bi-gru-model.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Import needed libraries\n","To make things work ensure that you have this libraries installed."]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:06:10.225056Z","iopub.status.busy":"2024-04-26T11:06:10.224776Z","iopub.status.idle":"2024-04-26T11:06:25.223996Z","shell.execute_reply":"2024-04-26T11:06:25.223218Z","shell.execute_reply.started":"2024-04-26T11:06:10.225033Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["2024-04-26 11:06:14.587395: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-04-26 11:06:14.587496: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-04-26 11:06:14.743878: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"]}],"source":["# imports\n","import os\n","import numpy as np\n","import splitfolders\n","from tqdm import tqdm\n","import seaborn as sns\n","import tensorflow as tf\n","from string import digits\n","from imutils import paths\n","from random import choices\n","from typing import Tuple, List\n","import matplotlib.pyplot as plt\n","import tensorflow.keras.backend as K\n","from tensorflow.data import AUTOTUNE\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.models import Sequential\n","from sklearn.model_selection import train_test_split\n","from tensorflow.keras.metrics import CategoricalAccuracy\n","from tensorflow.keras.losses import CategoricalCrossentropy\n","from sklearn.metrics import confusion_matrix, classification_report\n","from tensorflow.keras.layers import GRU, Dense, Bidirectional, InputLayer, Reshape"]},{"cell_type":"markdown","metadata":{},"source":["# Data\n","The **Dataset** class is designed as a helper tool for loading and preparing image datasets for training machine learning models."]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:06:43.958139Z","iopub.status.busy":"2024-04-26T11:06:43.957757Z","iopub.status.idle":"2024-04-26T11:06:43.985530Z","shell.execute_reply":"2024-04-26T11:06:43.984475Z","shell.execute_reply.started":"2024-04-26T11:06:43.958112Z"},"trusted":true},"outputs":[],"source":["class Dataset:\n","\n","    MAX_IMAGES_PER_GENRE = 5_000\n","    \n","    def __init__(self, path: str, output: str | None = None, ratio: Tuple[float, float, float] = (.70, .15, .15), split: bool = True, split_folders: bool = False, image_size: Tuple[int, int] = (224,224), batch_size: int = 8 , cache: bool = True):\n","        assert sum(ratio) == 1.0\n","\n","        self.path = path\n","        self.labels = [name for name in tqdm(os.listdir(self.path)) if os.path.isdir(os.path.join(self.path, name))]\n","        \n","        self.cache = cache\n","        self.ratio = ratio\n","        \n","        self.output_path = output if output else f\"./data-{''.join(choices(digits, k=5))}\"\n","        \n","        self.train = os.path.sep.join([self.output_path, 'train'])\n","        self.val = os.path.sep.join([self.output_path, 'val'])\n","        self.test = os.path.sep.join([self.output_path, 'test'])\n","        \n","        self.image_size = image_size\n","        self.batch_size = batch_size\n","        \n","        assert split == True or split_folders == True or output is not None\n","        \n","        if split:\n","            self.__split()\n","        elif split_folders:\n","            self.__split_folder()\n","            \n","        self.__generate()\n","    \n","    def __split(self):\n","        tmp_ratio = (1.0 - self.ratio[0])\n","        val_test_ratio = self.ratio[1] / tmp_ratio\n","        \n","        def get_genres(path):\n","            genres = {}\n","            for image_path in tqdm(list(paths.list_images(path))):\n","                genre = os.path.basename(os.path.dirname(image_path))\n","\n","                if genre not in genres:\n","                    genres[genre] = []\n","                \n","                if len(genres[genre]) >= Dataset.MAX_IMAGES_PER_GENRE:\n","                    continue\n","                \n","                genres[genre].append(image_path)\n","            \n","            return genres\n","\n","        \n","        def split_genre(genre):\n","            train, tmp = train_test_split(genre, test_size=tmp_ratio, random_state=42)\n","            test, val = train_test_split(tmp, test_size=val_test_ratio, random_state=42)\n","            \n","            return train, val, test\n","        \n","        self.train, self.val, self.test = [], [], []\n","        genres = get_genres(self.path)\n","        for genre in genres.values():\n","            train, val, test = split_genre(genre)\n","            self.train.extend(train)\n","            self.val.extend(val)\n","            self.test.extend(test)\n","    \n","        np.random.shuffle(self.train)\n","        np.random.shuffle(self.val)\n","        np.random.shuffle(self.test)\n","    \n","    def __split_folder(self):\n","        splitfolders.ratio(self.path, output=self.output_path, seed=42, ratio=self.ratio, group_prefix=None)\n","    \n","    def __generate(self):\n","        def load_images(image_path):\n","            image = tf.io.read_file(image_path)\n","            image = tf.image.decode_png(image, channels=3)\n","            image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n","            image = tf.image.resize(image, self.image_size)\n","            \n","            label = tf.strings.split(image_path, os.path.sep)[-2]\n","            label = tf.cast(tf.equal(self.labels, label), tf.int32)\n","    \n","            return (image, label)\n","        \n","        def get_paths(x):\n","            if isinstance(x, str):\n","                return paths.list_images(x)\n","            else:\n","                return x\n","        \n","        train_paths = get_paths(self.train)\n","        self.train_len = len(train_paths) // self.batch_size\n","        self.train = tf.data.Dataset.from_tensor_slices(train_paths)\n","        self.train = (self.train\n","            .shuffle(len(train_paths), reshuffle_each_iteration=True)\n","            .map(load_images, num_parallel_calls=AUTOTUNE)\n","            .batch(self.batch_size)\n","            .repeat(-1)\n","            .prefetch(AUTOTUNE)\n","        )\n","        \n","        if self.cache:\n","            self.train = self.train.cache()\n","        \n","        val_paths = get_paths(self.val)\n","        self.val_len = len(val_paths) // self.batch_size\n","        self.val = tf.data.Dataset.from_tensor_slices(val_paths)\n","        self.val = (self.val\n","            .shuffle(len(val_paths))\n","            .map(load_images, num_parallel_calls=AUTOTUNE)\n","            .batch(self.batch_size)\n","            .repeat(-1)\n","            .prefetch(AUTOTUNE)\n","        )\n","        \n","        if self.cache:\n","            self.val = val.train.cache()\n","        \n","        test_paths = get_paths(self.test)\n","        self.test_len = len(test_paths) // self.batch_size\n","        self.test = tf.data.Dataset.from_tensor_slices(test_paths)\n","        self.test = (self.test\n","            .map(load_images, num_parallel_calls=AUTOTUNE)\n","            .batch(self.batch_size)\n","            .prefetch(AUTOTUNE)\n","        )\n","        \n","        if self.cache:\n","            self.test = self.test.cache()"]},{"cell_type":"markdown","metadata":{},"source":["# Helper functions\n","The following functions are helpful tools for visualising model training progress and evaluating model performance through confusion matrix analysis."]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:06:46.778643Z","iopub.status.busy":"2024-04-26T11:06:46.778225Z","iopub.status.idle":"2024-04-26T11:06:46.788304Z","shell.execute_reply":"2024-04-26T11:06:46.787373Z","shell.execute_reply.started":"2024-04-26T11:06:46.778613Z"},"trusted":true},"outputs":[],"source":["def plot_history(history, metric: str = '', ylim: List[float] | None = None):\n","    h = history.history\n","    plt.figure(figsize=(15,5))\n","    plt.plot(h[metric])\n","    plt.plot(h[f'val_{metric}'])\n","    plt.ylabel(metric)\n","    plt.xlabel('epoch')\n","    plt.legend([metric, f'val_{metric}'], loc='upper left')\n","    if ylim:\n","        axes = plt.gca()\n","        axes.set_ylim(ylim)\n","    plt.show()\n","    \n","def show_confusion_matrix(y_true, y_pred, labels: List[str] = []):\n","    matrix = confusion_matrix(y_true, y_pred)\n","    plt.figure(figsize=(len(labels), len(labels)))\n","    hm = sns.heatmap(matrix,\n","                cmap='coolwarm',\n","                linecolor='white',\n","                linewidths=1,\n","                xticklabels=labels,\n","                yticklabels=labels,\n","                annot=True,\n","                fmt='d')\n","    plt.yticks(rotation = 0)\n","    hm.set_ylim(0, len(matrix))\n","    plt.title('Confusion Matrix')\n","    plt.ylabel('True Label')\n","    plt.xlabel('Predicted Label')\n","    plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["# Bi-GRU \n","The **build_model** function is a utility designed to construct and compile a model. The **STFT** and **Mel Spectrogram** are used to learn this model."]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:06:48.668705Z","iopub.status.busy":"2024-04-26T11:06:48.667846Z","iopub.status.idle":"2024-04-26T11:06:48.676194Z","shell.execute_reply":"2024-04-26T11:06:48.675165Z","shell.execute_reply.started":"2024-04-26T11:06:48.668673Z"},"trusted":true},"outputs":[],"source":["def build_model(height: int = 224, width: int = 224, channels: int = 3, genres: int = 10):\n","    input_shape = (channels, height, width) if  K.image_data_format() == 'channels_first' else (height, width, channels)\n","    \n","    # Create the model\n","    model = Sequential()\n","    model.add(InputLayer(shape=input_shape))\n","    model.add(Reshape((width, height * channels)))\n","    model.add(Bidirectional(GRU(256)))\n","    model.add(Dense(256, activation='elu'))\n","    model.add(Dense(genres, activation='softmax'))\n","\n","    # Compile the model\n","    model.compile(\n","        optimizer=Adam(learning_rate=0.0001),\n","        loss=CategoricalCrossentropy(),\n","        metrics=[\n","            CategoricalAccuracy(name='accuracy')\n","        ])\n","    \n","    return model"]},{"cell_type":"markdown","metadata":{},"source":["# GTZAN "]},{"cell_type":"markdown","metadata":{},"source":["## Short-Time Fourier Transform"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T13:59:26.321068Z","iopub.status.busy":"2024-04-10T13:59:26.320238Z","iopub.status.idle":"2024-04-10T13:59:38.195784Z","shell.execute_reply":"2024-04-10T13:59:38.194744Z","shell.execute_reply.started":"2024-04-10T13:59:26.321038Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Copying files: 4995 files [00:11, 429.91 files/s]\n"]}],"source":["batch_size = 32\n","dataset = Dataset(path='/kaggle/input/musicgenreprediction/Data/images_augmented/stft', batch_size=batch_size)"]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:04:22.415534Z","iopub.status.busy":"2024-04-10T14:04:22.414673Z","iopub.status.idle":"2024-04-10T14:06:50.699124Z","shell.execute_reply":"2024-04-10T14:06:50.698042Z","shell.execute_reply.started":"2024-04-10T14:04:22.415502Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 45ms/step - accuracy: 0.1741 - loss: 2.1956 - val_accuracy: 0.3003 - val_loss: 1.8923\n","Epoch 2/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.3137 - loss: 1.8621 - val_accuracy: 0.3180 - val_loss: 1.7183\n","Epoch 3/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.3387 - loss: 1.7285 - val_accuracy: 0.3431 - val_loss: 1.6692\n","Epoch 4/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.3575 - loss: 1.6729 - val_accuracy: 0.3821 - val_loss: 1.6140\n","Epoch 5/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.3901 - loss: 1.6223 - val_accuracy: 0.4170 - val_loss: 1.5716\n","Epoch 6/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4139 - loss: 1.5739 - val_accuracy: 0.4351 - val_loss: 1.5412\n","Epoch 7/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4361 - loss: 1.5225 - val_accuracy: 0.4589 - val_loss: 1.4995\n","Epoch 8/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4617 - loss: 1.4601 - val_accuracy: 0.4589 - val_loss: 1.4461\n","Epoch 9/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4906 - loss: 1.4123 - val_accuracy: 0.4770 - val_loss: 1.3963\n","Epoch 10/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5114 - loss: 1.3564 - val_accuracy: 0.4909 - val_loss: 1.3428\n","Epoch 11/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5417 - loss: 1.2885 - val_accuracy: 0.5160 - val_loss: 1.2903\n","Epoch 12/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5563 - loss: 1.2263 - val_accuracy: 0.5621 - val_loss: 1.2301\n","Epoch 13/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5800 - loss: 1.1716 - val_accuracy: 0.5914 - val_loss: 1.1556\n","Epoch 14/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6123 - loss: 1.0932 - val_accuracy: 0.6109 - val_loss: 1.1314\n","Epoch 15/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6299 - loss: 1.0312 - val_accuracy: 0.6276 - val_loss: 1.0766\n","Epoch 16/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6501 - loss: 0.9756 - val_accuracy: 0.6388 - val_loss: 1.0509\n","Epoch 17/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6675 - loss: 0.9372 - val_accuracy: 0.6346 - val_loss: 1.0378\n","Epoch 18/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6771 - loss: 0.9064 - val_accuracy: 0.6527 - val_loss: 0.9907\n","Epoch 19/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6845 - loss: 0.8846 - val_accuracy: 0.6471 - val_loss: 0.9626\n","Epoch 20/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7053 - loss: 0.8431 - val_accuracy: 0.6416 - val_loss: 0.9690\n","Epoch 21/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7168 - loss: 0.8088 - val_accuracy: 0.6430 - val_loss: 0.9489\n","Epoch 22/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7228 - loss: 0.7925 - val_accuracy: 0.6583 - val_loss: 0.9166\n","Epoch 23/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7391 - loss: 0.7628 - val_accuracy: 0.6722 - val_loss: 0.9172\n","Epoch 24/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7481 - loss: 0.7326 - val_accuracy: 0.6597 - val_loss: 0.9311\n","Epoch 25/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7631 - loss: 0.7018 - val_accuracy: 0.6726 - val_loss: 0.9039\n","Epoch 26/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7762 - loss: 0.6657 - val_accuracy: 0.6932 - val_loss: 0.8314\n","Epoch 27/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7940 - loss: 0.6306 - val_accuracy: 0.6904 - val_loss: 0.8222\n","Epoch 28/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7990 - loss: 0.6094 - val_accuracy: 0.7015 - val_loss: 0.8108\n","Epoch 29/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.8024 - loss: 0.5938 - val_accuracy: 0.7043 - val_loss: 0.7961\n","Epoch 30/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.8148 - loss: 0.5660 - val_accuracy: 0.7197 - val_loss: 0.8107\n"]}],"source":["model = build_model()\n","\n","epochs = 30\n","\n","# Train the model using generator for training data and validation data\n","history = model.fit(dataset.train,\n","                    batch_size=batch_size,\n","                    epochs=epochs,\n","                    steps_per_epoch=dataset.train_len,\n","                    validation_data=dataset.val,\n","                    validation_steps=dataset.val_len,\n","                    verbose=1)"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:06:50.701168Z","iopub.status.busy":"2024-04-10T14:06:50.700860Z","iopub.status.idle":"2024-04-10T14:06:50.995495Z","shell.execute_reply":"2024-04-10T14:06:50.994533Z","shell.execute_reply.started":"2024-04-10T14:06:50.701143Z"},"trusted":true},"outputs":[{"data":{"image/png":"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size 