4.ipynb

{
 "cells": [

  {
   "cell_type": "markdown",
   "metadata": {
    "id": "avs03Eqmt3rh"
   },
   "source": [
    "### Lab №04"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, InputLayer, Normalization\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.activations import swish\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Загрузите заданный в индивидуальном задании набор данных из Tensorflow Datasets, включая указанные в задании независимые признаки и метку класса. Оставьте в наборе признаки, принимающие числовые значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 'features/density'\n",
    "b = 'features/fixed acidity'\n",
    "c = 'quality'\n",
    "name = 'wine_quality'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You use TensorFlow DType <dtype: 'float32'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to float32.\n",
      "WARNING:absl:You use TensorFlow DType <dtype: 'float64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to float64.\n"
     ]
    }
   ],
   "source": [
    "ds = tfds.load(name, split='train')\n",
    "data = tfds.as_dataframe(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features/sulphates</th>\n",
       "      <th>features/total sulfur dioxide</th>\n",
       "      <th>features/density</th>\n",
       "      <th>features/free sulfur dioxide</th>\n",
       "      <th>features/chlorides</th>\n",
       "      <th>features/volatile acidity</th>\n",
       "      <th>features/pH</th>\n",
       "      <th>features/citric acid</th>\n",
       "      <th>features/fixed acidity</th>\n",
       "      <th>features/residual sugar</th>\n",
       "      <th>quality</th>\n",
       "      <th>features/alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.55</td>\n",
       "      <td>197.0</td>\n",
       "      <td>1.00080</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.32</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.34</td>\n",
       "      <td>7.6</td>\n",
       "      <td>18.35</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.42</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99110</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.49</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.20</td>\n",
       "      <td>6</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.48</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99076</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.11</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.10</td>\n",
       "      <td>4</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.41</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.99672</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.27</td>\n",
       "      <td>6.6</td>\n",
       "      <td>10.70</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.34</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.99016</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.34</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.80</td>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   features/sulphates  features/total sulfur dioxide  features/density  \\\n",
       "0                0.55                          197.0           1.00080   \n",
       "1                0.42                           92.0           0.99110   \n",
       "2                0.48                           51.0           0.99076   \n",
       "3                0.41                          103.0           0.99672   \n",
       "4                0.34                          135.0           0.99016   \n",
       "\n",
       "   features/free sulfur dioxide  features/chlorides  \\\n",
       "0                          44.0               0.054   \n",
       "1                          35.0               0.063   \n",
       "2                           6.0               0.029   \n",
       "3                          20.0               0.110   \n",
       "4                          57.0               0.035   \n",
       "\n",
       "   features/volatile acidity  features/pH  features/citric acid  \\\n",
       "0                       0.32         3.22                  0.34   \n",
       "1                       0.27         3.38                  0.49   \n",
       "2                       0.43         3.51                  0.11   \n",
       "3                       0.41         3.08                  0.27   \n",
       "4                       0.34         3.09                  0.30   \n",
       "\n",
       "   features/fixed acidity  features/residual sugar  quality  features/alcohol  \n",
       "0                     7.6                    18.35        5               9.0  \n",
       "1                     6.3                     1.20        6              12.2  \n",
       "2                     5.3                     1.10        4              11.2  \n",
       "3                     6.6                    10.70        6               9.0  \n",
       "4                     5.9                     3.80        6              12.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[list(set(data.select_dtypes(exclude='object').columns.to_list() + [a] + [b] + [c]))]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Визуализируйте точки набора данных на плоскости с координатами, соответствующими двум независимым признакам, отображая точки различных классов разными цветами. Подпишите оси и рисунок, создайте легенду для классов набора данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for class_label in np.unique(df[c]):\n",
    "    class_data = df[df[c] == class_label]\n",
    "    ax.scatter(class_data[a], class_data[b], label=class_label, s=100)\n",
    "ax.set_xlabel(a)\n",
    "ax.set_ylabel(b)\n",
    "ax.set_title(name)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Если признак с метками классов содержит более двух классов, то объедините некоторые классы, чтобы получить набор для бинарной классификации. Объединяйте классы таким образом, чтобы положительный и отрицательный классы были сопоставимы по количеству точек."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Исходное распределение классов:\n",
      "quality\n",
      "6    2198\n",
      "5    1457\n",
      "7     880\n",
      "8     175\n",
      "4     163\n",
      "3      20\n",
      "9       5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение классов после объединения:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "quality\n",
       "Other    2700\n",
       "6        2198\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def merge_classes(df, class_col):\n",
    "    while len(df[class_col].unique()) > 2:\n",
    "        class_counts = df[class_col].value_counts()\n",
    "        smallest_classes = class_counts.nsmallest(2).index.tolist()\n",
    "        df[class_col].replace(smallest_classes, 'Other', inplace=True)\n",
    "    return df\n",
    "\n",
    "print(\"Исходное распределение классов:\")\n",
    "print(df[c].value_counts())\n",
    "print(\"\\nРаспределение классов после объединения:\")\n",
    "df = merge_classes(df, c)\n",
    "df[c].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "1    2700\n",
       "0    2198\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "df[c] = df[c].astype('str')\n",
    "df[c] = label_encoder.fit_transform(df[c])\n",
    "df[c].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Разбейте набор данных из двух признаков и бинарных меток класса на обучающую и тестовую выборки. Постройте нейронные сети с нормализующим слоем и параметрами, указанными в индивидуальном задании, для бинарной классификации и обучите их на обучающей выборке, контролируя процесс обучения нейронных сетей. Определите нейронную сеть с более высоким качеством бинарной классификации по показателю бинарной классификации, указанному в индивидуальном задании."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import InputLayer, Dense, Normalization\n",
    "from tensorflow.keras.optimizers import RMSprop, Adagrad, Adam\n",
    "from tensorflow.keras.activations import tanh, swish, relu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FP(y_true, y_predict):\n",
    "    assert len(y_true) == len(y_predict)\n",
    "    return np.sum((y_true == 0) & (y_predict == 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[a,b]]\n",
    "y = df[c]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y.values, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization (Normalizatio  (None, 2)                3         \n",
      " n)                                                              \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                96        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,356\n",
      "Trainable params: 4,353\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n",
      "None\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization_1 (Normalizat  (None, 2)                3         \n",
      " ion)                                                            \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 32)                96        \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,356\n",
      "Trainable params: 4,353\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n",
      "None\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization_2 (Normalizat  (None, 2)                3         \n",
      " ion)                                                            \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 32)                96        \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,356\n",
      "Trainable params: 4,353\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import RMSprop, Adagrad, Adam, SGD, Adamax,AdamW,Adadelta\n",
    "\n",
    "def compile_and_fit_model(model, optimizer, X_train, y_train):\n",
    "    model.compile(optimizer=optimizer,\n",
    "                  loss=tf.keras.losses.BinaryCrossentropy())\n",
    "    history = model.fit(X_train, y_train, epochs=100, verbose=0)\n",
    "    return model, history\n",
    "\n",
    "score_metric = FP\n",
    "count_layers = [5, 5, 5]\n",
    "neurons_per_layer = [32, 32, 32]\n",
    "optimizers = [RMSprop(), RMSprop(), RMSprop()]\n",
    "activations = ['elu', 'selu', 'swish']\n",
    "\n",
    "def create_model(neurons_per_layer, activation, num_layers):\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Normalization(axis=None, input_shape=(X_train.shape[1],)),\n",
    "    ])\n",
    "    for _ in range(num_layers):\n",
    "        model.add(tf.keras.layers.Dense(neurons_per_layer, activation=activation))\n",
    "    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "    return model\n",
    "\n",
    "\n",
    "models = []\n",
    "histories = []\n",
    "\n",
    "for neurons, optimizer, activation, num_layers in zip(neurons_per_layer, optimizers, activations, count_layers):\n",
    "    model = create_model(neurons, activation, num_layers) \n",
    "    model, history = compile_and_fit_model(model, optimizer, X_train, y_train)\n",
    "    models.append(model)\n",
    "    print(model.summary())\n",
    "    histories.append(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 947us/step\n",
      "FP for model 1: 413\n",
      "31/31 [==============================] - 0s 903us/step\n",
      "FP for model 2: 413\n",
      "31/31 [==============================] - 0s 1ms/step\n",
      "FP for model 3: 413\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "for i, model in enumerate(models):\n",
    "    y_pred = np.array([1 if prob > 0.5 else 0 for prob in np.ravel(model.predict(X_test))])\n",
    "    score = score_metric(y_test.ravel(), y_pred)\n",
    "    scores.append(score)\n",
    "    print(f\"{score_metric.__name__} for model {i+1}: {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[413, 413, 413]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "лучшая модель: [0, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "score_metric_name = score_metric.__name__\n",
    "\n",
    "def find_best_model_indices(scores, score_metric_name):\n",
    "    if score_metric_name in ['TP', 'TN']:\n",
    "        best_score_indices = [i for i, score in enumerate(scores) if score == max(scores)]\n",
    "    else:  \n",
    "        best_score_indices = [i for i, score in enumerate(scores) if score == min(scores)]\n",
    "    return best_score_indices\n",
    "\n",
    "best_model_indices = find_best_model_indices(scores, score_metric_name)\n",
    "\n",
    "print(\"лучшая модель:\", best_model_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Визуализируйте границы принятия решений построенных нейронных сетей на отдельных рисунках на всем наборе данных из двух признаков и бинарных меток классов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/9600 [==============================] - 10s 999us/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/9600 [==============================] - 9s 966us/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/9600 [==============================] - 10s 995us/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "for i, model in enumerate(models, start=1):\n",
    "    layer_config = f\"Model {i} - {count_layers[i-1]} layers\"\n",
    "    plot_decision_regions(X.values, y.values, clf=model, legend=2)\n",
    "    plt.xlabel(a)\n",
    "    plt.ylabel(b)\n",
    "    plt.title(layer_config)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Визуализируйте на одном рисунке ROC-кривые для построенных классификаторов на основе нейронных сетей, вычислите площади под ROC-кривыми методом трапеций или иным методом и создайте легенду с указанием площадей кривых."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 993us/step\n",
      "Площадь под кривой ROC (метод трапеций) для модели 1 = 0.50\n",
