loss: 0.2057 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9530\n",
            "Epoch 488/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0744 - start_output_loss: 0.0182 - stop_output_loss: 0.0562 - start_output_accuracy: 0.9955 - stop_output_accuracy: 0.9883 - val_loss: 0.3972 - val_start_output_loss: 0.1609 - val_stop_output_loss: 0.2363 - val_start_output_accuracy: 0.9645 - val_stop_output_accuracy: 0.9575\n",
            "Epoch 489/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3235 - start_output_loss: 0.0547 - stop_output_loss: 0.2687 - start_output_accuracy: 0.9859 - stop_output_accuracy: 0.8967 - val_loss: 0.4021 - val_start_output_loss: 0.1147 - val_stop_output_loss: 0.2874 - val_start_output_accuracy: 0.9850 - val_stop_output_accuracy: 0.9515\n",
            "Epoch 490/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.0766 - start_output_loss: 0.0171 - stop_output_loss: 0.0595 - start_output_accuracy: 0.9958 - stop_output_accuracy: 0.9888 - val_loss: 0.2232 - val_start_output_loss: 0.0736 - val_stop_output_loss: 0.1496 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9820\n",
            "Epoch 491/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0730 - start_output_loss: 0.0134 - stop_output_loss: 0.0595 - start_output_accuracy: 0.9968 - stop_output_accuracy: 0.9848 - val_loss: 1.9303 - val_start_output_loss: 0.2005 - val_stop_output_loss: 1.7298 - val_start_output_accuracy: 0.9595 - val_stop_output_accuracy: 0.4665\n",
            "Epoch 492/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.1785 - start_output_loss: 0.0556 - stop_output_loss: 0.1228 - start_output_accuracy: 0.9821 - stop_output_accuracy: 0.9653 - val_loss: 0.2417 - val_start_output_loss: 0.0795 - val_stop_output_loss: 0.1622 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9775\n",
            "Epoch 493/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.1075 - start_output_loss: 0.0162 - stop_output_loss: 0.0913 - start_output_accuracy: 0.9963 - stop_output_accuracy: 0.9726 - val_loss: 6.3541 - val_start_output_loss: 0.1918 - val_stop_output_loss: 6.1623 - val_start_output_accuracy: 0.9565 - val_stop_output_accuracy: 0.0515\n",
            "Epoch 494/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.3021 - start_output_loss: 0.0372 - stop_output_loss: 0.2648 - start_output_accuracy: 0.9890 - stop_output_accuracy: 0.9032 - val_loss: 0.2668 - val_start_output_loss: 0.0739 - val_stop_output_loss: 0.1929 - val_start_output_accuracy: 0.9915 - val_stop_output_accuracy: 0.9795\n",
            "Epoch 495/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.0644 - start_output_loss: 0.0119 - stop_output_loss: 0.0524 - start_output_accuracy: 0.9973 - stop_output_accuracy: 0.9908 - val_loss: 0.2627 - val_start_output_loss: 0.0831 - val_stop_output_loss: 0.1796 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9670\n",
            "Epoch 496/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.1232 - start_output_loss: 0.0349 - stop_output_loss: 0.0883 - start_output_accuracy: 0.9898 - stop_output_accuracy: 0.9734 - val_loss: 1.7386 - val_start_output_loss: 0.5876 - val_stop_output_loss: 1.1510 - val_start_output_accuracy: 0.8595 - val_stop_output_accuracy: 0.6035\n",
            "Epoch 497/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.3174 - start_output_loss: 0.1481 - stop_output_loss: 0.1693 - start_output_accuracy: 0.9600 - stop_output_accuracy: 0.9455 - val_loss: 0.2327 - val_start_output_loss: 0.0862 - val_stop_output_loss: 0.1465 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9830\n",
            "Epoch 498/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.0709 - start_output_loss: 0.0186 - stop_output_loss: 0.0523 - start_output_accuracy: 0.9956 - stop_output_accuracy: 0.9880 - val_loss: 0.2300 - val_start_output_loss: 0.0899 - val_stop_output_loss: 0.1401 - val_start_output_accuracy: 0.9865 - val_stop_output_accuracy: 0.9870\n",
            "Epoch 499/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.0596 - start_output_loss: 0.0146 - stop_output_loss: 0.0450 - start_output_accuracy: 0.9966 - stop_output_accuracy: 0.9906 - val_loss: 0.4033 - val_start_output_loss: 0.0845 - val_stop_output_loss: 0.3188 - val_start_output_accuracy: 0.9875 - val_stop_output_accuracy: 0.9010\n",
            "Epoch 500/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2665 - start_output_loss: 0.0300 - stop_output_loss: 0.2365 - start_output_accuracy: 0.9920 - stop_output_accuracy: 0.9075 - val_loss: 0.2134 - val_start_output_loss: 0.0694 - val_stop_output_loss: 0.1440 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9825\n",
            "Epoch 501/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0587 - start_output_loss: 0.0113 - stop_output_loss: 0.0473 - start_output_accuracy: 0.9973 - stop_output_accuracy: 0.9908 - val_loss: 0.2205 - val_start_output_loss: 0.0756 - val_stop_output_loss: 0.1449 - val_start_output_accuracy: 0.9900 - val_stop_output_accuracy: 0.9835\n",
            "Epoch 502/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.0654 - start_output_loss: 0.0158 - stop_output_loss: 0.0496 - start_output_accuracy: 0.9950 - stop_output_accuracy: 0.9880 - val_loss: 0.2218 - val_start_output_loss: 0.0806 - val_stop_output_loss: 0.1412 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9810\n",
            "Epoch 503/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.3214 - start_output_loss: 0.0895 - stop_output_loss: 0.2319 - start_output_accuracy: 0.9757 - stop_output_accuracy: 0.9126 - val_loss: 0.2390 - val_start_output_loss: 0.0817 - val_stop_output_loss: 0.1573 - val_start_output_accuracy: 0.9905 - val_stop_output_accuracy: 0.9780\n",
            "Epoch 504/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0623 - start_output_loss: 0.0133 - stop_output_loss: 0.0490 - start_output_accuracy: 0.9971 - stop_output_accuracy: 0.9905 - val_loss: 0.2243 - val_start_output_loss: 0.0749 - val_stop_output_loss: 0.1495 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9790\n",
            "Epoch 505/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0802 - start_output_loss: 0.0144 - stop_output_loss: 0.0658 - start_output_accuracy: 0.9965 - stop_output_accuracy: 0.9797 - val_loss: 0.2718 - val_start_output_loss: 0.0864 - val_stop_output_loss: 0.1853 - val_start_output_accuracy: 0.9890 - val_stop_output_accuracy: 0.9690\n",
            "Epoch 506/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.4089 - start_output_loss: 0.0366 - stop_output_loss: 0.3723 - start_output_accuracy: 0.9901 - stop_output_accuracy: 0.8594 - val_loss: 3.9611 - val_start_output_loss: 0.8078 - val_stop_output_loss: 3.1533 - val_start_output_accuracy: 0.8915 - val_stop_output_accuracy: 0.2035\n",
            "Epoch 507/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.1116 - start_output_loss: 0.0225 - stop_output_loss: 0.0892 - start_output_accuracy: 0.9943 - stop_output_accuracy: 0.9825 - val_loss: 0.2654 - val_start_output_loss: 0.0721 - val_stop_output_loss: 0.1933 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9800\n",
            "Epoch 508/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.0602 - start_output_loss: 0.0109 - stop_output_loss: 0.0493 - start_output_accuracy: 0.9972 - stop_output_accuracy: 0.9912 - val_loss: 0.2153 - val_start_output_loss: 0.0681 - val_stop_output_loss: 0.1472 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9810\n",
            "Epoch 509/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.0693 - start_output_loss: 0.0135 - stop_output_loss: 0.0558 - start_output_accuracy: 0.9964 - stop_output_accuracy: 0.9838 - val_loss: 0.2226 - val_start_output_loss: 0.0801 - val_stop_output_loss: 0.1425 - val_start_output_accuracy: 0.9890 - val_stop_output_accuracy: 0.9860\n",
            "Epoch 510/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.0813 - start_output_loss: 0.0224 - stop_output_loss: 0.0589 - start_output_accuracy: 0.9945 - stop_output_accuracy: 0.9848 - val_loss: 3.0802 - val_start_output_loss: 0.1939 - val_stop_output_loss: 2.8864 - val_start_output_accuracy: 0.9710 - val_stop_output_accuracy: 0.4625\n",
            "Epoch 511/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.3064 - start_output_loss: 0.0352 - stop_output_loss: 0.2712 - start_output_accuracy: 0.9909 - stop_output_accuracy: 0.9122 - val_loss: 0.1781 - val_start_output_loss: 0.0695 - val_stop_output_loss: 0.1086 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9855\n",
            "Epoch 512/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0550 - start_output_loss: 0.0104 - stop_output_loss: 0.0446 - start_output_accuracy: 0.9976 - stop_output_accuracy: 0.9923 - val_loss: 0.2680 - val_start_output_loss: 0.1300 - val_stop_output_loss: 0.1379 - val_start_output_accuracy: 0.9770 - val_stop_output_accuracy: 0.9800\n",
            "Epoch 513/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0716 - start_output_loss: 0.0130 - stop_output_loss: 0.0586 - start_output_accuracy: 0.9967 - stop_output_accuracy: 0.9829 - val_loss: 1.6808 - val_start_output_loss: 0.1018 - val_stop_output_loss: 1.5790 - val_start_output_accuracy: 0.9840 - val_stop_output_accuracy: 0.5785\n",
            "Epoch 514/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.2810 - start_output_loss: 0.0369 - stop_output_loss: 0.2441 - start_output_accuracy: 0.9888 - stop_output_accuracy: 0.9060 - val_loss: 0.2313 - val_start_output_loss: 0.0722 - val_stop_output_loss: 0.1591 - val_start_output_accuracy: 0.9900 - val_stop_output_accuracy: 0.9790\n",
            "Epoch 515/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.0558 - start_output_loss: 0.0098 - stop_output_loss: 0.0461 - start_output_accuracy: 0.9978 - stop_output_accuracy: 0.9925 - val_loss: 0.1845 - val_start_output_loss: 0.0737 - val_stop_output_loss: 0.1108 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9880\n",
            "Epoch 516/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.1307 - start_output_loss: 0.0266 - stop_output_loss: 0.1041 - start_output_accuracy: 0.9931 - stop_output_accuracy: 0.9712 - val_loss: 1.4862 - val_start_output_loss: 0.0864 - val_stop_output_loss: 1.3998 - val_start_output_accuracy: 0.9890 - val_stop_output_accuracy: 0.5500\n",
            "Epoch 517/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.1647 - start_output_loss: 0.0162 - stop_output_loss: 0.1485 - start_output_accuracy: 0.9957 - stop_output_accuracy: 0.9550 - val_loss: 0.2137 - val_start_output_loss: 0.0659 - val_stop_output_loss: 0.1478 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9835\n",
            "Epoch 518/600\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 0.0608 - start_output_loss: 0.0105 - stop_output_loss: 0.0503 - start_output_accuracy: 0.9973 - stop_output_accuracy: 0.9889 - val_loss: 1.8510 - val_start_output_loss: 0.2940 - val_stop_output_loss: 1.5570 - val_start_output_accuracy: 0.9315 - val_stop_output_accuracy: 0.5300\n",
            "Epoch 519/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.3331 - start_output_loss: 0.0356 - stop_output_loss: 0.2975 - start_output_accuracy: 0.9905 - stop_output_accuracy: 0.8953 - val_loss: 0.2255 - val_start_output_loss: 0.0755 - val_stop_output_loss: 0.1500 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9845\n",
            "Epoch 520/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0559 - start_output_loss: 0.0096 - stop_output_loss: 0.0462 - start_output_accuracy: 0.9974 - stop_output_accuracy: 0.9919 - val_loss: 0.3650 - val_start_output_loss: 0.0772 - val_stop_output_loss: 0.2879 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9360\n",
            "Epoch 521/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.0541 - start_output_loss: 0.0085 - stop_output_loss: 0.0456 - start_output_accuracy: 0.9979 - stop_output_accuracy: 0.9891 - val_loss: 0.7337 - val_start_output_loss: 0.3130 - val_stop_output_loss: 0.4207 - val_start_output_accuracy: 0.9260 - val_stop_output_accuracy: 0.9160\n",
            "Epoch 522/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.3586 - start_output_loss: 0.1863 - stop_output_loss: 0.1723 - start_output_accuracy: 0.9621 - stop_output_accuracy: 0.9441 - val_loss: 0.2299 - val_start_output_loss: 0.0818 - val_stop_output_loss: 0.1481 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9875\n",
            "Epoch 523/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.0552 - start_output_loss: 0.0120 - stop_output_loss: 0.0432 - start_output_accuracy: 0.9973 - stop_output_accuracy: 0.9916 - val_loss: 0.2025 - val_start_output_loss: 0.0722 - val_stop_output_loss: 0.1303 - val_start_output_accuracy: 0.9940 - val_stop_output_accuracy: 0.9850\n",
            "Epoch 524/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.1673 - start_output_loss: 0.0156 - stop_output_loss: 0.1518 - start_output_accuracy: 0.9959 - stop_output_accuracy: 0.9352 - val_loss: 1.7140 - val_start_output_loss: 1.0622 - val_stop_output_loss: 0.6517 - val_start_output_accuracy: 0.9065 - val_stop_output_accuracy: 0.7960\n",
            "Epoch 525/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 0.1328 - start_output_loss: 0.0219 - stop_output_loss: 0.1109 - start_output_accuracy: 0.9946 - stop_output_accuracy: 0.9679 - val_loss: 0.2039 - val_start_output_loss: 0.0684 - val_stop_output_loss: 0.1356 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9860\n",
            "Epoch 526/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.0531 - start_output_loss: 0.0097 - stop_output_loss: 0.0434 - start_output_accuracy: 0.9977 - stop_output_accuracy: 0.9916 - val_loss: 0.2240 - val_start_output_loss: 0.0787 - val_stop_output_loss: 0.1453 - val_start_output_accuracy: 0.9900 - val_stop_output_accuracy: 0.9875\n",
            "Epoch 527/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.1577 - start_output_loss: 0.0172 - stop_output_loss: 0.1406 - start_output_accuracy: 0.9958 - stop_output_accuracy: 0.9536 - val_loss: 1.8444 - val_start_output_loss: 0.1963 - val_stop_output_loss: 1.6480 - val_start_output_accuracy: 0.9455 - val_stop_output_accuracy: 0.4110\n",
            "Epoch 528/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1172 - start_output_loss: 0.0163 - stop_output_loss: 0.1009 - start_output_accuracy: 0.9956 - stop_output_accuracy: 0.9722 - val_loss: 0.2060 - val_start_output_loss: 0.0676 - val_stop_output_loss: 0.1383 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9880\n",
            "Epoch 529/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.0794 - start_output_loss: 0.0186 - stop_output_loss: 0.0608 - start_output_accuracy: 0.9948 - stop_output_accuracy: 0.9841 - val_loss: 0.2833 - val_start_output_loss: 0.0986 - val_stop_output_loss: 0.1847 - val_start_output_accuracy: 0.9885 - val_stop_output_accuracy: 0.9780\n",
            "Epoch 530/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0717 - start_output_loss: 0.0154 - stop_output_loss: 0.0562 - start_output_accuracy: 0.9956 - stop_output_accuracy: 0.9848 - val_loss: 1.1715 - val_start_output_loss: 0.1246 - val_stop_output_loss: 1.0469 - val_start_output_accuracy: 0.9750 - val_stop_output_accuracy: 0.6095\n",
            "Epoch 531/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.3047 - start_output_loss: 0.0636 - stop_output_loss: 0.2410 - start_output_accuracy: 0.9818 - stop_output_accuracy: 0.9197 - val_loss: 0.2124 - val_start_output_loss: 0.0735 - val_stop_output_loss: 0.1389 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9865\n",
            "Epoch 532/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.0504 - start_output_loss: 0.0092 - stop_output_loss: 0.0412 - start_output_accuracy: 0.9977 - stop_output_accuracy: 0.9926 - val_loss: 0.2327 - val_start_output_loss: 0.0725 - val_stop_output_loss: 0.1602 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9800\n",
            "Epoch 533/600\n",
            "10/10 [==============================] - 3s 335ms/step - loss: 0.0540 - start_output_loss: 0.0088 - stop_output_loss: 0.0451 - start_output_accuracy: 0.9977 - stop_output_accuracy: 0.9880 - val_loss: 0.2563 - val_start_output_loss: 0.0650 - val_stop_output_loss: 0.1913 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9755\n",
            "Epoch 534/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.1680 - start_output_loss: 0.0480 - stop_output_loss: 0.1200 - start_output_accuracy: 0.9870 - stop_output_accuracy: 0.9669 - val_loss: 2.2582 - val_start_output_loss: 0.1330 - val_stop_output_loss: 2.1252 - val_start_output_accuracy: 0.9745 - val_stop_output_accuracy: 0.2605\n",
            "Epoch 535/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.1342 - start_output_loss: 0.0178 - stop_output_loss: 0.1164 - start_output_accuracy: 0.9962 - stop_output_accuracy: 0.9604 - val_loss: 0.2181 - val_start_output_loss: 0.0632 - val_stop_output_loss: 0.1549 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9880\n",
            "Epoch 536/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0437 - start_output_loss: 0.0101 - stop_output_loss: 0.0336 - start_output_accuracy: 0.9976 - stop_output_accuracy: 0.9941 - val_loss: 0.2590 - val_start_output_loss: 0.0680 - val_stop_output_loss: 0.1910 - val_start_output_accuracy: 0.9915 - val_stop_output_accuracy: 0.9750\n",
            "Epoch 537/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.3078 - start_output_loss: 0.0292 - stop_output_loss: 0.2786 - start_output_accuracy: 0.9919 - stop_output_accuracy: 0.8944 - val_loss: 0.2288 - val_start_output_loss: 0.0769 - val_stop_output_loss: 0.1519 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9900\n",
            "Epoch 538/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0519 - start_output_loss: 0.0098 - stop_output_loss: 0.0421 - start_output_accuracy: 0.9974 - stop_output_accuracy: 0.9917 - val_loss: 0.2239 - val_start_output_loss: 0.0664 - val_stop_output_loss: 0.1575 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9815\n",
            "Epoch 539/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0392 - start_output_loss: 0.0075 - stop_output_loss: 0.0317 - start_output_accuracy: 0.9981 - stop_output_accuracy: 0.9939 - val_loss: 0.2406 - val_start_output_loss: 0.0769 - val_stop_output_loss: 0.1637 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9820\n",
            "Epoch 540/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 0.2458 - start_output_loss: 0.0216 - stop_output_loss: 0.2242 - start_output_accuracy: 0.9948 - stop_output_accuracy: 0.9315 - val_loss: 0.2159 - val_start_output_loss: 0.0699 - val_stop_output_loss: 0.1461 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9820\n",
            "Epoch 541/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0576 - start_output_loss: 0.0096 - stop_output_loss: 0.0480 - start_output_accuracy: 0.9975 - stop_output_accuracy: 0.9871 - val_loss: 0.2119 - val_start_output_loss: 0.0683 - val_stop_output_loss: 0.1436 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9735\n",
            "Epoch 542/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0818 - start_output_loss: 0.0124 - stop_output_loss: 0.0694 - start_output_accuracy: 0.9966 - stop_output_accuracy: 0.9792 - val_loss: 5.8377 - val_start_output_loss: 0.4131 - val_stop_output_loss: 5.4246 - val_start_output_accuracy: 0.9285 - val_stop_output_accuracy: 0.1655\n",
            "Epoch 543/600\n",
            "10/10 [==============================] - 3s 323ms/step - loss: 0.1317 - start_output_loss: 0.0166 - stop_output_loss: 0.1150 - start_output_accuracy: 0.9954 - stop_output_accuracy: 0.9650 - val_loss: 0.2099 - val_start_output_loss: 0.0705 - val_stop_output_loss: 0.1393 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9900\n",
            "Epoch 544/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.0810 - start_output_loss: 0.0144 - stop_output_loss: 0.0665 - start_output_accuracy: 0.9959 - stop_output_accuracy: 0.9783 - val_loss: 0.2321 - val_start_output_loss: 0.0934 - val_stop_output_loss: 0.1387 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9885\n",
            "Epoch 545/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0518 - start_output_loss: 0.0146 - stop_output_loss: 0.0371 - start_output_accuracy: 0.9964 - stop_output_accuracy: 0.9912 - val_loss: 0.3398 - val_start_output_loss: 0.0895 - val_stop_output_loss: 0.2504 - val_start_output_accuracy: 0.9850 - val_stop_output_accuracy: 0.9545\n",
            "Epoch 546/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.2987 - start_output_loss: 0.0413 - stop_output_loss: 0.2574 - start_output_accuracy: 0.9875 - stop_output_accuracy: 0.9223 - val_loss: 0.2400 - val_start_output_loss: 0.0830 - val_stop_output_loss: 0.1570 - val_start_output_accuracy: 0.9880 - val_stop_output_accuracy: 0.9780\n",
            "Epoch 547/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.0474 - start_output_loss: 0.0093 - stop_output_loss: 0.0381 - start_output_accuracy: 0.9979 - stop_output_accuracy: 0.9926 - val_loss: 0.1949 - val_start_output_loss: 0.0648 - val_stop_output_loss: 0.1301 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9910\n",
            "Epoch 548/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.0500 - start_output_loss: 0.0086 - stop_output_loss: 0.0414 - start_output_accuracy: 0.9978 - stop_output_accuracy: 0.9902 - val_loss: 0.2511 - val_start_output_loss: 0.0684 - val_stop_output_loss: 0.1827 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9770\n",
            "Epoch 549/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.2715 - start_output_loss: 0.0252 - stop_output_loss: 0.2463 - start_output_accuracy: 0.9924 - stop_output_accuracy: 0.9089 - val_loss: 0.2831 - val_start_output_loss: 0.1205 - val_stop_output_loss: 0.1626 - val_start_output_accuracy: 0.9790 - val_stop_output_accuracy: 0.9700\n",
            "Epoch 550/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.0538 - start_output_loss: 0.0099 - stop_output_loss: 0.0440 - start_output_accuracy: 0.9971 - stop_output_accuracy: 0.9919 - val_loss: 0.2050 - val_start_output_loss: 0.0707 - val_stop_output_loss: 0.1343 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9835\n",
            "Epoch 551/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.0366 - start_output_loss: 0.0070 - stop_output_loss: 0.0297 - start_output_accuracy: 0.9978 - stop_output_accuracy: 0.9948 - val_loss: 0.1928 - val_start_output_loss: 0.0658 - val_stop_output_loss: 0.1270 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9910\n",
            "Epoch 552/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.1215 - start_output_loss: 0.0559 - stop_output_loss: 0.0656 - start_output_accuracy: 0.9844 - stop_output_accuracy: 0.9772 - val_loss: 1.6545 - val_start_output_loss: 0.9227 - val_stop_output_loss: 0.7318 - val_start_output_accuracy: 0.7430 - val_stop_output_accuracy: 0.8080\n",
            "Epoch 553/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.0926 - start_output_loss: 0.0241 - stop_output_loss: 0.0685 - start_output_accuracy: 0.9936 - stop_output_accuracy: 0.9827 - val_loss: 0.2189 - val_start_output_loss: 0.0741 - val_stop_output_loss: 0.1448 - val_start_output_accuracy: 0.9915 - val_stop_output_accuracy: 0.9865\n",
            "Epoch 554/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.0366 - start_output_loss: 0.0071 - stop_output_loss: 0.0295 - start_output_accuracy: 0.9983 - stop_output_accuracy: 0.9938 - val_loss: 0.3998 - val_start_output_loss: 0.0690 - val_stop_output_loss: 0.3307 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9165\n",
            "Epoch 555/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2539 - start_output_loss: 0.0346 - stop_output_loss: 0.2193 - start_output_accuracy: 0.9894 - stop_output_accuracy: 0.9258 - val_loss: 0.2215 - val_start_output_loss: 0.0765 - val_stop_output_loss: 0.1450 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9910\n",
            "Epoch 556/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.0405 - start_output_loss: 0.0088 - stop_output_loss: 0.0317 - start_output_accuracy: 0.9980 - stop_output_accuracy: 0.9952 - val_loss: 0.2116 - val_start_output_loss: 0.0740 - val_stop_output_loss: 0.1376 - val_start_output_accuracy: 0.9895 - val_stop_output_accuracy: 0.9835\n",
            "Epoch 557/600\n",
            "10/10 [==============================] - 3s 324ms/step - loss: 0.0606 - start_output_loss: 0.0095 - stop_output_loss: 0.0511 - start_output_accuracy: 0.9974 - stop_output_accuracy: 0.9851 - val_loss: 0.3901 - val_start_output_loss: 0.0822 - val_stop_output_loss: 0.3080 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9360\n",
            "Epoch 558/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1411 - start_output_loss: 0.0139 - stop_output_loss: 0.1273 - start_output_accuracy: 0.9961 - stop_output_accuracy: 0.9637 - val_loss: 6.1487 - val_start_output_loss: 0.2138 - val_stop_output_loss: 5.9350 - val_start_output_accuracy: 0.9635 - val_stop_output_accuracy: 0.1275\n",
            "Epoch 559/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.2008 - start_output_loss: 0.0275 - stop_output_loss: 0.1733 - start_output_accuracy: 0.9929 - stop_output_accuracy: 0.9397 - val_loss: 0.2137 - val_start_output_loss: 0.0734 - val_stop_output_loss: 0.1403 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9815\n",
            "Epoch 560/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0411 - start_output_loss: 0.0072 - stop_output_loss: 0.0339 - start_output_accuracy: 0.9979 - stop_output_accuracy: 0.9944 - val_loss: 0.1856 - val_start_output_loss: 0.0659 - val_stop_output_loss: 0.1197 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9905\n",
            "Epoch 561/600\n",
            "10/10 [==============================] - 3s 325ms/step - loss: 0.0827 - start_output_loss: 0.0173 - stop_output_loss: 0.0654 - start_output_accuracy: 0.9950 - stop_output_accuracy: 0.9815 - val_loss: 0.3458 - val_start_output_loss: 0.1223 - val_stop_output_loss: 0.2234 - val_start_output_accuracy: 0.9780 - val_stop_output_accuracy: 0.9375\n",
            "Epoch 562/600\n",
            "10/10 [==============================] - 3s 331ms/step - loss: 0.1275 - start_output_loss: 0.0829 - stop_output_loss: 0.0446 - start_output_accuracy: 0.9804 - stop_output_accuracy: 0.9898 - val_loss: 0.2372 - val_start_output_loss: 0.0816 - val_stop_output_loss: 0.1556 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9830\n",
            "Epoch 563/600\n",
            "10/10 [==============================] - 3s 317ms/step - loss: 0.1681 - start_output_loss: 0.0146 - stop_output_loss: 0.1536 - start_output_accuracy: 0.9961 - stop_output_accuracy: 0.9527 - val_loss: 0.9612 - val_start_output_loss: 0.1131 - val_stop_output_loss: 0.8481 - val_start_output_accuracy: 0.9845 - val_stop_output_accuracy: 0.7055\n",