1500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history, metric='loss')"]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:06:51.297905Z","iopub.status.busy":"2024-04-10T14:06:51.297425Z","iopub.status.idle":"2024-04-10T14:06:52.797415Z","shell.execute_reply":"2024-04-10T14:06:52.796404Z","shell.execute_reply.started":"2024-04-10T14:06:51.297868Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - accuracy: 0.7437 - loss: 0.7928\n","Accuracy: 0.7146666646003723\n","Loss: 0.8462721705436707\n"]}],"source":["evaluation = model.evaluate(dataset.test, return_dict=True)\n","print(f'Accuracy: {evaluation[\"accuracy\"]}')\n","print(f'Loss: {evaluation[\"loss\"]}')"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:06:52.799117Z","iopub.status.busy":"2024-04-10T14:06:52.798670Z","iopub.status.idle":"2024-04-10T14:06:54.155982Z","shell.execute_reply":"2024-04-10T14:06:54.154878Z","shell.execute_reply.started":"2024-04-10T14:06:52.799082Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0AAAANXCAYAAAAcsD9hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADFQUlEQVR4nOzdd3gU5drH8d9uyiYkpBJIAoQamogioDQBAUERFMECcg7dCojEikoRQRApoiBHERE9gh09igICgqL03iH0loQW0sim7fsHrytrQLOa3U12vp/rmuuanZnduZ+Zbfc+9zxrstlsNgEAAACAAZg9HQAAAAAAuAsJEAAAAADDIAECAAAAYBgkQAAAAAAMgwQIAAAAgGGQAAEAAAAwDBIgAAAAAIZBAgQAAADAMEiAAAAAABgGCRAAeIn9+/erQ4cOCg0Nlclk0ldffVWsj3/48GGZTCa9//77xfq4pVmbNm3Upk0bT4cBAHACCRAAFKMDBw7o4YcfVvXq1RUQEKCQkBC1aNFC06ZN08WLF1267z59+mj79u0aN26cPvzwQzVu3Nil+3Onvn37ymQyKSQk5IrHcf/+/TKZTDKZTJo0aZLTj3/y5EmNHj1aW7ZsKYZoAQAlma+nAwAAb7Fw4ULde++9slgs6t27t+rXr6+cnBytWrVKTz/9tHbu3Kl33nnHJfu+ePGiVq9erRdeeEGDBw92yT6qVKmiixcvys/PzyWP/1d8fX2VlZWlb775Rvfdd5/Duo8++kgBAQHKzs7+W4998uRJvfTSS6pataquv/76It9vyZIlf2t/AADPIQECgGJw6NAh9ejRQ1WqVNHy5csVExNjXzdo0CAlJiZq4cKFLtv/6dOnJUlhYWEu24fJZFJAQIDLHv+vWCwWtWjRQvPnzy+UAM2bN0933HGHvvjiC7fEkpWVpTJlysjf398t+wMAFB9K4ACgGEycOFEZGRmaPXu2Q/Lzm5o1a2ro0KH223l5eXr55ZdVo0YNWSwWVa1aVc8//7ysVqvD/apWrarOnTtr1apVuvHGGxUQEKDq1avrgw8+sG8zevRoValSRZL09NNPy2QyqWrVqpIulY79Nn+50aNHy2QyOSz74Ycf1LJlS4WFhSk4OFi1a9fW888/b19/tWuAli9frptvvllBQUEKCwvTXXfdpd27d19xf4mJierbt6/CwsIUGhqqfv36KSsr6+oH9g8eeOABff/990pNTbUvW79+vfbv368HHnig0Pbnzp3TU089pWuvvVbBwcEKCQnR7bffrq1bt9q3WbFihZo0aSJJ6tevn72U7rd2tmnTRvXr19fGjRvVqlUrlSlTxn5c/ngNUJ8+fRQQEFCo/R07dlR4eLhOnjxZ5LYCAFyDBAgAisE333yj6tWrq3nz5kXafuDAgRo5cqRuuOEGTZ06Va1bt9b48ePVo0ePQtsmJibqnnvu0a233qrJkycrPDxcffv21c6dOyVJ3bp109SpUyVJPXv21IcffqjXX3/dqfh37typzp07y2q1asyYMZo8ebLuvPNO/fLLL396v6VLl6pjx45KSUnR6NGjlZCQoF9//VUtWrTQ4cOHC21/3333KT09XePHj9d9992n999/Xy+99FKR4+zWrZtMJpO+/PJL+7J58+apTp06uuGGGwptf/DgQX311Vfq3LmzpkyZoqefflrbt29X69at7clI3bp1NWbMGEnSQw89pA8//FAffvihWrVqZX+cs2fP6vbbb9f111+v119/XbfccssV45s2bZqioqLUp08f5efnS5LefvttLVmyRG+++aZiY2OL3FYAgIvYAAD/yIULF2ySbHfddVeRtt+yZYtNkm3gwIEOy5966imbJNvy5cvty6pUqWKTZPvpp5/sy1JSUmwWi8X25JNP2pcdOnTIJsn22muvOTxmnz59bFWqVCkUw6hRo2yXfwRMnTrVJsl2+vTpq8b92z7mzJljX3b99dfbypcvbzt79qx92datW21ms9nWu3fvQvvr37+/w2PefffdtsjIyKvu8/J2BAUF2Ww2m+2ee+6xtWvXzmaz2Wz5+fm26Oho20svvXTFY5CdnW3Lz88v1A6LxWIbM2aMfdn69esLte03rVu3tkmy/ec//7niutatWzssW7x4sU2SbezYsbaDBw/agoODbV27dv3LNgIA3IMeIAD4h9LS0iRJZcuWLdL23333nSQpISHBYfmTTz4pSYWuFapXr55uvvlm++2oqCjVrl1bBw8e/Nsx/9Fv1w59/fXXKigoKNJ9Tp06pS1btqhv376KiIiwL2/QoIFuvfVWezsv98gjjzjcvvnmm3X27Fn7MSyKBx54QCtWrFBSUpKWL1+upKSkK5a/SZeuGzKbL33U5efn6+zZs/byvk2bNhV5nxaLRf369SvSth06dNDDDz+sMWPGqFu3bgoICNDbb79d5H0BAFyLBAgA/qGQkBBJUnp6epG2P3LkiMxms2rWrOmwPDo6WmFhYTpy5IjD8ri4uEKPER4ervPnz//NiAu7//771aJFCw0cOFAVKlRQjx499Omnn/5pMvRbnLVr1y60rm7dujpz5owyMzMdlv+xLeHh4ZLkVFs6deqksmXL6pNPPtFHH32kJk2aFDqWvykoKNDUqVMVHx8vi8WicuXKKSoqStu2bdOFCxeKvM+KFSs6NeDBpEmTFBERoS1btuiNN95Q+fLli3xfAIBrkQABwD8UEhKi2NhY7dixw6n7/XEQgqvx8fG54nKbzfa39/Hb9Sm/CQwM1E8//aSlS5fq3//+t7Zt26b7779ft956a6Ft/4l/0pbfWCwWdevWTXPnztWCBQuu2vsjSa+88ooSEhLUqlUr/fe//9XixYv1ww8/6JprrilyT5d06fg4Y/PmzUpJSZEkbd++3an7AgBciwQIAIpB586ddeDAAa1evfovt61SpYoKCgq0f/9+h+XJyclKTU21j+hWHMLDwx1GTPvNH3uZJMlsNqtdu3aaMmWKdu3apXHjxmn58uX68ccfr/jYv8W5d+/eQuv27NmjcuXKKSgo6J814CoeeOABbd68Wenp6VccOOI3n3/+uW655RbNnj1bPXr0UIcOHdS+fftCx6SoyWhRZGZmql+/fqpXr54eeughTZw4UevXry+2xwcA/DMkQABQDJ555hkFBQVp4MCBSk5OLrT+wIEDmjZtmqRLJVySCo3UNmXKFEnSHXfcUWxx1ahRQxcuXNC2bdvsy06dOqUFCxY4bHfu3LlC9/3tD0H/ODT3b2JiYnT99ddr7ty5DgnFjh07tGTJEns7XeGWW27Ryy+/rOnTpys6Ovqq2/n4+BTqXfrss8904sQJh2W/JWpXShad9eyzz+ro0aOaO3eupkyZoqpVq6pPnz5XPY4AAPfij1ABoBjUqFFD8+bN0/3336+6deuqd+/eql+/vnJycvTrr7/qs88+U9++fSVJ1113nfr06aN33nlHqampat26tdatW6e5c+eqa9euVx1i+e/o0aOHnn32Wd199916/PHHlZWVpZkzZ6pWrVoOgwCMGTNGP/30k+644w5VqVJFKSkpeuutt1SpUiW1bNnyqo//2muv6fbbb1ezZs00YMAAXbx4UW+++aZCQ0M1evToYmvHH5nNZr344ot/uV3nzp01ZswY9evXT82bN9f27dv10UcfqXr16g7b1ahRQ2FhYfrPf/6jsmXLKigoSDfddJOqVavmVFzLly/XW2+9pVGjRtmH5Z4zZ47atGmjESNGaOLEiU49HgCg+NEDBADF5M4779S2bdt0zz336Ouvv9agQYP03HPP6fDhw5o8ebLeeOMN+7bvvvuuXnrpJa1fv15PPPGEli9fruHDh+vjjz8u1pgiIyO1YMEClSlTRs8884zmzp2r8ePHq0uXLoVij4uL03vvvadBgwZpxowZatWqlZYvX67Q0NCrPn779u21aNEiRUZGauTIkZo0aZKaNm2qX375xenkwRWef/55Pfnkk1q8eLGGDh2qTZs2aeHChapcubLDdn5+fpo7d658fHz0yCOPqGfPnlq5cqVT+0pPT1f//v3VsGFDvfDCC/blN998s4YOHarJkydrzZo1xdIuAMDfZ7I5c+UpAAAAAJRi9AABAAAAMAwSIAAAAACGQQIEAAAAwDBIgAAAAAB4XNWqVWUymQpNgwYNkiRlZ2dr0KBBioyMVHBwsLp3737Fv574KwyCAAAAAMDjTp8+rfz8fPvtHTt26NZbb9WPP/6oNm3a6NFHH9XChQv1/vvvKzQ0VIMHD5bZbNYvv/zi1H5IgAAAAACUOE888YS+/fZb7d+/X2lpaYqKitK8efN0zz33SJL27NmjunXravXq1WratGmRH5cSOAAAAAAuYbValZaW5jBZrda/vF9OTo7++9//qn///jKZTNq4caNyc3PVvn17+zZ16tRRXFycVq9e7VRMvk63AgAAAECJsdCvtqdDuKr1L/TUSy+95LBs1KhRGj169J/e76uvvlJqaqr69u0rSUpKSpK/v7/CwsIctqtQoYKSkpKciokECAAAAIBLDB8+XAkJCQ7LLBbLX95v9uzZuv322xUbG1vsMZEAlTCdH9zl6RDc5ttZ9ezzLbus9GAk7rXqm9b2+XuHHfJgJO712dRq9nmjnu/W3X71YCTutfLL5vZ5zrf3u/x89xnp3C+xpdncMdH2eaN+fl/8cKwHI3GvwH+/6OkQSiWLxVKkhOdyR44c0dKlS/Xll1/al0VHRysnJ0epqakOvUDJycmKjo6+wqNcHQkQAAAAUIqZ/EyeDqFYzZkzR+XLl9cdd9xhX9aoUSP5+flp2bJl6t69uyRp7969Onr0qJo1a+bU45MAAQAAACgRCgoKNGfOHPXp00e+vr+nKqGhoRowYIASEhIUERGhkJAQDRkyRM2aNXNqBDiJBAgAAABACbF06VIdPXpU/fv3L7Ru6tSpMpvN6t69u6xWqzp27Ki33nrL6X2QAAEAAAClmNnXe0rgOnTooKv9TWlAQIBmzJihGTNm/KN98D9AAAAAAAyDBAgAAACAYVACBwAAAJRiJj/6NJzB0QIAAABgGCRAAAAAAAyDEjgAAACgFPOmUeDcgR4gAAAAAIZBAgQAAADAMCiBAwAAAEoxkx8lcM6gBwgAAACAYZAAAQAAADAMSuAAAACAUoxR4JxDDxAAAAAAwyABAgAAAGAYlMABAAAApRijwDmHHiAAAAAAhkECBAAAAMAwKIEDAAAASjFGgXMOPUAAAAAADIMECAAAAIBhUAIHAAAAlGImH0rgnEEPEAAAAADDIAECAAAAYBiUwAEAAAClmJkSOKfQAwQAAADAMEiAAAAAABgGJXDQPbdFqm/3Cvp66VnN+iTZ0+G4XLdOserZrbIiwv114FCGpr6dqN370z0dlktFhPqoV+cINawbKIufSUln8jTj49M6eCzH06G5nBHPd4N6Iep5V6xq1QhWuQh/vTBhj1atO+fpsNyC822M820ySXffEqzm1wUoNNhHqen5+nnzRf1vZaanQ3Mrb/38nrlyq97+eZvDsqqRIfrq0bskSZ9v2qfvdxzWnqRzyszJ1U9P3a+QAH9PhFpimMyUwDmDBMjg4qsG6LbW4Tp0LNvTobhF25ZRGjywhibN2Kdd+9J1350VNWXMter5yHqlXsj1dHguERRo1suPx2jn/my98k6S0jIKFB3lp8ysAk+H5nJGPN+SFGgxK/Fwpr5bnqKxz9bxdDhuw/k2zvm+4+YgtW1SRrMWXNCJlDxVjfXVwLtDdTHbph/WZnk6PLfw9s/vGlGhervXrfbbPpd9wc/OzVeLGrFqUSNWb/y42RPhoZQzdAlcmzZtNHjwYA0ePFihoaEqV66cRowYIZvNJkk6f/68evfurfDwcJUpU0a333679u/fb7//+++/r7CwMH311VeKj49XQECAOnbsqGPHjnmqSU4JsJj01MCKevODU8rIyvd0OG7Ro2slfbP4lL5blqzDx7L02lv7lW0tUOdboz0dmst0bReqs6n5euvjM0o8mqOUc3natveiks/meTo0lzPi+ZaktZtTNXv+Mf281rt7Af6I822c8x1f2U+b9mRr6z6rzqTma8Muq3Yk5qh6JT9Ph+YWRvj89jGbVS440D6Flwmwr/vXTXXVv0V9XVuxnAcjRGlm6ARIkubOnStfX1+tW7dO06ZN05QpU/Tuu+9Kkvr27asNGzbof//7n1avXi2bzaZOnTopN/f3XxKzsrI0btw4ffDBB/rll1+UmpqqHj16eKo5Tnn0gRit35ahrbuNUTLg62tSrZpltWHrefsym03asOW8rqkd4sHIXKvxNWV04JhVCX3K690xcZr4ZKzaNS3r6bBczqjn26g438ay/1iu6lW3qEKkjySpcgVf1arip237rR6OzD2M8Pl99Fyabn39c90xfYGGL/hZpy54b1uLg8nHXGKnksjwJXCVK1fW1KlTZTKZVLt2bW3fvl1Tp05VmzZt9L///U+//PKLmjdvLkn66KOPVLlyZX311Ve69957JUm5ubmaPn26brrpJkmXEqq6detq3bp1uvHGG6+4T6vVKqvV8U3aYrHIYrG4sKWOWjUJUY24AA0bd8ht+/S00BA/+fqYdO68YynMudRcValUxkNRuV75SF91aF5W365I05dLU1UzzqL+d0coL9+mleszPB2eyxj1fBsV59tYFv6cqUCLSROGlFOBTTKbpC+WZWj1Nu8sB7ucET6/r61YTmO6tFDVyBCdybio//y8Tf3nLtbnD3dRkMUYvXxwrZKZlrlR06ZNZTL9XlfarFkz7d+/X7t27ZKvr689sZGkyMhI1a5dW7t377Yv8/X1VZMmTey369Spo7CwMIdt/mj8+PEKDQ11mMaPH1/MLbu6cuG+erBHtCa9e0K5eTa37ReeYTaZdOh4juZ/d16HT+Ro6ep0LV2Trg7Nvb8XCIB3uvGaADVrEKj/fH5Bo2ae1awFF3R7iyC1uD7gr+9cihnl87tlzYrqUK+KalUIV/MasZreo63SrTlasuuwp0ODlzB8D5AnDB8+XAkJCQ7L3Nn7U7NKoMJDfDVtRHX7Mh8fk66JL6POt0To7kd3q8AL31cvpOUqL9+miHDHX48iwvx09rz3joZ2Pi1fx5MdfxU/kZyrpg2CPBSRexj1fBsV59tY7u9YVgt/ztTaHZd6fI6n5KlcmI863xysX7Z4by+QUT+/QwL8FRcRomPnvXtEx3+CP0J1juEToLVr1zrcXrNmjeLj41WvXj3l5eVp7dq19hK4s2fPau/evapXr559+7y8PG3YsMFe7rZ3716lpqaqbt26V92nu8vd/mjr7kwNGnXAYdnQfrE6fsqqLxad9co3T0nKy7NpX2K6GjUI189rzkq6NJRqo+vC9eXCEx6OznX2HspWbHnHL4Ux5f10+rx3D4Jg1PNtVJxvY7H4mewDFv2moOBSKZw3M+rnd1ZOro6fT1e5a6t5OhR4CcMnQEePHlVCQoIefvhhbdq0SW+++aYmT56s+Ph43XXXXXrwwQf19ttvq2zZsnruuedUsWJF3XXXXfb7+/n5aciQIXrjjTfk6+urwYMHq2nTple9/qckuGgt0JGTjtcgWa0FSs/ML7Tc23z81XG9MKyO9iSma/e+dN13V0UFBpi1cGmSp0NzmW9XXtDYobG6u32oVm/JVM04i9o3Lau3Pz3j6dBczojnW5ICA8yqGP17KVBMeYtqVi2jtIw8pZzx3t4QzvclRjjfm/da1aVVsM5eKNCJlDxVifFVx+ZB+nmTdw+BbZTP7ylLN6pVfCXFhAbpdHqWZv60VT5mk2675lICdCbjos5kXLT3CCWmnFcZfz/FhAYpNNBzPzCj9DB8AtS7d29dvHhRN954o3x8fDR06FA99NBDkqQ5c+Zo6NCh6ty5s3JyctSqVSt999138vP7/df0MmXK6Nlnn9UDDzygEydO6Oabb9bs2bM91Rz8heWrTiss1E8De1VVRLi/Eg9m6MlR23U+1Xv/I+TAsRy99l6yet0RoXs6hCnlXJ7e/+qcVm3y/hF1jHi+Jal2jWBNe7m+/fbg/pe+NHy/PEUTpid6KiyX43xfYoTz/d+FaerWLli9O4coJMis1PR8rdiQpa9WeO/ALkaSnJap4Qt+VupFq8LLBKhh5Sh90Pd2RQRdSvQ/27jP4Y9S+3+wRJL0Upfmuuu6Gh6J2dP4I1TnGD4B8vPz0+uvv66ZM2cWWhceHq4PPvjgLx+jW7du6tatmyvCc5vhk454OgS3+XLhSX258KSnw3CrTbsuatMuY5YBGfF8b9mZptbdfvV0GB7B+TaG7Byb5n2frnnfc02IN35+v9qt1Z+uf7T1dXq09XVuigbeyPCjwAEAAAAwDsP3AAEAAAClGaPAOcfQCdCKFSv+0f379u2rvn37FkssAAAAAFyPEjgAAAAAhmHoHiAAAACgtDNRAucUeoAAAAAAGAYJEAAAAADDoAQOAAAAKMVMZvo0nMHRAgAAAGAYJEAAAAAADIMSOAAAAKAUM5kZBc4Z9AABAAAAMAwSIAAAAACGQQkcAAAAUIqZ+SNUp9ADBAAAAMAwSIAAAAAAGAYlcAAAAEApxihwzqEHCAAAAIBhkAABAAAAMAxK4AAAAIBSzGSmT8MZHC0AAAAAhkECBAAAAMAwKIEDAAAASjFGgXMOPUAAAAAADIMECAAAAIBhUAIHAAAAlGJmH0rgnEEPEAAAAADDIAECAAAAYBiUwAEAAAClGKPAOYceIAAAAACGQQIEAAAAwDAogQMAAABKMZOZPg1nmGw2m83TQQAAAAD4e3Z3v9XTIVxV3S9+8HQIhZAuAgAAADAMSuAAAACAUoxR4JxDAlTC3D14v6dDcJsF0+Pt8y27rPRgJO616pvW9vn2PTd4MBL3Wjq/sX2+dbdfPRiJe638srl93qjn26ivb6O2u2OfLZ4LxM0Wz73ePr+/VyfPBeJm8R99Z5836vMcpRslcAAAAAAMgx4gAAAAoBSjBM459AABAAAAMAwSIAAAAACGQQkcAAAAUIpRAucceoAAAAAAGAYJEAAAAADDoAQOAAAAKMVMZvo0nMHRAgAAAGAYJEAAAAAADIMSOAAAAKAUM/swCpwz6AECAAAAYBgkQAAAAAAMgxI4AAAAoBTjj1CdQw8QAAAAAMMgAQIAAABgGJTAAQAAAKUYf4TqHI4WAAAAAMMgAQIAAABgGJTAAQAAAKUYo8A5hx4gAAAAAIZBAgQAAADAMCiBAwAAAEoxSuCcQw8QAAAAAMMgAQIAAABgGJTAAQAAAKUYf4TqHI4WAAAAgBLhxIkT+te//qXIyEgFBgbq2muv1YYNG+zrbTabRo4cqZiYGAUGBqp9+/bav3+/U/sgAQIAAADgcefPn1eLFi3k5+en77//Xrt27dLkyZMVHh5u32bixIl644039J///Edr165VUFCQOnbsqOzs7CLvhxI4AAAAoBTzllHgXn31VVWuXFlz5syxL6tWrZp93maz6fXXX9eLL76ou+66S5L0wQcfqEKFCvrqq6/Uo0ePIu2HHiAAAAAALmG1WpWWluYwWa3WK277v//9T40bN9a9996r8uXLq2HDhpo1a5Z9/aFDh5SUlKT27dvbl4WGhuqmm27S6tWrixwTPUBXcPjwYVWrVk2bN2/W9ddf7+lwit39nSLUo1Okw7LjSTkaMvaIhyJyr26dYtWzW2VFhPvrwKEMTX07Ubv3p3s6LJfpeVe0WjYJV+XYAFlzCrRrX4ZmzT+u46eu/ObjTRrUC1HPu2JVq0awykX464UJe7Rq3TlPh+VSRj7fkvFe378xWrs7t43UHW3LqUI5f0nSkRPZ+ujrJG3Y5n1t9gmPVLke/RR0XWOZLBblJp9S8ttTZT20X/LxUeS9vRV0fRP5RUWr4GKmsnZs0ZmP5yg/1fve64z2PPcW48eP10svveSwbNSoURo9enShbQ8ePKiZM2cqISFBzz//vNavX6/HH39c/v7+6tOnj5KSkiRJFSpUcLhfhQoV7OuKggToH8jJyZG/v7+nw/hbjp60atSbJ+y38wtsHozGfdq2jNLggTU0acY+7dqXrvvurKgpY65Vz0fWK/VCrqfDc4kGdcvq6yUp2nswUz5mkwb0qKhXh9fSgKd3Ktta4OnwXCrQYlbi4Ux9tzxFY5+t4+lw3MLI59uIr2/JmO0+fS5X7316UieSrTLJpFtbhmv00GoaNHKfjpwo+nUAJZ25TLAqj5qki7u26cTEkcpPvyD/6FgVZF760m/2tyigak2dWzBf1qMH5RMUrKh/P6LYJ0fp2IihHo6+eBnxee6MkjwK3PDhw5WQkOCwzGKxXHHbgoICNW7cWK+88ookqWHDhtqxY4f+85//qE+fPsUWU4k8WgUFBZo4caJq1qwpi8WiuLg4jRs3TpK0fft2tW3bVoGBgYqMjNRDDz2kjIwM+33btGmjJ554wuHxunbtqr59+9pvV61aVa+88or69++vsmXLKi4uTu+88459/W+1hg0bNpTJZFKbNm0kSX379lXXrl01btw4xcbGqnbt2hozZozq169fqA3XX3+9RowYUUxHpPjlF0ip6fn2KT3Tu78Y/aZH10r6ZvEpfbcsWYePZem1t/Yr21qgzrdGezo0lxk+Yb+W/HRWR45n6+DRi5o487AqRFkUX62Mp0NzubWbUzV7/jH9vNb7fgm9GiOfbyO+viVjtnvtljSt35auk8k5OpFs1ftfJCk7u0B1anjX8zy8yz3KO3taye9MlfXgPuWdTlbW9s3KTbn0S3fBxSydmPCCMtb+rNxTJ5SduFcpc99SQPV4+UZGeTj64mXE57m3sFgsCgkJcZiulgDFxMSoXr16Dsvq1q2ro0ePSpKioy+d7+TkZIdtkpOT7euKokQmQMOHD9eECRM0YsQI7dq1S/PmzVOFChWUmZmpjh07Kjw8XOvXr9dnn32mpUuXavDgwU7vY/LkyWrcuLE2b96sxx57TI8++qj27t0rSVq3bp0kaenSpTp16pS+/PJL+/2WLVumvXv36ocfftC3336r/v37a/fu3Vq/fr19m82bN2vbtm3q16/fPzwSrhMT5afZ46pp5uiqeqJPBZUL9/7OQF9fk2rVLKsNW8/bl9ls0oYt53VN7RAPRuZeQWV8JEnpGXkejgTuYJTzbdTXt1HbfTmzSWp9U5gsFrN2J2Z6OpxiFdSoqbIP7Vf048NV7a15qjzuTYXc0vFP72MODJKtoEAFWRl/ul1pwvPcOFq0aGH/Pv6bffv2qUqVKpIudVJER0dr2bJl9vVpaWlau3atmjVrVuT9lLhvvenp6Zo2bZqmT59u7+qqUaOGWrZsqVmzZik7O1sffPCBgoKCJEnTp09Xly5d9OqrrxaqB/wznTp10mOPPSZJevbZZzV16lT9+OOPql27tqKiLv1qEhkZWSibDAoK0rvvvutQ+taxY0fNmTNHTZo0kSTNmTNHrVu3VvXq1a+4b6vVWujiL4vFctVsuLjtP5ytN/+brBPJOQoP9dX9t0do3LBKGjruiLKt3lsKFxriJ18fk86dd+wqP5eaqyqVvOtXw6sxmaTHelfWjj3pOnzce8pEcGVGOt9GfX0btd2SVLVSgF4fES9/P7MuZhdozBuHdPSkd13r5hcVrdB2dyj1+wU6//UnslSvpajej8iWl6f0n5cV2t7k56dyPfspffVKFVy86IGIXcPIz/MiM3nHKHDDhg1T8+bN9corr+i+++7TunXr9M4779grtUwmk5544gmNHTtW8fHxqlatmkaMGKHY2Fh17dq1yPspcT1Au3fvltVqVbt27a647rrrrrMnP9KlTLGgoKBQtvhXGjRoYJ83mUyKjo5WSkrKX97v2muvLXTdz4MPPqj58+crOztbOTk5mjdvnvr373/Vxxg/frxCQ0MdpvHjxzsV/z+xaVeWft2coSMnc7Rld5ZennlSQYFmtbihrNtigGc83i9OVSsHauybBz0dCtyA8w1vdvyUVY+N2KvHx+zTtz+e0VMPVlFcrHt+SHQXk9kk6+FEnf10rqxHDirtx0VK+3GRQtt1Kryxj4+ihwyXZNLpOdPdHitQHJo0aaIFCxZo/vz5ql+/vl5++WW9/vrr6tWrl32bZ555RkOGDNFDDz2kJk2aKCMjQ4sWLVJAQECR91PieoACAwP/0f3NZrNsNsdejNzcwhfH+fn5Odw2mUwqKPjr62AuT75+06VLF1ksFi1YsED+/v7Kzc3VPffcc9XHcOZiMHfIuligkym5iony++uNS7ELabnKy7cpItyxnRFhfjp7PsdDUbnP4L5xuumGMCW8tEdnznHBqLcz2vk26uvbqO2WpLx8m06mXGpj4uGLql2tjLp2iNIb7x/3cGTFJy/1vHJOHHNYlnPimIKbtHDc0MdHMUOGy69ceR1/ZbhX9f5Ixn6eG1Hnzp3VuXPnq643mUwaM2aMxowZ87f3UeJ6gOLj4xUYGOhQ2/ebunXrauvWrcrM/L3G95dffpHZbFbt2rUlSVFRUTp16pR9fX5+vnbs2OFUDL/18OTn5xdpe19fX/Xp00dz5szRnDlz1KNHjz9N5Jy5GMwdAvxNii7np/MXvPsagbw8m/YlpqtRg9//TdhkkhpdF66de9M8GJnrDe4bp5ZNwvT02L1KOs2Hhbcz4vk26uvbqO2+EpNJ8vMtcV9r/pHsfbvkH1PRYZlfTEXlnrmsYuW35Cc6VifGP6+CDO8bFprn+V8zmU0ldiqJSlwPUEBAgJ599lk988wz8vf3V4sWLXT69Gnt3LlTvXr10qhRo9SnTx+NHj1ap0+f1pAhQ/Tvf//bfv1P27ZtlZCQoIULF6pGjRqaMmWKUlNTnYqhfPnyCgwM1KJFi1SpUiUFBAQoNDT0T+8zcOBA1a1bV9KlpKwk63N3OW3YnqmUc7mKCPVVjzsiVVBg088bveeCyav5+KvjemFYHe1JTNfufem6766KCgwwa+HSoo8dX9o83j9ObZtHaOTkRGVdzFd46KWXfWZWvnJyvfeaL0kKDDCrYvTvXeIx5S2qWbWM0jLylHLGOxMDI59vI76+JWO2u9+9MVq/LU2nz+YqMMCsW5qFq0GdYL0w6YCnQytW579foMqjJiv8zvuUsfZnBdSordBbblfK7DcubeDjo5ihz8tStaZOThotmX3kE3opScjPSJfyveeHTSM+z+E6JS4BkqQRI0bI19dXI0eO1MmTJxUTE6NHHnlEZcqU0eLFizV06FA1adJEZcqUUffu3TVlyhT7ffv376+tW7eqd+/e8vX11bBhw3TLLbc4tX9fX1+98cYbGjNmjEaOHKmbb75ZK1as+NP7xMfHq3nz5jp37pxuuummv9Nst4kM81VCv2iVLWPWhYx87T6YrecmH1daRtF6vEqz5atOKyzUTwN7VVVEuL8SD2boyVHbdT7Ve0uE7ry1vCRpykjH/8GZOPOQlvx01hMhuU3tGsGa9vLvw9QP7n9piPvvl6dowvRET4XlUkY+30Z8fUvGbHdYWV89/WAVRYT5Kutivg4dy9YLkw5o007v+iHPenC/Tr0+VpH391XE3Q8o73SSTv/3baX/ukKS5BseqeBGl0a+qjJ+hsN9j499Vhd3b3d3yC5jxOc5XMdk++MFM/hbbDab4uPj9dhjjxW6vscZdw/eX4xRlWwLpsfb51t2WenBSNxr1Tet7fPte27wYCTutXR+Y/t8626/ejAS91r5ZXP7vFHPt1Ff30Ztd8c+WzwXiJstnnu9fX5/rysMTOCl4j/6zj5v1Od5SXNyWE9Ph3BVsVPnezqEQkpkD1Bpc/r0aX388cdKSkoq0f/9AwAAABgdCVAxKF++vMqVK6d33nlH4eHhf30HAAAAAB5BAlQMqCIEAACAp5TU0dZKKu8aLxIAAAAA/gQJEAAAAADDoAQOAAAAKMVMZvo0nMHRAgAAAGAYJEAAAAAADIMSOAAAAKAUYxQ459ADBAAAAMAwSIAAAAAAGAYlcAAAAEApRgmcc+gBAgAAAGAYJEAAAAAADIMSOAAAAKA0449QncLRAgAAAGAYJEAAAAAADIMSOAAAAKAUM5kYBc4Z9AABAAAAMAwSIAAAAACGQQkcAAAAUIqZGAXOKRwtAAAAAIZBAgQAAADAMCiBAwAAAEoxk5lR4JxBDxAAAAAAwyABAgAAAGAYlMABAAAApRmjwDmFowUAAADAMEiAAAAAABgGJXAAAABAKcYocM6hBwgAAACAYZAAAQAAADAMk81ms3k6CAAAAAB/z/lxj3o6hKsKf2Gmp0MohB4gAAAAAIZBAgQAAADAMBgFroQZ8PJpT4fgNrNHRNnnW3ZZ6cFI3GvVN63t88OmZ3gwEveaOjjYPt++5wYPRuJeS+c3ts8btd1GfX0btd0XV8z3YCTuFdimp32+84O7PBiJe307q559vnW3Xz0YiXut/LK5p0O4OkaBcwo9QAAAAAAMgwQIAAAAgGFQAgcAAACUYiYzfRrO4GgBAAAAMAwSIAAAAACGQQkcAAAAUIqZGAXOKfQAAQAAADAMEiAAAAAAhkEJHAAAAFCamejTcAZHCwAAAIBhkAABAAAAMAxK4AAAAIBSjFHgnEMPEAAAAADDIAECAAAAYBiUwAEAAAClmZk+DWdwtAAAAAAYBgkQAAAAAMOgBA4AAAAoxUwmRoFzBj1AAAAAAAyDBAgAAACAYVACBwAAAJRmjALnFI4WAAAAAMMgAQIAAABgGJTAAQAAAKWYycwocM6gBwgAAACAYZAAAQAAADAMSuAAAACA0sxEn4YzOFoAAAAADIMECAAAAIBhlJoEqE2bNnriiSeuut5kMumrr74q8uOtWLFCJpNJqamp/zg2AAAAwGPMppI7lUBecw3QqVOnFB4e7ukwSo0Af5O6timjG2pbVDbIrKNJeZq/OEOHT+V5OjSX69YpVj27VVZEuL8OHMrQ1LcTtXt/uqfDKjbVY81q29BflcqbFRpk1uyFF7XjUL6kS38U3ekmf9Wt6qPIELOyc2zadyxf367OUVqmzcORF6+ed0WrZZNwVY4NkDWnQLv2ZWjW/OM6fsrq6dBcyqjt/o23v76vxmjtfm/Rz3pjwTI90PYmPXP/7ZKkAZPnaOO+Iw7b3dOqkV7s1cUTIbrFPbdFqm/3Cvp66VnN+iTZ0+G4VIN6Iep5V6xq1QhWuQh/vTBhj1atO+fpsFBKlZoeoL8SHR0ti8Xi6TBKjT6dg1Wvur/e/Tpdo94+p50Hc/Tkv0IVVtZrnhJX1LZllAYPrKE58w9rwBMblXgoQ1PGXKuwUD9Ph1Zs/H1NOnGmQF+sLPyF199XqhRl1g/rczX5kyzN+S5b5cPMGnhHgAcida0Gdcvq6yUpGjJyt559ZZ98fU16dXgtBVi8+zlu1HZLxnh9X4nR2r3j8Al9/tNG1apUodC6bi1v0NKJT9qnJ7rd6oEI3SO+aoBuax2uQ8eyPR2KWwRazEo8nKnXZx30dCjwAqXqE7GgoEDPPPOMIiIiFB0drdGjR9vXXV4Cd/jwYZlMJn388cdq3ry5AgICVL9+fa1cubLQY27cuFGNGzdWmTJl1Lx5c+3du9dh/cyZM1WjRg35+/urdu3a+vDDDx3Wm0wmzZw5U7fffrsCAwNVvXp1ff7558Xe9uLk5ys1qmvR50szte9orlLOF+h/P2Up5Xy+bmnkfV+EL9ejayV9s/iUvluWrMPHsvTaW/uVbS1Q51ujPR1asdlzNF/fr83R9oP5hdZl50j/+V+2tiTm6XSqTUeSC/TFT1ZVLu+jsOCS2U39dw2fsF9LfjqrI8ezdfDoRU2ceVgVoiyKr1bG06G5lFHbLRnj9X0lRmp3VrZVz8/+QiP/3UVlyxT+vArw91O50LL2KTjQOz/TAiwmPTWwot784JQysgq/13ujtZtTNXv+Mf28ll6fKzGZzCV2KolKZlRXMXfuXAUFBWnt2rWaOHGixowZox9++OGq2z/99NN68skntXnzZjVr1kxdunTR2bNnHbZ54YUXNHnyZG3YsEG+vr7q37+/fd2CBQs0dOhQPfnkk9qxY4cefvhh9evXTz/++KPDY4wYMULdu3fX1q1b1atXL/Xo0UO7d+8u3sYXIx+zST5mk3LzHEuecnOlmpW98xdDSfL1NalWzbLasPW8fZnNJm3Ycl7X1A7xYGSeFehvUoHNpotW7yqB+6OgMj6SpPQM7y/zvJxR2m3U17fR2v3K/O9087W11LRujSuu/37ddrVJeFXdX5qhNxYs1cWcHDdH6B6PPhCj9dsytHV3pqdDAUqlUpUANWjQQKNGjVJ8fLx69+6txo0ba9myZVfdfvDgwerevbvq1q2rmTNnKjQ0VLNnz3bYZty4cWrdurXq1aun5557Tr/++quysy91J0+aNEl9+/bVY489plq1aikhIUHdunXTpEmTHB7j3nvv1cCBA1WrVi29/PLLaty4sd58882rxmW1WpWWluYwWa3uq8/PzrEp8ViuOt9cRmHBZplMUtNrLapRyderS+BCQ/zk62PSufO5DsvPpeYqMtzfQ1F5lq+P1Lm5vzbvy5M196+3L61MJumx3pW1Y0+6Dh83RrmIZKx2G/X1baR2L1q/XXuOntLjd7e74vrbm1yrcf27adaTfdX/tpv17ZqtemH2l26O0vVaNQlRjbgAzf0yxdOhAKVWqfq226BBA4fbMTExSkm5+htAs2bN7PO+vr5q3LhxoZ6Zyx8zJiZGkuyPuXv3brVo0cJh+xYtWhR6jMv389vtP+sBGj9+vEJDQx2m8ePHX3V7V3j36zSZTNLkYZF6+/lyatckUGt3WlXg3Z0AuIzZLPW5LUAmSZ+t8O4L5B/vF6eqlQM19k1j1Y4btd3wPknnLmjiJ4v0yoBusvhduVLhnlaN1fyamoqvWEF33NRAY/vdreVb9ujYae8pmSoX7qsHe0Rr0rsnClVxwOA8PdIbo8C5jt8f3vRMJpMKCgqK7TFNpksn6Z8+5l8ZPny4EhISHJa5ewCH0+cLNPGDC/L3u3Rh4YWMAj3crazOnPfeWuILabnKy7cpItzxeRQR5qez572zTOJqzGapT8cAhZc16a2vLnp178/gvnG66YYwJby0R2fOeXFD/8Bo7Tbq69so7d519KTOpWeq57i37cvyC2zatP+IPlmxTutmjJCP2fE33WurVZIkHUs5p8pREW6N11VqVglUeIivpo2obl/m42PSNfFl1PmWCN396G5+yASKoFQlQM5as2aNWrVqJUnKy8vTxo0bNXjw4CLfv27duvrll1/Up08f+7JffvlF9erVK7Sf3r17O9xu2LDhVR/XYrGUmBHrcnKlnNwClQkwqX4Nf3221HvrifPybNqXmK5GDcL185pL14KZTFKj68L15cITHo7OfX5LfqLCTJqx4KKyvLgyanDfOLVsEqYnX96rpNPe82Xwrxix3UZ9fRul3TfVqa7PRz7qsGzk3K9VLbqc+nVsUSj5kaQ9x5IkSeVCg90Sozts3Z2pQaMOOCwb2i9Wx09Z9cWisyQ/QBF5dQI0Y8YMxcfHq27dupo6darOnz/vMMjBX3n66ad13333qWHDhmrfvr2++eYbffnll1q6dKnDdp999pkaN26sli1b6qOPPtK6desKXWtU0lxT3U8mk5R0Nl/lw310b/tgnTqTr1+2evG3YUkff3VcLwyroz2J6dq9L1333VVRgQFmLVya5OnQio2/n1Qu9PcvA5EhZsWWsykr26a0LJv63hagSlFmvftttsxmk8r+/+BgWdk25bu289OtHu8fp7bNIzRycqKyLuYrPPTS211mVr5ycr33W4JR2y0Z4/V9JUZod1CARTUrOg57HWjxU2hQoGpWrKBjp8/p+3Xb1bJ+vEKDArX/RLImfbpYjeKrqFYl7xkN76K1QEdOOpYsW60FSs/ML7Tc2wQGmFUx+vdR/WLKW1SzahmlZeQp5Ywxfuj5M6Yr/AiAq/PqBGjChAmaMGGCtmzZopo1a+p///ufypUrV+T7d+3aVdOmTdOkSZM0dOhQVatWTXPmzFGbNm0ctnvppZf08ccf67HHHlNMTIzmz59fqJeopAkMMKv7LUEKDzEr82KBNu7J0YIfM73qC/CVLF91WmGhfhrYq6oiwv2VeDBDT47arvOp3lMiVLm8jwbfHWi/3fXmS72N63bnatG6HF1b/dLL/umejsMiT19wUQdOeE8J5J23lpckTRlZx2H5xJmHtOSns1e6i1cwarslY7y+r8So7b6cn4+P1u4+qI+WrdFFa44qRISq3Q119WCnVp4ODcWkdo1gTXu5vv324P7VJEnfL0/RhOmJngoLpVSpSYBWrFhRaNlv//sjSTZb4V8269atq7Vr117x8dq0aVPoPtdff32hZY8++qgefdSx2/2PYmNjtWTJkj/dpqTZsMuqDbu8+9eiq/ly4Ul9ufCkp8NwmQMn8jVsesZV1//ZOm/SvucGT4fgEUZt92+8/fV9NUZs9+wn+9nnoyNCNfupfn+ytfcaPumIp0Nwiy0709S626+eDgNeotQkQAAAAACuwFQyR1srqSgYBAAAAGAYXtkDVLVq1SuWxLmCu/YDAAAA4J/zygQIAAAAMAxGgXMKRwsAAACAYZAAAQAAADAMSuAAAACA0oxR4JxCDxAAAAAAwyABAgAAAGAYlMABAAAApZiJUeCcwtECAAAAYBgkQAAAAAAMgxI4AAAAoDQz0afhDI4WAAAAAMMgAQIAAABgGJTAAQAAAKWZmT9CdQY9QAAAAAAMgwQIAAAAgGGQAAEAAAClmMlkLrGTM0aPHi2TyeQw1alTx74+OztbgwYNUmRkpIKDg9W9e3clJyc7fbxIgAAAAACUCNdcc41OnTpln1atWmVfN2zYMH3zzTf67LPPtHLlSp08eVLdunVzeh8MggAAAACgRPD19VV0dHSh5RcuXNDs2bM1b948tW3bVpI0Z84c1a1bV2vWrFHTpk2LvA96gAAAAIDSzGwqsZPValVaWprDZLVar9qU/fv3KzY2VtWrV1evXr109OhRSdLGjRuVm5ur9u3b27etU6eO4uLitHr1aucO1987ygAAAADw58aPH6/Q0FCHafz48Vfc9qabbtL777+vRYsWaebMmTp06JBuvvlmpaenKykpSf7+/goLC3O4T4UKFZSUlORUTJTAAQAAAHCJ4cOHKyEhwWGZxWK54ra33367fb5Bgwa66aabVKVKFX366acKDAwstphIgAAAAIDSzMnR1tzJYrFcNeH5K2FhYapVq5YSExN16623KicnR6mpqQ69QMnJyVe8ZujPlNyjBQAAAMCwMjIydODAAcXExKhRo0by8/PTsmXL7Ov37t2ro0ePqlmzZk49Lj1AAAAAADzuqaeeUpcuXVSlShWdPHlSo0aNko+Pj3r27KnQ0FANGDBACQkJioiIUEhIiIYMGaJmzZo5NQKcRAIEAAAAlG4mk6cjKBbHjx9Xz549dfbsWUVFRally5Zas2aNoqKiJElTp06V2WxW9+7dZbVa1bFjR7311ltO74cECAAAAIDHffzxx3+6PiAgQDNmzNCMGTP+0X64BggAAACAYdADBAAAAJRmZvo0nMHRAgAAAGAYJpvNZvN0EAAAAAD+nuwvpno6hKsK6D7M0yEUQgkcAAAAUJqV4D9CLYk4WgAAAAAMgx6gEqZll5WeDsFtVn3T2j5v1HbfO+yQByNxr8+mVrPPt++5wYORuNfS+Y3t80Z9nnd+cJcHI3Gvb2fVs89nfzLRg5G4V8D9z9jnW3f71YORuNfKL5vb54dNz/BgJO41dXCwfd6o72so3UiAAAAAgNLM7B1/hOoulMABAAAAMAwSIAAAAACGQQkcAAAAUJoxCpxTOFoAAAAADIMECAAAAIBhUAIHAAAAlGYmRoFzBj1AAAAAAAyDBAgAAACAYVACBwAAAJRmZvo0nMHRAgAAAGAYJEAAAAAADIMSOAAAAKA0YxQ4p9ADBAAAAMAwSIAAAAAAGAYlcAAAAEBpZqJPwxkcLQAAAACGQQIEAAAAwDAogQMAAABKM/4I1SkcLQAAAACGQQIEAAAAwDAogQMAAABKM/4I1Sn0AAEAAAAwDBIgAAAAAIZBCRwAAABQmvFHqE7haAEAAAAwDBIgAAAAAIZBCRwAAABQmjEKnFPoAQIAAABgGIZNgPr27auuXbt6OgwAAAAAbmTYErhp06bJZrN5OgyP6tYpVj27VVZEuL8OHMrQ1LcTtXt/uqfDcjkjtjsi1Ee9OkeoYd1AWfxMSjqTpxkfn9bBYzmeDs1let4VrZZNwlU5NkDWnALt2pehWfOP6/gpq6dDcwsjPs8vd89tkerbvYK+XnpWsz5J9nQ4xWbm8k36z4rNDsuqlgvV14/fI0my5uZp8uJ1WrT9oHLy89W8ZiW90Lm5IoMDPRGuSzWoF6Ked8WqVo1glYvw1wsT9mjVunOeDqtYVY81q21Df1Uqb1ZokFmzF17UjkP5kiSzWep0k7/qVvVRZIhZ2Tk27TuWr29X5ygt0zu/3xj9fe1PmQ3bp/G3GPZohYaGKiwszNNheEzbllEaPLCG5sw/rAFPbFTioQxNGXOtwkL9PB2aSxmx3UGBZr38eIzy82165Z0kDXv1hOb+75wyswo8HZpLNahbVl8vSdGQkbv17Cv75Otr0qvDaynA4v1ve0Z8nl8uvmqAbmsdrkPHsj0dikvUKB+mZU/3tE/vD+hsX/faorVaufeoXru/rd7rf4dOp2UqYf5SD0brOoEWsxIPZ+r1WQc9HYrL+PuadOJMgb5YWfiHG39fqVKUWT+sz9XkT7I057tslQ8za+AdAR6I1PWM/r6G4uX93wSu4vISuEWLFqlly5YKCwtTZGSkOnfurAMHDti3HT16tEwmU6Hp/fff1+HDh6+4rk2bNp5pWBH16FpJ3yw+pe+WJevwsSy99tZ+ZVsL1PnWaE+H5lJGbHfXdqE6m5qvtz4+o8SjOUo5l6dtey8q+Wyep0NzqeET9mvJT2d15Hi2Dh69qIkzD6tClEXx1cp4OjSXM+Lz/DcBFpOeGlhRb35wShlZ+Z4OxyV8zWaVK1vGPoUHXfrCm56dowWb9ump227STdVjVS+2nMbc3UpbjqVo27EUD0dd/NZuTtXs+cf081rv6vW53J6j+fp+bY62Hyz8XM7Okf7zv2xtSczT6VSbjiQX6IufrKpc3kdhwd53QbyR39dQ/AybAF0uMzNTCQkJ2rBhg5YtWyaz2ay7775bBQWXfiF/6qmndOrUKfs0adIklSlTRo0bN1blypUd1m3evFmRkZFq1aqVh1t1db6+JtWqWVYbtp63L7PZpA1bzuua2iEejMy1jNruxteU0YFjViX0Ka93x8Rp4pOxate0rKfDcrugMj6SpPQM7078jPo8/82jD8Ro/bYMbd2d6elQXObI2TS1f22+Ok39VMM/X6FTqRmSpF0nzygvv0A3VY+1b1stKkwxoUHa6oUJEAoL9DepwGbTRat3lcAZ/X2tKGwmU4mdSiLDXgN0ue7duzvcfu+99xQVFaVdu3apfv36Cg4OVnBwsCRpzZo1evHFFzV37lzVr19fkhQdfenXh+zsbHXt2lXNmjXT6NGjr7o/q9Uqq9WxO9tischisRRjq64uNMRPvj4mnTuf67D8XGquqlTy3l/Hjdru8pG+6tC8rL5dkaYvl6aqZpxF/e+OUF6+TSvXZ3g6PLcwmaTHelfWjj3pOnzcO8uifmPU57kktWoSohpxARo27pCnQ3GZaytF6eW7W6lquVCdTs/S2ys2q9/sb/XF4G46m3FRfj5mhQQ6fpZEBAfqTEaWhyKGu/j6SJ2b+2vzvjxZc/96+9LEyO9rcA16gCTt379fPXv2VPXq1RUSEqKqVatKko4ePeqw3dGjR9W1a1c99dRTuu+++wo9Tv/+/ZWenq558+bJ/CcXo40fP16hoaEO0/jx44u1TcBvzCaTDh3P0fzvzuvwiRwtXZ2upWvS1aG5cXqBHu8Xp6qVAzX2Te+9VsDoyoX76sEe0Zr07gnl5nnXr9+Xa1mrsjrUr6Za0RFqEV9J0//VQenZOVq8w3uTPvw1s1nqc1uATJI+W2GMgV6Af4IeIEldunRRlSpVNGvWLMXGxqqgoED169dXTs7vI2RlZmbqzjvvVLNmzTRmzJhCjzF27FgtXrxY69atU9myf/7Fcvjw4UpISHBY5q7eH0m6kJarvHybIsIdLxyMCPPT2fPeOyqYUdt9Pi1fx5MdfzU7kZyrpg2CPBSRew3uG6ebbghTwkt7dOacl/0segVGfZ7XrBKo8BBfTRtR3b7Mx8eka+LLqPMtEbr70d0q8MK8KCTQoiqRoTp2Lk1Na1RUbn6B0i5aHXqBzmVcVLlgfiX3Vmaz1KdjgMLLmvTWVxe9rvdHMu77mlNM9Gk4w/BH6+zZs9q7d69efPFFtWvXTnXr1tX58+cdtrHZbPrXv/6lgoICffjhhzL9oZ7xiy++0JgxY/Tpp5+qRo0af7lPi8WikJAQh8mdCVBenk37EtPVqEG4fZnJJDW6Llw796a5LQ53M2q79x7KVmx5xw+NmPJ+On3eu6+FkS4lPy2bhOnpsXuVdNoYH5JGfZ5v3Z2pQaMO6PExB+3TvsMXtWLtBT0+5qBXJj+SlGXN1bHzaSpXtozqxZaTr49Z6w6etK8/fCZVpy5k6rrK5T0YJVzlt+QnKsykmV9dVJaXVvga9X0NrmP4HqDw8HBFRkbqnXfeUUxMjI4eParnnnvOYZvRo0dr6dKlWrJkiTIyMpSRcem6idDQUB04cEC9e/fWs88+q2uuuUZJSUmSJH9/f0VERLi9PUX18VfH9cKwOtqTmK7d+9J1310VFRhg1sKlSZ4OzaWM2O5vV17Q2KGxurt9qFZvyVTNOIvaNy2rtz894+nQXOrx/nFq2zxCIycnKutivsJDL73dZWblKyfXS78N/z8jPs8vWgt05KRj6Y/VWqD0zPxCy0uzyYvWqnXtOMWEBet0epZmLt8kH5NZt19bXWUD/HX3DbU0adFahQRaFBzgrwkLV+u6yuXVwAsToMAAsypG/z7kc0x5i2pWLaO0jDylnPGOHzz8/aRyob//Vh0ZYlZsOZuysm1Ky7Kp720BqhRl1rvfZstsNqns/3f0ZWXblO9l/3RgxPc1uI7hEyCz2ayPP/5Yjz/+uOrXr6/atWvrjTfecBjGeuXKlcrIyFDz5s0d7jtnzhxJUlZWlsaOHauxY8fa17Vu3VorVqxwRxP+luWrTiss1E8De1VVRLi/Eg9m6MlR23U+1Qv7zi9jxHYfOJaj195LVq87InRPhzClnMvT+1+d06pN3jtKliTdeeulL3xTRtZxWD5x5iEt+emsJ0JyGyM+z40iOS1Tz32+QqlZ2QoPClDDuAr68KEuigi69EenT992k8wmk578ZJly8grUvGZFvdC5+V88aulUu0awpr1c3357cP9qkqTvl6dowvRET4VVrCqX99Hgu3//E9uuN1+qFlm3O1eL1uXo2uqXvsY93dOxxHH6gos6cMK7hoHnfe0vUALnFMMmQFar1T6yW/v27bVr1y6H9Tbb778Q/1Ui07dv3+IOzy2+XHhSXy48+dcbehkjtnvTrovatOuEp8Nwq/Y9N3g6BI8y4vP8j4ZPOuLpEIrdxPva/ul6i5+vnu/cXM97adJzuS0709S626+eDsOlDpzI17DpVx+t88/WeSPe11BcDJcu5uXladeuXVq9erWuueYaT4cDAAAAwI0M1wO0Y8cONW/eXLfccoseeeQRT4cDAAAA/CMl9Q9HSyrDJUDXX3+9srL4QzgAAADAiAxXAgcAAADAuAzXAwQAAAB4FUaBcwpHCwAAAIBhkAABAAAAMAxK4AAAAIDSjFHgnEIPEAAAAADDIAECAAAAYBiUwAEAAAClmZk+DWdwtAAAAAAYBgkQAAAAAMOgBA4AAAAoxWyMAucUeoAAAAAAGAYJEAAAAADDoAQOAAAAKM1M9Gk4g6MFAAAAwDBIgAAAAAAYBiVwAAAAQClmowTOKRwtAAAAAIZBAgQAAADAMCiBAwAAAEoz/gjVKfQAAQAAADAMEiAAAAAAhkECBAAAAMAwuAYIAAAAKMUYBts5HC0AAAAAhkECBAAAAMAwKIEDAAAASjOGwXYKPUAAAAAADIMECAAAAIBhmGw2m83TQQAAAAD4e9I3LPJ0CFdVtvFtng6hEHqAAAAAABgGCRAAAAAAw2AUuBKmZZeVng7BbVZ909o+T7u93+Xtbt3tVw9G4l4rv2xunzdquxf61fZgJO51R+5e+3z7nhs8GIl7LZ3f2D7P+5r3u/z1bdTzXdLYGAXOKfQAAQAAADAMEiAAAAAAhkEJHAAAAFCamejTcAZHCwAAAIBhkAABAAAAMAxK4AAAAIBSzCZGgXMGPUAAAAAADIMECAAAAIBhUAIHAAAAlGI2RoFzCkcLAAAAgGGQAAEAAAAwDErgAAAAgNKMEjincLQAAAAAGAYJEAAAAADDoAQOAAAAKMVsJv4I1Rn0AAEAAAAoUSZMmCCTyaQnnnjCviw7O1uDBg1SZGSkgoOD1b17dyUnJzv92CRAAAAAAEqM9evX6+2331aDBg0clg8bNkzffPONPvvsM61cuVInT55Ut27dnH58EiAAAACgFLOZzCV2clZGRoZ69eqlWbNmKTw83L78woULmj17tqZMmaK2bduqUaNGmjNnjn799VetWbPGqX2QAAEAAABwCavVqrS0NIfJarVedftBgwbpjjvuUPv27R2Wb9y4Ubm5uQ7L69Spo7i4OK1evdqpmEiAAAAAALjE+PHjFRoa6jCNHz/+itt+/PHH2rRp0xXXJyUlyd/fX2FhYQ7LK1SooKSkJKdiYhQ4AAAAoDQrwaPADR8+XAkJCQ7LLBZLoe2OHTumoUOH6ocfflBAQIBLYyIBAgAAAOASFovlignPH23cuFEpKSm64YYb7Mvy8/P1008/afr06Vq8eLFycnKUmprq0AuUnJys6Ohop2IiAQIAAADgUe3atdP27dsdlvXr10916tTRs88+q8qVK8vPz0/Lli1T9+7dJUl79+7V0aNH1axZM6f2RQIEAAAAlGJ/Z7S1kqZs2bKqX7++w7KgoCBFRkbalw8YMEAJCQmKiIhQSEiIhgwZombNmqlp06ZO7YsECAAAAECJN3XqVJnNZnXv3l1Wq1UdO3bUW2+95fTjkAABAAAAKHFWrFjhcDsgIEAzZszQjBkz/tHjkgABAAAApZhNJXcUuJKo9BcMAgAAAEARkQABAAAAMIwSmQAdPnxYJpNJW7Zscfm+3n///UL/KPtPrFixQiaTSampqcX2mAAAAMDV2EzmEjuVRIa/Buj+++9Xp06dPB2GR3TrFKue3SorItxfBw5laOrbidq9P93TYbkc7TZOuxvUC1HPu2JVq0awykX464UJe7Rq3TlPh+VyRmj3LfuXqUzVSoWWH575kXY+PkZmi7/qvvacYu/rJLPFX6eXrNKOIS8pJ+WsB6J1nZ53Ratlk3BVjg2QNadAu/ZlaNb84zp+yurp0NyC9zXvfH1fjRHPN1yjZKZlbhQYGKjy5ct7Ogy3a9sySoMH1tCc+Yc14ImNSjyUoSljrlVYqJ+nQ3Mp2m2sdgdazEo8nKnXZx30dChuZYR2/9LsHi2t1MI+renYV5J06vNFkqR6k59XhTtu0aYeT2h1u38rILa8Gn023YMRu0aDumX19ZIUDRm5W8++sk++via9OryWAize//HO+5r3vr6vxKjnG67h0XfIgoICTZw4UTVr1pTFYlFcXJzGjRtXaLv8/HwNGDBA1apVU2BgoGrXrq1p06Y5bLNixQrdeOONCgoKUlhYmFq0aKEjR45IkrZu3apbbrlFZcuWVUhIiBo1aqQNGzZIunIJ3DfffKMmTZooICBA5cqV0913321f9+GHH6px48YqW7asoqOj9cADDyglJaWYj4zr9ehaSd8sPqXvliXr8LEsvfbWfmVbC9T51mhPh+ZStNtY7V67OVWz5x/Tz2uN8evob4zQ7pwz52VNPmOfKtxxizITj+jcT+vkGxKsyv26a9fTE3R2xRqlbdqprQOfV0TzGxR203WeDr1YDZ+wX0t+Oqsjx7N18OhFTZx5WBWiLIqvVsbTobkc72ve+/q+EqOe7yIzmUruVAJ5NAEaPny4JkyYoBEjRmjXrl2aN2+eKlSoUGi7goICVapUSZ999pl27dqlkSNH6vnnn9enn34qScrLy1PXrl3VunVrbdu2TatXr9ZDDz0k0/8f9F69eqlSpUpav369Nm7cqOeee05+flf+xWDhwoW6++671alTJ23evFnLli3TjTfeaF+fm5url19+WVu3btVXX32lw4cPq2/fvsV/cFzI19ekWjXLasPW8/ZlNpu0Yct5XVM7xIORuRbtNla7YRwmPz9VfOBOHXv/C0lS6A31Zfb315llv9q3ydx7UFlHTii86fUeitI9gsr4SJLSM/I8HIlr8b5mLJxvFDePXQOUnp6uadOmafr06erTp48kqUaNGmrZsqUOHz7ssK2fn59eeukl++1q1app9erV+vTTT3XfffcpLS1NFy5cUOfOnVWjRg1JUt26de3bHz16VE8//bTq1KkjSYqPj79qXOPGjVOPHj0c9nfddb//Yti/f3/7fPXq1fXGG2+oSZMmysjIUHBwcJHabrVaZbU61mdbLBZZLJYi3f+fCg3xk6+PSefO5zosP5eaqyqVvPdXQ9ptrHbDOKLvai/fsLI6/sECSZIlupzyrTnKu+B4bUBOyllZKkR5IkS3MJmkx3pX1o496Tp8PNvT4bgU72vGwvlGcfNYD9Du3btltVrVrl27Im0/Y8YMNWrUSFFRUQoODtY777yjo0ePSpIiIiLUt29fdezYUV26dNG0adN06tQp+30TEhI0cOBAtW/fXhMmTNCBAweuup8tW7b8aUwbN25Uly5dFBcXp7Jly6p169aSZI+lKMaPH6/Q0FCHafz48UW+PwDgd5X7ddfpRT/Jeqr0lSMXp8f7xalq5UCNfdNY14YAkGwyl9ipJPJYVIGBgUXe9uOPP9ZTTz2lAQMGaMmSJdqyZYv69eunnJwc+zZz5szR6tWr1bx5c33yySeqVauW1qxZI0kaPXq0du7cqTvuuEPLly9XvXr1tGDBAqfjyszMVMeOHRUSEqKPPvpI69evtz/O5bH8leHDh+vChQsO0/Dhw4t8/3/qQlqu8vJtigh3LAOMCPPT2fNFb0dpQ7uN1W4YQ2BcrMq1a65j731uX2ZNOiMfi798Q8s6bOtfPlLW5NPuDtEtBveN0003hOmpl/fqzLncv75DKcf7mrFwvlHcPJYAxcfHKzAwUMuWLfvLbX/55Rc1b95cjz32mBo2bKiaNWtesRenYcOGGj58uH799VfVr19f8+bNs6+rVauWhg0bpiVLlqhbt26aM2fOFffVoEGDq8a0Z88enT17VhMmTNDNN9+sOnXq/K0BECwWi0JCQhwmd5W/SVJenk37EtPVqEG4fZnJJDW6Llw796a5LQ53o93GajeMoVKfbrKmnFXKdyvsyy5s2qGCnByVa9vMviyoVjWVqVJR59dscX+QLja4b5xaNgnT02P3Kum0Mb4M8r5mLJxvFDePXQMUEBCgZ599Vs8884z8/f3VokULnT59Wjt37ixUghYfH68PPvhAixcvVrVq1fThhx9q/fr1qlatmiTp0KFDeuedd3TnnXcqNjZWe/fu1f79+9W7d29dvHhRTz/9tO655x5Vq1ZNx48f1/r169W9e/crxjVq1Ci1a9dONWrUUI8ePZSXl6fvvvtOzz77rOLi4uTv768333xTjzzyiHbs2KGXX37Z5cfKFT7+6rheGFZHexLTtXtfuu67q6ICA8xauDTJ06G5FO02VrsDA8yqGB1gvx1T3qKaVcsoLSNPKWe894uiYdptMqlSn246/uFXsuXn2xfnpWXo2JwvVPe155R77oJy0zNU//UXdX71JqWu3erBgIvf4/3j1LZ5hEZOTlTWxXyFh176WM/MyldOrs3D0bkW72uXeO3r+w+Mer6LylZCR1srqTz6R6gjRoyQr6+vRo4cqZMnTyomJkaPPPJIoe0efvhhbd68Wffff79MJpN69uypxx57TN9//70kqUyZMtqzZ4/mzp2rs2fPKiYmRoMGDdLDDz+svLw8nT17Vr1791ZycrLKlSunbt26OQxycLk2bdros88+08svv6wJEyYoJCRErVq1kiRFRUXp/fff1/PPP6833nhDN9xwgyZNmqQ777zTdQfJRZavOq2wUD8N7FVVEeH+SjyYoSdHbdf5VO8unaDdxmp37RrBmvZyffvtwf0v/Wjy/fIUTZie6KmwXM4o7S7XrrnKVKmo4/8/+tvldj35iuoWFOiGT9+Q2eKvM///R6je5s5bL/2P3ZSRdRyWT5x5SEt+8q4/ff0j3tcu8dbX9x8Z9XzDNUw2m+0vfyLatm1bkR+wQYMG/yggo2vZZaWnQ3CbVd+0ts/Tbu93ebtbd/v1T7b0Liu/bG6fN2q7F/rV9mAk7nVH7l77fPueGzwYiXstnd/YPs/7mve7/PVt1PNd0iTv3ujpEK6qQt1Gng6hkCL1AF1//fUymUy6Wq702zqTyaT8y8oQAAAAALiWzVQyR1srqYqUAB06dMjVcQAAAACAyxUpAapSpYqr4wAAAAAAl/tb/WUffvihWrRoodjYWB05ckSS9Prrr+vrr78u1uAAAAAA/DmbTCV2KomcToBmzpyphIQEderUSampqfZrfsLCwvT6668Xd3wAAAAAUGycToDefPNNzZo1Sy+88IJ8fHzsyxs3bqzt27cXa3AAAAAAUJyc/h+gQ4cOqWHDhoWWWywWZWZmFktQAAAAAIqGUeCc4/TRqlatmrZs2VJo+aJFi1S3bt3iiAkAAAAAXMLpHqCEhAQNGjRI2dnZstlsWrdunebPn6/x48fr3XffdUWMAAAAAFAsnE6ABg4cqMDAQL344ovKysrSAw88oNjYWE2bNk09evRwRYwAAAAArsJmKpmjrZVUTidAktSrVy/16tVLWVlZysjIUPny5Ys7LgAAAAAodn8rAZKklJQU7d27V5JkMpkUFRVVbEEBAAAAgCs4PQhCenq6/v3vfys2NlatW7dW69atFRsbq3/961+6cOGCK2IEAAAAcBWe/rNTr/8j1IEDB2rt2rVauHChUlNTlZqaqm+//VYbNmzQww8/7IoYAQAAAKBYOF0C9+2332rx4sVq2bKlfVnHjh01a9Ys3XbbbcUaHAAAAAAUJ6cToMjISIWGhhZaHhoaqvDw8GIJCgAAAEDR8EeoznH6aL344otKSEhQUlKSfVlSUpKefvppjRgxoliDAwAAAIDiVKQeoIYNG8p02fji+/fvV1xcnOLi4iRJR48elcVi0enTp7kOCAAAAECJVaQEqGvXri4OAwAAAMDfUVJHWyupipQAjRo1ytVxAAAAAIDLccUUAAAAAMNwehS4/Px8TZ06VZ9++qmOHj2qnJwch/Xnzp0rtuAAAAAA/DlGgXOO00frpZde0pQpU3T//ffrwoULSkhIULdu3WQ2mzV69GgXhAgAAAAAxcPpBOijjz7SrFmz9OSTT8rX11c9e/bUu+++q5EjR2rNmjWuiBEAAAAAioXTCVBSUpKuvfZaSVJwcLAuXLggSercubMWLlxYvNEBAAAA+FM2mUrsVBI5nQBVqlRJp06dkiTVqFFDS5YskSStX79eFouleKMDAAAAgGLkdAJ09913a9myZZKkIUOGaMSIEYqPj1fv3r3Vv3//Yg8QAAAAAIqL06PATZgwwT5///33q0qVKvr1118VHx+vLl26FGtwAAAAAP4co8A55x8fraZNmyohIUE33XSTXnnlleKICQAAAABcotjSxVOnTmnEiBHF9XAAAAAAUOycLoEDAAAAUHKU1NHWSioKBgEAAAAYhslms9mK44G2bt2qG264Qfn5+cXxcAAAAACK4OCBA54O4aqq16jh6RAKKXIJXEJCwp+uP3369D8OBgAAAIBzbCZK4JxR5ARo8+bNf7lNq1at/lEwAAAAAOBKRU6AfvzxR1fGgf/X+cFdng7Bbb6dVc8+377nBg9G4l5L5ze2z3fss8VzgbjZ4rnX2+eN2u7TO9d6LhA3i7rmJvt8626/ejAS91r5ZXP7vFGf5y27rPRcIG626pvW9vlJXxZ4MBL3eqrb75eQG/V5jtKNUeAAAACAUsxmowTOGYwCBwAAAMAwSIAAAAAAGAYlcAAAAEApZqNPwykcLQAAAACG8bcSoJ9//ln/+te/1KxZM504cUKS9OGHH2rVqlXFGhwAAAAAFCenE6AvvvhCHTt2VGBgoDZv3iyr1SpJunDhgl555ZViDxAAAADA1dlkKrFTSeR0AjR27Fj95z//0axZs+Tn52df3qJFC23atKlYgwMAAACA4uR0ArR37161atWq0PLQ0FClpqYWR0wAAAAA4BJOJ0DR0dFKTEwstHzVqlWqXr16sQQFAAAAoGg8Xebm9SVwDz74oIYOHaq1a9fKZDLp5MmT+uijj/TUU0/p0UcfdUWMAAAAAFAsnP4foOeee04FBQVq166dsrKy1KpVK1ksFj311FMaMmSIK2IEAAAAgGLhdAJkMpn0wgsv6Omnn1ZiYqIyMjJUr149BQcHuyI+AAAAAH+ipJaalVROJ0C/8ff3V7169YozFgAAAABwKacToFtuuUUm09WzzOXLl/+jgAAAAADAVZxOgK6//nqH27m5udqyZYt27NihPn36FFdcAAAAAIqAEjjnOJ0ATZ069YrLR48erYyMjH8cEAAAAAC4itPDYF/Nv/71L7333nvF9XAAAAAAUOz+9iAIf7R69WoFBAQU18MBAAAAKAKbjRI4ZzidAHXr1s3hts1m06lTp7RhwwaNGDGi2AIDAAAAgOLmdAIUGhrqcNtsNqt27doaM2aMOnToUGyBAQAAAEBxcyoBys/PV79+/XTttdcqPDzcVTEBAAAAKCJGgXOOU4Mg+Pj4qEOHDkpNTXVROAAAAADgOk6PAle/fn0dPHjQFbEAAAAAgEs5nQCNHTtWTz31lL799ludOnVKaWlpDhMAAAAA97HJVGKnkqjI1wCNGTNGTz75pDp16iRJuvPOO2Uy/d4om80mk8mk/Pz84o8SAAAAAIpBkROgl156SY888oh+/PFHV8YDAAAAAC5T5ATIZrNJklq3bu2yYAAAAAA4p6SWmpVUTl0DdHnJGwAAAACUNk79D1CtWrX+Mgk6d+7cPwqoNDl8+LCqVaumzZs36/rrr/d0OAAAAAD+glMJ0EsvvaTQ0FBXxQIPuee2SPXtXkFfLz2rWZ8kezocl+l5V7RaNglX5dgAWXMKtGtfhmbNP67jp6yeDs2lOreN1B1ty6lCOX9J0pET2fro6yRt2Jbu4chcyyjtXrBomb5avFynUk5LkqpVrqi+93VVsxuukySdSErW9Pc/1vY9+5STm6ubGjbQsIH/VkSY972XN6gXop53xapWjWCVi/DXCxP2aNU67/5RzijP86vp1ilWPbtVVkS4vw4cytDUtxO1e7/3tP3UofXa9tN7OnNip7LST+vWf72pqte0t6+fNbzuFe934+1P6bpWA9wVpssZ/XleFDYbVVrOcCoB6tGjh8qXL++qWNwuJydH/v7+ng7Do+KrBui21uE6dCzb06G4XIO6ZfX1khTtPZgpH7NJA3pU1KvDa2nA0zuVbS3wdHguc/pcrt779KROJFtlkkm3tgzX6KHVNGjkPh054b3n3SjtjoqM0CP/uk+VYirIJpu+/3GVhk94Xe9Nelkx5aM07KXXVLNqZU176TlJ0rvzv9Czr0zV2xNGymx2+p8QSrRAi1mJhzP13fIUjX22jqfDcQujPM+vpG3LKA0eWEOTZuzTrn3puu/Oipoy5lr1fGS9Ui/kejq8YpGXc1ERMbVVq3E3Lf3v44XW93r+J4fbx/b+rJ++fFHV6ndwV4huYeTnOVyjyJ9+3nD9T5s2bTR48GA98cQTKleunDp27KiVK1fqxhtvlMViUUxMjJ577jnl5eXZ71NQUKCJEyeqZs2aslgsiouL07hx4674+Pn5+erfv7/q1Kmjo0ePuqtZf1uAxaSnBlbUmx+cUkaW9w9fPnzCfi356ayOHM/WwaMXNXHmYVWIsii+WhlPh+ZSa7ekaf22dJ1MztGJZKve/yJJ2dkFqlODdnuDlk0aqlmj61Q5NlpxsTF6uNe9CgwI0K59B7R9zz4lnT6tF4Y8pBpVKqtGlcp6YchD2nPgkDZu3+Xp0Ivd2s2pmj3/mH5e6929PpczyvP8Snp0raRvFp/Sd8uSdfhYll57a7+yrQXqfGu0p0MrNpVrt1KTDk+o2jW3XnF9mbJRDtOR3csVW/0mhURUdnOkrmXk5zlco8gJ0G+jwJV2c+fOlb+/v3755ReNHj1anTp1UpMmTbR161bNnDlTs2fP1tixY+3bDx8+XBMmTNCIESO0a9cuzZs3TxUqVCj0uFarVffee6+2bNmin3/+WXFxce5s1t/y6AMxWr8tQ1t3Z3o6FI8IKuMjSUrPyPuLLb2H2SS1vilMFotZuxONc96N0u78/AItXbVG2dlWXVO7pnJy82SSSX5+v3f2+/v7yWwyadvufR6MFK5glOe5JPn6mlSrZllt2HrevsxmkzZsOa9raod4MDLPyUo/o6N7Vqp24+6eDsWljPQ8d0aBTCV2KomKXAJXUOAdJULx8fGaOHGiJOmDDz5Q5cqVNX36dJlMJtWpU0cnT57Us88+q5EjRyozM1PTpk3T9OnT1adPH0lSjRo11LJlS4fHzMjI0B133CGr1aoff/zxL6+TslqtslodrzuxWCyyWCzF2NI/16pJiGrEBWjYuENu22dJYjJJj/WurB170nX4uPd3n1etFKDXR8TL38+si9kFGvPGIR096d3XPknGafeBI8f0yPAxysnJVWBAgF55dqiqVa6osJCyCgiwaOYHn+jhf90rm036z4efKL+gQGfPX/B02CgmRnmeXy40xE++PiadO+9Y6nYuNVdVKhmzV2D/pq/kbwlS1av0FpV2Rnyew3W8qwC8CBo1amSf3717t5o1a+ZQ3teiRQtlZGTo+PHj2r17t6xWq9q1a/enj9mzZ09lZmZqyZIlRRokYvz48QoNDXWYxo8f//cb5aRy4b56sEe0Jr17Qrl53tGz56zH+8WpauVAjX3zoKdDcYvjp6x6bMRePT5mn7798YyeerCK4mLdl3B7ilHaHRcbozmTx+rtV0ep621tNe7Nd3To2AmFh4bo5acG65cNW3TrAw/ptn89rIysLNWqXlVmc8n8VQ7OM8rzHH9u78YvVeP6zvL1885zz/McxcmpQRC8QVBQUJG3DQwMLNJ2nTp10n//+1+tXr1abdu2/cvthw8froSEBIdl7uz9qVklUOEhvpo2orp9mY+PSdfEl1HnWyJ096O7VeDFedHgvnG66YYwJby0R2fOeceFsn8lL9+mkyk5kqTEwxdVu1oZde0QpTfeP+7hyFzLKO328/NVpZhLpbl1alTT7sSD+uzbJXrm0X668fpr9enMSUpNS5ePj1llg4J0Z/8hiq0Q5eGoUVyM8jy/3IW0XOXl2xQR7uewPCLMT2fP53goKs85dWiDLpw+pHY9p3g6FJcx4vPcGfwRqnMMlwBdrm7duvriiy9ks9nsvUC//PKLypYtq0qVKql8+fIKDAzUsmXLNHDgwKs+zqOPPqr69evrzjvv1MKFC9W6des/3a+7y93+aOvuTA0adcBh2dB+sTp+yqovFp31+uSnZZMwPfnyXiWdNt6H5G9MJsnP13AdwIZpt63Aptw8x+Q+LKSsJGnj9l06fyFNLZvc4InQ4AZGeJ7n5dm0LzFdjRqE6+c1ZyVdanej68L15cITHo7O/fZu+ELlKl6jyBhjjH4oGeN5DtcxdAL02GOP6fXXX9eQIUM0ePBg7d27V6NGjVJCQoLMZrMCAgL07LPP6plnnpG/v79atGih06dPa+fOnRowwHF8/SFDhig/P1+dO3fW999/X+g6oZLkorVAR/5QN2u1Fig9M7/Qcm/yeP84tW0eoZGTE5V1MV/hoZee/plZ+crJ9d6sr9+9MVq/LU2nz+YqMMCsW5qFq0GdYL0w6cBf37kUM0q7//PfT9W0YQNViIpU1sVs/fDzam3euUdTRjwtSVq47CdVqRSr8NCy2rE3UdNm/1f3de6ouIoxHo68+AUGmFUxOsB+O6a8RTWrllFaRp5SznjnDx5GeZ5fycdfHdcLw+poT2K6du9L1313VVRggFkLlyZ5OrRik2vNVNrZ30eVTT9/XGdP7palTKiCw2IlSTnZGTq0fbFuuuMZT4XpckZ+nsM1DJ0AVaxYUd99952efvppXXfddYqIiNCAAQP04osv2rcZMWKEfH19NXLkSJ08eVIxMTF65JFHrvh4TzzxhAoKCtSpUyctWrRIzZs3d1dTUAR33nrpP6ymjHT8hWzizENa8tNZT4TkFmFlffX0g1UUEearrIv5OnQsWy9MOqBNOzM8HZpLGaXd5y+kaewb7+js+VQFlQlUjaqVNWXE02pyfX1J0tGTp/T2R58pLSND0VHl1PueO3V/l9s8HLVr1K4RrGkv17ffHty/miTp++UpmjA90VNhuZRRnudXsnzVaYWF+mlgr6qKCPdX4sEMPTlqu86nek9p8+kTO7VwVh/77TULX5Ukxd/QVW3uvXTt8IFt38kmm2ped4dHYnQHIz/Pi4o/QnWOyeYt41t7ic4Pet9/c1zNt7Pq2efb99zgwUjca+n8xvb5jn22eC4QN1s893r7vFHbfXrnWs8F4mZR19xkn2/d7VcPRuJeK7/8/Ycvoz7PW3ZZ6blA3GzVN7+XvE/60jtGyy2Kp7r9Xnpm1Od5SbNpX8n9IfeGWpGeDqEQiicBAAAAGIahS+AAAACA0o5R4JxDDxAAAAAAwyABAgAAAGAYlMABAAAApRijwDmHHiAAAAAAhkECBAAAAMAwSIAAAACAUswmU4mdnDFz5kw1aNBAISEhCgkJUbNmzfT999/b12dnZ2vQoEGKjIxUcHCwunfvruTkZKePFwkQAAAAAI+rVKmSJkyYoI0bN2rDhg1q27at7rrrLu3cuVOSNGzYMH3zzTf67LPPtHLlSp08eVLdunVzej8MggAAAADA47p06eJwe9y4cZo5c6bWrFmjSpUqafbs2Zo3b57atm0rSZozZ47q1q2rNWvWqGnTpkXeDwkQAAAAUIqV5FHgrFarrFarwzKLxSKLxfKn98vPz9dnn32mzMxMNWvWTBs3blRubq7at29v36ZOnTqKi4vT6tWrnUqAKIEDAAAA4BLjx49XaGiowzR+/Pirbr99+3YFBwfLYrHokUce0YIFC1SvXj0lJSXJ399fYWFhDttXqFBBSUlJTsVEDxAAAAAAlxg+fLgSEhIclv1Z70/t2rW1ZcsWXbhwQZ9//rn69OmjlStXFmtMJEAAAABAKVbg6QD+RFHK3S7n7++vmjVrSpIaNWqk9evXa9q0abr//vuVk5Oj1NRUh16g5ORkRUdHOxUTJXAAAAAASqSCggJZrVY1atRIfn5+WrZsmX3d3r17dfToUTVr1sypx6QHCAAAAIDHDR8+XLfffrvi4uKUnp6uefPmacWKFVq8eLFCQ0M1YMAAJSQkKCIiQiEhIRoyZIiaNWvm1AAIEgkQAAAAUKqV5FHgnJGSkqLevXvr1KlTCg0NVYMGDbR48WLdeuutkqSpU6fKbDare/fuslqt6tixo9566y2n90MCBAAAAMDjZs+e/afrAwICNGPGDM2YMeMf7YdrgAAAAAAYBj1AAAAAQClmk3eUwLkLPUAAAAAADIMECAAAAIBhUAIHAAAAlGLeMgqcu9ADBAAAAMAwSIAAAAAAGAYlcAAAAEApxihwzqEHCAAAAIBhkAABAAAAMAxK4AAAAIBSrMDm6QhKF3qAAAAAABgGCRAAAAAAw6AEDgAAACjFGAXOOSabzUbVIAAAAFBKrdyZ5ekQrqr1NWU8HUIhlMABAAAAMAxK4AAAAIBSzGajBM4ZJEAlzAPPHfd0CG4zb0Il+/zdg/d7MBL3WjA93j7futuvHozEvVZ+2dw+n/3D+54LxM0Cbu1rn2/fc4PnAnGzpfMb2+c79tniuUDcbPHc6+3znR/c5blA3OzbWfXs80Z9X7t32CEPRuJen02tZp9v2WWlByNxr1XftPZ0CCgmlMABAAAAMAx6gAAAAIBSjCHNnEMPEAAAAADDIAECAAAAYBiUwAEAAAClWAF/hOoUeoAAAAAAGAYJEAAAAADDoAQOAAAAKMX4I1Tn0AMEAAAAwDBIgAAAAAAYBiVwAAAAQCnGH6E6hx4gAAAAAIZBAgQAAADAMCiBAwAAAEoxG3+E6hR6gAAAAAAYBgkQAAAAAMOgBA4AAAAoxQoYBc4p9AABAAAAMAwSIAAAAACGQQkcAAAAUIrZbIwC5wx6gAAAAAAYBgkQAAAAAMOgBA4AAAAoxWyMAucUeoAAAAAAGAYJEAAAAADDoAQOAAAAKMUKxChwzqAHCAAAAIBhkAABAAAAMAxK4AAAAIBSjFHgnON1PUBt2rTRE088cdX1VatW1euvv+62eAAAAACUHPQAGdC0Z6MVFV741C9ZnaH3v051f0Bucn+nCPXoFOmw7HhSjoaMPeKhiNynQb0Q9bwrVrVqBKtchL9emLBHq9ad83RYLjV7yWq98b8V6tWmsZ6551b78q0Hj+vNb3/S9sMn5WM2qXbFCpo56H4F+Pt5MNri0/OuaLVsEq7KsQGy5hRo174MzZp/XMdPWT0dmkt1bhupO9qWU4Vy/pKkIyey9dHXSdqwLd3DkbnXPbdFqm/3Cvp66VnN+iTZ0+G4lBHf1yQpItRHvTpHqGHdQFn8TEo6k6cZH5/WwWM5ng7N5bp1ilXPbpUVEe6vA4cyNPXtRO3eb6zXOIoHCZABvTg9RebLBgupHO2n5wdGae32i54Lyk2OnrRq1Jsn7LfzC4zRZxxoMSvxcKa+W56isc/W8XQ4LrfjyEl9/stm1apY3mH51oPH9dhbn6p/h2Z67t5b5Ws2a++JFJlN3jN6ToO6ZfX1khTtPZgpH7NJA3pU1KvDa2nA0zuVbS3wdHguc/pcrt779KROJFtlkkm3tgzX6KHVNGjkPh05ke3p8NwivmqAbmsdrkPHjNFeo72vSVJQoFkvPx6jnfuz9co7SUrLKFB0lJ8ys7z3tf2bti2jNHhgDU2asU+79qXrvjsrasqYa9XzkfVKvZDr6fA8zmbzns8xd/C6EjhJysvL0+DBgxUaGqpy5cppxIgRsl2hOPLw4cMymUzasmWLfVlqaqpMJpNWrFhhX7Zjxw7dfvvtCg4OVoUKFfTvf/9bZ86csa///PPPde211yowMFCRkZFq3769MjMzXdnEfyQ9s0AXMn6fGtYJUNKZPO0+6N2/EEtSfoGUmp5vn9Izvf9DQ5LWbk7V7PnH9PNa7/91NMuao+Hv/0+jet6ukMAAh3WvfblMPds00oAOzVQzJkpVK0Sq4w115e/nPb8FDZ+wX0t+Oqsjx7N18OhFTZx5WBWiLIqvVsbTobnU2i1pWr8tXSeTc3Qi2ar3v0hSdnaB6tTw7nb/JsBi0lMDK+rND04pIyvf0+G4hZHe137TtV2ozqbm662PzyjxaI5SzuVp296LSj6b5+nQXK5H10r6ZvEpfbcsWYePZem1t/Yr21qgzrdGezo0lEJemQDNnTtXvr6+WrdunaZNm6YpU6bo3Xff/VuPlZqaqrZt26phw4basGGDFi1apOTkZN13332SpFOnTqlnz57q37+/du/erRUrVqhbt25XTLhKIh8fqWXDMlq5oeQmbMUpJspPs8dV08zRVfVEnwoqd4VSQJRur3yyWK3q11TTOtUclp9Nz9T2wycVERyk3pM/0C3Dp6n/6//VpgPHPBSpewSV8ZEkpWd4/xek35hNUuubwmSxmLU70RjvbY8+EKP12zK0dbcx2mtUja8powPHrEroU17vjonTxCdj1a5pWU+H5XK+vibVqllWG7aety+z2aQNW87rmtohHowMpZVXfvurXLmypk6dKpPJpNq1a2v79u2aOnWqHnzwQacfa/r06WrYsKFeeeUV+7L33ntPlStX1r59+5SRkaG8vDx169ZNVapUkSRde+21f/qYVqtVVqtjb4vFYpHFYnE6vn+qcb1AlQkwa+VG7//Q3H84W2/+N1knknMUHuqr+2+P0LhhlTR03BFlW0tHwoo/9/2GXdp9LFnznulbaN2JM6mSpP9897MS7m6n2pXK69t1O/TQm/P1xfMDVaV8hHuDdQOTSXqsd2Xt2JOuw8e9vyyqaqUAvT4iXv5+Zl3MLtCYNw7p6Env79lu1SRENeICNGzcIU+HAhcrH+mrDs3L6tsVafpyaapqxlnU/+4I5eXbtHJ9hqfDc5nQED/5+ph07rxjqdu51FxVqWSMXt6/YpCK/mLjlT1ATZs2lemymv5mzZpp//79ys93vixg69at+vHHHxUcHGyf6tS5VGt84MABXXfddWrXrp2uvfZa3XvvvZo1a5bOnz//p485fvx4hYaGOkzjx493OrbicEuTIG3dl63UdO8vBdu0K0u/bs7QkZM52rI7Sy/PPKmgQLNa3OD9v54ZQdL5NE384geN73unLFcoaSv4/17Ze1o2VNdmDVS3crSe7t5eVctH6KvV29wdrls83i9OVSsHauybBz0dilscP2XVYyP26vEx+/Ttj2f01INVFBfr/h+W3KlcuK8e7BGtSe+eUG4e34C8ndlk0qHjOZr/3XkdPpGjpavTtXRNujo053MMcIZX9gAVldl8Kf+7vFwtN9fx14WMjAx16dJFr776aqH7x8TEyMfHRz/88IN+/fVXLVmyRG+++aZeeOEFrV27VtWqVSt0H0kaPny4EhISHJZ5ovenXJiP6te0aOp/z7p93yVB1sUCnUzJVUyUd4z+ZXS7jibpXHqWerz6nn1ZfoFNGw8c1cc/bdTXIx6WJFWPLudwv2rR5ZR0/oJbY3WHwX3jdNMNYUp4aY/OnDPGBcJ5+TadTLk0Elbi4YuqXa2MunaI0hvvH/dwZK5Ts0qgwkN8NW1EdfsyHx+Trokvo863ROjuR3fzy7AXOZ+Wr+PJjq/nE8m5atogyEMRuceFtFzl5dsUEe74eR0R5qez571/9DsUP69MgNauXetwe82aNYqPj5ePj4/D8qioKEmXruNp2LChJDkMiCBJN9xwg7744gtVrVpVvr5XPlwmk0ktWrRQixYtNHLkSFWpUkULFiwolOT8xlPlbn/UunGQLmQUaPMe7y+NuZIAf5Oiy/lp5TrjXBvhzW6qXUWfPz/QYdmo/36rqhUi1e/WZqpULkxRocE6nOKY8B9JOaeW9arLmwzuG6eWTcL05Mt7lXTauF8OTCbJz9crCx3stu7O1KBRBxyWDe0Xq+OnrPpi0VmSHy+z91C2Yss7JgEx5f10+rx3f47l5dm0LzFdjRqE6+c1l97DTSap0XXh+nLhib+4tzGUkkvPSwyvTICOHj2qhIQEPfzww9q0aZPefPNNTZ48udB2gYGBatq0qSZMmKBq1aopJSVFL774osM2gwYN0qxZs9SzZ08988wzioiIUGJioj7++GO9++672rBhg5YtW6YOHTqofPnyWrt2rU6fPq26deu6q7l/i8kktWpURj9vylSB91e/SZL63F1OG7ZnKuVcriJCfdXjjkgVFNj080bvrZv+TWCAWRWjfx8RLaa8RTWrllFaRp5SznjHF+SgAIviY6MclgX6+yssKNC+vG/7mzRz4SrVrlhBtSuV1//Wbtfh5LOaPOBuT4TsEo/3j1Pb5hEaOTlRWRfzFR566W0+MytfObne+wnZ794Yrd+WptNncxUYYNYtzcLVoE6wXph04K/vXIpdtBboyB+uc7JaC5SemV9oubcxwvvaH3278oLGDo3V3e1DtXpLpmrGWdS+aVm9/emZv75zKffxV8f1wrA62pOYrt370nXfXRUVGGDWwqVJng4NpZBXJkC9e/fWxYsXdeONN8rHx0dDhw7VQw89dMVt33vvPQ0YMECNGjVS7dq1NXHiRHXo0MG+PjY2Vr/88oueffZZdejQQVarVVWqVNFtt90ms9mskJAQ/fTTT3r99deVlpamKlWqaPLkybr99tvd1dy/pX5Ni6LCfbViQ5anQ3GbyDBfJfSLVtkyZl3IyNfug9l6bvJxpWV4/5CxtWsEa9rL9e23B/e/VJ75/fIUTZie6Kmw3O5ft9woa26+XvtiqS5kZat2xfL6z+AeqhwV7unQis2dt17676MpIx3/F2XizENa8pP3lruGlfXV0w9WUUSYr7Iu5uvQsWy9MOmANu30/h84jMqI72sHjuXotfeS1euOCN3TIUwp5/L0/lfntGqT9w9ktHzVaYWF+mlgr6qKCPdX4sEMPTlqu86nGqPEF8XLZCst4zUbxAPPeW+t+h/Nm1DJPn/34P0ejMS9FkyPt8+37varByNxr5VfNrfPZ//wvucCcbOAW/va59v33OC5QNxs6fzG9vmOfbZ4LhA3Wzz3evt85wd3eS4QN/t2Vj37vFHf1+4dZpxR+D6b+vs1zi27rPRgJO616pvWng7hqr5cV3LLebrdWPJKkUteRAAAAADgIiRAAAAAAAzDK68BAgAAAIyCER+dQw8QAAAAAMMgAQIAAABgGJTAAQAAAKUYYzo7hx4gAAAAAIZBAgQAAADAMCiBAwAAAEoxSuCcQw8QAAAAAMMgAQIAAABgGJTAAQAAAKVYgc3k6RBKFXqAAAAAABgGCRAAAAAAw6AEDgAAACjFGAXOOfQAAQAAADAMEiAAAAAAhkEJHAAAAFCKUQLnHHqAAAAAABgGCRAAAAAAw6AEDgAAACjFCiiBcwo9QAAAAAAMgwQIAAAAgGFQAgcAAACUYjabydMhlCr0AAEAAAAwDBIgAAAAAIZBCRwAAABQivFHqM6hBwgAAACAYZAAAQAAADAMSuAAAACAUow/QnUOPUAAAAAADIMECAAAAIBhmGw2xo0AAAAASqs5P3o6gqvrd4unIyiMHiAAAAAAhkECBAAAAMAwGAWuhGnfc4OnQ3CbpfMb2+dbd/vVg5G418ovm9vn9/fq5MFI3Cv+o+/s8y27rPRgJO616pvW9nna7f0ub7dR38/vHrzfg5G414Lp8fZ5o55vo35+lzRc0OIceoAAAAAAGAYJEAAAAADDoAQOAAAAKMX4I1Tn0AMEAAAAwDBIgAAAAAB43Pjx49WkSROVLVtW5cuXV9euXbV3716HbbKzszVo0CBFRkYqODhY3bt3V3JyslP7IQECAAAASjGbreROzli5cqUGDRqkNWvW6IcfflBubq46dOigzMxM+zbDhg3TN998o88++0wrV67UyZMn1a1bN6f2wzVAAAAAADxu0aJFDrfff/99lS9fXhs3blSrVq104cIFzZ49W/PmzVPbtm0lSXPmzFHdunW1Zs0aNW3atEj7oQcIAAAAgEtYrValpaU5TFartUj3vXDhgiQpIiJCkrRx40bl5uaqffv29m3q1KmjuLg4rV69usgxkQABAAAApVhBQcmdxo8fr9DQUIdp/PjxRWhTgZ544gm1aNFC9evXlyQlJSXJ399fYWFhDttWqFBBSUlJRT5elMABAAAAcInhw4crISHBYZnFYvnL+w0aNEg7duzQqlWrij0mEiAAAAAALmGxWIqU8Fxu8ODB+vbbb/XTTz+pUqVK9uXR0dHKyclRamqqQy9QcnKyoqOji/z4lMABAAAApZinR3orrlHgbDabBg8erAULFmj58uWqVq2aw/pGjRrJz89Py5Ytsy/bu3evjh49qmbNmhV5P/QAAQAAAPC4QYMGad68efr6669VtmxZ+3U9oaGhCgwMVGhoqAYMGKCEhARFREQoJCREQ4YMUbNmzYo8ApxEAgQAAACgBJg5c6YkqU2bNg7L58yZo759+0qSpk6dKrPZrO7du8tqtapjx4566623nNoPCRAAAABQijlbalZS2YrQkICAAM2YMUMzZsz42/vhGiAAAAAAhkECBAAAAMAwKIEDAAAASrECLymBcxd6gAAAAAAYBgkQAAAAAMOgBA4AAAAoxYoyeprnmDwdQCH0AAEAAAAwDBIgAAAAAIZBCRwAAABQipXoCrgSiB4gAAAAAIZBAgQAAADAMCiBAwAAAEqxggJPR1C60AMEAAAAwDBIgAAAAAAYBiVwAAAAQCnGKHDOIQEyoJ53Ratlk3BVjg2QNadAu/ZlaNb84zp+yurp0FyuQb0Q9bwrVrVqBKtchL9emLBHq9ad83RYxc4nPFLlevRT0HWNZbJYlJt8SslvT5X10H7Jx0eR9/ZW0PVN5BcVrYKLmcrasUVnPp6j/FTvOxbdOsWqZ7fKigj314FDGZr6dqJ270/3dFguR7uN0W6jvp/f3ylCPTpFOiw7npSjIWOPeCgi9zDq+ZaM8/kN9yh1JXA5OTmeDqHUa1C3rL5ekqIhI3fr2Vf2ydfXpFeH11KApdQ9HZwWaDEr8XCmXp910NOhuIy5TLAqj5ok5efrxMSROvLMIzrz0SwVZF76Emj2tyigak2dWzBfR18colOvj5V/TCXFPjnKw5EXv7YtozR4YA3NmX9YA57YqMRDGZoy5lqFhfp5OjSXot3GabeR38+PnrSq3/CD9un5qcc8HZLLGfl8G+HzG+5T4l8xbdq00eDBg/XEE0+oXLly6tixo3bs2KHbb79dwcHBqlChgv7973/rzJkz9vukp6erV69eCgoKUkxMjKZOnao2bdroiSeesG9z6tQp3XHHHQoMDFS1atU0b948Va1aVa+//rp9mylTpujaa69VUFCQKleurMcee0wZGRkO8a1atUo333yzAgMDVblyZT3++OPKzMx09WH5R4ZP2K8lP53VkePZOnj0oibOPKwKURbFVyvj6dBcbu3mVM2ef0w/r/XeX43Cu9yjvLOnlfzOVFkP7lPe6WRlbd+s3JQkSVLBxSydmPCCMtb+rNxTJ5SduFcpc99SQPV4+UZGeTj64tWjayV9s/iUvluWrMPHsvTaW/uVbS1Q51ujPR2aS9Fu47TbyO/n+QVSanq+fUrP9P5hsIx8vo3w+f1PFNhK7lQSlfgESJLmzp0rf39//fLLL5owYYLatm2rhg0basOGDVq0aJGSk5N133332bdPSEjQL7/8ov/973/64Ycf9PPPP2vTpk0Oj9m7d2+dPHlSK1as0BdffKF33nlHKSkpDtuYzWa98cYb2rlzp+bOnavly5frmWeesa8/cOCAbrvtNnXv3l3btm3TJ598olWrVmnw4MGuPSDFLKiMjyQpPSPPw5GgOAQ1aqrsQ/sV/fhwVXtrniqPe1Mht3T80/uYA4NkKyhQQVbGn25Xmvj6mlSrZllt2HrevsxmkzZsOa9raod4MDLXot3GavcfGen9PCbKT7PHVdPM0VX1RJ8KKhduvKp+I51voDiVineL+Ph4TZw4UZI0duxYNWzYUK+88op9/XvvvafKlStr3759iomJ0dy5czVv3jy1a9dOkjRnzhzFxsbat9+zZ4+WLl2q9evXq3HjxpKkd999V/Hx8Q77vbzHqGrVqho7dqweeeQRvfXWW5Kk8ePHq1evXvbt4uPj9cYbb6h169aaOXOmAgICrtgeq9Uqq9WxXtdischisfyNo/PPmEzSY70ra8eedB0+nu32/aP4+UVFK7TdHUr9foHOf/2JLNVrKar3I7Ll5Sn952WFtjf5+alcz35KX71SBRcveiBi1wgN8ZOvj0nnzuc6LD+Xmqsqlbz311Labax2X85I7+f7D2frzf8m60RyjsJDfXX/7REaN6ySho47omxrCf3JuZgZ6XwDxa1U9AA1atTIPr9161b9+OOPCg4Otk916tSRdKlH5uDBg8rNzdWNN95ov09oaKhq165tv7137175+vrqhhtusC+rWbOmwsPDHfa7dOlStWvXThUrVlTZsmX173//W2fPnlVWVpY9lvfff98hlo4dO6qgoECHDh26anvGjx+v0NBQh2n8+PH/7CD9TY/3i1PVyoEa+yY1td7CZDbJejhRZz+dK+uRg0r7cZHSflyk0HadCm/s46PoIcMlmXR6znS3xwqg+Bjp/XzTriz9ujlDR07maMvuLL0886SCAs1qcUNZT4fmNkY63/hrNlvJnUqiUtEDFBQUZJ/PyMhQly5d9OqrrxbaLiYmRomJicWyz8OHD6tz58569NFHNW7cOEVEROj/2rvzsKjK93/g72Ebhn1TFmVTELEUdwXNPU3TXPqYmSkoaYlLZippueSSZmGpmabf3Co1zdTKyh1SJAQFTUVABFfcEBCQdeb5/eHPk6MokMDAnPfruua6zpxz5sx9z5xzZp557vPMkSNHEBwcjKKiIpiZmSE3Nxdvv/02Jk6c+Njj3dzcnrjt6dOnY/LkyVrzdNH7Mz7IDe1a2mDyx+dw+05x2Q+gWqEkKxNFV7UvBi66ehkWbTpor2hoCOcJ02HsUBdXPpmuV70/AJB9txglagE7W+0L4O1sjJGRqb+DqTBveeX9gNzP5/fyNbh2sxjOdfR3wIuHyf39JnpWtaIH6GEtW7bEmTNn4OHhAS8vL62bubk5GjRoAGNjY8TExEiPyc7ORlJSknTfx8cHJSUliIuLk+adP38emZn/1o4fP34cGo0GYWFhaN++PRo1aoRr1649FsvZs2cfi8PLywsmJiZPzEGpVMLKykrrVt0NoPFBbujYxgZT5yfi+i39/3IgJwVJZ2HiXE9rnrFzPRTffugatweNHycXXF04A5pc/RsmuKREIOl8Dlo1+7dnV6EAWvnZ4kziXR1GVrWYt7zyBng+BwBTEwWcHIyRma3/18Lw/SZ6drWuATRu3DjcuXMHQ4cORUxMDFJSUrBnzx6MHDkSarUalpaWCAwMxNSpU3Ho0CGcOXMGwcHBMDAwgEKhAAA0btwYPXr0wJgxY3Ds2DHExcVhzJgxUKlU0jpeXl4oLi7G8uXLceHCBXz33XdYtWqVViyhoaE4evQoxo8fj/j4eCQnJ2PXrl01fhCEiaPc0KOjHT756gLu5atha20EW2sjmBgrdB1alVOZGsDLwwxeHvevCXCuq4SXhxnqOjy5wVrbZP6xA6ZejWH7ymswdnSGZUAXWHftjex9v91fwdAQzu/OgLKBN65//RlgYAhDa1sYWtsChrWiU7jctuy8gn69nPFSN0e41zfDlBBvqEwNsHv/dV2HVqWYt3zyluv5PHCgA57zUqGOnRF8PE0ROsYFGo3A4eP6M5BLaeT6fgPy+Px+FkIjauytJqp133ZcXFwQGRmJ0NBQ9OzZE4WFhXB3d8dLL70EA4P77bklS5bgnXfeQd++fWFlZYVp06bh8uXLWoMSbNy4EcHBwejUqROcnJywcOFCnDlzRlrHz88PS5Yswaefforp06ejU6dOWLhwIUaMGCFto1mzZoiIiMCHH36IF154AUIINGzYEEOGDKneF6WCXnmxLgBgyazGWvMXr0zF3r8ydBFStfFpaIGl856X7o8f5QkA+OPgTSz6qnLKJ3Wt8EIy0r+cD/shQbAb+AZKbl3Hre+/Qc7RcACAka09LFr5AwDcF67QeuyV+aHIT/inukOuMgeP3IKNtTHeGuYBO1sTnL+Qi/dn/4PMLP0uGWHe8slbrudzexsjTB7pBEszA2TnqpFwoQAfhF3B3Vy1rkOrUnJ9vwF5fH5T9VEIUVMvT6o8eXl5qFevHsLCwhAcHFzqOleuXIGrq6s08IGu9Bgaq7Pnrm77N7eWpjsPOqrDSKpXxM8B0nTysFIGJtBT3j/8Lk137Behw0iq15FfO0vTzFv/PZy3XM/nA8cn6zCS6rXjq39Hj5Xr+y3Xz++a5vOfa+7/YE0ZVPMKzmpdD1B5xMXF4dy5c2jbti2ys7Mxd+5cAED//v2ldQ4ePIjc3Fw0bdoU6enpmDZtGjw8PNCpUyddhU1EREREVGE1tNKsxtLLBhAAfP7550hMTISJiQlatWqFw4cPw8HBQVpeXFyMGTNm4MKFC7C0tERAQAB++OEHGBvLYwQZIiIiIiI50ssGUIsWLXD8+PGnrtOrVy/06tWrmiIiIiIiIqKaQC8bQEREREREcqH/V/RXrpp3VRIREREREVEVYQOIiIiIiIhkgyVwRERERES1mIbDwFUIe4CIiIiIiEg22AAiIiIiIiLZYAkcEREREVEtxlHgKoY9QEREREREJBtsABERERERkWywBI6IiIiIqBZjCVzFsAeIiIiIiIhkgw0gIiIiIiKSDZbAERERERHVYhrWwFUIe4CIiIiIiEg22AAiIiIiIiLZYAkcEREREVEtJjS6jqB2YQ8QERERERHJBhtAREREREQkGyyBIyIiIiKqxQRHgasQ9gAREREREZFssAFERERERESywRI4IiIiIqJaTMNR4CqEPUBERERERCQbbAAREREREZFssASOiIiIiKgW4yhwFcMeICIiIiIikg02gIiIiIiISDZYAkdEREREVItpWAFXIewBIiIiIiIi2VAIXjVFRERERFRrfbS+SNchPNH8IBNdh/AYlsAREREREdVigjVwFcISOCIiIiIikg32ANUwPYbG6jqEarN/c2tpOnPBWB1GUr1sP1wpTXfsF6HDSKrXkV87S9NyzXvwe6k6jKR6bfvCU5qW6/st17x7BcbrLpBqtmdDc2k6cNZ13QVSzTbMdZKm5bqfU+3GBhARERERUS3GK/orhiVwREREREQkG2wAERERERGRbLAEjoiIiIioFtNwFLgKYQ8QERERERHJBhtAREREREQkGyyBIyIiIiKqxQSHgasQ9gAREREREZFssAFERERERESywRI4IiIiIqJaTGh0HUHtwh4gIiIiIiKSDTaAiIiIiIhINlgCR0RERERUi2k4ClyFsAeIiIiIiIhkgw0gIiIiIiKSDZbAERERERHVYvwj1IphDxAREREREckGG0BERERERCQbLIEjIiIiIqrFNBqWwFUEe4CIiIiIiEg22AAiIiIiIiLZYAkcEREREVEtxkHgKoY9QEREREREJBtsABERERERkWywBI6IiIiIqBYTHAWuQtgDREREREREssEGEBERERERyQZL4IiIiIiIajENh4GrEPYAERERERGRbLABVMmCgoIwYMAAXYdBRERERESlYAncE3Tp0gXNmzfHl19+qetQKt3Q/k7o2MYWri6mKCzS4GxSLtZsvoIr6YW6Dq1Smb7wMlSd+mrNU9++jrvffCzdN6znCVWX/jBy8QCEBiU3riB383KgpLiao616g/q4YOggV9jZmiAlNRdffHMeCck5ug6ryskxbztrQwzra4cWvioojRW4frsEK7bcwoXLRboOrcrJ8f0G5Jd33272eLmbAxwdTAAAF68W4Idd1xF7Sn9zBgCFAhjY1QIBfqawtjBEVo4ah+Py8UtEnq5DqxZy288rgqPAVQwbQDLUzNcSu/beROKFPBgaKBD8ej18Or0RgqeeQUGhRtfhVSr1zWvI2bT03xkatTRpWM8Tlq9PQP7RP3Fvz4+ARgNDx3p6+XfK3TrWwfi3GuLzFUk4m5SD116phyVzm2LoOzHIyta/xt4DcszbXGWAeROdcSa5AJ+svo67uRo41TFG3j39OrZLI8f3G5Bn3rfuFGPt1mu4eqMQCijwYkdbzHnXE+NmJeHi1QJdh1dlXn7BHN3amGHNjmxcvVkCDxcjvDXQGvkFAvui7+k6vColx/2cqo5elMB16dIFEyZMwKRJk2BrawtHR0esWbMGeXl5GDlyJCwtLeHl5YU//vhDeszp06fRu3dvWFhYwNHREcOHD8ft27cB3C9ji4iIwNKlS6FQKKBQKJCWlga1Wo3g4GB4enpCpVLBx8cHS5cufVJYNdb0RcnY+1cGLl4pwIVL+Vi8Mg2OdZTw9jTTdWiVTgg1RN7df2/5//5KZvbiYBTEHkJh1F5obqdDc+cGihNOAOoSHUZcNV4fUB+/7knH7wduIO3yPXz2dTIKCjXo+6KTrkOrUnLMe0B3a2RkqfH1lts4f6kIN++U4FRiPm5k6N9+/Sg5vt+APPOOjr+LmFM5uHajCFdvFGL99usoKNCgcUP9+xx7mLerMU6cK8DJpELczlIj9mwhTp8vQoP6xroOrcrJcT+nqqMXDSAA2LBhAxwcHHDs2DFMmDABY8eOxeDBgxEQEIATJ06gZ8+eGD58OO7du4esrCx069YNLVq0QGxsLP7880/cuHEDr732GgBg6dKl8Pf3x+jRo5Geno709HS4urpCo9Ggfv362LZtG86ePYtZs2ZhxowZ2Lp1q46zfzbmZoYAgJxc/fuCZGhbF9YTF8IqZB7M+o+EwsoWAKAws4RRPU+IvBxYBk6B9bufwuLN92BYv6GOI658RkYKNPKyROzJTGmeEEBsfCae87HSYWRVS655t37ODCmXCzE5sC7+b64bFr/vgu7tLXUdVpWT6/st17wfZqAAOrezgVJpgITz+l0Klny5GE0aKOFof/9z29XRCI3cjXEqWb9K2B/F/bxsQiNq7K0m0psSOD8/P3z00UcAgOnTp2PRokVwcHDA6NGjAQCzZs3CypUrcerUKezfvx8tWrTAJ598Ij1+7dq1cHV1RVJSEho1agQTExOYmZnByenfXxYMDQ3x8cf/Xj/i6emJqKgobN26VWo8lUdhYSEKC7VPVkqlEkql8j/l/iwUCiBkhCtOn8tB2hX9KhsouZaGvF83QnPnBhQWVlC98DIsR7yPu6vnwcDGAcD964TyD/wM9Y3LMGnaHpbD3sXd1fOgybyl4+grj7WVMYwMFbiTqV0icCerGO719ffXUrnmXdfeCD0DLPFb+F38vD8LXm5KjBpohxK1QERMrq7DqzJyfb/lmjcAeNQ3xZczvWFibID8Ag3mLkvFpWv63RDYfTgPKqUCiyY4QCPuN/62H8hF1Cn9+vx+lJz3c6oaetMD1KxZM2na0NAQ9vb2aNq0qTTP0dERAHDz5k2cPHkShw4dgoWFhXRr3LgxACAlJeWpz7NixQq0atUKderUgYWFBVavXo1Lly5VKNaFCxfC2tpa67Zw4cIKbaOyTBzpBg9XFeYvv6CT569KJSlnUHzuBNQ3r6LkQgJyt6yAgdIMJr6t7rf8ABTGHUHRqSiob1xB/v6foM64ARO/AB1HTvTfGSgUSL1ShM2/ZyLtahH2R+Vg/9856Bmg/71AJC9X0gsRMjMRE+cm4bdDtzFltDvcXKr/h8Tq1PY5U/g3U2HVT9mYvTIDa3Zko3cHc3Robqrr0IhqFb3pATI21q5/VSgUWvMU//8Lr0ajQW5uLvr164dPP/30se04Ozs/8Tm2bNmCKVOmICwsDP7+/rC0tMRnn32G6OjoCsU6ffp0TJ48WWueLnp/xge5oV1LG0z++Bxu39H/CwhFYT7Ud27AwLYOStISAQCa2+la62gyrsPA2k4X4VWZ7LvFKFEL2NlqHyN2NsbIyNTfUcHkmnfmXTWu3NA+nq/eKEb7ZuY6iqh6yPX9lmveAFCiFrh2836O59Py4eNphgE962DZ+is6jqzqDOllid2H8xB9+n6Pz5WbJXCwMUTfFywQGa+/vUBy3s/Lq4ZWmtVYetMDVBEtW7bEmTNn4OHhAS8vL62bufn9LwkmJiZQq9Vaj4uMjERAQABCQkLQokULeHl5ldljVBqlUgkrKyutW3U3gMYHuaFjGxtMnZ+I67dkcvIwVsLAtg5E7l1osjOgycmCgb2j1ioGdo7QZN/RUYBVo6REIOl8Dlo1s5XmKRRAKz9bnEm8q8PIqpZc805MLYBLXe0vCc51jXErU/+u8XuYXN9vueZdGoUCMDbS7681SmMFxCMjlWo090vh9Bn3c/n466+/0K9fP7i4uEChUGDnzp1ay4UQmDVrFpydnaFSqdCjRw8kJydX+Hn0+0zxBOPGjcOdO3cwdOhQxMTEICUlBXv27MHIkSOlRo+Hhweio6ORlpaG27dvQ6PRwNvbG7GxsdizZw+SkpIwc+ZMxMTE6Dibips4yg09Otrhk68u4F6+GrbWRrC1NoKJsX6dQVXdB8HIzRsG1nYwrNcAFv97G9BoUHT2/ntWELUPpq27wrhxCxjY1oFp534wtHdEUXykjiOvfFt2XkG/Xs54qZsj3OubYUqIN1SmBti9/7quQ6tScsz7t4hseLsrMbCHNZwcjNCxpTl6tLfEn0f0/0uCHN9vQJ55jxzsjOd9zOHoYAKP+qYYOdgZzRpb4FCUfv2A9ai4xEL062QBv0ZKONgYopWvEr0CzHE8QX97fx6Q434uR3l5efDz88OKFStKXb548WIsW7YMq1atQnR0NMzNzdGrVy8UFFTsGNCbEriKcHFxQWRkJEJDQ9GzZ08UFhbC3d0dL730EgwM7rcJp0yZgsDAQDRp0gT5+flITU3F22+/jbi4OAwZMgQKhQJDhw5FSEiI1vDatcErL9YFACyZ1Vhr/uKVqdj7V4YuQqoSBpa2MB8wCgqVOcS9XJRcTkHO+sUQ9+5fCF4YcxAwMoLZi/+DwtQc6ptXkLNpGTRZt3UceeU7eOQWbKyN8dYwD9jZmuD8hVy8P/sfZGbpd+mjHPNOuVyEz9bewLCX7fC/nja4eacE63fewZET+j06FiDP9xuQZ942lkaYOtoddjZGuJevRurlAnz4eQpOnNHfgT4A4PvddzGouwVG9LWClbkBsnLUCI+9h53h+p03IM/9vCJq6mhrFdW7d2/07t271GVCCHz55Zf46KOP0L9/fwDAxo0b4ejoiJ07d+L1118v9/MoxKN9qaRTPYbG6jqEarN/c2tpOnPBWB1GUr1sP1wpTXfsF6HDSKrXkV87S9NyzXvwe6k6jKR6bfvCU5qW6/st17x7BcbrLpBqtmdDc2k6cJZ8eiI2zP13hFy57uc1zTufZpa9ko4snWT2n0Y/VigU2LFjBwYMGAAAuHDhAho2bIi4uDg0b95cWq9z585o3rx5hf6bU5YlcEREREREVPUqa/Tj69fv/8jwYGTnBxwdHaVl5SXLEjgiIiIiIn1Rkwu6asroxw9jA4iIiIiIiKpEecrdysPJ6X7p5Y0bN7T+tubGjRtaJXHlwRI4IiIiIiKq0Tw9PeHk5IQDBw5I8+7evYvo6Gj4+/tXaFvsASIiIiIiqsU0ejIKXG5uLs6fPy/dT01NRXx8POzs7ODm5oZJkyZh/vz58Pb2hqenJ2bOnAkXFxdpoITyYgOIiIiIiIh0LjY2Fl27dpXuP7h2KDAwEOvXr8e0adOQl5eHMWPGICsrCx07dsSff/4JU1PTCj0PG0BERERERKRzXbp0eeqADgqFAnPnzsXcuXOf6XnYACIiIiIiqsVq8ihwNREHQSAiIiIiItlgA4iIiIiIiGSDJXBERERERLWY0JNR4KoLe4CIiIiIiEg22AAiIiIiIiLZYAkcEREREVEtxhK4imEPEBERERERyQYbQEREREREJBssgSMiIiIiqsU0/CPUCmEPEBERERERyQYbQEREREREJBssgSMiIiIiqsU4ClzFsAeIiIiIiIhkgw0gIiIiIiKSDZbAERERERHVYoKjwFUIe4CIiIiIiEg22AAiIiIiIiLZYAOIiIiIiIhkg9cAERERERHVYhoOg10h7AEiIiIiIiLZYAOIiIiIiIhkgyVwRERERES1mGAJXIWwB4iIiIiIiGSDDSAiIiIiIpINheBfxxIRERER1VpvfHBF1yE80aZF9XUdwmPYA0RERERERLLBBhAREREREckGR4GrYbI/m6DrEKqN9dTl0nTnQUd1GEn1ivg5QJoe/F6qDiOpXtu+8JSmO/aL0GEk1evIr52laeat/x7Ou8fQWB1GUr32b24tTcv1vDZwfLIOI6leO77ylqblenzXNEKj0XUItQp7gIiIiIiISDbYACIiIiIiItlgCRwRERERUS2m4R+hVgh7gIiIiIiISDbYACIiIiIiItlgCRwRERERUS0mBEvgKoI9QEREREREJBtsABERERERkWywBI6IiIiIqBYTHAWuQtgDREREREREssEGEBERERERyQZL4IiIiIiIajGWwFUMe4CIiIiIiEg22AAiIiIiIiLZYAkcEREREVEtphEaXYdQq7AHiIiIiIiIZIMNICIiIiIikg2WwBERERER1WIcBa5i2ANERERERESywQYQERERERHJBkvgiIiIiIhqMZbAVQx7gIiIiIiISDbYACIiIiIiItlgCRwRERERUS0mBEvgKoI9QEREREREJBtsABERERERkWywBI6IiIiIqBbTaDS6DqFWYQ8QERERERHJBhtAREREREQkG7JqAHXp0gWTJk0CAHh4eODLL7/UaTxERERERM9KaESNvdVEsr0GKCYmBubm5roOo1ooA3rDtEMfrXnqjBvIXTsfAGA+ZCKM3Ly1lhfGH0HBvh+rLcbq0qyJFYb2d0GjhhZwsDPBh4vO4cixO7oOq8rZWRtiWF87tPBVQWmswPXbJVix5RYuXC7SdWhVblAfFwwd5Ao7WxOkpObii2/OIyE5R9dhVTnmLY+8h/Z3Qsc2tnB1MUVhkQZnk3KxZvMVXEkv1HVoVU6O57Uhfezweh97rXlXrhdhwvyLOoqoesnt+KaqI9sGUJ06dXQdQrVS37qGvG1f/TvjkYvlik5GoiByt3RfFBdXV2jVSqU0wPm0PPx+8CbmhzbWdTjVwlxlgHkTnXEmuQCfrL6Ou7kaONUxRt49/b9gslvHOhj/VkN8viIJZ5Ny8Nor9bBkblMMfScGWdn6uY8DzFtOeTfztcSuvTeReCEPhgYKBL9eD59Ob4TgqWdQUKi/x7icz2uXrhVi9vKr0n11Df2FvbLJ8fimqqO3JXB5eXkYMWIELCws4OzsjLCwMK3lD5fACSEwZ84cuLm5QalUwsXFBRMnTpTWLSwsRGhoKFxdXaFUKuHl5YVvv/1WWh4REYG2bdtCqVTC2dkZH3zwAUpKSqolz3ITGoi8nH9v+Xnai4uLtJajqEBHgVat6LgsfLv5Mg5H63+vzwMDulsjI0uNr7fcxvlLRbh5pwSnEvNxI6OG7aNV4PUB9fHrnnT8fuAG0i7fw2dfJ6OgUIO+LzrpOrQqxbzlk/f0RcnY+1cGLl4pwIVL+Vi8Mg2OdZTw9jTTdWhVSs7nNbUGyMpRS7ecPP1v9AHyPL4rQghNjb3VRHrbAzR16lRERERg165dqFu3LmbMmIETJ06gefPmj627fft2fPHFF9iyZQuee+45XL9+HSdPnpSWjxgxAlFRUVi2bBn8/PyQmpqK27dvAwCuXr2KPn36ICgoCBs3bsS5c+cwevRomJqaYs6cOdWUbdkMbOrAcux8oKQYJddSUfDXrxA5mdJy4yatYdykDUTeXRSnnEZh1J9ACX9R0QetnzNDfGI+JgfWRZOGpriTXYI9kTk48Ld+lw0YGSnQyMsS3/10SZonBBAbn4nnfKx0GFnVYt7yyvtR5maGAICcXP1uCMj1vAYAznWM8e0CTxQVCySm5uP7XzJwO1O/328e31TZ9LIBlJubi2+//Rbff/89unfvDgDYsGED6tevX+r6ly5dgpOTE3r06AFjY2O4ubmhbdu2AICkpCRs3boV+/btQ48ePQAADRo0kB779ddfw9XVFV999RUUCgUaN26Ma9euITQ0FLNmzYKBweOdbIWFhSgs1K7PViqVUCqVlZL/o9TpF3Hvj++hybwJA3MrKAN6w2LoJOSs+wQoLkRRQizE3TvQ5GbDsE49mHZ+BYZ2jri36/+qJB6qXnXtjdAzwBK/hd/Fz/uz4OWmxKiBdihRC0TE5Oo6vCpjbWUMI0MF7mRqN+TvZBXDvb7+/jrOvOWV98MUCiBkhCtOn8tB2hX97MV/QK7nteS0Aiz//gau3iiCrbURhvS2w4L36uPdBRdRUKi/pXA8vqmy6WUJXEpKCoqKitCuXTtpnp2dHXx8fEpdf/DgwcjPz0eDBg0wevRo7NixQyphi4+Ph6GhITp37lzqYxMSEuDv7w+FQiHN69ChA3Jzc3HlypVSH7Nw4UJYW1tr3RYuXPhf0y1TSepZlCTFQ3PrGkrSziFv+yooTFUwbtwCAFB86ihK0s5BczsdxQmxuPf79zBu5AcDG4cqi4mqj4FCgdQrRdj8eybSrhZhf1QO9v+dg54BlroOjYgq0cSRbvBwVWH+8gu6DqXKyfW8duLsPRyNy8XFa0WIT7iHeSuvwVxlgA4t9TtvKpuuR3qrbaPA6WUDqKJcXV2RmJiIr7/+GiqVCiEhIejUqROKi4uhUqkq/fmmT5+O7Oxsrdv06dMr/XmeqDAf6js3YWBT+kAQ6vQ0AGADSE9k3lXjyg3tX82u3iiGg41edgBLsu8Wo0QtYGdrrDXfzsYYGZn6O0oU85ZX3g+MD3JDu5Y2mDIvEbfv6H/5slzPa4+6l6/BtZvFcK5jXPbKtZjcj2+qfHrZAGrYsCGMjY0RHR0tzcvMzERSUtITH6NSqdCvXz8sW7YM4eHhiIqKwj///IOmTZtCo9EgIiKi1Mf5+voiKioKQvzbwo2MjISlpeUTS+6USiWsrKy0blVV/lYqYxMY2DhA5N0tdbFh3XoAAM0TllPtkphaAJe62h8aznWNcUvPa8ZLSgSSzuegVTNbaZ5CAbTys8WZRP3dt5m3vPIG7jd+OraxwdT5ibh+Sx5fBuV6XnuUqYkCTg7GyMzW77zlfHxT1dDLn0osLCwQHByMqVOnwt7eHnXr1sWHH35Y6vU4ALB+/Xqo1Wq0a9cOZmZm+P7776FSqeDu7g57e3sEBgZi1KhR0iAIFy9exM2bN/Haa68hJCQEX375JSZMmIDx48cjMTERs2fPxuTJk5/4fNXNtMsAFJ8/Dc3dOzCwsIayQx9AaFCccBwGNg4w9m2F4gtnIfLzYFjHBabdBqHkcjI0t67pOvRKpzI1QD0nU+m+c10lvDzMcDe3BDdv6+cXh98isjH/XRcM7GGNqPg8eLkp0aO9Jb7ZelvXoVW5LTuv4MP3GuPc+RwkJOXgtf71oDI1wO7913UdWpVi3vLJe+IoN3QLsMOssPO4l6+GrfX9j/W8e2oUFdfM0pPKINfzWuBAB8T+k4ebd4phZ22E11+2h0YjcPi4/l739IAcj++KqKmlZjWVXjaAAOCzzz5Dbm4u+vXrB0tLS7z//vvIzs4udV0bGxssWrQIkydPhlqtRtOmTfHrr7/C3v7+n42tXLkSM2bMQEhICDIyMuDm5oYZM2YAAOrVq4fff/8dU6dOhZ+fH+zs7BAcHIyPPvqo2nIti8LCBmb9gqAwNYPIz0XJlQvI+2EJRH4uYGQEI3cfmLTqCoWxCTQ5mShJOomCqD26DrtK+DS0wNJ5z0v3x4/yBAD8cfAmFn11XldhVamUy0X4bO0NDHvZDv/raYObd0qwfucdHDmRV/aDa7mDR27BxtoYbw3zgJ2tCc5fyMX7s/9BZpZ+lwgxb/nk/cqLdQEAS2Zp/6/Z4pWp2PtXhi5CqhZyPa/Z2xhh8kgnWJoZIDtXjYQLBfgg7Aru5qp1HVqVk+PxTVVHIR6u3SKdy/5sgq5DqDbWU5dL050HHdVhJNUr4ucAaXrwe6k6jKR6bfvCU5ru2K/0klJ9dOTXfwdQYd767+G8ewyN1WEk1Wv/5tbStFzPawPHJ+swkuq14ytvaVqux3dN0zvolK5DeKI/1jfTdQiP0dseICIiIiIiOdDU0D8cralqxkUqRERERERE1YANICIiIiIikg2WwBERERER1WIcBa5i2ANERERERESywQYQERERERHJBkvgiIiIiIhqMaHhKHAVwR4gIiIiIiKSDTaAiIiIiIhINlgCR0RERERUi3EUuIphDxAREREREckGG0BERERERCQbLIEjIiIiIqrFhOAocBXBHiAiIiIiIpINNoCIiIiIiEg2WAJHRERERFSLaTgKXIWwB4iIiIiIiGSDDSAiIiIiIpINlsAREREREdViQsNR4CqCPUBERERERCQbbAAREREREZFssASOiIiIiKgWExwFrkLYA0RERERERLLBBhAREREREckGS+CIiIiIiGoxITgKXEWwB4iIiIiIiGSDDSAiIiIiIpINlsAREREREdViHAWuYtgDREREREREssEGEBERERER1RgrVqyAh4cHTE1N0a5dOxw7dqxSt88GEBERERFRLSY0mhp7q6gff/wRkydPxuzZs3HixAn4+fmhV69euHnzZqW9XmwAERERERFRjbBkyRKMHj0aI0eORJMmTbBq1SqYmZlh7dq1lfYcCiEEr5oiIiIiIqqlOvaL0HUIT3Tgp/YoLCzUmqdUKqFUKh9bt6ioCGZmZvjpp58wYMAAaX5gYCCysrKwa9euyglKkKwVFBSI2bNni4KCAl2HUq2YN/OWA+bNvOWAeTNvqtlmz54tAGjdZs+eXeq6V69eFQDE0aNHteZPnTpVtG3bttJiYg+QzN29exfW1tbIzs6GlZWVrsOpNsybecsB82becsC8mTfVbIWFheXuAbp27Rrq1auHo0ePwt/fX5o/bdo0REREIDo6ulJi4v8AERERERFRlXhSY6c0Dg4OMDQ0xI0bN7Tm37hxA05OTpUWEwdBICIiIiIinTMxMUGrVq1w4MABaZ5Go8GBAwe0eoSeFXuAiIiIiIioRpg8eTICAwPRunVrtG3bFl9++SXy8vIwcuTISnsONoBkTqlUYvbs2eXumtQXzJt5ywHzZt5ywLyZN+mXIUOG4NatW5g1axauX7+O5s2b488//4Sjo2OlPQcHQSAiIiIiItngNUBERERERCQbbAAREREREZFssAFERERERESywQaQHurSpQsmTZoEAPDw8MCXX36p03j0VVBQEAYMGKDrMPTew/tzabiP/zdpaWlQKBSIj4+XTRzr16+HjY1NpW0vPDwcCoUCWVlZlbbNR+nTeaasY1mhUGDnzp3l3l51vP5EpJ84Cpyei4mJgbm5ua7DqNG6dOmC5s2b80s0kZ4bMmQI+vTpo+swKmTp0qWQy1hF6enpsLW11XUYeiMtLQ2enp6Ii4tD8+bNdR0OUY3CBpCeq1Onjq5DoGdUVFQEExMTXYdBNQz3i4pTqVRQqVS6DqNCrK2tdR1CtanMf3mn8uO5hOSIJXC1XF5eHkaMGAELCws4OzsjLCxMa/nD5UFCCMyZMwdubm5QKpVwcXHBxIkTpXULCwsRGhoKV1dXKJVKeHl54dtvv5WWR0REoG3btlAqlXB2dsYHH3yAkpKSasnzgS5dumDChAmYNGkSbG1t4ejoiDVr1kh/kGVpaQkvLy/88ccf0mNOnz6N3r17w8LCAo6Ojhg+fDhu374N4H55SUREBJYuXQqFQgGFQoG0tDSo1WoEBwfD09MTKpUKPj4+WLp0abXlOH78eEyaNAkODg7o1avXU3MAgJycHAwbNgzm5uZwdnbGF1988Vi5SXp6Ol5++WWoVCp4enpi06ZNj5WPLVmyBE2bNoW5uTlcXV0REhKC3NxcrfiOHDmCF154ASqVCq6urpg4cSLy8vKq9DUpKSnB+PHjYW1tDQcHB8ycObPUX8VLK6fKysqCQqFAeHi4NK+s1/Onn35C06ZNoVKpYG9vjx49elR5jmUpbb8o65jUaDRYvHgxvLy8oFQq4ebmhgULFpS6fbVajVGjRqFx48a4dOlSpcdf3ljKc+yFh4ejbdu2MDc3h42NDTp06ICLFy8CAE6ePImuXbvC0tISVlZWaNWqFWJjYwGUXgL366+/ok2bNjA1NYWDgwMGDhwoLfvuu+/QunVrWFpawsnJCW+88QZu3rxZya/M0z1cAvfnn3+iY8eOsLGxgb29Pfr27YuUlBRp3Tlz5kjnsYdv69evl46NR29dunSp1nw0Gg2mTZsGOzs7ODk5Yc6cOdKyh0vgHsS7ZcsWBAQEwNTUFM8//zwiIiIe2+bx48fRunVrmJmZISAgAImJiVrLV65ciYYNG8LExAQ+Pj747rvvtJYrFAqsXLkSvXv3hkqlQoMGDfDTTz9VSq5P2uf/+ecfdOvWTTrHjBkzRutcW1q54IABAxAUFCTd9/DwwCeffIJRo0bB0tISbm5uWL16tbTc09MTANCiRQut9/rBPrVgwQK4uLjAx8cHc+fOxfPPP/9YDs2bN8fMmTOf+bUoy4Pz25PO85mZmRgxYgRsbW1hZmaG3r17Izk5WXr8g2N7586d8Pb2hqmpKXr16oXLly9XeexUO7EBVMtNnToVERER2LVrF/bu3Yvw8HCcOHGi1HW3b9+OL774At988w2Sk5Oxc+dONG3aVFo+YsQIbN68GcuWLUNCQgK++eYbWFhYAACuXr2KPn36oE2bNjh58iRWrlyJb7/9FvPnz6+WPB+2YcMGODg44NixY5gwYQLGjh2LwYMHIyAgACdOnEDPnj0xfPhw3Lt3D1lZWejWrRtatGiB2NhY/Pnnn7hx4wZee+01APfLS/z9/TF69Gikp6cjPT0drq6u0Gg0qF+/PrZt24azZ89i1qxZmDFjBrZu3VptOZqYmCAyMhKLFi16ag7A/X9NjoyMxC+//IJ9+/bh8OHDj+0HI0aMwLVr1xAeHo7t27dj9erVj32ZMzAwwLJly3DmzBls2LABBw8exLRp06TlKSkpeOmll/Dqq6/i1KlT+PHHH3HkyBGMHz++yl8PIyMjHDt2DEuXLsWSJUvwf//3f/9pW2XtE+np6Rg6dChGjRqFhIQEhIeHY9CgQTWiDOnh/WLOnDllHpPTp0/HokWLMHPmTJw9exabNm0q9Y/kCgsLMXjwYMTHx+Pw4cNwc3Or9NjLG0tZx15JSQkGDBiAzp0749SpU4iKisKYMWOgUCgAAMOGDUP9+vURExOD48eP44MPPoCxsXGpMe3evRsDBw5Enz59EBcXhwMHDqBt27bS8uLiYsybNw8nT57Ezp07kZaWpvUFtLrl5eVh8uTJiI2NxYEDB2BgYICBAwdCo9EAAKZMmSKdx9LT0/H555/DzMwMrVu3hqurq9ayuLg42Nvbo1OnTtWaw4YNG2Bubo7o6GgsXrwYc+fOxb59+564/tSpU/H+++8jLi4O/v7+6NevHzIyMrTW+fDDDxEWFobY2FgYGRlh1KhR0rIdO3bg3Xffxfvvv4/Tp0/j7bffxsiRI3Ho0CGtbcycOROvvvoqTp48iWHDhuH1119HQkLCM+X6pH0+Ly8PvXr1gq2tLWJiYrBt2zbs37//P51Hw8LC0Lp1a8TFxSEkJARjx46VGoDHjh0DAOzfvx/p6en4+eefpccdOHAAiYmJ2LdvH3777TfpfBcTEyOtExcXh1OnTmHkyJHP9DqU19PO80FBQYiNjcUvv/yCqKgoCCHQp08fFBcXS4+/d+8eFixYgI0bNyIyMhJZWVl4/fXXqyV2qoUE1Vo5OTnCxMREbN26VZqXkZEhVCqVePfdd4UQQri7u4svvvhCCCFEWFiYaNSokSgqKnpsW4mJiQKA2LdvX6nPNWPGDOHj4yM0Go00b8WKFcLCwkKo1erKS6oMnTt3Fh07dpTul5SUCHNzczF8+HBpXnp6ugAgoqKixLx580TPnj21tnH58mUBQCQmJkrbfPB6Pc24cePEq6++Kt0PDAwU/fv3f7aEStG5c2fRokUL6X5ZOdy9e1cYGxuLbdu2ScuzsrKEmZmZlFdCQoIAIGJiYqR1kpOTBQBp/yjNtm3bhL29vXQ/ODhYjBkzRmudw4cPCwMDA5Gfn/9f0i1T586dha+vr9a+FxoaKnx9fYUQ2vt4amqqACDi4uKkdTMzMwUAcejQISFE2a/n8ePHBQCRlpZWJfn8V4/uF2Udk3fv3hVKpVKsWbOm1O09eK0OHz4sunfvLjp27CiysrKqJPanxVLae/aoh4+9jIwMAUCEh4eXuq6lpaVYv359qcvWrVsnrK2tpfv+/v5i2LBh5c4jJiZGABA5OTlCCCEOHTokAIjMzMxyb6OinnaeuXXrlgAg/vnnn8eWRUVFCVNTU/Hjjz8+tiw/P1+0a9dO9O3bV6fnbyGEaNOmjQgNDRVCCAFA7NixQwjx736xaNEiad3i4mJRv3598emnnwoh/n399+/fL62ze/duAUA6HwUEBIjRo0drPefgwYNFnz59pPsAxDvvvKO1Trt27cTYsWP/c65P2+dXr14tbG1tRW5urlbcBgYG4vr160KI0j+X+vfvLwIDA6X77u7u4s0335TuazQaUbduXbFy5UohxJOPrcDAQOHo6CgKCwu15vfu3Vsr5wkTJoguXbpUKO//6mnn+aSkJAFAREZGSstu374tVCqV9P1n3bp1AoD4+++/pXUefO5FR0dXSw5Uu7AHqBZLSUlBUVER2rVrJ82zs7ODj49PqesPHjwY+fn5aNCgAUaPHo0dO3ZI5TLx8fEwNDRE586dS31sQkIC/P39pV9ZAaBDhw7Izc3FlStXKjGrsjVr1kyaNjQ0hL29vVZP1oNflW/evImTJ0/i0KFDsLCwkG6NGzcGAK3SkdKsWLECrVq1Qp06dWBhYYHVq1dXSWlQaVq1aiVNl5XDhQsXUFxcrPXLtbW1tdZ+kJiYCCMjI7Rs2VKa5+Xl9dgFx/v370f37t1Rr149WFpaYvjw4cjIyMC9e/ekWNavX68VS69evaDRaJCamlolrwUAtG/fXmvf8/f3R3JyMtRqdYW3Vdbr6efnh+7du6Np06YYPHgw1qxZg8zMzErL5Vk8vF+UdUwmJCSgsLAQ3bt3f+o2hw4diry8POzdu7fKrjcpbywPPO3Ys7OzQ1BQEHr16oV+/fph6dKlSE9Plx47efJkvPXWW+jRowcWLVr01OM8Pj7+qTEdP34c/fr1g5ubGywtLaXzY3WdBx6VnJyMoUOHokGDBrCysoKHh0ep8Vy6dAkDBgzAlClTtHqKHxg1ahRycnKwadMmGBhU79eAh8/fAODs7PzUskJ/f39p2sjICK1bt36sZ+bhbTo7OwOAtM2EhAR06NBBa/0OHTo8to2Hn+fB/WfpAXraPp+QkAA/Pz+tAYo6dOgAjUbzWPleWR7OXaFQwMnJqVxlmk2bNn3sup/Ro0dj8+bNKCgoQFFRETZt2qTVm1bVnnSeP3v2LIyMjLS+69jb28PHx0frPTIyMkKbNm2k+40bN4aNjc0z9+SRfmIDSEZcXV2RmJiIr7/+GiqVCiEhIejUqROKi4tr1YXBj5azKBQKrXkPTqAajQa5ubno168f4uPjtW7JyclPLf3YsmULpkyZguDgYOzduxfx8fEYOXIkioqKqiapRzz8wfhfc6iotLQ09O3bF82aNcP27dtx/PhxrFixAgCkvHNzc/H2229rxXHy5EkkJyejYcOGlRbLf/Xgy5x4qFzt4RIJoOzX09DQEPv27cMff/yBJk2aYPny5fDx8anSBl55VWREx/Ie03369JFKyapKRc4v5Tn21q1bh6ioKAQEBODHH39Eo0aN8PfffwO4fx3MmTNn8PLLL+PgwYNo0qQJduzYUeG4HpQpWVlZ4YcffkBMTIy0neo6DzyqX79+uHPnDtasWYPo6GhER0c/Fk9eXh5eeeUV+Pv7Y+7cuY9tY/78+dizZw9++eUXWFpaVlvsD5R2/n5QwlcZ23z4/K9Lz/qZamBg8FjZ7aPnMuC/v56lnUv69esHpVKJHTt24Ndff0VxcTH+97//VTByotqBDaBarGHDhjA2NpY+BIH7FwomJSU98TEqlQr9+vXDsmXLEB4ejqioKPzzzz9o2rQpNBpNqReYAoCvr69Ud/tAZGQkLC0tUb9+/cpLqpK1bNkSZ86cgYeHB7y8vLRuDz4ATExMHutJiIyMREBAAEJCQtCiRQt4eXmV2WNUVcrKoUGDBjA2Ntaq3c7OztbaD3x8fFBSUoK4uDhp3vnz57V6No4fPw6NRoOwsDC0b98ejRo1wrVr1x6L5ezZs4/F4eXlVaWjCD28jwPA33//DW9vbxgaGmrNfzDq4cM9Ao/+v0x59gmFQoEOHTrg448/RlxcHExMTJ74JVpXyjomvb29oVKpcODAgaduZ+zYsVi0aBFeeeWVJx7/z6q8sQDlP/ZatGiB6dOn4+jRo3j++eexadMmaVmjRo3w3nvvYe/evRg0aBDWrVtX6nM1a9bsiTGdO3cOGRkZWLRoEV544QU0bty42gdAeFhGRgYSExPx0UcfoXv37vD19X2sZ1IIgTfffBMajQbfffed1q/pwP3rQOfOnYutW7fWiB8syuNBwxa4f/3X8ePH4evrW+7H+/r6IjIyUmteZGQkmjRp8sTneXC/Is/zqKft876+vjh58qTWwCqRkZEwMDCQeu7r1KmjdR5Tq9U4ffp0hWJ4cE4ub0+5kZERAgMDsW7dOqxbtw6vv/56tf44+qTzfJMmTVBSUqK1/MHx8PD7WFJSIg14AtyvfMjKynqm95H0FxtAtZiFhQWCg4MxdepUHDx4EKdPn0ZQUNATSxrWr1+Pb7/9FqdPn8aFCxfw/fffQ6VSwd3dHR4eHggMDMSoUaOwc+dOpKamIjw8XLrwOCQkBJcvX8aECRNw7tw57Nq1C7Nnz8bkyZOrvYSiIsaNG4c7d+5g6NChiImJQUpKCvbs2YORI0dKHwoeHh6Ijo5GWloabt++DY1GA29vb8TGxmLPnj1ISkrCzJkztRoYNSkHS0tLBAYGYurUqTh06BDOnDmD4OBgGBgYSF+AGjdujB49emDMmDE4duwY4uLiMGbMGKhUKmkdLy8vFBcXY/ny5bhw4QK+++47rFq1SiuW0NBQHD16FOPHj5d6TXbt2lXlgyBcunQJkydPRmJiIjZv3ozly5fj3XfffWw9lUqF9u3bY9GiRUhISEBERAQ++ugjrXXKej2jo6PxySefIDY2FpcuXcLPP/+MW7du1bgP0bKOSVNTU4SGhmLatGnYuHEjUlJS8Pfff2uN7PjAhAkTMH/+fPTt2xdHjhyp9FgrEktZx15qaiqmT5+OqKgoXLx4EXv37kVycjJ8fX2Rn5+P8ePHIzw8HBcvXkRkZCRiYmKe+N7Nnj0bmzdvxuzZs5GQkIB//vkHn376KQDAzc0NJiYm0vHwyy+/YN68eZX+2pSXra0t7O3tsXr1apw/fx4HDx7E5MmTtdaZM2cO9u/fj2+++Qa5ubm4fv06rl+/jvz8fJw+fRojRoxAaGgonnvuOWnZnTt3dJRR+axYsQI7duzAuXPnMG7cOGRmZlaoLGvq1KlYv349Vq5cieTkZCxZsgQ///wzpkyZorXetm3bsHbtWiQlJWH27Nk4duzYM53XnrbPDxs2DKampggMDMTp06dx6NAhTJgwAcOHD5dKuLt164bdu3dj9+7dOHfuHMaOHVvhP3ytW7cuVCqVNNBLdnZ2mY956623cPDgQfz555/VWv4GPPk87+3tjf79+2P06NE4cuQITp48iTfffBP16tVD//79pccbGxtjwoQJiI6OxvHjxxEUFIT27dtrlYcTSXR6BRI9s5ycHPHmm28KMzMz4ejoKBYvXqx18eTDF4jv2LFDtGvXTlhZWQlzc3PRvn17rYtH8/PzxXvvvSecnZ2FiYmJ8PLyEmvXrpWWh4eHizZt2ggTExPh5OQkQkNDRXFxcXWmW+qFoQ/n+AAeupg2KSlJDBw4UNjY2AiVSiUaN24sJk2aJF1smZiYKNq3by9UKpUAIFJTU0VBQYEICgoS1tbWwsbGRowdO1Z88MEHws/PT3qOqhwE4dEcy8rh7t274o033hBmZmbCyclJLFmyRLRt21Z88MEH0jauXbsmevfuLZRKpXB3dxebNm0SdevWFatWrZLWWbJkiXB2dhYqlUr06tVLbNy48bGLvI8dOyZefPFFYWFhIczNzUWzZs3EggULKv11ePj1CAkJEe+8846wsrIStra2YsaMGVLuj77/Z8+eFf7+/kKlUonmzZuLvXv3ag2CIMTTX8+zZ8+KXr16iTp16gilUikaNWokli9fXmX5lVdp+0VZx6RarRbz588X7u7uwtjYWLi5uYlPPvlECFH6BdJhYWHC0tJS62LjyvKkWB6No6xj7/r162LAgAHSecrd3V3MmjVLqNVqUVhYKF5//XXh6uoqTExMhIuLixg/frx0QfyjgyAIIcT27dtF8+bNhYmJiXBwcBCDBg2Slm3atEl4eHgIpVIp/P39xS+//KIVa3UPgrBv3z7h6+srlEqlaNasmQgPD9c613Xu3FkAeOy2bt066SLxR2+dO3eustgfVdaF/ShlEIRNmzaJtm3bChMTE9GkSRNx8OBB6bGlvf5xcXHSefyBr7/+WjRo0EAYGxuLRo0aiY0bN2rFAECsWLFCvPjii0KpVAoPD49SB4+oqKcdf6dOnRJdu3YVpqamws7OTowePVoaXEMIIYqKisTYsWOFnZ2dqFu3rli4cGGpgyA8+tnn5+cnZs+eLd1fs2aNcHV1FQYGBtJ7XdZn1wsvvCCee+65Z02/Qso6z9+5c0cMHz5cWFtbS59PSUlJ0uMfHNvbt28XDRo0EEqlUvTo0UNcvHixWvOg2kMhRA0Y25WIKlVeXh7q1auHsLAwBAcHl7rOlStX4OrqKg18QEQ1z9ChQ2FoaIjvv/9e16FUq7S0NHh6eiIuLg7Nmzev0udSKBTYsWOH9H9LciaEgLe3N0JCQh7rYaxKXbp0QfPmzbX+l64i1q9fj0mTJlW4l4zky0jXARDRs4uLi8O5c+fQtm1bZGdnSxc/P1wecPDgQeTm5qJp06ZIT0/HtGnT4OHhUe3/A0JEZSspKUFSUhKioqLw9ttv6zockoFbt25hy5YtuH79erX99w+RrrABRKQnPv/8cyQmJsLExAStWrXC4cOH4eDgIC0vLi7GjBkzcOHCBVhaWiIgIAA//PDDE/8kkoh05/Tp0wgICEDXrl3xzjvv6DockoG6devCwcEBq1evfuwvEoj0DUvgiIiIiIhINmru8F1ERERERESVjA0gIiIiIiKSDTaAiIiIiIhINtgAIiIiIiIi2WADiIiIiIiIZIMNICIimQkKCtL608cuXbpg0qRJ1R5HeHg4FApFlf554aO5/hfVEScREVUfNoCIiGqAoKAgKBQKKBQKmJiYwMvLC3PnzkVJSUmVP/fPP/+MefPmlWvd6m4MeHh4/Od/hyciIioN/wiViKiGeOmll7Bu3ToUFhbi999/x7hx42BsbIzp06c/tm5RURFMTEwq5Xnt7OwqZTtERES1AXuAiIhqCKVSCScnJ7i7u2Ps2LHo0aMHfvnlFwD/lnItWLAALi4u8PHxAQBcvnwZr732GmxsbGBnZ4f+/fsjLS1N2qZarcbkyZNhY2MDe3t7TJs2DY/+//WjJXCFhYUIDQ2Fq6srlEolvLy88O233yItLQ1du3YFANja2kKhUCAoKAgAoNFosHDhQnh6ekKlUsHPzw8//fST1vP8/vvvaNSoEVQqFbp27aoV53+hVqsRHBwsPaePjw+WLl1a6roff/wx6tSpAysrK7zzzjsoKiqSlpUndiIi0h/sASIiqqFUKhUyMjKk+wcOHICVlRX27dsHACguLkavXr3g7++Pw4cPw8jICPPnz8dLL72EU6dOwcTEBGFhYVi/fj3Wrl0LX19fhIWFYceOHejWrdsTn3fEiBGIiorCsmXL4Ofnh9TUVNy+fRuurq7Yvn07Xn31VSQmJsLKygoqlQoAsHDhQnz//fdYtWoVvL298ddff+HNN99EnTp10LlzZ1y+fBmDBg3CuHHjMGbMGMTGxuL9999/ptdHo9Ggfv362LZtG+zt7XH06FGMGTMGzs7OeO2117ReN1NTU4SHhyMtLQ0jR46Evb09FixYUK7YiYhIzwgiItK5wMBA0b9/fyGEEBqNRuzbt08olUoxZcoUabmjo6MoLCyUHvPdd98JHx8fodFopHmFhYVCpVKJPXv2CCGEcHZ2FosXL5aWFxcXi/r160vPJYQQnTt3Fu+++64QQojExEQBQOzbt6/UOA8dOiQAiMzMTGleQUGBMDMzE0ePHtVaNzg4WAwdOlQIIcT06dNFkyZNtJaHhoY+tq1Hubu7iy+++OKJyx81btw48eqrr0r3AwMDhZ2dncjLy5PmrVy5UlhYWAi1Wl2u2EvLmYiIai/2ABER1RC//fYbLCwsUFxcDI1GgzfeeANz5syRljdt2lTrup+TJ0/i/PnzsLS01NpOQUEBUlJSkJ2djfT0dLRr105aZmRkhNatWz9WBvdAfHw8DA0NK9Tzcf78edy7dw8vvvii1vyioiK0aNECAJCQkKAVBwD4+/uX+zmeZMWKFVi7di0uXbqE/Px8FBUVoXnz5lrr+Pn5wczMTOt5c3NzcfnyZeTm5pYZOxER6Rc2gIiIaoiuXbti5cqVMDExgYuLC4yMtE/R5ubmWvdzc3PRqlUr/PDDD49tq06dOv8phgclbRWRm5sLANi9ezfq1auntUypVP6nOMpjy5YtmDJlCsLCwuDv7w9LS0t89tlniI6OLvc2dBU7ERHpDhtAREQ1hLm5Oby8vMq9fsuWLfHjjz+ibt26sLKyKnUdZ2dnREdHo1OnTgCAkpISHD9+HC1btix1/aZNm0Kj0SAiIgI9evR4bPmDHii1Wi3Na9KkCZRKJS5duvTEniNfX19pQIcH/v7777KTfIrIyEgEBAQgJCREmpeSkvLYeidPnkR+fr7UuPv7779hYWEBV1dX2NnZlRk7ERHpF44CR0RUSw0bNgwODg7o378/Dh8+jNTUVISHh2PixIm4cuUKAODdd9/FokWLsHPnTpw7dw4hISFP/Q8fDw8PBAYGYtSoUdi5c6e0za1btwIA3N3doVAo8Ntvv+HWrVvIzc2FpaUlpkyZgvfeew8bNmxASkoKTpw4geXLl2PDhg0AgHfeeQfJycmYOnUqEhMTsWnTJqxfv75ceV69ehXx8fFat8zMTHh7eyM2NhZ79uxBUlISZs6ciZiYmMceX1RUhODgYJw9exa///47Zs+ejfHjx8PAwKBcsRMRkX5hA4iIqJYyMzPDX3/9BTc3NwwaNAi+vr4IDg5GQUGB1CP0/vvvY/jw4QgMDJTKxAYOHPjU7a5cuRL/+9//EBISgsaNG2P06NHIy8sDANSrVw8ff/wxPvjgAzg6OmL8+PEAgHnz5mHmzJlYuHAhfH198dJLL2H37t3w9PQEALi5uWH79u3YuXMn/Pz8sGrVKnzyySflyvPzzz9HixYttG67d+/G22+/jUGDBmHIkCFo164dMjIytHqDHujevTu8vb3RqVMnDBkyBK+88orWtVVlxU5ERPpFIZ50JSwREREREZGeYQ8QERERERHJBhtAREREREQkG2wAERERERGRbLABREREREREssEGEBERERERyQYbQEREREREJBtsABERERERkWywAURERERERLLBBhAREREREckGG0BERERERCQbbAAREREREZFs/D9xTEhBHayeEgAAAABJRU5ErkJggg==","text/plain":["<Figure size 1000x1000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.62      0.73      0.67        75\n","           1       0.86      0.75      0.80        75\n","           2       0.63      0.83      0.72        75\n","           3       0.84      0.61      0.71        75\n","           4       0.74      0.52      0.61        75\n","           5       0.90      0.93      0.92        75\n","           6       0.78      0.67      0.72        75\n","           7       0.62      0.60      0.61        75\n","           8       0.51      0.83      0.63        75\n","           9       0.91      0.68      0.78        75\n","\n","    accuracy                           0.71       750\n","   macro avg       0.74      0.71      0.72       750\n","weighted avg       0.74      0.71      0.72       750\n","\n"]}],"source":["y_pred = model.predict(dataset.test)\n","y_pred = np.argmax(y_pred, axis=1)\n","y_true = tf.concat([y for x, y in dataset.test], axis=0).numpy().argmax(axis=1)\n","\n","show_confusion_matrix(y_true, y_pred, labels=dataset.labels)\n","print(classification_report(y_true, y_pred))"]},{"cell_type":"markdown","metadata":{},"source":["## Mel Spectogram"]},{"cell_type":"code","execution_count":40,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:07:07.958076Z","iopub.status.busy":"2024-04-10T14:07:07.957232Z","iopub.status.idle":"2024-04-10T14:07:51.962795Z","shell.execute_reply":"2024-04-10T14:07:51.961955Z","shell.execute_reply.started":"2024-04-10T14:07:07.958043Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Copying files: 4995 files [00:43, 114.18 files/s]\n"]}],"source":["batch_size = 32\n","dataset = Dataset(path='/kaggle/input/musicgenreprediction/Data/images_augmented/mel_spectrogram', batch_size=batch_size)"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:07:51.965317Z","iopub.status.busy":"2024-04-10T14:07:51.964686Z","iopub.status.idle":"2024-04-10T14:10:17.438065Z","shell.execute_reply":"2024-04-10T14:10:17.437113Z","shell.execute_reply.started":"2024-04-10T14:07:51.965282Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 71ms/step - accuracy: 0.2129 - loss: 2.1369 - val_accuracy: 0.3084 - val_loss: 1.8299\n","Epoch 2/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.3287 - loss: 1.8064 - val_accuracy: 0.3612 - val_loss: 1.6883\n","Epoch 3/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.3730 - loss: 1.6730 - val_accuracy: 0.3933 - val_loss: 1.5767\n","Epoch 4/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4248 - loss: 1.5736 - val_accuracy: 0.4226 - val_loss: 1.4948\n","Epoch 5/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4475 - loss: 1.5019 - val_accuracy: 0.4421 - val_loss: 1.4474\n","Epoch 6/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4683 - loss: 1.4534 - val_accuracy: 0.4616 - val_loss: 1.4237\n","Epoch 7/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4783 - loss: 1.4169 - val_accuracy: 0.4826 - val_loss: 1.3884\n","Epoch 8/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.4964 - loss: 1.3690 - val_accuracy: 0.5091 - val_loss: 1.3488\n","Epoch 9/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5182 - loss: 1.3276 - val_accuracy: 0.5021 - val_loss: 1.3218\n","Epoch 10/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 44ms/step - accuracy: 0.5309 - loss: 1.2784 - val_accuracy: 0.5007 - val_loss: 1.3087\n","Epoch 11/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5384 - loss: 1.2418 - val_accuracy: 0.5160 - val_loss: 1.2904\n","Epoch 12/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5641 - loss: 1.2085 - val_accuracy: 0.5300 - val_loss: 1.2909\n","Epoch 13/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5705 - loss: 1.1906 - val_accuracy: 0.5216 - val_loss: 1.3052\n","Epoch 14/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5900 - loss: 1.1493 - val_accuracy: 0.5370 - val_loss: 1.3011\n","Epoch 15/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.5970 - loss: 1.1155 - val_accuracy: 0.5425 - val_loss: 1.2891\n","Epoch 16/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6099 - loss: 1.0888 - val_accuracy: 0.5411 - val_loss: 1.2789\n","Epoch 17/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6156 - loss: 1.0638 - val_accuracy: 0.5495 - val_loss: 1.2517\n","Epoch 18/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6242 - loss: 1.0336 - val_accuracy: 0.5621 - val_loss: 1.2049\n","Epoch 19/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6398 - loss: 0.9995 - val_accuracy: 0.5802 - val_loss: 1.1823\n","Epoch 20/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6519 - loss: 0.9635 - val_accuracy: 0.5816 - val_loss: 1.1715\n","Epoch 21/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6586 - loss: 0.9498 - val_accuracy: 0.5969 - val_loss: 1.1628\n","Epoch 22/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6742 - loss: 0.9222 - val_accuracy: 0.6067 - val_loss: 1.1519\n","Epoch 23/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6921 - loss: 0.9008 - val_accuracy: 0.5886 - val_loss: 1.1703\n","Epoch 24/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.6955 - loss: 0.8863 - val_accuracy: 0.5830 - val_loss: 1.1671\n","Epoch 25/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7074 - loss: 0.8654 - val_accuracy: 0.5829 - val_loss: 1.1739\n","Epoch 26/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7254 - loss: 0.8284 - val_accuracy: 0.5774 - val_loss: 1.2233\n","Epoch 27/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7311 - loss: 0.8062 - val_accuracy: 0.5774 - val_loss: 1.2189\n","Epoch 28/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7453 - loss: 0.7860 - val_accuracy: 0.5886 - val_loss: 1.1828\n","Epoch 29/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7554 - loss: 0.7497 - val_accuracy: 0.5830 - val_loss: 1.1774\n","Epoch 30/30\n","\u001b[1m109/109\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 43ms/step - accuracy: 0.7646 - loss: 0.7246 - val_accuracy: 0.5969 - val_loss: 1.1419\n"]}],"source":["model = build_model()\n","\n","epochs = 30\n","\n","# Train the model using generator for training data and validation data\n","history = model.fit(dataset.train,\n","                    batch_size=batch_size,\n","                    epochs=epochs,\n","                    steps_per_epoch=dataset.train_len,\n","                    validation_data=dataset.val,\n","                    validation_steps=dataset.val_len,\n","                    verbose=1)"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:10:17.440116Z","iopub.status.busy":"2024-04-10T14:10:17.439815Z","iopub.status.idle":"2024-04-10T14:10:17.718519Z","shell.execute_reply":"2024-04-10T14:10:17.717565Z","shell.execute_reply.started":"2024-04-10T14:10:17.440091Z"},"trusted":true},"outputs":[{"data":{"image/png":"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size 1500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history, metric='loss')"]},{"cell_type":"code","execution_count":44,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:10:18.036082Z","iopub.status.busy":"2024-04-10T14:10:18.035797Z","iopub.status.idle":"2024-04-10T14:10:19.348077Z","shell.execute_reply":"2024-04-10T14:10:19.346962Z","shell.execute_reply.started":"2024-04-10T14:10:18.036057Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - accuracy: 0.6017 - loss: 1.1328\n","Accuracy: 0.5920000076293945\n","Loss: 1.1120434999465942\n"]}],"source":["evaluation = model.evaluate(dataset.test, return_dict=True)\n","print(f'Accuracy: {evaluation[\"accuracy\"]}')\n","print(f'Loss: {evaluation[\"loss\"]}')"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2024-04-10T14:10:19.349627Z","iopub.status.busy":"2024-04-10T14:10:19.349326Z","iopub.status.idle":"2024-04-10T14:10:20.743147Z","shell.execute_reply":"2024-04-10T14:10:20.742211Z","shell.execute_reply.started":"2024-04-10T14:10:19.349602Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x1000 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.52      0.44      0.47        75\n","           1       0.63      0.84      0.72        75\n","           2       0.56      0.55      0.55        75\n","           3       0.49      0.65      0.56        75\n","           4       0.40      0.27      0.32        75\n","           5       0.84      0.77      0.81        75\n","           6       0.58      0.64      0.61        75\n","           7       0.58      0.39      0.46        75\n","           8       0.61      0.57      0.59        75\n","           9       0.65      0.80      0.72        75\n","\n","    accuracy                           0.59       750\n","   macro avg       0.59      0.59      0.58       750\n","weighted avg       0.59      0.59      0.58       750\n","\n"]}],"source":["y_pred = model.predict(dataset.test)\n","y_pred = np.argmax(y_pred, axis=1)\n","y_true = tf.concat([y for x, y in dataset.test], axis=0).numpy().argmax(axis=1)\n","\n","show_confusion_matrix(y_true, y_pred, labels=dataset.labels)\n","print(classification_report(y_true, y_pred))"]},{"cell_type":"markdown","metadata":{},"source":["# FMA"]},{"cell_type":"markdown","metadata":{},"source":["## Short-Time Fourier Transform"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-04-25T21:17:38.218163Z","iopub.status.busy":"2024-04-25T21:17:38.217855Z","iopub.status.idle":"2024-04-25T21:18:36.566733Z","shell.execute_reply":"2024-04-25T21:18:36.565926Z","shell.execute_reply.started":"2024-04-25T21:17:38.218100Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 15/15 [00:00<00:00, 27025.15it/s]\n","100%|██████████| 64846/64846 [00:00<00:00, 273153.48it/s]\n"]}],"source":["batch_size = 32\n","dataset = Dataset(path='/kaggle/input/fma-images/stft/stft', batch_size=batch_size, cache=False)"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-25T21:18:36.568328Z","iopub.status.busy":"2024-04-25T21:18:36.567955Z","iopub.status.idle":"2024-04-25T22:01:51.750196Z","shell.execute_reply":"2024-04-25T22:01:51.749293Z","shell.execute_reply.started":"2024-04-25T21:18:36.568293Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 118ms/step - accuracy: 0.2559 - loss: 2.1932 - val_accuracy: 0.3893 - val_loss: 1.8667\n","Epoch 2/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 76ms/step - accuracy: 0.3986 - loss: 1.8311 - val_accuracy: 0.4136 - val_loss: 1.7906\n","Epoch 