      "31/31 [==============================] - 0s 997us/step\n",
      "Площадь под кривой ROC (метод трапеций) для модели 2 = 0.50\n",
      "31/31 [==============================] - 0s 941us/step\n",
      "Площадь под кривой ROC (метод трапеций) для модели 3 = 0.50\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAK7CAYAAAAA+kUOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADTYklEQVR4nOzdd3QUVePG8e/sbnolCaH33nsXEFCkiEhHUBBBQBFUFBVRERuvDREUFAQFBcQAAioivUiR3kG6lIQWIAnp2Z3fH9EoP+pCkk15PufkvNk7dzZPPOcNeTJ37himaZqIiIiIiIhIurC4OoCIiIiIiEhOopIlIiIiIiKSjlSyRERERERE0pFKloiIiIiISDpSyRIREREREUlHKlkiIiIiIiLpSCVLREREREQkHalkiYiIiIiIpCOVLBERERERkXSkkiUiItnCW2+9RcWKFXE4HAAYhnHVh7+/Pw0bNmTWrFk3fI+NGzfSpUsXChQogLu7O/nz56dz585s2LDhhufs2rWLPn36UKJECTw9PfH19aVmzZp88MEHXLx4MW1ekyZNeO6559Lt+xURkexLJUtERLK88PBwPvjgA9566y0sln//6fqnIK1fv54vvviC6OhoevTowcyZM695j/Hjx9OoUSNOnTrFBx98wLJly/joo484ffo099xzD5999tk150yePJlatWqxefNmhg0bxuLFi/nxxx/p0qULX3zxBX379k2b+/bbbzNhwgT+/PPPjPmPICIi2YZhmqbp6hAiIiI38/LLLzNjxgxOnDiRVrIMw2DQoEFXlaO//vqL4sWL06RJE1avXp02vm7dOpo0aUKbNm348ccfsdlsacdSUlLo0KEDixYtYs2aNTRq1AiADRs20LhxY+6//37mz5+Ph4fHVZmSkpJYvHgxDz30UNpYlSpVaNCgAZMmTcqQ/w4iIpI96EqWiIhkaUlJSUyZMoUePXpcdRXreooVK0bevHk5e/bsVeOjR4/GMAwmTpx4VcECsNlsTJgwAcMw+N///pc2/t5772EYBpMmTbqmYAG4u7tfVbAAHnvsMWbOnElMTIyz36aIiOQgKlkiIpKl/fHHH0RGRtKsWbNbzo2KiuLixYuULVs2bcxut7Ny5Upq165N4cKFr3tekSJFqFWrFitWrMBut2O321mxYgW1atWiSJEit5313nvvJTY2llWrVt32OSIikvOoZImISJb2z6YUNWvWvOaYaZqkpKSQnJzMoUOH6NWrF97e3owcOTJtzoULF4iLi6NEiRI3/TolSpQgLi6OyMjI2z7n/6tRowaGYbBu3TqnzhMRkZzFduspIiIirhMeHo5hGISEhFxzbMKECUyYMCHttZubGz/++CO1atVy+uv8c4uyYRjc6e3Kbm5uBAYGcvr06Ts6X0REcgZdyRIRkSwtPj4eNzc3rFbrNce6du3K5s2bWb9+PV9++SV+fn50796dQ4cOpc0JCQnB29ubY8eO3fTrHD9+HG9vb4KCgm77nOvx9PQkPj7e6fNERCTnUMkSEZEsLSQkhKSkJGJjY685ljdvXmrXrk2DBg3o378/8+fPJzY2lueffz5tjtVqpVmzZmzZsoVTp05d92ucOnWKrVu30rx5c6xWK1arlRYtWrB169YbnnMjly5duu5VNxERyT1UskREJEsrX748AEeOHLnl3MaNG9OrVy9++eWXqx4wPHz4cEzT5Omnn8Zut191jt1u56mnnsI0TYYPH37NOU8++SRJSUnXfK3k5GR++umnq8bCw8NJSEigYsWKTn2PIiKSs6hkiYhIlnbvvfcCsHHjxtua//bbb+Pp6cnrr7+eNtaoUSPGjh3LL7/8wj333MOMGTNYu3YtM2bMoHHjxixatIixY8fSsGHDtHMaNGjAxIkTWbZsGbVq1WLChAmsXr2aZcuW8eGHH1KxYkWmTp161df+J+Pt7IQoIiI5lx5GLCIiWV6TJk3w8/Pjl19+SRu73sOI//HSSy/x4Ycfsnr1apo0aZI2vnHjRj7++GPWrl1LZGQkQUFB3HPPPbz44os0aNDgul97586dfPLJJ6xcuZIzZ87g5uZG2bJladeuHc888wx58+ZNm9urVy927NjBrl270vG7FxGR7EYlS0REsry5c+fSrVs3/vrrLwoVKuTqONcVHR1NwYIF+eSTT3jyySddHUdERFxIJUtERLI80zRp2LAhtWrVuu6Vq6xg1KhRzJ49m127dmGz6QkpIiK5me7JEhGRLM8wDCZPnkzBggVxOByujnNd/v7+fPPNNypYIiKiK1kiIiIiIiLpSVeyRERERERE0pFKloiIiIiISDpSyRIREREREUlHue7uXIfDQXh4OH5+fhiG4eo4IiIiIiLiIqZpEhMTQ8GCBbFY0u/6U64rWeHh4RQpUsTVMUREREREJIs4efIkhQsXTrf3y3Uly8/PD0j9D+nv7+/iNCIiIiIi4irR0dEUKVIkrSOkl1xXsv5ZIujv76+SJSIiIiIi6X4bkTa+EBERERERSUcqWSIiIiIiIulIJUtERERERCQdqWSJiIiIiIikI5UsERERERGRdKSSJSIiIiIiko5UskRERERERNKRSpaIiIiIiEg6UskSERERERFJRypZIiIiIiIi6UglS0REREREJB2pZImIiIiIiKQjlSwREREREZF0pJIlIiIiIiKSjlSyRERERERE0pFKloiIiIiISDpSyRIREREREUlHKlkiIiIiIiLpSCVLREREREQkHalkiYiIiIiIpCOVLBERERERkXSkkiUiIiIiIpKOXFqy1qxZQ7t27ShYsCCGYTB//vxbnrN69Wpq1aqFp6cnJUuW5Isvvsj4oCIiIiIiIrfJpSUrNjaWatWq8dlnn93W/GPHjtGmTRsaN27M9u3befXVVxkyZAhz587N4KQiIiIiIiK3x+bKL966dWtat2592/O/+OILihYtytixYwGoUKECW7Zs4aOPPqJTp04ZlFJEREREROT2Zat7sjZs2EDLli2vGnvggQfYsmULycnJ1z0nMTGR6Ojoqz5ERERERER6jMqYCzXZqmSdOXOGfPnyXTWWL18+UlJSuHDhwnXPGT16NAEBAWkfRYoUyYyoIiIiIiKSRf248gvunVKJI6VPZ8j7Z6uSBWAYxlWvTdO87vg/hg8fTlRUVNrHyZMnMzyjiIiIiIhkTb/9dpg1+38l0mbhBhXirrn0nixn5c+fnzNnzlw1du7cOWw2G8HBwdc9x8PDAw8Pj8yIJyIiIiIiWVRysp1+A6czfeoJgusHk3/AUeon+rM/A75WtipZDRo04KeffrpqbMmSJdSuXRs3NzcXpRIREREREZeKvcBXcwYyL+kAjutcnUq46MXOSfW5fCQkdcA/CUwo6lMG+CPd47i0ZF25coXDhw+nvT527Bg7duwgKCiIokWLMnz4cE6fPs306dMBGDhwIJ999hlDhw7lySefZMOGDUyZMoVZs2a56lsQEREREREXWLFpDhN3vEOCYcfNTOGQhw3cr21YMbvycWpSXexXPLB4JVOo72bqVTnLszXep2rJexj15Hfpns2lJWvLli00a9Ys7fXQoUMB6N27N9988w0RERGcOHEi7XiJEiVYtGgRzz//PJ9//jkFCxZk3Lhx2r5dRERERCSXWbznGw542P9+9W+tae/ThlrBJbDbTWZ/E8vcmbGYJpQsY+PFN4IpWvwR7q/7CO7uHhm287hh/rNzRC4RHR1NQEAAUVFR+Pv7uzqOiIiIiIjcgWFT2rLYdoLaCd40L/ko8QGlKBNShGYlqwBw7lwsVatO5OzZWJ56qjZjxjyAp+fV15gyqhtkq3uyRERERERE/ivQGsBj9w++Zjw01IdZszpx9mws3btXztRMKlkiIiIiIpJtnDxzlPd/eoLDxgXg33uwHA6Td99dQ7lyIXTtWgmAZs1KuCSjSpaIiIiIiGQbYavHsNo9kn8Klp9bAOfPx/Looz+yZMkR/PzcadKkGPnz+7oso0qWiIiIiIhkGyn2RACKJ8F9gc0p7fs4NWp8yenTMXh52Rg/vrVLCxaoZImIiIiISDYUlOJF0vGutB/+E3a7SfnyIYSFdaFy5VBXR1PJEhERERGRrC8lJZkPZ/dnd+xOTDdY+UUtpu9aBkCPHlX48ssH8fV1d3HKVCpZIiIiIiKS5f28diozU7aAR+rdWHnyJXDOw8q4ca158smaGMa1DyJ2FZUsERERERHJ8qLjLmKPtxHsnkQDStDxg8fwc6tMpUquXx74/6lkiYiIiIhIljb1xzG8984pImLuodTzq/ngqZ9dHemmVLJERERERCTLmv/LKp4aGEHSuVAMq4PoYyGujnRLKlkiIiIiIpLlmKbJpElbGTJkDUlJvrgFx9L6yd30bP6wq6PdkkqWiIiIiIhkKdv3bWHAwIVsXmsFwK96OOX7bGLBkO0uTnZ7VLJERERERCRLafvYl0RsKwwWB/m77CG41UE8HQ5Xx7ptKlkiIiIiIpKlFOy0m4sR/tR+bCsFSl2GJCt1fOu7OtZtU8kSERERERGXiotLZuXKY7RtWxYA7/wxlH53Ce9UeYt7a3dwcTrnqWSJiIiIiIjL/PnnBTp3DmPfvvMsXtwdW+BuUgDD4upkd04lS0REREREXGLWrN307/8zV64kkT+/L+N/e4FjVY6DNbVhGdm0aWXP1CIiIiIikm0lJKTw2OOz6NFjHleuJFGnXh5mzKmJWe44AH52B1US3ahX+X7XBr1DupIlIiIiIiKZ5siRi7RsNZGjh1PAMMnbbj9xD+/j2cOAe+qcJ0O70+fB112a826oZImIiIiISKZZvvwYRw+nYPVLpMiTfxBc+Wzqgb93aA+2Q+Oq2W+zi/9SyRIRERERkQzjsNtJSklMe/1Yr/KELZvOyQYbaOhjMLXPHhemyxgqWSIiIiIikiEcdjsPfdCYtfMrUqj3Nqw+yakH2oIbQKK3K+NlGJUsERERERG5a2NmP8OsuFWkGP+OXdxZkNOTm2GPdcfiZqfwk1vSjhmmSSnvCi5ImvFUskRERERE5I59v+QTvjj5FZE2C1hSG5aZYnB2XmUuLCoHgF/xSBZ+9AzFi/mnnefu7k2+4EIuyZzRVLJEREREROS2Hb14li7zH8fCWTxJ4rLVANu/T4bqkfwgc74syIUtFwF4tHdxPh8/FH8/P1dFznQqWSIiIiIicksJiXH0+6YJOz0Twfr3GP+uDWznKE290Jfo9/jvXLhwEX9/D6ZOfYhOnSq6KLHrqGSJiIiIiMgtbdj1a2rB+lvZRDsvFnuMxODyBPrlpXq5e4iMjMPLayPVq+cnLKwLpUsHuTCx66hkiYiIiIjITQ396gH2cxrcDPztDt4o/yrNarbH3dOXqKgEAgI8AQgO9mbZsl4ULRqAp2furRqWW08REREREZHc6mzkaZa6hXPKLXVpYL4UKw807Im7py+rVh2nfPnP+frr7Wnzy5YNztUFC1SyRERERETkJuyOlLTPhwZ35bMOP+FwmLz33lpatJjOmTNXmDhxCw6H6cKUWUvurpgiIiIiInJDY38YwuZL6yB1NSCdmw0mMd6dtm1nsnjxYQAef7w6n3/eBovFuMk75S4qWSIiIiIicpWZv33M8uNhbPKMTStY/nYH27ZE8uijCzl1KhovLxuff96GPn1quDZsFqSSJSIiIiIiAKzYNIc5O8ax1uNSWrkC6EQlqhToTMuWs0hKslOuXDBhYV2oUiWf68JmYSpZIiIiIiK53J7DfzBl1essc4sAj3/HGyUGcl+p7nRuMQiAV15ZycGDF5k06UH8/Dxu8G6ikiUiIiIikkuduXCS8T8NYaHlMLj9O14m0eCBvG2pXuBpQvL6pI2PHHkvhgGGofuvbka7C4qIiIiI5FLTl76dWrD+5u1w8Jh7A77psZrk0x1o3OQbunadQ0JC6g6DFouhgnUbdCVLRERERCSXSkyJA6BIkkkd90r0az0af6+C9HviJ8LC9gFQvHggycn2XP/sK2fov5SIiIiISC5XyPRnVO/Z7NhxhuZdJnP48EVsNgsffXQ/Q4bU09UrJ6lkiYiIiIjkcqYJkydvZfDgX0lMtFO0aACzZ3emfv3Cro6WLalkiYiIiIjkIt/tWMnuswexnlvL2uTtYLVgOKx8NXU7iYl2HnywLNOmPUxQkJero2ZbKlkiIiIiIrnE9vBjvL9zyL8DVguFk006V3+KEbPbMG/efp57rj4Wi5YH3g2VLBERERGRXOJU1HkA3B0mwasKwJWKzJ3xHt6eqdu0Dx3awJXxcgyVLBERERGRXMIaH4kjyULE9Gps+70UADu2XaRhQ59bnCnOUMkSEREREcklIg6d5ejbzUk4GYjFYvDmm02pV6+Qq2PlOCpZIiIiIiK5wNjPFvHKsGMkJgTi5h/P4h8H0Lx5CVfHypFUskREREREcqCkpETW7fyZpJRERry5iq1LCgEWvMudp/qTG2je/H+ujphjqWSJiIiIiOQgDoeDbeFHef3XrpxyTwbgcvGiQCGKtd1H6Xb7aORZ0bUhcziVLBERERGRHOSxeaPYFTsPe7IbVvfUsbq1TjFoYmNeGTjbteFyCZUsEREREZEcJOH8KiIWVCV6U2FavLaUqT1/oEC+0q6OlauoZImIiIiI5BAnTkSx5sPqRB4LAqCzzwQVLBewuDqAiIiIiIjcvUWLDlGjxpdcOBaExTuJPi9epu+A+q6OlSvpSpaIiIiISDaWkuLg9ddX8L//rQMgqNhlQp5ZT+UitVycLPdSyRIRERERycbefXdNWsEa/GQ5YqqMZou/w8WpcjeVLBERERGRbKxiW0+8pkaTv+1+Ntf5gXjDAAziPUJdHS3XUskSEREREclG7HYHc+bso3o9Oy8tfZSj7lDyTTAscOWfLRdMC+XLtHRlzFxNJUtEREREJJs4e/YKPXvOY/nyY3Trm8zRxqnjhgVaxRl0q/4CiQXrUsAviJJB+VwbNhdTyRIRERERyQbWrPmL7t3nEBFxBat7CrssW7ECJZPg1bofUqdSCyw2N1fHFFSyRERERESyNIfD5IMP1jFixAocDhOPglEUGbQRa6EYAAqYgdSr1srFKeW/VLJERERERLKoyMg4evWaz6JFhwAIbPgXBXtvo4URxD0Fu+Nu86Blg0ddnFL+P5UsEREREZEs6s8/I/ntt8MYbikUeHQHeZocJ8juYFzfFa6OJjehkiUiIiIikkU1bFiESZ/fx8dnXsRSPIbH3OrTpm4/V8eSW7C4OoCIiIiIiKS6dCmeHj3msm/feQBm/vIWu+x9sBWLBqBF3YFULl3PlRHlNuhKloiIiIhIFrBlSzhdu4Zx7NhlDhy4wJYt/fk+PIxjPu4AGKZBaEhx14aU26KSJSIiIiLiQqZpMmHCZoYOXUJSkp3ixQOZNKkdFotBCiZgUMOoRctqvSkSGOzquHIbVLJERERERFwkOjqRJ5/8iR9+2AtA+/bl+Prr9uTJ48WpUxtJsKSWrAcL1KRr9WauDSu3TSVLRERERMQFTp2KpnnzaRw6dBGbzcL779/Hg22TeGfeQ+wjglM2A2ypWyi4ufm5OK04QyVLRERERMQF8uf3pUABP+LjU5j0VTP+PP0anTb+9fdv6AY206REghXvmBKUaNbO1XHFCSpZIiIiIiKZJDY2CZvNgoeHDdvhX5n9wmVsVoPFf/ZgSoA1bZ5fXAit84/CDA6haJAX1YroXqzsRCVLRERERCQT7N9/ns6dw2jRogTj3ijN+gVP8Jct9dfxP7w8AW/yJVsJCmjL6AcHUyo4v2sDyx1TyRIRERERyWDffbeLJ59cQEKCg1MRZ3CU+oLV+UOvmRfoUYcfurzrgoSSnlSyREREREQySHx8Ms8+u5jJk7cB4FPxLAUHbGJ1QGLanBCjDgBuFg8GNXzaJTklfalkiYiIiIhkgE27TtCx0yxOH07AMCD0ob2EtN9PlWQbtgQPDMPgnnwt6d9eV65yGpUsEREREZF0lpRkp2mzySRctGH1S6DIgE34Vj4HwBv3fUWFkrVcnFAykkqWiIiIiEg6cNjtrNgyj0sxZ/GMOUaNLrvYuaoc9futo6BfLI4ECwUshahQoqaro0oGU8kSEREREUkHb08azrRTm/ApG5k60ABK1DvDI27V6HfPU1CkLljdXBtSMoXF1QFERERERLK7b6eu4n8venBifEPcL3hQOAkCk33xNcvR+IEPoHgjFaxcRFeyRERERETu0Omj23l54GRmLM0H2PAqGUWllCCmP7nE1dHEhVSyRERERETuwJrf19H28e+5ciQfAMEtD1G4y06aFXrExcnE1VSyRERERESc9Ouvh+jWfTlXokOweCVR5ImdNGhRko/v/42CvgVdHU9cTCVLRERERMRJP/ywj5hoE89il6jdfwNrX/3D1ZEkC1HJEhERERFx0meftSbROMzOBvPwNuyujiNZjHYXFBERERG5hRUrjtGnzwIcDhMAHx93uj7mi8Xd4eJkkhXpSpaIiIiIyA3Y7Q7efXctb765CtOE+vULMWBAbVfHkixOJUtERERE5DrOnYvl0UfnsXTpUQD69q3BY49Vc3EqyQ5UskRERERE/p/ffz9Bt25zCA+PwcvLxsSJbendswJDp7ZgmzUSgBQDsOruG7mWSpaIiIiIyH9MmbKNAQN+xm43KV8+hLCwLlSuGMzhH7qz1OMS/39bg/x2L9cElSxLJUtERERE5D+qVcuP1WqhW7eK1H1wC8M3tMCxwcEpNwMMA4DXi71AkJcPFsOgYbU2Lk4sWY1KloiIiIjkehcvxhMUlHpFqnbtgmzfPoAKFULoOekdjnvCP1evQmIKcOFyLx7q8QieblbXBZYsTSVLRERERHIt0zQZP34TI0Ysp+yLf2Etcg4rKXiaCXisT+CyWxJg0MZenHsqPU1wSB1Kh/qqYMlNqWSJiIiISK4UFZVA374LmTt3PwB/bbhCgeKHAbiSNit1eWDdYi1pV6915oeUbEklS0RERERynW3bImjRajyXz3tgsTqo2nkbya2OUy4xif6Xo0n0DCHBuyBxPoUIzF+bdk0GuDqyZCMqWSIiIiKSa5imSZvnxvPbhEjMFA/cgmMpMmgjKSUvYQBF7O60fPYQePi6OqpkYypZIiIiIpJrzJ9/gMXjUrdh96seTqF+m+kbXBcAm8WNDvcPVsGSu6aSJSIiIiK5Rvv25fGrFo5P+fO81PgYNWsOo2mDHq6OJTmMSpaIiIiI5Ghz5uyjTZsyeHu7YbEYFH1uPYYBbe/9jrLFqrk6nuRAlltPERERERHJfuLiknniiQV06RLG4MGLUgcdjn+eJ8w/OweKpDddyRIRERGRnMOezPlDqxi9YAbfjc/P+dNeGIbJkUuLGTF1OsHxR+GfW66s7i6NKjmXSpaIiIiIZBsrj+4mbO9S/GOP4510IW3cMB34JkTgH3+ST/aVZf/0OjgSbdgC4ik8cBORFc6zEP4tWCZ4+4W64luQXEAlS0RERESyjeErnyPWdu66xxwWC2cW3sPFlaUA8Klwji5PHMQ7wA5JQQAkGZ6c8yxBpXx1KBwQlGm5JXdRyRIRERGRbMOLs8Ri0CguESzBOIx/txi4GJWHw5uLgWFS+uEEvv3kWeoXK+vCtJJbqWSJiIiISLZhYAIGHSsOo2XTJ685vrT6ERwOkwceKJ354UT+ppIlIiIiItmOw82bpCQ7L720lHvvLc7DD5cH4P77S7k4mYhKloiIiIhkQ2fPJNO48dds2nSaadN2cu+9xQkM9HR1LBFAJUtEREREsiCH3c6KLfOIij2fNuZ7YRcphkn0jgI8M+UkV2Ic5MnjybRpD6tgSZaikiUiIiIiWc7YOc/ydcLqq8bMFIOzc6tz4ddygIO6dQvxww+dKVYs0CUZRW5EJUtEREREspxzsSfACn52BwEOA3uyhc1jmnL5cAgATw+uzScftcLd3eripCLXstx6SsaaMGECJUqUwNPTk1q1arF27dqbzp8xYwbVqlXD29ubAgUK0KdPHyIjIzMprYiIiIhktJMRh4izXwGgjr0Av/bbw5KndtGjZSv8/T2YM6cLn49rq4IlWZZLS9bs2bN57rnnGDFiBNu3b6dx48a0bt2aEydOXHf+77//Tq9evejbty979+4lLCyMzZs3069fv0xOLiIiIiIZ4ac1U3notw6sdD+P6YDEuH+L1JgxD7BjxwA6darowoQit+bSkjVmzBj69u1Lv379qFChAmPHjqVIkSJMnDjxuvM3btxI8eLFGTJkCCVKlOCee+5hwIABbNmyJZOTi4iIiEhG+DN8EymGgf2yO6c+aMyaqfdgtzsA8PCwUaJEHhcnFLk1l5WspKQktm7dSsuWLa8ab9myJevXr7/uOQ0bNuTUqVMsWrQI0zQ5e/Ysc+bMoW3btjf8OomJiURHR1/1ISIiIiJZ15X9eTn+RkuiDuTjxBEru3efc3UkEae4rGRduHABu91Ovnz5rhrPly8fZ86cue45DRs2ZMaMGXTr1g13d3fy589PYGAg48ePv+HXGT16NAEBAWkfRYoUSdfvQ0RERETSh8Nhsny+D8c/aEJ8tCeVKuVly5b+VK+e39XRRJzi8t0FDcO46rVpmteM/WPfvn0MGTKEN954gwceeICIiAiGDRvGwIEDmTJlynXPGT58OEOHDk17HR0draIlIiIi4kJDJjdnlds5zP/8zpcS486pSXW5sju1UJVqcJJNy17F29vNVTFF7pjLSlZISAhWq/Waq1bnzp275urWP0aPHk2jRo0YNmwYAFWrVsXHx4fGjRvzzjvvUKBAgWvO8fDwwMPDI/2/ARERERG5I7ssZzGNqxdUnfyiHrF782G4p1Dwse30bOyjgiXZlstKlru7O7Vq1WLp0qV06NAhbXzp0qW0b9/+uufExcVhs10d2WpN3XHGNM2MCysiIiIiTluwahLjD39K3P+7QSXGljowosAT1C5/PwCHCkXz4nOb+WBMbapW7UOR/CUzO65IunHpcsGhQ4fy2GOPUbt2bRo0aMCkSZM4ceIEAwcOBFKX+p0+fZrp06cD0K5dO5588kkmTpyYtlzwueeeo27duhQsWNCV34qIiIhIrhd+/i8G//gQkdYUACJtFnC7/hYAHjFWYs2alG5ZGYDSRaHV7gY3vG1EJDtxacnq1q0bkZGRvPXWW0RERFC5cmUWLVpEsWLFAIiIiLjqmVmPP/44MTExfPbZZ7zwwgsEBgbSvHlz3n//fVd9CyIiIiICYJpsXjKCgx4O/v/eavclF6BT7efSXu/bc4W3PzjJwIg1VChbkoYNU++XV8GSnMIwc9k6u+joaAICAoiKisLf39/VcURERESytyWvYT+8jBeJYZ+7g3A3G0WSHTxX6TUA/H2DqV8l9ZE9pmny2WebeOGFJSQnOyhZMg9z5nShRo1r76sXyQwZ1Q1cvrugiIiIiGRTKYmM3zedWf5+xFgt/HMFK8GtJi0bPHLV1KioBPr1+4k5c/YB0LFjBaZOfYiAAM/MTi2S4VSyREREROSOxCTGMSlPwFVjL1YZy8MVG1w1tmPHGbp0CePw4Yu4uVn46KOWDB5cV8sDJcdSyRIRERER58Sc5eNln7Imch38vcv6Y0VfpGu11hQPCr1m+urVxzl8+CJFiwbwww+dqVevcCYHFslcKlkiIiIickNb9q5i0roRJJnJWLDj5YjFZiazwsc7rWD52h0806AT3p6+132PIUPqkZhop1+/mgQFeWViehHXUMkSERERkRsK++NjNnhE/2fEjbR2BXSiAvdV73FVwdq79xyvvrqCb7/tgL+/B4Zh8NJLjTIvtIiLqWSJiIiIyA3ZHSlghcqJblT3r0eCR17iPENxWNyolq8sj1ZvdtX8adN28PTTi4iLS2b48GV8/nlbFyUXcR2VLBERERG5pYKWvLzcfeINj8fFJTN48CKmTt0BQMuWpXjzzXszJ5xIFqOSJSIiIiJ35c8/L9ClSxi7d5/DYjF4882mvPpqY6xWy61PFsmBVLJERERE5I6tWHGM9u2/58qVJPLl82HmzE40b17C1bFEXEolS0RERETSpNjtvL3qO07FRGCYds6bkTedX6lSXvz83KlVqwCzZnWiQAG/TEoqknWpZImIiIgIADMWf8DCU0vZ53bm30H3v//H4p42dOFCHCEh3gDky+fL2rV9KFYsEJtNywNFQCVLREREJNdbsmEWK/bP4hfrsf/uzk6nmCskG+4ku+Xn0eZvAPDjj/vp02cBn3/ehp49qwJQqlSQK2KLZFkqWSIiIiK5WFxCLK8feIe4/2xS0TDRn4eDa9H6/j6QvxpYLCQl2Rk69Dc++WQjANOn76JHjyoYhuGq6CJZlkqWiIiISC4WnxBDnCW1YNVM8KR+3uY81fv9q+acOBFF165h/PHHaQBefLEB773XQgVL5AZUskRERERyqaMn9/Lblu/SXo/tvog8AXmvmvPLLwfp1Ws+Fy/GExjoyTfftKd9+/KZHVUkW1HJEhEREcmlnlvcnWP/7meBxWq96viff16gXbtZmCbUqVOQ2bM7U6JEnkxOKZL9qGSJiIiI5FIXrQ7AQrEkqGwtQYDv1RtYlCsXwrBhDYmPT+HDD+/Hw0O/OorcDv0/RURERCSXe7nWuzSu+RAAy5YdpUyZIIoVCwTgf/+7T/deiThJDzMQEREREex2B2++uYqWLb+lW7c5JCXZAVSwRO6ArmSJiIiI5HIXI5N54IHvWL78GABVq+bD4TBdnEok+1LJEhEREcnFYg+E0PfFA0ReSMHb240vv3yQRx+t6upYItmaSpaIiIhILhR+7gQnfynPiR+rgJlCxYp5CQvrQsWKeW99sojclEqWiIiISC7zwletWGxGcG5LMzANHmibh7mz++Hj437rk0XkllSyRERERHKZI45wLB4mRQZtxGNfCN+Pn6aCJZKOVLJEREREcgHTNPnkk40kJKRASOrYKxVa8/gLr13zEGIRuTsqWSIiIiI53KVL8fTps4AFC/7EMKDpiAAofRmb1U0FSyQD6DlZIiIiIjnYli3h1Ko1iQUL/sTd3crnn7fBv3CUq2OJ5Gi6kiUiIiKSA5mmyYQJmxk6dAlJSXZKlAik/YhgvuAlDKuegSWSkXQlS0RERCQHeuKJhTzzzK8kJdl5+OHybNs2gB1uy3HYLmA3wGqalM9X3tUxRXIklSwRERGRHOiee4pgs1n45JMHmDevK4GBnph/X8DqezmK387HU7dGe9eGFMmhtFxQREREJAcwTZMLF+LIm9cHgCeeqEGTJsUoUyb4mrkFUuzkK90WLNr0QiQj6EqWiIiISDYXG5tE797zqV17MhcvxgNgGMZ1C1aacq0yKZ1I7uN0yQoNDeXChQsZkUVEREREnLRv33nq1v2Kb7/dxenT0axadfyGcz3NOAAchhVKNc+khCK5j9PLBS9cuIDD4ciILCIiIiLihO++28WAAT8TF5dMgQK+fP99Z5o0KXbNvC27FzNpwzvEWaPABtE+xcDDzwWJRXIH3ZMlIiIiks3ExyczZMivfPXVdgDuu68kM2Z0JDTU59rJF4+yYtWzbPB3TxuyBtfKrKgiuZLTJcswDAzDyIgsIiIiInIbXnttBV99tR3DgJEjm/Laa02wWq+9CyT86B9MW9SXTZ6pr4slh9I+uC6PtnkjkxOL5C5OlyzTNGnRogU22/VP3bZt212HEhEREZEbe+21Jqxbd5J3321OixYlrzr2/sx+hMcew+ZIZLX7JRL9LEDqVaz8eVvyZPuXXZBYJHdxumSNHDkyI3KIiIiIyA0kJqYwe/ZeHnusKoZhkCePFxs29L1mddGKTXP4LvmPfzoV/93jrHneAbx8T4/MCy2Si6lkiYiIiGRhx45domvXOWzZEk5iYgpPPpl6P9U/BeuH3b/z08Hl5Lmyn/WOPWBJHW9rL0mUZyESvYswtH5PKucv6rLvQSS30cYXIiIiIlnUggUHePzxBVy+nEBQkBeFCvlfPcE0+XDTiyTYYlNfWwzyJ5v0LvQYj7bWskARV3G6ZOXJk+emG19cvHjxrgKJiIiI5HbJyXZeeWUZY8ZsBKB+/cLMnt2ZokUD0uZs37OMDb+/gekRA1hoGZtI/oCGDOw8Fj+fQNcEFxHgDkrW2LFjgdQNMJ566ineeustQkND0zuXiIiISK508mQU3brNYcOGUwAMHVqf0aPvw93detW8j9cNZaeXyT/3XfVsNZuaJWtkdlwRuQ6nS1bv3r3TPh88eDCdOnWiZMmSNzlDRERERG7XoUMX2bjxFAEBHnzzzcM8/HD5q45HRBxg6W/DOWdLBmwUTM5L0bytVbBEshDdkyUiIiKShTRvXoIpUx6iadPilCyZJ2089kokv/48nE/i1hFttcDfj9N5pOoLPF63raviish13HXJ0oOJRURERO5cREQM/fv/zMcft6Rs2WAA+vRJvSr10/7NHLt4kqCz6/j1/CJ2edrgPw8dLubWgi7V7nNJbhG5MadLVseOHdM+T0hIYODAgfj4+KSNzZs3L32SiYiIiORwy5cfpUePeZw7F8vlywmsWfM4hmGw5dRhpmz+nt+jZ/872fPfX9tCjDpMavMuZUIKuCC1iNyK0yUrIODfXW0effTRdA0jIiIikhvY7Q7efXctb765CtOEihXz8NjjZ1g2ZxDRlw/xpvuZq+ZXTEwh0hJMnLUA7zR5nealqroouYjcDsM0TdPVITJTdHQ0AQEBREVF4e/vf+sTRERERNLRuXOxPProPJYuPQpA3741OFPjdf7ydVwzt3SSg/aeVXm88wTwynPNcRG5OxnVDbTxhYiIiEgmOXQoknvvnUZ4eAze3m5MnNiWXr2qUe/r4YCFPClW7HiQjBu1g1oxocNrro4sInfA6ZJVs2bNmx7ftm3bHYcRERERycmKFw+kaNEA/P09mDOnC5UqXf2s0Y/qfkTdKtrIQiS7c7pk7d69G29vb/r166fldiIiIiK3cPFiPH5+7ri5WXFzszJvXlf8/Dzw9XV3dTQRySBOl6w9e/YwbNgwvv32W0aOHMnAgQOxWq23PlFEREQkl9m48RRdu4bRvXtlPvjgfgAKFPC7as75S+GuiCYiGchy6ylXK1euHAsXLmT27NlMnTqVypUr89NPP2VENhEREZFsyTRNxo7dSOPGX3PyZDTz5x8gNjbpmnnPf9WS5gsfIM7i9K9kIpKF3fH/o5s1a8bWrVsZPnw4Tz/9NM2bN2f79u3pmU1EREQk27l8OYFOnX7g+ed/IyXFQefOFdm8+Ul8fK5dHnjIjEj7vGgSVCpVLzOjikgGcXq54NChQ68Za9OmDTNnzqRu3bokJyenSzARERGR7Gbbtgi6dAnj6NFLuLlZGDPmAQYNqoNhGNdODt+BFTtgZVjeR3j0gZex6BYMkRzB6ZJ1o6tVtWvXvuswIiIiItlVbGwSDzzwHRcuxFGsWABhYV2oU6cQAEs3zuZ/e9/iyv9bQxTv9veAXzEVLJEcxOmStXLlyozIISIiIpKt+fi4M25cK77/fg/jXzAZtr0Vp3enPmA4ymoB2w3u0nC4Ub1Mo0xMKiIZzemS9cQTT/Dpp5/i5+d368kiIiIiOdju3Wd5c/F3HA5agB8xuJME7aDtMQt4wP+//b1pUgiPNxwJgOnuhcMjiOJBoeTzDcj88CKSYQzTNE1nTrBarURERBAaGnrryVlQdHQ0AQEBREVF6TlfIiIicse+/no7Tz+9iBSPOEq/9Ru2gMRr5thSfBhbfTgA3p6+1KnUIrNjishNZFQ3cPpKlmma1795U0RERCQXiI1NYtCgRUybthOAPKWiwGLy2oVLhFR6miS/IiR758f08Oe+UtXx8fBwcWIRyWxOlyyAIUOG4OXldd1jU6dOvatAIiIiIlnV/v3n6dIljL17z2OxGLz9djPWBj/NKU/wKtSdFq1edXVEEckC7ug5WaZp3vBDREREJCeaMWMXdepMZu/e8+TP78vy5b149dXGGJbU338SfIu6OKGIZBVOX8kyDINx48Zl23uyRERERJxlmiY//3yI2NhkmjUrzsyZncif3xfOH8SNFMANu7uvq2OKSBZxR/dkiYiIiOQmhmEwadKD1K1bkCFD6mG1WsA0YdELGKT+bpSoK1ki8jenlwv27t37hvdjiYiIiOQUc+bso3fv+Wl/YPbz8+D55xukFixg5bKPeT1mD2dsd3SLu4jkYE7/VPj6668zIoeIiIhIlpCYmMKwYUsZP34TAA88UIoeParwV/hBxi96ljhHHABrPS6C379LBAv4B7skr4hkPU6XrI4dO970+Lx58+44jIiIiIgrHT9+ma5dw9i8ORyAl15qSJcuFQEYt2gIS9xOX3NOaHI+WpUbyANlamRqVhHJum6rZE2YMIHu3bsTFBTE/Pnz8fPzo3379lit1ozOJyIiIpIpFi78k96953P5cgJ58njyzdftORT/KcOnj+GKPYZ1HlFpc9ubZQEI9MzL090+xtvTx1WxRSQLuq2StWfPHu655x727dvH0qVLeeGFF9iyZQsffvghbdu2zeiMIiIiIhnqgw/W8fLLywCoW7cQP/zQmRORa/hm9++pvy39/RtT9QQPhjX/jKpl6rsurIhkebd9Jcvd3Z0zZ87QokULtm/fzjfffMOAAQMoX748H3/8MdWqVcvorCIiIiIZ4p57imKzWXjmmTq8//79zFv1Kev/+hncwcfh4B57UeoWa03X+4e4OqqIZAO3tbvg5MmTCQkJSXs2lmEY9OnTh0OHDtGkSROaNm3KE088QXh4eIaGFREREUkvZ89eSfu8YcMiHDgwiE8+acWlmDO8Hz6Vle7nAQhKMfio368qWCJy227rStaff/7J8uXLsVgsjBs37qpjgYGBDBw4kM8//5ywsDBiYmIyJKiIiIhIerDbHYwatZoxYzawYUNfqlTJB0CpUkH8vmMRK3Z9R4phAHBPYiBNi3dwZVwRyYYM08mnC5coUeKmx48dO3ZXgTJadHQ0AQEBREVF4e/v7+o4IiIikonOnLlCjx5zWbnyOADvvNOMESOapB1vPqUS522pC33cHSZb++xxRUwRySQZ1Q2c3sI9q5coERERketZufIYPXrM48yZK/j4uDFpUjt69Khy1ZwoS+oVrCqJbtT0r+uKmCKSA9zxI8qTkpI4duwYpUqVwqYnnYuIiEgW5XCYjB69ljfeWIXDYVKpUl7mzOlK+fIhNzznlWYTtIOgiNyx29r44r/i4uLo27cv3t7eVKpUiRMnTgAwZMgQ/ve//6V7QBEREZG78e23O3nttZU4HCaPP16dTZuevGnBEhG5W06XrOHDh7Nz505WrVqFp6dn2vh9993H7Nmz0zWciIiIyN169NGqPPhgWaZOfYivv26Pt7fbNXNiEuP5YffvOAwXBBSRHMfpdX7z589n9uzZ1K9fH8P49ydRxYoVOXLkSLqGExEREXGWaZp8880OHnmkCp6eNqxWCwsXdr/q95b/r82sJ7ls7IS/51huMldE5FacvpJ1/vz5tOdl/VdsbOxNf3iJiIiIZLSLF+Np3/57nnhiIS+88Fva+K1+R4mxRwAQmpJCiziTiiVqZWhOEcnZnC5ZderU4Zdffkl7/c8PrcmTJ9OgQYP0SyYiIiLihE2bTlOz5pf89NNBPDysac+/uh1uJAPwwblIxt4/FotVm3qJyJ1z+ifI6NGjadWqFfv27SMlJYVPP/2UvXv3smHDBlavXp0RGUVERERuyDRNPvtsEy+8sITkZAelSuUhLKwLNWoUuO33CDCjSQCi89eF0vdlXFgRyRWcvpLVsGFD1q1bR1xcHKVKlWLJkiXky5ePDRs2UKuWLq2LiIhI5omKSqBr1zkMGbKY5GQHnTpVYOvW/rdVsBKS7VxJSOb8pQispABwqWirjI4sIrnAHV0Lr1KlCtOmTUvvLCIiIiJOuXQpgWXLjuLmZuGjj1oyeHDdG95/lZIUT8rZ/Rjn9nF0z0a2ndrMb8FRbPdyA7fUc0zD6b8/i4hc445Klt1u58cff2T//v0YhkGFChVo3769HkosIiIimap48UBmzepEnjye1KtX+IbzNu36jRe2DOWy9T8lqjDAv9u5h6Q4qFtRV7JE5O453Yr27NlD+/btOXPmDOXKlQPg4MGD5M2bl4ULF1KlSpV0DykiIiICcOVKEk8//QuPPFKZ1q3LANCqVelbnvfHwd+uLlgAJtT3q8XLjV4ixCcUbw8/3N09MiK2iOQyhmmapjMn1K9fn9DQUKZNm0aePHkAuHTpEo8//jjnzp1jw4YNGRI0vURHRxMQEEBUVBT+/v6ujiMiIiK3ae/ec3TuHMaBAxfIl8+HY8eexcvr3ytRj819i13R83EnGQuOq861Y5BoMSifaOPjrksA8HX3IsjbN1O/BxHJWjKqGzh9JWvnzp1s2bIlrWAB5MmTh3fffZc6deqkWzARERGRf0ybtoOnnvqF+PgUChb0Y/bszqkFKzqcyXMGMMM8SqTNAhZIAG60t1cxa36KBubNzOgikgs5XbLKlSvH2bNnqVSp0lXj586do3TpW1+uFxEREbldcXHJDB68iKlTdwDQsmUpvvuuA3nz+jD71/f57tQ3HHe38d9S9am1Kv5VH8W0ul/1Xh5uXlQuVS8T04tIbuV0yXrvvfcYMmQIb775JvXr1wdg48aNvPXWW7z//vtER0enzdVyPBEREblTMTGJNGo0ld27z2GxGIwadS+vvtoYy4U/4atn2J9whON+/y7365rnER6v1YEihSq4LrSICHdwT5bF8u9fiv7ZIvWft/jva8MwsNvt6ZUz3eieLBERkeyjX7+F/LRwPzV7rsK94ikAPMwkAMJtVqKsVmqZFXi25fvUKFjClVFFJBvKMvdkrVy5Mt2+uIiIiMh/JSSkEB+fTJ48XjgcDuIa/EieKrs5FZj4n1lXLwMsWbSdCpaIZClOl6ymTZtmRA4RERHJ5Y4evUSXLmGEhHjz6689ObpmHLttW3AL/HfOy6GPAuCwuhPrX5pQv2A6VKjvmsAiIjegpweLiIiIy/3443769FlAVFQiwcFejPioN+f9N4CXJxYTnij1Gh2r3EeRwGBXRxURuaXr728qIiIikgmSkuw8//xiOnb8gaioRBo2KMj2eZX4LXQbf3h5AmDa8/Bs424qWCKSbehKloiIiLjEiRNRdO0axh9/nAag5n1HaNjuOybuspPs5wNAszy96FbtQVfGFBFxmkqWiIiIZDrTNHm442y2b43A0yuJ0P6bSaoRwWI80+YYDguvNx9AXl/tBiwi2YtKloiIiGSa+CQ7ny4/xI79S8h73494x1Si8JObcc8bB0CwvSQVQpqAYVC/UDUVLBHJlpwuWTVr1rzp8W3btt1xGBEREcm5wsNj+Hz6ak56/Y8DQZexBxuULreKexx5Sfa5nxD/ErzR7DE83dxv/WYiIlnYbZWsl156if79+1O6dGl2796Nt7c3/fr108N8RURE5JaW7TvL3Plb+fZ/G4iLMygxwoJ3oMG9+PH8ve9QskRzV0cUEUlXt1WygoODuffeezlx4gR79uxh2LBhfPvtt4wcOZKBAwditVozOqeIiIhkQ+cuXWHo4HfZvTIITBueRS5j9U4mr6MA4/sscXU8EZEMYZimad7ORHd3d06cOEH+/PkBWLlyJS+++CJxcXF88MEHtGvXLkODppfo6GgCAgKIiorSlTgREZEMsHH3ElbumknMJQj7zIfDf4YCUKjJEZr0iCfevxiD6jzGfaWruTipiOR2GdUNbqtkvfbaa4SFhfHnn39ec2z69OmMGDGCMmXK8PHHH1OjRo10C5cRVLJEREQyVvvJVdh9NISTE+uREuWF4Z5Coce3cWTc97h7+Lk6nohImozqBre1XDB//vz8/vvvAAwdOvSa423atGHmzJnUrVuX5OTkdAsnIiIi2YzDzhWLg7ijQaREeRFQMJaKz1ykTYOeKlgikmvcVsl65pln0j7fvn37defUrl07fRKJiIhIlvXT/k0cuPDXVWOGI5k8F7bgf3k/fpf/JCmPJyGtDtK+QHPGvDscHx/tFigiuYvTW7ivXLkyI3KIiIhIFrfy6G5e3dT3usfiDgVzbmEFij4TgcVqxwAeGlBLBUtEciU9jFhERERuae/Zkyzc/gMAng4HFZOSADBN2L+8Anvn1cB0WIj5sQ75u5wn2KMI95fRKhcRyZ2cLlnNmjXDMIwbHl+xYsVdBRIREZGs5UzMJbotao9hSb3vulhyCtMoyKVYG32mV2DLzrwAdO9YnEnfvIKfn4cr44qIuJzF2ROqV69OtWrVbvjhrAkTJlCiRAk8PT2pVasWa9euven8xMRERowYQbFixfDw8KBUqVJMnTrV6a8rIiIitxafZGfloWMYlmQM06R0UhJNvOuypdYsan32EAt25sXd3cqECW2YOaeXCpaICHdwJeuTTz656vWhQ4fYvXs3lSpVoly5ck691+zZs3nuueeYMGECjRo14ssvv6R169bs27ePokWLXvecrl27cvbsWaZMmULp0qU5d+4cKSkpzn4bIiIicgumadJ1XBj+bhMgEHxMkx9DmjHfHEKjRlNJSrJTokQgYWFdqFWroKvjiohkGbf9MOLrmTNnDj169MAwDBwOB9OmTaNHjx63fX69evWoWbMmEydOTBurUKECDz/8MKNHj75m/uLFi+nevTtHjx4lKCjojjLrOVkiIiK35kiK49CKD+gRMYckS+ptAs2S/Bn3xCrCzyZQvfoXNGpUlK+/bk9goKeL04qI3JmM6gZOLxf8r/fff5/hw4eTmJjI559/znvvvXfb5yYlJbF161Zatmx51XjLli1Zv379dc9ZuHAhtWvX5oMPPqBQoUKULVuWF198kfj4+Bt+ncTERKKjo6/6EBERkZtIiGLg1Pp0Pjs3rWA979+TcU+uA6sbBQv6sXnzk8yb11UFS0TkOu6qZB09epTu3bsD8Mgjj3DkyJHbPvfChQvY7Xby5ct31Xi+fPk4c+bMDb/e77//zp49e/jxxx8ZO3Ysc+bMYdCgQTf8OqNHjyYgICDto0iRIredUUREJLdxJMUx75u2bPBKXehimhC/qhGDetiZO3df2rxixQJvuhGWiEhudlclKykpCQ+P1Btc3d3dSfp7O1dn/P8f0KZp3vCHtsPhwDAMZsyYQd26dWnTpg1jxozhm2++ueHVrOHDhxMVFZX2cfLkSaczioiI5BYvTn+YkV5RAFhiAyj/2wiOfFOAhIQU5s7d7+J0IiLZg9MbXwwdOjTt86SkJN59910CAgKw2+1OvU9ISAhWq/Waq1bnzp275urWPwoUKEChQoUICAhIG6tQoQKmaXLq1CnKlClzzTkeHh5pRVBERERuImIn5+3Hwc2DMifKsnfmvcw98CdWq8G77zZn2LBGrk4oIpItOF2ytm/fnvZ5w4YNOXr0aNrrJk2a3Pb7uLu7U6tWLZYuXUqHDh3SxpcuXUr79u2ve06jRo0ICwvjypUr+Pr6AnDw4EEsFguFCxd29lsRERGRv00IG8K3V5ZzxdODS+uK8sv0qiQlXqRgQT++/74TjRsXc3VEEZFsw+mStXLlynT74kOHDuWxxx6jdu3aNGjQgEmTJnHixAkGDhwIpC71O336NNOnTwegR48evP322/Tp04dRo0Zx4cIFhg0bxhNPPIGXl1e65RIREclttl/awhUPC/HHAzk9uS4A991XkhkzOhIa6uPidCIi2YvTJSs9devWjcjISN566y0iIiKoXLkyixYtolix1L+WRUREcOLEibT5vr6+LF26lMGDB1O7dm2Cg4Pp2rUr77zzjqu+BRERkWxt4ZopfH7wEy64ARh0LF6A6EE1KRjqz4gRjbFa7+r2bRGRXMnp52R17NjxpsfnzZt3V4Eymp6TJSIi8q+XprTl++12vEtH4pYngZdDH6Vnq5e0c6CI5AoZ1Q2cvpL1300nZs6cSbt27fDz80u3QCIiIpI5EhNTWDajGCdX5iNf6UssWtiVmhUaujqWiEi25/SVrP/y8/Nj586dlCxZMj0zZShdyRIREYFjxy7RpUsYW7dGAFC79Uk2LJyEzablgSKSe2SZK1kiIiKSvS1YcIDevecTFZWIp08K+fpvpFFFVLBERNKJSpaIiEgukZxs55VXljFmzEYA/EsnUq7/KuJDYyA5v4vTiYjkHE6XrHHjxqV9npKSwjfffENISEja2JAhQ9InmYiIiKSrpCQ7ixcfAaB+Rw9i2swl3pZ610CgLdCFyUREchan78kqUaLEjd/MMK56OHFWpHuyREQkt/lw1gAORO8E4HKED1FnfQiucYB9Hh5UTEzkYd9GPNh+DH4+eVycVEQkc2WZe7KOHTuWbl9cREREMk5KioPhry5hxvELBLeNTR0sEQsl4CweAOQ1Q3mkx9cuTCkikvPoniwREZEcKDw8hkcemcuaNX+BpTK+tU/TM39x3K2eaXOsFne63DfUhSlFRHKmuypZx48fp3fv3mzfvp2qVasybdo0SpUqlV7ZRERE5A4sX36UHj3mce5cLH4eyQQ+sRWPfLEMaDeW/Hm0wYWISEa7q71aX3zxRVJSUpg4cSKBgYEMHjw4vXKJiIiIk+x2B2+9tZr77/+Wc+diKVgwkkEvLCCg3qnUCTYP1wYUEckl7upK1h9//MGcOXOoV68eTZo0oVq1aumVS0RERG4gxW7nzZXTOXP5KHljD2JxJGOaEDauOId3BgCQp8lRAh/dwc/uqeeYphVPm7sLU4uI5B53VbIuX75M3rx5AQgNDSUqKipdQomIiMiNzdm9hgWnx/w78Pe6lNgqDoz9VSjYext5Gp0gIDmAUI9GADQoVJtALx8XpBURyX2cLlm7du1K+9w0TQ4cOMCVK1dITExM12AiIiJyHaaJ++5JAAQm26l63g8jxCv1UOPLXKm0Cb/gRAIdZXn10el4e6pYiYhkNqdLVvXq1TEMg38er/Xggw+mvTYMI90DioiI5DSzd61l55mDAFjtcfjHHMZqJt/Wud5xEUTFbCPFGsK+LxsQEVeWrVv7ExDgeeuTRUQkU+g5WSIiIplk7bF9zNyzmN8v3sVzqSwQd744JyfUJ/miN7Ee0WzadJr779fuviIiWYXTJatYsWIZkUNERCRHi01M5JmVj+GwJqWNNY2LByDe8CLJuPXOf6YJB1aUYO+8iph2C6FFPFjy0+NUq6Zt2UVEshKnS9a4ceNuenzIkCF3HEZERCQnOnH5PD/sWpZWsOrGJ9AqwaBLxe5QtTvkr3zL97h8OYEnnljA1h8PANClS0W++uoh/P21LbuISFZjmP/cXHWbLBYLhQsXxmq1XvtmhsHRo0fTLVxGiI6OJiAggKioKPz9/V0dR0REcoE6U+4nwXYm7fXGWm/jU+FBsN7+3zqffHIhX321HTc3C5988gBPP11H90KLiNyljOoGd7SF+5YtWwgNDU23ECIiIjlJSkoyK7fMIykldTmg1RIOWCiZlEwlCuNT+WGn33P06Pv4889IPv64JXXqFErfwCIikq6cLlmGYegvZyIiIjfxyrSH+M126t8BS+qDrN4o2Z9aLYbe1nvExCQye/Ze+vWrCUBIiDdr1vRJ96wiIpL+nC5Zpmny+uuvExAQgI+PDwULFqRGjRrUqlUrI/KJiIhkOxdSIsEG/nYHXo7UsUJ2b6o0HnRb5+/efZbOncM4eDASd3crvXpVy8C0IiKS3pwuWU2aNOHAgQMkJycTHR1NREQEFy9epFq1avzyyy8ULFgwI3KKiIhkCycjDpGMHYD2ng15qcfk2z7XNE2+/noHgwYtIiEhhcKF/SlTJiijooqISAZxumStWrXqmrEjR47Qq1cvXnjhBWbNmpUeuURERLKd8XOGMvnKEkxP55fVx8YmMWjQIqZN2wlA69almT69AyEh3ukdU0REMpglPd6kVKlSfPrpp5w+fTo93k5ERCT7sSdz7NIuTMPAME2CUxw0rND+tk7dv/889ep9xbRpO7FYDN57rzk//9xDBUtEJJu6o90Fr6d27dqsWbMmvd5OREQk+zi0DL5/BM88fuDuQyt7MT7o+8ttn/7XX1Hs3Xue/Pl9+f77TjRtWjzjsoqISIa7oytZ3377LY0aNaJgwYL89ddfAIwdO5YFCxakazgREZEsLykOfn6eH73c+MnPB4DSofWceotWrUrzzTft2bFjgAqWiEgO4HTJmjhxIkOHDqVNmzZcvnwZuz315t7AwEDGjh2b3vlERESytrUfsy8+gjdDUjeoqOTVjv7t3rjpKYcPX6R582kcO3Ypbax37+rky+eboVFFRCRzOF2yxo8fz+TJkxkxYgRWqzVtvHbt2uzevTtdw4mIiGRlh45u4eGjM+hWqAAOw8DNXoSZnd+56TlhYXupWfNLVq48zqBBizIpqYiIZCan78k6duwYNWrUuGbcw8OD2NjYdAklIiKSHazes5Aj7m5pr2uHNMNiuf7fLxMTUxg2bCnjx28CoHHjokye3C5TcoqISOZyumSVKFGCHTt2UKxYsavGf/31VypWrJhuwURERLKquIRYBkxryl+2eLBZKJ5k4a2286lRsMR15x87dolu3eaweXM4AK+80oi3326OzZYum/yKiEgW43TJGjZsGIMGDSIhIQHTNNm0aROzZs1i9OjRfPXVVxmRUUREJEtZv3MROzwT+WfVfSHT74YFa/v2CJo3n87lywnkyePJt992oG3bspmYVkREMpvTJatPnz6kpKTw0ksvERcXR48ePShUqBCffvop3bt3z4iMIiIiWceS18h7cBH4QYDdwatlX6Z5nc43nF6xYl5KlcqDzWZh9uzOFCsWmHlZRUTEJQzTNM07PfnChQs4HA5CQ0PTM1OGio6OJiAggKioKPz9/V0dR0REspOEKPhfUQ64u9GlUAHypMCavtdu+hQREUNoqA9Wa+qVrjNnrhAU5IW7u/WauSIi4joZ1Q2cXgzevHlzLl++DEBISEi2KlgiIiJ3xXQwKcCft4NTt2uPMvyumbJ48WGqVJnIqFGr08by5/dVwRIRyUWcXi64atUqkpKSMiKLiIiIy23YvpCpm94jEfs1xxIMO/uDAtNe2/j385QUB2++uYr33luLacKiRYd47bUmKlciIrmQ0yULwDCM9M4hIiLievZklm54jY0+t15J37HQC3Sp3BxIXR7Yo8c8Vq06DsDAgbX45JNWKlgiIrnUHZWsDh064O7uft1jK1asuKtAIiIirnDi8nk+mPckJ9ySADdKJOejsU+p686tU6YV99buAMDKlcd45JG5nD0bi4+PG5Mnt+ORR6pkYnIREclq7qhkNWjQAF9f3/TOIiIi4jKfr5nCausRsKY+XLhwvrYMa/f8Tc+5eDGehx76nitXkqhcOZSwsC6ULx+SGXFFRCQLc7pkGYbBsGHDtOGFiIjkKF5RewEomQRlijzNy/f0vOU5QUFefPZZa1at+ovPP2+Dt7dbRscUEZFswOmSdRc7vouIiGRZXkkXwQahZiAftXrqhvN+//0ENpuF+vULA9C7d3V6966eSSlFRCQ7cHoL95EjR2qpoIiI5Czxl8gbsw+Ai275rjvF4TD58MN13HvvN3TpEsaFC3GZmVBERLIRp69kjRw5MiNyiIiIuITDYXLg53FYHMkARNuCr5lz8WI8vXvP5+efDwLQpEkxPDy0c6CIiFyf0yWrZs2aNz2+bdu2Ow4jIiKS2VbtO8a+k98xK9AfAJvl6vuq/vjjFF27zuHEiSg8PKx8+mkr+vevpceZiIjIDTldsnbv3o23tzf9+vXD398/IzKJiIhkikkLRvDD+fmcDfYEwM8ozAv1+gGp9yCPH7+JF19cQnKyg1Kl8hAW1oUaNQq4MrKIiGQDTpesPXv2MGzYML799ltGjhzJwIEDsVq1ZEJERLKXzXuXM/7yQnCzEJzioGO+DgxqMwqr5d9/01atOk5ysoNOnSowZcpDBAR4ujCxiIhkF4Z5h9sFrly5khdffJG4uDg++OAD2rVrl97ZMkR0dDQBAQFERUXpSpyISC5z8K8drNn5IwDhlw8RZu4m0O5gfsflBAfmv2b+5csJzJmzj759a2h5oIhIDpRR3eCOS9Y/pk+fzogRIyhTpgwff/wxNWrUSK9sGUIlS0Qk92r9VWVOuV1dlvIlO1jWby+maTJp0lY2bDjF11+3V6kSEckFMqobOL1ccOjQodeMtWnThpkzZ1K3bl2Sk5PTJZiIiEh6i7SagEHJJLCZFgyggf89XLmSxIABPzNz5m4AOnWqQLt25VyaVUREsi+nS9b27duvO167du27DiMiIpIh7Clwdk/ay5GNPqNmxaYA7N59ltq1J/Hnn5FYrQajR7egbduyrkoqIiI5gNMla+XKlRmRQ0REJGPEX4Jv2sHZ3VCscOrY30sBv/lmB08//Qvx8SkUKuTH7NmdadSoqAvDiohITmBJzzfbvXt3er6diIjI3UmOh5ndSTq7m28Cg0j8u1zZffPz8stL6dNnAfHxKbRsWYrt2weoYImISLpwumT16dMHu91+1VhiYiKvvvoq9evXT7dgIiIid8WeAnOegJMbGRaan4/z+OIwDOzxhQnxyk+bNmVwc7Pw9tvN+PXXnuTN6+PqxCIikkM4vVzw4MGDtG3bljlz5uDr68vKlSvp378/efLkYd26dRmRUURExDmmScyCQTgO/comH39W+LgDEHusHnUK9qFokB8lmvpz9OizFC6snWZFRCR9OV2yVqxYwSOPPEKTJk2oVq0ac+fOZdSoUTz77LNYLOm6+lBEROSGHHY7MfFR/w6kJGE5tQnLsVUsPL6M9/xNKFYkdW6ShTMz65CwqQSjtpbDakldNqiCJSIiGcHpkuXh4cHcuXMZPHgwEydOZNGiRTzwwAMZkU1EROS6kpIS6fpNHY543OBRj//pTolnArnwZQsuHTOAFFauPEb58iGZklNERHInp0vWwoULAWjZsiV79+6lZ8+ejBkzhsDAQAAeeuihdA0oIiLy/x2P+PPGBesfDiv1zw7lh9HhREcnEhzsxXffdaRVq9KZE1JERHItp0vWww8/nPa5YRiYpsnjjz+e9vr/b4ohIiKSkZY9uDj1E8MAN28wDJKS7IweuZ6Jn20FoGHDInz/fSeKFAlwYVIREcktnC5ZDocDgKioKDp06IDD4WD+/PlpV7JEREQy0pDJzdliPQtWC4Zpki+40DVzxo37I61gDRvWkHffbY6bmzWzo4qISC7ldMkCOH36NK1atWLfvn18/vnnKlgiIpJp1tvOkfj3RkvFk43rznnqqdosW3aUfv1q8tBD5TIznoiIiPPPydq9ezf16tXj/vvvZ+7cubz77rs0b96cI0eOZEQ+ERGR6xpR4Alm9VwPQEqKg88+20RSUuqSdTc3KwsXPqKCJSIiLuF0yWratCnDhg1jzJgxPPzww+zfv5+