            "Epoch 564/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.0688 - start_output_loss: 0.0108 - stop_output_loss: 0.0580 - start_output_accuracy: 0.9977 - stop_output_accuracy: 0.9845 - val_loss: 0.2097 - val_start_output_loss: 0.0726 - val_stop_output_loss: 0.1371 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9875\n",
            "Epoch 565/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.0348 - start_output_loss: 0.0078 - stop_output_loss: 0.0270 - start_output_accuracy: 0.9985 - stop_output_accuracy: 0.9951 - val_loss: 0.3562 - val_start_output_loss: 0.1288 - val_stop_output_loss: 0.2274 - val_start_output_accuracy: 0.9825 - val_stop_output_accuracy: 0.9655\n",
            "Epoch 566/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.1376 - start_output_loss: 0.0168 - stop_output_loss: 0.1208 - start_output_accuracy: 0.9944 - stop_output_accuracy: 0.9647 - val_loss: 0.1967 - val_start_output_loss: 0.0711 - val_stop_output_loss: 0.1256 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9875\n",
            "Epoch 567/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.0329 - start_output_loss: 0.0075 - stop_output_loss: 0.0253 - start_output_accuracy: 0.9980 - stop_output_accuracy: 0.9952 - val_loss: 0.2153 - val_start_output_loss: 0.0721 - val_stop_output_loss: 0.1432 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9845\n",
            "Epoch 568/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2003 - start_output_loss: 0.0459 - stop_output_loss: 0.1544 - start_output_accuracy: 0.9887 - stop_output_accuracy: 0.9442 - val_loss: 1.6298 - val_start_output_loss: 0.4857 - val_stop_output_loss: 1.1441 - val_start_output_accuracy: 0.9045 - val_stop_output_accuracy: 0.6135\n",
            "Epoch 569/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.1028 - start_output_loss: 0.0133 - stop_output_loss: 0.0895 - start_output_accuracy: 0.9969 - stop_output_accuracy: 0.9672 - val_loss: 0.1996 - val_start_output_loss: 0.0736 - val_stop_output_loss: 0.1260 - val_start_output_accuracy: 0.9920 - val_stop_output_accuracy: 0.9855\n",
            "Epoch 570/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.0316 - start_output_loss: 0.0057 - stop_output_loss: 0.0259 - start_output_accuracy: 0.9984 - stop_output_accuracy: 0.9959 - val_loss: 0.1833 - val_start_output_loss: 0.0694 - val_stop_output_loss: 0.1139 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9820\n",
            "Epoch 571/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.0837 - start_output_loss: 0.0143 - stop_output_loss: 0.0694 - start_output_accuracy: 0.9963 - stop_output_accuracy: 0.9764 - val_loss: 0.1740 - val_start_output_loss: 0.0686 - val_stop_output_loss: 0.1054 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9865\n",
            "Epoch 572/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.0452 - start_output_loss: 0.0085 - stop_output_loss: 0.0367 - start_output_accuracy: 0.9979 - stop_output_accuracy: 0.9889 - val_loss: 0.9299 - val_start_output_loss: 0.6148 - val_stop_output_loss: 0.3151 - val_start_output_accuracy: 0.8635 - val_stop_output_accuracy: 0.8975\n",
            "Epoch 573/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.1589 - start_output_loss: 0.0898 - stop_output_loss: 0.0690 - start_output_accuracy: 0.9779 - stop_output_accuracy: 0.9800 - val_loss: 0.2308 - val_start_output_loss: 0.0892 - val_stop_output_loss: 0.1416 - val_start_output_accuracy: 0.9875 - val_stop_output_accuracy: 0.9835\n",
            "Epoch 574/600\n",
            "10/10 [==============================] - 3s 350ms/step - loss: 0.0447 - start_output_loss: 0.0088 - stop_output_loss: 0.0359 - start_output_accuracy: 0.9973 - stop_output_accuracy: 0.9905 - val_loss: 1.0429 - val_start_output_loss: 0.0892 - val_stop_output_loss: 0.9537 - val_start_output_accuracy: 0.9865 - val_stop_output_accuracy: 0.6570\n",
            "Epoch 575/600\n",
            "10/10 [==============================] - 3s 343ms/step - loss: 0.2073 - start_output_loss: 0.0139 - stop_output_loss: 0.1934 - start_output_accuracy: 0.9962 - stop_output_accuracy: 0.9249 - val_loss: 0.1723 - val_start_output_loss: 0.0661 - val_stop_output_loss: 0.1062 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9905\n",
            "Epoch 576/600\n",
            "10/10 [==============================] - 3s 345ms/step - loss: 0.0337 - start_output_loss: 0.0065 - stop_output_loss: 0.0271 - start_output_accuracy: 0.9980 - stop_output_accuracy: 0.9948 - val_loss: 0.1644 - val_start_output_loss: 0.0633 - val_stop_output_loss: 0.1010 - val_start_output_accuracy: 0.9945 - val_stop_output_accuracy: 0.9895\n",
            "Epoch 577/600\n",
            "10/10 [==============================] - 3s 349ms/step - loss: 0.0424 - start_output_loss: 0.0085 - stop_output_loss: 0.0339 - start_output_accuracy: 0.9977 - stop_output_accuracy: 0.9930 - val_loss: 0.7697 - val_start_output_loss: 0.2190 - val_stop_output_loss: 0.5506 - val_start_output_accuracy: 0.9670 - val_stop_output_accuracy: 0.8125\n",
            "Epoch 578/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2424 - start_output_loss: 0.0298 - stop_output_loss: 0.2127 - start_output_accuracy: 0.9921 - stop_output_accuracy: 0.9276 - val_loss: 0.2079 - val_start_output_loss: 0.0685 - val_stop_output_loss: 0.1395 - val_start_output_accuracy: 0.9950 - val_stop_output_accuracy: 0.9850\n",
            "Epoch 579/600\n",
            "10/10 [==============================] - 3s 336ms/step - loss: 0.0381 - start_output_loss: 0.0084 - stop_output_loss: 0.0297 - start_output_accuracy: 0.9973 - stop_output_accuracy: 0.9945 - val_loss: 0.1948 - val_start_output_loss: 0.0652 - val_stop_output_loss: 0.1296 - val_start_output_accuracy: 0.9945 - val_stop_output_accuracy: 0.9885\n",
            "Epoch 580/600\n",
            "10/10 [==============================] - 3s 338ms/step - loss: 0.0285 - start_output_loss: 0.0052 - stop_output_loss: 0.0233 - start_output_accuracy: 0.9989 - stop_output_accuracy: 0.9950 - val_loss: 0.2840 - val_start_output_loss: 0.0785 - val_stop_output_loss: 0.2055 - val_start_output_accuracy: 0.9850 - val_stop_output_accuracy: 0.9645\n",
            "Epoch 581/600\n",
            "10/10 [==============================] - 3s 339ms/step - loss: 0.3412 - start_output_loss: 0.0194 - stop_output_loss: 0.3218 - start_output_accuracy: 0.9948 - stop_output_accuracy: 0.8828 - val_loss: 0.6194 - val_start_output_loss: 0.0650 - val_stop_output_loss: 0.5544 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.7610\n",
            "Epoch 582/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.0413 - start_output_loss: 0.0063 - stop_output_loss: 0.0350 - start_output_accuracy: 0.9983 - stop_output_accuracy: 0.9955 - val_loss: 0.1899 - val_start_output_loss: 0.0579 - val_stop_output_loss: 0.1320 - val_start_output_accuracy: 0.9945 - val_stop_output_accuracy: 0.9890\n",
            "Epoch 583/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.0321 - start_output_loss: 0.0054 - stop_output_loss: 0.0266 - start_output_accuracy: 0.9986 - stop_output_accuracy: 0.9949 - val_loss: 0.1906 - val_start_output_loss: 0.0655 - val_stop_output_loss: 0.1251 - val_start_output_accuracy: 0.9930 - val_stop_output_accuracy: 0.9880\n",
            "Epoch 584/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.0853 - start_output_loss: 0.0081 - stop_output_loss: 0.0772 - start_output_accuracy: 0.9985 - stop_output_accuracy: 0.9757 - val_loss: 1.5216 - val_start_output_loss: 0.1317 - val_stop_output_loss: 1.3899 - val_start_output_accuracy: 0.9720 - val_stop_output_accuracy: 0.4095\n",
            "Epoch 585/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.1135 - start_output_loss: 0.0118 - stop_output_loss: 0.1016 - start_output_accuracy: 0.9963 - stop_output_accuracy: 0.9660 - val_loss: 0.2482 - val_start_output_loss: 0.0655 - val_stop_output_loss: 0.1827 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9725\n",
            "Epoch 586/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0652 - start_output_loss: 0.0098 - stop_output_loss: 0.0554 - start_output_accuracy: 0.9970 - stop_output_accuracy: 0.9837 - val_loss: 0.6303 - val_start_output_loss: 0.1251 - val_stop_output_loss: 0.5051 - val_start_output_accuracy: 0.9800 - val_stop_output_accuracy: 0.8805\n",
            "Epoch 587/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.0567 - start_output_loss: 0.0166 - stop_output_loss: 0.0401 - start_output_accuracy: 0.9954 - stop_output_accuracy: 0.9886 - val_loss: 0.1910 - val_start_output_loss: 0.0625 - val_stop_output_loss: 0.1285 - val_start_output_accuracy: 0.9945 - val_stop_output_accuracy: 0.9910\n",
            "Epoch 588/600\n",
            "10/10 [==============================] - 3s 329ms/step - loss: 0.2152 - start_output_loss: 0.0229 - stop_output_loss: 0.1923 - start_output_accuracy: 0.9937 - stop_output_accuracy: 0.9230 - val_loss: 1.2532 - val_start_output_loss: 0.6722 - val_stop_output_loss: 0.5811 - val_start_output_accuracy: 0.8400 - val_stop_output_accuracy: 0.8355\n",
            "Epoch 589/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0714 - start_output_loss: 0.0330 - stop_output_loss: 0.0384 - start_output_accuracy: 0.9907 - stop_output_accuracy: 0.9934 - val_loss: 0.2364 - val_start_output_loss: 0.0831 - val_stop_output_loss: 0.1532 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9900\n",
            "Epoch 590/600\n",
            "10/10 [==============================] - 3s 332ms/step - loss: 0.0278 - start_output_loss: 0.0055 - stop_output_loss: 0.0223 - start_output_accuracy: 0.9984 - stop_output_accuracy: 0.9974 - val_loss: 0.2395 - val_start_output_loss: 0.0725 - val_stop_output_loss: 0.1671 - val_start_output_accuracy: 0.9940 - val_stop_output_accuracy: 0.9900\n",
            "Epoch 591/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.1348 - start_output_loss: 0.0226 - stop_output_loss: 0.1122 - start_output_accuracy: 0.9940 - stop_output_accuracy: 0.9652 - val_loss: 1.3245 - val_start_output_loss: 0.1323 - val_stop_output_loss: 1.1922 - val_start_output_accuracy: 0.9815 - val_stop_output_accuracy: 0.8040\n",
            "Epoch 592/600\n",
            "10/10 [==============================] - 3s 342ms/step - loss: 0.0816 - start_output_loss: 0.0104 - stop_output_loss: 0.0712 - start_output_accuracy: 0.9972 - stop_output_accuracy: 0.9803 - val_loss: 0.2022 - val_start_output_loss: 0.0667 - val_stop_output_loss: 0.1355 - val_start_output_accuracy: 0.9940 - val_stop_output_accuracy: 0.9930\n",
            "Epoch 593/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0263 - start_output_loss: 0.0050 - stop_output_loss: 0.0213 - start_output_accuracy: 0.9987 - stop_output_accuracy: 0.9963 - val_loss: 0.3954 - val_start_output_loss: 0.0727 - val_stop_output_loss: 0.3226 - val_start_output_accuracy: 0.9925 - val_stop_output_accuracy: 0.9100\n",
            "Epoch 594/600\n",
            "10/10 [==============================] - 3s 326ms/step - loss: 0.2628 - start_output_loss: 0.0309 - stop_output_loss: 0.2319 - start_output_accuracy: 0.9894 - stop_output_accuracy: 0.9153 - val_loss: 0.1807 - val_start_output_loss: 0.0774 - val_stop_output_loss: 0.1033 - val_start_output_accuracy: 0.9910 - val_stop_output_accuracy: 0.9880\n",
            "Epoch 595/600\n",
            "10/10 [==============================] - 3s 328ms/step - loss: 0.0326 - start_output_loss: 0.0064 - stop_output_loss: 0.0263 - start_output_accuracy: 0.9984 - stop_output_accuracy: 0.9963 - val_loss: 0.1611 - val_start_output_loss: 0.0651 - val_stop_output_loss: 0.0960 - val_start_output_accuracy: 0.9945 - val_stop_output_accuracy: 0.9930\n",
            "Epoch 596/600\n",
            "10/10 [==============================] - 3s 330ms/step - loss: 0.0278 - start_output_loss: 0.0050 - stop_output_loss: 0.0228 - start_output_accuracy: 0.9987 - stop_output_accuracy: 0.9958 - val_loss: 0.3275 - val_start_output_loss: 0.0913 - val_stop_output_loss: 0.2361 - val_start_output_accuracy: 0.9840 - val_stop_output_accuracy: 0.9540\n",
            "Epoch 597/600\n",
            "10/10 [==============================] - 3s 334ms/step - loss: 0.0415 - start_output_loss: 0.0119 - stop_output_loss: 0.0296 - start_output_accuracy: 0.9969 - stop_output_accuracy: 0.9921 - val_loss: 0.2239 - val_start_output_loss: 0.0653 - val_stop_output_loss: 0.1586 - val_start_output_accuracy: 0.9940 - val_stop_output_accuracy: 0.9690\n",
            "Epoch 598/600\n",
            "10/10 [==============================] - 3s 333ms/step - loss: 0.2885 - start_output_loss: 0.0214 - stop_output_loss: 0.2671 - start_output_accuracy: 0.9936 - stop_output_accuracy: 0.9002 - val_loss: 0.1837 - val_start_output_loss: 0.0651 - val_stop_output_loss: 0.1185 - val_start_output_accuracy: 0.9940 - val_stop_output_accuracy: 0.9925\n",
            "Epoch 599/600\n",
            "10/10 [==============================] - 3s 327ms/step - loss: 0.0294 - start_output_loss: 0.0047 - stop_output_loss: 0.0246 - start_output_accuracy: 0.9986 - stop_output_accuracy: 0.9964 - val_loss: 0.1835 - val_start_output_loss: 0.0619 - val_stop_output_loss: 0.1216 - val_start_output_accuracy: 0.9935 - val_stop_output_accuracy: 0.9925\n",
            "Epoch 600/600\n",
            "10/10 [==============================] - 3s 337ms/step - loss: 0.0216 - start_output_loss: 0.0039 - stop_output_loss: 0.0177 - start_output_accuracy: 0.9990 - stop_output_accuracy: 0.9974 - val_loss: 0.1989 - val_start_output_loss: 0.0626 - val_stop_output_loss: 0.1363 - val_start_output_accuracy: 0.9950 - val_stop_output_accuracy: 0.9770\n"
          ]
        }
      ],
      "source": [
        "#Train the model\n",
        "history = model.fit(x_train, [y_start_train, y_stop_train],\n",
        "          batch_size=2048,\n",
        "          epochs=600,\n",
        "          #validation_split=0.2,\n",
        "          validation_data=(x_val, [y_start_val, y_stop_val]),\n",
        "          callbacks=[model_checkpoint_callback])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVnA5J6bskB1",
        "outputId": "ff7dd617-b831-47d3-885a-19d024056b79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f8dc00bdd60>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "model.load_weights(checkpoint_filepath)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CknKKJ3ghFTi"
      },
      "outputs": [],
      "source": [
        "model.save(\"/content/drive/MyDrive/tv_model_pred.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Go2tSnUW2kkn"
      },
      "outputs": [],
      "source": [
        "model = keras.models.load_model(\"/content/drive/MyDrive/tv_model_pred.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "sdA55sY4hEmD",
        "outputId": "cfe6f530-98a8-4288-da83-764859430e94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ],
            "image/png": 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FhOObOMfkx+7O5NSd3QnF7sQUklzicnFni5vJRy/DFVtCKhUkBMIk6cO4my9jh280ImY3aCZCzoHEWHcwwPsth6IeoF9aPIMa7sbrasFtgTf3H6UgNIQZFXMo7+rFnt7C4vfGkenSMy5/J1qGn1tim3izOpX7j8Uy/ICOQeUBLAYfy8YIjqUYGNN4IbG2If/Jj4P/OiIya+xnajwybW2llLL/95S9BWySUn7ccf8oMOGfTVsbPny4zMvL+862wsJC+vTp858KW1H+Y9RrU1EURflf930jM/4dnqAHg86ASW/6h7IfGm3xQ5pc/u8kXILhII/teIzr+89CH04mMzGSoKlq9ZBo03O0tZBByYN+8IfKb8q+wW6xMyJtBF/ur+GLfTXMu3IY/nCQu5avYEjqQMb1SCW3tJmSRjfeQIj+nePo3zmONQV1xEUZOa5/nUzLUOauTOHmKQ72tq2kqDKOWKskMaEFZ3siJWU5bJl9EwlWE7O/eJ9dR6NpdljpkRzNmf1SqdDPp5s9nSndp/B63nvsqjmIyxXD2RmX8+L55yGlZF1hA8+sKuR4kwt9dBHju4zk/jMHcOX8nTS7AwBceoqXLY6XiHXcwpHyWIYO2k2xK4+wro2k9vu5cvhAXtq4gxhTDGf16caS6scQlnK8xY9jMATI7P8OI203s6cojt791rOp6ht8nhR6GC5ixfUzGP/667Swi7joMJq5FF/YTcjdnQx5OW5nCk2+KizdXibo7I/NOR0tbMETciE6v4K+9Xwu6HUGHx36AnQBZPsoUhNb6ZmzmyO5OWQYjeQHYzGnL8MYcxhf5Q2Yg9nILn/G4O/HgyMfJD0+ijc2FLOrrIVBGfEcrnFwwZDOXDAmxKyvbwG9B4BA8yncMvAuFh6/n4Cuhh6+F7AnFrHH/9J3nv8urmQaGu9m+hkVfFD0VwwhCwJB0OAFwBYW6I7fzh8umMSTBRcRCpro5hlGsa4BYWxhbFjS6kqj3JaA3/otAKmGIZiar6XMWYQ+bSEY2jFKHTPogif2UT48vhRD0ipGVZ1DiifIVbMfY+GSl+m5/lMWn2qgNgHOtM2jNrCXo643COsgTqTSJJoRIkR6C7TqzqKxbiJRXedisEauzj21yy3QPp5l1U9hjDkMwHnG8bTJG4i1tXO4qZT9Rakk2A4QzPjou28EfwIb6yq5LXQ5BzrvwmiqxYhkvqcX13mT8Sdvx1s1g2GpQzlkuBshJBZN49K6wSz0XcXgzJfotb2SIeWSpWdI9qUYMPgFWY06rL4QNYmCOjuRYTyAWZP4dQKBwFj3IM2tNmJ7P0K6ppHgDpBVaMCq6Rk+aiLv+H3037OVplQzz+tLOZjUg7uj02gX5Vy8RWPiQcnd1+vxmgXpzZK+aUN4qWgV06yjsHiqONZV42y3h6cbW3jcPZ5Tt5VQmSI4NNjI5Eu+ZsP2lxi8bCVprZLiHEHGvevJ2/o8ExasZt5ZOuqy47jDM5S/tFdx5Y5jbOsr2NcTYqWkXRhxlN3OZPdcLtvuZc4lGp3qdcxerlGVomfp2UEKEgzowhBnykBGGygPlPNol6sxp1xA4sxzsbshLGDODIHVHmCdKYHr17s4c6+kuLvGw9ONJDMKmzTQ+fBWrlmrYfNB8fmxPGR+hGtK7md3H0FLmsZlK82MPuJnV47grbN1OK2Ci7rMIEE/gQ+PXs/dK/QMKPHz4dnQ2qU7+dGR147XlEi3+mau/SbMuxeA0ahx+VI9FqFja09JW+YwvnJM5/zgwyR7NbQEHynlBlxScN1AQYVb8lyUnVZ7G06j5LbFMKgskqtYcYaeWUnVzNJlcMl2I33L3CwZp2fjGEmLXk+6OZvHXj+EvUmH3wArf2/gwms/5vJvZiClxqXxo3F9s42r1mt8eN+fqG72cNfCFzCGIy/dR24y05LUndeeLuRIH433zhbkFBi4/uvIDqVp8NqsBKqD7Uxo9DHrXSNeix5zIIg/3sKRZ69k2rDriLXE/+TPof9WQoh8KeXw7yv7JUcerQBuE0J8AowCHP/Oekf/zV555RVmzZqF1Wr95zufZOHChZxxxhl06tTpPxbLpk2bMJlMjB079t9u45lnnuGhhx76j8WkKIqiKL9VDm+QyhYP/TvH/Ufak1Ky8WgDp2Qnn5jqIqXkcG07vdNiT0wj+XuBkEZuaTOFte38rm8q3ZP/9SH1gVCAsNSwGMzUt/tpcQfo2ykWKSXT/rqdZJuJp6f1JzUu6h/qLt9bzdOrCpl31UD6dYo/kfjZV9nG+9vLyEiwctfvcvCHgjy7/U2OtG/HpDfTdPwihnXuxuPn9SO/vJV9lW2sdzxMr4RePDb2se/0sa24iZsW5fPMhQM4Z0Aqd66ZQ1G9i6v6Xs1pvW2sKl+K0X067R6N2ydlU1DtYOrrW/njRVYcYh+HCsYjzDXk+ldQUZPI1r29+Oj60ZiNOi55cwdD+5ZxJDyXGX1mcGn3W1lX2EBuaTNVrV4eOLs39vgG7v72HgRwfrcZLN7QiUBY8lzuTtZUfE6Lv57126fx51WjAUiymTCYHCzfZwIi0z2ELoSt10aM7EYTd7Do2GtYjDo8Zi/uoBGjuwdV3jxMnbbw/q5RxNsCbHW+iKFTNNMH3UF1TSJvbjlMdM7XfFsDCw8tBKlH5++O0XaYbZUf4wtO4Z0tpbzwzTF6pti47ncBFlctYFu1i6mvO0iINrHoupH8Zc1RdlYdwGlx4jPOoXdWAkWBaqJIwmtwoffl89RXRhJ6z8NPkE/KemGKPwZA3ml5PHggxDZvBYtLd2HxjaO8aDc6kyTZXEaFeIlrvjiOM3YJ8ZiRwSjczTmEAsnEJG8loH+R9Ql9ua7RwgFCGGz7SLPuZ/nV+extOsDMNS3oklbyfl4isT2WoIkQT/Zy8KfGA+xpaaR75lYedjVzW1om7nA7emHhjHFVTOs5lFs2tGLWNfHAsoPozHVYktfTf7jG5V3HkJx3nEcKM/jG/REJYY3nGhp4L7ULu+27eeXb7dh6FqED3rw0iS8q9nBsFzRV3UtIGhnb9XGi9JX8lXuYXTgBnUHPcL8TgzmK3tlPoKv9lHmuQzxum8sDy8HcI4hJB2Wxm+nltDLxUBzhlko2DGjAbxX0FKfRxZfMJpZA7O3o4yRp1jRuHfRHdi+5jy36Mt72zGZ7ZjyVHpiasJaRgVY+2Xsdpy9eTkaDZHQNzL5CY+rpfnbuLeXBJ8LoJbTbqinOyaRbgx97VQOrLsun74w7eCi/kqk6O2s0B/HeXO47/TwObwAZyiaqtYAikcfiq+Zy16prqDbuZfGNa8kv2s5fa2G1qTeiZD2fD76Bt5pXccTsJrrbTozhNl5dJilDUjlxLymxMSQXaTgGVjB9cE8Knw3Ro2cvnulczEDDJroExnLlwkq6NEp0wKk1cQwx13LW9iGE9x878X7XLBoHkzNpNidwQTiX8l4hZg1J4M8XxJBpH8ya24NMKNGweIzIUJiQTuONQV+hea1ctlkDvGzqks4DV/hJElW8+HV3uuwtAmBOlRfD9fPRn38bW0cdRJcWYsr+GsYc0Jg3y8cxezpaUzs9iipIahektGtEBQOc+acUmj46zuDjkoBZklINY+wVHGo6RqITHlgS5s0pbcztvY6uTj3jCiXjCiWWDAu60Wlc2rWc+O5zyfzWS3qLxosfhQj59Bizc+haXcYd7+kAreMIlGPu34kZkySu/a/QPacUnR88w3th2XuUUw6G+WCykRsP2Jm810lUkp+epWae/9rLPWfupP8ByU2rNcJxUeDzkhMuY6HuT1jzJcNCek4LVVHs7EcowcKI4jbG7kjjubGV5NYu47z+MdzxRZgBZWGkXpJeo+OLwRW88H6IJEOQ2VeFuKQ1iezaeiblmaiNCdK7SqKLCZC1Uc+h62s5b2YO8ZcHiPXC/6UiJN7eToIhAw8vlCyfEsbRPcRAfRZR2Xo8xZUE3BpP9bIzdU2IATUCc0yIi3cb2Tc4xOzsSVw05iGOPX0KhtR4qG/juEvjkY23EG2w0sNs55O2HVzdHklEXZbWQHSmHt988PTrjPVQNVZnkIroIvQShrcILq5toqx6EN7UIIkJpVAYg7/NzTU9xvP7pStplSYG/+V2gkseomJTMpNXNhM77tefOPpnfrbkkRDiY2ACkCSEqAIeBYwAUso3gVXAOUAx4AGu+bli+aW88sorzJgx419KHoXDYRYuXEj//v3/48kjm832m0kehUIhDIZfxaxLRVEU5f8nDr+DGFMMOvH9I1D8oTDH6lx4AiEGdInDavrxz5Fdx1sw6gVDMu0UVDv466Zi/nLxIGwda2f4gmG+PdZInL2CwuYjrNrWkykD0pk5rht/3VjMu9vKyP/jZPQ6wd2L9zP79GziYp3MWn0PSeZM3jvvOQKhAO/t3cTmA3GkxZqpjX6BC7MvZKj9TJbvq8ao13HrxJ58c7ieGxfl8+jUvlwzrhuhsMbjXx5mUW45d07O5paJWVy9cjYxxjjmnfOXE4/huk8/ZUd5MaH2oewua+Wdq4dT6/Cys6yGTyv/yJj0U7EHz2ZDYT3tvhB/PLcvgzMiX4A9gRBvfVvKopKnCZgOYWgbjrPpdMJaNCtnjyfKpGd/ZRs6SzWTl93MJenP0y85m/lbj3NW/zRun5TNxt37GBb6iqu+eZLEaD0PDr+T0so+PPd15GTQYtRx84QePL35PZZXzSPNnENLoBSf6VmOFU6mvmwzGxq6EpReYnoV0OLxMOqZdUwb3JnbJ2Wz6WgjD3y2m8d5i4J9F/NF3TZ2Nu5ECsHDK7OI3pGPsK/HUxYg7M3ishEZrD1cj5Sw6PD7NMk8nEe6oreWYc2EIxVHmKk/zn1LLBgNegw6wf66Y5iT4YPCD1iwtQJ/wzn0TRRcF/iYfR8YWJJZgaaLJuzqxfLjizB1AxPwURGkm/oxdXsT3sltjOs+iER7Ax8UvcGuul1cPvJixifMIjuqnZs/+ILoOklTrBN7t7cI6py0ld7EIuMK9oe605CrkdI9jTnDj7PqyHLCZg1hEHS32Vnb/Bc2zNhAeWsSV34DaaWDObeugvfSrmHpLdO4f8VogroSPsgt56+bSjijbypvXDGU29bfSJJD0q1PiPJSK/MuzaFH1RK6hxZztbs7XXWSqhQv1cZqLug6i9/rNC45/g5XZ1cRldWHXe81UZmq43BGHja9BVfYBztf4bq0AWwDzhmUyBMTTuesz14liS7ML1/PBend2NP+Gdl+Ex/UFWGVktrBp7EvZQb+qnL+6D1KZdkaeqT0ZMQ3OrplxPNoTjPVtXsodZUBENY3ktH3Pbz+EEnBII871iHNAq1lBF65i9mWaGJ0GvNPf5+COU/xYngDAS0yAqhTYoDl8Vt5LcbAqtpDtHj0rNqTz/v1DbxrO5X9BHihUTDcmkrAp7HVGmBQ9ruUdEzUOPLxRdS1B1iwyIBmfIHq9Cw2RocozUgiQ1dCnPsg1dFG3m44DnozzJxK6eIPmQcY9TU832sTj4TgtdhBRB9bSXLtObR/swuAC1qdGC7pT5RnAM0LFnDq7w3UiGZiNI3z+84mypVKrw8DHOyi45ELWqn0eEBK2o+1oa8MYtr1FzIafGwcLZmU7+cPy/XsHLGZiqI89BLicgQxXi/xhY3obWaCgLehmbSUJkJoTGiqpERG0eLYAvtH4Mvpy5CodLpXeZljNlPtrGJbYz5+JAlxLTT5y4kJa3Se/Azi+ARu1LwsxcCfE+1UaPX8IesaUovmEUjR81paBimHmrl/icZDSdtZLUq5NR8suUcZfLGgOcXGs9b5uHyS4hwTvUr8nGM7hdTWtzl2vBrbsGyS7Nvx9b0Tzxdvk93UQj9fLU6dhfhyPafpNZp77iM9rjPjCiX6uGjsl1xGqLaW9lWr2RZlIdMZSb64EgTdqiTXy05cU1NB1d4iTElmAk1+Bre3YS5czxFvGL9Ho0GvR/oCGAOSoTUhXot140wZTFJbNcfTLKRqLgwBA76GQ/jamtAE6NKN6Hwhcg8uorY1sryvKVpw3Z54ZgxwMiwkAA17Lx+uRiu+z8p4alCIu87x0y++N3CYoFuPziDJmPsGrLyT9rzj0PscxJ4FhHMuo/mzNTxfIfnm1jBiXQoAACAASURBVF5EHf2CvlosscZ6jOl+RpfZuexoKSUb9NgGpNF5cBF1zWfBqo3MbY3GXuHEmuaj9qZriXtsAb743qSGKnBiITrUGWkLEWpwYJ80hMDBrfhrBBfZB3Cfr4gFB+czx2kmtreBsNvPmNY4NklBRk0T1gTY1usGWta/QAMwaa8Eow5rioeEvk6qNiXhbK+jZ4IHGQJfdz89rryfwrzFxH5VQ8vAO2hu8pDJx4yKOpXJjZ9R6ktHn2RFb5TEB4wstuk50w/WzCRSsqop+8bIO986SeqTjHTWooUE5qzOBOrbiA/r2RVo5ZmKbEY2FjB/UAzjDJ2BIlxVhWgmfSQx0TsHDlXzSG0rrVFpQIhg0A72aML1rdi69yDWfojmwhgWJz2AvUsaxSUbiJswFJPFgyk1QNIN19A0/z0Sr7sWc8+eP+Xryq/WTx/f+y+SUl4upUyXUhqllF2klPOllG92JI6QEbdKKXtIKQdIKfP+WZv/zdxuN1OmTGHQoEH079+fxx9/nJqaGiZOnMjEiRMBuPnmmxk+fDj9+vXj0UcfPVE3KyuL+++/n6FDh/Lxxx+Tl5fHFVdcweDBg/F6vd/b3/r16xkyZAgDBgzg2muvxe/3n2irqakJgLy8PCZMmEBZWRlvvvkmL7/8MoMHD2bLli3MnDmTm266ieHDh5OTk8PKlSuByKin22677UQ/5557Lps2beKBBx7A6/UyePBgrrjiih88DtOmTWPYsGH069ePefPmndi+Zs0ahg4dyqBBg5g0aRIALpeLa665hgEDBjBw4ECWLl0KgO2khcaWLFnCzJkzAU7EPGrUKO677z527drFmDFjGDJkCGPHjuXo0aNAJAF3zz330L9/fwYOHMhrr73Ghg0bmDZt2ol2165dywUXXPBjT6miKMqvQlgLM2fPHJq9zT+4jz8UxhMI/Wg7pY0u9la0Utni+d7y+nYfNW0eHtzyIPsa9gHw1v63+NO2P/H1oboT9RrafeyrbOP53c9zz7f3sPjoYtaXr6e4tfg77ZU1uXlsxSEcnuCJbcWtxVyz5hqcfieaJnls+2NMWz6Nsz47j6KWUgDWHq4nv7zlRJ2/Tb9v8jZx+uJJvLx9MasP1rJsTxX55a0caTly4tg8/uVhpr6+lUvn5TLtjW00OP/vKq3rDtdTUO04cX9NQR2Xv53LVQt2UdLUzH1L97PqYB0f7Sw/sc8zqwq5cVE+T337Pq/seZndZY2sLayn3l1PfvU24m2beGbbXD7ck8vqgjoeXPk1F6+4hApPAXm1B6lv9/HYhk+Zc/hudlQcZdn+EvY37ufDQ0uZ9NK3vLKuiOe/Psr+yjY+3lVBN1HLoh1laJrk9k/2sii3nLRYC/O3lnDLN/dQ0LadnbW7TsRX1eoh3/Ep0Z2XcemoBDYdbaDFHeCez/by4Nb7KGg+yFu7V/LH5QWUNrmpbvVy0dztfJAbeYzPrj7CnA1HCFsOE220oiVsJbvXHIShnY1HGthfeIzZ+mWcPSgfdH7ez9/B3Z/tp7jBwcL8b3mv4EMavC9zIONbBH7anHru2fYw3+Y+w5SB6SyxbueB/e+QV9bKgfLlpIdCxBacSqDyZqL1gmzDUu6c+xqnm/MY38/LkGKNjPzj6Anz1uZSBj7+Dbd+tIdrbLu5UL+Vo66PyKvPg5rfER0QTDnlEEb7DgBeip3HUtOjrD/SwPaj1dxr+Iim0EEAdCYHaQmR71GTTNt51PA+RsdxKhsd7Ep/kb7x5cS0xaLzDCQmeQ+b753AivFtjNm4hc5R3+AwNXCz/nz+nDObNOeDPFmSzjvv+fiD81R61szgym/9JOcf4OJhXdg5bzYZX+Yy0ufnq/IldO3UTM6B53kkej4Pfhpmaq6OoLGJS3bE8NcVHzPSn8vU4FEuLNnM+LVFzF4XxKnfTZvMZZTPw82lewlpIeraStHrPdz6ZZhXP83jjG8bWLj3MzI9JSSHQgQNfp5ZVYhfNjNy4HGKDy7icNEOXpsbZlBRHuvvnkDalj/g+fpBOjn2MrXiEM+9G+bFshaeaW7n2rd3krB+DwCBtkOM623m0i0at5Z24eFRD3ObMQN9WOLudTah9jIA+nayEBdlxG6DvjGSlHCYJ51J+BvO5NWjfkL+YWDPIqZgP1nXXcCQ8sj7dG/mUNrqHJyZHyBneQN/WRCmuGoHx+vysGga/Y12WgONXNvWznPpk5HAzOZEli7az6vzwjz9SZCP/DYyylz0+/wA3cq8bKraBECjs5pOB/+KdOwh3ZrCeFc7TVGxSGMM8dFupGake5mT+gPp9I2eTYZPT4lspn9sd3ToOBJ20uaPjLCLG9aTri3VXP6Rgc7lOkTfaaTSQI+WyNQ/wn5cqz6jU00JAqjsPARP/TYe/TBE1x0HGewPIFxOjGnJGKLCmK2pZBzfSrj8IFp7OxfWl3FHj4u5tt1NTEkplTfdBGFBsmZiW5SZBJ/GE0v1DF9tprkghtOP72JbXx31I8PE9xJkNGl8ffgj2tsaALB3baDLuFZyXr2SHveNQdNLDJ4QXxV/CUDv8AQeelvSpaorMuim6756xnxVxjhP5Fzkpdyn8BL5f3ukeicl7hoGtQbB3hW6T8BYtJbp7U4qjEZSpJ6zCwApiBMmygKtREfeYoT8QXZ6qjBKHWiSu5dpuO2jSBRlGENg13vQJ0QTaHYSDgjC7W6sXaKISpHYZ91L5/Ee+j55NpmvXEXPqfVYB/Xh1q802sqKqWs5hk4Dw4AsUu+9F3NODgDDjSlMaXcDkBoV+cy5JeUULI56AEy2yNQk2eNM5La5kf8LYfg62oqhY1pTdhWEZJhjmX1JawEZq2EwSqJ9UFOxmZCjhYBZEpvaG1MY3q/bjhaOfO6a+g3FKi28FTuMG1qckeejh4Mer96KIS2N3pZsnm5sZkRAIvQaXc/X03VSE0abxEg9iRN7knjLH0jIcZP8uyzSxkkS2qHZH01RSk90EmyOSmKnTSfU5qF6RwJS00gZb0ak5JD25FOkDWsjud5HVIqNLqd6MaVnRd7Pvabjm/ImAJaqZkKWHsighikmiDUVglU1nDr4fuLCGn4tgE0XhfC3EpWdSVRVC/NTHkBIiSZNiK0vorW3nvgMwhMmeWA7+sTImsUtAS/VTUcwhMGabCf28huxTr0ZAJfXhKnjOeqeMQCik9F8XnSaG71FT44hMqgixSPRGQJEZdiJPuUUWo7Z0JqrkM2VIAU6eyIAF3a/gAnSQt+VB/DtC/PQOQuwezsWFq+rwF0X+T9l7hZJ9qRp0Qypjnz2hZrb0GxdCbZ4MdmNmONCGFJT8G/ZRsPCL5GaIPF3vaGtEsxxJN1+F90+W/ybTxzBr2TB7H/F418e4nBN+3+0zb6dYnl0ar8f3WfNmjV06tSJr776CgCHw8G7777Lxo0bSUpKAuDpp58mISGBcDjMpEmTOHDgAAMHDgQgMTGRPXsiH8rvvPMOL7zwAsOHf+9UQ3w+HzNnzmT9+vXk5ORw1VVXMXfuXO68887v3T8rK4ubbroJm83GPffcA8D8+fMpKytj165dlJSUMHHiRIqLi7+3PsCzzz7L66+/zr59+370OCxYsICEhAS8Xi8jRozgoosuQtM0brjhBjZv3ky3bt1oaYl86X/yySeJi4vj4MHIl7fW1tYfaxqIXFlv+/bt6PV62tvb2bJlCwaDgXXr1vHQQw+xdOlS5s2bR1lZGfv27cNgMNDS0oLdbueWW26hsbGR5ORk3n33Xa699tp/2p+iKMovbW9FK+XNHqYN6fwPZbmlzbi0Ct4++DYp1hQu633Z97Zx72cHKGl0sXL2+O9ds+VIXTtnvbIFgCijng33nEb6SVOQNE3y+7dzcQTa8HdaiTvo5sUJL7KocBFR+mgWrRzFsEw7n900hps/3ENhfS2Gbosw6U18XfY1ABa9hZ1X7EQndBxtOcqH2/y8v6OSXcdbWHTdSBJtZpYXLyevPo8pby4l2dST4ujPSYvKpNpZyxXL/sj4mHtZtrcagNNykqlq9eDyh9h0z0RWFW8moPl5K3cbgcbIgriIILG9HifKaObGQbPYUZLFyG4JXDYig4c/L+Cyt3L58IZRNDr9zPlwCTldu3DRuSl43cnM/vgYfdNjKWopY9qXZ+B1n0+SbSxvbznGpSNTKajy8f6OcrrYoyhuasEYG8IQ1UBZdR3rpl9Lw9k6vJ0EK6sgWTcUmE6hYwdms5OU2nQQbp76qpAd5YWMadMYdmESz31VS2aDJLrkADmuMbzy0FVc8uYOHv/yEK6qAjaa7+Oqlvu55zM7a6s/5fzx2dw49FLO/+AZdjZsRAskII1tVLe56RwfzQe55egs1WiESO10mJCWxlNfHWZXyxdYUo9hFWn4o+r4cvYY+ney0+4L8Zf3lrHwy3XUhWL4OE/PWUP1sMvDjNTx6EIfcme6gfge82g/WMosxxykyc/E9iwAbpmczKjEUTy3/3aOuw7yQj4YYwUDAgHuck4g73Arb5xSQ2JsJS9eMohDZ89kVFWQzZ99TkV6JRe6vZiM5ex3TSSvtpxqOQI/hQxJriHcN5Wcv2p0adbQdX4AxyPPs8qZzrgeCUza8CjVDSYOdallZPIUpn6UT5QGd13x1YnXsE44GaZr5sVdx7DW7GJ0zFre1KcC0KeLxujeFvwfaoiekRPlx4YHaTXriNu9mwH05NLFHg68/nv+XHkAS5Qb987dOKuiWB60Mc7j5azP1mAd0cYFzz1L3ZVzaa0xcGHd56xw9gXA1d5ElbOKHnta6eqV9O3k5rwuMTyx4wk+bqzBJ9qxB+Lp5owjKdTMVLcJi6uecEAQ1d5CKxCVYeGU3T4WjGtAMwl+1+TFnnUaeA/TWruXUGwWpxVItAw/aSle6vcep/r+R0k6NcxhcxhNQv/ee3h1/ze8ISWZHtBL8DdGfnS823WQ6J6DeUGkkFZ9EJ2E0Q4fVi3Ekfx8vHsFgy+HxrgGGvd9hNUHxtZmTu19GdvffJ0528N4Xh2Fp3wDAN5Q5FimlzjIDEWuUjVe18K5aRfh+3gF7kAdUbfl0LKqBKTEXl5HcqqVbQZJnRZJUpiSwmQ06dlWc4DjtJAVDPHHYIhPMkYxs2wJUb0vYk3FbsxNFuo89RhjNDp7JAll23ElVQCQpUvhIE2cGTuSTU078Quod5STGtuZpFAQZwiKPotl4GkOCnrF0LTdjRauhO3Pc+NFg3gk5xDTozJ519/OzigPxmAkm5A0PpHU6aMoePhdhux1o7/yGno8+y0zN/sJXdsTWV9K5b1PkNjPReoFnaiMS8FYUMj4CjDZ6mEgSJ8LoRegk5DYB3QlyJZI3CG/Dn3vc+DY1zh3RhJK5rggybpEwMHMgz56F5n4Yjzcm1rLDZ7r2TNgDQ+G4tANmIDp0Fc06PX0CEaOpc4gwRKPqNsHbRXozBoxHh0LS75gQmmQ9mV56CQY2vy40zMZvz5MdnET9r6xJKSF+KZmGzZNI4DgWMNeHA3N3P+2nmbrfJJGngFHVnJxQE/V8TROT5S4N68CwIYFcNHHmgmUES0jn0E6TRCVboTaIJoriiZLHMYwGHQhLJldCFbXEYiJnEMZozwQnwkGMyR0J8pRSpTVDvZkEk67Bs/ee2lvrqOu7ThJEqxRkdGTomOWwjs5N+DfMZtyLOh0QcCAZusSWSQI0AkPYEIOvxG5MXI+ZgjDivgEpnQkgJJqI2MuNjnbOS8AXWNbMBjSsbY6Ka3ZjfAEkWaBPq4T0RzhgC7I2R2JJ509BVlUxtgup9DmXYMTCzo9iJTeCLMJkZjDeenR1G/OReisRJ13C3z9EDiqwVEFXUaCNQFiOkHJBvRaC5BATXslbWm90MkGRM8J2Gb+ETFvNd4miBuShClYCsmTEdEJ2AdZiT1/HMLbhM6tJyo+cmyDznZ87e3oAb3DhcfRO/LeMzWj69UZ9rQQPt7KxbYeLHcVYwlL0IJEDRkK60poXvBu5HuCsEJbMVLaEUYjyXfcTrCmBmvaBryJU2DpAtqkjuq9i+iuQWzmCADie/bFBfgqKwjVRa4+Zw0bIKUP0l+KCAXRR1vooU/nhigD0Z5j6IUbUnpjH3YZVVu24Csqw5QUGeWlT0wBoF/SIF47/yFKFownqOkgfRC65jbCgL+lEWmUxAPRXbvjA7SMSYTNDUAhAO7mWGQYTLYgIioO22mn0bZkKWgaSQP8mKNc0FIF8RkIgwFL3778L/jZRh79rxkwYABr167l/vvvZ8uWLcTF/eMaB4sXL2bo0KEMGTKEQ4cOcfjw4RNll1566U/u6+jRo3Tr1o2cjmz61VdfzebNm//lmKdPn45OpyM7O5vu3btz5MiRf7mNvzdnzhwGDRrE6NGjqayspKioiNzcXE499dQTl6xPSIhcgWPdunXceuutJ+ra7fZ/2v4ll1yCXh9ZH8DhcHDJJZfQv39/7rrrLg4dOnSi3RtvvPHEtLaEhASEEFx55ZV88MEHtLW1sWPHDs4+++z/z49XUZT/XVJK3tz31j+MqAEobnAx/Km1HKpxfE/NiPcPvc/TuU/T5G06se1glYPtxU3f2e/ldUU8sOwAobD2ne355a3MeGcnD6+IjDSpdkWSKrd9tIeJL2xi9cFapJT4gmHWHq7nUF09r+/+iLK2Gt7YWMytH+3hha8jIzaP1Lajtx3m4Sm9CGuSV9cVfaevrcVNlDS6afZEfjHdUr2FtWVrcfgdOPwupIS88lbuW3KA/PJWAsZjSCT3DnwJV9H9BJrH4wv7GPnn1fzlm91MXzmd9ZVfk5VopaTRxRXv7KTdF2TFschJZ727jT1V9WhSo6tpAoHmCXiNB1hRuoLegz5lzIhtFNS0YDHqqW/3U/DQY/jfmA/AmBw9q+84hY33TODWyUlIEYKwjZfzX6ZG/wkTspO4sOLPrJzURIPTz/S3dvCHxfuZo3+VsU2vMGvtLO7afgmm1GXMv2Yw/fvngi5AWmolL186CGfMx5z26SQ+WXE1Z9srWX3HKdhtkZOzWRnr6O9cw9ASycyCWL6oqsES6Eqjt5lzB6aTYHWhCxu4fYubP33ezMp9VUzdX8BdyzWSvdVYLE6e+CDMg4tDvPTNG3Rtr2PmuG7sqWijh6gBYJS5gi+ObMOSupptbW9hs7URk7qVkLsH/aOnIkSYDUUl+IJhPtm7l2SXl671ktyGr+mTHsuyPdVYoutJs6bzlP50utT7iLG1I4QgzmLgCc+T3GWdy3slT2JIXE/vbg2cWiBJXLyF0V4/r+g6kV3VyLgvP6EqN45lsRl4wyGm7NLAV87Y0G7qfMVEN/blub8m8NLeON6rbSBm9VbGbd/Mbas1bFY3FqMenzsyAqD/kjcwBcKc6vEyK8fFwikxmAPtJNRHTqKPNh+msKUAvSZosYFW7aPXnhU8dl4/Ylyf8YG7jtbViZx2UOJtmEg3Ty2d2iKjynJMke8czr5TASgLvIsuLo+tVgsZjZJzdmkM6AqWuiNcsUkjrToFDBaculw0wzoA/E4veg16t0S+fxS3FdNYWQJAD0MKLzY0EW5zEKyOvAeDLZEfMaNCXmaHPgXAFIysQ2QOSmxEYUvpw2wthsKWQg67qmjQ69FJ6K3Xs7GymjRnZEREwGUg2NgGgL1X5H9AUjsI4HS3h4RhNwDQ0nwUV3Nk6VBzTgIJI1NJOiUV94ES0lxhWnUwfWACnRO9JElBr5Dk/KxzAHD5PGgBN3sN0KjXIf4fe+8dJldxpn3/TurcPR0nahRGYZQzSoAQCJDARMs4LBgDtvkcFljva2OzxvbaZr2LA/6cMcYmGHhtg9HaJBMESCCQUEQB5dFIGo0m90zn7hPq+6PO9EiE3f2u3Xd330XPdc01M6f7nK5TVae6nrvu+64xZ+GzJU1EtRWEm2DjCD77Z8Ggo9G77keogLcgWTb+7jypDBTCTeRVlbN2OZi9R+HZr3L5c0MsXN2LEODJHOHr4k2sgoawHLpfypA/Iq9hDQwyx9/Ay6VONHe487l0laN97XQMdXLzQzBux36+VfHhV3RoXkB9sB6lKOvc8JsILQCKhtj2vwG4ML6E5aOXc/VPd/GpZx36pl1ONxZ1A0dI+hL4B8vYJcHCFwvctLaEYyu0fP+zKB4Ps0JTuaYEK4YGmKx42erz4XOJRWp6L4Z5DDMIwrQQzYvQKgaGrWC2XE0lK1kOuQ4vzYFajokKnhPymHDvT5SLKKpAUQVC80PDbMSgbEerpELjXIg24wzJhVcjZBFQvHxr0dc5vyDHnadna/T5VOYuVrA1hamBetSmySgCPBZEyy54VDcBxp8Lx96Arl14vBAtCEpOhSvXO2jxGKWwFz2Tp3/sAiIFgVAE6be8XLNdAihLC0XGmyYb+nZCTiIjvT/+CQNbc3RuiNL/z7Vc87Sg8SGLypHjKJrAi8FHWz/KOaPPB2BRbDpjTBPFcVBV+Zzmi1nSE87BsMCvOBgTZ1Dp6MB0JBDg0fshJvMIEhOg/yD0H4LEBBSfXCwYKGfoznagOeALyJxCMeSuhUTHgeMCRYasfBFsQgwf0+RYJBQf4sI75XNkw15NEBwGgHoMdDQOHpT5RkOwQiQ1mmAZNvZsI1AGNRRC0XUCig+A1DA4FQwgKhWom1Z9nhRNQHISiqYjhAJXP4qYdDGKJwDjpUqDvn1QTEPNKPl/3TRoexkU2aa2ZbK7T5ZHGXcmWihI6Jylspom9UOuG1Kt8tx4C1rhKGruKMTG4XMZOtbQEOXBETZvdr8Eer3FHfimTUPx+yls2sxNF/6Up473ohQGpdH5mSsBKO3aJetOyLp2wuNQ/H4Sn/oU9V//Otz4MupMqf7ICJ0TPTL/9YyeJ8tZN5aiB+zjnagn5C7VqmlB7TRExUK1MqiRCHq+xM1jL8OugGb1Qe1U9FRK1sNgP06/HH+1ZIMsj2WB7kV4Y5jpPMKycHrk/MrMZCgPyflZzXhZP3ZqHs6CESJGvl32TY84CvEWQsuWgeMQWLSI5NJaGDwKQ8dG2uZ9Ev/jmEf/GkPo/1RMmjSJrVu38vTTT3P77bdXpVnDcfjwYb7//e+zadMmYrEY1113HaXSCF0+GAz+h5RD13UcRw6KJ1//3eLtq8+Kopxy/r/lGifHyy+/zAsvvMDrr79OIBBg2bJl/7/Of7dyvf38k+vpa1/7Gueeey6rV6+mvb2dZcuW/YvXvf7667n00kvx+XxcddVVpz2TTsfp+L8whBBYwsJQjf/Q6x4bKHBsoMCSCcn3fM+6/b1MbYxUd2h67fhGfvbmT3nyrX386aM/OMWweN3+XvpyFX77+hH+adXMd73eb3b+lv5yF3869GfuvfBXzEjO4Jbfb6MjXeTpm89iQm0Y2xFsO5KmZDrs7cowvUmupnZnStz0yFaiAQ/9xTR+JHhUqFg8t7sbRYHPPryVb1w6lTGJAGZgI6H6P3PPngpr23eweetSQl6d56wu/ub8iWzu2kGg+UEmjlnENYvGcP9rh7l8dhNhn05rfZgHXmsnGfJw7owUfxkEy7G4/ZVvA1CyirSkgiDg0S0djE8F6dQPsPQtg65MLx4SXDxlOi/0vEptROHBjXu4rNPi+cbDfOKcDzIjbnPLI1u4/O4nScfkyuF1Z9Vyz/NSBtef0agTF6D7ttLX+CgDjp/juW0sXNDDd8++i3P+6XXKGzczZ+AI6kIV9DRTGiIALJ1q8GAH0HcV507r5iUeZaDyIGx/iPHTCjz0qR9y7a83ki/laPb1clAIwIfPaaFQs5G/XfcZ9ud2oKASrjnBkvFxvJH9WLbFy4mj/K3xHGHfZ2it97KlByr5jURcsGJK3qLFtNCKOo5/kIunpjA2b2OvUyTu+EkO2pzTv5+LDsp7HhrqZHx9kEAZ2lMKY3sFzmv3cZO9nXs9t7A0UYI0nJfq5279CeLeJFlziBuevQGTIX5ilVEWzuBvXoWBHQ+yqWc2BdHOZ9Y5zBoIc2PdHj42tcKeE9CUsIkHkoz66TNcknLY++G9pMtpntx5P7dnjpGNRIAogdhm3jywlys0PyKTp5LVmb6rh2/+SWZThYCHv9SEWHwswyfWODzbtJaM9VOKY5q5JBNn3NAO1LQXEQOzdxA96mfJjiLpxgwUBgjmYNcYhSlHy3xsrcKC1jJa+y7mmHK3KbuvGwgxVBxge+drXKN66YyWiOdAZKUU5yd7f0uWKGc4NvP6bb6618ZrmmDCt+bdytT9L/O/jm0kO1EmoYXoW2xRLdqcEJ9+xmbOTnj++hI9RzoA8GYVmDaThwZ30Z+BFYpCAQUQpHorEJDySrP7BLXA9eP/isCerTjFMqa7am4NlQCVX5Yv4RPOcxwnjs8UPHZgNQtNMGwN4uOZdfRViKm0izJdugfVgQRy3iNKBSCAmdepZBy0RAKP3g6EmZvz0NgYIBlyMJokS31g8DDenBwXg63nQdMAwePr6QPGHFOwGxS+fLbKZ9fvYUqxwM/P+ylFs5l2nsC2HPYdWUdBValoBow9C2H/SJbDAcdNsAOjvXC0jDbkJ62UGQXoRYmkGHlZ7qIawip4ufkJhzfEa4jQHkK5RoIZqIhReMwO+u97EG/UxDtvKZk1r0skTFGwihpz45N5rnAEw1XZ6j45Jz2S7cfCJtmjku/24t/+CIODUxD//BSxUD0UJIiv6gKBBstuQzz4fSDOjJzNMrWR3b1DnNGn0Lvg/6H7xa0sz2dJTjyX0DbJuvSYMPXNDDXjininzUH1+/GZ8OXkIspb17JIi/LMePC5Sls1vRdOAIaBZtmUnQq26gfyVBiNIxqBEuUhgynlep4095PsMAAb4ahQNx2xuQD4UVQQ6DBmCSL3EODD1uol26SmGSd/FMVroGpFhKNwZetVDKQeIsd+LA32eQwc7wm0gmBiZBwFVfqtxm0/kxwFyKJMPhdSjbB7tazbRIravJQKxnIK/oVzSe/bii/bFsskIwAAIABJREFUR2ddK5HCa5hjKvj7oszsdgCby9/0Y+23+Pz13UwZBlUCAbp/8FNUT5DovASRi1Yy9MgvKZUbMeiiWIavLvoqAwcfphu4ftoNfHLNVXQ4DahKBfBTLAwx0LSCBmcjvlAMT0srovBHiukokMbjdEB8sfzAxATY/ywU+qD1oipAlK4UsAtdaI5A8UfcDiTn+iLcXAWKNF0CL8JfV/Wg1nQXUDJNxDAjRgsDBSKmAyg4ZZX5ZiOxLiltSoUqDNS0ki/vZiNlppQVjEQdiqHjU31AieZIC3AANRBElMtQO6X6PCmBGggkUDQNLFc2F2oAXxBqXKbxMVeGXNMsf9dPh4PPo7jzDc0BZXjndE3ea+2tt1IzroA385g8nhwBjzi8ToJRE5YTiCYl4yabwRwaUX/kd7SjGg6aUUapn0JgjkVhyxa06O0EZ34E8dhzoOvoExdgjBmNeeQoKApOxYY5H8fJOKi+3ZwcilfOm0xHod2dvyk+yW4OGAH6oipGVx9GrwR0RMmtK3s1iuag1SQo92cQ8UkIS0U1bKidghqUVifOUBrHBc+1uJzHCcsFBC0LbJvygQOyDQC7aIMh6y3a1EK3YeBkM9iZbLXMud0SjPJY+yF+JaGzz6b2S1+i5sorUJ75DAwekeDR6MW8n+I08+g/KDo7OwkEAlxzzTV86UtfYuvWrYTDYbJZ2QkzmQzBYJCamhq6u7t55pln3vNaJ5/3btHa2kp7e3tVZvbb3/6Wc845B5AStS1btgBUPYTe65qPPvoojuNw6NAh2traaG1tZezYsWzfvh3HcTh27BhvvDHinWAYBqZp8l4xNDRELBYjEAiwd+9eNmzYAMCiRYtYt24dhw8fBqjK1i644AJ+9rOfVc8flq3V1dWxZ88eHMdh9erV/+LnNTXJwfX++++vHr/gggv45S9/iWVZp3xeY2MjjY2N3HHHHVx//f84f/bTcTreF3HPjnv4wOMf4JtPv8azu06wtXsrTxx6gqfanmKgNPCO9x/sybG/+73HU4B0vsJf3buB6+7fRL787r5AOzuGuPY3b3D+XWv503Y5obh3x8MAtGX2cutjO8iU5PhYskq8cOQlPMkXeGJHG/myRcVy6M+V6cmWeHFvN9c/8Ar95S4q6TMoVCx+vPH3bDmSpq03j2k7/K8/vIllO+zvzpItW4Dgpleu4SdbfslH73mdxf+4hr5chfuuO4PZY+RK9uHBo2xo66diO9xz7XwWtcT5+cuHeHLHCcbWrKMGwEyyp+8AH5zbxDcunYppC46lixwelImzJcr89XkTCHh0PvarDVzyk1dZ/oO1vLivh48tGM1VZ9RW68QkB0JBKCYXT6/l7y6egs9Q+eWEDdSHNvOxNRZjHr2PZa0plk6QXgWfO280gUwvH3/J4ez2ds6akGLxjq/xdP2vmLzzcRLuTiiJiIXibpV9YkBlemOSb535TVZNXMVTVz7Ft8/8Npu7NvOtDV/nnNYU+WKBYFnQelzlRH5k49bhv3vSfgpdK7HzE3m591m34Q8zuznKbz89i29eLNBwiGhy4lrsWc5M76d5s/dNQnqAT0y7lo7cEbb1bMNR8nxh0nX4HYd+U7JDiraU5+z2eugz5OQ0bMnyRxxQtBzLT/ySvJUmbjsY7qT2trceJ+z6Lg3kehkXkOUt+OREWxx6CePYa/zxU3O4fJxMLg7o7eA9zp17Z3DbkZl0F7qZq4ZZPrCf5oJMBFsHHubsTZ/nmvDD+E2FuPCjo+CzHuNzy8YT9GaJC1DLJrpQ2Dewj1/t+BV/6HiRtzwetnvlCnuFIpsqvUTdVfRir4fs7kEqAYPXp4ApFLZpOZZmpU9FtpSly01gFhZlef25MnZFRVRs4mdEsDWBb8CheGgDXhPaxghenK1w/nYHJa1x9LE+On/yOAB2yWVMOGA6Jn7FQEjyDyInV5DTdpFlObmwtGSoSERkUSoCYcOVkYk07z7C935jw4ksFUXD1iyEAgOaxqRuGxUYLPWQdpPowEAJGufQYxc5ISpsb1mMOpybHe0i7otzaOgQpQHZ5lElgoiMASGwursR5QJW3mWEKBfwQ0MyrGOWTtmpEKkIhOVAvIXmwQ5UFNoNg25NRxNAMQtjz64muqZvMmZex1Nbg+6R9fyZbosfZAXUTiHsjaAJSOdOUDwh54XBMbOhcS5+XyeKLkj1ykS6r3c3XeU09UYEWleCK8fxmoI1R54DoKwoUD8T4Va0iLYi3GHRE5JAUcX2MeST8y+laIHjoLkMpGIhQ8WQyZveN4AJ1Lg2arnKDLIdPion0iSnZkl+5nMAhBpL6BEfVkljboPcka62IpN5zeu4fcvG764nmuUgCJuBnULKSEK1CJd5pGgCYQtY+kVEi1zItTc+hFjzD6gVhUAZOje9RkVRqLNtkhNWEi7K9nppNhSiBqnpWYg0oQQDOIUijF5E/5YyrU/J8SEhZH9TVRO6dqAaHgwbspUstiu3q3R0UalEqwyRaYcU8pl+Rne4IIE/AYnxiEoZRbFA0xC2A2OWgMsytbwuYBBtxinkUT2aZCjZLvjRJOvKUmFvMMKe3FFaTBNfdAyqu1nPd8/4Fuc1XSDLO/fD0DSX4dCaJhJ1791X1NBTKZRohHBRsN8fJlIEj9dGSyZoEDG+19PH6Gwd8T6ZPnoseW7DP36Hxu/eyYSX1lD/678QWHQmDfOHGHelihbQEcPAiDvuGb4YIZ9kvagukFMqZBl0dx4MjFmE0SzZHPm2PLrPRnWGRphHyYngmFDol0CSO+ZkbZPjxT5UoaAEZJ8fBpYEGiIgZarDa09CqAh/vXvMrVOzIhlCQEyT1wiZDlpAPo8LuiM09QtKPhVt1AS0+vGoQLfQCZbBl2gEw0B3VFaOXcnU5Cx5/WBAAlOeMMJwJXV1E0FRQNclwAFg2xJM8obBVwNHZS41wjyaLn/HZN/QhILqAmCKuwunp7mZ8LnnVdv5ZOYR2U6wihAbS9AXoegBJ5vDyrgsaVXFKZbwhC0UBUhNxj9vHuV9+7AzGTjrCyAUlFASNB3/THl//pkzcUoluPynCCWA6j91581h8Mhjw2HV4x7zVF8finvxdg8S6pVjnCiXELVTEbaCogq0RB3OUAbbJ+tB8ziQmoIaks+ik8+NMI+G1T9unQ73u4JrDwNAWcUpq5Q8oHq9aOEwdiaLk8tW68E80YeiOxLAjregGAaJT96AHo9LCWXfQSgNve+YR6fBo/+g2LlzJwsWLGD27Nl885vf5Pbbb+fGG29k5cqVnHvuucyaNYs5c+YwefJk/uqv/oozzzzzPa81bAz9XobZPp+P++67j6uuuooZM2agqiqf+cxnAPjGN77BLbfcwvz586vyLoBLL72U1atXVw2zAUaPHs2CBQu46KKLuPvuu/H5fJx55pmMGzeOqVOncvPNNzN37siXzI033sjMmTPf0zB75cqVWJbFlClT+MpXvsKiRfJLLZVKcc899/DBD36QWbNmVSV6t99+O+l0munTpzNr1ixeeuklQPorXXLJJSxZsoSGhob3rKdbb72V2267jTlz5lSBIoBPfepTjB49mpkzZzJr1iweeeSR6mtXX301zc3NTJky5T2vezpOx+mQ8dLeHnZ2vLfs6t3Csh3W7e+tmhi/V6TzFZZ+9yU2tsmE7fhgkeOD775BwL0772V7125e2tdDe6adE/kT/P7oHXxr01f4xF8+wd+9+nd85ZWvsOz3y3hg9wOA9NrY3LWFTz6wic8+JAH17myeLz+1mo70iCG0ZTvc/LttHBsoUrEcXj/UT2+2zAd+/AqL/3ENV/58PSXT5smdnehGnlDDU9y28QYe3vkkW3tfQTg6hr+LP25tZ+E/rOEna/bxsac+xk7rh3hTL2BGXuSHz+/nnO+9xLw7XmDBP6zhhvs3s6lDysW+cOZlBJwW3jixhV+/epigR+POVTN5s2OI7738Ije+tIqg3k2D9xh95SO8cmQ3G9oG+Oyy8Txx01nMGFXDWa1y8nRkqIO1+3rxGxoLx8W5+byJ9GbLPL71OLV6mnFWCbPQjOLp4YsXtla3bG/rzdGVk4wJ0zGJBz08cMMZfHfVTL5/1SyCXp2AoXH1wjFYQmZvixvkSpuVHw/AuVNjnD+1jm1fuxC9uI0+XaDZKhN62rhkbAC/LieSkxt81AddgKhSYHJ9GDq3Ex/cy83rN3PFDvk+RykQDcpxvXtQY1pjhLNHnc3fL/l7UoEUV0y4gi9M+DBrjq7Bn3oF1d1JaUVnIz2FHixHntuVl/clrBpe2NNLTG+i4AI9pNsBeOLYvfzi2HcAKLi7tOWKGivGXMbPygF+OFRmSYPcqfTB9ZJttSIxmoAjKJSknKhoygnvLq/cwQggZMu6alIFilZG2fgT0qEkMQGauxDjDIwAnoN9h0ie+DMAKVWWv3NQyram+AYIFCSwtLsyQMgIkXzlLWZv7OOMuvl8oa8XBWjslVKqE7rGHebVtHtsahwNtZSnzjTpaV/DrWcYDA62ET/yBqJcJqKF2NC5gdc6XwNgTd04tvm8nOutZYIls5IwcpJf6PWQO6bizJtMzqtQUWxCqofz8vKZypoVupMtAEw4Ivu4kq1g5uV8xFB6KYcE/qxCx2YJEM1XC+xYEEVV4MiLdZh5DStXRkTHYldkezRYMnHzlIt4hpGcXD+UcwwiaC7IRFmvwEJ1D3ZFlav7/Qexujvduh4kFx8DwLW5Muc5fsL98vq92RMMum1Yk65gN8xi/AGFRXscngkFqslZZcsaJjgaB9IH0DIyIXYKBZwaaSMgTBPr8C4pNwJuXNDE0unytTpX6uQ3BU7ZhMR4DKBJC3DE0OlVXYZEqSDZJz7JYKtoYzHzGkbIZeGooPcNEu47ALWTURWVmOohXeyjNCD7u94wEZrmoqgQSJYJdMvE7Xj/W6SxqQ/LZEfY8h48FqzpkfvWmIoik3HNXdFvOAPHldnouqwju2iSS0hGpWMC6cOo7v2VchlMTSbHegEKU1cRcI2Ss4cq9O4K46mxCE8M4J0xn6Z/+Dvq5mTQfSZWUWXS6LMJGSHGmu5nukl7oCwIuWCiaccRAsyBokwMw/UIFwhTNSFBGEWBaR+W5R1/GfYnpXE6QHndegBql95GMjWFkAtKPb5IY9PNjRhBGyKNqP4ATlGCR7apoLn32KgnQFGk7AhQfX50W5A1s4iKfLYr7e1UstJo15vSaNjawZRjAn1YruavhXCjBI+wUAwDYZowelEVOLQUlwlb04wwQdVsFI8XYbpJsU++Xh8dxVPBANvMNFPLFagZVQWPpgTGETJSoOsozXOgYRagQDCF3thCuKhwU+8glAV6MokejxMpwMHBIwTKEPSr6Kl6HNPLynwBxw6g2oAQhF0vJc+oUdRcdhlaogE8QZlYA/TsRvGH5H25z4fsdw4kJKChuOBRuZhjMCMlS8b4JXiaJThSPjGEEXLn+PGTZGvDkZiAog8jyrC/JOcUiuF1r++CR5aFCMlFDDUswQVRLuME3WPD4FGlguOCR2HVZcZYAl+4iBbyMG17mqZ+QWFUAuWmTVWgIlCGYBn80YT0WbIsvnfO92jwSlmV6g9Ury988phSN1H+1rRq/xWWDcP3ExkFA4fcPuACFPUz5DlJeW6jr646PnFS3kdK+hah+0faI94y8np8HH7dT94LZPMSGAK0CbKOvQkX2Em14p81G4SgtGevBDx1fxWEDC9fjmfcOAKLF4FlyforFlHeDh555PUMi6oB+fAxgEIqTKQrh7fsjqulMiLaAiiomkBNjcLOZqvfCZpHQO1ktLCULDqmitMl60qLuu07nBu6z0tx24hvr1ZWUMoKhYAcd7VwGCebrTKPPC2yHjwhW4JoJ9cdQLRZgnDwvgOPTut2/oNixYoVrFix4pRj8+fP56abbqr+fzI75uRob28/5f9Vq1axatWqf/Hzli9fzrZt295x/Oyzz2b//v3vOD5p0iR27NhR/f/Xv/41559/Pnffffcp71MUhYcffvhdP/POO+/kzjvvfM8yeb3e92RUXXTRRe/wGAqFQjzwwAPveO+HPvQhPvShD73j+Nvrb/Hixafc6x133AFI6d5dd93FXXfd9Y5rvPrqq3z6059+z3s4HafjdMhwHMHf/H47qbCX57+w9F1Nlt8tHt54lG/8eTcP3rCApZNSp7z24OvtvLyvl19/Yj6bj6Q5OlDg8a3HWdiS4NMPbKYvV+bpW86uysIAOnOd/GjrjwiXdtF5+AIWLBxAV7zgP8aQOM7fzP0b1m6tY/3h49Q0/4kHdj7KgQNziTW8xgN7f0Z28KsIO8TR/gI/WP84L6a/y59+2svnlpzLX587ge8+u49XDvTx7cun8Y/P7GXN3k72nBhkd2eGi6bX88yuLh7d0sGTO9sJj/8RWSWHYgX4p623ARAsXkIh+CQ/vraBxzbAj15Zi3/cQco9K5g6NsOhxHp+/foSErWH+PQZ82j0tzK5PsJx6xW+uQFWTJpFV7GNx9ru55m32lk04wiDnqMsbpnJH/Y+hBXq4pL475ktOvgOKscG+2mtC/OlFZNH2kqRCZ1JnmfeOsyiljp8hsbi8QnmjI6y7eggQrXxWxXC1FMytlETdAh4JOh0qDfHQLkPPFCx5aR53pg488bI5PVif5ae3z5DXfhCdg1KkODG6TdwbvN5/Gb9Ybo4yKi4nND5PRqvFY6DBh7HQUOwoGcfe8ZK/4myXeaMMXISnTRLqJUM5LooWwpQw9RSnKAxxFB5iFHJIIcBYfuZ2hg5taO1r+fa5+5k67Qz+UvnvXxAkZPDOW0l7IU2fcU+6oP1dOW7SPgSNNXF2NuVZaLf5E1bkO4ah8/Tgb84yPrj68lYBSygMCw9dDxMrfMz/4U2sCtk3eT55ewh6vxJmiyTgHAoWBWo5CkWB/A7DkVVRXMnxj5T1uWyOh+bS5DWNNK6h1ZvDM3Msa8JWntUjo+voWlvmsHBTtSYF7BJKnnAS6eqMhFgoE2apgK9KqSMCFZfN8KyuHfKN1A3SCAmdHwrQTTaND8vBK+EwOt8vFRAFCukVC+9aglxzzIGGmqImxVEuUSNHmNXv/SsaLAs/uRV6bF1ru0+wNlmmX9MxAgJFQvIHPMhbJXmWeNR1+9EEXBt8wUE7d8wAOQFdI2aA32vYRwbwAHMnIJZlkCEQTdObBSJTJkDB99gHJCMBLhXL9MzKUf/njBGwMIs6NjjL8d5Rs5HZhTyrLUDaKaJVwHQEPkBzN49ZDWVWEEmIXZF5SxlJ46pSClU1y5EbgCIYWYzDNaNBesg0wpZbm64jjb7twCcyHRguYyR5KBDZ2wUq9Y7FD1w5/RjLHbxqnLXIBP6+vhdpY+YuzeLU8jjpFoAWYelbRvBNQReMaEG83iOTiBZKAMeDFNBWCaiZiwKMLZSpt0wyKs+oCK9cBITETXjgLeopE3MgkbEGEJRkQydfAHsMqTkQljME6Y/N0g87fqFhENQPxoUlUBdhfybeWryGrs6JZu8ISnPG06uPCYcNCUQWhHDWiQ/UETEJyPaZYKmeytAAF/Bor9Pyi0dU4HObagVBQeo5IawFDm2eEs6hdmfBV7HDAA79wAGTWcOoKQk+BS5/COw62Z0LYuZ96AFa/nZ8p8R23ILJfrQkrVAkUAZNLeNzJIXJzoVpzAo5T6huqqfjaKJKnNnGBxzHD+2LZNZW4HE1sMwF+rGnEXSnyTk5oA5HyQHj0MwBboX1e/HKRYgNQXH8SBMhZX1S5jkFaj+HpRwHeS6Uf0hjGw/A8UBFDdRrRw+jDNYwhu28E5qorzmMJ87AqYGaigAySkQaUBYNopWBI9Xtoc/hjAigIltybGSaDOOpaBQRvEFRkAYF+SY2TCXPx1+gmnlMh/PSNaUmlPd/lnAKRRQ/X75Pe4NSwApNgatK4ooK1zXlecQIfRUEm88RaQIHR3SNDg4cyXFbq80Vb/g21jb1qAIKZcaVbIBtcoqqUZkFLhSTyVQg7BkQj5cbmwLkZwEHK4yj0S5TG9GAr2Kx4vRNLJBhCfs9smq59HEkc+Kj0cpy3rQHBDOqRKuKvOoYiKCDcBR1GgK6JJATqAB6EANR6plHC5nQo0wI9JCyNqN4ofkRbOwH91ERIHs+RKQUUPy2ZDgkYoaDoMjqs+WMC0wDNRhNmm5jPDEUdRulNQwgKaPyNZsC8UtOzWjoGc3KCqEG6r3y/jzUELnANsZG2rmWEABOlDUk8EjCVqTnADDx08Bj1rQVI2iT8HIF3CyOQoeSEyaRHH/ITyNKdAHIDoGLZRx69BFT9FQ3EWCyMoVRFauoN/N0ZxSCVEqovp8nByq20dOBo/Uk/qNWRdFc3qq/4tSqeqhpISiaLEEolTC6pNjnBZLgK9GSvZUFdtUcHIZII4WGW7LYUDOBY/cTZ+yMS96uYzjQCUkvzvUSAQ7m8XJZsEw8E6cSOXgITwJ7zvrDkYAubf//T6I08yj0/G+iXnz5rFjxw6uueaa/+qinI7T8d8+2vpyDBVNDvbkeOVA37u+Z393li8++iZ/2n6cfNlCCMGDr7cD8MyuEelQppLhM09/na8/sZkX9/ZwdKDAzg6ZqLy4r4eDPVneOpGhJ1vmC7/fjuOMsJb+ckhuBpCt5GmK+mnrH0CzGil1XEuh/fMsSVzFjnYPM1PTqWRa6Skd5aGNB/ndjlcQCIIBafz40r4eNh2VE9OJY09w1/P7ufLn67lnXRsfXzSGjy8ey5kTkjzd/1Ue2Hc3i1sS/PzqucxujvLdZ/ZyIncCS8nw90v+nqbc1wnbc1Gyi5ifOBeAsn6Eb1w6FTUojabNoXl8YspnUBSL2MSfUI7+jiHPi1x/5jgWj0/QkWtHV3Waw82smLAYRRHowYO02Y/yizd/wdWLE1QM6RmQDg5xzJAA0VA5w4rp9ae0w1BlhB3WVzrBvBaNvmIfiqLwlZWTmdFUg6nY+ByHHw8VGHdC0D7UTjTgIRnysLszQ9GRsmHTeac0efszD1JcvZr8QDf5gpy41e39Cx+b8lE+f470GRzeVQlgrzVEwrLxuI6w1vpX8Lm08aJVZGa9nIxFimXolQsAL/lkktQ4qFDjqSFTyVAbHU4AA0xrfNsmFFsfQAG+u28r35h1Mx7HQCjgb+8mlhVVuVpXvov6YD3LWmvRsZhe2Y9iQde6CkOH/Rw7/gaeA8e4cr1DXlHIKm7CYRu0qifABdPCux5nnGkjFIX5obEouR7JPFIU6D9E0cwz05ET9LjL1sGVdDS6rKi0ppI288TDTaimRU9UIXppN5sWyvMyjkW/LushqMjZdd+wvmKgTXorNMyiV9NoMg3p3WDblNdJ4IjJl8CxN2i0LA4aNVyzNExOWIRsgXAEqbpZ9IZTZM0slqKQEB5wBGFNJvoTjChXD2XpsWVfm5PPcmXJYe3R4+jD3hG2CoogmspQb9toQuGaaZ9gmGhoCoXDukKwAk7aAk3FzGmYnrEAGEELvbGRZAaOZiUQWVs/BvoPkpyWo/H2m0nNkXVuByZiIxOz5bkifz7jGyDAb8jJfsk2GTz8smyfjJzO2hWVlZ59gCIllfuerbJm7HyOTE0DtWlBtCQopUfWTzsGj1ZX7yNFOJAbpLFfyuVydpGAIvusU1FpHSrhLzoE3DzKKRRwAiPJQ3H7m9W/nWIRpyhpLaMKFe7OG+CyqJyATAbH5AY44jEYcM1XhAMkJyBcWU9x1x4QCoaQ45dRX4dZ0BAOnHjkdcptbcT9SdKahumu2qvhsGSApKYQrJW0n6lHBLuK8rmob3B383XBlXBlZMwtu0bZ6BLwE/4GhFeWRffZCAWCJcHgQGe1T4hjW8BlClXyWWxkffnKBqW0HJ/yS6XcxpvSCI8qjbBHdA/UjEL3O1glHRSFuXVzibpyIr1B1m24LGgsyDoyewepLP+l/HzLglBdtQ+q2tsSd5CMhSH5nbNnopdUb4W6AUFdoI6oN0q4pOAoUPBBMtsLEQlcqH4/olCUUh41BCjcueT7jDXqUYIBaWYN6IEoui1lsoabGJfb26l09+MJWyQ+eCGhT36Czjj8ZZ6CFolKz6NwI0IoKHZBMoqGnzOXdWYN237WjMaxFVTVQvGHq0m8ME3QNG5f8nVeji3ld53dtFbMU5hHTr4gJW/u/wBc80e47CdoURfUz8hnQU8m8SfrCJQh09ku+9u8K9HiMex0Gs68GXtAfldMzZuMc6Xa7wCPdE8V7FACNe/OPIpLAEh1pUuGDR19bdXrqX5/1QzZM8w8io2Vv4MJ8McARbKRXMaN6nCShEseq4JHpinZXoAal/I1p1IZkbLF5GuiMiJb8wqNR86/R/oKqYLYpeehjx2DJmDUdKmu0MLyOQkVBb6ygxYKo5wkQxOWhaLr1TpyymUcT7Rqli3rS6sCnVh2texV36NwYxUMQ9Ph46thzAIAPjLxw9x19ver16mGNyyv3zhn5Ngwc0vRqh5KJb+GmitCJkfBB4EJskyehRfB0i9J4MktT5UdZdtVidxwDDOrhsc8NfDuzKOAMPC4zXlyvxENI5J4R9dwKuWqP5E6bgFqRH4XmB1SmqaukAt4iqKgBv04poJjyjKpkVOZR8P9zuyQCzBDTVG8JQWjrGKGJMilhUM4mQx2NoMWCuEZLVmqnjp3M6d/CTx6nzGPToNH/83jyiuvZPbs2af8PPvss//u695///3vyu75t0R/f/87yjR79mz6+/v/3eX6Pxlbtmxh3bp1eN/+JXc6Tsf/gDjaX2D+HS+w9aic2P31I1v54qNvnvKe7T3b2XBiw3teY/uxQe56fj+5ssXWI3Ki7Tc07lt/uPqeQ7059nVlyZZMbnxwM49t6eCW321n5Y/W8cetxznUmyce9PDs3r1s6drGsYECn1z9Pdb3rqapXvrDbGpPs+O4TCh6s2Xu/Ms+FH2ITy6L88qBPs7/4Vq+8/Qe3uqya6x6AAAgAElEQVTM8KvNcrxb2hrhCxdMomAWyZc0Vk1egVNq4n+/cZShosm1i8fw7YtWoCiCr6+KUVBkmZfP8DAmEeCB19rpzcokPlV7hK9fMpWdx4c4Y2yMr10it1ddNMGP4zlGzunkowuayVQyXDy/SLZsYbheI02hJi6YPI7O/R8m03EFZ49rJeKJsLtvN+NTIaKJw9ilehQ7woUTZ/LBiVeCViRoBE8BWA4NHWJsZCy6qjMrNQtN0Wge/yJFO4flWOwuPI7q7UM4Ovs9RXYOewOoRS56O3hUHkJT3MmzJ82L6Tu5dd2tACxsSfDETWdRFjaRoiD68Bou3+jQNiQn6S3JEOv29xLUJShkdr6T0TpYlGP7YL6fQlrWa6BNgnoRr5w4V+9NCHqcMvW2heEmw7lXXsW//ucAlIr9+DQ5oQtlK4heucvnk+5mCMaJfmo8EYbKQ8RCcsIX89VQGz5p3C6m4a0/QevFeIEP7VtL0hMhMHu2bMe9gs5cJ/Ts5cTgYRqCDayaXsNj4R9SN7Sf0b3IlWGhsKFjLWe+5fCxdQ6ZwVqGKgm+c7/F6tX/xImPfEom8pFR8MY9zCjJe5xnRCHXJZlHqgp9+ykKm8mhUYRUDxPKriTNncjG8vKZPB6MU3EqxOITUG3pU1IKCnKOuypvQ7fp7u7ikQlPXyAG/hhrjq1lbzlD/5FRDNkazUMjUu3SppelD8a0K6GSo7FSwoyFGN0g2zQUaATFQ21qKr3YDHzwHgDiMcleG5ZmXNzfxXkpmQwHNC+TKiZMuwK/7keYFTS/7GP+RAW9bwtnlEsENR/hxCRwvUp0B3aW+pjZ7yYK0ybgWCrlXARFd9A8gsDYVmJZGCrKe0yOlc+fqgtqPnIdepOclFtGA7Yl210L1hGd8WGE5icQl68PKDppFzzypWWS4VRUarIjwLXoPcwwkUbk8wyGUnztdzbRtQHK3SPPo+ZQ3d0LYGD9WnQHAi6DKO6JVl9r6XNIZkbe6+TzOP4RlkRxb9vIa8WilD0BiqWwuHPkNSE8EEgwzrQoKUoVNBeOAvHxI4m2e77hzYGiYYyZgFnQKKUNBp9YQ/6VV4iHm0hrKo41vLOTu7lIyzJ8kyeh+HxM7YRdbgJX3yQNgYe9aKKmvHkFMG0XEHCPOZUyjjdebSMCXoIl8LmeVgD24W04rpzEymVxL0GgAOU+Oe5bF5xPzYdW0XDFRCkDSZ4kPYq3YPht7JKoSoaGJVl6swQZxuZtxhRGpEVFl30vbBc8GmYeeT0gBMK2q8mjnRnCdndWOjBPJqnTj0HSn0RVVJKml4JPQSgKKduuJoNKwI9TkCCn8Mjy2AXZpmogALM+CtM+iB4IY1gSPBpmVYhCAWwHz8rPo551I81f+go//GSC3y7XMLx+WbZIA8KRxBLF6x9p8xqZ5NsD8nuYmlEIS0HVhQSPTgJjFMPAp/tIxE5KbiONqAHXC6ZQQAyXdziCSfDVoLm7HFdy8jUtmSRcK/tyXa+sOz0eQ4/FsYeGpCzT9Sf95fFeLsy4ffsk+VE13ORaCUalnEmIqgcNjo2Iy/ZXI/LZMizoz3S515Njg+FK1zxRHUL14DnpHhITZTsZ/irYcvJzPCxlUwxXDmqZCL9sQzXpyjYrFYRXHtPi9SPHKicxu4IpHEeVO+Ilx9JwmwQt4rPkMzTMWEq4Y4IaCcvPHG6jYfDI4x25frBRmkWPk361iqafAsyMyNbcceVdwInhe45oAZr8LlB3MvMI4BNPwoX/MPK/PyZ9lKLNoMk6rvgNtHwZJV+g4FMIzZ8v+9SFH4elXzzls4bBZuE4I2wmN4bBIuGOecNm2NXy6jpoGnE1VDXDH64TAL2psfp3pSmJKJVxXPBImX4ZmgsIDQNA2qQl1fdr4bCUrbnjnxYOgaqeapg9/N6aGuxEBF8JvCUFJ+L6l4Uj2LkcTjaHGgnjGe32vXFjIZCQbMSTo8YFj1RD9s33UZwGj/6bx+rVq9m+ffspP2+Xx/1nRyKReEeZtm/fTiKR+C8t1+n4vyvaenOs29/LliMD/6o/zn9m/GeX5Wh/ga6h996V0HZEFRD6l+KRN47Slyvz7K4uSqbNc7u7eWxLBwd7sry0r4cP/eI1vrPhu/xg8w8AeZ9DBbP6GZ9+cDNX/Gw9P15zgD9sOsbWo2lq/AY3Lm3hpX297OvK0p8rc9Xdr7Pi/13HB378KsfSRX5/4yIeuGEBg3mTLz76JvGgh9s/MIVC8Gmuf/Y6Lr73F+zJSznrl1dOJOLT2XR4gF3Hh1g+uRZFgeff6qa25XE69Qe5c9UMmqJ+7lt/mIt/vJYMkjqv6SaXzWrEMEyE4+FzyyYQD3r4/WYpnVjYkmBJswQPcuoOVEPO5KY2w7mttbT15VHcJcntPdv52KJ6Xvjbc3jwhoV4dPlVWJ+SIIlhFFkxrZ7f7f0dv9j3JWaMCjBrjJyAxnwxlk+pq9b7/LFxpiWmsatvFwWzQEU/hJ2fxKS6MEGvzu2LbmfNVWuYEJ1A2SpXz2sbbKOlRk72A0aAyfHJ9JU7GV8znkmxSTy8V8p1KgNn0q867HDBI90oSZ+gk2KwPEhLVF7r3Fl5Dgzt4c2eN6tJIGaRkgJ1Lr4/o13QNiAZUuNrg6QLJh5D9rHKwefgbc+AaclkLlfKUBiS9R04vg3y/VUvo4JrDk0xTa+qkHIA28EYPRonm8X7ugQti737ME2ZcETyDtmeXbR7A+xwJ5FOLkddUWEofYiQeRjheJjVEELZch+4hsbsfEwuyZ/zZTjnS7DnCURhCJ92FG+ywiVvOHQNdSD+9Dm6ch3U24KJa29itrUD/8yP0tLl3p8Dr/ftqK5U57d5mfQyjOqDrqbx2EN5HEeHs78ACOZ6EihCcIatQ7aLgGJQVBTs3f9MRVEIRsdy97TPcmlG1oVjKVAzmnhOgjiHIjJpjdXOQHXA0qAQiFNwfaR0BxSXtaQm5apnv+7Djo/jtuJeXuyP0vP4NprbVRo6j1fbp9R2HMafB80LAai3LDrtEms71pLwJfAFahFCJelPkjNzdMTkJD2ektskRyyHv62ZxUfSvTSf/22mxKewoH4B+oyrYPHnIVwPlomvTkcLqtSMKULvHnyaIb2AVa2alGkO7MkcZkq/XM0NT5GgQ/5gP0bARlE1YhPnogJ13QoVA/RREjwiIhNBbZpk89l5E7viyghr3Z10VQ+hWrkynlZVBrukDMHokcCAbWpSRuWG44xsMy8KRYb8NcRyoHR4ya4d8cDRxKngkb5ByvxrXNAkboww3+oGDVJDI8+IUyjgGLGR9mg/SXpRLCJc0NGx1SoLCqS8g3gLY9wkc/jzBR7wRUYkPm54ghaEG9BHjcEqahSLLnuiWCQWrGdA0xHDfiAhCepywTdRblyDGgpRY+lkXbZAnev9Mpywht06a9HDVBz5vA/Lr0SpjNBdg19NoIbDhIpUfYwAnGO7q/VuFfOSrQMEcxaVfslcNVK1NN5xB/6Z7u6TJ/vWxMah+2ViavXI50V4ZJ2qo6aApnFZVmFZZWTH3fyGjfIP04JwHS7RsQqayB2W5D04mSyOCx4VJo6iZEDrgA/d9ZmKVQwyPteLzZZ+R/JagSr45wx7seTzUgYWCMK0K+Cq+zB8AXRHyqyNEUwNAO+CiyQLBGgONUvAyvDI9g03gCMNgRWPv+rNMnwv1vCirCeA4xgSvAtKJo8QAlGpjGxFH3EBhmCtlNwFh5lHeZx84VTwyI1h8KjccCUAejKFx92palS/rA8tHkeLu/5bx45VASCvBaoir/l2iRIwAh6F3NzANEdAGduBmPTLU4JxhK7hsUQVVBiWM3lc02xjVCMkxp96/TNvgWVfcW9EtmPYckb8f9RTmUeYJiLkMsomniXLUTGrrDo1JcECYZqnMrtUFSF0FA2oaSZ0zjlMWLuWwELJ/BlmHqVc8EgLh08xwBZmxWUeye9wUS4jLIESjlfBsFN2W3u7bO3k3yeH+x5hO+DIcxX9beBRuA58J0m+FQXqZoyYbgOVgIFRKKPmihT9GoH585m06Q08o076zCrzaJgdZb3jsxS3DzjFogQr3+Z5BJJpFFOC+ByXIXSSYbavWX7npYOg1kSkbM3dcVv1edFc5lGlQ85BhqVp4AI/loZtjoDnw75TQogq6AWg19aiRCOEioJAwYaasHu98EnMozC+GTNQPB78q74In3xe1t3JEUxKP6lII6jvLzjl/XW3p+N0nI7/8ugaKnHL77ax/K61XPubN1j1i9f59auSzWDaDmXL/leu8G+PZ3aeYOF3XmDS7c/wlT/ueM/3dQ2V+PivNzL3289zxc9fQwhBvmzxnaf30J8rv+d5/94QQnDtbzZy3X1vnCLVMm2numPL3WsP8cGfv8amdgmyfenRN7n3lbZTQC7Tdnhsi1yN2dDWz9ajaSqu58M/PbOX//WHN9l8ZIA9/QfpzqX56uqdLL7zSRbc/QV+9vrTPLKxneff6ubm8yYwtSHC49s62HIkzZzRUa5e1Ey0po8bHniDWx/bQbZkct2SsfTnStx20WQWtiQ4Z1KK+64/g6BH4/olY1kxrR7Nk0bgQN39KJqsQ79XYf7YOM+91UVfrsI5rSlmN7sr+p5uspUsHzljNL/95EJev205nzzPi+oatBbMAh5dJRoUjE/EGZ0IMKc5SsVyGB0P0BT1UxeoI+aN8fiBx6t1UxIDnNMqV4zGJIa3ijXZ1r2NllQIv2dkAtRblqyAaMjEZ2j0FnuxhMWPrxnPpfPkRCXuizN7VJRkyEM0YNCSDDE9OZ2Dgwd5su1JbGGhlCZxxugwbPo1uuMQ88XwaT5KroFy2S7TkeuoAj4Ac2oltfzDrR/msvGXIRCMjYzlN6vkTk2OouB3gmh66R3+U0PlIZpDzYSNMLszLwBQcSrsS0vDYgoDlBSFRL88L1yE3FO/hK6dtCTlxLeiywTJzHbBwRdOub7tgkeFUpZ87gSqEPiEA4deJNjWzaw2h4LpgkfZE/TqGrWhRhAQmj8V1aehHpIJXan/AOWyfG9NHnp797A62YhxUuI+audWhtKHMdufRRd+PhVYB09+AX6+CJ79Kqy9E+pnQuNsOOtvYcnNcoKePUrtHIVUBjzPvkLmxDYKqkr9zscR+5+nOOWLBFrOY1y3fHYczcfGQgdBzV0x7bKoO1rmoeUKxXlyVZlYC0xfBeFGLl90K3/IKowtDErwyAhQ0D2UDjzL5a87zP35HmY1LqTGBYAcU4XG2cQyEkxocyfXsZrRKELFVqE4/3ryrueHblE101XqJdtiQNgciTZRVBSS7bKvxjKQGpBsJS0aoVxpgHnXydXkyCgaFQ9ZK8/aY2s5d/S5CNNCWBa1AQle7RuQ/SLWKMEmunZz/a7niUy5Ahpmcc8F9/CdpXfCqnulOWu4AWGZqKrJxC8vJDpL9hnFH6l6yxBwwSNb9r2x/Qaq4RCISODA6h3AqDEgPo7gGJkETuwUFCJG1feDhHwe9PNvkef0D2Dn3SSuXgLDwnEIBiSQk0cljU2gJFDyBRSvF7uiVA1VQbK5HFeWpxRKZDRvVTJROXKkuo23lLuMjKmNOyUDIuRKt6KGm6hoGqpZywe65LOsJZMSPCqOfE8MgwwATrFUla0JxY+wTgKPCkWIj2eMCxgM+4ALVfYTYZojsg4F9IANNU0YdfUIWyHfn6heJ+6Lk1MVFFPB8uojCbNmSGaGrhNQ5LGE6sWjuUmbm1SFbNm3pgdHUbbLCCFGwKNKGUeX969qAiMWJ1iCQOmk76BMSRYSsPOFKlsqUHSwu2X/96ZcWcpwe6dGfNuIt6D7XZlrj3y/GDYynvYBtFAIb2w6RqS1ekph0yb5PtsGbwTc+1P8rtzO7fcAdiZTZR4FUw0cTcH/x957B9hVlmvfv9V279Mnk8ykh3RIp4ZAAkgXEAsgKCoILzYQCyLYjkc90jzHBiKiICpIkSYgIEhII42EQHqbZHrZfe9Vvj+eVfaemSR4PH7vec/h/mfPrL3286y+nvt6ruu6x3Z55yJekMnYua4AjxzZWgV4ZDOQXPCoIjn2+YMe80gHK+4B/L62Nvfv09pO44yxorS8pQsw0DIlwTwKhCqkTgJk0SsM9U1LQ1IspFBCAPyGIa4Rh/UzBGhwZWsVnkdDQ0mI929h526QJNRUEiUlAKVRNm6lpFKo9rLi1m3uby1TwnSqho3E6B8CHlV6CWEaWAFHDjQGye9H03GBN6c9rbUVZBnfh34A7/tBdftHnQVHCxsKB8io0U1sD3OPeaQ6zCPdAxgbxTPIKhYx7XtbnnWWWFYqDZfZWYJ55ICKWkO9+x6WbRBjsSYAbjkqZGuV7DdJVV1AzCoKOZZcwbo5tGzt0OCRIxuzDN377VDm0Ujxgfvg3B+5/+ohP1qujJItUAraz8MhYGAl88gyTXH9DWUeubK1gm2YPRxQlH0+WgPNHFsj3q+V102yppl0ADoToAXDQt5XsJlHgYALFjmytSrwKBLFNP2Yuozk0wRYp6pCtupcc/Y+qPX1qPEkmgGRAsi24bkcdTyPBPMoMGkSk9etxT919nDgEgSYlBjzv87vCN4Dj96L9+K9+Afj1X2vc/rv388eh149JEq6yZX3rebuV3aIv3+1imc3HeSqk8bzh6sWccLEWu544R2e2PIax/3kK1z6mwdGbOdQ8fj6dk794cvs7M5WLe8YLHDjwxtIhnxMa47x2Lp2CmV75tAyq9b927ZuXtnazdjaMOv39rNh3wCPrN3Pz/66gztf2Oqud2Agzyd/tfqwpd93dmdZt3fkYwHQmS7w5Uc2cmAgzzsdGXb15NhyMM2fNx9017nq/jW8745X2Nub4ycvi+oRD6/Zx/IdPfz+jZ1868lN3PCHDS6w9ZctnXRniswYFWfj/gGe39yJLMGFc1p4/q1OMkWdb75/DMh5evOD/HHtfkY178FX8zI/eedGvrf+Cywcl+JzSydx4ZwW3tw/yNbODDV1W/n48x/AaP4B/fJyXtjSyXVLJvKh41Vqp32Xca273G2e25Zi9U1LuXbJBMJ+lWAojZlvwa8EmZwUA37d1JnTmqTPZjzNGBXn9GmN+FSdnNHvVsgCqI34GdUsALFJyUkes0UusqBNULSPaRWDz0XjxOBUkiSm1kylp9CDIimkAik6sh0sGlfD5IYoc9vsgS7SiPK9zb2bAVBsIKWvINg4g+U+Bkv9SEgk/AlkWeIzp0zk6pPGI8sSx406DtMy+ebr38Sv+PntpR/mixPb4cnPw1uielZADVCwDSx2DezCtEzGx70ByfvGvo/5jfM5e/zZnDnuTFRZ5eSWxRzXOoU6O6k9o3YCulX0GEUAnVsYLPSRCCRoibaQLqeJ+8VgaH2XLVvM9VCUJFK9EpImY0kQ2ivBa3cxvj4McpGSnbmWAzH4qz1Iz/fDfedQTgsmQLY4SD7XSQgZKVQD257D98CfuPJZ05WtlQb20qco1NXPEOPLPX8h0TqAscdHPGuR79tJuSSOg1+H3u6dLPcrzFY85kZDxs9AKMEAJhNCKkdveoRtT4+ipKdg+b9D/VQ4+3bskw7LvilmhqeeSfj9V9JebzHhiQ0csKV8jWqYTOpidt18P/GNu2mzmUeDWohBS2d0SAxa9VER9sysZcUMi4m2g66Vmiio/l94C3XmB5gSGQP9eyHTQVANkVM18ug091iEDg5CpNElbhm6BM1HEzcNZMtih+1jlAwkkdAE86h+Chmb+aYaFUaiLaL88WApy8ZAEMWwGLdTrFc3YJEUVl5ETjqZQlcZK9EmFhx7LU3jlwFQMAosGb0Eq1wC06TOnmF3wKO4ZtPwlQCMWwynfB2ARCBB1FfBbos2gqEjmUWkaD1SwpZSBGJu0uIwjxzwq6HDIJAoo+U2us1orQKI05pt5lMGysmI5/uREveDkkiAJKF3dAiABVx/DgwDzS+SlIzip19RqLMtv/yTJoEJ5ZyX1FiGhGWzgpR8iUxGHDg5IJIk/0QB0ikm+E1vSBzN2FXIULls6mVMSdg+IGPGUMyGOKG7hKRY+FpasOzkHHDZM47EzyzkMR3mkeVzZRWAYCSlxtFgGARlzQOPJJuhUC7jGyvkS2o8JHK1WDNqk5BIZLeJ94yZy5G0E/FACczg8EReUlWCkkjmGoOe/MKRrTWYAb7W3ctJz+f50EsGuqm74JFZKGLZ4JHkD6Ila4gVpSrmkZ71jrmZyyIVvC+tHXsoKxCI2vf49Avh0kc9EAmgZjxqwGYeddnMIweYjNUjR6MY0XEYWj2qXYXXtM+lpesgSVhqGCQh6xINlV3PI3NwEKO/HySJRE0zuxskGtsL7iRMpACZgEQCFQ08ACYYHAE8ymDmq5k8qj+EZgjmkWqA1NKE5PejJBIuQANw+fTL+eK8L9qJbRl8ISxL+IhJgRGqklVcW6ahCNlaJOmu48jWAM8fx/4cCh5J4eHMIzUptq20fQdKMomkaag2G6ml28KSJJRYzGUeFbd54yFLCmGp4liPKFsbvQDC9UjxRm97HXDMMMG0pZITTkV2wCPbJ06yfc1SH/kIY35xD8qk46Bh6vA+7HDAjaRuEnY850bwPHJYU46s0yp7EjW5RYA/ZoXnkftpyeK60kYA4GyW3/i8zWCJRqsqvFHWQRvieVQqVh2z4bI1m3mUEGwc1+upquMKKZlxCObRSBGutf2iROgRP/68jpYtUgqNcB4r+rL0Q/clBx3mUQ6zUEAOjMw8aguO4tIJF7v/O1ETqGHFFIk1E2R8oahgHpWK7nqyCx7tQwoGq46fEolgGhqmLnmSXRugda45x4BdbWjAZ1/PAFpS/K1EI1iFAkZvL4ptgi4diVH0vu+7783/TfEeePRevBf/zeJPG9o5/fa/0pkWCdaqXb38bVs3Xel/HgPmH4mfr3yR/bmtLL7zdxxrlzb/9pObWW0zZX7y8naef6uDbz35Fpfcs4I39w9yxweP5sbTpzC3LcXNZ02lFHyNr6z4FPnIk2zMPkL7IUq2D40f/WUr1z24lm2dGV7cUiEVsCy+8shGirrJjy+Zw3VLJpIvG7y+o4cbX/geR//yeJ7Z4lXq29ObQ5LgJ5fMQVMkHl/f7jJ5Hly5l4MDBbozRT5y9wr+vLmDZ948OGxbnPjiH9bzkZ+/XsVY6kwXWLO7F8O0+Oxv1/Hgyj385CVxXACa4wHueGEbpmnx5v4BXtjSydsdac688xXSBZ1jxiR4csMB7n5lJ9Hxd7B4/lv8Yc0+Fv7LC1z80+V8/dknqU1kueG0yZgWPLByN22tm5k3bR91UT83nXkUE0eLAaiiFFlz06lcvMAuH5tZCMGtXH6ygiRJnDO7GUWWAJNX+8XsVH2wnumTtnHl8WO5cEGca/9yLb2FXjb3bK7a96BPtGFaJqbcz7lHncizFzzNd0/4LiDAo3lt4kWtyhJHNcX42PFjuefKce73lbG2cy1j42NpjbW6zJa8nidkmwrPccCj8Z5k9qgaUUVoUnISY6Jj6Mx1EtAUnv3ciUxqFL+bWjOVFQdXDDt3W3qF/85AcQDLsugviuSsO9dNb7aDuAXKXlGt6NJFbXzqJJHszmmYw+PnPc7n53yery38GrNH1xHNihLr7BYlof2K32UeuX5DFcyjGXUzuOe0e4hqEWo3/pHf7z/IVcSQJIlFhTKNus4k25smXU6j6yXSL34b68eL6C/0EvfFGeUXicCZwdHUB2rY0CXYdla2m7wsE+uV8TeEGGz00bZbovzmw0wKZvCp3ux2uWUu7H0dOjYLX6GdL2P0iX3JFtJk832EJRVr3BLY+hzyQCeJDOTsMufdfQLsrK+fDpaEVEqTuOhCMC2WrLcoDO6lnPX66+ntY5tVZHLI84yp1RsYNIsMBOPUdh+k/ZlBygMW+ek3wZd2w0cfh1Fz3PUFLd1EapqGNGEJaxfqJHrK5FalwLJo+sCD6M2nAhB5ajmtIjelqIiBa0IOUlah84sn8crV8wkh0dQnWA3UTKq+SBKjSW/cS/8bXYR8EXKSRF6SbcmZBaEaLNv/xyzL0DgDGUiYJjvLAuVIBpJIpoShQM4XIqOJoZjflLwSxjZLQLIsXtB7mbLXIlwES5aoG4BIw0mgaYQWLsTK59l79dVsf9+ZGNMvo2nOlQCE1BALmha4UphaTdx7DiMtapsaW7VHwYcf8sxUh0a0SQzArbIwqnV8OEJxTw4QFPeg35ZlhdJl1AgoRh+yXySE2oLz4OSvoDZ6/hByfT1E6uHoSwXDC5EIKskkpV2e15pVUT3LSQZzskafIlM7KNoPHCXu/XLGM8I2DQlLFWCqv2jS3yNMnlMf+zj+qUcRWSw8RxQDUn4voXDDMLhh3g3U2t8FjjqK/J5B8j0aWkxFDoddQ2IAX8yuXlYvkg/LlnCIbVEwA03etuXzUH8UEtAaHoXPshkSfgccKAnZjqqiNdjbFhuF1thkf297a+VzpGyD5VARiAwHCSRVJWgn+o2pimvaTlhVy8cH0hmS2/o4aq9FySwh25MtVrGAJdvypHgtcixOrKgQrhiSVAF2hQJSBRNL2bmPgRAENXu7VB+MP7l6AycuQ73Afk84zCNbkiVJEnI0ijmYxkgPojU2uuXRxQ/spFsNCXZIyK60VJE4WuUy5c5O5FiM2kg9u+ol/Hkd/YDwxwrmDLJBqLVluC7zKBTEyuWqpExmNouVqwaPZJ8P1ZBsw2wLNRDC19ZWxTqqOh+a5vmxoIjtDkaGlRYHT7pmlYXnlBytZvK44FG0WZgn2UCr5PcLo++cI7M7tGzNKhZRa2urljX0gR4N2PejLVvbvt39rTnjQ1g1U5F8vpGrsU5aBjdsRQpFvPNRyTyywSNUBdkfIGRpnheOLWdS4nHCCxeOeAyrwgZbZueLnG0b8bvSr0rmkd2/cywqzbHlUEiAkOWyANypZB6pSLNGroIt+XxIgQBl+1qSbXKZZuYAACAASURBVMNs8XvdZh5pnudRUfRZCZxIqidbw9A9pk9iNFz2mPDWGtqv04dheMwj5V2AR0PCDAeRLQj1FymHRwaP3O2pPG/DmEc2g7dQOKxszSqWsOzqeA5ICAI8+tkZCo8eK+MPRTGLRU+25ve7TCO9s7OKdQQgRyIYZRmz7IFHjmm5W1GyVQBxan0dgZQHoPuTtmzR9q4qHzjgmnMfMcadBKPnvbt1/wfFe+DRe/Fe/F+MFTt6uPmxN+m2gQbdMPnXZ7aw5WCaW5/YzIMr93DRT5bzkbtXsOA7z/PlRzbyx7X7+PxD61izu/cIrf/zwzQt3rRfmOfP97NwfA0+Rea+13Zz4U+Wc+k9K/nRX7bxvhmNnDCxlpU7e/ngvNGcNs1LHiY2RJnZKh5FowJTkeQif1y7f8T+KmNbZ5of/PkdzprZREPMz/p9HttnZ3dWsGROmcjY2jALxqbwB7u5be13eWrf/Zhyms8+/mueefMgXbkudvcM0hgLUBf1c9Kken63ai/r9/Zz+bFtmJbFdQ+u5Zy7XqW9P08q7OPN/QMjblPHYIHVu/vIlgz+4yUxyNrUPsBZd77KBT9ezpJ/e4nXtvcwJhXiD2v28cT6dma2xLn+tMm8dWCQXy3fxS9e3UnIp/CV901hsKBzzqxmrl82mXRR5y9bOpG1PsY0ZPjz507ko4ta6ZD/RLbmdiZO/hvz2lL4FJlC2SQTfoIndj3I618+hcsWtbG9X2yPiYkllRgsCYH+PefehCb7WNUtjKlrI35OmlSHGmgnZ2S4atZVnDvhXLYMrOWqU+r58qtfZKA4QFAN0pXvGvE49BZ6KZtlpte3UhOsIWjP1ummzsyWOD5FZmJDlICmoCkyhtLjfl8ZuwZ2MT4+nqAaJKtnMS3BcAnZSciCsSnuvXweZ8/yjBaPSokEclrtNOpD9XTkOtzvnPan1kxl9+DuKulfrpxj18Auor4ouqWTLWfpLYh7rDvfTV/fdlLlEux4acR9bou3cUlyGcvyNpuoxx5k73wFsJlH5Tw8fh37ukQVtTHREejOL3wDnrqeCYUcoZ4dYFl8qbOTX7V3ECuJJHRwx4vcd98JnLvj1+R9YUpYxPwxRg2K83HSlr8wq7+DDZ3CULaUE8lYpE/GnwRjjMzE/bDt8Rr0T19Bm2+X2325ZoKoxLLhIXjzYUiMwbAZPLn+/RRKRb5zZ4F0Xyvke1Ha38SvQ+HP34b2dXQO7AagzvZz4thr8F9yG8EF8zllnUkh10v54Ca3v3dMH2VMxia9hDaej6KbOgf9QZa9ZFK2WQ3l/e3C7HNo2INDSVOh+RjKrTKPHQfBdxTOXGXRGG3GHBTXu+/lVa40QpfFwDWoG5gy5EIJcpJFMJCETsGYsZJD6Orx0fRvzNOzQSbki5HDJCdJohqPaQp/DFsGYRpeRZukYVCwK9kl/UnQdXRZgKGDNniUIuAxj2zZgGzC3zI7mLfVoqRA17Rm6gYsgkZMVEaaLmbLs6+8SmnHDvJvrKEpLMCFE1pOwKf43ASo1jZ93jW4i7g/jlzyPDYOG9FGLNMSlg/hOs9MOCjkM5ZludWhorJIiFRTQgqIpNEXt81vbcaRHAhQiIn98zU2CfbYuT+CsSe4Xao1KYrbPXNpB+TANpM1ZYkcEn2yQktatB+YaoNHFSwYq3khpu1zEixBf7eYFAhMn8m4Rx7BP0H47tRoCZL2ecsGKthBpgNaic/aa6/B0k3y3X60VEh44lSwQ/yjxbHX6mtA06pla2UT87xfuG2b+Txm26n0j/46k+pmMCpkMzQM2xS6XEbyBwhMnkxgkn0dxlvQmqrNWa183gWPgkVGZJhImkpAcGoYI9VQePsduy+bjSsJ816laOHThbTWAY/MYhHTBo+keINI6AsWoQJIcTvhqgCPyBVQCh47UtvXyWAY1x9txFA0lOM/DprmgUcVkiwlEsHIpDEH0yixWFUZdxdwUUNIEkhBr9KSA9AAlPfsRYnHqQvWsbtenOPC2wJI9WVLZAJQYwPwruQqKEysjbTHNnY9hCqSY8mnoRkWRaMoGIT+AE233kLDV7864u66zCNsVosi7peqCl32vhs2eGSWTeRgGKJ17vGxSiWPgaH64KJfwoKrRB+SSKSFrDI/IngkBYMuiOGCRzZTSrE8+Z0rW9vmgUfWlPdjRlpGlqwN2Vd3e10TacN7bisKss9HxPK5z2b57yxs4zBEZuoqn+wbtPt1mEcek88ayjyqAI8knw/J56taVul9JEVGAJftkKMRyu0CmFYcw2wQ7DdXtuZshwBPpAq/HxTVY3FWytZAsEJHYDy5rJhK5tF/AjzCfl4opoURHsG7qqLdKubRkGprjkG2kU6L4zWSbM3vE7I9B7CrOAbOMyzmi6EEAliFgidb8wdc5hHg+h+57UYiotqaEkMO27JqVRUm6fY15x8nJke0xibCNd4zNFjTUNWmVSq5zKP3YuR4Dzz6J8btt99Ozh5Q/N+KdevW8dRTT/1Dbfx32I//18KyLEq6gWFahzRgNk2Lrz76Jr9avptlt/2VZ948wJ82HGBvb57jJtTw5IYDfPWPGzlxUh0PXLmAjx7bxu9X7+VzD63nkbX7eXRt+/8v+/Lo2v2s3jUyULVyVy9ZXQApR40u88MPzOahTy3ijZuXctOZR7Fubz9Bn8Kt50znx5fM4Tvnz+Dms4dTj4+dmECVVKY2jCLo13n4jX1HNK7+6cs7CGgyt54zjaNHJ1lfIRVr7xeD9bk2M+Xbq76Or+0H7Cg+R6nnOGJKI5HU23zr6ZWc9cezeCN7P6NTIYpGkZOnBUgXdVRZ4tolE7h43mhW7uqlJRXi/o8v4PgJtWxqF4OTx9e3c//rHgjx7KaDWBbMH5vi/td3c8vjm/jAT5ajyhI3nDaZXMngojkt3Pmho8mW87zTt5VTpjRwzqxmlkyp55YnNvPouv18YO5oPnnieB64cgHfPG8q8UQnzfEAAvoxGCgOMKkhytjx6+jxPY4syUTCOYI+hdmjE0iqKL1+IHPAZhEJg2YnMuUM6VIan+xjbksrp7Ut46mdT7nSo6+eeRTnHyuO4fzG+ZzWdhqmZXL9y9ezpmMNX57/ZdpibXTmPLZXZXRkBWDTGLZLLdvlz3VLJ6ApfHBeCxcc4wE++9L73O+d0E2dfZl9tMZaCWthcuWcK/tykhBJkjh5Sr27jwAz62aiyirzGubREG6gI9fhnh+n/dZYK9ly1gXQQLAxLCwWNAofmP5iv8s86sp30Zs5QNIwRMn0YTu8GX7zAbruvI39139BLHPW69kK6YME1SDF4iC8cR+5g+tQJZWAOsJA7a3HhflxbJSQjZWyRC2TJsMgXhTshvSjV7PdyNClqrwz58MAJGQ/x2YzzDFV5l76DLMKRfZlD9CT7aSQ7SKSs/DlJPyRPK31vWSCkFd85Ld3cm7zbrf7sqrBhFNh3W9g1ysw84MYNqsk986fKZky0axFyWyAz2xAbhBeNHrvALx2J10Z8Vyqs8uQOwBCeMEC6gegKEmUe/e4/e2yq2mNtat8AYS7xXXYURxk0maFxHETUWpqKO/fN/x44c0OS5oGikpTvI3fnKCwvzXAyRssaoO1rt+J+xtNxbDNcoOFDKYEWX+InJ4jFGtBCttsttSEqt+RGI1lglGWCQWTlCyDTCCKYoDkzBxrYuBp6rIrIUnak7WarBGSA2Ca6IrEYGmQgq1XSipRUopNl7fBI8WEkqkzb7vJm60SO1Jl6gZA68ug1tbinziRUbffzrg/PQGaRm7lSupCdVww8QI+OvWjYl/thCkqh/ArfkzLJBVIYRVto36jWsY7LKJNYEkgWcLbyPb9kMK29MEwIC5mdeP29suGJx/SUiI5ccAjAL1eJKiRppH9IpRUDaU93nViGaa4jy0LSVawZIm8ZdKnyLRkfEh+v8vyKPk8INI65RtYIdFvoASDveLZJNtSE4edMKduNlPits9UrUhoyn7VY1bZhrT+ceOo+8x1Yn/qazzwKJsV3izThYmu2tiAHAgIgMiRreXzmHlvLGXl82ReeokD3/85n625iG8t/IZYPqSSVuuv7qPhxi+B4oPGGSiplMs2URsbMbOebC1UtFCi1bPyYkWNgCkSwJkv7mHPZZeJPhyTXlOBjz6OUizjK0PJKKHY8h+rUMSyJW9SogElFiOYM4iUJHz2Oa0Ej6RCEaVQpmQvknWTwaA08vOuIiRZRq2rdcEjswIYkaNRzHQGY3AQORZDsyszKXW1XtItB0EGKVRRpruiylJprwCPmsLC8wig+PbbWIaBmi2QCUJt3TS4/ElP+mX7uOjd3W47RiYjmDwVIJ2k+QTwa1loBij+AMHZswnO8IyJq/ZV0yrMsYUkSvL5q869w9DTe3oEu6SsI510rZukD2MeAUw9F5Kt7r+V4KZjJF61HZLkgkVqnQCPJFVFt8uXyzYLyVmntLOCDWh790iBI4BHFbKxEZlHsoLk9xMy1YoqXIeQTx0qHPNoX9T1NRpqmG2VPMNuB/gzi0VPGqVpNnhUrjoPlmkKg+jK4zy0+2jMKytvG2aDx34ThtmH9jwShtkjyNYOF24f/xjzSLINv0GwkA7bl3novpxqa0afGDeNKFvTHPDIk6M5EfPHUCVVGMr7A1XnRg74kW2GF4Acq55EUqIRzJKFWXdMFfOISubRuPGMuuMO4mefRbTWexdFasV9JlcARu+aefS/NN4Dj/6J8d8BdPmfBh7p+hFmSP8bhGlZbO/KsuVgmgMDBdcM+pk3D/Clhze48rNnNx1kW2eGzy+dRHMiwFW/foOv/HEjE+sj/OLyeUxrjjG+LsKPPnw0x06o5etnT+PF6xfzyKePZUpj9JDSLsfX51Cxdk8fK3e+O9aSaVp89Y8bueKXq9jbO/waeOSNfaia2I49g95AP+JXufKEcbx8w2Ke+swJ1EX9RPwqH14whpBv+EvRMHU0RSOoBgn4dHZ0Hdo3aCBfZm9vjkfX7efiuaOpifiZNTrBrp4c/Tkxm3FwsICkDlAXFQOQ1QdXMyY4k8y2G/APns9ZE5ZS1t6hS32SnJ6jT3mFhmSJa56/hjvf/jjhSAeLJ9dTG/Fz89lT+esNJ/O7Ty1iXluK6aNi7O/P050p8o0nNvG1R9/k9ueFD8BTGw8wsT7CbReLhPrXr+/mxEl1/PGa47jm5Ams/MopfO/CmcwenWDUuBcItf07S46qQVVkfnLJHM6Y3oimyFxxXBsAx06oZW33cj745MV8ckmSTy8Ryx1QY3PPZupD9Zw46kTXn+fESbUEwoK51ZnvpGSIY7J9wJsxzJQEeOT4mrx/4vvJlDM8v1sYJY+vi5CV36Yt1kZdqI5JyUmMjY9lTccajqk/hnMnnEt9qJ6u3MjMo4NZIetzwCPFHsQZdgLWGf53evx/cNffl7HBowrm0YHMAXRTpzXWSkgVSb3je+TI1kaKxnAjz17wLGeMPYOGUAN5PU+6nHbbl5BoibRU9Quwed0vAVjUvMg9xv0FW7aW76a32E/KNIeDR5YFz3wJtj6LtWc9Zto2o+ndASmbfbPrVfyK3wXn8oP7BAD2+P+BO2bB76+A3p1QGICebdB6nPAkKPRD0ZvxjuYF2DWoSHSPEmDLetvwO54f4NgD7/DLmhPwNR/NzLlXA7Bh1b9TyHYx2s59/L5OWsL9/PamyfxmiR+QSO36KwBBJSD8lGZ+ALJdYJkw/QIMu1x5oX8vBdsU1yqVINmKZEvpTK0Zdi93AUXHXwd7dtLxgChYErpVIXEoyGiyRnPS9p5JxPEdEM+nRMZCMSFw6ofRWkZR2nsI8MhlHohny9JJ59FsmGxJFEnkZWRJxhgYRKmpQZ4wloIGpaYadJuJEejcjiFDVvWTL+cJ+SJI8z8m2g43VXcWH41lSJglmZANqvVc8FPh9eMksZoYjJslC8sXAX+cpH3NJgNJdz1dgZ5CD7o9Bj8qMY1j6+zyz/bA22+zo+JF6InBFn8vfh30rdtdpkDs9NPwjx9PcOZMsitWIksytxx7CzPqZojtcRI2w6DO9rtJBVJuGWQ3GThURJuwLKGIIVznXdcR0b9lmq75bdQxn9UNpLAY4Gs2UKQ1e2yR6GjRRsOYCtPkilBrUlWJP6Y3440igyJTNgw6asdRn1bQmptdKVO5r8Jvp1h0QbJg0aKUFs9JJWqDdDY74bqZ13D66KUApOtE8pEbU+sxj3QvoUtdfgWp9y8jdvnnkcOVyXkIdbbwm9Imz0cOBLAKeSybeYRpugw4EMayDqgZlQI0BYWh9FDwSA6HkWrb4Et7oO14AbI0NqI1N6O1jMKsYB6FiqCNAB5JqorPEvsaLSkYtl+QW43MPkZKvohPt8GjsukdQ2wQJ9mEEo8hmxaj8kG0Jhs8qmB7SfkiWtGgN+ktGwgf/rnthFpX53keVYBHSjSKmU5jDg6iRKPuteRvG+uxdZQgkmS5oKYAKyomJA4eRInHmZScxHdPvwN1dAuFLW9j2OckE5CoDTVA2/Hubxy2jsP+AZt5NITJ42ynaoDPlKvNkEcISatgHqEizThfJLsV5d21BsGI0Lt7XPaaHAwNA2MOB2rIodCIBt+V4cjUFPt5Ah7jyFdjA0qahhyrrgBoFkYwfh5xXz3/nyrPowoGi+T3EzRV18z+SGymYX3Y97Hliwmgu2KZwwJyZGuSprll4wWgVHKld5Km2T5IHvOoanLiECFXADBKJFJt0q2Xq8CjET2Pqgyz9XfFIHKZR6bhgsD/GeZRJXhEdDjAWNVXJctpqGzNBnYM2+TdAZOq2vH7MUtF991TeZ5lSSYZSFITrEFymUfiuncnU6JOZbThsjWrWMQYGKyWrVU8AyRVJXbaMuRwmES9Z0Aeqxtlt+kBRk4/78XI8R549F8U2WyWM888k1mzZjF9+nRuvfVW2tvbOfnkkzn5ZKHtfvDBB5kxYwbTp0/nxhtvdH8biUT43Oc+x7Rp0zjllFPo6ho5IQMBBi1cuJCZM2dy/vnn09cnBkKLFy9m9erVAHR3d9PW1kapVOLmm2/moYceYvbs2Tz00EPccsstXHrppSxatIiJEyfy85//HICXXnqJs846y+3n2muv5Ze//CV33nnnsP0YKa6++mrmzp3LtGnT+PrXPfOwVatWceyxxzJr1izmz59POp3GMAyuv/56pk+fzsyZM7nrrrsAaGtro9ue3Vm9ejWLFy8GcLf5uOOO49JLL2XXrl2ccMIJHHPMMRxzzDG89tprbn//+q//yowZM5g1axZf+tKX2L59O8cc481ob926ter/f0a09+fJlXQaYgE0ReLPm8VM53+8tJ3frtrLstte5q4XtnLHC1sZWxvmmpMn8MdPH8dnT52Iblh8bukk/KrCw1cfy5PXnUAs4L2wRqdCHDMmSUsySPuQ8u66YfKDZ99m+tef5U8bPFaSaVps78qgGyYHBvJcds9KPnL366zY0cPQyJV0frV8F5/81Wp6syX29+fJlgzSBZ1P/+YNHly5h7tf2cFtz73DjX/YwBPrD5CIihfrnvSeYe3VRPyMSgTJlrPDJEmVUTbLaLJGWAuDXEKS4JWt3VXrLN/ewwd/tpxZt/6ZE773IqYFnzhRJCCzRtsGwfvEQHxVx98IT/gub6dfp2SUOJA9wAmj5yPpNXxkYSvLxp6CiY4v9Rpjo0dhobNB/z4rDq7AwqJuwm84e1Efr7W/hirDmBpvkDi9WfT1wIo9dGdKTG6IcscLW7n0nhWs3NnLGTOaGJUI8sxnTuD1r5zCjy+ZQ4Mt0ZAkCUmSREn34CokuUxrrYplWfxp56N896IJvP7lU2it8V7gjjRswYQgVy0WM4oOY6av0EdNoIZkIOmCR588cTyXLvZe6g6Qs6N/h1ttKV1OV4FHcxvmEvPFWNcpyl7rps4bnW8wv3G+u91njzsbTda4aeFNyJJMXajukLK1A1khZXTAI03W3HZBgDZuJTA85lHZ9Aamu9OCDdMaayWkhdBNnf6MuJdCapADX7uZgT89CXtWuMabTtSHRCWUhpAYfDtMKKOURUWi2V7envHuk+37/kbcMJggi2O/P7PfZSp157roM4qkRmIebf8L7HxZGCX37BDMAqMM/XvELLA/BrteJVAuUMTEUvzkMx0EVT+s/y3ImmAbrbobDtgG181HQyBhM4/sJE9SiNleQWlfiC5dLF9XEuc9ceBNATY1C+By4lwhXdjdvYlivpuWbsFu8cfEMf7guHPYERUDuGJGIWxJxPxxUaZ7ypngi4ry6PVTMG3pRx6JkuVJECo/MaOQbqcrvRcViNtVqpwBpjOYLmpRdMVjICSyFhMSE1DtAb9v/ASUwSyhguWWPtZaWvC1jKa8710wj4BR867mD+//Ey2jjiKSF+xPY2AAJR4n9uXruWeZjKFKmDbzyKeGBPMIw/PUqhdmvpY5hAEZH41lgWVKhFWRdPUUegTzyLQEkGKDR1jC/4HGGaTC4ppLBVKeia8i0ZP3wKNZqWmc13a22Bd7Nr/WLleuKSFUVaMjZlek6upywSMnwgvmU9i0yQMGhhwfq6xTF/LAI8sxNT7SpEy0EUxs2VotTH4fXP4UUrzJ+70NDMfliHiOG4aoCgXEFkwm8cGLXWYDQHKMAAsDDc2MFEqqpup/yzBdIEeSFVH1zIS3jQzxrIVaW+t5YlSMocxiEdP21wiWvPLyspMcVMgxnMR218JWHlsoYY5p8o6NabgJlKQoNHznDsInLa1OzkMhfGPFO0kbNxEpFMTM5V3DZRAV5Nxty+cxBtNe/w5Q5dxTpSHAQIV0JbpsKfHzz3ergUV9UVRJJVgEX8wzw3VCUlWicpAz2s6gwVcrqiZZlsc8yhewTBOpMJx5ZBaLmLoJsoQ051K3OlGwL4daU4OkKug286gcULEKBQJli3zMT8He/HRYRpWPzKbQ6uvRuxzPo3IV88hIpzHSaeR4DN/4caCq+CaM98pxy35RtcxhxFWAFaJBCyUeR5IkThlzCsEpUyhu2SKMtIF0EGqD1feUkwDr3RXg0eCgYNxUytYcJpgBPkM+InPGrbZmb6cUTlRL2cplVBs8Mnp7sGzGmhwM/N3gkdHfD7o+omwNQLFNs9VazwcmWi8S6liDl2Q7RtouC6VYwCwWjwz0VMnWxL04nHnkI2Aqrmm9dARAami4oIkWwfLZrJQRDLMtXQf7f0eiVslwc5lHFXI19/1ymHPqsP2kQEAAUUMMsx1WE4zseYSiVgBrQpp7xFA85pHz/P3PgEdVTMVKIKkyRmI5DdlGyb6+9D7xjJMCw5mGkt8vjq/jeTTk2lnQtIB5DfOQA34BWjvgkX09yLZMdhh4ZEvV9I6OCsNsu9qa7owPvOdPONXg/h23gSS54jjII7E33ws33gUv7v+xePpLcHDjkdf7e6JxBpzx3cOu8swzz9Dc3MyTTz4JwMDAAPfeey8vvvgitbW1tLe3c+ONN7JmzRqSySTLli3j0Ucf5bzzziObzTJ37lxuu+02vvGNb3Drrbfyox/9aMR+LrvsMu666y5OOukkbr75Zm699VZuv/32Edf1+Xx84xvfYPXq1W57t9xyCxs2bOD1118nm81y9NFHc+aZZx5yv6677jp++MMfuvtxqPj2t79NKpXCMAxOOeUUNmzYwJQpU7j44ot56KGHmDdvHoODgwSDQX72s5+xa9cu1q1bh6qq9PYemQWzefNmXn31VYLBILlcjueee45AIMDWrVv50Ic+xOrVq3n66ad57LHHWLFiBaFQiN7eXlKpFPF4nHXr1jF79mzuvfderrjiiiP29/dG2TDpyZTIFMvkSgZ1UT8NsQA7NYW1e/rY25tjw74BLpzTwp6eHP/2nPAb+N6FM1FkCQWJz546iWtOnkDRyLHywErmN80/ZH/NiSCrdvVVLbv2gbU8s+kgiZDGzY9tYuG4Gp558yB3v7KDXT055o9N4VdlyqbJqESQT96/hrNmNqHIEtecPIHBfJlL7llBx6AYXZ8xo5OoX7wAP3XiOH7+yg6+/Mg6tMQqfKlXUYvTmTX6w/QGCgzkYO/g3kNu70VPXMTpbadz3THXjfh9ySzhU3yE1BB5PUdbTZi3DngztD2ZIpf9YgU1YT/XnSKSjnG1YVqS4mU1Y1QcSYL1e/uZOUblhe4fIUkWW/vfYnLNeCwsptWP4/Frj2diQwRVhpgvwWCpn3HSJbyT/i29sY3Mb5zPF+Z+gcufuZybln8egHuW3VN1LqbZ4NHdr+xAlSV++8mFPLhqD7/82y4AzpopEqpxdcNfwrsGdhHWwvx1318pGGIwmNNzpMtpvv7a1zEtkwsnXchbPW+hmzoz6maQLWXdY+SwiAaKAiTrL/aTDCRJBpL0FoVBuU+V2Zt9B1mSMS2T/Zn9RHwR+op9LG1dynO7nyNbypIupYn57AGPJKqIOQydzT2byZazzGv0jAAvn34554w/hwY7Ca4P1rveRg449FbPW4yJjeFg9iB+xS+8XcBNGgzLZlyYOt15Dxx0ZWsVAOPuQQEejYmN4a3etwDovncpNNSQ2HKA/t//HuPADuKrn4CL7oNp5w073s62duQ6mJiciH5wI6qp09wn+nPBo0wXXYU+GlWVROcW91w50TG4m35FJhmqg553IN8nmEHpDvjzTcIf4yMPYz1zujCL7NuNZBmiitSYRfD20wSa2jAlifKiq8m9fT9BvQxGCc76Ibzyb7DtBcHqAAEeBRMCqCra90GylcDOXpJjowzGR7nHb32/eJbEtr0o1msSVbpCPjGAymY7yZsao7sszKCGGhID9gWTzsP35h+AneiFGHX+KLqsCQBPC8KFv3Arshg2OyHvj1FQJaA4DDySihpI0GUUqJNqkGzQZWi55HLTHIz+DkCc33hWmJs7A1L/uHHk16yhqRfq+m2j5eZmtJYWBp95xpUAVIYz6HYG7EgS0eQ4Tph2azt+gQAAIABJREFUJp1PbcbMZjEGBXiUWLCIl7fJXLYFjHJJAEVjF0L/8y67LagGvUoy5hBWTrwFTAF0RSTxLOjJ91Dv4Je6DqoH/prZLPIlD5PY8FN4824S/oSbQCmqj658D4Y9jWeVyy7zwJnFTfmSSHQjoxIKhOiKe4zMSjAGIDR/PvzHj8mvWUPkpJOGHR/0chXzyLInII7MPGrEcmRr4VohB2k7DmTbN8c0XRBidmo6953+dcx/ucitChWcdhTBj19d1aTWLJ6Tan0dI4VTLtyNIcwjSVWRrRLZchbNiglmRIWJsutdUiy5CUiwBCF7zsWpkOReS4bu7oPZ1sxvEhtZtiFS7Xk0QmImhUIiAR0YRA6FCEyeTOv9vyI4Zw5dd9yJWSi4sjXwZuUBrEIeM5Ou6N8zd3Y+DwUMNNxwAwD7rvsM5QPtyJJMIpAgVDxIIJYYtr6kqsimyfdO+h4Hnv8aeRDgiss8KmLl80iW8DwqmSVUV7ZWwCoUhVyqcTpKzH5uWhZyNCqAHVuqUkiF0YqDBEpAJEB/OEdjP+Qj2simykNCqalBX71GNF8lW4tg2r5DSjRG4vzzCc2dy+Azz9jHz8AKNyAFwlArvE2soeARVBlt+6dMIf38C65p9jETT2Tx6MVV6ztsHb3He1/pnQKcrJSBOdupGeAzDs9SAcBhRVhW1Xmu9DySQyHkaNRmHolrSAoGq8GQSs+jEUIOhTDsSdlDgkeObK3WA2xV2yBbrahKpaRSsHs3WmMj5X37xDXzLsCjKtmYc20PYR7J/gANhQTnjlkAPF7lhfOuwgE3xhwHY8+Ax37s3tseC6gabJOdZ4Suu/tQCSiBd4wr92OkcJhHzqfLdnIBK9UDj0rFYZ5HkqJUVVtzzb4PEy7bqoJ5NNTE+t2EVnFPyIcAj6pYTodgHkmaBrKM0Styk5GrrfkwMxlX4jf0mP7LCf8CQPeKnwG44LpsT6Y4QJcDIg3dbqOvz5WTSmp1tbXKcYPs95P3S/hKFv64XW2tinl0CBDtvQDeYx79l8WMGTN47rnnuPHGG3nllVeIx6v1mKtWrWLx4sXU1dWhqiof+chH+OtfhVRAlmUuvliULbzkkkt49dVXR+xjYGCA/v5+TrIHhR/96EfdNv6eOPfccwkGg9TW1nLyySezcuXKv7uNofG73/2OY445hqOPPppNmzaxefNm3n77bZqampg3TySgsVgMVVV5/vnn+dSnPoXqUMBThzahc+Kcc84haL/Ey+Uyn/jEJ5gxYwYXXXQRmzeLik/PP/88V1xxBSH7Bem0e+WVV3LvvfdiGAYPPfQQH/7wh/+hfS3pJu39efb352nvz7O7J8vbB9N0pQtISDTGAzTaTBO/KlM2LL7/rGBZXLaold9dtYi1X1vK769axEVzWqra1hSZR7Y+wiee+4RbXWqkaE4EGciXyRTFQzFdKPPs5oN87LixPPTJRaQLZU7+/kvc9OibJMM+LjlBY/3eHl7Z2s31yybzq48toCbi45k3D/LbVXt53x2v8MGfvY5pwW8/uZCQT2Hdnn7etkvSX7tkAiu+cirXnL+bQNOjaIE+po3r4befXETG9jzqzHcecpu7cl08v0dIol7b/xq3r6kGPMuGACBCWoiyWWZKY8j1FQJ4YUsnZcPi7o/O5fNLJ/H5pZM47+gKCURAY3xdhPV7+/n+qu9TstJIZoQdAztcUGtMbAzTR8XxqwqKrPCRoz6MlZ7Dmq0RSl1LmZlayC3H3sLUmqk8ft7jfOf47wAwUPK8UspmmZ9vuoNRtUUGCzrzx6ZIhn18evEEXr1xCS9dfzKTGg5Nd/3Ec5/g3EfP5ecbf+4uy5VzpEviOGdslslta27juysFYJ0pi2Ulo+SWaHeqgfUV+kj4E6QCKXRTJ11OY1kWm7o3MbdhLiBYQI5Z9qw6ASw4zKOIz3tBRnwRt//VHYLFOLdxrvu9JmsuGAO4DIaevF0JxrK47OnL+NHaH3Ewd5DGcKObLCi24bLDLCqbZRf8sCxrRNna7sHdhLUwNYEaV+7QLQkwIfawAEr0/bZXz87hz8HSrl3Ef/EEWJYrpdLT7agWxNrXE9Ei7M/Yxuw7XqRTVagzLeL7hcn0rsFdgKgAssPevlSzDab17oStz8F/LBDG2Gd8D+omQaRZsE32CQYXqXGw6BqQJAIdwiQ6P/kM8rJMMNcrWEljFgmPoa63YMuTojRvKAWBBIV9/bxz3qco52WomUDmeT/vf82kJ1JT5ccEEM90gawKthCCAh5CJlvoo5jvY8o+C6OtQbBHIo1IoRRXHPd/yAQg01eiPjUJTdHca4xJy9wqIqa9rJCaSMlm1VTOzAIoWQP8MXEctYgHSMjV0oGSGkK3xNDDUGXiWZiSmuIO8PwThcdQc08F86ipCa1lFBgG5YPDKx0eSlbgJEVGfz/mwCBKLEZACSAhYcoWpqET0SJi8C3LZMtZcuWcMGS3B8vDgBUtgCWJfkKmSB57Cj0oNlhm6TqW4iVpZjYLWoBkSAA9yUDSKxft84n7QJKwNLV6lttOZqYlj+LCSRcimSYhX5SuijGzMmRCJzhrFpKmkR3yTvdManWXfVgTqKmQrR2BeeQLgyWJBMLvbYBrmGp4M98+S2FSchKUy0ixBqF1q5kwrMnY+95H3WeuO2Q1KtVhHrnnwXRNqyVZQVZUFMdHypJBE5XPHIBHrbNNhUtFN1GpYh65nkd2AmYYroQrGhTXTcAfrvI8Gqlss5OQ693d7ox3aN48JFlGDgbtamseS9iZlQcwcxXMI8NwGZTvBjxy+w8GXbPulC9BqMSInkdVHjtlD6Bw96/CFNqVrdll061SEbNYcE2MlYrETYlF3Vl6yefDiATxl4W/lBQKMmDjK4Xou2OSyKGwK1Wpkq1V+JEIQ2IN/7hxbpJtGQaW7IdoPZI/6O6fpZerwBUl4Y3Ng9Ong2WRXb4cgEsWXEVbvK36uNnjzkrZmj4CGFMpW1MNkHyHP28u88hlcagCUHKrrYlzr9bUoPd44FGVbM2WVB2JeeSwpuQRjNTBYxRVMhkd8NapsiaWib+1JrvanytbOwLLymXhlL0qciN4HvkMmWlRMTn493oeufdmpAlrrK2ScJiCVcyjsgcq+Xzi+VDJPNK0am8mxH0q1j+M55F9fboMpCFsJ0nV3PvH9YqqlK1pqldt7V3K1pz9o5J59G4YS0NCjXtgsxw7BOOmiuVkjtiXJEnIweBhZWuyz+96Hh2ySh8eWGQMiHGOK1uLOcyjIZ5HEW8s64C6brU151wOuU8KIYVcSHa3Qa6Qqr3HPDp8/M9jHh2BIfTPikmTJvHGG2/w1FNPcdNNN3HKKaf8p9t6N7MzQ0NVVUz7hi4UCoddd2j7kiRV/f7dtFEZO3fu5Ac/+AGrVq0imUxy+eWX/12/d+Jw+xAOezM8t912Gw0NDaxfvx7TNAmMQI2sjAsuuIBbb72VJUuWMGfOHGpqag67/uGiJ1PkwEABC5AlwAJFkYgHNeqjfvxa9cPUr8r4VZnH17eTCvuY3hznno33ML12OgvaFozYx0BpANMyhXGrNvLLvjkhHsoH+vNMbIiyYd8AlgWLJ9cxuTHK55dO5pev7eRb509nwUSF0x4+jS9ecAulgZlccdxYFFniL19YDIiqZdf8Zi3pQplfX7mAcXURZoyKs27fAG01ZUYlgkQDGhG/xV/b/8z8xvk0hZtYfmC5ACxKacbGx7JzYCf7MvtE4jAkdFNn58BO9mf2c8faO9jev53Pzvms+73DXnFAgolNPp5+s4vBQplYQOPPmzoYlQgyrfnQD/RZLQlefqeT+tg6QvosVBm2929nb9oGj4ZUtvr07Kt5/rWZvNHbD9Rz2+K7qI+Ka6kx3MjsOiH/cUyaATZ0beC+zfcxrv4S9ndP59SjPDDFp8quvK1klPjCy1/g/Anns2TMEkCARAezBwkoAfZn9nNs87G81v4a2XKWoiEyGgcoGiwNun9XgkclUyTsJbNEXs+7zCPH76Kv0EemlKGv2MeSMUtY07GG/Zn9rtfO7HqxT5lShsHSIE0Rz88lqkVdEGt/ej9Jf3IYhb8ynCS0M9dJY7gR3dQpGAVe3vcyyUCSozvD9D/yRxKN+7DUFn5yl862b3bBDNzrpmgUGSwOUjSK+BV/FXi0Z3APrbFWJElyq7X1KAqjui18y9eDqlLuttl3u18btn29DzxA8VcPUnONKmRrloWe6UD1gbT3dZrjzR7zaNvzdKkaU/wNxHYth4aQyzyamOnjdVU8l5JNc7DM31DetArf2z8TTKGP/UYAR4AVbgR6sP72E9FuapwoQ/65TQRe/x5sfYBispW8ohIyyqJctWIbVP/5Jti3EqbaDKpgglJ3FiMtSo+rqfGYpY3UDlps0FTI47LLQJSCp346aN7zMKz4yZW7KPZ30NYJxbNmQ24l1IuqVKePPYMVzd+lsa+Lg6E6BooDVdJBJ0x7UFvUSxQNp3KUneDaIJLWn4PZC+jKb2SsP1HtTwPuzHC5lEfX7eu4Jko8O0BLajLsF/vha2sDVaWlxyCSBzMRFXKg0aJqWXnfPnwt1aC7Bx5VD2kcLw+jrw9jYAD/xAlIkjDu1SUwdZ2wLyYG36oiwCOXeeR5VgwNAR7pBG0goiff4wIZlq5jqUPAI7xKMkK2ZrOLNB89BTsp1VSsUtmd8XYYD1MTkzl+0aVsMR8lEoiRD0iUQj58udIw2ZocDBKYNo3C+g3etpreDL+lH0K2diTDbBDMI3/I1q45HToz3x6wU5UQR2vhqo1u2fPKUOvqqL366mHLnVBq7NngRAKjr89mHjkzyAqKqlWBR5KiCvPfWAyjrw+1tpby/v22bE3sZ6AE4aKFEdC8WWgnKSp7zKNESIwRQr4wxSMwjyrBI62xugqaFHQMswso8TjGwIA7Ky/5/eK7tAceVXqeWKb57sCjcAjLTm4bbCacHBlh1lxThzObjArGAl5JeNWEfCmPaoNHwtum5CVwlUyFSBQlEqGMnXgGAwQyFoEymOEwA2ExUCrGDj9Oc9sLBATgZll2Um+zRCqYAJUJbnVJ9DKSonhgRVkYZiupFLoNOldue2CG8ATLvPq3Yd+5fbmG2fZ9qqquLLIyOa6UrQnw6MiASmX1L0nTwLLANG1zbAFyKMkkRn8/lgMehYK2+di7lK2Fw+L+4XDMo+HgkQMoOc9Q8CquOcb3Vsn27jnCGHwkw+wq5pEqZGtWxb36dxtmV8qq3Cpu1cwjhhwvl52oG+51NrTaGnjP8Mqy8kPDMVh2mUdOnzbzRQ6FKjyPSsO9oiqrrb1L2Zp77Rv/GPPIXyFzVWPD7wHRlwOy64ftSwoGjyhbE55HpcMy1iS/+K3jEecyEGOHkK1VgkcO80jTBNtsBOYRQCnsx9C897scsieNTPM95tER4j3m0X9RtLe3EwqFuOSSS7jhhht44403iEajpO2Bwfz583n55Zfp7u7GMAwefPBBl0FkmiZ/+IMwj33ggQc4/vjjR+wjHo+TTCZ55RVR+vn+++9322hra2PNGkH1ddoCqrbBiccee4xCoUBPTw8vvfQS8+bNo7W1lc2bN1MsFunv7+eFF144bBuVMTg4SDgcJh6P09HRwdNPPw3A5MmTOXDgAKtWrQIgnU6j6zpLly7lpz/9qWt+7cjWKvfh4YcfPmR/AwMDNDU1Icsy999/P4b9wF26dCn33nuva+7ttBsIBDjttNO4+uqr/yHJWqZQZn9/npBPYXJDhGnNcaaNijOlMcboVGgYcAQCmJvXJgbBJ06sRZYlfrbhZ/xy0y8P2Y/D3nEkSiPFqIR4sO63TbPX7hGDg1mjxQzC1YvHs+Irp3Lu7FF05bowLZNwsMjVi8dXVaYCmFAf5anPnMBfrl/sSq1mj0nwVvsgG/cPMLlRvBS39W9j1+AulrUuoz5UT0++x/XZmVk7E8AFairDsizXO+a+TfexuWczRaNYBRSUzbKQrdlgWWudOJZvtQ+SK+m8srWLpVMbXOCzZJR4YvsTXP381bywR1yrU5tjdGdKlIwyxZJCjW80e9N72da/jagWJeEfTuWf0iReQAFNpi5S/SJzKsM4wAvApm7BHklEs0gSVeBRZdy98W5e2vsSr7V7oIbDcvnaoq9x15K7+Nh0Ycib1bNky2JwUvk5lI1UNIpV10RPvodMOUPCn3Ar7fQV+tjUI7ZxVt0s6kP1tGfaWd+5noZQA+PjotxzppwhU864nkcAUV+0CryK+Q8/8+LIXxzTbAd02Jvey5aeLcxfOUDnv/0b/OVbGC/+hlQGglt3wNpfu+v25Htc1tGY2Jiqa2LX4C5ao8LjKWzLgLoVhcUbTNBUEueehZ7Rhb9B11uQ7YFnvgIrfgpAbrlgcLYYUTpyHTCwF72cR0GCvatoDjeLc2Ka6NteoEeRqUtNRMt1E1GDLvNoYiHPnK0mU3eb1NROZmB3kO2fuwO9fQfMu9IFjgAs2+fH3L1GeAY5MjRZIdAgqu4UzDJ5f4SgacHE0+yDOQWitvdL89HiM5BwB7BmWcKKjQVLIpmFnaa4JqemROVCv6QSsCxonlV1jsJqiKwkYW0Wz0JlycnCY6lhmrvOqClzmZyL88HJH0STNRegdEI3PT+bfCFLuTwEPLI/fYN5GLOQTkWlLljvsUTcAbwYnJeKeUxHtlVfRyon28wjO5HwB/CNGUNLr0zdAMiN4h7TbMBoJN+jQzKPkh7zSFRpsr1a1CCGZGFVMI8kWSFXzrnMI3fm1xwOrDjMokBebLPjeQQ2eOL3BuNO4uHco0l/0h3MKj4/vQWbieIYtQ5hHlmmByhEgnZZ9PrhHiXePicx7D7d7an425WtBVOuSfIRZWuIvFbyV5upujPPFcyjytLIkqYJmd9/ZkLMnuRR4nFhbFvheYSsICkqAclmpViSe76cpMKRw1l2ogYgW5DIgBWuTPqdBMyTcC0bfwa3Lb6N4LthHtkTW3pX17DkXA6GMAsFrFzOAzJ7epACAeGVVMh7JeArjGj5/9h78zDJqvr+/3W32qt67559mBmHGRiYxRkWIWwqi4qAQUBCTNQoEg0Sjf4kxGWSkHzB7adGJSZGFCOLMcEYjDESEEQlCDiAjMgOszHdMz3dXd213uX3x7nn3HOrbnXXLDz5feN8nmee6a6+de+5S906n/d9L6B5fczBYMlmFSvl95ZeJLab0PhICQcQA5F01pkuqWtUplXylfQekQwTq9TKPApZF4UCRjZLJmQeOfmiYh41S3ObZUPUcMo4b9NJ2qYOHslr0AXXE8bEyiBZmOWamYxiEOnSRru/H2fhQuq/+lXb32RFnkeCbWQPRmlwScwjxwPL9ecE/STzSH1WbCeKlJcJXY4twNOJCY15pMnWpNnzrOCRNsYOhtnOksUYmYxKdwMNUNLko5KFZM8Xy/m1WnvkfNK+phLS1jxXY6eaGiOlCbbd1ujPVRGD0I2BUqCBV2H6ng4e+SFQJK9tI+WE4JHGPFLg0Wxpa3HmUTxtLTTMVp5HdfxmMwaexNPW3O5ka5qU7GCYR7lsUXmTpXra/dJi2/J8zV8p4X6YzUZpawnXmwAJEzyfWpeTzKOJSYx0OmKxS/CoVbamJ6XpaWtNVzPMjp+/kaM3MbAqmjMZpqnuZR0ZWIcLOAweHbJ67LHHOP7441m/fj1//ud/zkc+8hEuv/xyzjnnHM444wzmz5/PddddxxlnnMG6devYuHEj559/PiBYNQ888ADHHHMMd911Fx/72Mc6bufrX/86H/rQh1i7di1btmxRy37wgx/khhtuYMOGDcp0GuCMM85g69atyjAbYO3atZxxxhmceOKJfPSjH2XBggUsXryYiy++mGOOOYaLL76YDRs2qHXo+5FU69atY8OGDaxevZrf+Z3f4eSTTwaE59Jtt93GlVdeybp16zjzzDOp1Wq8853vZMmSJaxdu5Z169Zx8803A/Dxj3+cq666ik2bNmHNQtl8z3vew9e//nXWrVvHE088oVhJ55xzDueddx6bNm1i/fr1fOpTn1LvueyyyzBNk7POOqvzSZylXM9n274qadviiIE8qf24Qf/WSvE05/RVw4pR9MjYI4ox0FoSQKh5ndlbknkkI+m3bJtgxVCenmz7l9u+ugB4dAZNa1mmQcaxeGTsEcZr46xf1EvD83l2bAZKP+Oebfdw5wt3YmDwmqWvYSQ3ghd4KsFr7VAIHiX4HkmfG4Bbn7hV/SyTs0CTrYVP7Bf3i+O7ddcU9z45Rt31OWtNBNR85qHPcM1913Dfjvu468W7ADhyREyYa80mtQYsyB2BF3jct+M+FpcWJzL6VofA2JL+XNvfJXikHzcJzAz2VvmXPzwpZqQt65mJZ5QsTTWGRODREaUjOH3x6cpvaKYxo0AbeUymmyINLQgCdT00/EassZeG0v2ZftWYjtfG+fX4rzENk5V9K1lQEOyah0cfZsPwBtEUY1BuxA2zQcjWJGCl+yF1KslgGK2GkrBAi0T2GxSClJBTuDWCSbGMsfNJ+Nf3KpBoT3WP8js6wsrjhqBSozbJrumdLC0JtpgEFffYFoUaGD0l0kMOBAbemhAQvv9LcP8X4ef/gDc5Sf1pkXK4eLImTMO3PYBngO3koVFmkZlh59QLBN84n/H6PnxgeL4w0+/BVudk5eLf4vfv9LnwJwF9+Xk0m33gBTSnLTjynPhBUU8ODehfFmuadTCy6mTJBsBKke6EYcArQraqBI+yvSpy2HcN/Jxgb/TOBDwfHnPpSdUj5Yfz18eGk0uVmDFNnG0OO/ohveIV8Hv/Cr/1AbVMaukSMnumWNd7NI7l4LZImKpuFTMcx3RtImLYtMjWslN1qq94NWXLZHhgZdsEU3keNWp4IXg0uGQV8+oZcR1qrJLU8mUs3mswNBngLBT77cybB5aVnLjW4cmilK25e/bil8uKWZCzc7imT+C5wtzZ9TAsi/HaOAGBuA9JVk0S8ygEolPVCAS1Qx8kwTyKQBYJ5Ej/r75MnzpmtpNW3wNSLtHqeaSuKd+nmAmb23nis9fqeQTCUDfQmLt6ExQ0XZb3LsfAYGlpafeG2QCBAUe9rmVjsmGLM4/aPKgOoKTXitXTIxoXrSnEMjEsi7wpjpHto57USwBAydbqdQWSAQyUAwyNyaxAQg286cn18dqlrwXT0tgAszOPglqtTRZkZjIElYpgHoVgmLtvH2Y2KyRtFY15pBvRgpKizS1by4mm2/NYnwsTCxOYR4bdzjxqBax08Kg2M4kjcTNpjCxjsluYR/Kpv1ksYmQj2Vqq0MNEeEjc3u7AI3ndB7Va3PMoJlvTvpv0Jj2MOG9lfRiOrTWdcYBIso/a1ivHI5lloeeRPTSkDLbj4JE4T44bgkdzMo/sNuZRnEUljJ1bwSMjk20DY+byPEr6Wa+eN76RFT/4QSxhSjH/NKa+lLLZQ0MC7A79xMy5PI/085HoeWQpRkqrnKvr0iRc8l6UaJjd1GRroXmzSD5Lq2V1nyPQwKNZgFwFOijmUathto1hmmL9cj91z6NY2prXnWwtycQ6AeCeq7J2lpmMsPJLF5OZR2pbOvMoAeAyMxnFFkoCj8x0Wsn2ZpM7quS2yckYg0kxvFo+q1Yhuqcr8ChkWwYJhtkAqz/1eVZ/8e9jr8l7Z9I99HBF9bLK1gzDOAf4HGABXwmC4LqWvy8Bvg70hstcHQTBweXK/w/V2Wefzdlnnx17bdOmTVx55ZXq90svvZRLL7008f2f+cxnutrO+vXruf/++9teX716NY8+GlHVr732WkD4/kjmDwjD7LVr13LTTTe1reMTn/gEn/jEJ9pev/LKK2P7kVRf+9rXEl8/7rjjEsf7mc98pm2fTznlFJ588sm2ZTdv3hz7feXKlbF9vf7669XPV199NVdffXXbOu677z7e/va3zwpKJVUQBIzPNBgt13G9gBXDOUxz/56i/vaGhTw3NsNrjx5RrKJyo8wzE8+wMoyn1ksCCK3MI8/3VOT5cDGDZRrsnBD07l+8OMHpq4YTty/NlWcDo2Rd8cMreMvqt3DxisvVa1ur/8T77v4HSqkSG4Y3MJgdVJKlJ8fF+VpcXExvujcxcU0CBVJiYxkWXuBRaVYUQKFkayFIkE17DBbSbN05xU+f2UtP1uH4IyLt/T3b7uGUhacwXhtXnjvSa6jmNfEDixV9K7hvSpglS7lWa60aicCj1lLgkXbctu4V/lpjtd1sWJL8hOYrj32FrJ1lJDeixgaRKfTCgmiG805oaOzOKIBKZx41/SZ1r65AjKbXjPxoiAyde9O9DGTEBG+8Ns6L5ReZn59P2kqzIL+AO1+8k6pbZcPwBkzDJO/kGa+NU/fqMYCo4BTizKNUCb7zHmEO/brrhSm0Vv2ZfizDiphHXpNluwJ290ElY5ANHNEABOBPhh4R0xXIgOtFTfe28jYMDJa88HP8vIO/5Ra2/+Kr+GbAkqfvhXV/GHkeWSZLPTExc1wBxjUXvQ77sS8L02mAPb+m8tMfQRiSNTRe5sWxx6FRwDUd7LABX/D8z6iYTSbLLzG2/i0w/mOG+o+EkWPprU2ywwYnCFiy5LVkJn5Gwwnoy/TRpARM0XSWku1bGjsmcgLnu0YUZx5W2hKT07pXp5rKkl15tpC0ydrwVhj9FSzcKH7P9hGEgITfNPHTAjwtVgxxvRgGm+Zt4sbHb6QnOwTnf0mku2mVy/TiNgzSL1k8eJzBKisrjI61Si1ZCr5PY8cOHGmYrVWlWcEMj2WjWcOSIEkL8yhXbrCnIBr2of4j6eR55DZrKr3NnjciYs6r1WjCb5qkl69g6K7/wjMhu0jI1Qzbxpk/f7+YR1J60dwm7kuyOcw6WTzKBJ4XMo8mMCxL+XDFDLOTWDnhk8xURWx3vDaOE4Q6ZteNgwDT4jO9vHc5v7P6dzh10akEYSqkndYmxY4TM5RVDAzPF0lcmQw/AAAgAElEQVRSnsdIcQEXH3kxQy80qdz/eJtsTbwvqzxjwgMe/ey5rBlYx48u+RH9mX5GG/8WbmN25lHg+xAEGKUWWZbyPIrYabheV9HWc5VsWq3e3oh5pHkeYVvkzAwwg+kb6km9Yh6F4JFfD9kRoRylvwzmIs2XTvPyaG2KDNvqwvOovWmRZWQz+JUKQb2uGBze3r0CEMuK8+Qp2Zobu9a6Bo9CQMCvVtW1pgMtaiy2rZqo6H+tESSeBFebmlCfe8nekmwA5S3leVjFQtRwFYtYeZNiFawAsqVettoGv3ohwO3v7km+3IZfq+E3G1hh2qDVSbYWO39StqazPlywHaxSEXf3bqyeOAM5e+yxlP/jPzBLpcSGXTbA3lgEHrX+DaLzlGqCETC3D5DjCBlVQzKP7MjzqlEH3xeyNQkeVSLZmh8+d+tGtmbEwKMOMeyWhTMSnz8WX/ta/GvKpI+MmLUS0LUHBkIQoBYDXmbdVzleLb2w1fNIMFLm9lBK3IZhCM8ozbssSmCLZIxtsrV6XTB9JEiZSuFXqnHPo/1hHoWfvTYgUAOs/JmKSKPU9/NAZGuKHauZWO8nYwvEw5SxtLh2ZdBG27a6ZB4ZupQzyTDbSUWeR7PK1qTn0WQMnJReR60gsC5bs+R9WALmHR4uGakUrd2cWSrBjh3J0t/DpeplYx4ZhmEBXwReBxwNXGoYxtEti30E+FYQBBuAtwBfernGc7h+c+tNb3oTN910E1ddddV+va/W9Hh6bJodE1Ucy2TZUJ5cav9vzMOlDNe/eS2FtK3AAYBfjP4icXnFPNIYL/+967855dZT2DUtUkEs02BeKcPOiSrbxqvsnWmwYUm7LAtQ0rLZmEcggLLp5jST9UnmlTIMF9NAQM0rYxkWE/UJzlwqmBLDeTHRkHHrfZk+lhSX8OJUO3gkmUcbR0RTrHsAyWr4DRwrYh5V3ApHLyhxx6O7+OHW3fzBby3DDr+oRiujbJ/ezgnzT2AwO6g8Q4aLaUoZWwAsgcmq/uWYoTdAq9+RrNXzxCRUprbp5ZgOtmGr41ZulJWU6aWZdtNeWc9OPsvawbWs6F3RxjzK2lnlfSKBspnmTMzfyPM9JZWbbk6r66FVtibHItPWQJzr7eXtLCoKic+CwgK1rg3DgtFSSBXYNSOuo6ITNRmlVImZ5gye70Xg0ZM/gF//O3zxRAgNn2WZhslgdlCZUTe9Jn/xjx7nPSq+6LO+BUFA4BkEZfGk1poRY3HDa2LPL7/F83ufYEGqh1xd7Kf7nSt4YUxsa+nTd8Md7ycfGm7vsWxMXzTa9oyQGrgTM6GxcwDLTgWgctcdYgYP5OnHnd4ND30NP9PL0c96UFrIgrGnxXm55KuMrhNyj+HcMJx9LT0NMc6+wKC/OR8TKFUEUOe6oql3s6sIXJdnL3gTU//5n4AGHs0/AY6MP0zI2mIiVXNrVL062UK8EWfJCfCu/4J0OGnJaMwj38ZHTIgcVxj/Ghi8clgwpeY1czz9wRup/vrZ2Crz6RILdqQwfIOHVpqk7fbJWmqp+Gw0X3wRx3LaQGudeWT5qJ9bwaN82VUstKHsULt0wJbgUR1PMm9CcMefnlbyGcO2SS1fhuUL497Mouiz6yxalAwe6b4hWpmlEpgmjeefF+MPqe5ZO0vTEN4ignkkEtzkvSTn5KII9wQ/ILk9u1JXxyjGPHLbGw/HdPjTE/6Uefl5ikZva02XkUrHZB2KeeRHBqWOk+ajr/ooI5dcxtD735/8ZDf0jFFj1ZogOW55D/Jr0jB7Dtma16FZ0D2PZOS7q8sEDvz5pFkoYDhOxDzSpBlYJoZpkTVCA2ffb5OtWX19AnQK2RHS7Le/DHaxXfaky9YUcGhaUdqa66kGMDbOWZgdZjaHG7JUpAzIn5nByAnmkV+tKOYRnhe71roHj7JqeZncZhbaG0Ap4QDts6ulrYGIhJdVn4h+FmlrNeXRIr2lQHzGdOaRncsrU/JcsZ/Hl5p8/K026XRyU9q2P+E13cY80lkx2s+KTeA2lWwNjfUhJUNSsqobZgNk1wrmUZLfEYAhPY/GxzFSqdhyRoJsLdsIYr93KiM0yNdjxCWzRRk024J5FNRqkW9Ra9paF4bZST/PVVahQP/vvTXGyJZG2c78+QIEUV5Y+wEedUxbCz2P5pAzzbqd8D4RaIwm0IClppAxRobZkUQtMsyexfNoNqaMkjuF4JEunQwNs0GAIp78nCbI1oIg6Fq2pr6jXC/G4trfyjk5KhmYyaDm4IkVgnPq+yIJbNUAIzPbyfOoMafnkWIeTU3FmEedGIT6/UF+LlXa2n48zLCKwmPxYL67fhPq5ZStHQ88HQTBs0EQNIBbgfNblgkA+S3eA+x8Gcfz/9uanp5ue+29730v69evj/278cYbD3pbmzdv5oMf/OABv/+EE05oG9djjz120ON6Oev222/n0UcfZTDhKW2nmq41eXp0mqbrs6Q/x4qhPIX0wd9MdPBoy+iWxGWSPI9eLL9IuVnmjmfvUK8t6M2wY6LKL7aJScX6xcngkUxlkqbMnUoyDipuBcMwWLe4F8tq4gUeV6y7gmtPvpaLVokmeyQnWBBP7XsKEE318t7lPDXxlHhKrpVkHr168au58ewbOX/F+Wo7attenHlUaVY4en6JatPjhGX9vPeMKK3n4d0PA7BpZBMD2QHF7jEMgyNHiniBC4HFot4SiwoCRFlcXJy4zz05h+t++1h+/6QjEv+esTMKfPnVXgFWrBlYw2hlFK81wjusHdM7WFRcRH+mPwYebZ/ezsLCQjUZK4SpVTPNGeVrVGlWmHGja0Q3ztYNsyECj3rTvaStNDk7x3htnG3lbWp/FxSEj07eySuWW8EpKBCyVbYGArCaqk9RTBWgOg7HXgzNGXjqP9v2dTg3rNK+3EaVtAtrM8t5Re8ryBM+QXYNpJWRWW3gAUFIC9rz5B28uO0+ljRqihHkHnUuEycK5lv/qjfCo98iNy2e+u510tg+mIaPXXkCgObu3bDq9ZAfhjd9GQyLygMPkBtsgGmQNwdp5gfBb7LwhSzvvHEX9eLxLEwLZsPOyktqH4ZyQ7D8dHpK4rrpTfVQ2CU+P6UKmBh49TA5jhGaO3ZQf+IJ6k+Jz4HyLDjto7A+nuqYsSImW9WtKjCpY2V7hUkx4PlpJVsAEW/fl+mjkCqwIL+AVdsRY3kmDh7lnBwLdhh4dsBTCyI2nV6pJQKcabzwYjLzyI2YR5ZPomzNtwwyjYDdewV4PC8/T2MShRPMcDLWqFejiW5asmvik9/08oi1lVoYmWM7ixbS2LGjbR8UQNL6ZNE0sXp6aDwvJJ6mLlszfPB8CikRx25YlpKQ5WzN8yghiUyCMOZ0dE4cDTzC9dQE1p+ZYdt73iv8v9R4Q5PxVHQNWKkUQTOMiDaMyB/D05shsX+ZVasYfHfEDo3tcyYTYx7FPI+a8X1R8ra5mEeqWWg5vlraWuBH4BEdZAL7U4Zh0PPbv03h9NMSmUeGbZEz0xgYGF6gntRLcMAqlUI2g2hIFWvCh5TGPkmSranm0jLFvgXBLJ5HGoDQBh5llAzR0lJlzUw2BPl05pEGjrE/srUQbKlW8cO5pA6uqJJgBUSJV+G1KkuZQgPNSTGvCAxDmI43GslNXKGgpDpWqYiT075TeqI515z3u7DkNvxaLdbUKyaAYcQaxQjk9RRjI4pJdxUwrJghrbK1o48G01QS19ZSDbDnYebzMXZZjHWmwKNwmF14HgGRQbQuW6tG516Oq7lrVzieFvBIA9gSx6+DRx3S1rqt7KZNLL3lZjJr18blR13K1mKeR74X+44wpOdR7QBlaxAyTSLDbP1+pSdvyeNnSnNsTUJmpELvOR086uKzGHkexZlHUqoXSeVS+OXpcFvacZOAte+HIGgXzCPDANMMge/OgM5clbWzjPUYjPUYHUN6ILxX6p5SszD1Wn9W60inxDGpVmb1ypJzg1bmUfGsMxm55hpSy+PMbiOVUgwzS/M80g2zk8D/tvGXiof9jrqolxM8WgjoBijbw9f02gz8rmEY24F/B2bXRv0G1Re/+EW2bNkS+3cwZs+Hqv77v/+7bVzHaprx/9srCAL2zTR4bm+FlG2ycqRIb65znOT+lgQCcnaOX4z+gq/98mt86J4PxZaRoIoul6q7YoLxvWe/p8CZBb1Zdk5WuffJPWQcU/n3tJb0PNKNn5NKgksSvLr81OW89zXiSdNQdojzX3G+kt5IydLTE4K90ZPuYXX/asZr40r6IUuCR7Zps2nephhAJKvhN2KeRxW3wplHD7NhSS+fe8uGmMn3g7sfJGfnWNW/ioHMAOO1cdX0HTmvCIZPEFjM68mwvFd8wSwpJTOPAN5y/BKWDSY/Fc3YGXUepN/Ra5a8Bi/wFOCg11Rjisn6JAsLC+nP9DPVmFJSs+3l7QrMAiFjsgyR8KTL1fTjMt2YZqYReh55jRig+MKUaIiVEW+mj23lbUzUJ9rAo7WDa7FN8cVZTBXZOSNwegkYQQRmlRtlwTwyUhD4sGgT9CymsfVB9t74tdj+6swjN/QVGbR6uP3827G9MKnHNYSMC7BrAa72UdpjWbzo11kyNYa9cBMAzQu+RHNEkFRTy04Ht0rqwTtwmgH7DJ+UF2A0ythpD0wTd/conHAFfGArlBbg9a2hNlonN9zA6ukhN+Pi5gfhrd/BdEQD565+BwvecScAO6d3MloZxTRMxcjoXXqKOKa9S+F5wXQxAzGR8ZriM9CcDmi8IM6B8hKRzKNqO8tPsn4qbkWAR84czVSmN5Kt+Wk1gQXonYFF9FN/6ik+edoneb27Wmy/GWcN5Z08S3f4lIc9PMtQAJZe1sAAZi5H48Vk8KjqVhV4ZOrgUbMpWBmuS703jKzf+QwrtwcCXFbSAel5JCZ2tfqMWod8Yh3zbLAsUsuWqe07CxdEYy31qKfAeimmS8Lk3urrU+fJ0gyzm4Z48p138gSei2lF79UNs5NYOaoBKs+QMkNDXz1tzfNUE+FPl5n5yU+o/kJ7WBBeL04YKe6YDqaTEhIW6WGiywRak+tmKTMjPI+Clth3ObbYfjS6Yx4pM/PWZkHFpPsqsa3NMPsgav6fb6b0utclMo+wbBbm5vOF13wBww8UsCYn/maxJCQodcGa0b1b0roprC57kqCKFTGPgDABa3bPo9afgZhBsW48bGazGLks3vR0xBJrST7zZ7oDj+Q2/EoFL2xKkyQXSZ5HyicoLFdjHnmhb4mfSyvDbL3ZM0OQziyV1LVuFoo4mmQuXxpU7N8573dyvRI8qlZjwIhiOhUKMRDPiLGMBMMjkq1FTBPJOmxLacrnSa9aFZOj6SX9eECc3xh4lG9nHi02B2O/d6xwjBHLyI5ek2bpoWwNwH1JgEdGLtfueTRH2pr6uYNhdrdlGAa5DRswDEMwSKQXVteyNVf7rozLn1QS2fT0nAbcHbdjWYmG2XIMSjbnSOZRWrGM4syjZM+jWT16ivK+E17/LdJJeU2aqTReOUwQ0z2P1L00/C7shnmEBHT8g2Me2Tm+crbJZ95kzg7ytoD4Sf5KhsY2Skpbk0CQV55WZvhJZcq5QaUSYyhZxWIbIw5CNmR4rXdrmJ1UpbPOoudNF8y53G96/U8bZl8KfC0IgkXA64FvGIbRNibDMC43DONBwzAeHBtrb9gO1+Ha36o1Pbbvq+D7EUum0nB5fm+Fbfsq5ByL5YN5nC4m67I832tj3bSWBAlOnH8i26e38+mHPs0PX/hh4jI6UCABjGcmn1FSsQW9WXbsq/Ivv9jOW45bomRdrTVR6455JLcnwavjjujndevEBLE1eUtKlpp+k6ydJWtnWdW3CoAnxp+ILauDRxCXa8lqTVubac6wcWk/t7/nZOb1xL+AHh59mPXD67FNm4HsAF7gKV+nlUN5DMPDwGSwkFbpYp2YR3NVxsoo2drjex9nYWEhq/rFfiZJ13aUBStCMo9A+KEEQaAYSbIMQzzl0WVrOgsJBJCjmEcthtly+z1pcY4GMgM8tuex2P4uzAu8XkrWQIBEEkjUPY8kC2m0MooXeJSkGjw3AMNHM/mTrYxefz1uSJ+HOPOoWRfrtGo1+I9rVGOqg0dW08DVVOZPpzOULZOlnoG9+HhAXC8SwHAWnyD29S9u5/UPhlHuPtCYxEjnsYeHcXfvBsOAsPlvOKshMMgcMYw1MEBu2hUA3oozMFyxDnffBKXifPoz/Ty570nGqmMMZAbUNdpbEKBpX2EB9WcjNo+3dy/uhJj4ubt3K0aLYhzJ/2vtQG3WEpOyyZq4VrtiHkm7Fd+JgSY9MwHn/HiG5y66mGNyK8g8vSO2fVklN8XC3QH75vmYGDhm++TJMAzsBfNpvrSLlJUiO1GlfNdd6l5WaVYUMCJka+J13b+iMSiund5v3cVffcPDevqFyM9CSgfCybPlgxUCi2oy7jZjk1+rWFTNnIyFhrABaMQBMjkWsY0E8Ki3Vx07yUrJ2llqfgMzCEFTz8fUnkrm7JwGjCSAR+F+e+Updc+y5WJSKuM4GLkctSd+TVCv0xwbbRuvkxbvzTm5qLkJm0H1VNlvZx7NVnLyLhPG4uBRHBj0uzXM9pPBK8U80oGdpm6YfYio/6pp0Qx2TRPbNzh10amKXQK6J0bIPKpUwPcV8whaJA5aWlfguWBZqjFpTZNL9jzSmR3xhxC6hENnHhm5LGYmq1K7IAToDoR5FLJffI15lOx5lJC2pvnDAHia55E0vfXzWcHeqtUw0zrzqEewgPJ5zLyUrRVI5aPvFKdQVPe5JOA6cX9a0tZUhHo6LQykW1hVSnYok7Q0zyNpmI1tYQ+PYA0OJh7PRX/zeeZ97KOdxyST2nK5mCQwyfPoylV/EP7ehecREcsIx4nYSBJQcmyVGNncuQtCw+UIjGnsn2ztIMGj2PgzmcgLay6ZWaJszY0ZW0sgxZsuxyPs92dMlhU3zNai5IVBefx4ibS1ekvaWgpfS72ECDxiluPsLFyAs3AhmaOOCrcXAWY0m4oNZ6TT+FNlta1ofOFYteu4q7KsEHBq3+duK+fkqKYNZrLGrLK1CJzrfH83Q5mnkU4n3i8l0OiXy3OkrekgVHfXg5IOthlmu+r3uarnvPMY3k+Lk9/EejnBox2A3rEtCl/T6w+AbwEEQfAzIAO0aYuCIPi7IAg2BUGwaajD04HDdbj2p8bKdcZnGuydaeD5Ac+MTfP06DSVusuC3izLh/IdwZik8nyPJ/c9yVRjatblJGBy5hFn4pgOiwqL8AIvFlGelLYmgR3btPnes98DBHjkBzBUSPMnZ0Wmhq0lZWtzeR4p2ZrGfJlsiEa3J9XuByClaxK8kKCKBLdkyRQuK/St0dlFatstaWudWFKT9Ume2veU8nqRRtFSuvaKEfGlkXXSOJbJm498Mx8+7sMqnnp/K2NH4NEzE89wZN+RzM8LYCERPAoT1RYWFsZMrPfV91F1q8osW1beyceYR5VmRYFFcn/lcap79ZhhNgjPIgkI9GX6lExOgkeLiov4+Ks+zqWrI6N+yTCCuGxN/iz3oSSB0PwgjKzBnxDr9vbtEybaXziOofIeJuuT1J+4A/eX3wXA3P0k3P9Fgmlx3QXpYYIQPHIaBu5AZBL/WNgYLD33b7Bz4tbv+q76PDilhQQ9R+DVoL8ixpPywMCFpa/CGRnBHd0dOyYNQ5yf1Kp12H39ZKYb6ho0widQbmh+unFkIw/tfojRyqhKj4Pomu5N99J45hm8rJjkuXv2qKSd5ksvacyjKE4dZmcejdfFcZzVWwAg0xMxjzw7Bh71zsDCXcLLZea/H6C2VRi504xfH/OeL2MFsPuY5aTtTEcGpTM8grt7FMd0OOm+fWx/z3t56S/+gsDz2jyPbE22JoEcd1A0OMt/LBLuvOnptiehcuJmewFWAIFlagkuulmxWD61YkWM1SDW4YTeMHFAR4FHCWk4MiIdNMNsO0staGD5hMwjD1MDnrJ2NibJai05GfWnyuo86swjmZZj5nNUH3lEHKPRMQXIyfenQ9lazs4J89ZWJkHbU+VumEchEyVkL8QAxVbmkVymS9laG3hl6pIhLW1NXocHyTySZViWAFb0a8pONpi1B0Oj7f5+4S8yOaV+lxW7pjSGAF5LypHu6dSJeaTLNFpla5p5rFkoqOvdzOZEpLWWbhZrponAo7mOYeR5VBVeKoYR267aT8dpZx41Wwyz9fGUxf0mKITyy3I51sRZodeRiLeWhtmlWFqTmcsp0Gi/ZWstzCPDMIRErkV2lgQUtRpmG7bDwOXv4ohv/mPiNlOLFok0xw4lz2uMeWSaseZXjrObZC4xbkftp9yP6LVk2ZqZzQrWjwRjanVxzc4KHuXV+A6WCRhbbyol9tXz5mQKJXke4fmRx5hlKUaKPzU7qDBrtRpmtzKPVPqeZpjdmraW6sw8mlW2Vijwiv+6k9wrXxku22raHoFHUqoak/tZEQgaeN3J1kCXkrXvc7elA7uzytZMU5w3vzPzSIK/nYBKZYQ9B3ikHxsdtJ6tlPdai2G2Sls77GN0yOrlBI9+Dqw0DGOZYRgphCH2d1uWeRF4DYBhGEchwKPD1KLD9bKW5/tMhhHLY+UaO/ZVmKm7zO/Jsnp+kcFCer9lal7g4Qf+nACNBAbWD63n/t+5n4tXXQzEWUYSOGllHqXMFMfPO56f7PwJAK8YEjfKj79xDcVM5y81BR7NkbbWKluDKKlNNtN6ycQ1GUFdTBVZVFikvIFkSW8gyeqQKWMx8ChMW5MTTH0Mev3Hc/8BRObbA9kQPAqNbpcNiS+ZYjiZWVRcxO8e/bsHLDvM2lmqnjgfMr5+Xl5MMpPAI5motqi4iP5sxDzSX9er4BRigNF0czq277srETDS9JrqmpCMod5M5NMg5WsQgUeGYfDmI9+slpv6/vc55v5oncVUkerjjzP1wx8qCZsCj2RDmRPgkbwc3T174Bf/CHueZOCp/xL7+K9/iPfTGwCwpgQwE1TFhMvPzscnjNSuQ/OIk6J9Ck/L0uFjFQjmBhrzyHIIFgj2UdoLASjfwDCBZadij4zQ3D1Kc8cOpr7/fbHOujg2znGvx+rrIzvdiEC3sFFy94rrZdPIJnZM7+DxPY8znI3SZuT13pfqofH885SOE2OoP/ss+D5msYg7OkrjOcFKapOtJTCPpN+QZALO2UxZDhhhQ+KacfBoOqD3JfH75O23KwZDK/No8OkxfODZVx476/bskRHc3btxTIe+fU2wbSZuuZWxz32+TbamG2arJnREfA7lcjGAJ5F5BIFlaZITL9ZIAPS++c30v/WtsXEqH6AWkGy2yaFukCvBo5yTwzfFvgjmkYelgUfCMDuSM8W2FQQKpPPKZTXZNr0IGJLSCCuXxwuvNd2TRo4/lRH3wpzdzjwCMVmPG5R24dkgmUcSGGp0lq35ITsQ35+dNesmNyYxzyMvYrW8fMwjzSNFAkqIz50Etopnn83iL/8tqUWLMNMp1ajZAxrzSGfmtKataWOOAYgdmEeG40S+PK2yNT1iOpuNGCyZjGCI6cdca6YB/MrcDatcLwjAwZ+eaZN1qbHosjV5HbjNFsPsCDwywuQ2QvDIm5qKNXSF00+ndO4bgCjW2iwWyBQ0Q+lsNmIeJfitJe5PK/NI23+zWGz3c2rxPDJsJzLMbkaSIatYJLU0no7ZbckEKTOfi/Y1l4vNK+Q14IWf8W6ZR7o5tgIcpBeZLlsbHVXjiN5biW07qXTg61CWkcko+dVczb0Cj3RgWQsCkJ5HEIIKB+h5JO8JrYbZagzy/mrr4FGrYbYTeUlpvnUwu2ytbSy6dFIzzDZTKWWSHwMfD1C2lnRv3N+yTEsBSHMbZmv394QxSuDa6AgehZ+TqanZPY8ys8vfkqpdtubE09YOIXj6m14vG3gUBIEL/BHwA+BXiFS1xw3D+AvDMM4LF/sT4F2GYTwC3AK8LZhL93O4DtdB1mTVxQ8CFvRmcf2AiWqTkVKGoWIaK2HS1U3Jy1ZnECWVZJjknTwpK0XKEjdPCQroSVs6EFV366StNCO5EQXonLi8n3s/dAZvWDt/1m12SlvbVt4W8zdpla0Bikk1G3jUm44AjNX9q9uZR5JFEoIDinnUjDOPUlYKy7TI2tnYGGS9MPUCn37o05ww7wReOZLMPOrJivNXPNAnVy2lM48qzQp5J08xVSTv5HmpkgAeTW+nlCpRSpVizCOdkaRXzsmJRLXQ16jpN5VHFaBS0SAuW5NMKh0wkj/3p/sUQKfqqR/Cp45k383f5Mi7IxlWMVVk/GtfZ/e1f0XxF7cAsPMnnwGg5IZoUV6CR6F585498MDfQ2Ee6RkBFDW9Bt6IkMaZZg7ywwShjM23e/ENcc6dpok7JFhyhXDiaBkWCwoLFLjo+q4CexzTIZgnznUmEOfWCQwMI1Dgkbt7N7v+/M/Z8f4P4JXLNEb3YQ0OYK5/M1Z/H+lyXTGPzGYIHu0RzyiOm3ccIHzBdOaRvKaHJ0Sjn9sklms8LTy+MkcfDb5PRfrYeHHwKKi1A7VStravto9sLaDnyc6JfbICM3wa2zTUBNYr5hiZgOxe0aSU77wzWr4FVOl98iW2D8FLZln5lSWVPTKMu2cPaWz6Jl3yxx9HeuVK6s88IwyzNeZRzPNIAhMjIWssvH0mpr/o4JFPyBCQT1ubyjNHLt9z7hsYuvKPYuPUn2DHjtMsHjsy0U14hYSmtnYW3xBgVyFVEB5FOnhk52JypljpcepTUxHzSAePQjCj1XtGgXwhAy4TJlBl7ayS5OkNsxHGoe8P80gajUr2my5VazfM1qTMs7CP9EjtWCnmke55pHlMzNFAd1viibfb4nlkxhh/8nyZ6TSF004T70ul8afE96XV1wdhs6/Hvremre0v8wj0Br1FtpaNS4bUU/lcNvY3uX0SmEdzy9bEeoKKSG7rFDFtOAzU/fsAACAASURBVLZonoNAkw6F15ZsdOt1xSYwyyEAXgz3yfPUtQXQ88Zzmf/xjwORCbXV04OTj8AdM5dToFHXzKNsxKRqNYPOHHUUmaNWx5fX5UHh+dNZH7jNgwYxlRRHYx61MisUoNNFMpcYd+hvJJlHibI1B1saeQdBNI7Wbc3qeSTHfugkayBAAH9Sevd06XnUaEQS75gMVfM8mgNUmLXCe4K6L+hAsG0LMLGp3V/TqXbPoxBQ8psNdey6BXJj+9zCiIsS3lLqIYLuFRWZvDf3S7YmpWRtSZH7WfIhyGyfU/V95Mvv6gSQOhMB5EmlALhmc1Z5on6vMbu8HpRsTaWt2THA8jDz6NDVy+p5FATBvwdBcGQQBCuCIPir8LWPBUHw3fDnrUEQnBwEwbogCNYHQdAe53O4DtchLD80xE7bFgP5FIOFND1ZJ4ylP4j1Im6mrWazraWDR0AEHvntwI3OPKp7ddJ2mkKqQLkRPrUwDJYMzP40KQiCtrS15yef54o7r+D1//J6bn/q9rbt6WOQQJXujSMrCTxa1b+KF6dejPkZKdlaOBFPYhc1/aYCD7J2to15FAQB1/z4GhzT4drfulaZcLYyj7zQJOakFSOzHpduS3oeBUHAjDujztv8/HyVWKaXTFQDYp5HncCjvJ1vk6qNVSLypQ4e1b26OkcS6JCsL4D+sGFbZCc0D0//F0zvxt+3BztsUOwAMlj45TL+zDSFn34RgJ0hfaTUCAGQ3AAMvAK/GYI7T/wEJl6Ac/4PzqCQKjbXX4q78Z0AGD3LYWSNon37Vkkxj1INcIvCw2ZeOIlcWFiIYzpx8MhvYhs2pmHiD68X7w3CNKXAEpO4eWtxRobxp6eZuffH4hg9+STNbdtJLVkKhoHV10dquoEbAmGG9KrZI0CvFb0r1DHUwSP12m5xDHIbXwmGQf3pZ4AQPAJldhvo6UUky9ZsU+zPeH2c1z4SMPyhz6uknU4VGDKxTjSTRioFQ/0c/aI4R/mTToqxF3RwIPA8cr/ezq8XGeyt7Z31yb8zMgK+T77s0jfpY8+fL7blunHmURBPW1PePfkiP9xgcPurwsQxPf3FjBtm255Yh2FZUTOuMVdmS0RRRrGtvkezMF2kbE03ys3aWbyQeZR38iHzKJqgxg2z/dj6dPaOVy4rI2AjBI90zwrVaIYNfQQehcyjEDzKObkw5Sd8Mi4n2JaIiu8I3iRUxDyS16YGHrUAYX5dS2WbTbo2F/PI19PWmod+st6atmZZGJYmW3PdRGDHSKfxpL9IOhOBPLpsTb7P9ZTcsHX/lKStw8MldZ5b0qzMmHlsNmKOZLPtMdb6Z4DuwSMFtlSFbM0q5JMXlOfCi9KopGzN0rya5OfFroQpYPqx6uA9klm7lvl/dS2Fk09uMRDPK3C1W/DI1IyTIc72WPS5zzLyp38aWz7mWdUUIGKbYfZBpP5Bi+eRPNetLDMlW5ubDST+3ipRszVAKXzNFqw2dd1mOzCPuvA8OtTMIzOdUabqc8rWWvZL/OJFYKkZeR75MzNzprd13I4lmDHKiFs3Vg/vrzR18+qUSoxrBY9oNrGkn1iXgGCstIQ5PE9LW9Oljtp5k/cdlRzaJQhkx6XNSVKybiprZ0WIy2zfMfI+rCTmSZ5H2dj/rRXb/9lkaxl9ue5la0Y6HR1rOwTMm3PPLQ7X/tX/tGH2b2QVOjwZmq3++q//+oC29dnPfpZKJVkCdKD1ne98h63SY+MAamJigi996UuHcERzV9PzeX7PDI/vnGKm4dKfF4akC3qzLB3IH3Samkz7mot5NN2cJmVGjCOZ1JMkGYulrXmCeVRMFam61TlBKlnlZlkBKpLR9Imff4Ito1swDZOd0zvVsgrA0sYwVZ8ibaUTG08FHmnSqaP6jyIg4Kl9T6nXlGG2ETIPEthFMm0NxFP/VubRZH2SR/c8ytuPebuSjYEAtWzTVswjCVStHGpnSh1ISeZRzavhB756OjOSH0lkHu0oR6bYeSdPykyxt7aX5yafYzA72KYnL6QKyvNIHh8pVbMMi90zkcSs4TXUeZfMIx246w8BksVBwrW8+5cAeFOTmGGDUvI9jAf+Dr9Swa9UKIQTuR3hpKVUn4ZMj5BPWQ6eH0anPvJ9KC6Ao96Is/FtADQ3/C5uyHAwcWD4aPWFHZh5Aj+83uvg9szH8gJevcXnI7d4rK0K1koreOSEBthBCLilQiDFMbOw5HgwLewRcS3IyXPtiSdobN9GarE4B3ZfP0YQkJ6Rnkch8yj0PDINk03zRMqbLls7auAorjnhGlZNi8YpvXIlVl8fdZ15pFWsiYVEw2zDEGln+2r7KM0EGJ6vGqROFUjZWiMQk+p8ntzIQvrDt/W/7ffFMVm6REzsNKDA3bMHs1Lj+WGD8er4rIa19ogAW4t7q/SWA5z5C5QRZ6thdgo9djmMnE/n+PtzLH5+pC5jivvkyAYuYh5FHh8xptIsk98DYR5J2Yce0Z2zhWzNCmVrgedha+BRxspocpgWto5GgfenpshZWYwgwAzaZWuy0cydIGSPzd27Y+vIdCFbw/M6gjeJx6jV80g/Vm2eR/WOf4st1ykGOiFtDdeLZIQH2bTLaktbM00B7HieANZ8P1FCYaRTqsE1M+k2MA+I+25pLByxf3ramtex+ejEPIrL1jKKOWJmc22yDmGYHQGVwX4yj/xKFW/PXqyBNttQsR49lSxmWuzGjL5lKlxqRny2TY2l1ampN0yT3gsvFECH7gGVzynQaH+ZRyqRqksGj0qOs+021sfBNo1x8EgcDyPfATxSsrW5PI/CcevMo7bXwntmeA+TbA4JvHfHPJJgVwdQ8QDLSKeV/GousMewLDDNKFkQ2tLW9HUcKGNRGWYngRt2wv01pTGP0pFsDc/Dr9XVses2+TA2FgnwhfdY3TBblpkgW/Prjdjvc27HtGIPaw4UsM/a2Tl9GJW/knbeWmtO2VqqO/BITx/s1jDbWbigJWCj1TD7sGztUNVh8Oj/kjoQ8MjzvMPgESJF7andZabrLgP5FEv7cwwWopuRO1fSTBclZWtzgTpS+iRLSkmkTGfGjRg7bcwjK60YQFLmNFdJfxUDQwFUe2t72TC8gb50nzLEltuQ+yDHM9mYTDTLhsgwW2e/SNNsPXFNgkf6E41WdpGUrQEqgUyvHTOCubOsZ1nsdcMw6M/0K+ZRa7LbwVbWzlLzamqs8tzNy81r8zzyA18kqhUWRWPL9rO3upfH9zzO0QNxwAFE0zjjirQ1yXyR4NFQbiguW3Nr6poYzIoGISZbCxv5xfUW1ksQKPDIn6lghdd7wUzBj67DL0+CH2CbOTJWmpfCL+xSdUr4HYXluSG4Mz4Ob/g0WA5O/3IAmnYaL/RPMZoeDB8VJYWRwfdDM1sXmnaRD9zu89r/sln7fMAxL4WMFKOFeWRqT+6AlBXKgwIDIytAHXsk9N267DLMnh5qj/0Sd9dLOIuE55M0yc3OhM2s9DwKmUcgfI/k8ZZlGiaXrr4UqypkHFaphD3QrzxBMmtawCNXk4GQzDwCAUbuq+0jF360dR+jxvPPs2vz5hgDJCCcgNY9AR7lcmSGQ1ad45A/6STSq1eT27QpZogLKK+dibyQ5c3GPLLDdfY/uxcTsOaNKDp81a1ih1OFjJEiR+Q7FDFoQl8UXbbWQm1Xnkca80iXrSmGz6zMow6eR7M8WezIPApla5J5ZIcNS8bKYElPHWiTc8ltW/39BM0mRTJK1if2RZOthc1H/qRXAcI0W6xDXADZjGhEs05WRESHUkDdMDvwvQNiHkk5jA4KtXoeBRrzbTbmUZKHiPg9iXnkzgrmHVAlpK0hmUcJsdyyzFQ6asTTaQUaWUnMowTZWsQ+6+x5BBp41MY80qLcMxnNTDYTS2IDYl4toLNKZm+kledRpUJz927BIkyomDG4/PxIc3fNt8nqF2zeVDX83tY+N90wAFrTvfZXtmZq8iXoAkjQPas8F8Oy2wClbiK6ZyvleZTLa7K1FvCoTbY2B3jU5nlkR6+F3x/ydwke6dIzw3H+hz2PdAbN3M29Pl4gvN61tLUuQYVZy5bJY90aZqfFvSoIYmlrgHpYA5psbX88j5SpeXgflmwnjaUVY+HI70I5j9ofw+wuH77MVjknN6tZNhDJAhXLKYHtOYdsTWepzfUZkfeCbg2zB//wDznilpujF+wW8Ogw8+iQ1f+6I3n9A9e3RYUfbK3uX82Hj/9wx79fffXVLF68mPe+970AbN68Gdu2ufvuu9m3bx/NZpNrr72W888/f85t7dq1i0suuYSpqSlc1+WGG27ge9/7HtVqlfXr17NmzRq++c1vcsEFF7Bt2zZqtRpXXXUVl19+OSBYTe9+97u58847ufDCC9m5cydnnHEGg4OD3H333YnbvOWWW/jrv/5rgiDgDW94A9dff71a13T4FOXb3/42d9xxB5dffjnf/e53ueeee7j22mv553/+Z/7gD/6AdevWcc899+C6Ll/96lc5/vjj2bx5M4VCgQ9+8IMAHHPMMdxxxx1cffXVPPPMM6xfv54zzzyTT37yk21jmp6e5vzzz088fjfddBOf+tSnMAyDtWvX8o1vfIPdu3dzxRVX8GwYq33DDTewYMECzj33XL5/7wMEwL/945epVyts3ryZ008/nfXr13Pfffdx6aWXcuSRR3LttdfSaDQYGBjgm9/8JiMjI0xPT3PllVfy4IMPYhgGH//4x5mcnOTRRx/ls5/9LAB///d/z5bHtvCej70HP/DxA1/Jqtr2qzkdA48ku6Jb5pFMyio3yjHGT6eSkrWh7FDMu2dxcTE96R4lS4M4WFVxK/RY4u+ldLtkDQT7BuLMo5HcCAYGe6pRcy6ZTzqgI0ETEMCbNMyWf2tlHkmGVKvsC4TvkWQeSfDuUIFHGStD1a22gUfz8/MZr41TdatqUjxaGaXpN2Om2P2ZfraXt/Ps5LOcvezstvXnnTzlRpmqW2VV3yp2zexirDJGxsrQl+7jVzPCfNwOAhozozSKi7FNWx1znXk00BBjXDItjgXP/RjsDJQWQHUfQQBepUFIdqPYewQ8/zz+XgFQ+cPHUUiV2ePVMYF8dR/kI0DFD/tMb+hVsPr1QORj1fSbynzXaLqCeSSTwoIMOqbqTk+zanvA+Ios/c9UGTQFOCnPmQQv5bplg5vyxWfK9qNJQHb9egaueDf9v//71LZuZfpHP4IgILUkBI/CmONiJcDzPUxXTFa9iQn1BPKsI87i4dGHWTe0ru386P4zoqkSzKPUokWY+Tz+zIxgtCgD2lC2lsA8AnE9jVZHyYUfbR08Gr/pJiZuvY3Bd70LZ6G4zgNDgkcuXjiZlcyC9BFLMWybpf/4jxiOTfk/fxhnHoWx21N5Az/wZ/U8cuaFn+WnBXBpzBtSk9KKW8EOTMAjSwrPdIDpmGwtnclDFXx52/O0FCdpmB3+b4fMI8O24wBNi2F2UsW8M7SKwIr2yb1iHmnG2VknG2MeVT0PO0zDUxNozcA0VuE5tvr7cXfvpqdhY2uL6JHhsvnIrl2LWSi0GZtnQvBIpK15CWlrYbrNfjGPIsNh/diIn1tla92BR23+VbKkJ1Ao+dL3X4z3UDOPopQf6b8xm5l4q0RCNf4J4FGQIFtTEpC5PI/yyQ26Lk0zc7nYU/lWtobOvoPu2Q6GZQkWyMwM7tgYdofUMAWoxOLSI7aVkckQNBpYpRKeCdmqGItVLCFH1Y0XjSF9eTIZDMvab8Nsw3HAtiO54VzMI+VvpEmSFENRJC0dtOdRQtpaZ9lal0BDgudR4Afx1yTgLplHOhipg0ezXCNGOg2meehlazrY0wUzxLBtBZRBBPaBvIZ1UOVAmUehbM1NNsyO7q+R/5D6uwKPwu/c6WkFNntdMLzaxqLObwgE2hFgFW0zIW1N3pO79DzCtiNPtvD3A6msnZ0T4DUsWzwkmA2wVyy95HWZHZhXidvLZGBmpmsw0UynIQbIOeLeHc4XDjOPDl39rwOP/ifqkksu4Y//+I8VePStb32LH/zgB7zvfe+jVCqxZ88eTjzxRM4777w55VE333wzZ599Nn/2Z3+G53lUKhVOOeUUvvCFL7Blyxa13Fe/+lX6+/upVqscd9xxXHjhhQwMDDAzM8MJJ5zApz/9abXc3XffzeBgMpV5586dfPjDH+ahhx6ir6+Ps846i+985ztccMEFicufdNJJnHfeeZx77rm8+c1vVq9XKhW2bNnCvffeyzve8Q5++ctfdtzH6667jl/+8pex/WmtTCbD7bff3nb8tm7dyrXXXstPf/pTBgcHGQ+ZAO973/s47bTTuP322/E8j+npafbtE8bD5ZpLMe3gWCa6w0ij0eDBBx8EYN++fdx///0YhsFXvvIVPvGJT/DpT3+av/zLv6Snp4fHHntMLWdaJpv/cjN/+X/+kmK2yI033sgn/yYCwJp+s2OjNtOcUalWEDGPkiRjMeaRWydjZ1Sc+lRzquOx00uCR/MK83hu8jm1jbyTpzfd2xE8qrpVBS4lmWUDLCku4UObPsRZS89SrxmGQcEpxD2PWgyzQTRocl/dwCUgiMAjJ8d0Iy7nkeDR/Hy7OfhAdkAxjw41eJS209TcmgK68raYOB7ZJ0yft+7dqpLfknyN+jP9/GznzwgIOHbw2Lb15528OlZSBjhaGSXn5NS5Buj1fBq1SRp+g7SVprcujl1fM7qij6rV2Dy2lzMrVWjW4PZ3g52Gc64DICgsgcDFdF3ApJgbglWvw//XhwETf/iVFMyH2FPdQ9EHs7oXQmZR4Hn4VbEttx5NGHTwSDGPGi7B4JEReOTb+C4EBBgYeHvHKVVhZt2J8MzdHFMSx7JNtmbGgQInNMyWZssgPAuG//iPxblavYrKAw+IZRcL8MgOmUeligAoTQloBAHu+D6ckWEGs4N86rRPtZ0bINbIy7Qms1TCSKWw583DHRvDGhpUQImSr83CPHJ9l6xkHoWT/8D3Kd8pkut0dkgQfkX7taZIUsrnscN7eWr5CnE8CjJZxI6ZI3vj4jMxmYu23ams/n5wHIpPCiAxGBkUk9JGI2QeiXOZNVK4mmwNHTwCUk4GmFFMDdAm8OHkWaatmWGTKPdZgU2zsGs6p61JWnpS2ppgHpkag0LK1iTzaNT3cJzITFuMW0vaStiW3ddHHSjVTSXrA5RUxsxllTFzevly7OFhBR7J45YNGXQ5J4fh1NoNs03BPGKWaOTWMjNx5lEwG/OoVlNNFZ5HdcsWJv71X5n3sY/F5iqdAJo5PY8ONfNINYW2klWq1xKaplaJiGz8rSTZmiY3jPZPA5Zm8TwyFPOo1TBbk3Cl09FT+WxONamqqfU6MY/mPoZmNktz+zZwXQUEt41RM+SNeR6FbCszk8GfmsLM52k6Jvla6I1X6lHgUTcMADMXSbyA/WYegbiGlZ/OXOBRzDfNE0b8hiGuD9eNedwcaCm5YS6n7rdt4JFpgm0nejUljluxjMI5n21jhJ/zyPMoBI9C9qR+PXUNHhmGAL06yIgOtGKSzC6ae8NxIjYkhCBExKiMSbgOMG1Nge3KWF8DjzQWii5bi7aZVuMEMe9ola3tDzCjwKNaBA5CC6Ctb1/J1uqx3+fcjgwT6OLhy2x12qLTVLhOx21JWeAsyW4SMDdamZVyHekO4FlCmek0HnH/o/0pBZjXqmAYB3xsDld7/a8Dj2ZjCL1ctWHDBkZHR9m5cydjY2P09fUxb9483v/+93PvvfdimiY7duxg9+7dzOvwVEjWcccdxzve8Q6azSYXXHAB69evT1zu85//PLffLsyOt23bxlNPPcXAwACWZXHhhRd2Pfaf//znnH766QwNCYbBZZddxr333tsRPOpUl156KQCnnnoqU1NTTExM7Nf7WysIAq655pq243fXXXdx0UUXKTCsP2wO77rrLm666SYALMuip6eHffsE28L1fQqZ9kv9kksuUT9v376dSy65hF27dtFoNFi2TMij7rzzTm699VYm6hOUG2UW9y2m5tY4/reO5zvf/Q7HrTuOZrPJUWuOUgbKru/OCh7pumLpeSSBGx10iaWthYbZElBoBVc6lfwymJebx6/2ChbLjCvGUEqX4p5HOvMoBHYmG5MsLixOXLdhGPzemt9rez2fyitTb4gAHcuIbtx5J6/YRVIiJ2VreSfPaGU0ts4d0zsoOIVE4+6BzIDyWEoCqg6mMlaGmldT50WyEjYMi2Sxh3c/rMAjBXAVIoCrP9OvmFdrBta0rV9noSnwqDrKgvwCxTID6Pc8Go0yDa/Be/65ysjiv4UNBr0PfBWO+X1I5THKL3GhjFd+8vswJcAsHvo6AN6y1wH/FoJHKXEtvept+M13AOAPrqU0LY5j0XNhehQWiZQxOZGHSA4FEXMuBh41myolDMD3LPyGR6XokC+7+M+/iAWwWGjT06HWSYFHQRw8kpMpJwSjTC9IlCFkVq2KxrVIsL+kbK1YIQSPoi7f3TOGMzLMbKWDR1afWJdM70qvXIk9NCRYTCpBJpStJaStQQQW5+rhE+bwiWbtscfazJQBgtAknCDA27sXe2QEe1DIStIrVsTWrUAAuX8h80iBR7N4HhmmiT00CDtD8GioP5StCc8jO6QUZY0UrhE1oX4I7EnwaLA4AjybSG1X/h0eWAHt8hK/89NMfR9hFuZRomxNeh5FLD2Ztmb54TXsejhOK/NIAw4StiWvrUILeKS8Vyyb3ksuIb36KAH6jQy3MY9ymRA8snMYTsQ8Up48lhnz8OimoVBR5wmeRzq4COIpt5nPi2vY85j+8X1M3HIr8/70T0Fv4DqlvSWkrdGMZAKHyqA0SvnR0plCWSWzpAzF2AyZTKLnkWGawo9FydaSPI+EdLBTU2vmcqH0Jv535fsRJmnpZrJKJtbbizs2FnqJaGlrkn0yh7QDwMhlqT8nHg5JH7i20n2AJPPIbQqWhm0pMMDM5XAdk/xMBB7JRxTdMEx0fyBgvz2PxHYykWytSwaPAgakx1oIpguw4NB5Hhm5HBhGIhhjOE7XLBXlQaUlq8nwg6CDbM1sla11KZFLLVmCsyR5HnegFTc0PgDZmgby6Glr0B0YlbgNyTxKYEpK8CpoNiMvHe24GS2yNYjAYH96GiOV2j9vVPn9oZhH0vNIW79+f5KyNel5tB+G2YEblwAeSL316LfOvVAY4DDrPXcuw+z9kCfKe1K3htlt73ci9tdhydqhrcOeR4eoLrroIr797W9z2223cckll/DNb36TsbExHnroIbZs2cLIyAi1Dg2FXqeeeir33nsvCxcu5G1ve5sCRPT60Y9+xJ133snPfvYzHnnkETZs2KDWnclksA4RuqrfKOcae+tN1TAMbNvG1yZD3ey/rAM9fnrZto0bfokUM3bb+/PaU8Irr7ySP/qjP+Kxxx7jy1/+ctuy+2r7FCASBAEX/u6F3PyNm7nxxht5+9vfrjyPYHbfo+nGdIx5pNLWJHgUMlwMjDbPo4wVMY90cGa2Usyj/DyafhPXdzszj/xoexIsmapPdWQedapW5pHnJ8vWJEAlj5cEC5LS1nZO72RBYUHil/dAdoDx2jhBELwsnkd+4CvvKNlY9mZ6WdGzgodGH1LLynOie0QNZESjv6iwKOZPJEsHj6SHlOu75J18jHnU5/s0GjM0vAbrft1gaGuFPjPNyj3PwfdDwLz8EvQsET/f/7fif8OCX38PepfghwwfI/xIllIlgsUn4of56n52obo2S74HlT2QFyCtMsYsFoXnUViKeeQ1o4a+6caa+6DRJKhUmOkJGRHPvSj+sEDsr6Rpy3W1GWaHcfC2BI802Zpe6VUiwtnIZLBDMFw+sS1VxHp18MjTfI86la9F+Foh80iCBvOvvZZFn/9cFJUbBFEMcYd7lWqkwm5MTv7Ld96plokxRYLoK9odG4szj1Ysj61bJItonkfje8FxqIbzs7lkI07oezSRA9cxlX9E1a1ihcyj44ZeyWnzT5GDU34y2RAEGSyErIfYBD7ueSRla6aTapGtdZBG6fvYyTDbbUZSppayenpIrVhB5qij1Gs5OyfS1sLbduD72HYK0zAj5pG8xvwW8EjJ1sS1la8Tl61pbI7U4sX0nPsGAByNeSTHn8sKMDxrZ5MNsy07NITWIurnqCh9KwSPGnHDbL/RYO+NX8MPZYeKQaF5Q7RK2DoZZqvmoc3z6BAblMrUOd0jJUwZ6mjmTYu0JpXCKuQxcrm2+4fyDWlLW9M8j2ZhHlnFElah0Pb91Jo8ZGQ1z6PwZymnjKUYsb/MoxyNF8R9tTPzSAPj5XwlZOsYlh35MeXzeI6JFS6S0kDXrhgmth1LCJP3nW5la9DKPOrSeNoVxvKxpKVDZZgt2VT5HIZhYPX2xgz41VhSqa5laxHzqN0wWzF07Dh4pLM54syj2be19JabGQqVEYeqOsqvOpVjz5G2tp/rSxpTq2G2dt4VmKjdX5PYTmYCeITn7fe9zDAM0NhWEsgw0x3AE3mvaeyfbE2yU4ODZB51tS3LEp8przMTNrrnJX/ezf2QJ0qwulvD7Lb36+yvw5K1Q1qHwaNDVJdccgm33nor3/72t7nooouYnJxkeHgYx3G4++67eeGFF7pazwsvvMDIyAjvete7eOc738nDDz8MgOM4NMMJ5+TkJH19feRyOZ544gnuv//+jusrFouUy52BhuOPP5577rmHPXv24Hket9xyC6eddhoAIyMj/OpXv8L3fcVy6rTO2267DYD77ruPnp4eenp6OOKII9T4H374YZ4Ln4zNNSa5j0nH79WvfjX/9E//xN6Q/SBla695zWu44YYbAGEUPjk5ycjICGNjo1TLE/hukzvuuGPW7S0MfUa+/vWvq9fPPPNM/uYLf0OlWSEIAsbHxwkIWLtxLTu27+Dmm2/m0ksvVWlrMHviWsWNG2ZL8KjV86gv0xfzPKp5bY+3cgAAIABJREFUNZW2Bt2DR/tq+7ANW0XaT9QnCAjIO3l6Uj1MNSJGSavnEcBUYyqR7TNbFZxCLHo+yTBbl621gke6H5KsHdM7WFBYQFINZAZo+k2mGlOHHDySE14pi5OyNYBXjrySR0YfUeCYPCc66NOfEUBDkmQNIvAo1QxYt/mfWL4rUK/L9eR9n3QQUHerNOplTD/Aqg1y71sfZPGGt8GWm8GtQ3kXLDtFAEbb7ofepXDUuWJDI8fipcKmIrxUi6liDOTwa3XFdipJ0Dc0zPYmxXWSWrYMf2oqkpLpnkeyoW80Y+CRX6niV6tUesOnSM9vB8AcHgrTTsS1H5Ot6Z5H8u+KeeQng0crXwGWRWrxItXEmakUXjZNqSpka5br46bD7XQBHqHL1kIjWQkeWYU8VqkUgTa65KQDeCSvp5wGHgVBQPmHdyZKsqT0D4RPk5nLkd24kYEr3k3htNNj605iHtkDA5jh5242zyOIEtf2lASQLFJchOeRFSb4LciOsExjIsrmxUlnsQ2b4aJg3cWfhGrNHCHbxzdjaSox2dosk185sU9iHnWa3BumyYrv3UHPG89Vr2Vt4XmkjK5DxkLejqLFJVDQzjwK7zHh9ZCrBXHmkRdKZVrYDvbwMM2xMYIgUOso5fr5k41/wjnLzhGfhTDBrjVt7cCYRwmytaZL9cEHGb3+eqbvuUcsH/r1CAlYs+09+u9tn7sE5tHLYZitpBl6BLcpZWuyaZpDtpbJkDl2LbmNG9s3YNvJaWu655Hf2fOo/+1vZ+Fn/9+O24+MssNrK5uNmqxSj2I+6UDlfoFHuZySQtodDbOlgW90bwqagrllWBrzKJ/DTUX7meqJHnp0HZmdy7Uxj+ZKcoqNNZPuXram7iHN0BzbUq8HIQvuoA2zW9hUi//uywy8653ty6WcyKC9S68mJWvSUuKUbK01be0AZGsQSiYPMfNCb+jNbrywHEexrCAES3XmkS7hOlSG2a2eR/WGAIKU/1C7ebN+LHUD/AOR0hm2HTu/Yj3J4JG6jvdTtoYtDbO7lzYfcIXHt02SrpUCyjvJ1nRwbo7zLGWyB8pEU95n1eph5tEhrsPg0SGqNWvWUC6XWbhwIfPnz+eyyy7jwQcf5Nhjj+Wmm25i9erVXa3nRz/6EevWrWPDhg3cdtttXHXVVQBcfvnlrF27lssuu4xzzjkH13U56qijuPrqqznxxBM7ru/yyy/nnHPO4Ywzzkj8+/z587nuuus444wzWLduHRs3blTG1Ndddx3nnnsuJ510EvPnR1Kct7zlLXzyk59kw4YNPPPMM4BgPG3YsIErrriCf/iHfwDgwgsvZHx8nDVr1vCFL3yBI48UzIeBgQFOPvlkjjnmGD70oQ8ljqvT8VuzZg1/9md/xmmnnca6dev4wAc+AMDnPvc57r77bo499lg2btzI1q1bMS2by6/6f7jk9a/mzDPPnPUcbN68mYsuuoiNGzfG/KE+8pGPMLZ3jAtOuYDfPv23uevuuxRQdO6bzuXkk0+mr68PP+zITcOck3mUlLbW6nnUl+6LgTkNr3FA4NFEfYLeTK+awElj6byTpyfdQ9WtKuCqVbZW9+rK+2h/Kp/Kx5lH0jDbaGEetcjWJNNEB5ZAML12zexKNMsGFDC2t7b35QOPtOMm65Ujr2S6Oc1TE0LqNd2cJm2lFSAI0P//sXfmcXJVddp/zl1r7e6kO+ksnQUSSAiEJIAgIKIiAzqooEBQliCDijoKIiOzqDB+HEfe18EZlG0clkFQEBDxVQFFQRAiECAQZV8Ssi/dnV5qvct5/zjLPffWrapb1d2JzPTv88kn3dW37l637nnu83x/aSY0HNhTG1kDAidT7yCQ+tMbWLqeiUc5MxcWj6wcHN9BdXgTNB+o7hxlT7TmHglQj3VTK+4CpsyXnCLs+x7gYB7NnHEQfHBRiwKdVgf6cn3SYg8wIUMss0PciEjnEReP5s8DALicJxYHzI6KR97gIEApSl38BnwDi/fpvdMY6LUSIx7FMI8McW/kUck8UkuzbaQWL4a9336h173ObOA88nwUu9kxdHc2F49CsTXuPBKtrGUJlkJIPIoHZsvYWpXzoIpFeP39qK5fL9u5h8QjI8JQyWah2TamX3SRZG/I4m1pRbkD/TCmTpWDtmaxEdG5rr+TwPEdNghzWbc1IR5R1wstQwxeNNvG14/8Ok5ZdCqfrradr9ptzaRaaLDEurN5ACENu8XUcx5B4VgkqYyZgcdjawCkMJAxM8EgV2nhrpYQWITzKFXyoEeA2SK2ppYxbTrgOEHM0TCgaRrOPehczM7NlrEVv1yJdFvza/ZloyKSeRQTW+MtqAHAeYs5VUQLb+p5Spwp8gCkDnNJrA/13LDzyK0fI2yrIt3WoOvyKXgA524eW5t61pmY+4P/rJ2Ot6Bu2G2tgfPI6puN7JFH1s5X0xg4WjiOFBFJCBJ6Pl/rmIAqICRjHolphduyZl2irhbw48Zja7KzUSYLzwqOm901NVhOQgcAyaSl0CIeSLTGPErL2FpTdpDKPHLd4HMnuk+648s8AoD00qXSARpaF+VYNY2tiWuZEluTryWNrbUgMI53hRw0dTprqRXXbQ2KkzDsAmpve4LYWu31iphGIOzFMI/E8mteM2tFpcTrYxiBYCZja8p2qucLP4/9FmNrAmJNPZddF1uJ1rVYRNOZyzQGSC6nSTWJrbXgMBPTth1bE9e8YmkSlj3ONSnFjWMJqDIA9PT0YPXq1bHTiQ5mcbVq1SqsWrWq5vUrrrhCdkEDgPvuuy/RvL/whS/gC1/4QsP1/vjHPy6ZRWqdeuqpISi2qKOPPhovvPBC6LWzzjpLdh8TlU6n8etf/zp2mT/60Y9iXxfVaP/F7aPe3l7ce++98ndKKd4aKOIT530GX7v0y8ja4VP94YcfDv3+kY98JLYbXi6Xw3eu+46MLS2euliKHk+sfgL/+Hf/KJenEQ2GZjR0HhWcQohlU8M84o6brlSXFHUAxj+yDVs6X0acFsQju0vyTgbKzKml3sgNVYYwPTM9tLyiW8Rwhd28tRNb2zyyWf5eD5gtBKY455HsuKWbGK4Oo+AUMCtbx3kkxKNSv4wPjifzCAicR2or00OnsyfYT29/GounLsZIdSR0bAEGFQeCdvDREtN3Fdh6Tx9hNztZKyv/lvMpzM7ZqAy/DmfwTWggAKWovPY60r1clHr9d+z//Axg2iKg/1UmHi08Hjj808DS0+E98TKbhhL88qO/QtbMwtuwUa6LXyggP42LR9J5xPateApszZ/Pfu/vh9nbG2IeaVzkoU41JB6JmFuZi0f6xm2oGIDZ0Qli2zLjL8TFGvGIi0tigE48v+6T5DnXX1dzk+B3ZJEvDnLnEYWbsVjnK4XdVK/U2JrRLZxH3aFpBIgz1BK9ATAblCKtMI+8IRYdNWex81vtiEWpxm5e+aA+CuQNrQdv9S7K6x+A3t2NjLlbCpuNSrT43pVngq7FW6EXnWogHnmRAa7C9zhlwSnwhobwChDbbU3chJs+F490PRjUyUFs41uSusBsp7WOSsx5RABQJs7w7mjH9B2DBZ2MJSVvjCOxNXGcBYw9XfIQOh3dWhECYM4jAHB37Ih1SqmtvoPYmta0o1i0iKbxzxUXjxTOEXUd6eQTMSc1miHP4RrBrI7zSbhJPT/UbW1inEcRcLghomz1902os08DEYLoeny3tRoYc+u3zFo6DU0MqDLB/zI6k89L55N6rtGE3dbEMgDmOqoL9TZjxCPZyt6QQpuWzcDnziMfgJUPvv+TOkKMrilS8Pjwgg9jVnZWS/cRWirVQteyoLMa+LYA/LpcESLM+HVbazidqTpZkjmm1M5qkplXt9uaIh5ZVmKX00SUOqBPEjMjhgmvFACZqeeHHCytOFLqLkPXQCvxQooK7I4HZtcyj4Sg18jZ2nB9DEO6kKXbiX/OiGmGP6uiuyc/Z5Nc6wF+beTOo0YPXsajxHUyDkguSgLl87mavwFR8ajxeSsF7bZja/w7tTzJPBrvmnQeTdb/uPJ8ik2DJQyVHMzsTNcIR0nLpz586ofg1BQUg4OD+Osj/hp2ysZxxx0npyWEwNTMus4j13dR9sohASLKPCo6RaSNNFJGKiTmCOaRrunImbmWYmtTUlNq41fceQRAco9U5lHRLcrXO+zWY2uq80gCs9XYmuI8EtuuOo/EOgDxXczUEk6BkluaEOYREIhuqvNoZm4mZmZn4pntLJo56oyGImsAsHz6cjzwsQfqOo/E/Dr57pLikRHE1nK+D6trLqqEwC0EIPrKK68A3QsBzQReE+LRLGDGwYBmAPscCxgW8MH/C/QshD/CzmNKNXTanTA0I+giAuYikbE1IS5y55EQj2wOkhcwZtV5JAf0lbB4JADb5Sk8suG4GMgDhmZCsywpDolj5vhOiHkkoMw6b2NMlAFCtIyenhoWBc1lkKkwHpbu+qCGAaOnB+6unbHzCL03FFuLdx5JlkKS2Jqegu0ErB0mHrF9K7q5hVurO6HtaSgeGfHOI3GONWOOGJx5tEs4jzjLqeSWglgWd2fI7Yw++RY3wK6HqLVddECyYQbOIwmljhdcaraxATC7lZt7EVsDwMQe3wd0DZcdeRnOWnIW35Y6wGy+j7VMBjBNzDGn43MHfVpZl/gOTyHxKKZ9eEg8EjfVmgCUJnceAcwJUOM8EryKihCPWBQ8FFtz4plH9QCpcd3Wwm6gcXQeKUIR0XUeqww4TfHOI2WA22hAahgcmB0WiALnERvotjM4I+mUwjzi4lEqJd0aej4vY3ky6okWY2v8GBp1eEeAEotRxCMITpCuBxGRbOA8cg1AV9vDJ3QAzPrOdzD9K8xV3ml34rh5xyV6n1xOShUmksW/hCiqxtb8CKy43UodsBjW/Pkw585tvC4xYkTdaaPMI8OI5SAB9ZlHcT/vqaoHfq47fbTbmvqAQdMj+67NmJJgxHle7XeJKh7FRcisQNRRX4sTmpJWKLYWYR5Fr0fyPK6Ez+OmVS9yOxHFm2g04hPqXV2Yc/116Pzwh2NnEY4nNmMejQ8wezK2Nv41uTf3Uq1btw5nnx2m29u2jSeeeGLClnnEEUegUqmEXvvhD3+IpUvjeSxJK+riaaXGez8UKi7eGijC8XxMz6cwLd/elxClFK/tfk1GqlJGCmW3DAqKzq5O/PKJX8I2lI5S1JfOoyjsWa4bF1RCzqMI86jgFJA1s7A1u0Y8EtPmrXwi8WikOoJXB1/Fe+e+V7oOBkq1IoiAakdja0NVJh6pAOgkFWUeydiaIuhkzaxk20SdR2LdRGROdDGrxzxSoc0QXcHJ+Fza1Niards1otSCrgXYNMoYPqPV0RrnUaP19kslpLexfd/JT5meYTZ4yFoK80i3Yed64RAClwZP0iqvvALoJnMabeSfl/wM4MjPAfufAGTDDhmPR8/gU1BKQQiRT3cBNmjNcb5Vh90FYItkHvkR55HbzyJfIWC24nCQ9nRNkxE3L2OjYhLYDsVAHpivmaC2Hc888h25L4UzyaYGpmemg7g7kt9YAYBtw3KZKKV7FNTUYeR64CWIranMI3POHEw552zk3ve+8DSGaF8rrFEkPEBTKmWkJO8IALxCAd4w+5zp3NkU6ojlONC7OiXcW2UwREtlHlFKA+cRF1cbdVsDAHMm69K0qwNBbM3zUXJLkg0k3Bmi/Ig7QnaMUR1KimhMDAM21VkEMcQ8SvbktL7zKNxivVmZmskjDX4QSYq2oSckYNFElwW2/pppQnM8HD/7OKzHtcG285bhaokuee6u/nixSwhjFTW2JpxH9Vsjx5WWSslBi9hXWjoN6rgyJlqVsTUuHqnAbCeyzfWWX495JASdcXQe1Qxa+PkpX4sZHMjBiRKRjJ2/aOvuRgRMTRHHvPrMo0bFImq8Xf3BByO9fDn0adOkABo4j/zQuUad+hD4mvXnopRZr9MaID9rvuKKlBFLQw+idZkMKI8NOQYijpBkA2h7330STVevVNhuUxFGxH14HFPGg5SB+1gH1fbChVhwf7zbP7QuSty0qRAejRGaJojsthYWOax5c2HOno3Uov1rl4Xx+5y1UloqoTArpjHN8PdilHmkaYHLp00nlQQ6x7hYiWFI3pfkG4W6rdXG1ohlxbKQEpdp1HZbs+qIR5HYWnJgtiYbTuwR51Gl0rBJAQDkODc3dh6EcNZltTnzKDVW55Hgik06j8a7JvfmXqqlS5di7dq1e3SZEylMtVvjuR+qro8N/UVoGrBgWq5txxEAGZvKW3lYugVTM7HN3cZgp/wL3lMt5mCxNVMz4fquHJyrJUQlVWAQoo4QqUQnNNsIxCPP9+D4jhz85SzmPPJ8D8/veh7Lpi2DRmq/NH704o8w4ozgzAPOlLE71UEjBusinia4ShWvEnIetcM8KrkleL4HXdMDYDYJA7MB5i4S4pGI8InBrthfwnlUT4QRoprjO+BNocaPeaTE1lTBTVSX3YU3hxgIfsQZCXXSa1YDt96K6jXXQP8ClbG1KbsdACZyqvPIzME00qhoOlwaDC4qr77Cfpi+hDGPAKBjFmDngVnLa5YnnEcA2M2GYYTFo2IReYu5uzp4FDBwHo0AhgGzr4/9Huc8Uro6eRyIr3d2MuYRAN+2UEpx8ShHWMTTtuXNkhSPKAdm2+HYmkk1PHjqg3jpa0taGgwQ24Lpss+Y4VHANKBP60E5Er2NKzW2RnQdM/7xH2vnb5ihqI6WzTYEZqvikV8oyE52IhYXch5VHejTArZGc+eRw+dbBK1UYHQHziNV7I6r9IoVKF90Dp4xb+NCrCGdRxrlEGYvwnaKtKUO+CMBA0cV+ph4pMGgfph5FOP6iN3GcXIeEUJgmikAo4ozp/YaKrtwqcuSThdT3gQjGg1ToKyihEjjl4qxHBYtEpcAUNtJJ6FoqqVSQXRSrG8qxc5T/nlzt25l04pzyveDfREVzOo5j2K6rcH3pUA1bk/C47qtCWB2o25rIvbQrCW0YUgBWFMHlAmZR43KmjtXdn/MHHII5t/+YzZPw0D22Hcjc/g7sPvOO0OsFnFeJT2nBZOnsfNIuFoUnqCIrelGyHlE+bXX1RmDTK5PArbNeFQ4EpUQmC2EAQHoDw3c94y4EudeqVuSeVRk4iYhisAXFr30jg4s/O2DobeHHTJ7wXnUQst1oFbcpRHmkZgPdZymjpS6pQCdo+Kd2pFOrIuWMLYWfT1pEcNUYmtCPKpdDgAlttY6MJuWK3vGeWQ0dx4lKWLb7HrS7Lpsp+T0bZXSbW2SeTS+NRlbm6y3fe0cKWPrUAlvDRTgU4r53dm2hSMhDJVc9uU9LTMNM7IzZOSKUgoKLh5RZRBFfWhgziMKGhrke76Hp7Y9Jd04amxNRHOk88gtIGNkpIij/k0M/vImcx49vuVxnHPfObj+uetrtmO0OopbXrgF7+l7D5Z0L5EOGiEeZcyMdBQJ51HFq7DInJ5CySnJTmztdFsDILdXiGyqoCMEooJTqBtbE06tLaNbkDWzdddDcqP86oTF1vpL/bHdYjrtTimyjVZrY2t1yynB3boNKJUxdQToHmXnVKZMYFcpsrs3IseFq2xqCizdQpUQ+Ai+rMuvMFA3epew/3ULSMfDUgHAHw3capKtEAVmm5x51HMAMP1AgJ9z3vAQ9HweWi4HYllwB1gUTWUeqaKHP8rmG4qQpS2UbKbusdga42xEY2s1zCOHs5RcRzJPWnmKRCwLpsfOb8MDqKHDmj0b7patzOLeoJKIEtK1wPepls2Clsux807pKaRV8ahYlLE10cUNqnjkujKyADRmbqjOI48fH31qt/w8NXMeEV0H+egH4BgitqYFES0es2NQX+U4R8SjqJOIvVmBlhoGLKrD8Albnrj5dIOuTw3XsQ4wu52OSqcs+ih7LxeiYm/WdT0QReSyBM/HYMD3aiUkqMF1mWMtsi0iukRLJdBqfeYRoDwZF+JVHWB1vSLptBy0iHNYxBqjwlus86gmtlZHoIlxHrFtLAcD4nEoBrRWopCaJoHudTvBAQpwtcngQ41+qOeB2N+e17DbWqPq+95VmHHZ12vXTdMw9/rrkTv66ACY7XvMbdSKCIHg3GrkPFIjHLI8AZRWnEfZLCgfvLsmh90n3Y/jVK04j+QAUfCNhKAZM3Cf6KorDMRNq0TUQuK7pimxtQacrpDzaM8zj4QbpIbdU6eicbCQ80gLn2ftM48MGYeLrpN6DsQzj2odRsSyJMeqbWB2KQrM5myxyDkiXbvV1mJrApjdbqy2lSKaHnbCjkE8AsICZOx04hwbIzCblsp7TED+31KT4tFkva2r7HjYOlTGzpEKSlUffVPSSJntXdCKThEvDbyEqldFyS2BECKdQYRbWigC5xGlVHZeE8wjITL5ysDx95t+j/MeOA+PbX4MQNh5ZBADGtFC3dYyJheP3HAXNLEuHVYHRp1R6Xi55rlr8OCG8FOpu1+9G8PVYVyw/AL2Xi4CqC3nJfOIx9Mc34GlWazb2RicR2L7hPgjhLQoMFtsbzS2JgQbwTzaVtiGmdmZdQciQsCoeuMvHgnRbdQZjXUeddqdGHVG4fhO3dhaqHwfePjbwLdmw3uTsZJ6hoEp5WB9u0eA7OuPIv8Cs8nnuubD1m04oHCy7MmyOXcuvF27GIx6Oucp5WcADQZr3rAiHvFYioyXmSb8QgG9fP4zlp8FfO7xYLWHR1hbekKgd3dL55GIBzq+E3Jf+BzcHxKPUikU+b3CQJ7xwTTLluJR6bqbcdqjHlzfheu78nyQLgZHGSgmfSoH5sKwHKDslWF4bFvNvj5Qx4G7Y0fD9yYSjwyDQ5K5eJQTcbtKzbTMecSVGE1jzCMeW5POIzccXdE7kjOPwI+rgIGrzqNmzCMg/FmCrsMX+5vzpqgXia1FuCyCa0QVNlJIRDFNzM/OQYeeZTEzRWxKEguSsbUxOo8AYFbHnNC84nhCcc4jlecjHBkhyLkbH1sT0SK/WOJiVzzzKPSzcNy0yBDSUilQGVtzWSRGsLmq4fNSBWYLYaw2thb/pDnUbS3SbXBcn/TGOI9UvkncugHB4IQ0iT3IbmsRFpW4zlAej2uLeaSwveqWYIm4HnNVtdjhqRXmUYg74wSdAaXzKJMB+ADXM9j3idh/bbfMbrHqsX1ip5XOo9rYGo3wZia6WnGpqC7K0DmnxqsarPdedx61KCiG1te2a7qtsdeTi2+xyxAirOfWxtZirq/qumtxziM1ttY28yjsfqvLPJKxNX4eJxVmdC3o1NhKnL+dUpxdANoWq8Q+bRaDldeksQKzS5POo/GuSfFost5WVXY8+D6Vv+8uVkEALJ7RgSWzOtCVaf8JTNWrwqc+hipDrLuZbss4WEg8QrB84aoR3dZENEuISgCwfng9AOCRzY8ACPOGCCGwNCvotiaYR4rzqOyxLx8hHonY2qbRTciaWRww9QB8Z813QtuyestqLOxaiAO7mbCQ1jn4WWEepY00TM2UIpHgKqWNNGMeVYagE725IBIpEd0SzqPY2JrRILZmhmNrUch4tNTo1ESJR+p6qdVlM1fIcGW4eWzN94E7zwEe/lcg1wtvw3MAgNlDQGdJl6JDzxDFnDs3wfnnGwAA2elLZDSvzM/D1AEHAAAqr7waOI/y8bE+ufgRhZPFB/bCOWJM64FfLGJJ9xLc+5F7sWzastB7veFhaB3M+WVMnSqdR7qmQyc6izlVVUcKO/ZaVyB80JSNkhSPhPOIOTcAoLr6CRy0ngbOIz0cUWLRMMFSaSW2ZsPyWMdCJh4ZMGez+J2zcWPD91IltlZ3/ma425rGu43ERdds3Uaa6x5GTw/8QhH+8AhIJhM4UyLAbC1lB229G4lHluo8Yp9zfWq37NDYzHkEhD9Lsu0xAE2KR41ja4DiEhERHLX1uWFgccd+6NRzIHo0tjYW51Fr3dbYuognvYIxEfN+pQOSXJboJCbEo0o1LPhxN0z0SafoguaXS5zrEc88AhQHQ1yXsSTblkpJBozqPIIbMI9EBc4jT4qP0dhaXYFG7bam7Cf2pHf8BuwCKB1yHulayHkUd/yCp/zNYmtKtzU1ZhkCgrfnPEpS4jNDuXugZfFIOo8aiEfS6aIwjzxPfu5U51EgHrHt16QIt2dia+pgsemgnX8mpPNIxNZ0fdyA2UmrFcdYVDBq9nPN+/c6MLu1SJH6nU1SNmN8+R5AiHwwqNXhASUuBehcI3Sr+zISIVOXGY6tWdLV1ZbzyDRrhMC6opuMrTVwwsYtQzeUz/HEnudBkwJ+rW/zc6XF8KVil5ca23VHdpicZB6Ne02KR5P1tqiiU8Su4jBe2T6CN/sL8Dn0d7DoIJcyYRkadG1sFnkh+AxXh1FyS6F29kJEUmNrQBBd88GA2WI6NdImgM/Pbn8WAGocLJYeiEdFt4iskY2PrXHxSACzN41swtz8XBzSe4jkFgFMrHl2x7M4tPdQ+VrUeZQ20iCEhGJXVa8KS7eQNbMoOAUMlAfQaXe2HD0Q2yedRzGCjhpNE7wnGVtThCWxXqprKVpqxzqxrEbTt1LqgDtOPBLRv4HyAEpuSca+Yuvx/wBe/H/A8d8APv8EfJ/Nb0bBQseoj9SBTOibPgSkN5mobjXwoep+OKT3ECmslatsn9ocnFl543WgYzaQ6gQ6ZjbcFm+kfmzN6Jkmf963a9+Y9w5D5+KR3tkR4idJzpfiVvDinEfpFAoW++wI5hFrKc4H7+UKdB8SpC5ja5VAPAq6PrXgPLJtmC6LogrxyJrDxKPqps0N35vI0aIrkRcAepY7j2Kg2WkjLZlHRm8vdx4NQ+fQXLbMsPOImKZ0hzRyHkHpttau80icZwKYLZxHms+vAW7A3wG480hhdQTrEd/OV3KZOJg4FFuLtkmPqfrA7DZaKQuXaCPnkaaF2qcDijOMtzen1WooyieZR3EMnnSaxdbuFUErAAAgAElEQVRcV7YYl3+LYR4FzqP6UOjYTYsAsxm7gx+XSti1RYR45AXcrhrBTAwWolBx4TwS4gqvcX/Sq+scKK2cU3yg2KjbmnzK32zwodeJrand1jwvcbe7lssInEekDeeRMWMmi+fOmVN3msB5pDKPHCmYWXPmQO/qYgBvvt887uImqVTieNJ4VKjbWpNIFiEEME3pPAp1Wyu31vZ8rBXE1hKIR3XEH/lzE1h6TQxsD5eMrSXkE6nrq9mp4AGDKpYljDPVXYYQYWNcOCE4dmxsLabbmso8ajO2Fv05DszN/s6vNS12W1Mf1kx4bE00KRir8yiha03jDsS2nWhi/zutP1yarMY1KR7thcrlWnNyAMC3vvWtCViT1urhhx/G448/3nzCBtXudmwv7sD2wnYYmoZCxcWGgSL6C1U4no8pmfG5SfXBbn7Lbhk+9UPiUVxsDVDEI+qDgEjxSHUeiW5cIr4VdfJYuiUFIjW25lEW4RF/E4O/vJXHqDOKjSMb0ZfvY+KTHwwIXh54GUW3iMN6D5OvCRFkoDyAtJGW8bouu0uKR47nwNItZAwWW3tr5C3MzTduTRtXYvtERzjpPFI69YgBreo8EmKBykMCEOLfxNVEOo/Uc0A4ONQSziMB9c5ZOSYQPXxF4BYAgE1rgN99E1hyMnDUF4FUB7wUg1P3jprIjrpILVkCSoDDXqUgDjvfvrjtEBw166igK1+F3fibs2ZBy2ZRff0NNng/5XrgmC833BbVeSQGXH6hAJLJQM/lZOes2PcODUPrYMKYls2F+EmmZjKxQXFfBMyjgNdD0ikUbC4eceeRpjCPaKkUiEe+I49hyHnUwGVQr7RUCqYHlCoFmC67CTRmzQIIgbNpU830tFqVg9SksTV13bQ820+V19/A6yedBGfLFjltSk8p4tF0xjwaHmKRwBhXTSAesc9Ew9iayjzqF8yjqYmZR0A0thaIURqtH1sjphkSmAmPrUm3SoR5JNwiJBpb873mzg5dBwiRgo8spzYG1qwC55HSyj5awpGiVOB+M6GZApitOHWECy2udXwmzWJrMedVbKxCF86j1m7WSToAZsuInAC7R5xHugrMFswjdXuAYPlR8USPdx755fFtjRznwCK6wX6XUdaY/W0nG+CKLk3Rbmtyf/tCWJoo5xFntXB3U6sD1vzx78fC3z4IQ3DT4krhf8hyg9hax0knYeHvH4ZmWfKJv28IFo21x3hHQLiTl5ZEiNF10ErEZWQaNV3LJrrEIDfKs4mdll/LgMj6yc6VTZoHqMLHXhgYB3yiZK6QaGxNumXU7wc5z3EAZkc6Q4aWbzQQj2pia2OI0pm14pHYtug2Sjh/tdXYmtI4YKLPAyGyu7UPhlqppOJR/oS/wrSLLgpxH1taTohzNSkejWdNikdvk/rfLh5VXBcUPuZ1ZzC7K43Rsostu0vQCUFHqj3xyI3cIKuCDxAWDsTgiFIqRSYgiK35NOw8Uue1eWRzqHV81goPAG3dlgJKwSlI8QhgriPBPpLOIzMPn/p4a+Qt9OX6pEtJiFprtq8BABzSe4hchhCeKl4lBH7usDpCwGxLD5hHG4Y2YG5H++JRlHmk7gO1o1o0tibWVbixHN+R4klcyW5rnhO7rJZq8zPAW0FXQrHPgVrHGAB0ppizZtMGFknMmTlg9TXAw98C7vs7gFLAKQH3fAbIzwQ+9B/yhtEbZWLN/v02dI/CnNGLalcWy97g8Ox3vhNDP/85/FJJbqPOTytimLD23Zc5jwDs+t16jPy5Mb9HuIEAyAGXXyhAy2RYh7Bioc47mWtJsHe0XA7eaDCtqTPxCI4yeFS6rclK2yjYACXA7hyPrSnMI79chu7zTocqMFuIS03guPVK5ze31XKBtYg3TWiWBaO3N1Y82nD2Odj5H1exZVabtw0mhg44TiC08Nja6O9/j+prr6Py2mtyWtuwJTDbnD6ddVsbHoHW2SGfsNMIMBuGETiPmgGzpfNoAFo+D822E3dbA6KxNR3wfSaQqrE1VSQsFGpFEMmd4Cdr5AZO5ayojKREziPe5hfj6DwKmEcx4oMS3ZPLcqOxtUp4n1SrAKWx56iWzsAvlWJjdvHug7DzKKloqqVigNm6Duo68KuVkAgpfg6Js250m+s5j9RuawowuzhBzCPVgcWFLL9B7FA85W86wDX0oPOYKvrpgbjJmEcTJR4xbongKsmIS8J9SDRNdnSrO40CaBZFXSW2RkjQnY6LN8J5pNmpPRZZAyJOsYQRsIAVI5hHCjB7TzOPEgKs40RC6VBJ2HkShrHHHGGh5bcKUVe2R0ulgm5rynU3jjvU0jop4kaj2JrYdyG3p9geMyIejcl5pLxHArPruKsk+L1as74Nl6E8rJl4YDaPCguwf5sNEbSEIqvV14eeCz7TfuOFmP0/WeNT/+P25rZvfQuVF18a13naByyObdEs6u///u8xZ84cfP7znwcAXH755TAMAw899BAGBwfhOA6++c1v4iMf+UjTZW3duhUrV67E8PAwXNfFtddei1/+8pcolUpYvnw5DjzwQNx222248sorceONNwIAzj//fFx00UVYv349TjzxRBx66KF45plncOCBB+KWW25Bps6A47e//S0uueQSuK6Ld7zjHbj22mth2zbmz5+PNWvWoKenB2vWrMEll1yCm2++Gddddx10Xcett96K733ve7jhhhuQSqWwZs0aDA8P48orr8RJJ52Em2++GWvWrMH3v/99AMBJJ52ESy65BPfff3/NdsTVySefjI0bN6JcLuPCCy/EJ887H67v4bHfPYJzrvg4PM9Dd3cP7vnFfSiVivibvzkPa9asASEEl112GT72sY8hl8thlA+W77rrLvziF7/AzTffjHPPPRepVArPPvssjj76aJxxxhm48MILUS6XYdgG/vnf/xn7L94fpWoJ/3TpP+GBBx6Apmk497xz0TWvC5fedCn+68f/BQB4/OHHce8t9+IXP/+FZB5FxSMKii2jW3DU7KPwyCYmMES7dpmaiYpXgU99Flszs3KgV/EqNcwj0dHLpz768n2Bc4iLLE9vfxpz83MxPTNdLqOeCNJld2HjKOO+VP0qA2YbGWwY3oAdpR2Y1zEv9hg1KjF/tduaQcKdd1SukXCYCNeDOoAFmsfWNKLBIEao25qYV+IqDwE/WQW88RBgdwKXrgfcMvRH/g9jUvlVtl3b/gT8+qvA+y8HZi2XsbXNz9wIdObRYeWBHS8AmR7gqf8CRncAmW6g/zXg7J8BafYEhVIKf4gdt6mbhkDB+DTOtE7YgwVoc/vQ87nP4q1zVmHkN7+BdRD/suWmN2LosPfdF4XVq+GXSth59TXoOOEE5I87ru4m+sPD0Do74Q8NhWJrWjYDLZOBV4gXjyilLFolnEe5nARiA0wEqnEeFSKxNUKgp9J4cLmGeYccA0//AzsnbFsO/mixBCNN4FKXHXPBPOLd1uA4gcughcGAlkrBAxePvOAm0OybjWqMeFTdtAnm7Nl82QlECcHFEbE17iwtr1vH9kUxcHSl9TQyFQovZULLd8jYmjlrVtANSe145Xkgpgk90zy2RoxwtzXhQhBdCuPEz2ipsTXoGojns2uAx7YFnl8TW6vZP2J/xLllDFNGjeQgibs+4iDTsdtpmjXOI+q6oae8SaqGeRRz480cL+GHClLANE0Q24Y3MhyOGor4TIyYoaVS8EvFxM4jRJ1HCWNTWjolXRdCqJKur0qVdfajFH6xGAiSSgSMeh5otYqNf/u3mH7RRQH8PHp8hADnehHn0UQwjyLd1oSwI8S/mOMvnu43bQktzstIbG1PMY8E/Fu6B8TxH0fRQ8bWyqp4xETb6OdOiG2+jK3Zidw041VqXCXJwJHoemxsTbof95jzqDWhgRgGO3/NWmGjqeN1DKLGeJSW0NUnKnR9S6WY6B7pEDbWrn7h75LIMQ9dX2OYRzLKFgaRtwJBr1mfuA5vTYDZMrbWCjBbRHr3BDDb9yXYv93aU90bQ5ytvdCR8H9yJboTIYT8lBDy14SQSadSTK1cuRI/+clP5O8/+clPsGrVKtxzzz145pln8NBDD+HLX/5yKO5Ur370ox/hhBNOwNq1a/Hcc89h+fLl+Pa3v410Oo21a9fitttuw9NPP42bbroJTzzxBP74xz/iBz/4AZ59lvF0Xn75ZXzuc5/Diy++iI6ODlxzzTWxyymXyzj33HNxxx13YN26dVKoqlfz58/HBRdcgC996UtYu3YtjjnmGADA+vXr8eSTT+KXv/wlLrjgApRjILGiottRr2688UY8/fTTWLNmDa666iq8snELBnb14/KLv467774bzz33HO666050Zix89//8Kzo7O7Fu3To8//zzeN/73td0H2/atAmPP/44rrzySixevBiPPvoonn32WXz5n76Mf/+Xf0dvphf3//h+bNiwAWvXrsXzzz+PMz5xBg5/1+F45eVXsGvnLhAQ/OzHP8PHz/m47LpGQCQUWsbZfB9Vv4qjZx2NDqsDaSNdE6mydRtVr4qyy/adYB4BQMWt1HRbU9vB9+X6gkgTF6Ce2fFMiHcEsAG+WK46iIwyj2zdRsbMyBhWO84jsX6FasA80iNPbOOA2VERSWy32nmrXpm6iapXrZlX4nrh50w4WnAcUBkCBl4HXnsQ+MN3IZ6BZkZ2ADeewKZ76F8ABLG1TfxLO+dWgfJu4NhLmcD0yv3A0zcBh6wCFrxXLo6Wy2ygT4gc/Bg93cj37cPmc/gRyBzKjmH1rY1yUC+cR9B1WAsXwN2xA4XHHuMg3PqfPep58AsFGNz+K2NrxSK0bJa1Z64TW6PlMuA4Epit5bLwR0fl9czUTDieAxLHPOLAbJJOQ9cMbO0mePOoeVJMJDy2RimVziMJzJbd1mpja60MBgybDUScwihML7g5tPrmxMfWSiX5FDtZbI3HgbgrQuPMo/JL7CGGXwwGa7bBgNleJsWEIM+Du3Mn9Hw+gEc7CuMJ7KZHy2ZDT0Fj10N1Hg0MMoEAwPHzjsd3jv0OZudmN9wOIPjsuT6Db2oUmJebG8T4uENI3DjGiUfiSWictT3EPJIttQ3FAdH8uBLLGifmEef1OA0ApTHA7JpuayowmxAlPlOHeVQsxcfs4oDZksUjuhIlfBptp2rOYWJw11e1CmJbMGawtu5EAWZL5pHrwe3vR+GRR1F89tm6wO4Q8ygSWxtP4UMMWtRuawEjpIFzTDppEnRbi4mt1TCPJujJvlx+m8DsRMsQ1xfleiQA+NFzXziPKD+GmmXvYedRMqCuLNMMgNlxzp09DcxOuN5xTqUgdtfke2cMXcDGo+RnKyGfSN1GzbZjnUdjFhUkMLu2+UKckAPDAAgJRa9rmEctQNCjFc88qnOORMSjxNd6PXhYM1HOSLksAcz2m7uEG87Htvk1fIIB3wnh85PVeiXdm9cA+CSAqwghdwK4iVL68sStVvvVyCE0UbVixQrs2LEDW7Zswc6dOzFlyhTMmDEDX/rSl/DII49A0zRs3rwZ27dvxwx+w1av3vGOd+C8886D4zg4+eSTsXz58ppp/vCHP+CUU05Blj+F/uhHP4pHH30UH/7whzFnzhwcffTRAICzzjoLV111FS655JKaebz88svYZ599sP/+DLy7atUqXH311bjooota2vbTTz8dmqZhv/32w7777ouXXhq76+uqq67CPffcAwDYuHEjnvvTSxgcfhWHHnko5s+fDwCYygdGDz74IG6//Xb53ilTpjSd/2mnnQadX/iGhoawatUqvPrqq8zx4FSRt/L44yN/xAUXXACDX3C6p3ZjcPcgTv/E6bjr9rvwwZUfxHNrnsN/3vifEqCtEU2ykYTzSIhIffk+LJ++HC/2v1izPgKYLWJe0dhaXLc1UX35Prw18hYAJrbsLO7EUGUIK6avqFlOWk9jxB8JgZ+j4pGpm6G43rx8686jtJEGAcGIw5lH1K0Rc1JGCgQERTdwHgkRzCAGCIjkOAmQd6MS3B0pHlEN+ON1wLyjgJkHN1/pbc8DVo7BrF//LbDlWWAr64aWcqsY1oDsCz8HuuYB845krqIdLyE7bREMEGziN9m5ke1sfr1LgPnvAhafBKy7Czjy86HFecMMcG7Nm4fq+vUAAL27G1Pm7Y8BPIbc4YezKIFlwS8Va2NrugF7wQIAwCAXrqMgXLUEDFvv6gI2bJBPZP1CAXqGiUdeMV48Euuq5zkwO5cHKAUtFkGyWYV5pMbWws4jLR2IpmW3LH/WeOwHjgN4HgxfQ8WtgIIGsTXhDKG0cWesOqXzp9gu5zSJm1izrw/ujh3wKxX5FFWIWDIqV602F4+UG1YAsmuejOMp+zVlMOYRzaal48Pr72exNQEBFgN40dWLA7MbwrLBhRn+Hr9YlIDzjJnBCfNPaLyTeIl9XvWqcAg72fbJzQ1cJbwNvZZOMwGxUqkbW4PopFOHeRRqqS3gqQmcNcQ0g/OAF2MotXZzH3WuxC1btHAPLctRxKMIMFvtchY3YCWZNLyBQcD3a9Y3DpgtmEfS+dOi84hSGghrpgFaroBWKtAsG1pnB6obNgRCgecCrhCPghgmdRxAOh4j2xRlHpkmi3CWylJsHo8iml7LfpKxw/qDraQAXmLo8EtOzdP7EPNoIp1Hhh6IhIYxoeJRqAukwwWzyHHV0lxQ5N9r+RNPhLujcSx6PEt0j0sswijOI9n2PcSb2TPunJZdKjF8Iyky/IU7j4iuA9x9mWj6KBibd1sLO4+EUNNubI0Ds73az2pIoBP7jhAQ2w5zzriYRB2H8b/G4jyKcZTVc2zJz2erwGyjPiR83EsAs8cI595TDLVJ8WjiKtHRp5Q+SCk9E8AhANYDeJAQ8jgh5JOEkLpXLkLIiYSQlwkhrxFC/r7ONKcTQl4ghPyZEPKjdjbiL6FOO+003HXXXbjjjjuwcuVK3Hbbbdi5cyeefvpprF27Fr29vQ1dOaLe/e5345FHHsHs2bNx7rnn4pZbbmlpPaIW33ayooZhwOdPGputc9zy1PcnmYdaDz/8MB588EGsXr0azz33HA46eBlK5TLE3WuUS5RkvaLLzyqDsK997Wt473vfiz/96U+44fYbUC3HD8DF/D5x9ifw0zt+il/d/Suc+JETQXQi10kjLAOsEa1WPMr14YsrvoivvvOrNfMWwOt64pFkHvEom4ihaETDzNxMOW3Vq2KwMggA6M3WtuwV7486jypeBWW3jIpXga3bob+3E1sjhCBn5kLd1oQjS5RGNKSNNApOQTqMxMCVECIFCaA5MBsIBDjXd5kDbNvzwP2XAte/G7jjLOAP3wUG3gi9h669C/4fuNtu2zqg9yBg2mLAzDD20cYnAaIhxbvBZatF4OSrgff8I2CkgdXfByEEHT7FZv7FlN+8ls1v+hL2f89+wHv/AUiFB1Iej6zZixfL14yeHljz5wO6jszhh7P9xLszCfFIk8wjFlsDgMKjf2Db0+Bz5g1zBhEXV1VgNhMmMmzQFwURA/B2s3XVO4XziIkjwl0UxNY8OAYXT0fri0cltyR/JpYNWq1KgUX32d8B1DCPgGDg05LzKJXh68TFI0uIR7MBSuFsDoDWcBwGDq5UuD3bbc48Mo1Y5pFcZ0U8mpGZgXxVh5nvCIlBgiclxBUgLB5l3nkEcu95T5P1CGJrtFyWT+9bKXFcHN/BkMv217xM4FgSkFMxuBPLDc9EgWrGPP0VzCO1pTbrUJbQeaRsp1wvx2l9gChcM9X6ziNi6NLtMvLww/BGRgKXEWdnqcBsLZUKWjPHxdYk88itiVk1Yh7J6FzCJ8uEC6ZMmHV5bI075KoVENuGOb0Xmjpw8v0gfud58mfqOEpsLnI8NcV55PtSABv31si6Vus8koBZwQiJcR6Jp/zNPgtKt7XQcRPLEDyiCeq2JtlaHCA8IcKAEcM8Etsc2XeGOH/4Odp1ysno+cynx29dmlRc2/SG0xtG8LkT14G9AMtt1aVC4sSjpMDsvSweAUwISfo9o3aQJIbBrik1zKOxAbOJFGGbOI/Un61aIUMVjMbiPEKcKCh5P5FlCmdgq7E1jW/znnAeqV0px3B91ywL2h44byfFo4mrxN+EhJBuAOcCOB/AswD+A0xM+k2d6XUAVwP4AIAlAD5OCFkSmWY/AP8A4GhK6YEAWrO9/AXVypUrcfvtt+Ouu+7CaaedhqGhIUyfPh2maeKhhx7Chg0bEs1nw4YN6O3txac+9Smcf/75eOaZZwAApmnC4TfMxxxzDH72s5+hWCyiUCjgnnvukTGyt956C6tXrwbAInDvete7YpezaNEirF+/Hq9xmOsPf/hDHHvssQBYRO3pp58GANx9993yPfl8HiNKxyYAuPPOO+H7Pl5//XW88cYbWLRoEebPn4+1a9fC931s3LgRTz75pJxe3Y64GhoawpQpU5DJZLDuzy9gzVNPImvpOPjQpVizeg1e54DggYEBAMDxxx+Pq6++Wr5/cJCLJ729ePHFF+H7vnQx1VvebM43ufO2O+Xrxx9/PK6//noJ1R4cYPOdMWsGemf04torr8WpZ54Kz/dkfEcITDrRpXgkGDyzcrOwaOoivG9ubazO0li3tYLLxJacmQuLR6Lbmh50WwOAmdmZMDVTRk3iplVLvKYylzptNmgdqgwxZhJnHgHAtPS02Pb0SSprZTFaHZX7IC5GljWzIWC2KhBZugXHa0E80izZbc3QDGAXN0YuPxPY+BTw4OXAzR8CHC6w+D62XvY1bLj0SvbatnXAjKWobt2Gors/sPGPwNa1wPIzkeJP3TPTDwJmrQCy3cCKM4Hn7wDefARdroMSH0TlXvwVkJsBZBp0vQHjDwFASohHmga9sxNdp5yMfe/9GUwRKclm4BdLsbE1s6+P3dDw88+PdFEKLW80Kh6pzKMsNM7UieMeef27ADBxCwjEESEQBc4jH5UUj20WRgFNk0KTlk5LAVF1HombNk8AtuPEI0XQom0AUOVAiMfyxE2gxdtaO5uD6JoQp3wFgtz0plFEuPj+1/P50J/V1tjd6W4cll+CfNd02UENgORJhQQgIR4ZBqacfjpm/WvjZgNSxKJUuktaLY1oUgwcdNk5Oic1S/5dgq1V8SjSDUm6dWKs7axdfMTtYHCnUgJgNlve+MTWapg5ccIAt+l7w8PYdMFnMXTvz+OB2cKNlEo1fHKsMo+iN9+Nuq3VZQ7VKeEm8kulcGzNc+FXqiC2jc5TTsHUvzkv3PFOia2J7aSOU7+7TsR5JD7Pfml8gdlEC2Jd7HctAGaX6w+2JAC6GfNIxMYcJ+xCiALBJwqYrQluSXvd1hItQzDVuJhNUikJCY+6tgzhPLL2zoCrVecRDL3mc6eKyXtq4Ngq8DnWZSR4PG8D8YjYdvLYmhon1PUQrF2dHzStfWFCRLjigNlx4jzCApH6mphuvIDZoe3n3x2haaOxtRaA2XCTNZwYazGw/9jh3OacuTDnto7EaLmM2s/VZI1PJWUe3QPgUQAZAB+ilH6YUnoHpfQLAOr1nT8cwGuU0jcopVUAtwOIEqM/BeBqSukgAFBK95wvdpzrwAMPxMjICGbPno2ZM2fizDPPxJo1a7B06VLccsstWKw4DBrVww8/jGXLlmHFihW44447cOGFFwIAPv3pT+Pggw/GmWeeiUMOOQTnnnsuDj/8cBxxxBE4//zzsWIFiyktWrQIV199NQ444AAMDg7is5/9bOxyUqkUbrrpJpx22mlYunQpNE3DBRdcAAC47LLLcOGFF+Kwww6T8S4A+NCHPoR77rkHy5cvx6OPPgoAmDt3Lg4//HB84AMfwHXXXYdUKoWjjz4a++yzD5YsWYIvfvGLOOSQoOuXuh1xdeKJJ8J1XRxwwAG45O8uxcErDsPUrIWpPVNx+b9djtNPPR3Lli3DypUrAQBf/epXMTg4iIMOOgjLli3DQw89BIDxlU466SQcddRRmDlzZt39/ZWvfAX/8A//gBUrVsBxHfDUGc4//3zMnTsXBx98MJYtWyajcZRSnHz6yZg5ayYWLV4Ej3qy+5qAZWtEk6951ENPukd2EIsrW7fheI5kBGXNbKx4JNwnoptZX65Pvl9OG+nMppZYh5DziAOfd1d2h7qtAe3xjkSpziOPenXFo4JTgOM7ctAqSjiJAITgyfXK1Bl3R4pHO18CdIt1N7vkZQarHt4EPHk9e8PGP6LaX0G530D1gWuA6igq3kysP30lNt47CLr5WcCrAvufgBQX2LIHnRos8JhLADMN3H4muvwgrpUf3cEia01KRMHsxYsAsJbqIqZmL1wopxNOBXE81dgaMQzmVAIXHRo6j3j0TMQ6+WDUK3JgNhcy/BjukbuLiUd6NxOPBBBaikd832uuB4eLR/7IKIhlBYOATFqKQWVPFY/YOe3t3i23T4pH/Jj71VrnUSvxETEQAu9up0nnEfv8qNwj2aGqUlFAvM2ZR+w9bHohmImikTigP1pgndAySscrHvERzhy2YoHzKEnJ6VwXfqXSti3c1Bg/bKDKjsnsVADeh8ugylqDbkjEEAODmJiPYQTsKmWQR103cSyIxdYi4lFM9KbpfJJ06+IRPJ+3N/dHR9m6axpziHDnHI1xHtWLrdFiKT5ml8h5lOyGnaR5FK1cZsuymHgEx2VRQ9tC9p1HYNrnPx8MAtROep4bcsAFsbnIgIwQQNOk80icc7RUGmfnkR4ApfnvUvxrMNiSg8Ak3dbkE/UGzKMJch5Jt57ofGaOLb4TVzIWw51HWioV8Mcix1Xn18yoMLynSlxfki6fGMr3n4it7QXXQasRp1jnkZFMrIgDPO/pMvtmw5w9q/mECNZTdn70a6PKJGWzGFmb3bUaAbND+0kVdSyzvnikOo/a2M+h8045ntO+8AV0fPAD4YnF57PaovNI8OD8sUGsE1UU7N9m9Xzus5h/+4/HccXiKwQ/34si6//ESvmk250AACAASURBVHr0r6KUPhT3B0rpYXXeMxvARuX3TQCOiEyzPwAQQh4DoAO4nFJ6f3RGhJBPA/g0wMSKv9Rax7vrAEBPT490AEVrVG2ZHalVq1Zh1apVNa9fccUVuOKKK+TvF198MS6++OKa6QzDwK233ppofY877jgJ2lbrmGOOwSuvvFLz+v7774/nn39e/n7DDTfg/e9/P6677rrQdISQukDs6HZEy7Zt3HfffShWXLy2cxTT8yn05A28PPAyjnn/MTjnY+eE3DC5XA7//d//XTOfU089FaeeemrN6zfffHPo9yOPPFJu62uDr+HvL2PpSsMwcOWVV+LKK68EwDqGvTTwEigonlz9JFauWgmd6Kj6Vek80rgWq2kaPC4oeL7XFFRr6qzbmuhOljNzkvfTyHnUlw+LR47vyGljxSPhPFL2n5jXqDMq2ULCedROZE1UzswFzCPfhUFqLzV5K48RZwSOV+ssEu4HsV1NgdmaKbutMfHoFWDqgmBAuOC9wH5/BTzyb8CKs4F1d8GrsL+N3H0j8t06NvzrT+ANMpHFLWkwMz7QdzjSry8A+tchM2NpsMCOmcAH/i9wz6fRQXkrdAqYQBBZ40UprbkZ8oa4eLTPPiCWBaO7O3a7tHQafrGoxNa4y40PcKwFC1B59VWkDz0UztYtsfMAlBiZAGZL5lERWibg6fjFWueRu6sfAGBME86jcGxNOI80x0M1ZQCoMEdTLie5Plo6E888Es4jEY2Lja0FziMx8GklomSm2LaRAh808ZtBY9o0EMtCdWMgHlFVPBLiTbPYmrSbs/cKYDaxbdaZTgHUAuxYaLlsxHnEY2txzqOkN63SQcIFgjZia0BwPPud3VgAwHaDm/u42JoWHeDqykA4Clg2OK9IaWUvWw3HDGLjijmPIsyjdoDZSZxHYlAvWFIVBo9XIwi+Kq6kUpIv1ii2RtLpmnM4+jRcrpMXwKiTDoJFtyq/xMH8+ZxsRw9EWiQr7egl50iB0zMemc/XJ+b4CMaV6wad25TjOx4lu60J8UjTarrlxQKzDQPmrFmw5s5pPH/dkC6ckFtAxPK4C2uiBmdE11l3PD6YnhDnka4DmhZEf9PpwNUZcbSZQtjeSx2KiBSPkjOPAudRTOzrLxWYHeM8Shpbk7ykvdhFat4ttyQWOULntOgQFolaTTn9dKQPPLD9FRJRZMeJ+e6pjQYCHAZfJ0KsNqloZz/XEzDjIqBE08AaLoiHGQn3K4+t7RnnURjs3/Z8JqjxQM1yYkTZyRqfSnoElxBCusQvhJAphJDPjcPyDQD7AXgPgI8D+IG6HFGU0v+klB5GKT1s2rRp47DYyfpLLkoptg6XYWgapuXtEOcoKfOonfKpLwWgaInB/3FHHYeX/vwSTj79ZOiaDs/3QswjgIlIwnnkUhezco2fzIhua8KpE4qtuYF4JJhFlm7hg/t8UEbg1G5rEq5t1A4c45hHHTZzPAxXhuH4juy2BoxNPMpa2VC3tTjnUd7KY6Q6EisOWZolhTnHd5oCsy2iSeaRqZkstjZt//BE7/9noDoK3Pox4IWfwXXYPEdeLWLHc53wy1XMuPxyAEBlyGRw7HwvUmkWQcsaEWDxwacDy89C1xTeIU1sgyIeFZ96Cq8c8U5UN24MvdUfFhyhThgzZzQWj0pF6cJRY2sAY1FMXXUOzNmzAmBoTPkiFjaFi0cOc3pQpdsaEIC11XJ37ZJCCABoOSY4+qNsWikeeT6cFD/Ovs9uvKR4pMTWvLIUEwXIVjCgdI8mi621MBgQwGzCRRzN5DwNTYM5a1as88ivVKS7pbnzKBxbE6KQtWBf1pku6jwaGYGey0eYRw1iay06j6jjMAh4G7E1gEdGfQe7qiyqG+JgidiaZUkXTDww22VQzTjmUYQJJNw9ceyVetsZdR6pgk7Skp3CqvWFASFayA54pTKo0ilNxNZCzCMxiI1rHZ9OM2EyBjQeB8yWziM1rpVk27hwSCtlLqxZ0uHlVyshgLQUYXxVPFJ+VsSxevso6jwKbcN4lK50W9N17niKArPjz50Fv34AXWec0XD2oS6Acd3WxLVgooHZfDA9UZEkYhhh55GM/IXPVTPN3ZN72XlUI0zXqTDzaC8Cs9tkHsGMERmaxGy0Fpc1EaXZduLrriqUES3eeWQvXIjOj0QDKskr5EaMfJeExflIbM2McR5pGosmj8HhJc9B7lRtWoYRfH8k3q/cbeX7Ew/M1rUA7D/RLqdxqEnm0cRV0r35KUqpBMtQSgcJIZ8C68JWrzYDUB/39PHX1NoE4AlKqQPgTULIK2Bi0lMJ1+ttW+vWrcPZZ58des22bTzxxBNtz3P+/Pn405/+VPP6KaecgjfffDP02hVXXIETTkjWgadeRV08rVR/fz+OO+64mtd/+9vfwsx0oFBxMbsrDV0jcF0q/z6h4hF8KQBFS3RR+81jv0HRKYKCQic6i63xdVKZR67vss5Nvt/UeSQiWsJ5lDEzcCm7WRcwa41oIffOFe8O3FuCh1PxKjLqFcs8ErE1RQTpMJl4NOKMoOJVYGpm4Dxqo9OaqJyZw+YR9nF3fRd6DCsib+WxZXRLrDikArABNHYePf59mFvXodoLuJlpMIgODK4HDoo4z3qXACt/CNx9Pmi5CL9sQ0uZKLFUFqZd+Gnk/+p4bLv8clSKeeTmHgkgPu4HACAEOPlqdK35N+DPNyMv9uv0AwAwEXTHv10Jf3gY5RdflIwdIHAeafk8ei+9tIaTIxeRScPfuas2tsa/CHPHHovcscdi2ze+EQJLR0u4hITDBZ4rHTGMecRjazEd17z+XTC6u4PzO4Z5NOKPQHN9lFPKl7XFeQG6XrfbmmQe8diapjiPxDnBbgQNFseKDBCSlIjG6UUu7iiDdHPOnJB4FHYeJYutScdPObjpI+k07IULUXn1tdA+pZ4Hv1iElstBz9bG1iCYQGhDPBLxOek8aq+1tqmxGOKgM8TXQxHvhMhjMzs/LZdrb6q5wyXORk8Mo/YY8m2mvpeIKVOXedTqzb0QBpwGwoAY1AvnUbnEBihiQGFbgOcFroeUrYhjMQyeDHcEjYzU3MzGuw/4wEAwh9pxHrkCmM3PLUrDIo9YT5V55LkyPulXq9AUx09N6aIDjxeOSo4jY0I4sNQn3oFo2/hzmmTQQIygy2Go21rkHJk45hEfTPOOSRMpHknmUVoROiPXUyudRRXJHTTjXa06j2AYkv0nrzl7A5jdamxNcnBi2Dhvg9haS6U6quo4j8ZaqhsxKog2BGbXRNxqu6yNBZidWAjS9faA2aK5R5vf+UmL6Abg+wzsPyke/a+upHtTJ4QQyvM5HIbd7Or4FID9CCH7gIlGZwD4RGSan4E5jm4ihPSAxdjeQBsVFw35S66lS5di7dq1e2RZjYDRe6u6u7vrbv/rO0ZhGRqmZHnnFgSCkeheNhHl0wbiESEghHVWo6CsoxfRQSmVQk+IeeT7cHwHPnxMSzd2ywmXjXQeWTnpNhKxNVuvnwNXu62VXTZwiXPqNIqtDZYH4VMflm5h6bSleP/c9+OwGfUSqc0r2m2tkfOo6lVr/i7cLHEw7VD9+WfAr/8J1ozpcEa2wu1ZDINSgPrAtEW10y/+a+Bvfg139U+AO+9Cxwnvw+57H4DRaWPqJz8JLZWC3t2NSvdS4PjLAcTvN7UEdDyXnwn89bkMqg2g8IfHUOLnuLt1a+g93vAwtHweRNeRf18tRF2Uls7AKQXAbE20zI4Oyu1UQ2C2GNAKhwv1POkyauo82rkLOo+sAUFsTUC4pfPIpfBsxQ5uWqz7YDoNkgnEo4pXQdrgbiDBPBoSzCOKoluU8wXYjaCWycAfHm7LeSSeYhtFziRSBrhm32yUlCiuYNtQ1XnULLYmn3YG69Z76VeQOmgptn/rW6HuRnKf53PSlQUEXelCziMB7E564ymcR6USH8i3N/AzNRNFtwjXY+sacn6J2JqRDThbNc6jAFqKSBSMmEYNE4g0gJzW2071PKWUcoZSq86jcOwp1lXDu2AFXewqgB0cE9ldrFhkEFTDVLhcMQwefi7GxuwaMY9adB5pknkUAWa7LIIWOjeE+Ol5krPFWj8rwGzRQj7mO0jwS6jvhz5bE+k8Eq8BjWNryedvyLhIbLc1KTBOULc1Do2faOeReo5pqXQgbEf2nZnLowoAbUZfx1paq7G1OGbQXgBmt8rHiTvOgXCcUDx6m7Bc1PWt5zwacynOo6TAbH3KlFghPyoatSOkxkYoG02v6w2/P+KXsWedRwDnBE4U/208ay8IyP9bKunevB/AHYQQTpvFZ/hrdYtS6hJC/hbAA2A8oxsppX8mhHwDwBpK6c/53/6KEPICAA/A31FK+1vdiFQqhf7+fnQrT8cn6+1ZlFKUXQ9daQsaP5Z7IrbmUx+U0rriEcDcRxSUTadpQVyMQ6ol84hocH0Xu3btwsbSRsya1ji2Zums29podRSGZsDSrBpgdpyTSH0/wMQjyUeKAXTHOWhyFhMC+kvsY2frNqampuK77/1uw3VuVjkzJ51UHvXqM4/qxdZ4tzUhHlm6xZ4sqp/v0Z3AvZ8H5hwBK1XGcHEHHKcEQ5wjPZHYmqgZS+EtNgDchez7Pgjs+DM6PnSqvGG1Fy5EZcsAfHsKdv/3fyM7n617jfOIlxSP7E7gHX8jX9919dUwZs2EN7gbzpaoeDQEXbhNGhSLrZXkMQ5ia5GbHe54qCeiC1FEiETUdeuIR/HAbFNxTYlpPRWY7TvQPR++qYPYNrt54zdbU8/7JNLLlsHQ2AC45JbQYbFtl8wjHlsjFCg7YfHIr1ahd3bCHx6W24EWYgjCDWGW2LmkK5Edq68P/tAQvOFh6B0dUgDyVeZR0yfAHHQpYoO6gSk8JqNlMhJWDijsqYjzSDjPRLQIaMd5xNbD43FCrRkkuE5ZuoUto1swlTClMhRbc13W+r1BRygJLfW9mqe/UJ1HodhaPOQ0rmqcR57H3DStDqIEz6YRM0fXQKu+FFX8chmayqRRxCPp7mkYWwsEwxrOBiFscK8IS5J5VK/bWZ0iwnlULoO6XDwyOTDbcUKRxjhgNnUCYDZjHtXyq0Lvj3EejWdUiGi6ZB5J51GkO9FYxKPQE3/VeSTOEeG+m6hW2EJA5VHPiXQeidJSNjzetTb6OU1NnYZvn6ThiGMOGtflJ612mEfyZwni3xvA7BZdKjGd1UjCbmt4u4lHQkixIsyjhCJJomWIzqdONaZZQ7yYOPNfvllzzxQGZbfvPApiia1xC9k6JrzWKAL3eLq44iroUOqM63GbqCKESNf62+Vz8nappEf/UjDBSLTu+g2A/2r2JkrprwD8KvLa15WfKYCL+b+2q6+vD5s2bcLOnTvHMpvJ+gso36fYMlRGIW1ieFsQcxkos5ucglXADmtsTflc34VO9NAXhk99bCtsQ9EuYpe5K/Z92wrbMGQMoepVoWs6hswh7CztxKAxiIpbAc1Q6JqO4eowRqojKOfK+MFbP8D3Fn2v4fqIbmujzihyZg6EkBrxqBHzJzotAZFOFbWkg8YIBjCGZiBrZrGLZ7easYWSVtbKouSW4PpuXedRh5lH1a9i9K3HYaXCrh4BwBYxPJMC+O5BwMGnAe+/nE306HcApwScfC3M1V9HtbAd7vAmGL4LgAA9+9VdP7efnU9GTzdm3vSb0N/s/fbD0E9/iqGf3oPt//ptzP67EwAjvN/U6rIZR0i4uAAmgpaeew7dn/oURh58EE7EeeQPDUPrbF88it5YaHYKoJTHd2qPIS2XWBcT4U4JiUcZJbYW4zzq70d6+XL5u4hlhZhHngPdpYAZtC8X6zHtcwyPZ2x+HEAktmaFY2sAUK2UAK3WeQSo7p4WYmuiu1qJDYZ1U3UeMVHM2bQJ+pIlgTjluqBlDuduyjyKOo+CddMyGbjbt8nfvREmHmm5vLTGUwBEdKUzzYCv06p4xJ92+wW2jLF0W9s4shFdXCuodR7xaE29m2pDZw4dz48HZkfFFRHVS9j+V3VnAapDq7WbQ3ncGjlXBPxbiCqlEmAHMQdx/vqFAhePYtq8KyVia2I76m2b/AxrSicdQlp2Hvkl7jzi7aEZ86gaPjdkl5+qjP7QkJDkMPGq3mCWA8+p54WuPePrPBLgajfGedRaa+vYMgLxKDTorGEeTZDzSBVQ94R4ZJpMyK3Gx9YyRgaPHWzgPdN6x3X5SYt1MqxtoV53+hhmUEic3WPOoxZja3HHudXY2l4EZrdScnsMMwR5Hl/nEb9OVKr1nUeEhD7j5vTpiFZIPBrDZzGIJSZ3HsX9nOQ9tFJp6b6onQrHAt8GziMgiGtPxtbGtRLtTUqpD+Ba/u8vrkzTxD777LO3V2OyxqHWbtyNT/3wMfzn2Yeio3c3FnYtxEMbH8LFzzB98ZMHfRIXL2tfa9wyugUf/OkH8Y2jv4EPL/iwfH1bYRvOuOsMXH7k5fjY/h+Lfe/f3vm3OHrW0Vi7cy32n7I/Lj/ycpz54zPRm+nF9uJ2PLLyEUxJTcEN627Av7/w7/j6kV/HiDeC7lQ8DFmUqZtwqYvh6rB0twi4dcWroOJWYp1EoqLOo3oRt3rsng6rY9zFo5zJHE0FpxCIR24FePpmQDOA4gDyr94NmMDA6FaYfhfLUt/+CWDKPNaBzq0EsbXdG4HhTcAfvstA1vOOAp66AVhxFtC9AGa2F45uwh1cD4P4QNdcwEzXXT9vgDmt9KlTa/5mL1wIv1jELt5FcFnXEnxy3txYbhNQRzwqlwFKoeVzMGfOrBGPmNOls+l+1DJp0GIxiK0J8Sh6YyQAueUyEHPj6pfKzFkl3qfG1jL1nUfUdeENDMDo6Qm9rudyIeYRcx7RsKgQWQ+x/ypeRWEeidjaUGiZsAhM3eQ3mG6wfqX2Y2t2mYtHiiPH7GM8suqmTUgtWSIFICBwVjUdCPAncH6MA0JLp0P7VAg7Ej6eyYTiQGMCZgsRgK+3NoZua8PVYXhR8cg0lXbiRt2bamKY8L2iHAjHrSOAoKW27M7mJbLdM2C2Imi1uJ9kRZxH8bE1PqhXnUdONgTMBgLnUegGNbbbmnJNijmHNdOEp2yLZP3w2FjSEq3pabkMVEVsje83xwnF1uTgQwXue55kHlHH4Q6BOsdGr+c8Gn9HAa1WFeeR+NyNPbZGdEOJpilCxJ5iHnG2lhBQJ1o8kudDOUYwA3Mk33zizVg0JSb6vYeKpNPJt1+v/dzV6641kSWEHC1x3K421pS029rbjXkUOqcN5iQcd+ZRAmA2qRO/jU5bwzxqJ7aW1EUmpo8RrpuWGt+dYOdR0KRgDyxrnIoYBntAN9ltbVwrkXRICNmPEHIXIeQFQsgb4t9Er9xk/e+oQsXF717aDs+n2NDPBrRd+QpW/mIlHlj/gOT4AEChWsD2wnZc/vjlcDyn3izr1lPbnoJHPWweDbPbBWelHtcGCAbJVa8KUzORs3KYmpqK7cXtAGrFmY3DrMNWd7qxeCScQ7vLu6XoYhADGtFQdstSEKpXoW5rbrmuACTmERWP8lYeu8pcPIpxLLVTIfGIMqcXnr0VuO8rwC8vBh76Jjp4RLA/OwVmaQj4zdeAV+4DXrlfcqCkeLTzJUAzgX2OBX5xEfz/OByeYwDHXsrW27BQTeXhlgZgVIaBaYsbrp/L7fpxXc7s/RayabYxx8g+mTm4+LD6gqWIYIltBgLwtJbJwJxVTzxq7jwi6TSLmfgUBjGC2FrkZkQIJDIvHym/HG4PTl1Puoy0bJYN+nS9xnnkDQ4ClELvCe8nLZeTQogqHlHTkAPT6M2WEIw86klXkWbXOo/ENppaIBKIDma+cAO1MpDm62GXWfQnGlsDAGcjg2ZL5xEAn7uEEjuPyrUOCC2bCQGzZdc7Dh3XstkQLJ0YLFoEKAPWFjv3CPGoXeeROE4+vzvwxTEwTRZl4myiZrE1eLWCQyybRLou3ERWeGLVcx61yDwSx81p5Dzizh/JPOIAajEoEOJRocBcd004KyJOBtQ5r6zIPhXOowaxsbiSziNlfYmuBy4r9bMpRDRFOKWO4jyqOuyY142tqcwjRZQazye9miLiSNFRrHfjbmtJKi72pM4zcKdN0NN2sQ95dHPCxSPDiMB5a4/ViukrGt4PTXQZU6ZA76ppvhxb4etKDDB7jzOPWnUexVwXEzuP3h6DYnnNlMwjf9yZRw2B2crym5WWSsl7kz3pPJLnbNLubFAEs2qt22q8KyTivw2A2UAbx2CyElXSvXkTgMsAfBfAewF8EgmFp8marEZ16x834Du/fhm7iw6+9/EVeKufDbS6sqyL2Y7iDil2pPQURp1R/H7T73H3q3fjEwd8AvtPqcO1qVNPb38aADBQGgi9XnLYoLReNAkIOoCp3cHm5ufKSF1UnNkwvAGWZoVEhdj5csFmoDwg3yuia6qbqF6Jvzm+05CPJMSt6A1h3srjzaE3Q/Maa4lljDqjcH2XAZLX3AT0LgXOugvQTOR3vwg8+FkMwMd0EGD19wHeKc3Eu1CtFlB99N9w/DM+enY/A7zvSOCM24DnbsfO236H0Zd2YkEnc46YmomqlYHb9w6cdOerGDj8UNR6ioLy+gcA04QW0+XMXrgw9Hu0s1O0hPNI8KMAVTzKwpg5E96uXaxrEb+p9IaHoCeJrWWE46YEUzeh+/EDXRGfqddxjZZKDFwtbqw9V06rpVMMbJ3NyiiaKHcXExWjziMtl5MRLME8MlwKGKZsrxy92VKji9Fua/7uwHkUEo/EOvL9EAg0LTCPdB2uDmTEGFNxHumdndDyeTibN/H5K3BrDgRvLh4J23htly3CY4eihJtJnHdaNgsSiTIJ8Uc4PxI/MZPMI+GYau+zLK5tppkCUAwJDsIhRHg8UaxzeD30wElUEx2oHeQR02Cd/zjvpVnVOI+q7TmPSNR5FPMkleg6+6woziNmgecDClt1Hpnxg1ilQrG1mOMaHaiozqNWbtalmFwsBsBsZd+HwNacC+ErziMWW1OB2Y1ia6rzKPhsjeegVgpFjhPsB2XQBF0fE+8yFHFSP79asNzo38azBDA7cB4lH+i2tBxFnCSmEbglJxq020bN+a8fQM81vncSFYqLNuhgNtHVqtAQ11ktNnY3Dsva2xVaX8FyG2fnERoIKa3sr+4LPgOfswrF91xSN1moWo2tySYS/5+9946T5Kqvxc+t2GHyzM5szpJWAS2KSCIoIYNBGIPIhufwELbBxs8ZY2xh/DDm98EC4wDP/hks2xh4NtGYIBAIE4wAZRR3tTnP7OTuru4K9/1x6966t7qqu6qne7SzmvP57GdneqqrbqXu+z11zvnmsOXr0vdYrwmd5dxWt2Bmu59WkQ9ZCaAipfQuAIRSepBS+m4AL+3dsFbxTMDkQh23ffERnDvej6Kp496DMzg4XcXEgA0t9OcsNBZE6+7R4igqbgWnqizziL+eB/edug8ABOHDkUV5JNQwfhTwvHlgMwBGbPGwbU4AHVo4hLHiWNtJLS/WZpwZRRVk6zZTE/lOZuVR3a8Ly1scnFRKsq3NODMAGBHQDfBt1b06/MCH0agAJx8GrvgloH8tUB4VNq+a78Ac2AhYfcALb2P75DrwatNwH/xXXPEkxcCPGwg2XwfY/cCVt8Kl42gcPCoKc0uzWL6SVcSOpzzMf+f+luPzpk/DGB5OPDf64CDsc85B/003AYiK+DSMFEewe81u7B7bLV5TlEfrWGA6VzIBQDC/ELVnbwFucwmqLPdIE4HZscyjQmvyKKg5IMVC9OTI8yJSIJxM6YODin0MkMkjtWOg1ldWbGue70L3ITKPgATlEUkgjziZNtdMHhmaEalempRH+SYunqEJ8siIkSrmpo1oHAmVR1IBLUiYdpNNbhdzeCEmFemlEuvcFrY65+Qct61Z27crZOWSbGtCeRRuYwm2NQAYKDJSVHShsm1hW4NsW4udZ96hTG6rLpCkBpBta1nIo3hgttehbY3bnvh9kHBNEUMHlVrYB06NBVDHuq3RCg/Mbm07kG1raZlHyt9yHhuxnlIJpFCAf3qakV1hxo34e+weIJoWdcEDGGEllEcNdi5Tti+65QGqba2bk/U2yqMlkwOKVa2ZSIosbb0KzA4J1yDoaeYRZFWLbkSh6Gdg+K21cWNm5ZFqF20OzF6uQrdT5ZHaFar5teT3LqGF/NMAxTbWY+URgHTLdIbjVbzwQpSvvpq9b0nKIx6YndO2lufzjH+PycR6j7CcKqduIauSbxX5kPWurRNCNAB7CCG/Rgh5BYBsjwRWsYoUfOGBo/ADij975UXYvWkQ9x+awcHTFWwZKQu70nxjXpBEa4prUHErwiaWlzyaqk3h4PxBAAnkUdjhqZXySG4fL8ij/pA8kjKJhG1t4XBbyxoQkT/TzrSiUuLkUcNvpBJCQGRxE+RRCtF0xdorcNOWmzBSUDU5/VY/KKjYZhYEtVrL1vB8vI7vwKMejLmjgNUPPOs1ynY5zNEdwG89BpzPcqisxiIabhUuITB9CuITVBfXRtsPC/vGoUNs+VAV5gUeDI+ivmcPaBj+CgAzn/wkpu+4Q/zun56GnmBZ49j2uc9i4l1/CKC98sjUTHyk9GY8p3xBNL6KTB6tAwDRcS2o10Hr9cyZRwBAa1XYmi0FZsck2cK2lkIeOTVohWLUllsqiAV5NDSk2McAwJti2VBGzLYWzzzyPRcaWMHIC8g02xoQEUkiMFvqSCaUR7pkW+OB2R1kHgGAZ2oocvIo1oXM2rAxsq3JyqOFbJlHwgoYa0HPxh0px4BIzcSfqG/40Aex7n//72hdPDwaUsGas+3zUm1r/LNtsMQ+JxSrU+Zua75o7678TVbbCBKABS7D8zpTHonrOKdtTTxFbZFnw+1EwrZWjwKoIZGf1UpT5lGSskghj5Iyj/i11qQ8yhYmLtZNCIw1a+CePAEEQUhsSeqG+LVhubgIowAAIABJREFUGMrnOfX85sDsNPJI06L7VLKtZe4wlAGK8ogfB95ZqQuFjKIWlNVxhACEtFSndQMkJHKYdbP3gdm8M2D0+sooBNOg5FTFlTumuWxdmPMHZicojzKqzvJu6+mGorLqkfKoVeB0p/cUtwDzOVau8SSo4FrCiL4TM29DUQP11hAk2wJXivJo1bbWG2S90n4DQAnA2wFcBuCNAH6+V4NaxTMDn7nvKHZvHMTO8X5csnkYjxybx95Ti9g8WorIo/o8HM+BqZkYtAcV5ZGchZQF3LK2rrwuVXlUbBGyzNvHN/yGUOgI5ZFEHnECqO7X24ZlAxFh41EPZStFeaSlF4KEsO5qDb/RUqV00dhFuP2625s6n/HMHiB75tHht74Vx//wXal/F8ojvw7Pc2DMHwN2vxawI3JM3q6pW0BhgIVhW30wa7NwgwYaO26AGT4crTx8SCzvh3k7jf3MbmdqJrMU+ix7J6hW4R07Jpaf/cxncfLP34/aQw8BALyZaRgJYdkcxIgUNO3II39xEUd+9a2Y/fznxWtCeVRmmUcARO4RV9lksa3xLlyRbS18PT4xsrnyKPmeoGFgtsh5kaw4fD/1oSGWcSTBm2IdLOPZUFpfvzgHpmaCcHWWaUpPX9vb1kSxGQTib3rYlVzNPApJmHpn5JFvarDDIRqWeo+bGzfCPXqUZctI5Fte25roXBQLzAYiMtFfXGR5BiEZRghRChtVeaQqw9qBT1CX2m2Nk9lDpdFwHNF1krnbGg/Wbuq2lpTtwXKeaBBkC8yOKY86DsyWbU9IUR5xIqwR2dbgetGElN93Qnkk208S1tdGeYQwqFVcE1xx00G3GGN8HG74GUhMSzn2CskT7qesPKKepwZm+y2IPSVLqTeB2YoCKKYsoY3G0jvptFKMGXKYdo+KM12TlBgSedTlMGQ1MLuDcN4zFOrnipH4/3LA3roVQ695DUpXPifT8pEaRzrPGQmHlWxbI5rOvvM9v7vEpUxExQOzO7weylddhbV/+h4ULrww93DyB2bnt62Je9fLlhm4JITHN3BXkvIoPCar5FFX0fabkBCiA3gtpXSRUnqEUvqLlNJbKKU/WIbxreIsxSPH5vDY8Xm86jIWWHvp5mF4AcVM1cWWkZJo0T7vMuVRwSigZJaw6C7mtq199+h38Zr/eA0+9pOPoWgUcc36a3DaOa0sk1V5JDKPQpJFtq1xyNa3TMojibBRlEeGLbqttVIeAZHypuE3cucWKeRRhm5rlFI4D/8E1XvuSV7Ad8UY6l4dXnUKOg2Aq96qLKYoj7SwaNY0YPx8mNMH0ADgbroSVlj0L373u2J53imsceAAe3+Yu+NRD7rHiIj63r3R8ouLAKU4/q4/AnVdpjxqQR4B0WSHP4FPA1+3nBcU1CLlkbGWKabc46yQ4176LIHZWjG0a9VC2xoXU8UmCe0Dsx0WmC0mGn6zbS1BeeRPnYZWKgnyRmyvr0/sr6mbMELCh5hWqnRfJ9FkI555pCyXmHmkKo/yTgQ8I/qqM+wYebRpI2ijAW9ySlEeRdlBOQOzJcJE2O3CIPJgYRFaX1/6k3BDUh7xIOictjWfd9FbovJoqJygPArb1rfstsZta34zGZSUecStWfC8TE+hiWkCQSCsUuL+zBuYLT+xBZTzJsCJME7o1WpqYHZoHQlqNRZ03MYqo9rWmscrd/mJjzGP8ggAUx5x8iiuNIldG0TXBTELgOU8yQq4FplLRNcickXpttZN5VFEHolua5pkW1uy8ijFthZup+fd1vg9ELQOo1/yduQC2kjf5xUHOfMoZv1ZziKXWBbWvedPYE40t39PXD7hPGfvtrZybWv8fLE5XxeVR8p1ELuPO7yniGVh+NWvzv35y8aTlzzKb1tTVXc9tq0J5ZHbc5VTt5Bb/bWKTGh79imlPoDnLcNYVvEMwv/90WFYuoaX7WZ5MJdsjrztsvJoob4Ax3dQNIroM/uYba2Sz7Z2/6n78dj0Y3j09KO4fOJyTJQmMFefE9sAJOWRka48MnUTNa8GCioKrE39m5reJ2cK5bGtxd9ra7awoqWFYItl9YhoklVQWSCTOInk0d67gMknxK/+9DSCxUV4k5NwT51Sl63NAB84F/Z9nwAAOM40PGcOxsBGYHRH05g5cXbB94/h8d3PxsE3vgmLsxOwPAcNQuCN7hTKo8a+fWgcOQogynXhyiNLs0BB4XgOdI8xLPU9e6IxVxZhbtyI+pNPYvYzn4E33Vp5BEjkURvlUVBl16EctkylzCPNsqCvGYPHlUcheaTlsK0F1RpsXbatxZVHYWvuVoHZhYJkW/My2tamoMfCsoEo84hSdi8YfFyWKUKE4wGTiYHZCZL75G5rofLI6VB5ZEnEVaxwtjaw0HX36FFVeSS6rbUhVLl9pu4wAkEihjhZQIVtbRFaX7l5HSFk5RH4+ck68QzJCH5vdCKzB6Lcs+EiO+9CAWVF9wPRZeVRTMXCw3+TLAlKV6uouKO+x4K4M0x+Rc5QeG10rjyKddJK6o6m6YpKjzYaitJFtLyntCnzKDEQu1AAwusjLfNIeV3O+smtPFoDf3JKWm965hEMQ7n2mW0tFpidVoRrepOKkW+za5DDzeOB2QltufNCPW/N12yvlUfsnvFCe6DWO/JI7njVRiW3kqDcu7Fi8Yy2qySQC7m7ra0Q25rYV648AlcSdvGeUmxrsfU+HUqtDrut5VMeSfvZI3I7vn5ar3c36LyXWA3M7gmy3rX3E0K+SAh5EyHklfxfT0e2irMW05UGPv3jw3jZ7vUYKrEvvrE+G5tH2FP6LaOxzCO3JsijufocFlxmJ8lKHjmeg5JRwtdu+Rre9/z3icyfWScqlLMojyzNQsVlhRknWQasAQzbwwp5JKuHstjWZMJGfu9gYRBHFo6g7tfbKoK48sjxnUzqIRkDdmvb2vwHfgW1j/+m+L1x8KD42Xn0UfZD2K0O9/0TUJtG4Yd/DwCoP/oF+KAwJpIlv5y4Wrd3FlqxiPrevTj93VMwKYVLNDTsMkwfwIWsq17le98DEOW61CXlEQBU3QoMrjzaIymPKlX033QT7HPOwez//TfQarVl5hEgKUrcRsvluEWNk0jya5w8MNetF5lHeWxrwvZUq8LSLJhh//T45ILbUGia8qhWg1aKlEfU96KgYGFbG2SEkESWeVNTTZ3WgDCzJwhAq1UYmiEpj0xR3MdJBZk84uQr0fWmbBQ58yhoUh7xwOx8E4HAjL7qzIJ6j+vDwwAAf3ZWzTzKrDzimUfNwb183Px68BcXoPc1d/gT6+pKYPbSuq3xczPSx867OAe8o5/jsGyrtMk4D/8NC2FljErmkTSx5ra1DAqIOKkrFFo5nyySOHmUojyCHyj3hF9ZFNe2TJA2Zx4l2NYIiaxrSWSVaQLS9bYU5ZE5HqkfSFNgdozw0zRFeUQ9LwrMdl2ghW2NxMmVHtiF1BbR4WegsIs2t+XODXnf4pZgKdOpZ8WZCMxmhGvvArN5DpChns8VYkFJgzj/UovzKPPozC0ao/PcIrMp7b3W00CGLAGKFVPJMOum8khWfsaUR2FXyeUkEwURmDW3UCiP8mQeyQrCZVIeue6SCfvlwoogkVcgss5GCgBOA7gBwMvCfzf3alCrOLtxx/cPwHED/Op125XXLw3VR1tGSip55DPyqGyW4VNfLJ+HPCoYBazvW49BexAjRUYeyblHVa8KUzNbdhszdVMolHiBBQAXjF2AifKE+L1oFEHAni5nUR7JNjNZefSiLS/CgfkDmK3PtlUeWbqVWaUUR78ZFbNNlrdGBSd/QHD6ricAjxWRjYNR9pDz8EPA534VeP82YN+3gR/+PbD+EtgWK5rr+74FzyzBKCfLuDl5NHRsAcXdF6N05ZXwqgEsCniEZSZZHqCdsx3ENOEePgRKaWRb238AlFJBejXq0TXBlUfU90GrVWh9ZQy89CWC8DJGhlseF6LrgGxZSAENLWqyZUzutgYA5rp1IvMon20tVK5Uq7B0Cyb/yI4XONy2Vk8mugLHASkUoy9QbsUhRKxLkChS5zNvaqop7wgAtDIjOf3FClMeSeSRkNNnUB4BzQol1bYWzzyqJ+5/O/impDwy1ftDH2QKMH9+DrTmRCHIGbut8Uk+dZxmUq8U2Q4BpgrSWrSfJobZHJids+0zJ4+W2m1tpMw67NEYyQhK1W5rCbY1eB4rhOMT+CRyJbSGscyGDFMSTh5x5VGjM+VRnDxKJHN4tzPpMyBYrDQFZrP9iVnDUq5RbjFNVB5ZFjSZdNWkMeacrBtrog6JxDSUcxHPPIKhq8ojP5551Bx+LiBlHkHTpWPTTeWRrFQw1Ne6bVuLh7zLmU49KpiIpocd7kL1Xc9sa1LujExYrHDbWlKL85VgV0lSGWXttqaVy9CHhmBt3Niz8XUT4mFS2G0N6L7yqFVgNhuDuaxkm7jHctrW8tyPyndmr0lgmcRfIcojJah9FV1DpiuUUvqLvR7IKp4ZqNQ93PHfB3DTBRPYOa4+gX/Dc7ZgsGhiqGTCnVS7rRX0QlOL+czkke8ohApXHsm5RzWvpmQVJSFJeQQAt197uxp6SwhKZgkVt5JJeSQTVgp5tPVFeP+P3o+KW2mbeWTrNhpBo2W3tTQo2UNx8mzuKAKPwKtQ4PAPgW3PR+PgAUDXYa4dh3PnPwGX7QHKa4B/uQUIXODF70PBmQUe+v9Q3/XT8BaebArp5hiwB0AoxcDxeVgv2AnaaMBfqMEKz0XVq2LCA/RCEcHgIPy5eWYJoxTGxAS8kyfhz8yI8+Fy8sgwUH/qKVDfF0STVi6j/4YbMPmhvwQA6CPtzw0xzaidcQo4USTb1nhAMlcZGBPjqISZTf4ct61lCcyWMo9KFkyqK09XxXJtA7ND2xr/8vd8wHVBLEtcu0bYFtmfnYUxNgbq+3APH0bftdc2rU/r7wv3c1Ehj3TLArEZ+9NEHpFk8ojYNlCpQCuVEFSrCnnkuSp5RHm2TM7uOUE4ifM0QIsTPOF5CObnEdQd6IOD8CYnESwsJO5HHHzCFyQE94rzF14PweIi9BakZZPySCL32kEoj5YYmM0/A0b7xnEKEjkjEyW6nvrkm3AyyA9ArNh1mvSEPSTMsrajF7a1uPIor8Igg/KI6BojwmTyaGGhKTCbb18Nvk2erGrFInwkF7VNmUfSk968k3UjrjySv6OaMo8MVbUod1trNBLDz8V7NU2Qo0TXQAwDFN1WHkVKBU6oK51/lmpbM1uQfpKyKjEXqxuIZcD0PvPIjFlIV0YhmAa5s5p4bQV0WUoiirJ2W9MsC+d89zsrRjWm7FePlEetArOBZoK/18jfba0T29oyKo/4+oNgxXxmrITPgZWITN+EhJCPE0I+Fv/X68Gt4uwCpRR/8NmHMVt18dbrdjT9/cptI/iTl18EQghcn03WvMDDjDMjlEcy8iqPODh5pCiP3GpLyxrAiqq6z57OysqjkllqykriYx0rNlt+4pA7qcm2tZJZwku2vYQt04YQ4ra1TjKPZNtafDt05gAjj2o6sO9bAFjOkDlcRNHYD+foAvCzHwXe/A2gOAQ6sBn73vEPcE+xp97OxstZiDVJ/qLpt/oxNgcYdQ/2jp0wRkbhz87CfP7vA2DnxfQBzS5AHxiAPz8PP8x0KVx0ERvPgQORDSp8Wm7v3Alar8M9ckQoMfS+PlibN6Nw8cUAAGO0deYRoBbzaRCqkpqqPCKlkii49KEhBJUKI8e48qg/3b7EIWceWZoFi2qJE4RWgdk834gUC2w8mgbquczuIU1QdYk8AgD32DHQRgP29m1N6+St5oPFRTUw22oRmC1N7GQiiReyWng89AAgINCJ3hSYHThOR5OAwArJo4TLkCvA/Ll50JoDfWhQ7BuQYdIhArMTlEdl1bYWLLSxrRmGyDqirqd23mqHcBz8/ug0MPuy8ctw4+YbUbb7w3EkdH0z5I5QMXKNq3V8v8nmk2QrILrOiAq/WamUBBJXHuXsSifWE96bgZuuPIJusC58cne3RiNReYSmzKMU5VGJkx/N2yvu3o3i5ZdJC0tPepeiPMoUmC0rj3xVAef56QSNQq5IyqOuZh7JSgX+dF4uZJa4LcXCFTsvciB4j4ozft3zdtu9yzySlEeKhXRlFIKpSOpStQKKxqR7JQ/hQDp4kPJ0oanbGnqgPJI/fxNIKWKaXe9g2Ho8+a7BjmxrStfE3oZYK/fXSlEeZVTyrSIfsl5pXwLwn+G/uwAMAFjs1aBWcXbib761F1988Bh+90Xn4ZLNrS1DXhCpPU5WT6JgFNBnRcRK2SzD8ZJVFnE4fgp5VFNta23JI4kwamVv4+MDOgjMtlSC7JZzblHWlwYemN1R5lGLbmt08gAAAq9mgO79JjC9H+7934RlTqFw0YXwqhq8jTcBw1uBX/ke/Fd9GvUnnoD7xB4YxEDdr8MLvHTlkTmAjVMs4NreuQP66AhAKYoVdv4rjUVYHmAI8mhOKImKF7Ecpcb+/eJ88ODmwgUXAGAd14TyKCQ8Bm++GSBEdEFrhUzkUZh1pOTlVKuC8AAAQ7KE+fNz0MrlTBMKJfNIt2BCT7bXhOSRbD0RYwlf0wph0arrwraWRB55MzMApDDybc3kkSaTR5LySGvRba2dbY2rmXSfhXATQpoCszu1qNAW5BHRdWh9ffDn5xHU69C4jW1xMZTYt/6aFNlYCWOLbGth5lGljW0tvN4opez85Om6ImceEdKUJZUV1266Fh+6/kNRlhNXHskqG91I7fbD2ttzMih2wBOesMOQyIcMk1+SpjzKOzkUrd7D/UtUHulNyiMAUQinYlszY7a85PFwNVpSETN2661Y/973StuPntDnnazLyiM0ZR7FiEVdF3liAEA9Vw3MTgo/5+uSrL3McpXPqpEFRFEqNFt3u2tbi2ceqeRYLyCrqIhuwNqyFcbatTA3b+nydqJiViU6V3ZhlWxbO/OzTqIOZAnd1s4ym42yX3KWWzctk23UdPFcul4ja+c8ASO/bU1WQ/bcfqrJn7mr3daeych09imln5H+fQLAawBc3tuhreJsgusH+Mtv/wg3XTDSpDqilOI/nvoPoTYCoHRCm6vPMeWRwQrIAWsAQ/ZQPuWRZFsbsAZgaEZT5lFb25pErMhEUhLKRhmWZilKoizrjS9/4diF+PD1H8bN21tHjFlaqDzqJPOoRbe14OQBAAANAH//Q6AffQEaMy6sZ9+Awmv/GIAUmt0/gYCwdQW1GmzDhuM5LcmjfqsfG0P3oL1jh+iAVlxkpEGtxqxDeqEIbXAAwdy8sOXY550HYpqYv/NO2KF4gHdmszazLnje5JTIruEExPDPvQHbPvsZmBNRTlUaiGFkII9C25qsPKrVFPJIVvUEc/PQMoRl8+0TywKt1XD95uuxo39bqpcfhKjttkNwO51WDK8L02TdlELbWtIYgWzkkb8QI48sS+SpxIvjNPKIkxJckaMHETkbD8zmxyQvKLetGclPafWBAQTzc6C1GvRBdhxorZapk00UrO42hbMK8k9SHnGSLHFd/JhxtViOAkImj4htL/mJtCjYw3Mgn092XaYUOby9fYINTSmSuILEMIVlKpfyKN6VrkPlkcjrSdq2rjVlHrExJ2ceKYVJyr5EtqsM13E4WQ/cRm6CROvvF/cWU5qkZx4RXRcB+gCY0khWHrnpgdnNyqPmgnjJ4BbDJOUR0N1ua022tSgwu2cFE7e+ui6IrsHauAHn3P0tWBs3dHc7Sre15bO79Bri/Mn2wxURmG0o/wNYEYqpTiAX8bLSrpv3VKvAbABKJuOyIGP4OYf47MxxP6r73GPlkaJyWiHX52q3tZ6g0yvtHADJCbirWEUCDp6eg73lgyiu+V5TUfPo9KN453ffibsO3SVek8kjgIVQc1XOeGkcRaOYL/NIUh4RQjBSGFEzj9xaLuVRUlcyGWWrjLHiWKYCTlEeJSiMrt98PQbt1m3dLd1CtT6LgAaq9cytAX7rzJ6SUYJOdBAQZieqzQBPfg2gFMHUEbGcVyPwyzsRuATWJdehcMGFACGoPfywWIZbsgKnJtRQPvVb2tY2TlI0hlj4I88hshdYwepU2fqMQhH6wCD8ubnIhtbfj7G3/zoq//UdrP1ft6NYp4LE4B3C2PI884gV7UTXUTj//JbHhIMpQdpkHvHA7Fq68khW9fjz89AHWp9PGVqxiKBaw83bb8azR56VTB4RAmLbycojnkciKY+o77GJWwvbWn3/fmiDgyJIWxkTD8yemQ7JI6Ye061CuvKoVeYRVNsav9e4KoQTfwA6UtQENnuPryffj9rgIPzZOQT1uhJknoWQUIuwWOZR2Jqd1mosO6ZeF5a/VuviVkPkkNiLsVLasWVNgWylQZwoSc9lIboBBEHYoStD5pGus+WR/LQ4DqE84ra1TpVH8cyjhIk33xfaUIPoxSSfEOXpcpb2560Cs5u3H45JIk2yghAi1EdMFSWNLU6Khq3iwzeGhFn4O6XM2pempIp1W+uFbY1I5FGy8miJhYFCRDVfzz1XHvH1yoHgvdiOTExkIDpXDMRnScLnyxmsOEj6DI1CtFf4OYmB2DZKV12F4rMuUjKPunlPtQ3Mtu3mZgE9hLj2ctvWciiOu/k52A7ytnqV/9ZlJIXSr2LpyHSlEUIWAFDppRMAfr8nI1rFWYnvH3kYRHdA9bmmv83XGUGwb26feE1WIQGMPOKqnInShAjSzgLHczBWULOHRgujTcqjteXWNiZFedTGtnbp+KXY2JetC4ZM9mRRKiWugwLzswcBXYPNx+m7wN/fADQqwMv/Btj2fPa66wCH7wGO3Qdc9CqQoU3ot/pRcysgX3sncN8/AY1F4I2fQTB9VGzDve52BBt2AR/5BVhbt0DvK8PeuQO1hx4SywQLjNihNab2qnk1BDRIVWr1W/1YM0VR28TOD88hsucdgAD1GiN+jEIRNMw8km1oY7feCnN8HMd+/x0474iG0/0k/Fs/SKHAyKNQqaT1tbb+JSGLbY3bPVTbWiVVecTIo2zKIwAgpZIggKjvpU4sNNsWKhEZ/L1C8SBZcZRJa7EIYtuR8mjfftjbtiUSoMbYKPTRUZx475+h/5U3wgy/HTTLEiHJ8S5qhLAcI5/6MfKILadz25pMHgnlUXTuOnoia7Ymj3ieFq3VoBUZARYn11LR4gk+IYSRf5UqfHHdtsg8klQ1uZVHLTJtOoESSIzY+dSNiCRMCMwGws5/LbqtieMmkywZCom48ihvVzqxHk1jRIkgj5JsDuExiGWJKcfassJzlc0KRFpkHjWBK48aDRil/J9fxpo1cA8fZseGBtEYEgKzxc/FYmhbi0hz6tRByinfTWnd1rpZ/MoqsQTlUW9taxoCiRzrCYzlKcqUbmst9nmlge9Lkm30jFbwJIzxrLWtaRq2/OPHAQCzn/kse5HS7qpllOyy5vWu/aN3JT4M6xWEsiwjgUmM5s+2tlhGK1k31Z7LhRXxObACkeloUkrbJ7uuYhUtcP/JBwEAut5c3FZdptzYP7dfvJaoPApVORPlCbiBmz3zKBaYDbDcIyXzKEtgdg7l0Vuf/dZMY4uvq511LnUdU09iPqyL7ROPABcBuPcfgVOPAn0TwB03A8/7LeDZbwA++XrgNGtjj6P3Aa/9Zwy4dfhuHfjh3wEXvBx49AvAge8hmD4ptuE1ivCPHGPb28KyGAoXX4zFu74JSikIIQgWmc0scBzYhi260+kpRWG/2YeNp4GFS9kTcn2Ek0c1YBBocNuazbqtBQsLUbeyUMFhn3suAMD02T+AFXT60BD8uVlBNrVSfKTCzGBbqyTY1qpVJRiZT1j82VkE83Owtm7NPAStWIxUTV5CjkwIUigotjXniSdYYRsWvpFtzUi0rRFC2DGTbGvl5z0veUyFArZ99rM4+b73AZ/+KjZdzyYthlUAsdKLeUMz4Pu+ci9pYf6K1pegPOLd1myLTQyTcnSyINxP30ieXOkDA6jv28cCuQtFkEKBkUcZlD8qIZJAQJRZFzmumGuVeSRyeFxWvOcij3SdFdlBIDKwloJI7ZFPeSQUPfV6c1fANsVdlnPbHJgdkhydKAykgPJEZU9YlAYxUlZRUIXdAhHv5JOyLxrPPMqhPKKu21FWh1AemQZrBc/XGyd2pbFqtq10WwNCYjylWCCaausSBVAvlEcNyb63bLY1lRzrBRTFQA8tZOL6NI3WHeZWGJIyj1aC/StReWSe+eNeMiSSo5vd1tqpcMpXX921bWVBbuIiKfi97TaWz0qmHN+VEpi9Aj4HViKydlt7BSFkUPp9iBDys70b1irONuyZewQAEJBm8qjiseK+FXlUMApClZPbtpZGHuXNPNKyK4/ywNAMEBCUjBI0knJLBj5w8pHkv80chDX5JOphoVZ47EvAk3cCd/85sOV5wNsfAC79eeC7twN/cyVQPQ28+g7g6l8DHvsP4MFPob82C8uwgd/ZA7zqY8C63cDB7yGYi6x93smTqD+1FzBNmOvXAwCKF++GPzsL9/BhAIA/z8geWquhoBcEMZiWeTQ8H6DYALxNTPWlDw4CmgZjnp3bRqg8IrYNPcwJck+eABBZmfhTdMuNkUfcihTLPMoDYlpKEZUE0W1NUifQFNuaPzMLf25etIfPAkYehQSVn971iBRU29qJ296Nk3/2PtEFLrKtGYm2NT5Of3YO/uIivMnJxLwjDnNiHMOvex0AYGKWSY80y061rQEQ9kVFeRQPzI5nHmmaUph3NAngtrW0zKMhZomkjgOtYAs11FJtawDLawpqNQQLIRHaKvOIEyNcGZaTEOFj6Yo0P1xXIGxrklpFl7utxZVHErnTKjBbKEhak29xNAVmc1VIB110FHIrMTA7JG8cRw0Ml4s9fr0bUii1rqdalrkCMBPZJayDbkeKFN5xjUiB2azbUWxdclFQLITd1qQOczW2mtCeAAAgAElEQVQn3RIhZx7putivrk7WeaFCaYryaInbamHhInLmUY+etrckr7q5HaVdejPRsmLBj1+SgucM3rekUG/x8xk87qVCucZ7pDw6E9R0nXdby3HuteX57ACgfjaeAcc3C6LGHGeXku/pRta79jZKqfAbUUpnAdzWmyGt4mzEyfqTAIBqSBTJ4OqUA/MHEITS+obfUHJyikYRfVYfbrv6Ntxyzi35M4/0ZPKIUlb0Vt0qikax5Xpkwqid8igPCCGw9Dbh2j/6/4GPXMOUQoEPfPqNwCOfY3+7+32wERUqtlcH/vXVQHUK+Kn3IICBo3cbqF/+bmD7dcCbvwFc+LNMiWQWgc/9CvphwC6OAqWwff3mq4HD9yBwI6uDe/IEnAcfQuGC88XErLibtb2vPcisa4rySLex6DLiRs67kdFfYcefjjFyheg69OFhGHPsmnAdRpoQ24IW5gS5x5j6iSs4eFFr+hDZO8Q0GXk0N9sUmJ0HmbqtcUuZnHlUUckjrVBgNjphW8uXeUTDjm4sRybNtqYqj4LKIrypqabAbGZba+62BoTk0cwMGvsPAACsbVtbjs2YYMqG8dnwd7sQBSknkEecNJKvBxGYzTOPfDnzqAFiWUq2TEcBqCGZEqRMVLWBAfjTjEwmhaJQQ2VTh7SevGlFpjzyMyiPeJhnJ7Y1ebzE7qLyiHcji3cWS7OtJalEpPcJiIl1PhJABIML5VFntjV5rKlkDw8ydhxoA5IAO2ZbA6BkHrUqFsR9mEd51Gh0NFk3xiPySIwtwdKoKo8KzB6rKI+c1CKP6BojdcL19CbzSFYqaE1j7qVtDcuQeaSst5fWEzm0OCG8fqUiUcUoqazOVJhrJwBdhz62Rrx2ttrWFPSoQ1gW5eeyIm9+VQe2NVV51GvbWm8UYz1FL2zUq8hMHiUtt3omVpEJp2un4WASQGRRk8HJo7pfx/HKcQBMeWTpFvpNNmHnyqFXnfsqrC2vRcEoLE15VByB4zuoelVQSlHz8gVmpylpOoWlWyIQvAmUshwiAKc/+G6c+O1bmWLoS78FHPw+8OCnYK3dLRa3f+avgJ/7DPD6TwEbLoN7+DDmv/wVVCsbgDd9DhjdAQCY/vcvobH11QAo1oxfhH57EAff9D8w+9nPAVuuAQAELrv19dFRuMeOofbIIyheLG1r506QYhG1B5kt0V/g5FEtk22tHLDjqBci4s4YGYE+ywptTh5pliWUR96xYyCmGbV4D4kB0wMMnvlqMfIoCAOzSbHY0RPIXN3W3CgnJKhWoZXV60kfHoZ38iSo44h9yTSGUmRbo21sa7LyKKg34M/MSMqj8B4w9EjZEiN4uG2tcYCpAO3t21uOjXesG5/jgdmtlUf8vpGJWK7yEbY1KmceNaKOUQmhqJnR1rY2KEKbOdEHRGROS8jXVcIEhSmPqk3B7UmQO7dRN2PmUsL7u5F5xAvavIHZfAIcNBpNk9mkwGxVjdR+SiLGwZVHnQZmA8mdu+RtceVPrQa9XwpSlwpv0V3Q0DNlVhCePZZlMiudg04m6+Y6phDVisVIdZJEHskEXrEAuJ7SKIDWai2UR9LrmtYb5YRCriQ8nV9iYdBK+UM0mRzrTXGmbL+HRZlM7J1xhfYSQBLsPishMLu4ezfO/e/vK131zPXrofX1wdq8+WkcWW+hEr9d7LbWRUK5G4hUL1mVRwk5gO2wrIHZ8mfGamD2MxlZr7QfE0JuB/A34e9vA3Bvb4a0irMND0+xblw2GRRkggyZUNo/tx8b+jbADVyYmok+sw8L7kKTKiir8sgPfDSCRpPyaLzEFBMnKicwaA+CgmK40DpITw7Mjre0XyosLaY88j3gno8CxSFg7bOAkz8BSmOofPtR1KYsTLxpHKQ2BfzzKwF7APaWa4BHWY6RXRgCNkRZNTzzh0qBzt7MDE6+78/h/8qbsebmD+E3z3sxKr6D6u+9BMb4OIZe/E72Xo89jbe2bUXtvvtB63UUd0fkETEMFC+8UIRmB8K25qCgrxHnOy0we505ikkAW8bPFa/pIyPQwtwdLxwzsW1o4Rewe/SYoiLihb7lJWQezc4hqFQ6CssGsimPaC26fgPHgd7X19RtDWDETCO09+WzrZXg1pjaqpVtTZPyjQBWcDLyKBaYbZjMltJwoQ2o9xUnj+pP7gF0HdamTa3HVioBfWWsCZVihlmAZrOTkEgehYojmXzVhPIotK3FlEciqHkJWRB8LEEaeSSReUS2rWXJPNK0KGsoxbbmL8wLVV4m21qHyiPena0b3dbigdnysSB6m25rAOC6zd3n2tnWciiP+H3pnTgBrVzuiDBLzM+REU6Qg3pd2CqB2H5wglHOPGpxjRqjo4z8zpBLpWYe5S+GBn7qJmh/+zewtmxBfe9ets4kS6Mu348FhQjn208l2BQVgd6Tol0ld7SE7S6xaGpl4ZL/1rPMo+UJouXXKlPJNRMtKxVJRfpKyTqJN88w16/HeT/+0dM0mmVCr+6pZcoOy4q8n4WJ34kZ3wNgeZVHK6RD40r5HFhpyHql/TqABoBPA/gUAAeMQGoJQsiLCSFPEEL2EkLe0WK5WwghlBByecbxrGIF4UfHHwClGjaXLkokjypuRVjUeO4RJ48GbPbFWtQ7I4/qPlNixJVHW/pZ4POh+UM4vMAK+s39rZ/0yARIGhnSKWzdFoHgOPU48A8vBO78Q+ALbwM+/1ZAt4BX/h18hyJwKbwLfxm47BcArwY89+2w7EFlXTJ45k8gBTr7MyE5M7MAXP6LWNO/DpsMRqg1Dh9m9rU1uwR5ZG/bJjpfFZ+9W149CrsvRv2xxxA0GvBTbGtD9zyBxsGDzfsdrn/dyFbxmjE6Am0uXE9owyKWLQgX9/hxxfrDi0bTAwxOHpkmy7GZnUWwuAC9g05FfD1y9kcSeGA2wJ7QUy/ME2oijwbFMchlWwuVKwBa2tZYYHakPKL1Omi9LuxYSrc1v4XyaG4O81/5CkpXXJFIADWNb3xMkHaGXRDnidvQZCTa1kRgdpR5xJej9bqaKYMOyaMC20arwGyxP8V8tjV5TMm2tSJoVtuaJREjbr7AbHm8Xem2Fm9lH+u21vfc52Lk5/+HCM8X72sho08OzM6XUyGIwHBczuOPw961KzVjqCVyKI+UPK+EznZy5lGra3TwFa/A1s/8exO5nAheWPl+R4oUYlnov+EGZUxagppOsa2JzKNY1lsqwaYWgkquTregZFM1E35Ltq216piokFQ9Ks5k29wyKY9adYlcceD3sdK84MzPPHqmonfKozOL3MhNXHRiW1tW5VFvzlsvsUoe9QaZzj6ltEIpfQel9HJK6RWU0ndSSptZAAmEEB1MqfTTAC4A8HpCyAUJy/UD+A0A9+Qf/ipWAu498QCC+lqMl9ak2tbGimMYsoci8sh3YeomBqyQPIopjwpGAW7gwgtahxk7viOWl7F5gBFFhxYO4eD8QeW1NPRMeeQ6KPkuBhYmgW++F/g/LwBmDwG3/AOw40amOtr1UmDHDfB9th/1wrOAF/4J8JIPAFe/TRlPXGXFW4TLreT92RkAgDc5KV4LqmHOECd5dtyIQB8EMU2YGzYCYKogc0MksQYAe+c5oK4L78QJBAusQKa1GmzdFgTfhg/+O6bv+KemXRedwApRsauPjAIzLGItaDAyhNgsABsI1SgyeRQWKaZHBYmhhYHZ1HXhTU617nDVAnkyjwBGmgmlT6w4NIaHEcyzTnF5bGty5lFr25otCD4ganPPM6J4YDazrfnJgdnDQ4Dvwz1yBEO33JJpfPp4lNdg2EWUr74aW/7ln2Gfc07zslpCYLad0G2NB2Y36s0FeweTAK7EoSnkkSaRecS2JdtaPvIoSTWglUoIqjVxb2gJpFrTekRgdr59FQRboQu2NalrGqCqmYihw1izBhN/8AfNY2zVkUUp7hLOZ4ZQaEWdFQSoP/44Crt2tX1f4rraKI84mRU4DrP6JFjOEjOPWkz+NdtG4dxzU/+ujk8qhpaqSGmReaScM7vArr8YaZ5JeWToPcmYSCo2heIPWHq3NeXpfYtrtmfKI+m672FRRqRzk1fxdyYj6b57RnQtW6lIIoO7gTMtMLtYhDYwACO097ddviPbWj7b91KgWl1Xxn2llcvsO3r1c6CryHQ0CSFfB/DqMCgbhJBhAJ+ilL6oxduuBLCXUrovfM+nALwcwKOx5f4UwPsB/G7Osa9iBcANXDw59xP41csw3jeA6lRVtHXnqHpVlM0y1pXXNSmP+i0184iD5xM5noM+K50YcLyQPIoRKoP2IAasARyaP4RBexA60bG+b33LfVG6rXWiPNr3bZZRdM2vA3Y45se/DHzhbbgtWMRAEADut4BdNwM3fxDoGwfO+2ngW3/GuqURAj8oAqigvv8w+m58EXDlrQBUtVGc2OJZK3Ired6OXSaPaJjd48/NwZ+bg37jHyP4HqDt/6b48ivu3t30hN8YGxPr8hcYORI4DmwjfCJPKbRaA97p04gjCDN65Nbi+sgwsFCB7uuwQm5Qs21VHSLb1ggBbBuW34iUR5YFLSSb3KNHYbaxX6WByK28UxDUaiCWBdpoiJ+BZvKId1wDmqXqraDJmUe+l/ol2BSYHY7bPc5yxDhBR3QjyjxKCMwGmK2u/6YXZhqfMT4BfoQMuwCiaShdniwiFcojhTxix0uXuq3xe43WHMlutwTbWlgwBymTMpnM04pFybaWkSRukcdESkUE1SpTAJpmy3Uu1bbGl9esLiiPwuI8WXnUItOnRfiw2B9Nk4KPjcT3pq6fd1trNOAeOoSgWkXhgvPbvi8RbZRHvLDh5JFWKCCYm0sOzDYlK1C3JqpyYbXEIkvkMbUJzCa2zVRHrgcQIvJ+UgsaRXmk9SZjIu046HqqXTQXlGs2ZrVcFuXR8qgHZFWYmj/29BfaS4G4tpMy1VaDcs849MpqRZaB6M0DzbKw8xtfz9ysJcrMy2Nbk4nn3l7ry/JZ2GUMv/51KD3nys6UyatIRdazP8aJIwCglM4AGG/zng0ADku/HwlfEyCEXApgE6X0P1utiBDyFkLIjwkhP56Uit1VnPl4YvoJuEEdfnUr1vYPwqe+sJJxVF1GHm0b3IZ9c/sASLa1FOUR/73m1fDAqQdwutZMTAASeWQ050tsGdiCQwvMtra+b31bQkj+e27lEaXAl38X+Pafs65ph+4BqtPAF38NGFiPZ7/237D9N/cAv/0E8Np/YcQRAFhl4EXvBdacC+p5ggiq79mrrF4mjwpGAe7JU1i4+24AUeaR3Epe2NampqLXKpGYsHHoMGAWEDQCaOUyzLCrlpx3xGGs4eTRlFBXBE7U4c4KmQU/gTyida48is6PMTIKABioMisaEKlBBDETyzDSLAuWG7etMSLEPXlyicqj1uq2oFqFPsrGTB1H2Ni0Yjp5lCfziBSLzILm+0A75VFIxlHfF6SXe/y4EpBKDKOlbQ0ABm++OVMmCxCFZgOAYbV+TxJ5xBUtXJGjB9G9JmdHLYU80sLuY4GZQh7JxGSh0LltLWFs5vr18GdmUH/qKeh9fS0nMWpgdgfkUTcDs8Em+aKbWazbWvoY0gmPJHtfbtuaRLA5jz8OALCXrDxK6STGc58cB8QyhaJL6VRly8qj7qodutnCPbo2EmxrfDvh5wT1WTdGTtwCCSoyMa5Y5lGShWiJiG8j/vOSj41MOsTX1UqV1CWotrnl6LZmnHHhwkuBOH567DwSsqo8OhMhkxBdJHnSvleeTugDA9nvr04+z5ZRebQcKsxuQx8cROmSS57uYZx1yHqlBYQQ4ekhhGwFQJeyYUKIBuB2AL/dbllK6d+FlrnL16xZ027xVZxBuP/U/QCAAbITw0VWHMZzjypuBSWzhHXldZh2puEHvui21o48qnpVvOXrb8Edj96RuP2azxQbceURAGzq34TDC4dxaOFQ27wjQO0QlVt5dOi/gakngCt/mT3NveNlwCdfDzhzwCv/Dth+HcsZ6l/L/p4A3skMAOp79ih/k8ksW7cx88l/xZG3/VpIOPHMo8gyKGxrU1Og4ZNlrjwCgMYhZl3jXcPs88+HvWsX+m+8oWlcQnk0NYWAj9F1UaDsi6YQkkdemL0jo6kTGAB9dARAjDyybBBCoIUKET3WsYrYNkxfXj6yucH3mzqfZUWSbc1frIhjxvahBmN4WOwP777W1G1tKApkF2PLAE5CBbUaqJ9OHml2QdgAuVoECLvTyUWgnm5bK+zaBfvcczH8xp/LPD5r7Tq2HU29R5KQFJitD4+IgHMgZlurVkH4cRShqPknLZw8SretyYHZhfy2NdEBqnls/TfeCABY/K//aktiCmIkRRnWdhxmF21rAKDrQsEmd55rOTFvkYsQqQHM5teATBNScYwaDTiPPQ4YBuydO9u+L3mszeonZVuSdY8pj1QVHIAo0N0wJAValybWSV3GOoTIPEpSpekSqWAYoW3NU3LbUs95XHnUC7tQynEgPbCttVYe9cq2Ju9fD5VHnPSMZR6dCeHCS0JCtzWAX8+rXZbONPSM5Fnh17S4VnMcE1V51Nt9brIor+IZi6zfUn8I4LuEkG8DIACeD+Atbd5zFIDsFdkYvsbRD+AiAHeHT2LXAvgiIeRnKKU/zjiuVfQQpxfrcLwAnh/ACyiCgIKCiWgoKPs/7WcAlFJ84oFvI2gM46fOOw9l8wAApjQaLY6K7VS8CkYKI8LmVPfrbW1r/PcTlROoeTXMOrNIQt1LDswGWMbRV/Z/BTPODC7ecXHb45E7MDsIgLveDWy4HHj8S4A9ALzwNuC6dwCffB1w+AfAc38DmLiw/boQWc300VHU9+1TiIR45pEzPw/4PvyFhajbWoJtDa4Lf3aW5fFI5JEbdgULKhWQUgnG8DC2f/5ziePSh4cBTYM3NSlCgQGgGLCx2SGP0VJ5JCkljBFGHg1WqLCt8SBhfWAQ/uRUkwxYKxRgeUTttjYo2cQ6VB7BNBTyyJuawt4bX4iNf/3X6Hv+80CDgLXxDpVHQa3KPiGRYFsblsbTIvcmDq3ECtagWmUFXQqhQGxbBGbL5JE/NwdjXBKKGkaqssVcuxbbv/iFzGMDAHvtWgCAp7e/LzhpJC83+IqfRemKK6LAbJ8qyiNzE8vbigKz8xcDepGTR8lfeXHlEVdnaFlta7xgTij87O3bYe3cgcbep5SOXcnrkWxrLc51GgRB0E3lUUhIyoqVlq3oW+QiJAaLt7C5pY0JoSLKefwx2Nu3d7y//Kk3SctakkgLuUOaolQRHazMjib/LccnK24y5EG1RIvMI0UtZOiMvPQ8aMUifL5QGsEmHyNdzjzqYrc1PcUu0a3Obk9zt7XlUg/IyjhxzCQL6UqFnOUkQxsYgD6Q/bt2FcsD0qN7qqsdGJ8GdGJb62bjgLZYgcqjVfQGma5QSulXw05obwFwP4DPA2jX6upHAM4hhGwDI41eB+AN0jrnAIzx3wkhdwP4nVXiSMW9B6fxnT1T7RfMAN8PMFVpYMHxMDFQQJ9tJIpcGl6Au5+YxKPH55e4RYryOY9iS99uvPcVz8Ldh5nlsOKpyiNuW+PWK8d34PkeTM3EucPnYqQwgiF7SHkPVx4dWjgEAFhoLCAJrWxrm/s3g4Ki6lWxZWBL09/j4ASNoRnZ/LM/+Xfge3/JfiYacPkvAVYZR//gNvQ991YMXvkWlm+UEcEcC5EuXXYZFu68E+6RI6LTUTzzqMqtagsLCCqRlYxDkEcAvFOTjDySbWsH2XENqlXobfzaRNehj44w5dH8PLT+fgQLCyh5IXnEbWtzc02EReDUWYtr6TWdk0fVyIYm2rmHRX5cwUFsG7ZPYPhMEcS7rXFo5e4EZjcOHACt1+EePQIgDPymFMbIsPg94NtMyTzSSqVchRW3jtBaDfA8RaWljLVggzoOKKVK1zV5HQA7X0Gtmmhb6wTmOCOPXF1VFCUh0bZmWbC3bwMAUF2DTmnXbWu6HR7DFNsa0XVx3RLJtobMtrXWQcn9N92E03ufalLMpa2Hukx5lDevI+q2ls1y2BZJChugpTqiZSedhA5Iane2bBNSfl/WH30M5WuuzvSexPW0yShSxmaaiYq0qNtalHnUNbWDPEFfqrqGE5xJ9zxXYJkmy0QLu63Jn2GpBEPM1qWoW7qFlMyjrtnW+PknpGk/e9UZSt1GjwKE49sRmUeWVKiu/CJQ3Kexz6Ut//xPQhm9ijMIvbqnzkDbWi4IEj+H8mgZ1VZnk9V1FUtD1sDsN4N1RNsI4AEAVwH4bwDNHpYQlFKPEPJrAL4GQAfwMUrpI4SQ9wD4MaX0i0sd/NmOk/MO3vQPP0S14bdfuA30vsdRWPfvMBduxJB7I+567BRqbtp6fVy4ieB/3lDEQFGHTjQYOgEBASGQ/keksiAAIYT/CkKABXcGH31iAb9w6fXQNYKSySaiabY1bi2re5Hy6PrN1+P6zdc3jZCTR4fnmUJmwU0mj1rZ1uTuau06rQFRiK8cnA0A+PHHgae+CZglZjtbcx5w7ouBu/4UWHsxsPX5wH13AFe8Ge6JE5j/z/8EDXwMvvKDbbcpw+fk0eWXY+HOO1Hfu1eQR4ryyCjAD7OR/Ll5SXkU8b3ezEz08+QkcN65QnlkbtiAxuGQPKpUoI9FKrE0GGNr4B49Cuq6MDdtQmNhAQWXXQ0FyfXlzczAlFQw1Kk1qQY4ydJXg6Q8CkOVOXkUI7SIbcH2NBi+B6qxnAPZGpY1sDAOYppKy2r3xEkAQBB2P+NB1voIVx450EJLWzN5xAgmLYdlDYDokhY4Ye5RysRCWLNcV1EeAVBta4YBpNjWOoER5mFlUR7phI2d29fiILoB3Xcj8qhSEeduSeRReAzTlEcAu7aChQWmPOqibQ0ABm66Cac/8tHstrWOA7PTc206gTJZtLLa1tKfTpIE62GrNump47IsuIcOw5uchH1+h2HZ0viyECOK8ig1MLu7tjVVedTDzCPZtqbrotuaVhyJFko553FbVy9aI6vZTwk2jW7Z1pLGvAyZR8tV9ApyWcrnOhu6EKXdd/b27U/HcFbRBqRH3dYIIexe8v0VqYwRiqM8gdnLqbZS7MMrW624iqUh65X2GwCuAPADSun1hJBdAP6s3ZsopV8G8OXYa3+csux1GcdyVuATj30CBboBf/EFH5/+5auxaaQ5k+X9X3kcnk/x7d+9DpsT/p4GL/Dw+b2fR9VjZMBsfRb/+JN/QcEoYNH4Aq47x8C2wW3Ke0YLo3jp9peCUoqXfO4lOLR4FIeOL20fOS6buAwAUDZZEZhEHpXNslAHOb6Dht9A0VRzjmTElUeLjcXE5Vra1qSco0397btx8RwWJddl/hjwld8DCkOAUQAWjgOBC+g24NfRuOKPYFz2Mmg/9aeApqP6BWYJ8kISYuEb38Dcl/4TGz8UEUmL3/kuJj/8YWz9108oxWNEHrHjWd+zR+SpcOWRqZnQiCYII39+ThBJceWRsXYtvBMnRMc1Th7Zu3bBeegh8Vo75RHAco94DpMxPo7Gvn0o+uzLxXajfCB/elohjwKn3qSk4RazvoYG3fcRaFHoJc88ag7MtmHVwWxrfNmQBKCO03lgtqEqj7yTJ9i4w/wofsyMMKeJOjUEASNl02xreTqtAVGxRxv10KqYQryEOTfUcUR7dQ4l/NowGLnk+90hj0ZH4RNGHnWiPFIX0KEHLqCbTEGVqDzKPyk0QvIIKcojILy2jh5lmUc5bWtR8ZK8X/b558M+5xxhwUtdj7m0wGyu9tC6pDxKJ49aKI9aBGBH51Dar3jIbZZxmSYWvv51EMtC3wtekOk9ietpQz6QGHkkSNgk8qgHgdldVR6FAcJJFr9IMWWAmEx5RFxPWGbZ+1P2qanbmsEUSN20QrVVHi3xeKdk5gCx4qxH9q7l6hIV2bvMlvu84qB3+b5bRW/RQzUf0XX2+bUClUcdqQGXK2wfcRJ/9V57JiPr2XcopQ4hBIQQm1L6OCHkvJ6O7CzHX9//15jQno9jc9fiyZMLTeTR/Ydm8Nn7j+Kt1+3AltF8qomHpx7Ge37wHuW1q9ZdhQ9c+wH81f1/hU8/8enE9102cRlGi6M4ungUN225CT+97aehkaV9GA3ZQ9gxtANARB5VXSlbx3fhBi4jj7jySMo8SgMngwR55CaTR47PCJN44DYfW7/Vj4pbwca+1kUdAFihasJy5oEv/Sbw3P8F/OAjQOAD//NOYGQb+/nwPcD3PgxaWoP9b38/Rn9lDmNvuRUAULnnh2y/QxJi4a5vYuGrX0XQeL8oVKv33Qvn4YfhTU8rnaz8WUYeGevWwVy/HvW9T0VjC9VQnEQSIdnz81K3tZq0rlnYO3eq5FG4XGHXLizedRdrL16tKqGpaTDWrEHlO98RPwORXc2WlUex3CPqOE1dvXiR1tfw4AWALxX8+gBT7cQzjIhtw54hzOZmSRa4wUF4jtNENmVF3LbmnmSkH1dxcQWSPszIo6DmgITd2dJsa3nyjoDIskfrdWZbSwvMDo9j4DgJyqPoGBNdF7a2btjWiK5jrl+DpwddIo8ATTMFwaWVwnMnVCu9Uh6xa0srFAT5kpm8aUMaEEKw9dOfaru+KDC7025r6bk2HSGBJAGQ2bbWpDziHZDSlBYZJ6R8LGtv++OlqQs4+ZBWsCvjNMW9mNRtjQVmm9HPXUA3lUdASA61CMyGaQC6ITKPiNwxMq0jXczKICuwuoW0zKMoq2mpqqwWRdtydFtbBmscACmPyohUimcBeZSkaFzFGYweKY8AsGu80ViZ13UntrVlCtsHYuT5qvLoGY2sV9oRQsgQWNbR1wkhMwAO9m5YZz9KZgn7TrLuU7NVt+nvdz/BCvq3Xb8z97rnGyyr6GMv+hjOH2GS/rJZBiEE77rqXfity34LAQ3E8ncdugvv+t67hH0MAC4dvxQ3bbkp97ZbIUl5xAD7TMkAACAASURBVH8uGSURmO14TlvyqGSwcQrbWkrmUc1jxb2cCcRBCMHm/s2Yrc+27RIFAOYDn2L/gwAPfgp45POAWwMufi0jjgD2RbjlGmDLNQgWFxG84wrRuQwAqvfcAwDwTp4C9X24R1h2jj8zCy20//inp8PXZlTyKFQe6QMDsHbsQH2fRB7pMfKoItnWQiKJVmXyaA6lSy6FVi4ryiNimrB3sGKscfgIsw1lIY+kXIGIPAptaxKP4cc6rgWOk5jho/WVUWoswAHgm9GXVKptrWDD9ELlkRkjj06e7Dgwm5gm4PugQQCiaUIxxkkjGiqQ9DDzKHBqosiLHzetXAZMM79tjZNHjUZL2xrPuaH1ulAekWIRtFYTXaIANjHhyqluhdrODegwHdqWbE7qtiaD6AaMAIBmRl3rhPKo88Bss68fM2WgOpF+7PWBAZG/JciXzJlH7YuXLPeRYlvrJDCbK4+61G1NTBa5ooS/ntG2lqjkMIzYuvIFZgNA6dJLYUxMYOiWWzItn4b2yiM5GNsUJKwczKtZcmB2l3NkcoaJt4Oxdi3M9euaXhckjGmKJ/c8MDtaJk15JBeCGvpf/GJBpncNWkqBxI9ztzKPEkgvhdjpVbD0cljjIH2GhucZwNlhWxNF92pntZUAlfDosvJI01gr8BWojOnEttZLFVcT5O/qFWgLXEX3kDUw+xXhj+8mhHwLwCCAr/ZsVM8EBDYaAStAZ6qNpj/POy76CwbKdv4PQK7CWVNcgz6ruWjmBBEHD6N2fKdlwPRSkZR5xMOz5cDsLMojriTiyqJOArMB4A3nvyHV8qZgeh/M//oLYOMYrMFNwMs+C3zqDcDUHuD5v534Fk7geKdOAQAaR47CPXpUdF7ypk6jEQYv+7MzMDl5NBORRzL8uTloAwMgug57xw5Uf/QjQWrwY8f3kxNGvqI8Ctu4Uwp/dhb68DCMNWsk5RGzCJmbWY5SY/8+0EYjU16QQh6NM/LI8phdra3yKEEloff1o6+xCF8DAiu6B/TB5MBszbJheYDhqYWdCKleQuYRwFqnE8sSirFAKI9C8qi/n6mUajVQorFw3ZiqhxACc2JCHJ/MYwiVAkG9Dup7qUWcJtnWgjr7TDEnJtA4cABaMWZbC8kvYnVnsn1wSxHF6Urb5dorjwxoAaBrprhum2xrHRRXhlXAL7/dwGvPS1ep6IODkbLE5jk2+cijpUq5+XpozWEqs5zKsCjXplvKI6k4z2gvU5VECZNZ01TJmg4672z4iw9kWq4t2iiPFJWLaQoSNjHzyOh+3k+3g5S3f+HzrQOzDZN9fkrd1sRYUjOP1Pyq0iWXoHTJJUseq7KNdsqjHtrWkJSx1GUsVxCttXEDtIEBmBs3dT2f62nF2bQvzwT0MKcn+kxYecqYTh4+kGUinoGQPCcEoHRF2gJX0T3kvmsppd/uxUCeaag6OizThasRzNWalUfzNQ8Dhc4KO24L40qfduCEQ82rCTKmJ+RRqBaSu63xsSqB2Tlsaxxu4KLu15sURnWfKTCSlEcA8DM7fqb9wIMA+OLbYem88DWB0R3Ard9iGUejO5LfFhI43ilGznDV0eDPvByTt98O98hhoWSRiSJveqbpNYCRRzwE2tqxHdRx4B47BmvjxlTlUbDQTB4Fi4uA50EfGoqRRxWQcgnWZpb/5Dz2OIBsigljTUQe8Uwj020mj7iqShyjenPmEcDIoZIDVE0gkGxrXLUT755GbBumH3Znk4ojfrw6zzwKi/mGC1hWpDyqqYHZpFgEKRYR1NgxlosuGZv+7v8IQivzGEKCh9YbgOenTiw4YRA4dWFbM9auRePAATUwWzeicXdJefSVl03gVPUUfqHNcnpYaKbd28QwYPiAoZsIKnHlUWS5yAu+PR7YnYSh17wGhQsvZNsMVVyZM4+61fUpPB/ucRY4l7dTkAjETbImdTIeXlRrWmKL9EQoSo0U5ZGiqOmuuiYP2p63uPKoEHVWE69bcre1LttnFGJm6etM+yyXA7Oh6wCl7MGBlHmUSl4p1rplUOYk5ED10rZGEjKWuo5lKgDNDRtw3g/ZPMQ9yR5qnQ1Wr67fd6voKdQmCV3+zODrXoHKIzHmPNex1kym9xRhQ4WVGEi+iu5h5VGzZwGqDQ/zVQ2j/cBg0Uy0rc07LgaKnRV2XHmUlzxyvEh5VNTTw6o7haEZKOgFJfOIq5DKZlnY1mpeDa7vtrSSWZrVZJFJUh85ngNbt/NlNx29l3VKu+tPgT3fAO79OHDgOzBfyHKkRGczq5RKHAHNyqPq/fdBHxpC3/Oey/bz/vsZMQWVKPJDdY7XRB7NCjLE3sG223jqKWVMtm6DBoFQxCi2tVpNqI4AROTRVGRb08tl6AMD0IeG4Dz+GIBsqp0k25rVYPvGu63pQ0Pwpk/Dm55GLQzkTso8AgC9vw/FOoXlA1QiCwoXXABz0yZYW7coyxPbgulSmL5KiOhDyWRTVkQ2ogao50VEmwjMZiSMVipBKxQQODUl5DkOe/t2GCP5LB0i86jRutuasLfVHdAGI0257TFuW+PXHTG705XL1My2ndaA9sojEiqPTM1EUA2VR7Fua53YLNoqngAUn3URhl/3WrYtrkDKSq51KXNDkEdHj7LVSeHymd4vwuK7RR7JyqNsRbRiHUk4Hi1ta8utHOCFSxohKweESsqjxCwowxA2x249TV+2Fu5yYDYfu+/HSOc0ddYyZAJpKcojfkyWqPRq3W1tGZRHT8M9EBFmK7DIjiExiH8VZyx61W0NkB4IrEAisZN7UnSYA3quPAJW9vFdRfew8r81ViBKloHnbF2H2fppkKKJ2UTlEbOtdYKKW4FGtMSQ6CTIFjCeEdQL5RHAFEaybU1WScWVRzwEOgmEEBSNIipuBWuKazBZm8RiYxFjRfVJfc2rtd6X+WPAwe8D9Xlg4iKgOg38288DHu9WFXYK23YtjMt+AdqjH8pUJANS7tD0NKjrwj14CNaOHTDWscyJ6o/vFcvKRBH/2Z+ZVdYnK494SGx971Pou/ZaoTiydVsQRwALxg6qVRDLYpk5jYYgqvRhrjyaYuOVwrHNLZtRz6E80hPII8MNACPstqZrMNauhX96Gqduvx0LX/8GzrvnBwgcB3oCmaKVGXlkmjHy6NxzsfPrdzYvb9swPE4eJSmPOrStcVuX58GbmhKkC7d9ybk8WrEIWnNAXbdjm1ziGOystrVCtFyYeWRw8ki2rSkdtLoz2TY1s21YNiBlHpHkZXXTwq6Bc7B9840IHmJZYVo5vP5EwOsSlEcZJ6oiBDmrbU3vUvESFkHusWNs83nJI05edDkwmzRlHrUIzJYJlxTlUaptbZnJI9LKrgSoyh/TFMRFUmA2MUxpYt2lqdUy2ZmEfc80FWUfsSx2DXheerGgdEJ7mpRHS7atpZ83cQ2zhjFL204K1GJ6eZ7pnlW2tS4pP1exTOhlTk+3c+eWEZ0SM6LD3HIpj4BV5dEzHKvk0dOE0VI/Jp2jGCyZmE3MPPKwYagz9U/FraBklDJPdLjKqNe2NYCRREmZRyWjJAiQLIHZAAR5tH1wOyOPEjquOb4jSCkEAfPr8uMy+QTw8ZcA1Sn1Tet2A2/8HA685e3oP38Eo+fMYt54ERb/8F0wLzAj5VEbcPIIALypKTQOH0b5Oc+BPjQEYtuo3nef+DsndKjrIgiDsZvCpWfnYG1gXeH0oSHoY2MiNFsojwxb2S7P6NHHRuEdOw5aq6nKo/E1oNUq/MWKEo5tbd4C50GmDspmWwtzfDQN+ugoAMCse4w8agAoFGGMjsI7fRrOk08gmJtjHX0cJ1ElofX3o+BQGEWAZsj9IpYNww2g+4BWiM6PsX49iG2LoO28ELY11xWqI+i6ZFuLyCNSLCJwHHjHj8Nc1xxK2ykiRVFoW8sQmB0I2xojj4iiPJIK3y7Z1jKTR+2UR7qO7f2bsbF/I+arofItbltbivIohbSKgxNxmW1rXSrECCGAaXauPOIEm92dz2/Z1qXsWyvyqE0YKlMemcrvie9dDuRUHgE8cDghMNs0RDe5btlnlqu7TRSYbTR1mCOWBdrCprAceT1p3cg4sbNk25qmsVD4pPHLuV+9gnIPLNO0vBOLzBkK8T12FuzLMwG9VR5x29oKvBaMDse+jOQp0XVQ9ID0W8WKwip59DShZJZQ9arYXDQxtdhMHi04LgaK+Vp6c1TcSmbLGpBsW+sleZRmW+PbFJlHbTqgcVJo6+BW3HPiHtFlTkbdq7P1NqrAP74UqM0AV7+N/fG/PgAQDfilrwH964AjPwJm9gNXvgW+q6F2732o7xnA4Ne+ipMv/1l4p06heP5QbuURADQOH4Z38iTMTZtYcPLatWgcPAjoOjTbFiojWYHkzyZkHg1F3aLsHSx4G2BFsUY0FPSCsKkBkYLBGFsD79hxBHHyKCR9vMlTCKpVmGOM+LE2bRLryKKi0cplkEIBxLJE3o9e94Eys62RYgH66Aiq994LKmUvscyjZpJU6yvDrvuwPAra3/54kwJXHtFINQJg6FWvQt811yRa47JA7n7lhnlH1saNTYHZWrEIrVAArVXROHwYg8/e3dH2ksCLU1oPbWtpyqNQXRRUa4xoAmCuXaf8DYgVYt0ij/R8trXUe9s0QF0PABICs5sL98zjC8eW9d4VOTZZlUddbBVNDIPdw7qeqMprPY6w25rdHTuiQq5kJXnaFcKcZOHLyETUMlto2imP4vdK6TnPwdDrX6eQw3GLYzzTaUlYZuUREvKoiGmyYiHt2taXQTWTojxSbJVLBNH1ZJslJ6h6qAhKI8d6CbIEJeeZhq4r/lbRW/Tweuf36YpUHnXYeIMTOstpW1uR5NwquoZV6vBpQskooebWMFSyMFtLUB7V3I4Dszslj2peraeZRwDbbzkwO4k8cjwHXuC1Vx6ZbIzbB5mFa7GxCMweAvZ8XSxT86qMZPrSbwLH7gesMvDl32H/dAv4+S8Cm68ChrcAz3oV8ILfBQqDaOzfDwAI5udx+M23ityi0ZoOmxqY/Nu/bcokisOXyKPaAw8ClIowamPtWgCAuW4d9LExoTzyE+xrAECDAP78vNLm3d6xHfV9+0ApBSEElmbB0q2o6O7vhx9a0ngmUVBzxDaMsNsaAHiTk0pWj7Vls9iOVmp/LRFCYIyNQe/rEwWH3mAkgO0ycsUYGRXEEQD4i5WwjXxytzW7HsByoQRgp0GzbWgBhd0ANMm2plkWrK1b274/db+kbmteqOKytm0TpBGt1ZjVwzBAigW4x08gWFiAuXlz6jpzj0HKPILnpU6K+LkLalVhWzPXsetMIQBlNUXObl5pyK08SlEAEd1gBBlUSyAgFQYdFDt8u1lta1rObmtRt6alFy98m8bYWP6ClRMYHZKlTWMRT3E1ZFWYtCuEWUv7ZCJquZ9miu2lbTdm8bQ2bsC6225Txl++6iqMve1tKOzaxV4wza4VscvVSUe2XSpqq/CzvNX2493WejI+OfNIJni61OWQrytxPW3UaV1BBx0Hl4puhfyfCehWt8tVLBMURWWXz9kKDszuOE9oOe/ls+hzYxWdY+XdXWcJikYRVa+KgbLRFJgdBBQLdQ8DS8g86jOzBwTLpE2vM4/KZhmnnahdu5x5ZGomCIiwnynkEaUADZSJFc9q2j4UkkfVSeCL7wCmnwIueRNgleEcvBMFQoDjJ4Dr3glc+3vAyZ/A9ywsPrAHg+PnJ46zvm8fG8OmTXAeeQSkWASt1TC2qGHd0RqmPvxX+H/tnXmYHGd17n+nqnqdfUajkUaLtVmWZMu2JK/Y2MYLtmMjE7Ax2NiGkMslJCwJIUAISwjkJiEEEgKJHSBh35yQAJcbIGY3GNnGNgYbyZtky9YujTT79PLdP2rp6pnume5Rd3WP5vyeR4+6q6urvuqp7q7v7fe8x0qm6PmdV5c91rDzaOT++9ztLXXFo5gvHi1dSn50pCAeeaVqVnt7UeZRfmgI8vkgwwcgvno1+cFBsvsPEOtbSNyOFzmPYv39jG/fDhTEIzM2SnZgACwLq709WJ49cKCobC22LCQetcxctubvI9x9zBp3z+tEBiSdwu4pdlHkhwZd51GJEhurrQ0x0D4KVJDL4ztF0hOVlxpVxCTnkcTjOIsXYR54wD2GkdHgNbOSKddNBsRrKR55HZDynvOo3IWFPw4zMuKWmQDxk06i/2/+mtaLLy5sb1IHqVoQt+IVuXpssRGkrIgjjoPJuueNmSwe+b+Uz2KCmnSSxK04bfHK3JzOwoVg24HIOxOFkroaOCB88ajKkrWicdSs25rvurCLL/gr7LZWaiI8JfOoyHkUddna9JPOSlx6VksLvW/4g+Ln1Kp8ZobOdbUiCGqNxYqdY7GCeFS+I13juq0FLoMaOf6m67ZWV+dRIzoOThcSPseQVIru1/wOrS+4pNFDUSqgnt3W5nSg8yxL7gpuoPq/l9V5pIA6jxpGOpYmZ3K0p4XBsSzZXD54bGgiizHMutvacGaYdKyyCT94rgFxGMuNRZJ5NLlszbEc4nYcESFhJ1wHESHxyBj43Evh/X3wj2fD43cBBfFoZftKAAYf/JxbdnbGTfDAZ2HbHYy19pJsXwIvfL/rKhKBRRsZ+O7PeO6P38rE7mdLjnPiqZ3gOCx697vBsljw+t8DYMlYioWD7ttmZNu2aY81PzQclHuM/sIVG+LL3MyiwHm0dAlOZxdZr0Qt67WyT6xaVdyBzctBsjsKbd6Djmte7tGS1iUsaVsSOJ7CpRWOV46WHxsjNzCA3d6OWNZU55HnUKnWeQTQecMNdN5wg/ucZBIJiUd2Ko3T7Y7B7vVcUIODmLGxoP11GD/gumOYojK0cvjunPQ4WDWaOEPhIsdkMmT37sVZtAgrnS4qW/NDxq1UKgjUDpf91WQciQRmfGKGsrVUMKa85zySeJyOrVuLRMeiC7caiUe3bLiF15/x+hnX60x00pnoLPu42DZkQ84jkULHp+PIPErYCb547Rd58ZoXV7R+rL+ftXf/hPTmzRWtX9PyGe/4ZiUeea67WnVbC4ePikhFf4OZJsItz3seLeecU2b9iMvWrJmcR9W/V9wS1to4d8s6bmpNqOynSEirxHkURbe1UFZh0WTTqd2kSSaVZgbLI8g8qmcGTNl9evlqJ4KDQEToe+tbSZ5ySqOHolRAXc93//0aUfB8LZltB8SCizCCY57DgeRK7Zj7PznMUdKOO+FMJ12HwNHRDD2t7gX/4Ji77HjK1npTvVU9J+WkijOP7IgCsyeV2CWcBIOZQSCUi/LQl+CJu+DU34Y9D8HX/je8/uek7BQpJ0VvsgcBhvb/Gi79M3j+W2DzrZDuZvyed9GRXgjPe0PROPyytMxzzxJfumTKOCeeeor40qW0Pv9C1v7sp5hslgMf+jtevejFxLKGY9zt5vdM0+EgPzyM1dKC1dJCds8eJJUKupL55UTxJUuYyOYY27EDKDiP4qtXMfbII0FJWm7AF49CzqNQx7WW88/ns7/1WWyxGfqvb7r76C+IR3ZQtjZK7sgAdlcXAFZHBxKPk927DzM+Hrg87O5urJYW7xgqEyI7X/qS4LakkpixMRJ2guTEMFbIedR26WUMfPnLrlBmTMmJlt3mOkQmd08rhy8w1Vw88vZtMhky+/cR6+vDSqXdznW5HPnR0UC0kVCuUKzG4pEVi2HGx6YtW5N4HInFXNHFssG2S0/G61C2ds7ic2ZeCbh5/c1ctfKq8ivEHMyYK3zlh90ySj/4P3DVzCLzCGBt19qq1rc7y4tcUwhcUccvxhWcR9V9hgOkN51J68UXY7VW7jyddiyTfgkV2/bCk8tfpEoZl4hP39v+pOz69XR3lMSe/mJ9amD2zCz5yEeKPnuPi4jKmcKZMcVla7GQ86jM+y6qv59tw6Tg7kK5XQ1emzLOI39/dZ0sNcJ5BFOD8BUlCuqZeWTP/kemRnO8ZWuRZB75n79zUJxTaof+9RuE7wxKJVyhaGC0ULp2zLvdnpp92Vo1ziNwnUbhzKN6OY/SsXSReDSSHaHFCYlHdoLBCU88smIwchi+805Yeja89FPwss+6odd3vpq+Zx9k5fg41icuozWXZ7D/DLjgD90NnXQ+9J7CaHa05LFM7NwJFAKlAca27+CpG17mdkZ76qlAnLE7OlyxxXFID4xhH3DLyfKDg4z95jdlj9UXj/xJYHzp0mAi7ITK1uzu7sBllD1yGCyL+IoVmIkJ8sPD7P/wRzj6tf9wxxIKzHZ6e7Ha24OOawk7gWM5QblcrL+/sG6PX7bmOY+8ibGfVeSXW/kuIxEJcnt8caQarKTbeSxhJ7zMozTps86i88Yb6XzJb7vHevCgt24J51FLYfJbifPI8pxH8Wxty9YKgdlZcgcPYS/oKTh8RkfJj44Ula2B6xiZbUB32XEkEuRHvbyoaS4srHSa/LCbeVSuXXs9ytYqJR1Ls6ytvLBWnHk0XNTpLxBmmnCyU9OyNW9bsVk4j9Jnn82y2/+5ZhPCQtlaKITUcabv5Bk+vyqZGIQFt6idRzP9Yht2/lT4Xklv3hSUJR8vUTuPiDlTnGAF8aj0a1RUTheBO6doHLZftlYb51HJ900EmUczCa5126/jnBBla8rcoq7d1uZwYHahbG22zqMIxKMZfnBR5gcqHjUI33kUj3ni0UgJ8WiWzqOhzFBVmUfgikVjuTFGc6Mk7ASW1OfUaIm1MJIdIW/c8p6RzEiR0JW0k8Vla9v+xRWQrv2IeyG/6DS4+G3w1A/5o4OHuSO5Fkye1kQ7g/2nT1HDx3JjrPnlYXa98pZgUgoEYkl2z55g2bFvfYuxhx/m6Ne/wcSuXcRXrgge80u8svv2kdnzXODcGbn33rLH6rt2/Elg2I2SOuMMWi6+iPQ552J3dWLGxlxX0KHD2F1dQYnXyH33cej22znyhS8Cxc4jESGxahUTTzw5Zb8ATrhszS8VGxkld+hgUf6Q09tbEI9CLqP48uVIKjWrLyTLy4jyxSM73YLd2sriP38vsSWu0yt74IB7HCUyj+y2sHg0s5MovI1auWmg4HIxmQz54WHs1lasdKg8LJQT5Xc0q2XeUTCORKKQJzXNl7a