3/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 75ms/step - accuracy: 0.4376 - loss: 1.7284 - val_accuracy: 0.4439 - val_loss: 1.7072\n","Epoch 4/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 77ms/step - accuracy: 0.4639 - loss: 1.6500 - val_accuracy: 0.4591 - val_loss: 1.6627\n","Epoch 5/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 77ms/step - accuracy: 0.4759 - loss: 1.6055 - val_accuracy: 0.4592 - val_loss: 1.6526\n","Epoch 6/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 79ms/step - accuracy: 0.4893 - loss: 1.5481 - val_accuracy: 0.4873 - val_loss: 1.5682\n","Epoch 7/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 77ms/step - accuracy: 0.5089 - loss: 1.5001 - val_accuracy: 0.4974 - val_loss: 1.5422\n","Epoch 8/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 71ms/step - accuracy: 0.5244 - loss: 1.4496 - val_accuracy: 0.5064 - val_loss: 1.4994\n","Epoch 9/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 73ms/step - accuracy: 0.5363 - loss: 1.4147 - val_accuracy: 0.5222 - val_loss: 1.4540\n","Epoch 10/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 74ms/step - accuracy: 0.5514 - loss: 1.3573 - val_accuracy: 0.5284 - val_loss: 1.4159\n","Epoch 11/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 74ms/step - accuracy: 0.5672 - loss: 1.3170 - val_accuracy: 0.5369 - val_loss: 1.4258\n","Epoch 12/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 74ms/step - accuracy: 0.5738 - loss: 1.2975 - val_accuracy: 0.5549 - val_loss: 1.3577\n","Epoch 13/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.5845 - loss: 1.2642 - val_accuracy: 0.5460 - val_loss: 1.3833\n","Epoch 14/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.5953 - loss: 1.2291 - val_accuracy: 0.5709 - val_loss: 1.2913\n","Epoch 15/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 74ms/step - accuracy: 0.6068 - loss: 1.1874 - val_accuracy: 0.5653 - val_loss: 1.2974\n","Epoch 16/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 74ms/step - accuracy: 0.6192 - loss: 1.1517 - val_accuracy: 0.5691 - val_loss: 1.2995\n","Epoch 17/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.6288 - loss: 1.1239 - val_accuracy: 0.5824 - val_loss: 1.2612\n","Epoch 18/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 74ms/step - accuracy: 0.6365 - loss: 1.0981 - val_accuracy: 0.5947 - val_loss: 1.2261\n","Epoch 19/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.6584 - loss: 1.0534 - val_accuracy: 0.5905 - val_loss: 1.2212\n","Epoch 20/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.6640 - loss: 1.0172 - val_accuracy: 0.6005 - val_loss: 1.2147\n","Epoch 21/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 76ms/step - accuracy: 0.6761 - loss: 0.9805 - val_accuracy: 0.6031 - val_loss: 1.1924\n","Epoch 22/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 76ms/step - accuracy: 0.6835 - loss: 0.9640 - val_accuracy: 0.6142 - val_loss: 1.1813\n","Epoch 23/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.6918 - loss: 0.9314 - val_accuracy: 0.6218 - val_loss: 1.1398\n","Epoch 24/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 74ms/step - accuracy: 0.7040 - loss: 0.8921 - val_accuracy: 0.6303 - val_loss: 1.1351\n","Epoch 25/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.7145 - loss: 0.8713 - val_accuracy: 0.6242 - val_loss: 1.1403\n","Epoch 26/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 74ms/step - accuracy: 0.7240 - loss: 0.8380 - val_accuracy: 0.6383 - val_loss: 1.1049\n","Epoch 27/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.7288 - loss: 0.8183 - val_accuracy: 0.6312 - val_loss: 1.1008\n","Epoch 28/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.7460 - loss: 0.7760 - val_accuracy: 0.6293 - val_loss: 1.0989\n","Epoch 29/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 74ms/step - accuracy: 0.7534 - loss: 0.7534 - val_accuracy: 0.6489 - val_loss: 1.0706\n","Epoch 30/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.7678 - loss: 0.7179 - val_accuracy: 0.6494 - val_loss: 1.0722\n"]}],"source":["model = build_model(genres=15)\n","\n","epochs = 30\n","\n","# Train the model using generator for training data and validation data\n","history = model.fit(dataset.train,\n","                    batch_size=batch_size,\n","                    epochs=epochs,\n","                    steps_per_epoch=dataset.train_len,\n","                    validation_data=dataset.val,\n","                    validation_steps=dataset.val_len,\n","                    verbose=1)"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-04-25T22:01:51.752078Z","iopub.status.busy":"2024-04-25T22:01:51.751772Z","iopub.status.idle":"2024-04-25T22:01:52.044467Z","shell.execute_reply":"2024-04-25T22:01:52.043555Z","shell.execute_reply.started":"2024-04-25T22:01:51.752052Z"},"trusted":true},"outputs":[{"data":{"image/png":"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metric='accuracy')"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-04-25T22:01:52.046085Z","iopub.status.busy":"2024-04-25T22:01:52.045755Z","iopub.status.idle":"2024-04-25T22:01:52.266702Z","shell.execute_reply":"2024-04-25T22:01:52.265818Z","shell.execute_reply.started":"2024-04-25T22:01:52.046058Z"},"trusted":true},"outputs":[{"data":{"image/png":"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size 1500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history, metric='loss')"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-04-25T22:01:52.270288Z","iopub.status.busy":"2024-04-25T22:01:52.269560Z","iopub.status.idle":"2024-04-25T22:02:17.054686Z","shell.execute_reply":"2024-04-25T22:02:17.053714Z","shell.execute_reply.started":"2024-04-25T22:01:52.270260Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m242/242\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 102ms/step - accuracy: 0.6615 - loss: 1.0702\n","Accuracy: 0.6528946757316589\n","Loss: 1.0678775310516357\n"]}],"source":["evaluation = model.evaluate(dataset.test, return_dict=True)\n","print(f'Accuracy: {evaluation[\"accuracy\"]}')\n","print(f'Loss: {evaluation[\"loss\"]}')"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-04-25T22:02:17.056094Z","iopub.status.busy":"2024-04-25T22:02:17.055791Z","iopub.status.idle":"2024-04-25T22:02:45.638080Z","shell.execute_reply":"2024-04-25T22:02:45.637277Z","shell.execute_reply.started":"2024-04-25T22:02:17.056067Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m242/242\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 59ms/step\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1500x1500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.66      0.73      0.70       750\n","           1       0.65      0.62      0.64       750\n","           2       0.68      0.60      0.64       750\n","           3       0.78      0.60      0.68       142\n","           4       0.50      0.53      0.51       185\n","           5       0.59      0.38      0.46        89\n","           6       0.66      0.78      0.71       750\n","           7       0.75      0.36      0.49        25\n","           8       0.65      0.65      0.65       750\n","           9       0.98      0.97      0.97       612\n","          10       0.70      0.86      0.77       743\n","          11       0.60      0.53      0.56       750\n","          12       0.54      0.45      0.49       461\n","          13       0.77      0.60      0.67       214\n","          14       0.41      0.42      0.41       750\n","\n","    accuracy                           0.65      7721\n","   macro avg       0.66      0.61      0.62      7721\n","weighted avg       0.65      0.65      0.65      7721\n","\n"]}],"source":["y_pred = model.predict(dataset.test)\n","y_pred = np.argmax(y_pred, axis=1)\n","y_true = tf.concat([y for x, y in dataset.test], axis=0).numpy().argmax(axis=1)\n","\n","show_confusion_matrix(y_true, y_pred, labels=dataset.labels)\n","print(classification_report(y_true, y_pred))"]},{"cell_type":"markdown","metadata":{},"source":["## Mel Spectogram"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:07:14.936476Z","iopub.status.busy":"2024-04-26T11:07:14.935558Z","iopub.status.idle":"2024-04-26T11:09:06.996414Z","shell.execute_reply":"2024-04-26T11:09:06.995423Z","shell.execute_reply.started":"2024-04-26T11:07:14.936442Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 15/15 [00:00<00:00, 25554.25it/s]\n","100%|██████████| 64842/64842 [00:00<00:00, 273534.41it/s]\n"]}],"source":["batch_size = 32\n","dataset = Dataset(path='/kaggle/input/fma-images/mel/mel', batch_size=batch_size, cache=False)"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:09:06.998496Z","iopub.status.busy":"2024-04-26T11:09:06.998184Z","iopub.status.idle":"2024-04-26T11:56:31.880081Z","shell.execute_reply":"2024-04-26T11:56:31.879214Z","shell.execute_reply.started":"2024-04-26T11:09:06.998470Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 127ms/step - accuracy: 0.3041 - loss: 2.0832 - val_accuracy: 0.3839 - val_loss: 1.8767\n","Epoch 2/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 90ms/step - accuracy: 0.4011 - loss: 1.8250 - val_accuracy: 0.4141 - val_loss: 1.7891\n","Epoch 3/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 80ms/step - accuracy: 0.4299 - loss: 1.7242 - val_accuracy: 0.4374 - val_loss: 1.7281\n","Epoch 4/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 83ms/step - accuracy: 0.4553 - loss: 1.6515 - val_accuracy: 0.4549 - val_loss: 1.6673\n","Epoch 5/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 83ms/step - accuracy: 0.4745 - loss: 1.5957 - val_accuracy: 0.4741 - val_loss: 1.6149\n","Epoch 6/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 83ms/step - accuracy: 0.4983 - loss: 1.5275 - val_accuracy: 0.4886 - val_loss: 1.5781\n","Epoch 7/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 86ms/step - accuracy: 0.5021 - loss: 1.5032 - val_accuracy: 0.5038 - val_loss: 1.5279\n","Epoch 8/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 81ms/step - accuracy: 0.5191 - loss: 1.4594 - val_accuracy: 0.4986 - val_loss: 1.5165\n","Epoch 9/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 80ms/step - accuracy: 0.5303 - loss: 1.4187 - val_accuracy: 0.5294 - val_loss: 1.4677\n","Epoch 10/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 78ms/step - accuracy: 0.5508 - loss: 1.3638 - val_accuracy: 0.5233 - val_loss: 1.4601\n","Epoch 11/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 75ms/step - accuracy: 0.5597 - loss: 1.3340 - val_accuracy: 0.5349 - val_loss: 1.4221\n","Epoch 12/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 75ms/step - accuracy: 0.5642 - loss: 1.3169 - val_accuracy: 0.5353 - val_loss: 1.4261\n","Epoch 13/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 77ms/step - accuracy: 0.5767 - loss: 1.2873 - val_accuracy: 0.5453 - val_loss: 1.3818\n","Epoch 14/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 69ms/step - accuracy: 0.5876 - loss: 1.2573 - val_accuracy: 0.5479 - val_loss: 1.3881\n","Epoch 15/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 74ms/step - accuracy: 0.5927 - loss: 1.2229 - val_accuracy: 0.5612 - val_loss: 1.3436\n","Epoch 16/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 85ms/step - accuracy: 0.6039 - loss: 1.1946 - val_accuracy: 0.5491 - val_loss: 1.3804\n","Epoch 17/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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22/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 73ms/step - accuracy: 0.6630 - loss: 1.0217 - val_accuracy: 0.5916 - val_loss: 1.2524\n","Epoch 23/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 71ms/step - accuracy: 0.6738 - loss: 0.9932 - val_accuracy: 0.5974 - val_loss: 1.2469\n","Epoch 24/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 85ms/step - accuracy: 0.6731 - loss: 0.9782 - val_accuracy: 0.5944 - val_loss: 1.2487\n","Epoch 25/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 103ms/step - accuracy: 0.6813 - loss: 0.9482 - val_accuracy: 0.6112 - val_loss: 1.2179\n","Epoch 26/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 99ms/step - accuracy: 0.6943 - loss: 0.9241 - val_accuracy: 0.6103 - val_loss: 1.1993\n","Epoch 27/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 99ms/step - accuracy: 0.6997 - loss: 0.8957 - val_accuracy: 0.6092 - val_loss: 1.2320\n","Epoch 28/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 90ms/step - accuracy: 0.7200 - loss: 0.8595 - val_accuracy: 0.6104 - val_loss: 1.2177\n","Epoch 29/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 92ms/step - accuracy: 0.7178 - loss: 0.8421 - val_accuracy: 0.6098 - val_loss: 1.2084\n","Epoch 30/30\n","\u001b[1m1125/1125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 90ms/step - accuracy: 0.7294 - loss: 0.8215 - val_accuracy: 0.6016 - val_loss: 1.2269\n"]}],"source":["model = build_model(genres=15)\n","\n","epochs = 30\n","\n","# Train the model using generator for training data and validation data\n","history = model.fit(dataset.train,\n","                    batch_size=batch_size,\n","                    epochs=epochs,\n","                    steps_per_epoch=dataset.train_len,\n","                    validation_data=dataset.val,\n","                    validation_steps=dataset.val_len,\n","                    verbose=1)"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:56:31.882003Z","iopub.status.busy":"2024-04-26T11:56:31.881651Z","iopub.status.idle":"2024-04-26T11:56:32.203368Z","shell.execute_reply":"2024-04-26T11:56:32.202366Z","shell.execute_reply.started":"2024-04-26T11:56:31.881957Z"},"trusted":true},"outputs":[{"data":{"image/png":"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metric='accuracy')"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:56:32.205839Z","iopub.status.busy":"2024-04-26T11:56:32.205558Z","iopub.status.idle":"2024-04-26T11:56:32.485135Z","shell.execute_reply":"2024-04-26T11:56:32.484189Z","shell.execute_reply.started":"2024-04-26T11:56:32.205815Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABNEAAAHACAYAAACMHv7nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACDqElEQVR4nOzdeXzU1b3/8ffMZN/3PSFhS1hCWAUEFJSKoijVulSvS116bbVW7WoXq7+219vb1uu9rbVXb9XaulWvWCu4VYkgssiSsAeyhyRk3/fMzO+Pb5gkJEMCJJksr+fjMY8k53sm8xkYBvLmc84x2e12uwAAAAAAAAA4ZXZ1AQAAAAAAAMBoR4gGAAAAAAAADIAQDQAAAAAAABgAIRoAAAAAAAAwAEI0AAAAAAAAYACEaAAAAAAAAMAACNEAAAAAAACAARCiAQAAAAAAAANwc3UBI81ms6mkpET+/v4ymUyuLgcAAAAAAAAuZLfb1dDQoJiYGJnNzvvNJlyIVlJSovj4eFeXAQAAAAAAgFGkqKhIcXFxTq9PuBDN399fkvELExAQ4OJqAAAAAAAA4Er19fWKj493ZEbOTLgQ7dQSzoCAAEI0AAAAAAAASNKA23659GCBJ554QosWLZK/v78iIiK0fv16ZWVlDXi/N954QykpKfLy8lJqaqo2bdo0AtUCAAAAAABgonJpiPbpp5/qvvvu044dO/TRRx+po6NDl112mZqampze5/PPP9dXv/pV3XXXXdq3b5/Wr1+v9evX6+DBgyNYOQAAAAAAACYSk91ut7u6iFMqKioUERGhTz/9VBdddFG/c2688UY1NTXp3XffdYwtWbJEc+fO1R//+McBH6O+vl6BgYGqq6tjOScAAAAAAMAEN9isaFTtiVZXVydJCgkJcTpn+/btevjhh3uNrVmzRm+//Xa/89va2tTW1ub4ur6+fsA67Ha7Ojs7ZbVaB1E1RorFYpGbm9uAa5QBAAAAAACG2qgJ0Ww2mx588EEtW7ZMs2fPdjrv5MmTioyM7DUWGRmpkydP9jv/iSee0OOPPz7oOtrb21VaWqrm5uZB3wcjx8fHR9HR0fLw8HB1KQAAAAAAYAIZNSHafffdp4MHD+qzzz4b0u/7yCOP9OpcO3VsaX9sNpvy8vJksVgUExMjDw8Pup5GCbvdrvb2dlVUVCgvL0/Tpk2T2ezSLf0AAAAAAMAEMipCtPvvv1/vvvuutmzZori4uDPOjYqKUllZWa+xsrIyRUVF9Tvf09NTnp6eg6qjvb1dNptN8fHx8vHxGVzxGDHe3t5yd3dXQUGB2tvb5eXl5eqSAAAAAADABOHSVh673a77779fGzZs0CeffKKkpKQB77N06VJ9/PHHvcY++ugjLV26dMjqosNp9OL3BgAAAAAAuIJLO9Huu+8+vfLKK/r73/8uf39/x75mgYGB8vb2liTddtttio2N1RNPPCFJ+va3v62LL75Yv/3tb3XllVfqtdde0+7du/Xss8+67HkAAAAAAABgfHNpW88zzzyjuro6rVy5UtHR0Y7b66+/7phTWFio0tJSx9cXXnihXnnlFT377LNKS0vTm2++qbfffvuMhxEAAAAAAAAA58OlnWh2u33AOenp6X3Grr/+el1//fXDUNHYtXLlSs2dO1dPPfWUq0sBAAAAAAAYd9hgCgAAAAAAABgAIdo4MXBPHwAAAAAAAM4VIdoA7Ha7mts7XXIbzHJXm92u0roWtbRbZeuaX1NTo9tuu03BwcHy8fHRFVdcoePHjzvuU1BQoHXr1ik4OFi+vr6aNWuWNm3a5LjvLbfcovDwcHl7e2vatGl64YUXhucXFwAAAAAAYIxw6Z5oY0FLh1UzH/3AJY99+P+tkY/HwL9FDa1G4NbY9fGOO+7