KFStSrVo13nvvvYzIKCIiwgez+tNuchUS/95+vWLxevh4+3H6dDTNm09j8OBfeeWVZS5OKSIicgcla9KkSTz77LNpr319ffnss89Yvnw5s2fPTtdwIiIin238ifpTH+TbpA0cT32mMN4OB4VCS7J06RFq1PiStWtP4OfnToMGhV0bVkREBDBM07zFHri3z263Y7Vm7RuLo6OjCQgIICoqCn9/PYRSRCSraz2lDadsJ9NeP+5WjwfqDCTsuxjefnsNpgnVq+cnLKwLpUsHuTCpiIhkNxnVDe5o4wu73c78+fPZv38/hmFQoUIF2rdvn+ULloiIZB+/HdrO6PWfkGz8BVh4NCqWe2q9TunSXejZcx7Llx8DoH//mowd2wovLzfXBhYREfmb0yXr8OHDtG3bllOnTlGuXDlM0+TgwYMUKVKEX375hVKlSmVEThERyS2iI2DFOyw7u51Izxiwpq5sL1q6H40a9eHQoUg2bTqNj48bX375ID17VnVxYBERkas5vVywTZs2mKbJjBkzCApKXZYRGRnJo48+isVi4ZdffsmQoOlFywVFRLKmyMtneHdOb2yJhwDY4+HOSTc3msYm07RAOzq0G43Nlvq3wV9/PUTx4oFUqJDXlZFFRCSbyzLLBVevXs3GjRvTChZAcHAw//vf/2jUqFG6BRMRkdxl7urPWeoWDm4+V407AvoxbUpBgvxP0KJFSQBaty7jiogiIiK3xemS5eHhQUxMzDXjV65cwd3dPV1CiYhI7pOUEg9AwWSTRp51uehXjsvHA1jwYRzhpw+xd+95Dh58Bjc33f8rIiJZm9NbuD/44IP079+fP/74A9M0MU2TjRs3MnDgQB566KGMyCgiIjmYw+HgxcVfsPjybgCCHG683nMKRQ43ZsaL5wk/HUPZssEsWNBdBUtERLIFp69kjRs3jt69e9OgQQPc3FJ3ckpJSeGhhx7i008/TfeAIiKSsy09spPfzn7+779IV7zo0GE2Cxb8CUD37pWZNOlB/Pw8XBdSRETECU6XrMDAQBYsWMChQ4c4cOAApmlSsWJFSpcunRH5REQkh7sUEwmAn91B6wg7Ez9/kIiIP3F3tzJ27AMMHFgbwzBcnFJEROT23dFzsgDKlClDmTK68VhERO6Qw8FXC17l+8ifwM1CXrud1zqN4eCBRNavP8kPP3SmVq2Crk4pIiLiNKdLVseOHW96fN68eXccRkREcolja7n026uM87xESoo7QXEmHUp0wijfhokTk0hJcRAY6OnqlCIiInfE6ZI1f/58unbtipeXV0bkERGRnOz8n7D0DQ4cX85mT0/izhfn5Of1KVajCr0X9QLA11c71YqISPZ2R8sFx40bR2hoaHpnERGRnCrmLKwaTfjOGYzJ48figgW4tKY4Ed/VwEy2sm/fJcLDYyhUSA+JFxGR7M/pkmUYhm5AFhGR25MUC+s/I2r9OHZaUxhUJD+ORCvhk2tyeX0xAErWdWfTov4EB3u7OKyIiEj6cLpkmaZJixYt8PLywsfHh4IFC1KjRg26d+9OoUKFMiKjiIhkNw47bP+OqFXvMc8Sy+T8gcRYLSSc9uPk5w1IDPfHajV4770WvPhiQywW/fFORERyDsM0TdOZE0aNGoVpmiQnJxMdHU14eDjbtm3j/PnzLF26lAYNGmRU1nQRHR1NQEAAUVFR+PtrWYqISLqyp7B23SS27ZvMJiOWPR7uOP5e/WAkBXPozfokhHtRoKAvs7/vTOPGxVwcWEREcrOM6gZOl6zrMU2Tfv36cfToUVauXJkeuTKMSpaISPo6HX6Agztm431uG/sv7+HjPFcv+7Mk5+PBYj0Y2awXO7ad5a23VjN1antCQ31clFhERCRVRnWDO35O1n8ZhsGoUaP49NNP0+PtREQkG4iJucD6XYt58fD7qQMG8J+CFXqlEzW8G1PLPz+P3F8JgLp1C/Hzzz1ckFZERCTz3FXJ+ucimGEYFC5cmA8//DBdQomISNblcJiEnzvPoIXNOOphSRv3TXEnCXdS8KBTyX6UP1+Dfv0WkpRkp0LZYKpXz+/C1CIiIpnHcusp15o+fTpVqlTBy8sLLy8vqlatyrfffpve2UREJAsaNHMz73376FUFq1lSXjb03crWvhvY9Ogyzs0Nolu3OcTEJFGvXmEtDRQRkVzF6StZY8aM4fXXX+eZZ56hUaNGmKbJunXrGDhwIBcuXOD555/PiJwiIpJF7I4dTVRoBADB9hCW9V6KzZr6z8mxY5fo0iWMrVtTjw8ffg9vvdUMm+2O/qYnIiKSLTldssaPH8/EiRPp1atX2lj79u2pVKkSb775pkqWiEhOZZrE7f8FT+shojB4whHC4MeXYrOk/lMyf/4BHn98PlFRiQQFefHddx1o3bqMi0OLiIhkPqdLVkREBA0bNrxmvGHDhkRERKRLKBERyXpGfdOJucZBTLfULdmLVxqcVrAAdu48Q1RUIg0aFGb27M4UKRLgqqgiIiIu5fT6jdKlS/PDDz9cMz579mzKlNFfLEVEcqLDR5Ywx3II8+9nXlnsftQpdy//fQrIa681YeLEtqxe/bgKloiI5GpOX8kaNWoU3bp1Y82aNTRq1AjDMPj9999Zvnz5dcuXiIhkTwtWf8Unhz8h1mKQaAB/F6ynQ5+hz319WLH0Lx4f8zM//fQIXl5uWK0WBg6s7drQIiIiWYDTV7I6derEH3/8QUhICPPnz2fevHmEhISwadMmOnTokBEZRUTEBf44+guRNgsJFgPTMKh8xYO8x3vTtGp33hq5lrZtZ7J8+TE++WSjq6OKiIhkKYb537UeuUBGPdVZRCSn2HVoI2+s7E+Em504i4X6iX48d98X+AeWJu5yAv37LGTNmr8AeOaZOnz0UUs8PNLl2fYiIiKZKqO6gdP/KkZHR9/0uIqLiEj2tmL7TI54mPyz2KG4dxkqlazK8uVH6dFjHufOxeLn585XXz1E166VXBtWREQkC3K6ZAUGBmL8vS7/v0zTxDAM7HZ7ugQTERFXSV3gUC7RwtB6/6N+5ZZ8880OnnhiAaYJVavmIyysC2XLBrs4p4iISNZ0R+s75syZQ1BQUHpnERGRLMTTtNGwWmsAmjcvQZ48XnTsWJ5x41rj5eXm4nQiIiJZ1x2VrEaNGhEaGpreWUREJAu5EumV9nnRogHs3v0UBQv6uTCRiIhI9qA7lUVEcrFnF33GlnPrcDMT8bNH4+uIJtqM5/yycuydV4mfqx7kwQfLAqhgiYiI3CanS5ZhGNe9J0tERLKP85fC+en3r1hxMQwMwIBIC6RccefUpEZc2VUAgCVLjqSVLBEREbk9Tpcs0zRp0aIFNtv1T922bdtdhxIRkfSx79B6JqwaQaKZmDYWRRyH3FNI+c8fzF6NvMyfEZUY93VtrkS64eZm8u7/GvLi8/e7IraIiEi25nTJGjlyZEbkEBGR9BZzhuWL+7Ha33qdgwb5k03KmXlpWaQVR87W5d2xv5OS4qBMmSDCwrpQrVr+TI8sIiKSE+hhxCIiOU3iFaL+XMaUtW+yyS2OvR4eFErOwz0exQDwsHnTuHIn6lZsgcVqZfXq49x77zQAunatxOTJ7fD393DldyAiIpIpsszDiEVEJItxOODMLjiyHI6shBMb+dnXg6+Dg4DUshQU2JzXOr953dObNi3OkCF1KVs2mKefrqP7bkVERO6SSpaISDb127ppbNz7DQGJp3GzJ6SNRwX6Mivg350A6wf2YnjjXmmvTdNk6tTtPPhgWfLl8wXg009bZ15wERGRHE4lS0Qkm/p034ec9DDAw4N/rlj9lz2uKPX8BzK5fbu0sZiYRPr3/5nvv99DixYl+O23R7FaLZmYWkREJOdTyRIRyabiLSZgUC6lKMneZYF/y1KIrRLFgurRo16xtLFdu87SpUsYBw9GYrNZaNu2DBaLlgaKiIikt7sqWf/smaH1+yIimWvpr28RZU392TukxlM0qfngDeeapsnXX+9g0KBFJCSkULiwPz/80JkGDYpkVlwREZFc5Y7WiEyfPp0qVarg5eWFl5cXVatW5dtvv03vbCIi8v/ZU9g5ZwBvR3xPsmFQMdGbe6rd+H6q2Ngk+vRZQN++C0lISKF169Js3z5ABUtERCQDOX0la8yYMbz++us888wzNGrUCNM0WbduHQMHDuTChQs8//zzGZFTRCTXu3jpNBPmduVHaxRJViv5k3z57JFfsViv9xysVA6HyYYNp7BYDN55pxkvv3yPlgiKiIhkMKefk1WiRAlGjRpFr169rhqfNm0ab775JseOHUvXgOlNz8kSkezq2SmtWGE7DUBAbF6eqfcZ3WtVvO5c0zTTlnLv2nWWS5fiadq0eGZFFRERyRYyqhs4vVwwIiKChg0bXjPesGFDIiIinA4wYcIESpQogaenJ7Vq1WLt2rW3dd66deuw2WxUr17d6a8pIpIdxdmjAagRU5iaeT6iXeWy18yJj09mwICf+PTTP9LGqlbNp4IlIiKSiZwuWaVLl+aHH364Znz27NmUKVPGqfeaPXs2zz33HCNGjGD79u00btyY1q1bc+LEiZueFxUVRa9evWjRooVTX09EJFtKTmDDsg+xOy4BUDogmHGP1MTH4+oV34cORdKgwRQmTdrGK68sIzw8xhVpRUREcj2n78kaNWoU3bp1Y82aNTRq1AjDMPj9999Zvnz5dcvXzYwZM4a+ffvSr18/AMaOHctvv/3GxIkTGT169A3PGzBgAD169MBqtTJ//nxnvwURkazNNOHCQTi8HMfh5bwVtYO5ft7glfojO8qn1DWnhIXtpW/fhcTEJJE3rzczZnSkYEG/a+aJiIhIxnO6ZHXq1Ik//viDTz75hPnz52OaJhUrVmTTpk3UqFHjtt8nKSmJrVu38sorr1w13rJlS9avX3/D877++muOHDnCd999xzvvvHPLr5OYmEhiYmLa6+jo6NvOKCKSaRKi4fAyLh/8jbN/rcEt7hwAC319mBsYgMU08UvxJtESSqdaj6SdlpiYwosvLuGzzzYD0LhxUb7/vrMKloiIiAvd0XOyatWqxXfffXdXX/jChQvY7Xby5ct31Xi+fPk4c+bMdc85dOgQr7zyCmvXrsVmu73oo0ePZtSoUXeVVUQkQzkcJE1qwl8xp+hYuAAE2SCo4FVT7s8/iI9aPXXVmN3uoHnz6axffxKAV15pxNtvN8dmu6Onc4iIiEg6cbpk3epKkLO7cvz/Bxn/d0es/7Lb7fTo0YNRo0ZRtuy1N3vfyPDhwxk6dGja6+joaIoU0fNhRCRrcCTFc2rVh/TySSIyoMC/B+xegAHYaBLa5ZqCBWC1WujYsTwHDlxg+vSHadv29n82ioiISMZxumQFBgZetwT9U47sdvttvU9ISAhWq/Waq1bnzp275uoWQExMDFu2bGH79u0888wzADgcDkzTxGazsWTJEpo3b37NeR4eHnh4eNxWJhGRzHQlJoK+s1qxz8MBtn+fdVXc/T5+euST656TnGzn7NlYChdO/YPW0KEN6NmzKvnz+2ZKZhEREbk1p0vWypUrgdRS1aZNG7766isKFSrk9Bd2d3enVq1aLF26lA4dOqSNL126lPbt218z39/fn927d181NmHCBFasWMGcOXMoUaKE0xlERFzCnkJSUgxD5rZLLVh/83NU4Pfe32OxXH+536lT0XTrNofIyDi2bOmPr687hmGoYImIiGQxTpespk2bpn1utVqpX78+JUuWvKMvPnToUB577DFq165NgwYNmDRpEidOnGDgwIFA6lK/06dPM336dCwWC5UrV77q/NDQUDw9Pa8ZFxHJsrZNZ89vL/FBkB/bPT3xdjjw/6sz4wa8QIUCN15uvXjxYR59dB6RkfEEBHiwZ8856tcvnInBRURE5Hbd0cYX6aVbt25ERkby1ltvERERQeXKlVm0aBHFihUDUh98fKtnZomIZCdfbvuOzwrmBcDDYZLv9P1c8GhCsWDv685PSXHw5pureO+9tZgm1KxZgB9+6EypUkGZGVtEREScYJimad7pyX5+fuzatStbLdWLjo4mICCAqKgopzfpEBFx1sqju3l+5SAslihs2Im3pN7T6pviwactvqNcvlL4uFuxWa9dIhgREUOPHvNYteo4AE89VZsxYx7A09Olfx8TERHJMTKqGzj9L3WNGjXSNr6Ij4+nXbt2uLu7px3ftm1buoUTEcnulu36EbvtEnYgmdSfnVbTZGixx6lbvPxNz33++d9Yteo4vr7uTJ7cju7dtTRaREQkO3C6ZD388MNpn19vgwoREYHw838x5MeHCLelgNVCw7h4nq72KskBJcmbpyDFCt56u/WxY1tx8WI848e3ply5kExILSIiIunB6ZI1cuTIjMghIpKjLN88iz89HEDqMsAA93JUa/jETc85fz6WuXP3M3BgbQDy5/dlyZLHMjqqiIiIpDMt7BcRyQAmqVuzF042eanaKJrWfPim89eu/Yvu3ecSHh5DnjyedOumpYEiIiLZldMlK0+ePNd9GPE/Ll68eFeBRERyEg+HhWZ1Ot3wuMNh8tFH63n11eXY7Sbly4dQqVJoJiYUERGR9OZ0yRo7diyQ+jDip556irfeeovQUP1CICLirIsX4+ndez4//3wQgJ49q/DFFw/i6+t+izNFREQkK3O6ZPXu3Tvt88GDB9OpU6c7fhixiEhutWnTabp0CePEiSg8PKyMH9+afv1q3nSlgIiIiGQPuidLRCQ9mSas/oDQv36B6z9fGIAzZ65w4kQUpUsHERbWherV82deRhEREclQd12y9FdXEZH/uHScr7eP53cvL8ATu2FNO2SaZtrPzIceKsd333XgwQfLEhDg6aKwIiIikhGcLlkdO3ZM+zwhIYGBAwfi4+OTNjZv3rz0SSYikg1tCz/EmKA8aa+jrCUA2L49gqee+oWwsC4UKRIAQM+eVV2SUURERDKW0yUrICAg7fNHH300XcOIiGR3cYmxAHg6TOrlfZLe1drwxRdbeO65xSQm2hk2bCnff9/ZxSlFREQkIzldsr7++uuMyCEikm1sO7CWsPUfkmwmXzVumA5sicfB1x13E0Y3HciAAT8za9YeANq1K8vEiW1dkFhEREQykza+EBFx0qQ1r7LO4/L1D7qlbr9++WQB6tSZzJ9/RmK1Grz//n0MHdpA97GKiIjkAk6XrJo1a970+LZt2+44jIhIdpBEEgCVEm0UtFz9nMB4mz/7j5diw/98SEqMpFAhP2bP7kyjRkVdEVVERERcwOmStXv3bry9venXrx/+/v4ZkUlEJFuo7lODV3pOvWY8Pj6Z+j9OoUABX779tgN58/pc52wRERHJqZwuWXv27GHYsGF8++23jBw5koEDB2K1Wm99oohIDnb8+GWKFg3AYjHw8nJj2bLHCA72xmLR8kAREZHcxuLsCeXKlWPhwoXMnj2bqVOnUrlyZX766aeMyCYiki3MnLmbypUnMHr02rSxvHl9VLBERERyKadL1j+aNWvG1q1bGT58OE8//TTNmzdn+/bt6ZlNRCRLS06Cp576mZ495xEbm8yqVX9htztcHUtERERczOnlgkOHDr1mrE2bNsycOZO6deuSnJx8nbNERLIvh8PBvL0bOBN9huDwlSSaUSSeDeSLiQUIP74Vw4DXXmvCyJFNsVrv+G9XIiIikkM4XbJudLWqdu3adx1GRCSr2LV/FftOpv68+/XMDraZ/+6cGrW7OKen1sYR70ZIiDfffdeBBx4o7aqoIiIiksU4XbJWrlyZETlERLKE0yd2smnZq7xhPXHd40XPurH4y7o4UqzUqBPKwnk9KVxYO62KiIjIv5wuWU888QSffvopfn5+GZFHRMQlYhLjWbdtPsMOvgf/2TC1aOojsXADHg9oxMPPfcyXAfs5cuQS777bHDc37a4qIiIiVzNM0zSdOcFqtRIREUFoaOitJ2dB0dHRBAQEEBUVped8iUiaRlPaEm379+qVW4ovNYLbM+XhV/j554Pkz+9L7doFXZhQRERE0ltGdQOnr2SZpolhaFtiEclZ7EZqwfK3Qx73Rvzc+wuSk+28/PJSPvhgPcWLB7JtW3/y5PFycVIRERHJ6pwuWQBDhgzBy+v6v2hMnTr1rgKJiLiCFQdg4cOqo2hYuyOnT0fTvftcfv89tXw99FBZfHzcXRtSREREsoU7KlmmaeLkKkMRkSzrUtR5/vmJ5rB5smTJEXr2nMeFC3H4+bkzdWp7Oneu6NKMIiIikn04XbIMw2DcuHHZ9p4sEZH/evGr1vzmdgqsFkwHfDXhNNO/2oxpQvXq+QkL60Lp0kGujikiIiLZiNNPzdQVLBHJSQ6Zp9I+L5RscuaEO6YJ/fvXZP36J1SwRERExGlOX8nq3bv3De/HEhHJVs7sxmraMU0rzwV15PE2rxHVKZnly4/RtWslV6cTERGRbMrpkjV27FiSk5OvGb948SI2m03bootIlrT00A5eXPMsWGKw4sCKHYvDwV+/VSTxvC/GqGLYbG4EB7upYImIiMhdcXq5YPfu3fn++++vGf/hhx/o3r17uoQSEUlvS3bPxWG7iMOSTLLFzpVYd/aNbcLZeVW4vLYEKRfKuTqiiIiI5BBOl6w//viDZs2aXTN+77338scff6RLKBGRdGVPpsTZpQA0jYvn1UNVuDjqIa7syY+7h5Uxn7dgQJdGLg4pIiIiOYXTywUTExNJSUm5Zjw5OZn4+Ph0CSUiki5Mkws7w9i89l22u8Viml5sXNGQL34sjd1uUrZsMGFhXahaNZ+rk4qIiEgO4nTJqlOnDpMmTWL8+PFXjX/xxRfUqlUr3YKJiNyNuNhLDP7uPrZ5JJLibwBehE+txaU1JQCT7t0rM2nSg/j5ebg6qoiIiOQwTpesd999l/vuu4+dO3fSokULAJYvX87mzZtZsmRJugcUEbkTuxe/zibPJMAgIMWDYM/6NHy4Dt9uPsWYMQ8wYEAtDMNwdUwRERHJgZwuWY0aNWLDhg18+OGH/PDDD3h5eVG1alWmTJlCmTJlMiKjiIhzzh/E7+AczAJ54UwQv7+yOu3QWz2ukC+frwvDiYiISE5nmLns6cLR0dEEBAQQFRWl7eZFciKHnU8n3cPGhCgWzWzIlT35Obh7KKVK6aHCIiIicrWM6gZOX8kSEcmq3lk5g52HP2f7OSsnJ7Qh6awfWB1s3hyukiUiIiKZ5rZLltVqva15drv9jsOIiNyN+Uc/JmJ9YSK+q46ZYsU/1Mqnkx+g+0OVXR1NREREcpHbLlk2mw2bzcbgwYNp0KBBRmYSEXHalStJHP+qOpEbiwFQu2leFs99nOBgbxcnExERkdzmtkvWoUOHeO211/joo49o37497733HuXKlcvIbCIit+3TTzemFiyLg0FDijPu48exWLR7oIiIiGQ+y+1OLFq0KNOnT2f79u0kJCRQpUoV+vfvT3h4eEbmExG5tTN7GFZrJXlqnKLEy2t49IlCKlgiIiLiMrddsv5RpUoVfvnlF5YtW8aePXsoU6YMw4cP5/LlyxkQT0Tk+uLjk/nww3WkpDjglxdw3ziG8s+sx6fcBUyrl6vjiYiISC7mdMn6R5MmTVi/fj0zZsxg4cKFlCpVig8//DA9s4mIXNfBg5HUrz+Fl15axuuvr+BI/GWm+fsRb6T+SHP4hLg4oYiIiORmt31PVseOHW94rFSpUhw7doxXXnmFYcOGpUswEZHrmT17D/36/cSVK0mEhvpw330lee1wHHv88qTN8fPQlSwRERFxndsuWQEBATc93q1bt7sOIyJyI4mJKQwd+hsTJmwBoGnTYsya1YkCwRbGH7UDVvKlFKF8vgeomr+4S7OKiIhI7nbbJevrr7/OyBwiIjd07NglunQJY+vWCABeffUeRo1qhs1mYd/85zhnTd3kolfVwfSq09qVUUVERERuv2SJiLhKfHwK+/dfIDjYi2+/7UDr1mUAOHzwd1648BsxbjYCk4PoVLW5i5OKiIiIqGSJSBZlmiaGkXqFqmLFvMyZ04XKlUMpUiSAmMR4wnavZdrOYVx0sxGUYuHLh77Dx8PDxalFREREVLJEJAs6cSKKnj3n8d57zWncuBgArVuXISUlmU8WjGTuhZ+JsiWl/QS737M25UOLuDCxiIiIyL9UskQkS1m06BCPPfYjFy/G07NPGB/NrY/FYnL5xDp+Dp/JEXcTbBBkt5M/xY6n6UWX+592dWwRERGRNCpZIpIlpKQ4eOONlYwe/TsAXsUv4zVwEW/vmPbvJHfwdjh4KKUgz9R8nIDyD4CeiSUiIiJZjEqWiLhceHgMjzwylzVr/gKgQ6tY/uy0Aoubg0LJKQAYQBmzAM+0HEvZYlVdmFZERETk5lSyRMSlTp6MonbtyZw7F4ufnzuVuq/hUKNTWP4+/kuxR7CWaQFF6oLVzaVZRURERG6H5dZTREQyTuHC/jRtWoyqVfOxZUt/7A1OAGB1WClgaYT1/jegeCMVLBEREck2dCVLRDLduXOxeHhYCQjwxDAMpkx5CJvNgpeXG6xLnfNV3bHUrnSvS3OKiIiI3AldyRKRTLVmzV9Ur/4F/fr9hGmaAPj5eaQWLBEREZEcQCVLRDKFw2Hyv//9TrNm04iIuMK+fee5eDH+6kmJV1wTTkRERCQdabmgiGS4yMg4evWaz6JFhwB47LGqTJzYFh8f938npSTBD4+lvbR7a2t2ERERyZ5UskQkQ23ceIquXcM4eTIaT08bn33WmieeqIFhGACM/WEwc66swE7qNu3x1r8vsFs9XJZZRERE5G6oZIlIhklOtvPII3M5eTKaMmWCCAvrQrVq+a+as/XSBqI8/9/KZbsf5fIWzMSkIiIiIulHJUtEMoybm5Vvv+3AxIlbmDixLf7+Hvy+/Wc+3DKcOCN104tL7gAGHahAq4YvYbr7UDFvEQK9fFyaXUREROROqWSJSLp5YfFEfl63BFuknWI1jv574F7oPvsNAE66G/B3sfqvGkWb07BM7UzLKiIiIpJRVLJE5I7N2b2OjzZ/gK/jHN6OWLb8XoIzs6qBxSRp5AU8C8Xc8NxqCe50qzAIgEIhpahZsWlmxRYRERHJUCpZInLH5m2fTKz1KNFJNsK/rkvUpiIA1K10jNGJJ7iYbwgOq+c153l5+NK64WPYbHo2loiIiOQ8KlkicsfyJP5FwtkAIsfXI+qcP1YrDHoqgLGDe2CEvgWBRV0dUURERCTTqWSJyB3b/3s+jswqi5lspXBhf374oTMNGhRxdSwRERERl1LJEpE7Y5rEnPPETLZSqko0G1cMIyTE29WpRERERFxOJUtEbtvlmAu8M7s3VxxReDjiCG0fT6Ei5+nUwFcFS0RERORvKlkicksOu513ZvZi5uqLnFpfkmJDf8fi4QBs5Gl0gqTgvq6OKCIiIpJlqGSJyA0lpaSw8eSffL3mCxZ8bePS6voAXFpdgifv9+GSTyk8fQvzcuNHXJxUREREJOtQyRKRq8zft5HJO2ZxNuEoCcZpks55cPLz+iScLAmGSZOHzvPRS89Rp8q9ro4qIiIikiWpZInIVd7a+BbJ1pNghehNhTg9tTaOBDe8feMZOczGS2987uqIIiIiIlmaSpaIXMVuJgDgvbo7e75OAaBx0VN8/9RRCr60xJXRRERERLIFi6sDiEjW9EjHWgT62xh+zx+s6B1GwZ6jwWJ1dSwRERGRLE9XskQkzcGDkWmf5yvkzqHXlhISux1qPAZF6rowmYiIiEj2oStZIkJysp1hw5ZQocLnxOzNA0CeE4tTC5ZXHrhvlIsTioiIiGQfupIlksudPBlF9+5zWb/+JABxR/zxqQYhB2enTmgxEnyCXZhQREREJHvRlSyRXGzx4sPUqPEl69efJCDAg3nzupLvob8AsKbEQcGaULOXi1OKiIiIZC8qWSK5UEqKg9deW0Hr1jOIjIynZs0CbN3anw4dKuBJIgAmBrT9WJtdiIiIiDhJJUskF1q8+DDvvrsWgKefrs26dU9QqlQQmCaBZhQAl4vcD4VqujKmiIiISLake7JEcqGyVeLo0DWIylU8eKDeHnb/tAD3qKO4Xz6KmccDsBFdsJGrY4qIiIhkSypZIrmA3e7g00//oHfvamw6MJdXDr2Po43BIeDH8L8nuQF5fdLOSQgs7YqoIiIiItmeSpZIDnfuXCyPPjqPpUuPsmzZUR54fCsOw8BmmvjZTexYsRtWUrCmfo4FT/LxQPmGro4uIiIiki2pZInkYGvX/kX37nMJD4/B29uNDp3LccGxDoAKie7MHLDNxQlFREREch6VLJEcyOEw+eij9bz66nLsdpMKFUIY+1ULXjzQGzMxztXxRERERHI07S4oksNcuhRP+/bf8/LLy7DbTXr2rMKS5R34Zt9gTGtqwbKaJpU9y7o4qYiIiEjOpCtZIjmMYRjs3XsODw8r48e3pkeHPHT5sSUn3Q0AmsVZ+Oi+T3Av09zFSUVERERyJpUskRzANE0gtWAFBnoyd25XDMOgell3Tk1pyUl/A8M06evTgkE9P8Jmc3NxYhEREZGcS8sFRbK5qKgEunQJ44svtqSN1ahRgOrV8zNkeiu6+MYDYJo2nu3yqQqWiIiISAbTlSyRbGzbtgi6dAnj6NFLLFlyhO7dK5Mnjxd957/P9sgfSfaK55+/pXiYRVwbVkRERCSXUMkSyYZM0+TLL7fy3HOLSUy0U6xYAGFhXciTxwuAPyPDSLYlps3/qO4kGpeu6aq4IiIiIrmKSpZINhMTk8iAAT8za9YeANq1K8u0aQ+nFSwALzOBKAxeTMpP67afElqwoqviioiIiOQ6Klki2UhiYgr1609h377zWK0G779/H0OHNsAwjKvm/fMqT7nuKlgiIiIimUwbX4hkIx4eNnr2rELhwv6sWdOHF15oeE3BEhERERHXUskSyeLi4pI5cSIq7fUrr9zDzp0DadjwxhtZmJkRTERERESuSyVLJAs7cOAC9ep9Rdu2M4mLSwbAYjEICvK64Tnf//YcZ9xSr275+BTNlJwiIiIi8i+VLJEsaubM3dSuPYk9e85x4UIcR49euuU5M359j9ERywCoEZmfYkXqZ3RMEREREfl/tPGFSBaTkJDCc88t5ssvtwLQrFlxZs7sRP78vrc8d9GJuTg8DWpe8aNjs68pHeqX0XFFRERE5P9RyRLJQg4fvkiXLmHs2HEGw4DXXmvCyJFNsVpv76Kz3XAAULdAI9rXKJyRUUVERETkBlSyRLKQIUN+ZceOM4SEeDNjRkdatizl6kgiIiIi4iTdkyWShUya1I727cuxY8cApwrW/nOneGL+aM5Y7RmYTkRERERuh65kibjQ8eOX+eWXgwwaVBeAwoX9mT+/+7UT7Sl89/NIDlw6cN33WWAcTP3k7/9H+7j5ZERcEREREbkNKlkiLvLTT3/Su/d8Ll1KoFAhfx5+uPx15/22+ku27/mMGb7ALZ47bJgm3VMK06n5s+kfWERERERui0qWSCZLTrYzYsQKPvxwPQB16xaiRo38N5z/1pFxRPv+u7K3SVLQded5G54MvvcNipZulL6BRURERMQpKlkimejUqWi6d5/DunUnAXj22Xp88MH9uLtbb3hO9N87CxY0q3BvifYMb9otU7KKiIiIyJ1x+cYXEyZMoESJEnh6elKrVi3Wrl17w7nz5s3j/vvvJ2/evPj7+9OgQQN+++23TEwrcueWLj1CjRpfsm7dSfz9PZgzpwtjx7a6YcE6+NcOJsx7Ke31xBYjVLBEREREsgGXlqzZs2fz3HPPMWLECLZv307jxo1p3bo1/9fefYdFdaV/AP8OfWiD9CIggogoAooKWGOMaCwYFSG