0uCV1ZmKirIg2GzdFVIQzj3znUfgx9//mGjNMdegc17Z851Fv9c6jmjP5F81y7owQRSJHBa9H0fkYeeaRLwyUcx41TmgFInQe+a6+2KRSPafweVrWeRRBtzUovBYlnEe167ZWomwtgsyjejoxpkOdR0pDqOf57r+H56AoGpStVftDQZSlZFGWyClNy9x7d50g+IJJLOZOlI6GOq4d88rW2mYhHhljGMmMVNVtDTznUcZ1HtXLdQQELqNv7/w2O47sKFm25gdmO5YDv/4aLD/fFY18LnorrN9KesHa4GK19esvCUSnMGPZMZY/vJ+R+55g/LHHSK5bR35khOw+t+162Hk0fM/PADj8mc9gJiZIrFxZtC1nYS/ZA/vJDRwlufE0JnbtYmTbvfS86lUljzVwHnmTwHAOjtPVxfLbbw9ug9tpLXfkME53VyBODf/4JwD0/ek7yA0cJT5pTPE1qxn6/g+K9zs0BLFY4DaCULe1sVGyBw+R2ryl6LhGH3kEKM43ar/6qiKxqhoklQycR8lMsXvJ8krSsgdd8SjcRj5Yp7Va8agglNRykldwHk14QlFLkMFjRkfJDQyQWOVmT4nnPKplp7VgHIk4+RFXFJzuwsJKpd2yNdsqK6KFJ8Q1DRevBU5x5pGExMwgMHuWZWv1pJbdfo4nMLvWTHbmiG3POFGvtCvbbNevJYFoUkYYqCQwu55E5TwqTFrKd1srt/9w3te0jrTjHaPXjjo8aalpi/ZygdlRZB6JuNcy+Xy0zspYrHbh7opSIXV1Hk1yy84l/Gu2an8ADdyfETqP5mI3O6V26F+/QfiCiWO7olFJ59EsytbGcmPkTK5q8Shlp9zA7OxY3fKOoCCavevudzGeGydhJ9i8sBBKm7JT7B1xW6LHhg7AgUfh6g8Wb0QEFq4rWtQWawtEpzDjuXE69rrLRx98kOS6dUw8/XTweOY513mUGxxk7OFfYXd1kd1baMkeJrawj/GnniR38BDJ0zfiLFzI4He+i8lkSk4s8sPD2C2twSSwXA6OLxRljxwhe/gIdlc3dpdbVjb0kx9jpdN03XxzyQvbxKrVHL3z38keORKIUO5+W7A72t2XKxbDbndv54eGyQ0M4PT0BNtwensh455zYadH+1VX0X7VNPk002AlU2SOHiXpJL2ytZB4lEggsVhQtlau21p4/Rn3F1qnts4jTzyayASB4lbKfY2C/CivBNA/xvjy2uYdgZvj5Leun+4CwUq74pHE4+VFt/CXftM5j2JBp7j8SLHzqNCWuwknO7P9xbAUsdkHZtecyYHEjg25Gb6Xqg1Qbmi3NW+CUe7v1mDxKLIW7mERJrzPcGB2ucmYXyZS78laCedRLdtGS7mMuCicR97+TT4f6efblIB0RYmC8OdqjcXLKMTeepE45RQWvfe9tFxwQVXPkyidR8F12NwT55TaoX/9BuGXrdmOKx4dCYlHg2OzL1vz84Rm4zzyA7P9Lmb1wB9XLp9jRfsKxnPjU51HnoMo/uz9gMCGrTNutzXeGmQl+RhjGM2O0rrHDZseffAhACZ2uiVaseXLA+fRyH33QT7Pwrf8UfB8P/PIx1m4kMzuZ8kNDBBb3E/7FVeQP3aMo9/8v+427r2X3FAhzyk/POQ5j9xJoN9pbTJ24DwaIHfoEHZPdyAEZXY9TXLDhrJfConV7hgnniyUrvn79QUjq7U1cMtk9jwHxmAvmCQeefjd1o4XK5XEjI6RsOIkJkDSxeeU1dZG7oAnHs2QeWSXEJcmI/USj7wvytyxo964WorK1nJHjxbyozwHVa3DssErW/PEo+ku9n3xyExMlM2KCl+sNZvzSGwbkwuJVGDujQAAIABJREFUR6HzUZrYju6X0tUke6WpnEfFzhyxnRmPUSyrIChU4jwSCQkD0V6SFI5vBlcNNOS8E8si6DJWT+dRUWB24bqjKDC7zPFH1bo5OAdLlTnWwvE3WTgL9lH/zCOgcH5F+B7QsjWlIdSz25rt1N0FWS9EhK6X31jRD6ZFROk8CjqUNt91mBIdKh41CF88MjKBCBwdKVW2Vv2b83jEo9HsKKO50gHTtcLPYrplwy38xQV/ARSPNWEnGMm6k+TY43fBSc+DtuLw0fHHH+fp33kNuaGC06gt3jZFPBrPjdM2YogPukHDow8+CBTCslvOO4/Mnj1uqd899yCJBO0vehHJ00/H7ugIBBwfZ+FCzJi7rVj/YlouuojE+vVuJtEXv8iuW27l6VtvJeuFX+e8srX0ls0kTz+d5Omnl3xN7M5C2ZrrIOoOBCWA5GmnlXweQHz1GvdYn3giWJYbHsZqbcXySs6slhZXUBEhs/tZ91jCJW1h8ShdXdB6OcQLzG7Jx7EgcOsE+2lrDQVmTxUrrZY0xvvur0g8CnVYq0fZWm5gwBtXS+Awyh44CNlswXmU9J1H9ck8MsOeeDRTYPaIG5hdtutco90U0yCOAxlPPBouHZjdlJlH/sSvJpPYGMRi1XV7qxOThYFKJ5tSpRhUURB3HZAZnEeNLlsDohHW/L9vbJqytXJ/96jKREo6j6YKSrOl53/9Ll2vePnUByJ0HkGNSvAq3Wc83pRlwMqJTfgHg5p3W7OseSeIRhmYXchXUvlgPqPfGg0iFXMnmmPZUTpSXVO6raXjNs4s3pyzFY9STjRla2cvOps3b34zr1j3CtJOir/su4Q1rWuCx5NW4QI9NnIYnvc2wO2ERj5Hcv16jn7jmwz/9KeMPvgQrRe69s7WWOuUsrXx3Dj9h73j27KF0fvvJ3vkCBO7duH09pJYszroPjZ8z89Jbd6ElUiw6M/eSWbP3iljDzsBYosXIyIseN3rePZNb2Lvn7+P5MaNjG/fztOvejUrv/oVN/C3pYX4ihWs/MqXy74mfola9sAB8kePYnd3u2Vbtg253LTiUax/MZJKMRESj/JDrmhltbSAbbvOIxEklSKze7d7LJMCs33CgdnHg5VMYkZGaM27f8/JHdvs1jbGPCGrVLc1ESGXiuOMTGCXEJem7q++ZWth8ajIxUXBOdZ68UUseP3vkVy/vmb7D8aRiFdRtjaMmRifJvMo5CxoMvEIxw51W5sUmO1nHjVhRsesgy5LbSsWw1mwoDkyG5xJE2fHhnwFlw2OA5lMxRNhcRy3DXvUzOA8KhZaG+PSE8vysn6iCMx2Jglm4bK1GQS2OosepbqtBeOuwWvTsbW0wzky51EDsloWvu1Pin5IUpSoEMtyv+tr7Twq4yA8oQl+vIrCeVRCxFfmHU1wdTo/8Z1HI9kROlOx4syjscysO63N2nlkF8rW6uk8SsfSvGbja9zso++9nxfd8xnWf+OtsNMNhk5kxoN1Yy/5BGy+DZPPs/sNb2D3G97ouoS2bQNgfPv2YN22eBtDE0MYY4Jlo9lR+g+59/328GO//KXbSW3FiqCV/egDDzC+fTst550PQOr002m/8oVTxu4sLIgs/nPbrricxLp1xFesYPknP0HfO9/J+PbtjD/1VBCYPRN2eztYFmNeaLXT041YVuA8SJ12atnnimWRWLmS8SfCZWvufkUEu60tGIOVTAbikR3KPLIXFC4ea1W25gdmt2TdLxirRNkankggZdra59Lu8urL1urtPHLfu36nPrvTdXg5PT30vvGNdfnl2IrHqypby09MlM08KhI4mkw8mpp5NLVsLcpf5iumhhdvqY0babngece9nVowOYhT7Jm7rbnrVTkRblDLcJnJuRIOrG6086ie4pETEoDCHeYqCMxuZOaRTM7kqst+o8lai6r8L0zbJZeQ2lj+xylFqRv1ch7Nw1JMv1Q8CuduwSE5v15jpZi6ftuLyFUisl1EHheRt5d4/I9E5BER+aWI3CUiJ9VzPM1E0kkiCCPZETrS8SLn0eBYdlZh2VAQj/zysGrGM5YdYyxXP/Fo4M47Gfezee75Z/jx38IZr4CulfDFm+DILpLjhdKzWNcKsCxGtm0j8/TTZHbvZvSBBxl9+GEAxnfsCNZtjbWSNVnGcmPBsrHsGEsOGfIxh7YrrwTbZujuu5nYuZP4ipOCVvaH/+3TALRdccW044/19bk3LCtwIYllcdLnPsfK//pP7PZ2kqesBbxcpWy2IjFGbBu7o4Nj3/wmEo+T2rQJcB1JVlvbjN274qtXF5Wt5YeGsFo9waijvXA7lQoECGdBfcvWrGQKMz5Oe66088gfE1BW5Mi1uOehk5jZeVSvsjV/IpUbmJp55IetR1FeJKHA7JnK1szwCGZ8msyjcIlKk+UCiG1DNosxpmxgdlNmHgUTzOMfW+8b30D/+99/3NupCZOCOKsuW6vwIr5hF/ylSqFCiEghILSGonQ1SBX5UbMmmBDEisVZxyk4GMvsP/LMI3tqXko9S6+ich5FWnqiKA2mkJ1T+8DsefceivKYG5DNpjQfdfvri4gNfAy4GtgAvEJENkxa7QHgLGPM6cCdwN/UazzNhiUWKSfFSMZ3HoUzj2bvPPJLt/yuZpUSLltL2bUPzM48+yx7/uxdHPnCF2Hbv8B/vw3WXQtb/xFu+jKMH4OHvkhidCB4TswrYRv4ylfd1u2Wxf4PfjAQZcZC4lFb3O3O5Ydtg/taLDkEuaULsVtbSZ52Kkc+81lyhw8TP+mkwD00sm0biZNPJrGquLvaZHzByOnrK7rAtltbgoA7Z7G7zfHHHwOKRZLpiK9YgdO/mJO+8AWS69xOcolVq2k577wZf9FNrF5Nds+eIKw77HjqvvlmOq+7DiAIdJZ43H09/ePq6Qm+CCaLPLPF8vb1272XefueWrZWWLf0Po3nPHJSM5/LVkgoqW3Zmrut3NGpmUd+2Hok4lEiAb6rbjrnUUua/OgoZmys/OsQlKg0l+sI3EmgyWYx4+OQy03KPGpe51EwthMsPyQQw8KlSZUc40w5OZP34zgNKdPznVXTCbKBaNFg51F9y9ZCgnJRJ6TYzJlHUTmPnKlCXySCS0SZR1GWnihKw6lXmabdGBdrIxE7ulK9QuOC+fUaK8XU869/DvC4MeZJABH5EnAd8Ii/gjHm+6H17wFeWcfxNB3pWJqR7AgjiZ/xTPbHGHMBIsKx0Sy9bVWm7XuMZFx3wmycR6PZUUYyI3VxHg3edRcAmV/fDdm7Ye3VcP2/uh/03Sth+fnw6DdIJDLgfQbGrBjZI0cY/O536bzxRsZ+8yij990Ptk3HdVsZ+OqdmEwGicUC8WhwYpDetOuk2XVsF/2HDPFNrii07J/+ieGf/ozxxx+j/UVbsTs7kVQKMzpK21VXzngMVlsbkkwS8xxLpXB6F0Asxvhjj7vPqbAMbNkdt7uiTsiFs+RDf1sQDKYh7ndce+pJUhs3kh8exva6lXXfemth/F52kL2gp8hxIraN3dNNfnCoZl8I4u1rcaaFZykVmF0Qj8o5jzq7+8nwNN3tfTPvL7SNWnYQ890Gftma3RoSj/ZE6DwKi2MzOI8whtzgIIkygdlBdlCTdVoDwHHFI99lFX7/pDZudMtETz65UaMrS/CanmATv8nlZ2JXlnlUtRvFcSCfn9UYj4sSgsTUdRyYmGiYeCQzuKNquQ+341iobC2ceVTWeRSN6BEIfWHnUQRla+o8UpTaU8tOiZO3O9/eQ2JHGBI+OQdRmZfU86+/BHgmdH+3t6wcrwH+Xx3H03SknTQjmRGGrV+TTWznts/9F08cGOLYWGZWndbg+DKPAI5OHK2PePSd7wKQ2bkd1m+Fl30anNDkdf2LYN+vSB3bFyyK2TEGv/1tTCZD5w3X03b55QCkTjuN1KZNmEyGiV27gIJYdmziWPD8XYeeoG8AOta6mUFOdzcd117Dwje/mVjfQkQkEILar7pqxmMQERKrVpFYW37yKpZFrK+v4DyqUDyy29qmtOeUWKyiCX4i1HHN5PNls5YsL1uoVECm09tbs5I1KDiPsofdxPLJmUd2W0HctMpkHqU63FDvStqWFmUe1bLbWomyNYnHwXHIHT4MIm5mVZ0p6pw2zUWC7/DKHTlSPvMo5DJoNsR2MLlcQTwKnZPOggUs/ehHsUPCY9NQpwvhhuMUi0DVlq1VeoHZqAv+giAxzXuqAV2wiogiYyIcmB1ylhVnHpU+/iA3qu6B0lNbRFfiHDv+/fqh6vXOdIoomFtRmoE6ZR4lN2wgdcYZNd1m0xOp8yj6bDal+WgK6VBEXgmcBXywzOOvFZH7ROS+AwcORDu4OuI7j1paXMHjgQM/4613PsCA/WPSidystjmUGUIQUk515Uf++uO58ZqLR9nDhxn5xS9ADNnxNNzwb+BMmtiuvxaAhCkcd8yKMbJtG86iRSTWrqXtcjeTKH3eeSTWutlCY15o9sK0W1K2d6TQJe3w47/GMpBeXejmNpnE6lUk1q8nsXp1Rcey/F8/Rd/b3jbtOrHFi93MI8CuUQD1dMSXL4NYjIknniA/MgpQVJbm4wsLTnf3lMdqLh5528ru2+/en5J5NLPzyHcnlXs8jFhWYZJTS+eRZYFtkztaEI+gcDx2e3skk94icWzawGx3fGa8fLe1pi5b87pu5YddEbyW52Q98TvYnWghklO6WVUabB2U4FTRba0R4lEgjpW/FPI/Axr1a3YgWtTTeeQUBLLisrWQeFROPIks82jqZFOcmcW/495vZM6jiEQqRWkC6pV51H3rLSz96D/UdJvNjlgNcB6peDSvqedPac8Cy0L3l3rLihCRy4F3AhcbY8YnPw5gjLkDuAPgrLPOmrmOZ46QdtKMZkbZP+qWv/T37+Thnf9DcvF/cIBOXD2tOkYyI7TEWqoOwg2LTbXOPBr63vcgn6dt2RiDzwj58YmpOTedy6F/E4mhx4JFjjgcve9+0mefjYgQX7qE5f/2byRP3eC6URyH8e074JprWNbmnmq7B3cHzx/dtROA+Enlc9gXv//9QWvwSrA7OmZcJ9a/GO69F6hd97LpEMchseIkxh9/gvzwUNn9+g4fe0HPlMe6b76ZzJ69U5bPloSX2zTy85+7Y5xStuaKW5JIlL1Y9vOiKhGP/PVMJlPzciyJxTBjY243C++8tVIp8oOD2F1dNd1X2TFUU7ZW4jlF2woCgJuxbM09tvygG5xvtcwV8egE/TVu0sTZF1JmQqr9VdmxkWwDXrtAkJjmUigkoDSESJxHITdi+LUIH3uZ10iiyjwqVb7nj6merrCIMo+KBFpFOdGpV+bRfCTCH1+iKlNWmpt6fkvdC5wsIitxRaOXAzeFVxCRTcDtwFXGmP11HEtTkoql2De8j8Njh2mNtbJ3YjuJBa74MZDbMcOzSzOUGaq6ZA0ochvV2nk0eNf3iPW00tZ/hMFnUmT27C0dTr3lVSTv+wfA7Txn7z1Idv9+0mdtCVZpOe/c4HZi1SrGPedROpamJ9nDM4NupWTe5OE5VwyJLQtrmMVUIgZVixPKRIpCPAKIr17D2COPFBwbJYK6/dKxUmVrrRddVNvxrFiB3buA0YceKtq3j196JGVK1orWiVUmckgiAUNDNZ/oieNgcIUZX5QNnEcR5B3BpNK9ab60w+JRuXK/4Nf6pnQeeRlTx1w35txxHjVvmPfxMNkN0XnD9ZjczNlE1YppbuBndnaDPA4KrpJpnEe23dD3SsF5VL9Jlt3R6ZbgdncXl63FQoHZ5cSrBnZbi6JkLirnUdDVTyfTyjygXs6j+UiUZd+RlSkrTU3dvqWMMVngD4BvA48CXzHG/FpE3iciW73VPgi0Al8VkQdF5Ov1Gk8zknbS7Dy6E4Dr1lxH3uSQ2DHymU72TvxmVtsczgzPTjyy6yMemYkJhn/+c1pWpYj1uaJFZs9zpVfe8ioSV7wvuJt74GEAUlu2lFw9sXYtY48VRLZlbcsC8Wjf8D56DmXIpRKRTfB9/C5uULp8rB4kVq8ms3s3Y48+CoBTwhHjh1g7JZxHtUZESJ91VhD4Xa5srVzeEYDlhX5bZRw0U/bprVcP55E7nsL7SjxRI6pzK3xM05athZw6ZUU3/xe/JnQe+RckuaNzSzxKn3ceXTffTGLl9B0b5xqTRaCOrVvp/O0Xz/zEyeVuM+6nQR1ygnFOs+8Gi0cF51H9Xp/40iWs/vZ/kz733Klla/7nRLmyuYY6jyLIo6ogF6sW6GRamVeo86h22Na0P4DUdl/qPFLqnHlkjPmWMWatMWa1MeYD3rJ3G2O+7t2+3BjTZ4w50/u3dfotnli0xFrIGvfX1itXXElPsodVracxcfgCjmb2sW943wxbmMpwZrjqTmswyXlk1048GvnFA5iREVq7D+Gs2QhAdm/58qjwvjMPPITV0UFiTenMosQpa8k+tydwKaw1C3n62NMAPHXsKRYOAP19VZfwHS+xxSHxKCLnUWL1Ksjn2f83H8TpX0z67LOnrBOUrfXUXzwCCmMQmeIw8gOzJVm+JK314ovovPFGnEWLKtqfHyodhXgUtfNIQoHZlZetlXMeNXHmked6yB11u9tF9f45Xpzubha968+aUpA7LmZ5gV91hzDHbsgkovAr6vx2HgHEly93vyvLZR6V+dwJhJuGOI/qX+pVSS5WTXBstzQ64usVRWkEwefaCebWbQRiO9P/AFLTfWnmkdIkgdnzlbRTmOgta1vGp676FJ+8+qO887LfAuCBAw9Uvc3hzDDpWPW/1hdlHlUZtj3teO7+Cdg26dZniZ1yDoiQeW5P2fUTdmHCO3b/A6Q3by47qUh6odnjO3Ywcv/9XP+Wb9G1Yy/juXF2Ht1J34Ahtbx83lG9iPWHytYick7EvVDw7L599Lzq1SUnO5Iq322tHrR44pGkUlMuiH1HlpUoL1TGly9n8Z+/t3L3gidQ1bxsrRnEo1mUrc2YedSE4pF/bLmDB927EbsGlWKCC9Jq83aCAOYqytYa4jyauUteo8UjnAjcNSHCxyqOQ6x/MVY6XdTkoGh9K6IyBmca51E99x04qyIIzNYJmTJf0DLNmtF10030vulN0exMu0IqqHjUUHyRJ2EnXNdRxyoWpBdw86bzSTkpHtg3O/GomTKPhn5yN+l1K7BjBjnpbJzeXjJ7yotH/r67Rm0mdu4kvWVz2XUTp5wCwNiOHQzf/VMAzns0z7ODz7Jz4CkWHoXWFZV1UaslMc8pI6lUZOp8fOUKsCzszk46r39pyXUsv2ytZ2q3tbqMafVq7M7OqeHoFMrWpss8qpa6la15FznhHCk/wym6zKNwYPZ03dZmzjxq6rI1L/Moe+AAxGJBxz2lMQT5WFVOnAPRqcKJweQuX1FRUbB3w51H0ZZ3FP0dHIfWSy/l5B//CLtEjh4Qfbe1kCAZX7IEu6tr2vLn495vVMfXwI5+ihI1WqZZO9KbN9Fx7TWR7Eu7QipQ38BspQzGGMYfe4zug25zuf7W/iJnRsyKsXHBRrbt3caD+x+satsD4wNsiG2oekxFzqOBUUzfRMnJ5dj27eSHRzAT4+QOH0aSSeIrVgYB2NkjR8js2gWxGGZ8nPFHH6X3xZsBgcVnEFu8uGzmkcnnSY65YawrDrkfTIn168uO2enrw+roYHz7DjLPuOVqZ+8wPHPsaQ7s3kE8O31Ydr2wWlrcIO4IJxxWPE7nS19C8vTTy7qd7J5uiMVw+voiGZNYFi3PO5/xxx6fOhavbK2WF/11K1uLl8g88p1HkXVbC5WtTec8Cgl15V6HZnYe+RPD7IEDOJ2dWsLRaHzRqMqSnWqt7RKPI5lMVfuoCRUIA2Lb0MjzMCLxwidcCua//2Sa8tHouq355XuF16Ht6qtpu+KK+n6WRSXe2Y0p3VSUhqCZR3MTdR4pqHjUEEwmw86X3cjyi06GLdCf8ibz2/8bUl2w/FzOWnQWH3/w49zy/26pevs9VgJyGRjaB+ODkF4A+SyMDUBrn7uPSRfDSa9dfcuooe3Wd3DglltYeOML3Od5jPxqB7v+8AMl97n4T/43HVdcwNOvexfjT+wqeqw180NYeQYk2nD6FzP+aOkw8AMf/SjOF7+A/VrD0sPu+BKrVpU9ThEhefLJjD36KBOPP47V3UXv4SM8+ODdDDzp7iO+bPkMr1Z9cPr7MSMjke5z8V/8xbSPd1x3HelNm7Db2yMaESx697uDDnBhJJVyL5anyTyqFl9gqflEwtueXVS2FnVgdrhsbZoSmyLxqEzmUbgtd5PhC1vZAwciE+aU8szWeRSUWlV4gdn7pjdiGiAeVSRyhUSURhBZWZhPteJy1M6jcOaRSN1/pKm2c+Cs92PbOiFT5g3qPJqbiO1oNpui4lEjsOJx0mduoGPbL2CLw9LhIzC4D75yC+RzcMk7ePUz97Bpzz7y1b5BDZy58+/gOx8qv46dADvuTkKtGKS7SY4egt4UFz5ikNFxjn3hDnqPvq9IYzp2fwdip1h6wRHENjjJPPmssPe+Dg5+/GPYP/tLxp/oYcGpgyS7JzB5wYoZkpueD9d+BHDDpIe+932MMUUfPrljxzjymc/C8DBLDtksOQSSTs8Ylpw45RSOfP7zACx885vY+3/+D7v/73/Q0joGQHzZ0upevxqRXL+e7P79Ddl3Oax4vGz4eL2wOztLCiwigt3aOm3mUbUE4lHNy9aaIfMoXLY2jUvCspB0GjMyMnPmUROWrfnCWHb/gWldh0pE2LN1HlUXopzauLGq7deKSkqSGl5OFLXzyLLcyUGFGUsSlYOghPMoEqJyVjlatqbMI9R5NCdp+Peh0hSoeNQIRg7TarYxPODQO2DYdM8jPPnR62jtSpCLL2H4G3ew+IIs5219A/SsgUQV3dOMgZGDMLQfWhdCoh1GDrkXXMlO1400uNd1FOUykJuA0cOk8qtg4ldc8ss8CGSGLMa3fIDkhnXuZvN5Br/zLlrPWUnrW3/X3xkAPT97iGf/8pPseXAZzoI4C973oaBrEsl2WHZu4HSKLV6MGR8nd+QITnche+fIl74cOFRW7jUsPpQjsXLNjOp24pS1we32yy5j25f+gTN/NUzHpj6Q/cT6+6d5dv1Y9N73BG3qldI4ixdjL6hd5zc/F6hsi/pZUjIwO/LMo8rK1sDNPcqNjJQXh5q6bM3vtnYUu0vDshuNVNLKvuTz5sjEoJI27LbVFN3WorxgF9uuPKA7KHOL3nkUBRLV8WlgtjKPUOfR3MTu6sTq7Gj0MJQGo+JRI0h30/LS18HPP8GWxw0n3ZMjmzvCob1tSDKDFe/kuYdaWfWBP8RurUI4Og5ixrDiQ6ezei8kf/dWxv718xx7MktuYRsTTz1J4pRTyB4+RttLXgknX1703LbVlxH/8g+YeOIJFr7+Dcipv1V+P14nsszTTwfiUX58nMOf+Qzp889j9IEHWLFvgr4DOeLPL1+y5uN3XHP6+nD6+3nyBWu49JMP0f/ToziLFzXMXWE1o6ujyVh2x+0lw7Rni3guJj+jqGbbLSEeSdTOo/D5NMOkzkqnyVE+MLu5y9YKF5JOVzTB7kp5Zt2m3D9Hm7wNs1RQXhdftrysiy8S/LFF6bhxnMqdRxF1I4vagRUQWbc1zTxS5hGOAyJ6zs8xum+7jY6tWxs9DKXBNPeV3QlM/EV/hPWh/+IVPzmMPWrRf/Ehkr/3Kay1lzK+Ywc7b7qZ597+dlovfD4TzzzN2COPYMbGp27npJOIr1jB+I4d5IeHSZ52GrmjRxnfvh2TzeL09tJ9262kt2wpet7hT3+aY//97aJlf/xUjowNi265mWO/2sHAl77MoU98ErJZ7N4FSCxG6wsumTIGsSwWvuUtHLz9n+m84YZpjzt1+ukADN9zD6kzzwRg6Ac/JHfwID1/9Vfs/4e/55RnH6Z7IBuEcE9H4uST3e1u3oSIsOy3X8GOB/ew9v79xE+LPixbqZzYwoU13V7Qba3GokggHqUL4lHrRReTee45nN4FNd1X2TFU6Tya/JwimrjbWlhs0MyjJiBwEFXbbW1uOY+mE2aW/O0HIxpMaYLXss7Ol6J9Og7EqnQe1dkRFFkw9+T9+p9J9XY82XbTi62KUivEstR1NAexUqma/uirzE30m6pBiAhtz7+I/J3/TnxpLy0vfD5yxjUgQurMM1nwutdx8OMfZ+h/7kJiMRKnnILdXty22mRzDP3oR+T+8z9xFi/Gakkz9KMfIckkyXXrsFJJRu69l8HvfAdJpbDicZZ94l9IbdzIwJ13kj0yQHLtycH2DnXH+PamLO9dsAiufCF777mH9NlnkzrzDA79yydoveSSsk6otktfQNulL5jxuJ3eXpKnncbQD37Igte9DoDB734Xu6uLlvPOJfXdDaz55cMAxFfO7DyyWlroe8fbSW12xbGta64j908v4KnrbyB52mkzPl85cbASCSQWq3mQnz95CDuPkqesZfF73lPT/Uw7hqLA7ArFozLle74Y1pTOo1B5lN2t4lGjkVkKA+LMjfDfwHkUoTBTNUHWT3SiiVu2VtnnQ2TOo8AFF/FlqzqPFKX26PmuKHMWFY8aSOuFF3L0zn+n+3/9AfLSlxU91vvGN9B1802YbM6tMS3jEjDGkB8awm5zhaX88DCSSAQT3vzoKANf+xoTTzzJkc9/ntGHfklq40Yy+w/Qcc01LHr3u4Jtvfbfr2LP8B4+YMXofOlLsdraabvicqxEgvQ550zb+ayq4774Yg5+/ONkjxzBamlh6Ac/oO2qKxHHIblhQ7BeYnVl++u+7bai+3Z7O6u/+Y26d2FRmou2yy8v22HseChVthY1VlFg9sxla0D5wOwmdh5JyO0QzkRTGkTQjazKSwXHmRuhmhU4jxpNkPUTpSulirK1RnZbi4LZCqjV0nrJxcSWLKnrPhSlWVDnkaLMXVQ8aiBtV1ydE381AAAPVUlEQVTBko98mLbLLy/5uNMzc5iwiATCEUyd4FqpFN033YTJ5xn4ylfI7ttLfmyM/NGjOJPKhlJOiqSddJ0b8Tgd114TPNb6/OdXc2jT0nrJxRz82McY/vGPsTs7yQ8N0X7FFQAk17sB3XmB2EknzXofzTgxVupL+uyzSZ99ds23G4hHEeWPlRxDNWVrnqV4TmYehY5Ny9YaT6Fr2iy6rc2BiUEh86iJfwG3oy/XqiYwO/g8qfff225QwG5EzqOOF72orttXlKZCnUeKMmdR8aiBiG3TftVV0ezLsnD6+sjs3Re0kHf6+orWSdpJkk7tWqeXI3nqqdgLFnD0m9/ESqawWltJn38+AIm1a8lZcKw7qaHTSlNQKFtLN24MYSFoBhdI4Dwq9/6Jxei65RZaL76oVsOrHUWZR+o8ajSBuFJt5tFcaTsedPxp3kuh4LWP0Hkks3Ie1TvzqDE5WhKRs0pR5hPqPFKUuUvzXjEpNcdZ1Ed2796CeLSwt+jxpJMk5dQ/CE0si7YXXMLAV+8EoOO6rYFQZCWTPLc4wWhf23SbUJTI8Lu3NbJsLSwEzZTP4otc5QKzRYRF7/zT2g2uhoRzVuyuaDrZKdMwW2HAnhuZR4k1a0ht2RI0XmhKGhE+7tgVB2ZH122tMc6jORP+rihzCXUeKcqcRcWjeURsYR+jv/oVmX37vPvFZWtJJ0nSrr/zCGDh295O+zXXAmZKsPWXfnc1Czv6uS6SkSjK9PjlXXYjxSMRJBbDZDKVd1urQ/5TvQkLY46WrTUcv2ytWmEgvXkz+aHhegyppjjd3az4/OcaPYxpCSZYEYomYjsVB2af6M6j4PiaOVRdUeYYot0FFWXOou/ceYSzaBHZ//kfsvtKl61dueJKDo4ejGQsdmsLLeedW/KxP7nyA5E4oBSlIkp0W2sEkkhgMpkZJ5F2dw+STBaFbM8VghLBtramzGSab/hZQNV2I+vYupWOrVvrMaT5h2ODSLSZR1WUrZ3wzqOojk9R5hPqPFKUOYuKR/OI2KI+zMQE4zt2uJPLtuLSsBeveXGDRlbMuu51jR6CogQ0Q7c18MrQRkZmvODqevmNtFx4wdwUXzyni92trqOmwM8C0olzwxCrASWATuWB2e76Tt2dOQ13HjVzqLqizDE080hR5i4qHs0jnL5FAIw+/DBO30K3q5qiKNOSOu000uee23AxRhLxikKIrXSa5Nq1EYyo9oiXs+JoWHZTMCe6kZ3o2Fbk4ePJk9dW1e1QLKv+zhxfnGpY5pFOdBWlVkgsVp1ArShK06Dv3HlEbJFbpjbx5JOkt2xp8GgUZW7QfvXVtF99daOHgRVPkDvBL7b8iVo1E1eljvgTdZ04N4xGOI/6//qvqnuCbUeTeRRx+R6gziNFqQPdr34VbVdH021aUZTacmLPRJQigowjY6bkHSmK0txIIjE32p8fD46WrTUT/i/D1WYeKTWkAc6jaonEeWQ3pswlyDxq8r+BoswlkuvWkVynERWKMhfRn1LmEc6CBeBdCDmTOq0pitLczAfxyBcrtNNacyDqPGo4YjV/sGz7NdfQcv55dd1H6vQzaL3wwrruoyT6HlAURVGUAHUezSPEcXB6e8nu26fikaLMMax4/IRvbWslk+A4OIsWN3ooCgQTZs08ahyxJf04S/obPYxpWfy+P6/7PtqvfCHtV76w7vuZjJVIYHd1Eetv7r+BoiiKokRBXWciInIV8PeADXzCGPNXkx5PAJ8BtgCHgBuNMTvrOab5jrOoj+y+fcT6VDxSlLmExCsLzJ7LWOk0K778JRKrVzd6KAruOQc0PCx+PtPz2tfS85rXNHoY8xaJxzn5hz8AfQ8oiqIoSv3K1kTEBj4GXA1sAF4hIhsmrfYa4IgxZg3wYeCv6zUexSXmdVxT55GizC0kkah7O+xmIHXqqa4DSWk4sSX9LPqL99F62WWNHsq8RSxLxbsGI/G4dqdVFEVRFOqbeXQO8Lgx5kljzATwJeC6SetcB3zau30ncJnoN3Rd8YOyNTBbUeYWbubRiV22pjQXIkLXDTdgt7Y2eiiKoiiKoihKg6nnTGQJ8Ezo/m7g3HLrGGOyInIU6AEOhlcSkdcCrwVYvnx5vcY7L0iuX4/d2anOI0WZY3Rcew2pjRsbPQxFURRFURRFUeYhc+JnbGPMHcAdAGeddZZp8HDmNB0vvo72a34LK5Fo9FAURamCtssvb/QQFEVRFEVRFEWZp9SzbO1ZYFno/lJvWcl1RMQBOnCDs5U6IZalwpGiKIqiKIqiKIqiKBVTT/HoXuBkEVkpInHg5cDXJ63zdeA27/b1wPeMMeosUhRFURRFURRFURRFaRLqVrbmZRj9AfBtwAY+ZYz5tYi8D7jPGPN14JPAZ0XkceAwrsCkKIqiKIqiKIqiKIqiNAl1zTwyxnwL+NakZe8O3R4DbqjnGBRFURRFURRFURRFUZTZU8+yNUVRFEVRFEVRFEVRFGWOo+KRoiiKoiiKoiiKoiiKUhaZa/nUInIA2NXocdSIBcDBRg9CUaZBz1FlLqDnqdLs6DmqNDt6jipzAT1PlWbnRDhHTzLG9JZ6YM6JRycSInKfMeasRo9DUcqh56gyF9DzVGl29BxVmh09R5W5gJ6nSrNzop+jWramKIqiKIqiKIqiKIqilEXFI0VRFEVRFEVRFEVRFKUsKh41ljsaPQBFmQE9R5W5gJ6nSrOj56jS7Og5qswF9DxVmp0T+hzVzCNFURRFURRFURRFURSlLOo8UhRFURRFURRFURRFUcqi4lEDEJGrRGS7iDwuIm9v9HiU+YuIfEpE9ovIr0LLukXkuyLymPd/l7dcROQfvPP2lyKyuXEjV+YLIrJMRL4vIo+IyK9F5E3ecj1PlaZARJIisk1EHvLO0T/3lq8UkZ975+KXRSTuLU949x/3Hl/RyPEr8wcRsUXkARH5pndfz1GlqRCRnSLysIg8KCL3ecv0+15pGkSkU0TuFJHfiMijInL+fDpHVTyKGBGxgY8BVwMbgFeIyIbGjkqZx/wbcNWkZW8H7jLGnAzc5d0H95w92fv3WuCfIhqjMr/JAm8xxmwAzgN+3/vM1PNUaRbGgUuNMWcAZwJXich5wF8DHzbGrAGOAK/x1n8NcMRb/mFvPUWJgjcBj4bu6zmqNCMvMMacGWp3rt/3SjPx98B/G2PWAWfgfqbOm3NUxaPoOQd43BjzpDFmAvgScF2Dx6TMU4wxPwIOT1p8HfBp7/angReHln/GuNwDdIrI4mhGqsxXjDF7jDG/8G4P4n5JL0HPU6VJ8M61Ie9uzPtngEuBO73lk89R/9y9E7hMRCSi4SrzFBFZClwDfMK7L+g5qswN9PteaQpEpAO4CPgkgDFmwhgzwDw6R1U8ip4lwDOh+7u9ZYrSLPQZY/Z4t/cCfd5tPXeVhuKVTmwCfo6ep0oT4ZUDPQjsB74LPAEMGGOy3irh8zA4R73HjwI90Y5YmYd8BPgTIO/d70HPUaX5MMB3ROR+EXmtt0y/75VmYSVwAPhXrwT4EyLSwjw6R1U8UhSlLMZtx6gtGZWGIyKtwL8DbzbGHAs/puep0miMMTljzJnAUlyH8boGD0lRAkTkWmC/Meb+Ro9FUWbgQmPMZtxyn98XkYvCD+r3vdJgHGAz8E/GmE3AMIUSNeDEP0dVPIqeZ4FloftLvWWK0izs8y2V3v/7veV67ioNQURiuMLR540x/+Et1vNUaTo8+/r3gfNx7emO91D4PAzOUe/xDuBQxENV5hcXAFtFZCduXMKluLkdeo4qTYUx5lnv//3A13DFeP2+V5qF3cBuY8zPvft34opJ8+YcVfEoeu4FTvY6XMSBlwNfb/CYFCXM14HbvNu3Af8VWn6r1zngPOBoyKKpKHXBy9n4JPCoMebvQg/peao0BSLSKyKd3u0UcAVuNtf3geu91Safo/65ez3wPe+XSkWpC8aYdxhjlhpjVuBed37PGHMzeo4qTYSItIhIm38beCHwK/T7XmkSjDF7gWdE5BRv0WXAI8yjc1T0uyB6ROS3cGvPbeBTxpgPNHhIyjxFRL4IXAIsAPYB7wH+E/gKsBzYBbzMGHPYm8T/I253thHg1caY+xoxbmX+ICIXAj8GHqaQ1fGnuLlHep4qDUdETscNyLRxf5T7ijHmfSKyCtfl0Q08ALzSGDMuIkngs7j5XYeBlxtjnmzM6JX5hohcAvyxMeZaPUeVZsI7H7/m3XWALxhjPiAiPej3vdIkiMiZuI0H4sCTwKvxvvuZB+eoikeKoiiKoiiKoiiKoihKWbRsTVEURVEURVEURVEURSmLikeKoiiKoiiKoiiKoihKWVQ8UhRFURRFURRFURRFUcqi4pGiKIqiKIqiKIqiKIpSFhWPFEVRFEVRFEVRFEVRlLKoeKQoiqIoitIgROQSEflmo8ehKIqiKIoyHSoeKYqiKIqiKIqiKIqiKGVR8UhRFEVRFGUGROSVIrJNRB4UkdtFxBaRIRH5sIj8WkTuEpFeb90zReQeEfmliHxNRLq85WtE5H9E5CER+YWIrPY23yoid4rIb0Tk8yIiDTtQRVEURVGUEqh4pCiKoiiKMg0ish64EbjAGHMmkANuBlqA+4wxpwI/BN7jPeUzwNuMMacDD4eWfx74mDHmDOB5wB5v+SbgzcAGYBVwQd0PSlEURVEUpQqcRg9AURRFURSlybkM2ALc65mCUsB+IA982Vvnc8B/iEgH0GmM+aG3/NPAV0WkDVhijPkagDFmDMDb3jZjzG7v/oPACuAn9T8sRVEURVGUylDxSFEURVEUZXoE+LQx5h1FC0XeNWk9M8vtj4du59DrM0VRFEVRmgwtW1MURVEURZmeu4DrRWQhgIh0i8hJuNdR13vr3AT8xBhzFDgiIs/3lt8C/NAYMwjsFpEXe9tIiEg60qNQFEVRFEWZJfrLlqIoiqIoyjQYYx4RkT8DviMiFpABfh8YBs7xHtuPm4sEcBvwz5449CTwam/5LcDtIvI+bxs3RHgYiqIoiqIos0aMma3DWlEURVEUZf4iIkPGmNZGj0NRFEVRFKXeaNmaoiiKoiiKoiiKoiiKUhZ1HimKoiiKoiiKoiiKoihlUeeRoiiKoiiKoiiKoiiKUhYVjxRFURRFURRFURRFUZSyqHikKIqiKIqiKIqiKIqilEXFI0VRFEVRFEVRFEVRFKUsKh4piqIoiqIoiqIoiqIoZVHxSFEURVEURVEURVEURSnL/wcPo9G346yfHgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (20,3)\n",
        "\n",
        "plt.plot(history.history['start_output_accuracy'])\n",
        "plt.plot(history.history['stop_output_accuracy'])\n",
        "plt.plot(history.history['val_start_output_accuracy'])\n",
        "plt.plot(history.history['val_stop_output_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['start_output_accuracy', 'stop_output_accuracy', 'val_start_output_accuracy', 'val_stop_output_accuracy'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(history.history['start_output_loss'])\n",
        "plt.plot(history.history['stop_output_loss'])\n",
        "plt.plot(history.history['val_start_output_loss'])\n",
        "plt.plot(history.history['val_stop_output_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['start_output_loss', 'stop_output_loss', 'val_start_output_loss', 'val_stop_output_loss'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlEBb_YIpxP1"
      },
      "source": [
        "Тестиру"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPGIsW_cpdAk",
        "outputId": "8438bd83-eff2-455e-e985-bd41131d2b16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate on test data\n",
            "16/16 [==============================] - 27s 2s/step - loss: 0.1295 - start_output_loss: 0.0330 - stop_output_loss: 0.0965 - start_output_accuracy: 0.9965 - stop_output_accuracy: 0.9925\n",
            "test loss, test acc: [0.12945647537708282, 0.03297673910856247, 0.09647972881793976, 0.9964995384216309, 0.992499053478241]\n",
            "Evaluate on train data\n",
            "40/40 [==============================] - 61s 2s/step - loss: 0.0771 - start_output_loss: 0.0259 - stop_output_loss: 0.0512 - start_output_accuracy: 0.9969 - stop_output_accuracy: 0.9941\n",
            "train loss, train acc: [0.07710107415914536, 0.02590973861515522, 0.05119132995605469, 0.9969499707221985, 0.9940500259399414]\n",
            "Evaluate on val data\n",
            "4/4 [==============================] - 5s 1s/step - loss: 0.0520 - start_output_loss: 0.0194 - stop_output_loss: 0.0325 - start_output_accuracy: 0.9980 - stop_output_accuracy: 0.9965\n",
            "val loss, val acc: [0.05196806788444519, 0.019429847598075867, 0.03253822401165962, 0.9980000257492065, 0.9965000152587891]\n"
          ]
        }
      ],
      "source": [
        "print(\"Evaluate on test data\")\n",
        "results = model.evaluate(x_test, [y_start_test, y_stop_test], batch_size=128 * 4)\n",
        "print(\"test loss, test acc:\", results)\n",
        "\n",
        "print(\"Evaluate on train data\")\n",
        "results = model.evaluate(x_train, [y_start_train, y_stop_train], batch_size=128 * 4)\n",
        "print(\"train loss, train acc:\", results)\n",
        "\n",
        "print(\"Evaluate on val data\")\n",
        "results = model.evaluate(x_val, [y_start_val, y_stop_val], batch_size=128 * 4)\n",
        "print(\"val loss, val acc:\", results)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(s):\n",
        "    x_input_seq = [s]\n",
        "    y_input_seq = ''\n",
        "    input_seq, _, _ = build_data_for_model(x_input_seq, y_input_seq, token_index)\n",
        "    prediction = model.predict(input_seq, verbose=0)\n",
        "    prediction_start = np.argmax(prediction[0][0, :])\n",
        "    prediction_stop = np.argmax(prediction[1][0, :])\n",
        "    return prediction_start, prediction_stop"
      ],
      "metadata": {
        "id": "BuTeVG81x27c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pd9OIznBuSAf",
        "outputId": "0658c1cb-866c-4846-d2c2-ea48bff9f5a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input      :LG 47LE5510 47\n",
            "Prediction :47LE5510\n",
            "Right      :47LE5510\n",
            "\n",
            "Input      :Philips 32PFL3018K\n",
            "Prediction :32PFL3018K\n",
            "Right      :32PFL3018K\n",
            "\n",
            "Input      :LG 27TK600V\n",
            "Prediction :27TK600V\n",
            "Right      :27TK600V\n",
            "\n",
            "Input      :Samsung UE78JS9502T\n",
            "Prediction :UE78JS9502T\n",
            "Right      :UE78JS9502T\n",
            "\n",
            "Input      :SUPRA STV-LC4215DF\n",
            "Prediction :STV-LC4215DF\n",
            "Right      :STV-LC4215DF\n",
            "\n",
            "Input      :Samsung CS-29Z47ZQQ 29\n",
            "Prediction :CS-29Z47ZQQ\n",
            "Right      :CS-29Z47ZQQ\n",
            "\n",
            "Input      :Samsung SP-54J7PFR 54\n",
            "Prediction :SP-54J7PFR\n",
            "Right      :SP-54J7PFR\n",
            "\n",
            "Input      :Skyworth 40E2AS 40 (2018)\n",
            "Prediction :40E2AS\n",
            "Right      :40E2AS\n",
            "\n",
            "Input      :Philips 40PFL6636H\n",
            "Prediction :40PFL6636H\n",
            "Right      :40PFL6636H\n",
            "\n",
            "Input      :Philips 47PFK7179\n",
            "Prediction :47PFK7179\n",
            "Right      :47PFK7179\n",
            "\n",
            "Input      :DEXP F22D7100E 21.5 (2017)\n",
            "Prediction :F22D7100E\n",
            "Right      :F22D7100E\n",
            "\n",
            "Input      :LG 55UQ76003LD\n",
            "Prediction :55UQ76003LD\n",
            "Right      :55UQ76003LD\n",
            "\n",
            "Input      :Thomson T55FSE1170\n",
            "Prediction :T55FSE1170\n",
            "Right      :T55FSE1170\n",
            "\n",
            "Input      :Samsung QE82Q77RAU\n",
            "Prediction :QE82Q77RAU\n",
            "Right      :QE82Q77RAU\n",
            "\n",
            "Input      :Samsung QE55Q6FNAU\n",
            "Prediction :QE55Q6FNAU\n",
            "Right      :QE55Q6FNAU\n",
            "\n",
            "Input      :Samsung GQ55Q9FNG\n",
            "Prediction :GQ55Q9FNG\n",
            "Right      :GQ55Q9FNG\n",
            "\n",
            "Input      :LG 50NANO856PA 49.5\n",
            "Prediction :50NANO856PA\n",
            "Right      :50NANO856PA\n",
            "\n",
            "Input      :TCL LED55D2930\n",
            "Prediction :LED55D2930\n",
            "Right      :LED55D2930\n",
            "\n",
            "Input      :Toshiba 24P1300\n",
            "Prediction :24P1300\n",
            "Right      :24P1300\n",
            "\n",
            "Input      :BBK 32LEX-7388/TS2C LED\n",
            "Prediction :32LEX-7388/TS2C\n",
            "Right      :32LEX-7388/TS2C\n",
            "\n",
            "Input      :LG 42PC3R\n",
            "Prediction :42PC3R\n",
            "Right      :42PC3R\n",
            "\n",
            "Input      :Samsung PS51D450\n",
            "Prediction :PS51D450\n",
            "Right      :PS51D450\n",
            "\n",
            "Input      :TCL L24E4104FR\n",
            "Prediction :L24E4104FR\n",
            "Right      :L24E4104FR\n",
            "\n",
            "Input      :Philips 32PFL3605\n",
            "Prediction :32PFL3605\n",
            "Right      :32PFL3605\n",
            "\n",
            "Input      :BRAVIS PDP-42720F 42\n",
            "Prediction :PDP-42720F\n",
            "Right      :PDP-42720F\n",
            "\n",
            "Input      :Thomson 52FE9234B\n",
            "Prediction :52FE9234B\n",
            "Right      :52FE9234B\n",
            "\n",
            "Input      :Samsung UE60KS7000U\n",
            "Prediction :UE60KS7000U\n",
            "Right      :UE60KS7000U\n",
            "\n",
            "Input      :LG 49SK8000\n",
            "Prediction :49SK8000\n",
            "Right      :49SK8000\n",
            "\n",
            "Input      :Philips 42PF9952\n",
            "Prediction :42PF9952\n",
            "Right      :42PF9952\n",
            "\n",
            "Input      :Saturn ST-TV2108\n",
            "Prediction :ST-TV2108\n",
            "Right      :ST-TV2108\n",
            "\n"
          ]
        }
      ],
      "source": [
        "X_EVALUATION_SET = x_data_raw\n",
        "Y_EVALUATION_SET = y_data_raw\n",
        "\n",
        "X_EVALUATION_SET = x_data_raw\n",
        "Y_EVALUATION_SET = y_data_raw\n",
        "\n",
        "indexes = [random.randrange(0, len(X_EVALUATION_SET)) for _ in range(30) ]\n",
        "for seq_index in indexes:\n",
        "    #take one sequence (part of the training set) for trying out decoding.\n",
        "    prediction_start, prediction_stop = predict(X_EVALUATION_SET[seq_index])\n",
        "    print('Input      :' + X_EVALUATION_SET[seq_index])\n",
        "    print('Prediction :' + X_EVALUATION_SET[seq_index][prediction_start: prediction_stop] )\n",
        "    print('Right      :' + Y_EVALUATION_SET[seq_index])\n",
        "    print()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}