6iF1jLEREjIaAJXGT6KrZGFdRgSQIii32xPiLBVGjCLERKyoKCAJSBAaYOb8/WO4yTqEITMD38zzzbObcc899z9wjO++cc+998uSJzPrnzp3De++9h+PHjyMlJQXvvPMOxowZg9TU1FaOnJDGKyyswIsXZXB3N8e1a3MwYYKzwvqf/hKErSUnuPfq6vyWDpEQQgghhDQDHmOMKevg/fr1Q69evbB161aurFu3bhg3bhzWrVvXoDa6d+8Of39/fP755w2qX1xcDIFAgKKiIujr6zcpbkIaijEGHu9/d6vYu/cWxo7tCi2t+lfqDtvRHc/VVWAv5KGbuh3WTf+5JUMlhBBCCHnrtFRuoLRrsiorK5GSkoLly5dLlA8fPhzJyckNakMsFqOkpASGhrJvBAAAQqEQQqGQe19cXNy0gAlppN9/z8CSJb/i+PHJsLDQAwBMmtRdqp64ugq/X92Hl2WFEuWV/83N5jgvxfsDprZ4vIQQQgghpHkoLcl68eIFRCIRzMzMJMrNzMyQk5PToDY2bNiAV69eYdKkSXLrrFu3DqtWrXqjWAlpDLGYYd268/j88zMQixkiI89g27YxEnXuZKTiYdZNqFXk49ydGBzSkfEQYbWa1bwqKvJvikEIIYQQQv5+lH53wbpLqQDp5VXyxMfHIzIyEj///DNMTU3l1vv000+xZMkS7n1xcTGsra2bHjAhCrx4UYZ//OMAfvnlAQBg+nQ3bNw4QqLOtb/OY8aljyCqHed1nhvcsUpy9a6JiI9Bvca1ZMiEEEIIIaSZKS3JMjY2hqqqqtSsVW5urtTs1uv27NmDmTNnIjExEcOGDVNYV1NTE5qamm8cLyH1uXDhCfz99+HZsxLw+WrYsuV9TJ/uLlHn1oOrWHvuY4g0axIs02oGITRQDgME9/oMge5DlRE6IYQQQghpRkpLsjQ0NNC7d2+cOnUKH3zwAVd+6tQp+Pr6yt0vPj4eM2bMQHx8PEaNGtUaoRJSr+PH72Hs2HiIRAxduxohMdEPLi5mEItEuPfkOsorXyHhwtf4jfcQwv8mWKPFnbFuJt3MghBCCCGkvVHqcsElS5YgMDAQHh4e8PLywvbt2/HkyRPMmzcPQM1Sv2fPnuGHH34AUJNgTZ06FZs3b4anpyc3C8bn8yEQCJTWD0IGDbJFly5GcHc3x7Zto6GnVzN7OmtHf1zRelVTSRUAeOgqVEFg14XwHTxLafESQgghhJCWo9Qky9/fH/n5+Vi9ejWys7PRo0cPHD9+HLa2tgCA7OxsiWdmbdu2DdXV1fj444/x8ccfc+VBQUGIiYlp7fDJW+6vv16ga1cj8Hg86Opq4MKFGejQQUvimsJHaiUAVKDCGKyqgHGmvpg1ejVUVOlmFoQQQggh7ZVSn5OlDPScLPKmGGP47rs/EBLyK7788l0sXeott+7Qnd2Rp6aC9Y6fwsdrcitGSQghhBBC6tPunpNFSFtUVFSBWbOOYN++2wCAlJTsBt8RkxBCCCGEvB0oySKkgdLScuDnl4j79wugrq6C9euHY+HCvpRgEUIIIYQQCZRkEVIPxhiioq5h0aITEApFsLUVYO9eP/Tta6Xs0AghhBBCyN8QJVmE1OPBg0IsWHAcVVVijB7tiNjYcTA05Cs7LEIIIYQQ8jdFSRYh9XBwMMS//uWD8vIqhIR4Q0WFlgcSQgghhBD5KMkiRIYffvgTrq5mcHU1BwAsWNBXyRERQgghhJC2QkXZARDyd1JWVoWZM39GUNAh+PkloqREqOyQCCGEEEJIG0MzWYT81507L+Dnl4gbN3LB4wGBgT2hra2u7LAIIYQQQkgbQ0kWIQASEm5i9uwjKC2thKmpDnbvHo933+2s7LAIIYQQQkgbREkWeatVVooQHHwSW7deBQAMHmyL+PgJsLDQU3JkhBBCCCGkraIki7zVVFV5uHevAACwcuVAREYOgZpa0y5VFIvF+PbiIZy5vw86VXngi16hWIPuREgIIYQQ8rahJIu8lcRiBhUVHlRVVfDTTx8gLS0HPj4OTW7veNIP2HH737inWVFToP7fF2qSLJMO1m8cMyGEEEIIaRsoySJvlcpKEcLCTqG8vBr/+c9oAICZmW6TE6zn+c/w2X4/XNIsATQBTbEY40uF0NXsiFItS4h5auhk3B29nAY2ZzcIIYQQQsjfGCVZ5K3x5EkRJk1KxOXLzwAAc+f2hru7xRu1GX/6K1zSLAGPMXhVGmBxnyVw7jkGUKW7EhJCCCGEvK0oySJvhWPH7mLq1EMoKCiHgYEWYmJ8m5xgbT34KR4X3gIAPK3KArQAt0o+ts1Jas6QCSGEEEJIG0VJFmnXqqvFCA//P/zznxcAAB4elti7dyLs7Do0qb07Gan4vvgooPrfgv/+ryZo5ooQQgghhNSgJIu0a35+iTh06C8AwIIFfbB+/XBoajZt2O+/lYwj1/cAANQYw6AqMwCAKk8Nfp6fNE/AhBBCCCGkzaMki7Rrs2a54//+LwM7doyBn1/3JrdTWPQMkVfncu/1RQybZ59ujhAJIYQQQkg7Q0kWaVdEIjHu3SuAk5MxAGDUKEdkZCyGoSG/0W3df3IdCefWIl14H9fVqrjywWWVcDcd1WwxE0IIIYSQ9oWSLNJuPH9eiilTDuDatWykps6Fra0BADQ6wSosysP+s99hc+GBmoL//iuxqGLoz7NHRFA0oG3YjJETQgghhJD2hJIs0i6cPfsIAQH7kZNTCm1tddy6lcclWY21JvEfOKWexb3v/koDjgafYOboD2BrpNNMERNCCCGEkPaKkizSponFDP/8ZxLCw3+HWMzg7GyCxEQ/ODubNGj/5D9P4FHOTYmyZ+JcAIBlFYOtWIB1k/fDyMC82WMnhBBCCCHtEyVZpM168aIMgYEHcfLkfQBAYGBPbN06Cjo6Gg3aPyn1KD66/qn0Bs2a/xnC98CnU2KaKVpCCCGEEPK2oCSLtFkbNiTj5Mn70NJSw3ffjcSMGe7g8XgN3j8ju2YGS1PMYFEtuZ8OU4NPv5nNGi8hhBBCCHk7UJJF2qyIiCF48KAQK1cOhKtr45bz5bzIRPbLBwAAq2oefp59oyVCJIQQQgghbyEVZQdASEMVFpZj9eqzEInEAAAtLTXs3evX6ATref4zjD88Aj9WXWqJMAkhhBBCyFuOZrJIm3D1ahYmTUpERsZLAMDnnw9uclv3nqSiRLXm94UO1WL00erdHCESQgghhBACgJIs8jfHGMP331/BkiW/orJSBDs7A4wa1aXR7YjFYuSUvgSqyyG6fxQAoC8S49zMW80cMSGEEEIIedtRkkX+toqLhZg9+wj27q1JhMaNc0J0tC8MDLQa3dZ7cXORK5ZcHlgN1WaJkxBCCCGEkLooySJ/SzduPMeECXtx714B1NRU8PXXwxAc7NmouwfWlVd1A6/nVAJ172aIlBBCCCGEEEmUZJG/JbGYITOzGNbW+ti71w+enh0b30jODXx9aB7itYrAVGuSs90vKtD5H8cAA1voaGo2c9SEEEIIIYRQkkX+RsRiBhWVmmTI1dUchw75w8PDEkZG2g1uI+HXjfg+cwfKVXjggaGcrwKgpk2jajEsP/gBOmaOLRE+IYQQQgghACjJIn8T6el5mDz5AP7zn1Ho169m1srHxwEAsPbHQPxadQ3iBrRTpKoCqNU+meB/Swsjun2BkT3egY62XjNHTgghhBBCiCRKsojS/fTTdcydexRlZVUIDv4FyckzJK69ulKRhkKNxj3SbazYAeMHfAox3xDWAiOY63Vo7rAJIYQQQgiRiZIsojTl5VVYvPgkoqKuAQDefdcOcXHj5d7cIlDdE15OY+ptV6BrjJ6OdFMLQgghhBCiHJRkEaW4dy8ffn6J+PPP5+DxgIiIwfjss0FQVZU/Y2VhYI+Bvca2YpSEEEIIIYQ0HiVZpNXdufMCffpEoaSkEiYm2ti9ewKGDesss25W3mOU8xjqXl9FCCGkZYhEIlRVVSk7DEIIaVYaGhpQUWncpSdvipIs0uocHY0wZEgnFBUJER8/AZaWNTejuPU8Ex+fDIdGVSYMxIXggeGZWiWK1Gv+UZh2sFFm2IQQ0m4xxpCTk4OXL18qOxRCCGl2KioqsLOzg4aGRqsdk5Is0ioePXoJY2Nt6OpqgMfjIS5uPDQ1VPDpj6PxXPQCAPCnVmVNZXUgm9tTBR2rGCaZ+8PHa7IyQieEkHavNsEyNTWFtrZ2kx/8TgghfzdisRhZWVnIzs6GjY1Nq/19oySLtLiff/4L06b9jFGjuuDHHz8Aj8eDnp4mfjrxFX5VzwLUJesbV4swld8XFVpm0NESYOLQxdDW0lFO8IQQ0s6JRCIuwTIyMlJ2OIQQ0uxMTEyQlZWF6upqqKur179DM6Aki7SYqioRPv30NDZsuAgAePCgEGt/SUBS7ikYCx/iukY+V3cmfwgAQFNVE+MHzYeZiexrtAghhDSv2muwtLUb/uB3QghpS2qXCYpEIkqySNuUmfMQm49+jNwX1fg1qgeePzQAAPR89wl6jv8/7H1eAADI+u+S2G5CVXzkHo53+kxQUsSEEEIA0BJBQki7pYy/b5Rkkaa7sQ/IvAwAqBCJsD73DvarPkZhujmebu8LUakmVPiV6DjrKsS9s5BcZ9eAUsC+82RMGr4MKqqqyomfEEIIIYSQFkBJFmkaYSlOnVyEGxo1U64X+Fq4q6kBsVANz3Z5QFSqCXPbckyY/wQdTPUBps/t6tjBEYH/WAWo0vAjhBBCCCHtD33LJU2SV5yPZSaGqH5t+lVFU4SAOTkQZQ3Brqh/QEuLhhghhBCibOHh4Xj+/Dm2b9+u7FDeiFAoRJcuXXDw4EH07t273vrtpd9EvtzcXHTv3h1paWmwsrJSdjic1n0qF2k3yqqEXILV4f5o6N30RXftcdg8IA4/fhWD3T9OowSLEEJIi5o2bRp4PB7mzZsntW3+/Png8XiYNm1a6wfWQrKzszF58mR07doVKioqCA4ObtB+z58/x+bNm7FixQquLDIyEjweT+Jlbm7eQpE3H01NTSxduhRhYWH11pXVb9JyCgsLERgYCIFAAIFAgMDAwHqfvVf7b7juy9PTU6KOUCjEwoULYWxsDB0dHYwdOxZPnz7ltpuamiIwMBAREREt0a0mo2/BpMGu3T6LE9eiwZgYQlE5GAPyjnTDrUNa0NXVwA9z58LB3lDZYRJCCHlDjDGUV4mUcmy+umqjLlK3trZGQkICNm7cCD6fDwCoqKhAfHw8bGza10PshUIhTExMsHLlSmzcuLHB++3cuRNeXl7o1KmTRHn37t3x22+/ce9V28g10lOmTEFoaCjS09PRrVs3ufXk9Zu0jMmTJ+Pp06c4efIkAGDOnDkIDAzEkSNHFO43YsQIREdHc+9ff2BwcHAwjhw5goSEBBgZGSEkJASjR49GSkoKN2anT5+Ovn374ptvvkGHDh2auWdNQzNZpMH+mbQYCaIU7BGnYl9JBh5tGIjcg93BGDBpUndYWekpO0RCCCHNoLxKBOfPf1HKq7HJXa9evWBjY4MDBw5wZQcOHIC1tTXc3d0l6gqFQixatAimpqbQ0tLCgAEDcOXKFak2hwwZIvXr+qZNmyTqREdHo1u3btDS0oKTkxO+//77RrXz6NEj8Hg8pKWlNbivnTp1wubNmzF16lQIBIIG75eQkICxY8dKlaupqcHc3Jx7mZiY1NvW4cOH4eHhAS0tLRgbG2P8+PES22XNkI0bN47bXlhYiKlTp6JDhw7Q1tbGyJEjce/ePYk2oqOj0bVrV2hoaHBt1J21MzIygre3N+Lj4xvdb1nnhMfjwc3NTaLemTNnpOoYGBhw28ViMVavXo2OHTtCU1MTbm5uXHIByD6/48aNk5hZrT1G3dmesLAwODo6QltbG507d0Z4eDj3mIW6ZPWh7rE6deokNWZbUnp6Ok6ePIkdO3bAy8sLXl5eiIqKwtGjR3Hnzh2F+2pqakqMQ0PD//1gX1RUhJ07d2LDhg0YNmwY3N3d8dNPP+HGjRsSPxC4uLjA3NwcBw8ebLE+NhYlWaTByng1/8dnetMMT8Lfw6tbZtDQZIiNHYcdO8aCz2+d5w4QQgghdU2fPl3il/Bdu3ZhxowZUvWWLVuG/fv3IzY2FteuXYODgwN8fHxQUFAgVXf27NnIzs5GdnY2OnbsKLEtKioKK1euxBdffIH09HR8+eWXCA8PR2xsrEQ9xpjCdlpDYWEhbt68CQ8PD6lt9+7dg6WlJezs7BAQEICHDx8qbOvYsWMYP348Ro0ahdTUVJw+fVqqXcYYunfvzvV50qRJEtunTZuGq1ev4vDhw7h48SIYY3j//fe5ROKvv/7CrFmzMGPGDNy/fx/Z2dnw8vKSiqVv3744f/58k/pd95xkZ2cjJCREqg5jDABw584dZGdnSyUsmzdvxoYNG7B+/Xpcv34dPj4+GDt2rFTC2Fh6enqIiYnB7du3sXnzZkRFRcmdtYyOjkZ2djb++OOPNzpmLV1dXYWvkSNHyt334sWLEAgE6NevH1fm6ekJgUCA5ORkufsBNcmmqakpHB0dMXv2bOTm5nLbUlJSUFVVheHDh3NllpaW6NGjh1S79Y2J1kbLBUmj5B13xO19PSEWA926GSMx0Q/du5sqOyxCCCHNiK+uiturfZR27MYKDAzEp59+ys0eXLhwAQkJCThz5gxX59WrV9i6dStiYmK4L4tRUVE4deoUdu7cidDQUK6uUCiEQCDgrlF6fRndmjVrsGHDBm4Wx87ODrdv38a2bdsQFBTE1auqqlLYTmt4/PgxGGOwtLSUKO/Xrx9++OEHODo64vnz51i7di28vb1x69YtGBkZyWzriy++QEBAAFatWsWVubq6StSpqqoCn8/n+szn8yEUCgHUJHWHDx/GhQsX4O3tDQCIi4uDtbU1Dh06BD8/P1y/fh2qqqoS11y9vnwMAKysrPDo0aNG9xuoefB23evPdHV1perUJn1WVlbQ0dGRmjlcv349wsLCEBAQAAD46quv8Pvvv2PTpk3YsmWL3Ljq89lnn3H/3alTJ4SEhGDPnj1YtmwZV177eZqYmMDc3BwVFRVNPl5d9c2q1i7HlSUnJwemptLfB01NTZGTkyN3v5EjR8LPzw+2trbIyMhAeHg4hg4dipSUFGhqaiInJwcaGhpSSwDNzMyk2rWyskJqaqrCPrQmSrJIo1QXaUEsBqZMccF//jMaurrSf/gIIYS0bTweD9oabecrgrGxMUaNGoXY2FgwxjBq1CgYGxtL1Hnw4AGqqqrQv39/rkxdXR19+/ZFenq6RN38/Hzo6+tDlry8PGRmZmLmzJmYPXs2V15dXS31Rby4uBg6OjoKY/f29oaKigoMDAzQr18/rF+/HnZ2dg3qd0OUl5cDALS0tCTK685KuLi4wMvLC/b29oiNjcWSJUtktpWWlibRZ1kU9Tk9PR1qamoSsx1GRkbo2rUrdw7s7OxQVVWFxMRETJw4Ue71eXw+H2VlZXLjkNfvhiouLoaKiorMxKK4uBhZWVkSYwkA+vfvjz///LNJx6u1b98+bNq0Cffv30dpaSmqq6ulxmJ+fj4AyB2jtcLCwvDZZ5+Bz+fDyckJq1atwtChQ+XWd3BweKPYZZ0rxpjCayz9/f25/+7Rowc8PDxga2vLzZrKI6vd+sZEa6PlgqReYjHj/tvc7waCP+Pjxx8/oASLEELI38aMGTMQExOD2NhYmUsFa5d/vf7F7PUva9XV1cjMzJR7swSxWAygZhYsLS2Ne928eROXLl2SqJudnS1zJqWuPXv2IC0tDYmJicjOzsbUqVPr7Wtj1CabhYWFCuvp6OjAxcVF4XI3RTMZtbKysuT2ufYcyCqvPQd9+vTB6tWrMWPGDGhpaUFXV1fmErCCggKF15A1tN/yZGVlwczMDCoq8r8q1zeWGuvSpUsICAjAyJEjcfToUaSmpmLlypWorKyUqFe7rLO+G3qEhoYiLS0Np0+fhpOTE3x9fVFUVCS3/pssFzQ3N8fz58+lyvPy8mBmZqYwzrosLCxga2vLjUNzc3NUVlZKncfc3FypdusbE62NkiwiF2MMmzdfwrvv/oCq/16IzFNj6DdQ/Y3+iBBCCCHNbcSIEaisrERlZSV8fKSXOjo4OEBDQwNJSUlcWVVVFa5evSpxh7rLly+joqICAwYMkHkcMzMzWFlZ4eHDh3BwcJB41Z2BevDgAQoKCqRuvvE6a2trODg4wMvLC/Pnz2/25U729vbQ19fH7du3FdYTCoVIT0+HhYWF3Do9e/bE6dOn5W4Xi8W4du2a3D47Ozujuroaly9f5sry8/Nx9+5diXOwePFiWFpaYtWqVUhLS5N5XdXNmzcVfrYN7bc8V65ckdu+vr4+LC0tJcYSACQnJyu822F9Lly4AFtbW6xcuRIeHh7o0qULHj9+LFXv7NmzsLGxgbW1tcL2jI2N4eDggJ49eyIiIgKlpaUKk+i6PxrIeu3YsUPuvl5eXigqKpK4Puzy5csoKiriloY2RH5+PjIzM7lx2Lt3b6irq+PUqVNcnezsbNy8eVOq3frGRGtrO2sBSKsqKqrAjBmHceBAzfR9QsJNJUdECCGEyKeqqsotOZN17ZOOjg4++ugjhIaGwtDQEDY2Nvj6669RVlaGmTNnAqi5riQ8PByenp7g8/ncNR8ikQglJSUoLy8Hn89HZGQkFi1aBH19fYwcORJCoRBXr15FYWEhlixZgqtXr2LRokVwcXGRmSDUVVlZiYqKCuTl5SE+Ph4uLi4K69deN1NaWoq8vDykpaVBQ0MDzs7OMuurqKhg2LBhSEpKkrjL39KlSzFmzBjY2NggNzcXa9euRXFxscQ1Za+LiIjAu+++C3t7ewQEBKC6uhonTpzAsmXLkJmZicjISOTm5nLXKb2uS5cu8PX1xezZs7Ft2zbo6elh+fLlsLKygq+vL4CaH3iDgoLg5uaG5cuXA5A9g3b+/HmsWbNGbqzy+l2f0tJS7NixA7t378bevXvl1gsNDUVERATs7e3h5uaG6OhopKWlIS4uTqJe7fkFapJQkUjEva+doaq9xsrBwQFPnjxBQkIC+vTpg2PHjkndLS8tLQ1btmzBhx9+yI3PvLw8ADUJikgk4sZ/dXU1KioqUF5eju3bt4PP58Pe3l5un95kuWC3bt0wYsQI7twCNbdwHz16NLp27crVc3Jywrp16/DBBx+gtLQUkZGRmDBhAiwsLPDo0SOsWLECxsbG+OCDDwAAAoEAM2fOREhICIyMjGBoaIilS5fCxcUFw4YN49otKytDSkoKvvzyyyb3odmxt0xRUREDwIqKipQdyt9WSkoWs7b5mgGRTE1tFQsO28XOXTvKhkd1Zz1ierD4X/6l7BAJIYQ0k/Lycnb79m1WXl6u7FAaLSgoiPn6+srd7uvry4KCgrj35eXlbOHChczY2Jhpamqy/v37sz/++IPbPnjwYAZA7is6OpqrGxcXx9zc3JiGhgbr0KEDGzRoEDtw4ABjjLEBAwYwPz8/lpmZKRGPra0t27hxI2OMsYyMDIm2BQIB8/HxYXfv3lXYZ1lx2draKtzn5MmTzMrKiolEIq7M39+fWVhYMHV1dWZpacnGjx/Pbt26pbAdxhjbv38/129jY2M2fvx4xhhjISEhbNCgQez8+fMS9V8/RwUFBSwwMJAJBALG5/Ol+rxu3Trm5OTESkpKuLLBgwezxYsXc++Tk5OZgYEBKysra3S/X2+LMcYiIiKYq6srY4yxAwcOMGdnZxYVFSVRJzo6mgkEAu69SCRiq1atYlZWVkxdXZ25urqyEydOcNtfP7/1vWqFhoYyIyMjpqury/z9/dnGjRsljltfOxkZGYyxmrFWW6alpcV69erFjh8/rvDzelP5+flsypQpTE9Pj+np6bEpU6awwsJCiTp1/x2VlZWx4cOHMxMTE6aurs5sbGxYUFAQe/LkicQ+5eXlbMGCBczQ0JDx+Xw2evRoqTq7d+9mXbt2lRubor9zLZUb8BiTs0C2nSouLoZAIEBRUVG9Fwy+bRhj2LYtBQsXHkN1NaBu/ArW8y9Bu7PkOtiVFjMQMPwTJUVJCCGkOVVUVCAjIwN2dnZNvklAezFkyBBERkZiyJAhUtuCg4Ph5uYm8ZyjtoIxBk9PTwQHB+PDDz9UdjhvzM/PD+7u7lixYoXCem2l3wYGBhLPylKEx+PJvbbNzc0Nhw4deisfvty3b18EBwdj8uTJMrcr+jvXUrkBXZNFOJ9//js++qgmwdJzz4JT5CmY2+ZDXyTmXvZCHob0mqjsUAkhhJBmZ2hoKPN24UDNdTgNufHD3xGPx8P27dtRXV2t7FDemFAohKurKz75pP4fe9tKvxvz44aim0gYGxsr5TEBypabm4uJEyf+7RJpmskinDt3XsDLayf6+DzBM59z6F8pwLY5SfXvSAghpM2imSxCSHtHM1mkVZVVvMIfVx+hrOIVyipewdqWj5u3Z8FzxAvQzQMJIYQQQghpGrq74Ftq6ndeOLLHCS8v2KLTsnPQ7Zan7JAIIYQQQghpFyjJekv8c/dM7BNeRjUPqMjSw6PvvSB8JgB4DMJn+lJJlgpjcNRXfBtZQgghhBBCiDRKst4SN0rSINTi4eVFa2TF9IZYqAZ1QTl2/uc9vDN0mlR9voYWjAzMWz9QQgghhBBC2jhKstq53b98g12ZMchTUcWzmF4oPNMZAODpZYzd8X6wszVVcoSEEEIIIYS0L3Tji3Yu+clxPFdXQcG1jig80xk8HvD554OQdP4jSrAIIYQQQghpATST1c7cf3ITK08GolilCgDwUpUBUME49wq8DOqMwMneGD7cXrlBEkIIIYQQ0o7RTFY78+vVH3BbsxpPeCq4dswZRRWaAADHDq74MSaQEixCCCHkLRQeHo45c+YoO4w3JhQKYWNjg5SUlAbVby/9JvLl5ubCxMQEz549U3YoEijJaicqhGWYs70/fi44jso8bWStfQe5h7pDL94P3zpH4uPx65UdIiGEENKspk2bBh6Ph3nz5kltmz9/Png8HqZNm9b6gbWQAwcO4L333oOJiQn09fXh5eWFX375pd79nj9/js2bN2PFihVcWWRkJHg8nsTL3Pzvf8MrTU1NLF26FGFhYfXWldVv0nIKCwsRGBgIgUAAgUCAwMBAvHz5UuE+tf+G6748PT0l6giFQixcuBDGxsbQ0dHB2LFj8fTpU267qakpAgMDERER0RLdajJKstqwL36ahoDt7gjY7o7Bu/vgomYx/rppiQeRw/DykSE6dNBCeNgEvNNnAlRUVZUdLiGEkLaCMaDylXJejDUqVGtrayQkJKC8vJwrq6ioQHx8PGxsbJr7k1Gqc+fO4b333sPx48eRkpKCd955B2PGjEFqaqrC/Xbu3AkvLy906tRJorx79+7Izs7mXjdu3GjB6JvPlClTcP78eaSnpyusJ6/fpGVMnjwZaWlpOHnyJE6ePIm0tDQEBgbWu9+IESMkxuHx48cltgcHB+PgwYNISEhAUlISSktLMXr0aIhEIq7O9OnTERcXh8LCwmbvV1NRktWGHaq6ilua1bilWY1XYlXk7HHBk839IXqlgV69TZGaOhejRzsqO0xCCCFtTVUZ8KWlcl5VZY0KtVevXrCxscGBAwe4sgMHDsDa2hru7u4SdYVCIRYtWgRTU1NoaWlhwIABuHLlilSbQ4YMkfp1fdOmTRJ1oqOj0a1bN2hpacHJyQnff/99o9p59OgReDwe0tLSGtzXTZs2YdmyZejTpw+6dOmCL7/8El26dMGRI0cU7peQkICxY8dKlaupqcHc3Jx7mZiY1BvD4cOH4eHhAS0tLRgbG2P8+PES22XNkI0bN47bXlhYiKlTp6JDhw7Q1tbGyJEjce/ePYk2oqOj0bVrV2hoaHBtBAcHc9uNjIzg7e2N+Pj4Rvdb1jnh8Xhwc3OTqHfmzBmpOgYGBtx2sViM1atXo2PHjtDU1ISbmxtOnjzJbZd1fseNGycxs1p7jLqzPWFhYXB0dIS2tjY6d+6M8PBwVFVVSfVNVh/qHqtTp05SY7Ylpaen4+TJk9ixYwe8vLzg5eWFqKgoHD16FHfu3FG4r6ampsQ4NDQ05LYVFRVh586d2LBhA4YNGwZ3d3f89NNPuHHjBn777TeunouLC8zNzXHw4MEW62NjUZLVhol4Nf87psQT5evH4cWJrgCA4OB+uJg8B7a2BsoLjhBCCGkl06dPR3R0NPd+165dmDFjhlS9ZcuWYf/+/YiNjcW1a9fg4OAAHx8fFBQUSNWdPXs298t6x44dJbZFRUVh5cqV+OKLL5Ceno4vv/wS4eHhiI2NlajHGFPYzpsSi8UoKSmR+FL6usLCQty8eRMeHh5S2+7duwdLS0vY2dkhICAADx8+VHi8Y8eOYfz48Rg1ahRSU1Nx+vRpqXYZYxIzZJMmTZLYPm3aNFy9ehWHDx/GxYsXwRjD+++/zyUSf/31F2bNmoUZM2bg/v37yM7OhpeXl1Qsffv2xfnz55vU77rnJDs7GyEhIVJ12H9nVO/cuYPs7GyphGXz5s3YsGED1q9fj+vXr8PHxwdjx46VShgbS09PDzExMbh9+zY2b96MqKgobNy4UWbd6OhoZGdn448//nijY9bS1dVV+Bo5cqTcfS9evAiBQIB+/fpxZZ6enhAIBEhOTlZ43DNnzsDU1BSOjo6YPXs2cnNzuW0pKSmoqqrC8OHDuTJLS0v06NFDqt36xkRro7sLtkG7f9mAM48PoFqj5v37/abip6+vQl9fiF27xmLCBGflBkgIIaRtU9cGVmQp79iNFBgYiE8//ZSbPbhw4QISEhJw5swZrs6rV6+wdetWxMTEcF8Wo6KicOrUKezcuROhoaFcXaFQCIFAwF2jpPrakvs1a9Zgw4YN3CyOnZ0dbt++jW3btiEoKIirV1VVpbCdN7Vhwwa8evVKKpGp6/Hjx2CMwdLSUqK8X79++OGHH+Do6Ijnz59j7dq18Pb2xq1bt2BkZCSzrS+++AIBAQFYtWoVV+bq6ipRp6qqCnw+n+szn8+HUCgEUJPUHT58GBcuXIC3tzcAIC4uDtbW1jh06BD8/Pxw/fp1qKqqSlxzpaGhIRWLlZUVHj161Oh+A4C2trbE9We6urpSdWqTPisrK+jo6EAgEEhsX79+PcLCwhAQEAAA+Oqrr/D7779j06ZN2LJli9y46vPZZ59x/92pUyeEhIRgz549WLZsGVde+3mamJjA3NwcFRUVTT5eXfXNqvL5fLnbcnJyYGoq/WggU1NT5OTkyN1v5MiR8PPzg62tLTIyMhAeHo6hQ4ciJSUFmpqayMnJgYaGBjp06CCxn5mZmVS7VlZW9S6dbU2UZLVB8U9ikKFeM1Wsxhg6d7LBwYP2MDDQgr29/F+zCCGEkAbh8QANHWVH0WDGxsYYNWoUYmNjwRjDqFGjYGxsLFHnwYMHqKqqQv/+/bkydXV19O3bV+ranvz8fOjr68s8Vl5eHjIzMzFz5kzMnj2bK6+urpb6Il5cXAwdHcWfo7e3N1RUVGBgYIB+/fph/fr1sLOzq7fP8fHxiIyMxM8//yzzy22t2mvVtLS0JMrrzkq4uLjAy8sL9vb2iI2NxZIlS2S2lZaWJtFnWRT1OT09HWpqahKzHUZGRujatSt3Duzs7FBVVYXExERMnDgRPB5PZlt8Ph9lZfKXlsrrd0MVFxdDRUVFZmJRXFyMrKwsibEEAP3798eff/7ZpOPV2rdvHzZt2oT79++jtLQU1dXVUmMxPz8fAOSO0VphYWH47LPPwOfz4eTkhFWrVmHo0KFy6zs4OLxR7LLOFWNM7jkEAH9/f+6/e/ToAQ8PD9ja2nKzpvLIare+MdHaaLlgG1RarIFH3wyC2e89sNxqFixNbNG7tyUlWIQQQt5aM2bMQExMDGJjY2UuFaxd/vX6F7PXv6xVV1cjMzNT7s0SxGIxgJpZsLS0NO518+ZNXLp0SaJudna2zJmUuvbs2YO0tDQkJiYiOzsbU6dOrbeve/bswcyZM7F3714MGzZMYd3aZLO+GwLo6OjAxcVF4XI3RTMZtbKysuT2mcm5qUndc9CnTx+sXr0aM2bMgJaWFnR1dWUuASsoKFB4DVlD+y1PVlYWzMzMoKIi/6tyfWOpsS5duoSAgACMHDkSR48eRWpqKlauXInKykqJerXLOuu7oUdoaCjS0tJw+vRpODk5wdfXF0VFRXLrv8lyQXNzczx//lyqPC8vD2ZmZgrjrMvCwgK2trbcODQ3N0dlZaXUeczNzZVqt74x0dooyWpjfv89A8mrh+NVuimu/NwNo73nKzskQgghROlGjBiByspKVFZWwsfHR2q7g4MDNDQ0kJSUxJVVVVXh6tWr6NatG1d2+fJlVFRUYMCAATKPY2ZmBisrKzx8+BAODg4Sr7ozUA8ePEBBQYHUzTdeZ21tDQcHB3h5eWH+/Pn1LneKj4/HtGnTsHv3bowaNUphXQCwt7eHvr4+bt++rbCeUChEeno6LCws5Nbp2bMnTp8+LXe7WCzGtWvX5PbZ2dkZ1dXVuHz5MleWn5+Pu3fvSpyDxYsXw9LSEqtWrUJaWprM66pu3ryp8LNtaL/luXLlitz29fX1YWlpKTGWACA5OVmiH4114cIF2NraYuXKlfDw8ECXLl3w+PFjqXpnz56FjY0NrK2tFbZnbGwMBwcH9OzZExERESgtLVWYRNf90UDWa8eOHXL39fLyQlFRkcT1YZcvX0ZRURG3NLQh8vPzkZmZyY3D3r17Q11dHadOneLqZGdn4+bNm1Lt1jcmWhstF2wDkv88geMpu3DuqCGSDlmAMS1odizC2rWdoaMjvU6ZEEIIeduoqqpyS85kXfuko6ODjz76CKGhoTA0NISNjQ2+/vprlJWVYebMmQBqrisJDw+Hp6cn+Hw+d82HSCRCSUkJysvLwefzERkZiUWLFkFfXx8jR46EUCjE1atXUVhYiCVLluDq1atYtGgRXFxcZCYIdVVWVqKiogJ5eXmIj4+Hi4uL3Lrx8fGYOnUqNm/eDE9PTy4+Pp8vtVSxloqKCoYNG4akpCSJu/wtXboUY8aMgY2NDXJzc7F27VoUFxdLXFP2uoiICLz77ruwt7dHQEAAqqurceLECSxbtgyZmZmIjIxEbm4ud53S67p06QJfX1/Mnj0b27Ztg56eHpYvXw4rKyv4+voCqJkNCgoKgpubG5YvX87173Xnz5/HmjVr5MYqr9/1KS0txY4dO7B7927s3btXbr3Q0FBERETA3t4ebm5uiI6ORlpaGuLi4iTq1Z5foCYJFYlE3PvaGaraa6wcHBzw5MkTJCQkoE+fPjh27JjU3fLS0tKwZcsWfPjhh9z5z8vLA1CToIhEIm78V1dXo6KiAuXl5di+fTv4fD7s7e3l9ulNlgt269YNI0aM4M4tAMyZMwejR49G165duXpOTk5Yt24dPvjgA5SWliIyMhITJkyAhYUFHj16hBUrVsDY2BgffPABAEAgEGDmzJkICQmBkZERDA0NsXTpUri4uEjM4paVlSElJQVffvllk/vQ7NhbpqioiAFgRUVFyg6lXi8Ks9nC7e+w7pvdmW6PeQyIZEAkMxgYxJy3ubJfkncrO0RCCCFtXHl5Obt9+zYrLy9XdiiNFhQUxHx9feVu9/X1ZUFBQdz78vJytnDhQmZsbMw0NTVZ//792R9//MFtHzx4MAMg9xUdHc3VjYuLY25ubkxDQ4N16NCBDRo0iB04cIAxxtiAAQOYn58fy8zMlIjH1taWbdy4kTHGWEZGhkTbAoGA+fj4sLt378rtj7z46vZRlpMnTzIrKysmEom4Mn9/f2ZhYcHU1dWZpaUlGz9+PLt165bCdhhjbP/+/Vy/jY2N2fjx4xljjIWEhLBBgwax8+fPS9R//RwVFBSwwMBAJhAIGJ/Pl+rzunXrmJOTEyspKZHo9+LFi7n3ycnJzMDAgJWVlTW636+3xRhjERERzNXVlTHG2IEDB5izszOLioqSqBMdHc0EAgH3XiQSsVWrVjErKyumrq7OXF1d2YkTJ7jtr5/f+l61QkNDmZGREdPV1WX+/v5s48aNEsetr52MjAzGWM1Yqy3T0tJivXr1YsePH1f4eb2p/Px8NmXKFKanp8f09PTYlClTWGFhoUSduv+OysrK2PDhw5mJiQlTV1dnNjY2LCgoiD158kRin/LycrZgwQJmaGjI+Hw+Gz16tFSd3bt3s65du8qNTdHfuZbKDXiMNfKpf21ccXExBAIBioqK6r1gUNnW/hiI3WU3cH+FD6oKtKGmLsKgyffg3D8HJpoW+GzKj/SQYUIIIW+koqICGRkZsLOza/JNAtqLIUOGIDIyEkOGDJHaFhwcDDc3N4nnHLUVjDF4enoiODgYH374obLDeWN+fn5wd3fHihUrFNZrK/02MDCQeFaWIjweT+61bW5ubjh06NBb+fDlvn37Ijg4GJMnT5a5XdHfuZbKDWi54N9QdXUVfjixDvfK/oKqlgg9vLJQdr8f9u2bhB495N9BiBBCCCFNZ2hoKPN24UDNdTgNufHD3xGPx8P27dtx/fp1ZYfyxoRCIVxdXfHJJ5/UW7et9LsxP24ouomEsbFxsz8moC3Izc3FxIkT/3aJNM1k/Q1F7fsa65/sg4ZRze1HB1eY4J9TTkJXl66/IoQQ0rxoJosQ0t7RTNZb6Pq9S7h46zD3PuMuD99/LUaVrjfcPz0NZ+jCv08wJViEEEIIIYS0EZRkKVnwuZnIU1MBY0DBbw7ISegJJlKBhmo1TPIMsOMz6WdDEEIIIYQQQv6+KMlSsgJVHkRlaija4YHsax0BAFa9nqHv1FRMdJqk5OgIIYQQQgghjUVJlpKVPTbA4++9UJmrC3V1FWzYMBwLFvR9oyeGE0IIIYQQQpSHkiwlYowhK94Nlbm6sLTi4+CBKejb10rZYRFCCCGEEELegIqyA3hbVVdX4dKNX2A96w8IvB7jwJHBlGARQgghhBDSDtBMViu7eTMX8YnncKvjajzQYFAzAaznXoGBwUJlh0YIIYQQQghpBjST1QryCrPwMPMWvvnXMbj3+g5frr6FtL/+91BhtwpNdLHpqcQICSGEENKehYeHY86cOcoO440JhULY2NggJSWlQfXbS7+JfLm5uTAxMcGzZ8+UHYoESrJa2I/H/4lhe0ei95RvsCzkKqqrVKHbIwf8ToWwrQT+b+wv+HHuVai8hU/oJoQQQt7EtGnTwOPxMG/ePKlt8+fPB4/Hw7Rp01o/sBaSlJSE/v37w8jICHw+H05OTti4cWO9+z1//hybN2/GihUruLLIyEjweDyJl7m5eUuG3yw0NTWxdOlShIWF1VtXVr9JyyksLERgYCAEAgEEAgECAwPx8uVLhfvU/huu+/L09JSoIxQKsXDhQhgbG0NHRwdjx47F06dPue2mpqYIDAxERERES3SryWi5YAuqFolwPi0Vd7a+C+FTAcBjMP3gFrqOuI1N/b9HL+fByg6REEIIkcIYQ3l1uVKOzVfjN+oOu9bW1khISMDGjRvB5/MBABUVFYiPj4eNjU1LhakUOjo6WLBgAXr27AkdHR0kJSVh7ty50NHRUThbs3PnTnh5eaFTp04S5d27d8dvv/3GvVdtIz/4TpkyBaGhoUhPT0e3bt3k1pPXb9IyJk+ejKdPn+LkyZMAgDlz5iAwMBBHjhxRuN+IESMQHR3NvdfQ0JDYHhwcjCNHjiAhIQFGRkYICQnB6NGjkZKSwo3Z6dOno2/fvvjmm2/QoUOHZu5Z09BMVgs5l3ELnReMx841XSB8KgBfX4jTvwXh+f5EnJt9ixIsQgghf1vl1eXot7ufUl6NTe569eoFGxsbHDhwgCs7cOAArK2t4e7uLlFXKBRi0aJFMDU1hZaWFgYMGIArV65ItTlkyBCpX9c3bdokUSc6OhrdunWDlpYWnJyc8P333zeqnUePHoHH4yEtLa3BfXV3d8eHH36I7t27o1OnTvjHP/4BHx8fnD9/XuF+CQkJGDt2rFS5mpoazM3NuZeJiUm9MRw+fBgeHh7Q0tKCsbExxo8fL7Fd1gzZuHHjuO2FhYWYOnUqOnToAG1tbYwcORL37t2TaCM6Ohpdu3aFhoYG10ZwcDC33cjICN7e3oiPj290v2WdEx6PBzc3N4l6Z86ckapjYGDAbReLxVi9ejU6duwITU1NuLm5cckFIPv8jhs3TmJmtfYYdWd7wsLC4OjoCG1tbXTu3Bnh4eGoqqqS6pusPtQ9VqdOnaTGbEtKT0/HyZMnsWPHDnh5ecHLywtRUVE4evQo7ty5o3BfTU1NiXFoaGjIbSsqKsLOnTuxYcMGDBs2DO7u7vjpp59w48YNiR8IXFxcYG5ujoMHD7ZYHxuLkqwWknTrGMSiaogr1KHjlIuV35Rg6FA7ZYdFCCGEtDvTp0+X+CV8165dmDFjhlS9ZcuWYf/+/YiNjcW1a9fg4OAAHx8fFBQUSNWdPXs2srOzkZ2djY4dO0psi4qKwsqVK/HFF18gPT0dX375JcLDwxEbGytRjzGmsJ03lZqaiuTkZAweLP+H28LCQty8eRMeHh5S2+7duwdLS0vY2dkhICAADx8+VHi8Y8eOYfz48Rg1ahRSU1Nx+vRpqXYZY+jevTvX50mTJklsnzZtGq5evYrDhw/j4sWLYIzh/fff5xKJv/76C7NmzcKMGTNw//59ZGdnw8vLSyqWvn37KkwuFfW77jnJzs5GSEiIVB3GGADgzp07yM7OlkpYNm/ejA0bNmD9+vW4fv06fHx8MHbsWKmEsbH09PQQExOD27dvY/PmzYiKipK7JDQ6OhrZ2dn4448/3uiYtXR1dRW+Ro4cKXffixcvQiAQoF+/flyZp6cnBAIBkpOTFR73zJkzMDU1haOjI2bPno3c3FxuW0pKCqqqqjB8+HCuzNLSEj169JBqt74x0dpouWAzE4nEUFVVgXbZU3To/wRdVCuxZ8FSmHb3UXZohBBCSIPw1fi4PPmy0o7dWIGBgfj000+52YMLFy4gISEBZ86c4eq8evUKW7duRUxMDPdlMSoqCqdOncLOnTsRGhrK1RUKhRAIBNw1Sq8vo1uzZg02bNjAzeLY2dnh9u3b2LZtG4KCgrh6VVVVCttpqo4dOyIvLw/V1dWIjIzErFmz5NZ9/PgxGGOwtLSUKO/Xrx9++OEHODo64vnz51i7di28vb1x69YtGBkZyWzriy++QEBAAFatWsWVubq6StSpqqoCn8/n+szn8yEUCgHUJHWHDx/GhQsX4O3tDQCIi4uDtbU1Dh06BD8/P1y/fh2qqqoS11y9vnwMAKysrPDo0aNG9xsAtLW1Ja4/09XVlapTm/RZWVlBR0cHAoFAYvv69esRFhaGgIAAAMBXX32F33//HZs2bcKWLVvkxlWfzz77jPvvTp06ISQkBHv27MGyZcu48trP08TEBObm5qioqGjy8eqqb1a1djmuLDk5OTA1NZUqNzU1RU5Ojtz9Ro4cCT8/P9ja2iIjIwPh4eEYOnQoUlJSoKmpiZycHGhoaEgtATQzM5Nq18rKCqmpqQr70JooyWpGBw6kIzz8d5w5878/sJZuBZRgEUIIaVN4PB601bWVHUaDGRsbY9SoUYiNjQVjDKNGjYKxsbFEnQcPHqCqqgr9+/fnytTV1dG3b1+kp6dL1M3Pz4e+vr7MY+Xl5SEzMxMzZ87E7NmzufLq6mqpL+LFxcXQ0dFRGLu3tzdUVFRgYGCAfv36Yf369bCzU7zy5fz58ygtLcWlS5ewfPlyODg44MMPP5RZt7y8ZvmllpaWRHndWQkXFxd4eXnB3t4esbGxWLJkicy20tLSJPosi6I+p6enQ01NTWK2w8jICF27duXOgZ2dHaqqqpCYmIiJEyfKvT6Pz+ejrKxMbhzy+t1QxcXFUFFRkZlYFBcXIysrS2IsAUD//v3x559/Nul4tfbt24dNmzbh/v37KC0tRXV1tdRYzM/PBwC5Y7RWWFgYPvvsM+4mKatWrcLQoUPl1ndwcHij2GWdK8aYwmss/f39uf/u0aMHPDw8YGtry82ayiOr3frGRGuj5YLNoLJShE8+OYkJE/bi9u08fPON4mlRQgghhDSvGTNmICYmBrGxsTKXCtYu/3r9i9nrX9aqq6uRmZkp92YJYrEYQM0sWFpaGve6efMmLl26JFE3Oztb5kxKXXv27EFaWhoSExORnZ2NqVOn1ttXOzs7uLi4YPbs2fjkk08QGRkpt25tsllYWKiwTR0dHbi4uChc7qZoJqNWVlaW3D7XngNZ5bXnoE+fPli9ejVmzJgBLS0t6OrqylwCVlBQoPAasob2W56srCyYmZlBRUX+V+X6xlJjXbp0CQEBARg5ciSOHj2K1NRUrFy5EpWVlRL1apd11ndDj9DQUKSlpeH06dNwcnKCr68vioqK5NZ/k+WC5ubmeP78uVR5Xl4ezMzMFMZZl4WFBWxtbblxaG5ujsrKSqnzmJubK9VufWOitVGS9YaePCnCoEHR2LSpZllFaKg3vvhC/q8EhBBCCGl+I0aMQGVlJSorK+HjI72CxMHBARoaGkhKSuLKqqqqcPXqVYk71F2+fBkVFRUYMGCAzOOYmZnBysoKDx8+hIODg8Sr7gzUgwcPUFBQIHXzjddZW1vDwcEBXl5emD9/fqOXOzHGuOVjstjb20NfXx+3b99W2I5QKER6ejosLCzk1unZsydOnz4td7tYLMa1a9fk9tnZ2RnV1dW4fPl/S1Hz8/Nx9+5diXOwePFiWFpaYtWqVUhLS5N5XdXNmzcVfrYN7bc8V65ckdu+vr4+LC0tJcYSACQnJyu822F9Lly4AFtbW6xcuRIeHh7o0qULHj9+LFXv7NmzsLGxgbW1tcL2jI2N4eDggJ49eyIiIgKlpaUKk+i6PxrIeu3YsUPuvl5eXigqKpK4Puzy5csoKiriloY2RH5+PjIzM7lx2Lt3b6irq+PUqVNcnezsbNy8eVOq3frGRGuj5YJv4Nixu5g69RAKCsqhrl0J92lXcKfLPkyMAUpVxIAa5bCEEEJIa1BVVeWWnMm69klHRwcfffQRQkNDYWhoCBsbG3z99dcoKyvDzJkzAdRcVxIeHg5PT0/w+Xzumg+RSISSkhKUl5eDz+cjMjISixYtgr6+PkaOHAmhUIirV6+isLAQS5YswdWrV7Fo0SK4uLjITBDqqqysREVFBfLy8hAfHw8XFxe5dbds2QIbGxs4OTkBqHlu1vr167Fw4UK5+6ioqGDYsGFISkqSuMvf0qVLMWbMGNjY2CA3Nxdr165FcXGxxDVlr4uIiMC7774Le3t7BAQEoLq6GidOnMCyZcuQmZmJyMhI5Obmctcpva5Lly7w9fXF7NmzsW3bNujp6WH58uWwsrKCr68vgJqkMSgoCG5ubli+fDkA2TNo58+fx5o1axrd7/qUlpZix44d2L17N/bu3Su3XmhoKCIiImBvbw83NzdER0cjLS0NcXFxEvVqzy9Qk4SKRCLufe0MVW2S7ODggCdPniAhIQF9+vTBsWPHpO6Wl5aWhi1btuDDDz/kxmdeXh6AmgRFJBJx47+6uhoVFRUoLy/H9u3bwefzYW9vL7dPb7JcsFu3bhgxYgR3boGaW7iPHj0aXbt25eo5OTlh3bp1+OCDD1BaWorIyEhMmDABFhYWePToEVasWAFjY2N88MEHAACBQICZM2ciJCQERkZGMDQ0xNKlS+Hi4oJhw4Zx7ZaVlSElJQVffvllk/vQ7NhbpqioiAFgRUVFTW5j15HVrN+s9xkQyYBIxrdbxBy/6ct6xPSQen28fXDzBU8IIYQ0s/Lycnb79m1WXl6u7FAaLSgoiPn6+srd7uvry4KCgrj35eXlbOHChczY2Jhpamqy/v37sz/++IPbPnjwYAZA7is6OpqrGxcXx9zc3JiGhgbr0KEDGzRoEDtw4ABjjLEBAwYwPz8/lpmZKRGPra0t27hxI2OMsYyMDIm2BQIB8/HxYXfv3pXbn2+//ZZ1796daWtrM319febu7s6+//57JhKJFH5OJ0+eZFZWVhL1/P39mYWFBVNXV2eWlpZs/Pjx7NatWwrbYYyx/fv3c/02NjZm48ePZ4wxFhISwgYNGsTOnz8vUf/1c1RQUMACAwOZQCBgfD5fqs/r1q1jTk5OrKSkhCsbPHgwW7x4Mfc+OTmZGRgYsLKyskb3+/W2GGMsIiKCubq6MsYYO3DgAHN2dmZRUVESdaKjo5lAIODei0QitmrVKmZlZcXU1dWZq6srO3HiBLf99fNb36tWaGgoMzIyYrq6uszf359t3LhR4rj1tZORkcEYqxlrtWVaWlqsV69e7Pjx4wo/rzeVn5/PpkyZwvT09Jienh6bMmUKKywslKhT999RWVkZGz58ODMxMWHq6urMxsaGBQUFsSdPnkjsU15ezhYsWMAMDQ0Zn89no0ePlqqze/du1rVrV7mxKfo71xy5gSw8xuQskG2niouLIRAIUFRUVO8Fg687mRyHuBsbkaYlhKhMDQ9WvQtdlxyY+9+AiroYizt8AC31/13sqaaqgVH9p0NPx6CZe0EIIYQ0j4qKCmRkZMDOzq7JNwloL4YMGYLIyEgMGTJEaltwcDDc3NwknnPUVjDG4OnpieDgYLk3yGhL/Pz84O7ujhUrViis11b6bWBgIPGsLEV4PJ7ca9vc3Nxw6NCht/Lhy3379kVwcDAmT54sc7uiv3NvkhsoQssFFcjMeYh1h6ehhNXcqeTicy1o2QjBA6CqXY3ZK2/DzdYDQGe42Q+FR/chygyXEEIIIW/A0NBQ5u3CgZrrcBpy44e/Ix6Ph+3bt+P69evKDuWNCYVCuLq64pNPPqm3blvpd2N+3FB0EwljY+Nme0xAW5Kbm4uJEyf+7RJpmslS4N+Jn2B72W9gYiD3Z2fkHe4GiylpeGdAJsbZT8eEdz6Cyls4mAkhhLQfNJNFCGnvaCbrb6ZKXInqIk3kbe2H/L9qHrDmVOiKqKA4aGspfu4FIYQQQggh5O1ESZYM1dVVWB03BeeuF+D+zmGoLuJDR0cd27aNxpQpPZUdHiGEENLs3rKFLYSQt4gy/r5RkvWaTXsX4mzBWST/1hW5BwYAjIcOliW48FsYunX7+zzgjBBCCGkO6urqAGpugdxWrzkihBBFam+X35rXrFGS9V9xJ7/G+SdHcEHzJSpeCJB70BlgPLj2f4n/bJlACRYhhJB2SVVVFQYGBsjNzQUAaGtrg8fjKTkqQghpHmKxGHl5edDW1oaaWuulPpRkARCLRNicHYtyzZqHB2tZF+HdSc/gbt8P36yNUHJ0hBBCSMsyNzcHAC7RIoSQ9kRFRQU2Njat+gPSW59knU35Gb/fjEfmKUfo9ngOHxNNDLAdg4D4JcoOjRBCCGkVPB4PFhYWMDU1RVVVlbLDIYSQZqWhoQEVFZVWPeZbm2TdyUjF/936EbEvr+HpDg+UpFpB07IYSy8vQqeOtsoOjxBCCGl1qqqqb+VzdgghpLm1bkonw/fff8/ds7537944f/68wvpnz55F7969oaWlhc6dO+M///lPk44bdPEjRN29i/sRw1CSagUVNRFGjWCwtbJpUnuEEEIIIYQQAig5ydqzZw+Cg4OxcuVKpKamYuDAgRg5ciSePHkis35GRgbef/99DBw4EKmpqVixYgUWLVqE/fv3N/rYBb/b4eEXQ1D1QgcGJkJcTp6D/Tv/RRf7EkIIIYQQQt4IjynxwRj9+vVDr169sHXrVq6sW7duGDduHNatWydVPywsDIcPH0Z6ejpXNm/ePPz555+4ePFig45Z+1RnYDkALYwebYcff5wEAwN6yj0hhBBCCCFvk9rcoKioCPr6+s3WrtKuyaqsrERKSgqWL18uUT58+HAkJyfL3OfixYsYPny4RJmPjw927tyJqqoq7lkfdQmFQgiFQu59UVFRzX/whFj35TB89JEHeLxKFBdXvmGPCCGEEEIIIW1JcXExgOZ/YLHSkqwXL15AJBLBzMxMotzMzAw5OTky98nJyZFZv7q6Gi9evICFhYXUPuvWrcOqVaukG2Mb8emnG/Hpp03vAyGEEEIIIaTty8/P/+9qt+ah9LsLvn4NFGNM4XVRsurLKq/16aefYsmS/92O/eXLl7C1tcWTJ0+a9YMk5HXFxcWwtrZGZmZms04/E/I6GmuktdBYI62FxhppLUVFRbCxsYGhoWGztqu0JMvY2BiqqqpSs1a5ublSs1W1zM3NZdZXU1ODkZGRzH00NTWhqakpVS4QCOgfLWkV+vr6NNZIq6CxRloLjTXSWmiskdbS3M/RUtrdBTU0NNC7d2+cOnVKovzUqVPw9vaWuY+Xl5dU/V9//RUeHh4yr8cihBBCCCGEkNam1Fu4L1myBDt27MCuXbuQnp6OTz75BE+ePMG8efMA1Cz1mzp1Kld/3rx5ePz4MZYsWYL09HTs2rULO3fuxNKlS5XVBUIIIYQQQgiRoNRrsvz9/ZGfn4/Vq1cjOzsbPXr0wPHjx2FrawsAyM7Olnhmlp2dHY4fP45PPvkEW7ZsgaWlJb799ltMmDChwcfU1NRERESEzCWEhDQnGmuktdBYI62FxhppLTTWSGtpqbGm1OdkEUIIIYQQQkh7o9TlgoQQQgghhBDS3lCSRQghhBBCCCHNiJIsQgghhBBCCGlGlGQRQgghhBBCSDNql0nW999/Dzs7O2hpaaF37944f/68wvpnz55F7969oaWlhc6dO+M///lPK0VK2rrGjLUDBw7gvffeg4mJCfT19eHl5YVffvmlFaMlbVlj/67VunDhAtTU1ODm5tayAZJ2o7FjTSgUYuXKlbC1tYWmpibs7e2xa9euVoqWtGWNHWtxcXFwdXWFtrY2LCwsMH36dOTn57dStKStOnfuHMaMGQNLS0vweDwcOnSo3n2aIzdod0nWnj17EBwcjJUrVyI1NRUDBw7EyJEjJW4FX1dGRgbef/99DBw4EKmpqVixYgUWLVqE/fv3t3LkpK1p7Fg7d+4c3nvvPRw/fhwpKSl45513MGbMGKSmprZy5KStaexYq1VUVISpU6fi3XffbaVISVvXlLE2adIknD59Gjt37sSdO3cQHx8PJyenVoyatEWNHWtJSUmYOnUqZs6ciVu3biExMRFXrlzBrFmzWjly0ta8evUKrq6u+O677xpUv9lyA9bO9O3bl82bN0+izMnJiS1fvlxm/WXLljEnJyeJsrlz5zJPT88Wi5G0D40da7I4OzuzVatWNXdopJ1p6ljz9/dnn332GYuIiGCurq4tGCFpLxo71k6cOMEEAgHLz89vjfBIO9LYsfbNN9+wzp07S5R9++23rGPHji0WI2l/ALCDBw8qrNNcuUG7msmqrKxESkoKhg8fLlE+fPhwJCcny9zn4sWLUvV9fHxw9epVVFVVtVispG1rylh7nVgsRklJCQwNDVsiRNJONHWsRUdH48GDB4iIiGjpEEk70ZSxdvjwYXh4eODrr7+GlZUVHB0dsXTpUpSXl7dGyKSNaspY8/b2xtOnT3H8+HEwxvD8+XPs27cPo0aNao2QyVukuXIDteYOTJlevHgBkUgEMzMziXIzMzPk5OTI3CcnJ0dm/erqarx48QIWFhYtFi9pu5oy1l63YcMGvHr1CpMmTWqJEEk70ZSxdu/ePSxfvhznz5+Hmlq7+jNPWlBTxtrDhw+RlJQELS0tHDx4EC9evMD8+fNRUFBA12URuZoy1ry9vREXFwd/f39UVFSguroaY8eOxb///e/WCJm8RZorN2hXM1m1eDyexHvGmFRZffVllRPyusaOtVrx8fGIjIzEnj17YGpq2lLhkXakoWNNJBJh8uTJWLVqFRwdHVsrPNKONObvmlgsBo/HQ1xcHPr27Yv3338f//rXvxATE0OzWaRejRlrt2/fxqJFi/D5558jJSUFJ0+eREZGBubNm9caoZK3THPkBu3qJ05jY2OoqqpK/QqSm5srlZHWMjc3l1lfTU0NRkZGLRYraduaMtZq7dmzBzNnzkRiYiKGDRvWkmGSdqCxY62kpARXr15FamoqFixYAKDmizBjDGpqavj1118xdOjQVomdtC1N+btmYWEBKysrCAQCrqxbt25gjOHp06fo0qVLi8ZM2qamjLV169ahf//+CA0NBQD07NkTOjo6GDhwINauXUsrj0izaa7coF3NZGloaKB37944deqURPmpU6fg7e0tcx8vLy+p+r/++is8PDygrq7eYrGStq0pYw2omcGaNm0adu/eTevISYM0dqzp6+vjxo0bSEtL417z5s1D165dkZaWhn79+rVW6KSNacrftf79+yMrKwulpaVc2d27d6GiooKOHTu2aLyk7WrKWCsrK4OKiuTXVlVVVQD/m2UgpDk0W27QqNtktAEJCQlMXV2d7dy5k92+fZsFBwczHR0d9ujRI8YYY8uXL2eBgYFc/YcPHzJtbW32ySefsNu3b7OdO3cydXV1tm/fPmV1gbQRjR1ru3fvZmpqamzLli0sOzube718+VJZXSBtRGPH2uvo7oKkoRo71kpKSljHjh3ZxIkT2a1bt9jZs2dZly5d2KxZs5TVBdJGNHasRUdHMzU1Nfb999+zBw8esKSkJObh4cH69u2rrC6QNqKkpISlpqay1NRUBoD961//Yqmpqezx48eMsZbLDdpdksUYY1u2bGG2trZMQ0OD9erVi509e5bbFhQUxAYPHixR/8yZM8zd3Z1paGiwTp06sa1bt7ZyxKStasxYGzx4MAMg9QoKCmr9wEmb09i/a3VRkkUao7FjLT09nQ0bNozx+XzWsWNHtmTJElZWVtbKUZO2qLFj7dtvv2XOzs6Mz+czCwsLNmXKFPb06dNWjpq0Nb///rvC718tlRvwGKM5VkIIIYQQQghpLu3qmixCCCGEEEIIUTZKsgghhBBCCCGkGVGSRQghhBBCCCHNiJIsQgghhBBCCGlGlGQRQgghhBBCSDOiJIsQQgghhBBCmhElWYQQQgghhBDSjCjJIoQQQgghhJBmREkWIYQQ0gbduHEDQ4YMQWVlJS5duoS+ffu22rGPHDmCwMBAiMVi7NmzBxMnTmy1YxNCSFugpuwACCGkLeHxeDLLGWOtHAl52/Xo0QMGBgbQ0dGBmpoaYmNjW+3Y7733Hr744gtoampCR0cHR44cabVjE0JIW8Bj9M2AEEIajMfjITo6GiNGjAAAnDx5EtOnT6ckiyhNbm4udHV1oa2t3erHzsnJgaGhITQ0NFr92IQQ8ndGywUJIaSBqqurAQCGhoYwNzeHubk5DAwMpOrt378f3bt3h6amJjp16oQNGzZw28rKyuDp6Ynp06dzZZGRkXBzc+PeHzp0CB06dEBaWhoA4MyZM+DxeHj58qXEcXg8Hg4dOsS9v3HjBoYOHQo+nw8jIyPMmTMHpaWlcvtT266sV2PbldVW3c9GLBZj9erV6NixIzQ1NeHm5oaTJ09y2+/evQsTExNER0dzZUOGDEFwcDD3fsmSJXBxcUFRUZHMz62yshL29vbcZ6Wof3VnJJOTkzFo0CDw+XxYW1tj0aJFePXqlUT/IiMjpfYfN24ct71Tp07YtGmT3M962rRpEvUBICYmRuIzer0/dR06dEgi5rp1TU1NoaamJtF3eXg8HrZu3YqRI0eCz+fDzs4OiYmJEnWePXsGf39/dOjQAUZGRvD19cWjR49k9sXc3BwlJSUwMDDg+hITEyP3M+/UqZPMvr5+7mR9PgDw6NEj8Hg8qX8br79u3rwp9zMghJDWQEkWIYQ0UGVlJQAo/NU+JSUFkyZNQkBAAG7cuIHIyEiEh4cjJiYGAKCtrY0jR44gKSkJ4eHhUvtfvHgRQUFB2Lt3r9wv3LKUlZVhxIgR6NChA65cuYLExET89ttvWLBgQb373rlzB9nZ2dyrKe3WzuTVtvV6wrF582Zs2LAB69evx/Xr1+Hj44OxY8fi3r17AABHR0f8/PPPWLx4MX755RepGDdu3IjExEScOHECAoFAZj++++475Obmcu+9vb25Pu3fvx8ApPp548YN+Pj4YPz48bh+/Tr27NmDpKQkmf3r3r07t++kSZPq+1hb1et9VyQ8PBwTJkzAn3/+iX/84x/48MMPkZ6eDqDmfL/zzjvQ1dXFuXPnkJSUBF1dXYwYMYIb/69btWoVRCIR997f35/7nDZt2oSOHTty769cufLG8cvy+hh2cnJqcluEENIcKMkihJAGKiwsBADo6urKrfOvf/0L7777LsLDw+Ho6Ihp06ZhwYIF+Oabb7g6JiYmOHHiBLZt24aoqCiu/M6dOxgzZgy+/fZbvPfee42KLS4uDuXl5fjhhx/Qo0cPDB06FN999x1+/PFHPH/+XOG+pqam3Mycubl5k9qtqqoCAFhZWcHc3FwqEVq/fj3CwsIQEBCArl274quvvoKbm5tEMubt7Y3Y2Fj4+/sjNTWVK09MTMSaNWtw/PhxdOzYUWYfCgoKsHbtWoSFhXFlGhoaXJ8MDQ0BQKqf33zzDSZPnozg4GB06dIF3t7e+Pbbb/HDDz+goqJCon98Pp/bl8/nK/xMW5Osvivi5+eHWbNmwdHREWvWrIGHhwf+/e9/AwASEhKgoqKCHTt2wMXFBd26dUN0dDSePHmCM2fOSLV19+5d7Nq1C5988glXVvdzEggEUFVV5d6bmJi8cfyyvD6G1dToknNCiHJRkkUIIQ2Uk5MDoOYLnTzp6eno37+/RFn//v1x7949iV/77ezsYG1tjfnz5+O3335DYWEhRowYgZKSEnh6espsu2PHjtDV1eVerx/X1dUVOjo6EscVi8W4c+dOo/va2HaLi4uhoqIiM/koLi5GVlaWzM+ldgalloeHB0QiEd5//31kZ2fj8uXLCAwMhImJCRwdHeXGuXr1arzzzjsYMGBAo/qXkpKCmJgYic/Vx8cHYrEYGRkZEn2o+xnIEhYWBl1dXZiYmGDgwIH4v//7P4ntR48elTjOvHnzpNq4ceMGdHV1IRAI4OTkhMjIyHqv92ts3728vKTe156HlJQU3L9/H3p6elychoaGqKiowIMHD6TaWrZsGebOnYvOnTs36NiNjb+oqEjiM+vevXuTj0MIIa2JfuohhJAGSk9Ph7q6Ouzs7OTWYYxJ3YFQ1pfkzZs3o6SkBLt378aHH34IkUiEuXPnQk1NDXPmzOGuNanr/Pnz0NPT49536dJF4XFryStviIa2m5WVBTMzM6ioyP/tTtbn8nrZxx9/jHHjxsHS0hJff/01eDweYmJisHHjRnzxxRdYvXq1VLv37t3Djh07kJaWhqdPnzamexCLxZg7dy4WLVoktc3Gxkaif5aWlgrbCg0NxbRp01BWVoZ///vf8PX1xdOnT7lZvXfeeQdbt27l6h84cABffvmlRBtdu3bF4cOHIRaLkZKSgpkzZ8La2hpGRkYyj/kmfa+r9jyIxWL07t0bcXFxUnVen4U6e/Yszp8/j+joaPz8889NOm598evp6eHatWvc+2fPnmHIkCFNOhYhhLQmSrIIIaSBjh8/Dk9PT6irq8ut4+zsjKSkJImy5ORkODo6QlVVFQDw+PFjRERE4Oeff8bQoUORlJSEo0ePYsuWLSgrK4OzszN27NiB2bNnS7RjZ2cn80YbtceNjY3Fq1evuBmXCxcuQEVFReEMUH0a2u6VK1fg7u4usw19fX1YWloiKSkJgwYNkvhc6j7bad++fUhOTsZff/0FIyMj/PLLL3B1dcXUqVPh7OyMgQMHwt/fX2o2IywsDLNmzYKDg0OjE41evXrh1q1bcHBwkFtHLBbj2rVr+PjjjxW2ZWxszLUTERGBHTt24N69e/Dw8AAA6OjoSBxH1oyohoYGV8fR0RE7d+5Eamoqhg0bJvOYTen7pUuXMHXqVIn3teeuV69e2LNnD0xNTaGvry+3DcYYQkJCEB4ejg4dOjTouE2JX0VFReIzo2WAhJC2gpYLEkJIPbKysrBs2TLs3bsXy5cvV1g3JCQEp0+fxpo1a3D37l3Exsbiu+++w9KlS7k68+fPx8SJEzF06FAAQIcOHaCnpwdVVVXo6enhu+++w7Jly7jliQ0xZcoUaGlpISgoCDdv3sTvv/+OhQsXIjAwEGZmZk3reAPaLS0txaZNm7B7927MmDFDbjuhoaH46quvsGfPHty5cwfLly9HWloaFi9eDKBmWdiiRYuwYcMGGBsbc3cnrP0C7+Hhgblz52LOnDkSM4P379/HmTNn8Pnnnzepf2FhYbh48SI+/vhjpKWl4d69ezh8+DAWLlwIAMjMzMTs2bORm5uLgIAAhW1VV1ejoqIChYWF2L59O/h8Puzt7RsVD2MMFRUVKCsrQ1JSEq5evQoXFxeZdZva98TEROzatQt3795FREQE/vjjD+5GH1OmTIGxsTF8fX1x/vx5ZGRk4OzZs1i8eLFEEnT69GkUFRVh/vz5jTp2c8RPCCFtASVZhBBSj927d+PKlSs4ceIE3n//fYV1e/Xqhb179yIhIQE9evTA559/jtWrV2PatGkAgD179uDKlSsSt3V/na+vL4YOHSpzCZs82tra+OWXX1BQUIA+ffpg4sSJePfdd/Hdd981uI2mtHvq1ClERUVh27ZtmDBhgtx2Fi1ahJCQEISEhMDFxQUnT57E4cOHuSWPy5cvh7OzM4KCguS2sXbtWjx9+lRiyd2rV6+wcuVK7sYWjdWzZ0+cPXsW9+7dw8CBA+Hu7o7w8HBYWFgAqFnWef/+ffz666+wtrZW2FZoaCj4fD4sLS1x7Ngx7N+/v9GzPNevXwefz4eenh4CAgIwd+5cqRnNWk3t+6pVq5CQkICePXsiNjYWcXFxcHZ2BlBzvs+dOwcbGxuMHz8e3bp1w4wZM1BeXi4xs/Xq1Sv885//fKPnY73puSOEkL8zehgxIYQQ8pbg8Xg4ePCg1DO7CCGENC+aySKEEEIIIYSQZkRJFiGEEEIIIYQ0I7pNDyGEEPKWoCsECCGkddBMFiGEEEIIIYQ0I0qyCCGEEEIIIaQZUZJFCCGEEEIIIc2IkixCCCGEEEIIaUaUZBFCCCGEEEJIM6IkixBCCCGEEEKaESVZhBBCCCGEENKMKMkihBBCCCGEkGb0/zKlrO9aD4nYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "for i, (model, history, num_layers) in enumerate(zip(models, histories, count_layers), 1):\n",
    "    y_pred = model.predict(X_test)\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(fpr, tpr) \n",
    "    plt.plot(fpr, tpr, label='Модель {} ({} слоя) (площадь = %0.2f)'.format(i, num_layers) % roc_auc)\n",
    "\n",
    "    roc_auc_trapezoidal = np.trapz(tpr, fpr)\n",
    "    print(\"Площадь под кривой ROC (метод трапеций) для модели {} = {:.2f}\".format(i, roc_auc_trapezoidal))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Ложно положительный рейтинг')\n",
    "plt.ylabel('Истинно положительный рейтинг')\n",
    "plt.title('(ROC)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Определите исходном наборе данных дополнительный признак, отличный от указанных в задании двух независимых признаков, принимающий непрерывные значения и имеющий максимальную дисперсию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data[list(set(data.select_dtypes(exclude='object').columns.to_list() + [a] + [b] + [c]))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Признак с максимальной дисперсией, отличный от features/density, features/fixed acidity, quality: features/total sulfur dioxide\n"
     ]
    }
   ],
   "source": [
    "excluded_features = [a,b,c]\n",
    "\n",
    "selected_features = data1.drop(columns=excluded_features)\n",
    "\n",
    "d = selected_features.var().idxmax()\n",
    "\n",
    "print(f\"Признак с максимальной дисперсией, отличный от {a}, {b}, {c}:\", d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Визуализируйте точки набора данных в трехмерном пространстве с координатами, соответствующими трем независимым признакам, отображая точки различных классов разными цветами. Подпишите оси и рисунок, создайте легенду для классов набора данных.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "6    2198\n",
       "5    1457\n",
       "7     880\n",
       "8     175\n",
       "4     163\n",
       "3      20\n",
       "9       5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[c].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "df = data1[[a,b,d,c]]\n",
    "df.head()\n",
    "\n",
    "ax = plt.axes(projection='3d')\n",
    "\n",
    "classes = df[c].unique()\n",
    "for i, k in enumerate(classes):\n",
    "    data = df[df[c] == k]\n",
    "    ax.scatter(data[a], data[b], data[d], label=f'Class {k}')\n",
    "ax.set_xlabel(a)\n",
    "ax.set_ylabel(b)\n",
    "ax.set_zlabel(d)\n",
    "ax.legend()\n",
    "ax.set_title(name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Разбейте исходный набор данных на обучающую и тестовую выборки. Постройте нейронную сеть для многоклассовой классификации с нормализующим слоем и параметрами, соответствующими лучшей нейронной сети для бинарной классифиации из п.4, и обучите ее на обучающей выборке, контролируя процесс ее обучения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features/sulphates</th>\n",
       "      <th>features/total sulfur dioxide</th>\n",
       "      <th>features/density</th>\n",
       "      <th>features/free sulfur dioxide</th>\n",
       "      <th>features/chlorides</th>\n",
       "      <th>features/volatile acidity</th>\n",
       "      <th>features/pH</th>\n",
       "      <th>features/citric acid</th>\n",
       "      <th>features/fixed acidity</th>\n",
       "      <th>features/residual sugar</th>\n",
       "      <th>quality</th>\n",
       "      <th>features/alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.55</td>\n",
       "      <td>197.0</td>\n",
       "      <td>1.00080</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.32</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.34</td>\n",
       "      <td>7.6</td>\n",
       "      <td>18.350000</td>\n",
       "      <td>5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.42</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99110</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.49</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>6</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.48</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99076</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.11</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.41</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.99672</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.27</td>\n",
       "      <td>6.6</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.34</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.99016</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.34</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.30</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>0.75</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.99058</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.16</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.44</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>7</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>0.37</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.99476</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.24</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.27</td>\n",
       "      <td>6.1</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>0.39</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.99886</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.36</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.59</td>\n",
       "      <td>8.1</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>5</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>0.42</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1.00295</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>6.8</td>\n",
       "      <td>26.049999</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>0.27</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.99054</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.36</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.28</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>7</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4898 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      features/sulphates  features/total sulfur dioxide  features/density  \\\n",
       "0                   0.55                          197.0           1.00080   \n",
       "1                   0.42                           92.0           0.99110   \n",
       "2                   0.48                           51.0           0.99076   \n",
       "3                   0.41                          103.0           0.99672   \n",
       "4                   0.34                          135.0           0.99016   \n",
       "...                  ...                            ...               ...   \n",
       "4893                0.75                          122.0           0.99058   \n",
       "4894                0.37                          133.0           0.99476   \n",
       "4895                0.39                          134.0           0.99886   \n",
       "4896                0.42                          122.0           1.00295   \n",
       "4897                0.27                           93.0           0.99054   \n",
       "\n",
       "      features/free sulfur dioxide  features/chlorides  \\\n",
       "0                             44.0               0.054   \n",
       "1                             35.0               0.063   \n",
       "2                              6.0               0.029   \n",
       "3                             20.0               0.110   \n",
       "4                             57.0               0.035   \n",
       "...                            ...                 ...   \n",
       "4893                          39.0               0.051   \n",
       "4894                          51.0               0.050   \n",
       "4895                          60.0               0.051   \n",
       "4896                          27.0               0.031   \n",
       "4897                          12.0               0.029   \n",
       "\n",
       "      features/volatile acidity  features/pH  features/citric acid  \\\n",
       "0                          0.32         3.22                  0.34   \n",
       "1                          0.27         3.38                  0.49   \n",
       "2                          0.43         3.51                  0.11   \n",
       "3                          0.41         3.08                  0.27   \n",
       "4                          0.34         3.09                  0.30   \n",
       "...                         ...          ...                   ...   \n",
       "4893                       0.16         3.11                  0.44   \n",
       "4894                       0.24         3.22                  0.27   \n",
       "4895                       0.36         2.96                  0.59   \n",
       "4896                       0.45         3.06                  0.28   \n",
       "4897                       0.36         3.19                  0.28   \n",
       "\n",
       "      features/fixed acidity  features/residual sugar  quality  \\\n",
       "0                        7.6                18.350000        5   \n",
       "1                        6.3                 1.200000        6   \n",
       "2                        5.3                 1.100000        4   \n",
       "3                        6.6                10.700000        6   \n",
       "4                        5.9                 3.800000        6   \n",
       "...                      ...                      ...      ...   \n",
       "4893                     6.4                 1.200000        7   \n",
       "4894                     6.1                11.500000        6   \n",
       "4895                     8.1                13.600000        5   \n",
       "4896                     6.8                26.049999        6   \n",
       "4897                     6.6                 6.100000        7   \n",
       "\n",
       "      features/alcohol  \n",
       "0                  9.0  \n",
       "1                 12.2  \n",
       "2                 11.2  \n",
       "3                  9.0  \n",
       "4                 12.0  \n",
       "...                ...  \n",
       "4893              11.3  \n",
       "4894              10.8  \n",
       "4895               8.7  \n",
       "4896              10.6  \n",
       "4897              12.8  \n",
       "\n",
       "[4898 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data1[c] = le.fit_transform(data1[c])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data1.drop(c,axis=1).values, data1[c].values, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_score_index = scores.index(max(scores)) if score_metric.__name__ in ['TP', 'TN'] else scores.index(min(scores))\n",
    "\n",
    "best_count_layers = count_layers[best_score_index]\n",
    "best_optimizer = optimizers[best_score_index]\n",
    "best_neurons_per_layer = neurons_per_layer[best_score_index]\n",
    "best_activation = activations[best_score_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 5, <keras.optimizers.rmsprop.RMSprop at 0x23e1172ce50>, 32, 'elu')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score_index,best_count_layers,best_optimizer,best_neurons_per_layer,best_activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization_2 (Normalizat  (None, 2)                3         \n",
      " ion)                                                            \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 32)                96        \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,356\n",
      "Trainable params: 4,353\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3918, 7), (980, 7))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_one_hot(labels, dimension=len(data1[c].unique())):\n",
    "    results = np.zeros((len(labels), dimension))\n",
    "    for i, label in enumerate(labels):\n",
    "        results[i, label] = 1.\n",
    "    return results\n",
    "\n",
    "y_train = to_one_hot(y_train)\n",
    "y_test = to_one_hot(y_test)\n",
    "y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization_3 (Normalizat  (None, 11)               3         \n",
      " ion)                                                            \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 32)                384       \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_20 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_21 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_22 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_23 (Dense)            (None, 7)                 231       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,842\n",
      "Trainable params: 4,839\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/100\n",
      "98/98 [==============================] - 1s 5ms/step - loss: 5.9322 - val_loss: 1.5541\n",
      "Epoch 2/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 6.5842 - val_loss: 1.6334\n",
      "Epoch 3/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 6.4512 - val_loss: 1.6233\n",
      "Epoch 4/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 2.0205 - val_loss: 1.3121\n",
      "Epoch 5/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.9178 - val_loss: 1.5195\n",
      "Epoch 6/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 19.8299 - val_loss: 1.3162\n",
      "Epoch 7/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.4579 - val_loss: 1.9149\n",
      "Epoch 8/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6836 - val_loss: 2.1600\n",
      "Epoch 9/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.7453 - val_loss: 1.2544\n",
      "Epoch 10/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6349 - val_loss: 1.6415\n",
      "Epoch 11/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6822 - val_loss: 1.2835\n",
      "Epoch 12/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6761 - val_loss: 1.4586\n",
      "Epoch 13/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6601 - val_loss: 1.2460\n",
      "Epoch 14/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6300 - val_loss: 2.2704\n",
      "Epoch 15/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6874 - val_loss: 1.7028\n",
      "Epoch 16/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6706 - val_loss: 2.3045\n",
      "Epoch 17/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6704 - val_loss: 1.3959\n",
      "Epoch 18/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6740 - val_loss: 2.6026\n",
      "Epoch 19/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6505 - val_loss: 1.8418\n",
      "Epoch 20/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6730 - val_loss: 1.7803\n",
      "Epoch 21/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6764 - val_loss: 1.6968\n",
      "Epoch 22/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6700 - val_loss: 1.3574\n",
      "Epoch 23/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6710 - val_loss: 1.7559\n",
      "Epoch 24/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6009 - val_loss: 2.0674\n",
      "Epoch 25/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6707 - val_loss: 1.4316\n",
      "Epoch 26/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6497 - val_loss: 1.9952\n",
      "Epoch 27/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6675 - val_loss: 1.7343\n",
      "Epoch 28/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6655 - val_loss: 2.0453\n",
      "Epoch 29/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 2.2596 - val_loss: 1.2676\n",
      "Epoch 30/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6448 - val_loss: 1.5441\n",
      "Epoch 31/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6406 - val_loss: 1.4633\n",
      "Epoch 32/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6584 - val_loss: 1.7960\n",
      "Epoch 33/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6858 - val_loss: 1.3005\n",
      "Epoch 34/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 15.1667 - val_loss: 1.5009\n",
      "Epoch 35/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.5117 - val_loss: 1.6544\n",
      "Epoch 36/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6588 - val_loss: 1.6597\n",
      "Epoch 37/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6312 - val_loss: 2.7056\n",
      "Epoch 38/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6439 - val_loss: 1.2726\n",
      "Epoch 39/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6678 - val_loss: 1.3990\n",
      "Epoch 40/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6565 - val_loss: 1.4192\n",
      "Epoch 41/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6475 - val_loss: 1.3430\n",
      "Epoch 42/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6833 - val_loss: 1.3058\n",
      "Epoch 43/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6624 - val_loss: 1.6529\n",
      "Epoch 44/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6466 - val_loss: 1.3470\n",
      "Epoch 45/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6573 - val_loss: 1.3799\n",
      "Epoch 46/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6292 - val_loss: 1.6314\n",
      "Epoch 47/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6682 - val_loss: 1.5894\n",
      "Epoch 48/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6414 - val_loss: 1.7294\n",
      "Epoch 49/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6686 - val_loss: 1.5368\n",
      "Epoch 50/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6974 - val_loss: 1.6442\n",
      "Epoch 51/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6355 - val_loss: 2.5320\n",
      "Epoch 52/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6102 - val_loss: 1.3612\n",
      "Epoch 53/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6965 - val_loss: 1.2478\n",
      "Epoch 54/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6569 - val_loss: 1.3647\n",
      "Epoch 55/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6629 - val_loss: 1.3182\n",
      "Epoch 56/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.7033 - val_loss: 1.3781\n",
      "Epoch 57/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6347 - val_loss: 1.2642\n",
      "Epoch 58/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6216 - val_loss: 1.4441\n",
      "Epoch 59/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6371 - val_loss: 1.5881\n",
      "Epoch 60/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6465 - val_loss: 2.0628\n",
      "Epoch 61/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6609 - val_loss: 2.2253\n",
      "Epoch 62/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6531 - val_loss: 1.5839\n",
      "Epoch 63/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6792 - val_loss: 2.2968\n",
      "Epoch 64/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6438 - val_loss: 1.4773\n",
      "Epoch 65/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6634 - val_loss: 1.5012\n",
      "Epoch 66/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6332 - val_loss: 2.2487\n",
      "Epoch 67/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6577 - val_loss: 1.7623\n",
      "Epoch 68/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6751 - val_loss: 1.3583\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 69/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6272 - val_loss: 2.3530\n",
      "Epoch 70/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6581 - val_loss: 1.7230\n",
      "Epoch 71/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6570 - val_loss: 1.2562\n",
      "Epoch 72/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6528 - val_loss: 1.4457\n",
      "Epoch 73/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6703 - val_loss: 1.5213\n",
      "Epoch 74/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6696 - val_loss: 1.3327\n",
      "Epoch 75/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6584 - val_loss: 1.7204\n",
      "Epoch 76/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6791 - val_loss: 1.2576\n",
      "Epoch 77/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6380 - val_loss: 2.0612\n",
      "Epoch 78/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6485 - val_loss: 1.4001\n",
      "Epoch 79/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6872 - val_loss: 1.5991\n",
      "Epoch 80/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6759 - val_loss: 1.5165\n",
      "Epoch 81/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6038 - val_loss: 2.2040\n",
      "Epoch 82/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6505 - val_loss: 1.5697\n",
      "Epoch 83/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6928 - val_loss: 1.8380\n",
      "Epoch 84/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6803 - val_loss: 1.7800\n",
      "Epoch 85/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6346 - val_loss: 2.2185\n",
      "Epoch 86/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6653 - val_loss: 1.6303\n",
      "Epoch 87/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6849 - val_loss: 1.3938\n",
      "Epoch 88/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6293 - val_loss: 1.4438\n",
      "Epoch 89/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6633 - val_loss: 1.4734\n",
      "Epoch 90/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6636 - val_loss: 1.6966\n",
      "Epoch 91/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6580 - val_loss: 1.8364\n",
      "Epoch 92/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6424 - val_loss: 1.3409\n",
      "Epoch 93/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6388 - val_loss: 2.2942\n",
      "Epoch 94/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6628 - val_loss: 1.5697\n",
      "Epoch 95/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6545 - val_loss: 1.8825\n",
      "Epoch 96/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6484 - val_loss: 1.7022\n",
      "Epoch 97/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6650 - val_loss: 1.4687\n",
      "Epoch 98/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6221 - val_loss: 1.8516\n",
      "Epoch 99/100\n",
      "98/98 [==============================] - 0s 1ms/step - loss: 1.6432 - val_loss: 1.4342\n",
      "Epoch 100/100\n",
      "98/98 [==============================] - 0s 2ms/step - loss: 1.6230 - val_loss: 1.3466\n"
     ]
    }
   ],
   "source": [
    "feature_normalizer = tf.keras.layers.Normalization(axis=None,input_shape=(X_train.shape[1],)) \n",
    "feature_normalizer.adapt(X_train)\n",
    "\n",
    "model = Sequential([\n",
    "    feature_normalizer,\n",
    "])\n",
    "\n",
    "for _ in range(best_count_layers):\n",
    "    model.add(Dense(best_neurons_per_layer, activation=best_activation))\n",
    "\n",
    "model.add(Dense(len(data1[c].unique()), activation='softmax'))\n",
    "print(model.summary())\n",
    "\n",
    "model.compile(optimizer=RMSprop(learning_rate=0.05),\n",
    "              loss='categorical_crossentropy')\n",
    "\n",
    "history = model.fit(X_train,\n",
    "                    y_train,\n",
    "                    epochs=100,\n",
    "                    verbose=1,\n",
    "                    validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Постройте кривые обучения в зависимости от эпохи обучения, подписывая оси и рисунок и создавая легенду."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, \"r\", label=\"Потери на обучающей выборке\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Потери на тестовой выборке\")\n",
    "plt.title(\"Функция потерь при обучении модели\")\n",
    "plt.xlabel(\"Эпохи обучения\")\n",
    "plt.ylabel(\"Функция потерь\")\n",
    "plt.legend();"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Lab_report.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}