Q8ePH9c477yggIEA/+MEPtHbtWh0+fFju7u6677771N7eri1btsjX11eHDx+Wn5+fJOmnP/2pDh8+rPfee09hYWHKzs5WS0vLcD9VAAAAAACAUY0QbYwzm0yKD/aRJLVbbdp74LDeeecdbdu2TRdeeKEk6eWXX1Z8fLzefvttXX/99SosLNR1112n1NRUSdLkyZMd36+wsFDz5s3TwoULJUmJiYkj+4QAAAAAAABGIUK0AXi7W3T4/61x2WMPap6HRe5uxsrc7Xv3y83NTYsXL3ZcDw0NVXJyso4cOSJJeuCBB/SNb3xDH374oVavXq3rrrtOc+bMkSR94xvf0HXXXae9e/fqsssu0/r16x1hHAAAAAAAwETFnmgDMJlM8vFwc8nNZDINuk4Pi1luZpNsNmNftDPtp3b33XcrNzdXt956qw4cOKCFCxfqd7/7nSTpiiuuUEFBgR566CGVlJTo0ksv1Xe/+93z+0UEAAAAAAAY4wjRxhE/T3dNnpaizs5O/XPLNsd4VVWVsrKyNHPmTMdYfHy87r33Xr311lv6zne+o+eee85xLTw8XLfffrv++te/6qmnntKzzz47os8DAAAAAABgtGE55zjiZjFpUdpMrbpsre7/xr3632f/R8FBgfrhD3+o2NhYXXPNNZKkBx98UFdccYWmT5+umpoabd68WTNmzJAkPfroo1qwYIFmzZqltrY2vfvuu45rAAAAAAAAExWdaONMuL+n/uO/n9GM1DRdffXVWrp0qex2uzZt2iR3d3dJktVq1X333acZM2bo8ssv1/Tp0/WHP/xBkuTh4aFHHnlEc+bM0UUXXSSLxaLXXnvNlU8JAAAAAADA5Uz2M22eNQ7V19crMDBQdXV1CggI6HWttbVVeXl5SkpKkpeXl4sqPH+tHVYdL2+U3W5XfLCPgn09XF3SkBkvv0cAAAAAAGB0OFNW1BOdaOOQl7tFkf6ekqSSuhZ1WG0urggAAAAAAGBsI0Qbp8L9PeXtYZHVZldxTcsZT+sEAAAAAADAmRGijVMmk0nxwT4ymUyqb+1QTXOHq0sCAAAAAAAYswjRxjEvd4siA4xlnaV1LeroZFknAAAAAADAuSBEG+fC/Tzl4+Emq82uE7Us6wQAAAAAADgXhGjjnMlkUlywt0wmkxpY1gkAAAAAAHBOCNEmgF7LOmtb1M6yTgAAAAAAgLNCiDZBOJZ12u0qZlknAAAAAADAWSFEmyD6Lutsd3VJAAAAAAAAYwYh2gTi5W5RlGNZZ2uvZZ2JiYl66qmnBvV9TCaT3n777WGoEAAAAAAAYHQiRJtgwnos6zxR08yyTgAAAAAAgEEgRJtgTi3rNJtMamzrVDXLOgEAAAAAAAZEiDYQu11qb3LNbZBdYs8++6xiYmJks/U+dfOaa67RnXfeqZycHF1zzTWKjIyUn5+fVly4REf3bJMknaxtVXun9bx+iQ4cOKBLLrlE3t7eCg0N1de//nU1NjY6rqenp+uCCy6Qr6+vgoKCtGzZMhUUFEiSMjMztWrVKvn7+ysgIEALFizQ7t27z6seAAAAAACAoebm6gJGvY5m6d9iXPPYPyqRPHwHnHb99dfrW9/6ljZv3qxLL71UklRdXa33339fmzZtUmNjo9auXatf/vKX8vT01EsvvaRbb7xOH2zbq6CIaJ2oaTnnEpuamrRmzRotXbpUX3zxhcrLy3X33Xfr/vvv14svvqjOzk6tX79e99xzj1599VW1t7dr165dMplMkqRbbrlF8+bN0zPPPCOLxaKMjAy5u7ufcz0AAAAAAADDgRBtHAgODtYVV1yhV155xRGivfnmmwoLC9OqVatkNpuVlpbmmP/zn/9cGzZs0N6tH2n1V25XY1unbOe4N9orr7yi1tZWvfTSS/L1NQK/3//+91q3bp1+9atfyd3dXXV1dbrqqqs0ZcoUSdKMGTMc9y8sLNT3vvc9paSkSJKmTZt2TnUAAAAAAAAMJ0K0gbj7GB1hrnrsQbrlllt0zz336A9/+IM8PT318ssv66abbpLZbFZjY6Mee+wxbdy4UaWlpers7FRLS4tKik8oMsBLpXUtstrs6rTaBn6g0xw5ckRpaWmOAE2Sli1bJpvNpqysLF100UW64447tGbNGn3pS1/S6tWrdcMNNyg6OlqS9PDDD+vuu+/WX/7yF61evVrXX3+9I2wDAAAAAAAYLdgTbSAmk7Gk0hW3riWPg7Fu3TrZ7XZt3LhRRUVF2rp1q2655RZJ0ne/+11t2LBB//Zv/6atW7cqIyNDqampam9vV5ifh3w9jCy1rqVjWE7rfOGFF7R9+3ZdeOGFev311zV9+nTt2LFDkvTYY4/p0KFDuvLKK/XJJ59o5syZ2rBhw5DXAAAAAAAAcD4I0cYJLy8vXXvttXr55Zf16quvKjk5WfPnz5ckbdu2TXfccYe+/OUvKzU1VVFRUcrPz5fUfVqnJLV12lTddHandc6YMUOZmZlqampyjG3btk1ms1nJycmOsXnz5umRRx7R559/rtmzZ+uVV15xXJs+fboeeughffjhh7r22mv1wgsvnOsvAwAAAAAAwLAgRBsP7DbJ1qlbbrlFGzdu1PPPP+/oQpOMfcbeeustZWRkKDMzUzfffHOvkzw93S2ymI2ut9K6szut85ZbbpGXl5duv/12HTx4UJs3b9a3vvUt3XrrrYqMjFReXp4eeeQRbd++XQUFBfrwww91/PhxzZgxQy0tLbr//vuVnp6ugoICbdu2TV988UWvPdMAAAAAAABGA/ZEGw/qS6TWOl2yfLFCQkKUlZWlm2++2XH5ySef1J133qkLL7xQYWFh+sEPfqD6+vpe38JsMsnDYpbNbteJmhYlhfk6TtA8Ex8fH33wwQf69re/rUWLFsnHx0fXXXednnzyScf1o0eP6s9//rOqqqoUHR2t++67T//6r/+qzs5OVVVV6bbbblNZWZnCwsJ07bXX6vHHHx/aXx8AAAAAAIDzZLIPxyZYo1h9fb0CAwNVV1engICAXtdaW1uVl5enpKQkeXl5uajCs2SzShVHJWvXMkz/GMkv4qz2UzulrcOq4+WNstntig3yVqif5xAXe/7G5O8RAAAAAAAYtc6UFfXEcs6xzmyRwlMkr2Dj64YSqSpHsnac9bfydLcoKtAIpkrrWtV2Fss6AQAAAAAAxjNCtPHAbJGCJ0mB8ZLMUnuD0Z3WWj/gXU/3/ttvaGlynC6YHquQwED5+fk5brNmzRr62gEAAAAAAMYA9kQbL0wmyTdM8vCVavKlzlapOsdY2ukfLZkGl5dec801mr9wkfIrm2Wz2xUR4KUQXw9Jkru7+zA+AQAAAAAAgNGLEG28cfeWwpKl+mKpuVJqLJfaGqXgRMlt4D3O/P39NTMlWRGNbSqpbZHZZFJ8pJ883SzDXzsAAAAAAMAoxXLOfoz5sxbMZikoXgpOkkwWqaNZqsiSWmoG/S1CfT3k6+nmOK1ztPyajJY6AAAAAADAxEKI1sOp5YrNzc0urmSIeAdJ4cmSu69ktxrLPGsLJZttwLuaTCbFB3vLbDKpqa1TVU3tw17uYJz6vWFpKQAAAAAAGEks5+zBYrEoKChI5eXlkiQfHx+ZTCYXVzUE/OKkpkpjeWd9pdTUIAXESu5eA9411Muk8oY2lVR1yMPuIw931yzrtNvtam5uVnl5uYKCgmSxsLwUAAAAAACMHEK000RFRUmSI0gbVzpMUnOV0ZVmKpa8gyUPvzPexW6X6hvb1NppU22ZWWF+nnJlrhgUFOT4PQIAAAAAABgphGinMZlMio6OVkREhDo6OlxdztBrqpL++ZhUtN34euqXpFU/kjz9nd7Fu7ZF97y0Wy0dVn1j5VR9ZUHcyNR6Gnd3dzrQAAAAAACAS5jsE2yn9vr6egUGBqqurk4BAQGuLsc1bDZp+++kj/+fZOuUgiZJX3leilvo9C4v7yzQjzcclJe7We99+yIlhfmOYMEAAAAAAADDY7BZEQcLTERms7Ts29KdH0hBCVJtgfT8Gmnbfzk9dODmCxK0fGqYWjts+t4bmbLaJlT2CgAAAAAAJjhCtIksbqH0r1ulmeuNjrSPHpVe/orUWNFnqslk0r9flyo/TzftLqjRC9vyRr5eAAAAAAAAFyFEm+i8g6TrX5TW/Zfk5iXlfCz9cZmUs7nP1LhgH/34yhmSpF9/kKWcisaRrRUAAAAAAMBFCNEgmUzSgjukr6dL4TOkxjLpL1829kyz9j5c4aZF8VoxLUxtnSzrBAAAAAAAEwchGrpFzJDu+URa8DVJdmnrb6UX1kq1hY4pxrLOOfLzdNPewlr96bNc19ULAAAAAAAwQgjR0JuHj7TuKWOJp2eAdGKX9Mfl0uG/O6bEBnnrJ13LOn/z4TFll7OsEwAAAAAAjG+EaOjfrC9L926VYhdKrXXS326T3n1Y6miRJN24KF4XTQ9Xe6dN33uTZZ0AAAAAAGB8I0SDc8GJ0p3vS8seNL7e/SfpuUuliiyZTCb96rpU+Xu6aV9hrf53K8s6AQAAAADA+OXSEG3Lli1at26dYmJiZDKZ9Pbbbw94n5dffllpaWny8fFRdHS07rzzTlVVVQ1/sROVxV360uPSv7wl+YZL5YekZ1dKe/+i6AAv/fSqmZKk3350TNnlDa6tFQAAAAAAYJi4NERrampSWlqann766UHN37Ztm2677TbdddddOnTokN544w3t2rVL99xzzzBXCk29VLp3mzR5pdTRLL1zv/R/d+v61ACtTDaWdX7njf3qtNpcXSkAAAAAAMCQc2mIdsUVV+gXv/iFvvzlLw9q/vbt25WYmKgHHnhASUlJWr58uf71X/9Vu3btGuZKIUnyj5T+ZYN06c8kk0U6+KZM/3ORfnOhVf5ebsosqtVzW/NcXSUAAAAAAMCQG1N7oi1dulRFRUXatGmT7Ha7ysrK9Oabb2rt2rWuLm3iMJulFQ8be6UFJkg1+Qp7/Sr9dcYumWTTf350TMfLWNYJAAAAAADGlzEVoi1btkwvv/yybrzxRnl4eCgqKkqBgYFnXA7a1tam+vr6XjcMgfgLjNM7Z1wt2TqVduQ3+nvwf8vPWqvvvJHJsk4AAAAAADCujKkQ7fDhw/r2t7+tRx99VHv27NH777+v/Px83XvvvU7v88QTTygwMNBxi4+PH8GKxznvIOmGl6Sr/lNy89Kcll163+sR+ZZ8rv/ZwmmdAAAAAABg/DDZ7Xa7q4uQJJPJpA0bNmj9+vVO59x6661qbW3VG2+84Rj77LPPtGLFCpWUlCg6OrrPfdra2tTW1ub4ur6+XvHx8aqrq1NAQMCQPocJreyQ9MbXpMos2ewmPWNbr9Xf+E8lxwS7ujIAAAAAAACn6uvrFRgYOGBWNKY60Zqbm2U29y7ZYrFIkpxlgZ6engoICOh1wzCInCV9fbPs826T2WTXfZYNcnt2ufa894JkY2knAAAAAAAY21waojU2NiojI0MZGRmSpLy8PGVkZKiwsFCS9Mgjj+i2225zzF+3bp3eeustPfPMM8rNzdW2bdv0wAMP6IILLlBMTIwrngJ68vCV6Zrfqf7KZ9Vg8tMUndCCnQ/qxK8WqjHzHWl0ND0CAAAAAACcNZcu50xPT9eqVav6jN9+++168cUXdccddyg/P1/p6emOa7/73e/0xz/+UXl5eQoKCtIll1yiX/3qV4qNjR3UYw62RQ/np72xRnte+4VmF70sf1OLJKk+JFUBVzwmTb1UMplcWyAAAAAAAIAGnxWNmj3RRgoh2sg6eDxP+9/4uda3vSsfk7E3nTX2Alku/bGUdDFhGgAAAAAAcClCNCcI0UZea4dVT7+7XQF7/qBbLR/Ky9RhXJi0XLrkx9KkC11bIAAAAAAAmLDG5cECGJu83C36zpeXK/XO3+lG7z/qhc41arO7SQWfSS9cIb20Xjqx29VlAgAAAAAAOEWIhhGzZHKoXn5ovY7N/6lWtv2nXu68VJ2ySLmbpf+9VHr5Bqkkw9VlAgAAAAAA9MFyTrjE5qxy/eDN/fJoLNK33TboOstnMstqXEy5Slr1IylylmuLBAAAAAAA4x57ojlBiDZ61Da362fvHNLfM0qUaCrVT/3f1SXt6TKp6yU568vSykek8GTXFgoAAAAAAMYtQjQnCNFGn00HSvXjDQdU09yhFEuJfh/zgaZWfGRcNJml1Ouli38ghU5xbaEAAAAAAGDcIURzghBtdKpoaNMjbx3QP4+USZLWx9Tol0H/kG/u+8YEk0Wa+1Xpou9LwZNcWCkAAAAAABhPCNGcIEQbvex2u/5vb7Eef+eQGto65eVu1q+X2XVV1YsyHf/AmGR2k+bfJq34rhQY69qCAQAAAADAmEeI5gQh2uhXXNui77+ZqW3ZVZKkC6eE6qnlnYr44jfGSZ6SZPGQFnxNWvGw5B/lwmoBAAAAAMBYRojmBCHa2GCz2fXXnQX6t01H1Nphk7+nm366bqauDyuQafMTUsFnxkQ3b2nRXdLyhyTfMNcWDQAAAAAAxhxCNCcI0caWvMomfedvGdpbWCtJWj0jQv/25dmKqNwpffJL6cQuY6K7r7T4X6ULvyX5hLiuYAAAAAAAMKYQojlBiDb2WG12PbslV//50TG1W20K9nHXL9an6srUKCn7Y2nzL6SSfcZkzwBpyTelpd+UvAJdWzgAAAAAABj1CNGcIEQbu46erNfDr2fqcGm9JOnqtBj9v2tmKcjbXcp6T9r8b1LZAWOyV6DRlbb4XsnT34VVAwAAAACA0YwQzQlCtLGtvdOm331yXH9Iz5HVZleEv6d+dd0crUqJkGw26cg7UvoTUsVR4w4+odKyb0uL7pE8fFxbPAAAAAAAGHUI0ZwgRBsfMopq9Z2/ZSinokmS9NUL4vXjK2fKz9NNslmlg28ZYVp1jnEH3wjjJM8FX5PcvVxYOQAAAAAAGE0I0ZwgRBs/Wjus+o/3s/T8tjxJUlywt35zfZqWTA41Jlg7pQN/k9L/XaotMMb8o6UV35Hm3ya5ebqocgAAAAAAMFoQojlBiDb+bM+p0vfezNSJmhZJ0p3LkvT9y5Pl5W4xJlg7pIyXpU9/LdWfMMYC46WLvifNvVmyuLuocgAAAAAA4GqEaE4Qoo1PjW2d+uXGw3p1V5EkaUq4r568Ya7S4oO6J3W2SXtfkrb+VmooNcaCE6WLfyCl3iBZ3Ea8bgAAAAAA4FqEaE4Qoo1vm4+W6wf/t1/lDW2ymE365sop+tYl0+ThZu6e1NEi7XlR2vqk1FRujIVOlS7+oTT7WslscUntAAAAAABg5BGiOUGINv7VNrfr0b8f0juZJZKkmdEBevLGNKVEnfb73d4kffG/0mdPSS3Vxlh4irTyh9KMaySzWQAAAAAAYHwjRHOCEG3i2Li/VD95+4BqmjvkYTHr26un6a7lSd17pZ3S1iDt/B/p899JrbXGWGSqtOoRKXmtZDKNeO0AAAAAAGBkEKI5QYg2sZQ3tOpHbx3QP48YyzajA730wKXT9JUFcXK3nNZp1lon7XhG2v601FZvjEXPlVb9WJr2JcI0AAAAAADGIUI0JwjRJh673a4N+4r16w+yVFrXKkmaFOqjh1ZP17q0GFnMp4VjzdVGkLbzj1J7ozEWt0ha9SNp8irCNAAAAAAAxhFCNCcI0Sau1g6rXt5ZqD9szlZVU7skKTnSXw9fNl2XzYyU6fRwrKlK2vaUtOs5qbPFGEu40AjTklaMbPEAAAAAAGBYEKI5QYiGprZOvfh5vv74aY4aWjslSWlxgfrummQtnxrWN0xrKDPCtC/+JFnbjLGki4xlnglLRrZ4AAAAAAAwpAjRnCBEwyl1zR16dmuOnv8sXy0dVknS4qQQfW9NshYmhvS9Q32JtPVJac+Lkq3DGJtyqRGmxS0YucIBAAAAAMCQIURzghANp6toaNMf0rP18o5CtVttkqRVyeH6zmXJmh0b2PcOtUXSll9LGS9LNqOTTdMvN5Z5RqeNYOUAAAAAAOB8EaI5QYgGZ4prW/S7j4/rjT0nZLUZfyyuTI3WQ1+arqkRfn3vUJ1nhGmZr0p2I3xTylVGmBY5awQrBwAAAAAA54oQzQlCNAwkr7JJ//nRMf1jf4nsdslskq6dH6dvXzpN8SE+fe9QmS19+ivpwBuSuv44zfqytPIRKTx5RGsHAAAAAABnhxDNCUI0DNaR0nr99sNj+ueRMkmSu8Wkr16QoPtXTVVEgFffO5QflT79d+nQBuNrk1lKvV66+AdS6JQRrBwAAAAAAAwWIZoThGg4W/sKa/TbD4/ps+xKSZKXu1m3X5ioey+aomBfj753OHlQSn9COvqu8bXJIqV9Vbr4e1Jw4sgVDgAAAAAABkSI5gQhGs7V5zmV+s0HWdpbWCtJ8vd0010rknTX8iT5e7n3vUPJPmnzE9LxD4yvzW7SvH+RLvqeFBg3coUDAAAAAACnCNGcIETD+bDb7dqcVa5ff3BMR0rrJUnBPu76xsopum1porzcLX3vdGK3tPmXUs4nxtcWD2n+7dKK70gB0SNYPQAAAAAAOB0hmhOEaBgKNptdmw6W6skPjym3skmSFOHvqW9dOk03LoyXh5u5750KththWv5W42s3L2nuzdLib0jh00ewegAAAAAAcAohmhOEaBhKnVab3tpXrP/653EV17ZIkuJDvPXgpdO1fl6sLGZT3zvlbZE++aVUtKN7bOqXpCXfkKZcIpn6uQ8AAAAAABgWhGhOEKJhOLR1WvXariL97pNsVTa2SZKmRvjpO1+arstnR8l0ejBmt0sF26Qdz0hHN0rq+mMYnmKEaXNulNy9R/ZJAAAAAAAwARGiOUGIhuHU3N6pP39eoD9+mqO6lg5J0uzYAH3nsmStnB7eN0yTpOpcaeez0r6/SO2Nxph3iLTwa9Kie9g3DQAAAACAYUSI5gQhGkZCfWuH/ndLrv70WZ6a2q2SpEWJwfruZclaPDm0/zu11kn7/irt/KNUW2iMmd2kWdca3Wmx80eoegAAAAAAJg5CNCcI0TCSqhrb9MdPc/Tn7QVq77RJki6aHq7vXjZdc+KC+r+TzSplbZK2/0Eq/Lx7PGGpEaalXCWZ+zkFFAAAAAAAnDVCNCcI0eAKJ+ta9btPjuv1L4rUaTP+yK2ZFamHvjRdKVFneB2W7DP2TTv4lmQzlocqKEG64F+l+bdKXoEjUD0AAAAAAOMXIZoThGhwpcKqZj31z2PakFGsU3/yLp8VpW9dOlWzYs4QiNWXSrv/JO1+XmquMsY8/KS5t0iL/1UKnTL8xQMAAAAAMA4RojlBiIbR4FhZg/7rn8e16WCpI0z70sxIPXDJNKXGnSFM62iR9v/N6E6rONI1aJKSr5CWfFNKXC71d3gBAAAAAADoFyGaE4RoGE2OlTXo959k6x/7Sxxh2iUpEXrg0mmaGx/k/I52u5SbLu34g3T8w+7xyFRj37TUr0hunsNZOgAAAAAA4wIhmhOEaBiNsssb9fTmbP09o1hdW6bp4unheuDSaVowKfjMd648bpzomfGK1NFsjPmGS4vulhbeKflFDG/xAAAAAACMYYRoThCiYTTLq2zS7z/J1tsZxbJ2pWkrpoXpgUunaVFiyJnv3Fwt7X1J2vWsVF9sjFk8pNQbpCX3SlGpw1w9AAAAAABjDyGaE4RoGAsKqpr0h805+r+9JxyneS6dHKpvr56mJZNDz3xna4d05B1j37QTX3SPJ66Qlt4nTVsjmc3DWD0AAAAAAGMHIZoThGgYS4qqm/WH9By9uadIHVbjj+oFSSF68NJpWjolVKaBDhEo+sLYN+3w3yW71RgLmSwt/oY092bJ02+YnwEAAAAAAKMbIZoThGgYi4prW/RMerb+9sUJtVttkqSFk4L1wKXTtGJa2MBhWt0JY5nnnhel1jpjzDNQmn+rtPhfpaCE4X0CAAAAAACMUoRoThCiYSwrrWvR/3yaq1d2Faq90wjT5iUE6YFLp2nl9PCBw7T2JinzVWOpZ1W2MWYySzPWSUu+KcUvlgb6HgAAAAAAjCOEaE4QomE8KKtv1f98mquXdxaorStMS4sL1AOXTtMlKREDh2k2m5T9T2OpZ+7m7vHI2VLKVVLKWilqDoEaAAAAAGDcG2xW5NLdxbds2aJ169YpJiZGJpNJb7/99oD3aWtr049//GNNmjRJnp6eSkxM1PPPPz/8xQKjSGSAlx5dN1Nbf7BK96xIkre7RZkn6nTXn3frqt99pg8OndQZ83GzWZp+mXTb29I3tkvzb5MsnlLZQenTf5f+5yLpqVRp0/eknM3GYQUAAAAAAExgLu1Ee++997Rt2zYtWLBA1157rTZs2KD169ef8T7XXHONysrK9Itf/EJTp05VaWmpbDabli1bNqjHpBMN41FlY5v+d2ueXtqer+Z24wCBGdEBeuCSqVozK0pm8yA6ypqrpaz3pKxNUvbHUmdL9zXPQGnal4wOtalfkrz4swMAAAAAGB/G3HJOk8k0YIj2/vvv66abblJubq5CQkLO6XEI0TCeVTe160+f5erPnxeosa1TkpQc6a9vXTpVV8yOlmUwYZokdbRIuenS0Y3Ssfelporua2Z3KWmFlLzWuAXGDv0TAQAAAABghIzLEO2b3/ymjh07poULF+ovf/mLfH19dfXVV+vnP/+5vL29B/U4hGiYCGqb2/X8Z3l6YVu+GrrCtKkRfvrWJVN11ZyYwYdpkmSzSid2S1kbpaObpKrjva9Hz5VSrjQCtchZ7KMGAAAAABhTxmWIdvnllys9PV2rV6/Wo48+qsrKSn3zm9/UqlWr9MILL/R7n7a2NrW1tTm+rq+vV3x8PCEaJoS6lg69uC1ff/osV/WtRpg2OcxX918yVVenxcjNcg7bIlYeNzrUsjZJRbsk9XgLCZpkhGkpa6WECyWL29A8EQAAAAAAhsm4DNEuu+wybd26VSdPnlRgYKAk6a233tJXvvIVNTU19duN9thjj+nxxx/vM06IhomkvrVDL32er//9LE+1zcYhAYmhPrpv1VStnxcr93MJ0ySpsdxY7nl0k3HKZ2dr9zWvIGn6GiNUm3qp5Ol//k8EAAAAAIAhNi5DtNtvv13btm1Tdna2Y+zIkSOaOXOmjh07pmnTpvW5D51oQLfGtk79ZXuBntuaq+qmdklSfIi37ls5VdfOj5OH23kc2NveZJzkmbXJOKCgpbr7msVDSrrY6FBLXiv5R53nMwEAAAAAYGiMyxDt2Wef1YMPPqjy8nL5+flJkv7+97/r2muvVWNj46D2RWNPNEBqauvUyzsL9OyWXFU2GmFabJC37l05RdcviJOXu+X8HsBmlYp2Gss+j26UavJ6X49d0LXs80opPIV91AAAAAAALjMmQrTGxkZHV9m8efP05JNPatWqVQoJCVFCQoIeeeQRFRcX66WXXnLMnzFjhpYsWaLHH39clZWVuvvuu3XxxRfrueeeG9RjEqIB3VrarXp5Z4H+Z0uuKhqMjs0QXw/9y5JJum3pJIX5eZ7/g9jtUsXR7n3Uivf0vh6cZIRpKVdK8Ysl83kGeAAAAAAAnIUxEaKlp6dr1apVfcZvv/12vfjii7rjjjuUn5+v9PR0x7WjR4/qW9/6lrZt26bQ0FDdcMMN+sUvfsHpnMB5aO2w6rVdhfrfz/J0oqZFkuThZtZ182N11/LJmhrhN3QPVl8qHXvP2Ect71PJ2t59zTtEmn65sexzyiWSh+/QPS4AAAAAAP0YEyGaKxCiAc51Wm364FCZnt2aq8yiWsf4pSkRunvFZC2ZHCLTUC69bGuQsj82OtSOfSC1dj+m3Lykyauk+bcawRodagAAAACAYUCI5gQhGjAwu92u3QU1enZLrv55pEyn3iVSYwN194okrU2NPvcTPZ2xdkiF240OtayNUm1h97XAeGnhndL82yXf0KF9XAAAAADAhEaI5gQhGnB2cisa9fy2PL2x+4TaOm2SjEMIvrYsUTcuipe/l/vQP6jdLpUdkg78Tdr7l+6TPi2e0uzrpAvukWLnD/3jAgAAAAAmHEI0JwjRgHNT3dSuv+4o0Evb8x0nevp7uumrixN0x4WJigka3L6EZ62jRTr4lrTrf6TSzO7x2IXSBV+XZq2X3IbgAAQAAAAAwIREiOYEIRpwflo7rHp7X7H+97M8ZZc3SpLczCZdNSdad6+YrNmxgcPzwHa7dGK39MVzRqhm6zDGfcKkBbcbyz0D44bnsQEAAAAA4xYhmhOEaMDQsNnsSj9Wrue25Gl7bpVjfOnkUH39osm6eHq4zOYhPISgp8Zyae+fpS+elxpKjDGTWUq50uhOS1whDeUBCAAAAACAcYsQzQlCNGDoHSyu03Nbc/Xu/lJZbcZbytQIP929PEnr58XKy32YTta0dhqHEOx6Tsrf2j0enmLsmzbnRsnTf3geGwAAAAAwLhCiOUGIBgyf4toWvbgtT6/uKlJjW6ckKczPQ7ctTdS/LJmkEF+P4Xvw8iNGmJb5mtTRZIx5+EtzbzYCtbBpw/fYAAAAAIAxixDNCUI0YPjVt3bo9V1FemFbnkrqWiVJXu5mfWVBnO5aPllJYb7D9+CtdVLGq8beaVXZ3eOTVxlLPaevkczD1BkHAAAAABhzCNGcIEQDRk6H1aZNB0r13NZcHSyul2RsVfalGZH6+kWTtWBSsEzDtXeZzSblpRvdaVnvSep6qwtMkBbdJc2/TfIJGZ7HBgAAAACMGYRoThCiASPPbrdrR261ntuaq0+OljvG58YH6Z4Vk7VmVqTcLObhK6AmX9r9vLT3JamlxhizeEqpXzGWesbMG77HBgAAAACMaoRoThCiAa6VXd6gP32Wp//bW6z2TpskKT7EW3cuS9INC+Pl6+k2fA/e0SIdfEva9T9SaWb3eNwiY6nnzGskN8/he3wAAAAAwKhDiOYEIRowOlQ0tOkvOwr0l+35qmnukCQFeLnpliWTdMeFiYoM8Bq+B7fbpRO7pV3PSoc2SDbj8eUbLs2/XVp4pxQYO3yPDwAAAAAYNQjRnCBEA0aXlnar/m/vCf3pszzlVRqnarpbTLo6LVZ3r0jSjOhh/nPaWC7t/bP0xfNSQ4kxZrJIKVca3WmJy42N3AAAAAAA4xIhmhOEaMDoZLPZ9c8jZXpua66+yK9xjM+JC9TVaTG6ck60ogO9h68Aa6eUtdE4iCB/a/d4+Axj37Q5N0qefsP3+AAAAAAAlyBEc4IQDRj9Mopq9dzWXL1/8KSsNuMtymSSFiWG6Oq0GK1NjVaIr8fwFVB2WPriOSnzdanD6I6TZ4A092Yp+QrJL0ryj5S8guhSAwAAAIAxjhDNCUI0YOyobGzTewdK9U5mSa/uNDezScunhenqtBhdNitKfsN1GEFrnZTxqrF3WnVO3+sWT8kvUvKLkPyjjI9+UX2/9g2X3IYx9AMAAAAAnDNCNCcI0YCxqbi2Re9mluidzBIdKql3jHu6mXVJSoSuTovRqpQIeblbhv7BbTYpd7O050WpIktqLJNaa8/ue3iHOAnaIrtv/pFGxxvdbQAAAAAwYgjRnCBEA8a+nIpG/aMrUMutaHKM+3m66bJZkbo6LUbLpobJ3WIeviI6WqWmcqmhzAjVGk8ahxQ0dH089XVjmWTrHPz3dfPqHar5RfbT3RYp+UZIlmHqwAMAAACACYQQzQlCNGD8sNvtOlRSr39klugfmSUqqWt1XAvx9dDa1CitmxOjRYkhMptd1N1ls0ktNV2hWlmP0K28b/DWVncW39gk+YRKwZOk1OultJsk7+BhexoAAAAAMF4RojlBiAaMTzabXXsKa/SPzBJt3F+qqqZ2x7XoQC9dNSdaV6fFanZsgEyjdblke/Np3W1dtz7dbeWS3dr7vm7e0uzrpEV3SjHzWRIKAAAAAINEiOYEIRow/nVabfo8p0rvZJbog4Mn1dDWvZwyKcxX69JidHVatKZG+LuwyvNgs0rN1UbAVrRD2v2CVHaw+3p0mrTwLin1K5KHr+vqBAAAAIAxgBDNCUI0YGJp7bAqPatC/8gs0T+PlKmt0+a4NiM6QFenxWhdWrTign1cWOV5stulol3S7uelQxska5sx7hlgLPNceKcUMcO1NQIAAADAKEWI5gQhGjBxNbZ16p+Hy/ROZom2HKtQp6377W/BpGBdnRajtanRCvf3dGGV56mpSsp8xQjUqnO7xxMulBbdJc1YJ7mN4ecHAAAAAEOMEM0JQjQAklTT1K73Dp7UO5nF2plXrVPvhGaTdOGUMF2dFqM1s6MU6O3u2kLPlc0m5aUbYdrRTd17qPmESfP+RVr4NSk40ZUVAgAAAMCoQIjmBCEagNOV1bfq3f2leiezRJlFtY5xD4tZFyeH6+q0GK2eESlvD4vrijwf9SXS3r9Ie16UGkq6Bk3S1EuNvdOmXSZZ3FxZIQAAAAC4DCGaE4RoAM6koKpJ/8gs0TuZJTpW1ugY9/GwaPWMSK1Li9FF08Pk6TYGAzVrp3T8A+mLP0k5H3ePB8RKC+6Q5t8m+Ue5rDwAAAAAcAVCNCcI0QAMVtbJBr2TWax3MktUVN3iGPf3dNOXZkbqyjnRWj5tjAZq1blGZ9q+v0rNVcaY2U1KXmvsnZZ4kWQ2u7REAAAAABgJhGhOEKIBOFt2u10ZRbV6J7NE7x04qZP1rY5r/l5GoHbVnGgtnxouD7cxFjx1tkmH35F2/0kq3N49HjLFONVz7s2ST4jr6gMAAACAYUaI5gQhGoDzYbPZtbewRu/uL9WmA6Uqb2hzXPP3ctNlM6N01ZxoLZsaNvYCtbJD0u4XpMzXpPYGY8ziKc2+1tg7LW6hZDK5tkYAAAAAGGKEaE4QogEYKjabXXsKa7Sxn0AtwMtNl82K0pVzorVsyhgL1NoapYNvGnunndzfPR6ZKi26U0q9XvL0d119AAAAADCECNGcIEQDMBysNrt251dr04FSbTp4UhU9ArVAb3dd1rWH2rKpYXK3jJFAzW6XivdIu5+XDv6f1Nm1jNXDX5pzg7F3WuQs19YIAAAAAOeJEM0JQjQAw81qs+uLU4HagZOqbOwO1IJ8TgVqMbpwSujYCdSaq41lnrufl6qOd4/HLzaWes68RnL3cl19AAAAAHCOCNGcIEQDMJKsNrt25VVr44ESvX/wpCob2x3XgnzcdfmsKK1NjdbSsRKo2e1S3hYjTDv6rmTrNMa9Q6R5t0gLviaFTnFtjQAAAABwFgjRnCBEA+AqVptdO/OqtHF/qd4/eFJVTd2BWrCPuy6f3RWoTQ6V21gI1BpOSvv+Iu35s1RX1D3uHy15BUlegcbNu8fnZxr3DJDMY+B5AwAAABhXCNGcIEQDMBp0Wm3alVetdw8YgVp1j0AtxNdDa2ZF6crUaC2ZHDL6AzWbVTr+odGddvwjSef614pJ8groJ3ALGlwQ5+7D6aEAAAAAzhohmhOEaABGm06rTTvzqvXu/lK9f7BUNc0djmshvh66fHaUrkqN1gVJYyBQa6yQ6oul1rquW2335y21zsc7W87/sc3u/YdrAbHS9DVSwoWSxe38HwcAAADAuEKI5gQhGoDRrNNq047c7j3UegZqoV2B2pVzorU4KVQW8zjquups6xGwnQrcas8QxJ02ZrcO/BjeIVLyWmnGOmnySg5CAAAAACCJEM0pQjQAY0WH1abtOVXadKBU7x86qdoegVqYn4djD7VFiSFj41CC4WK3S+1NzgO3kwekrE1SS3X3fTz8pGlfMgK1aZdJnv6uqh4AAACAixGiOUGIBmAs6rDa9HlOlTbtNwK1upbuQM3fy00XTQvXqpQIXTw9XOH+ni6sdJSydkqFn0tH3pWO/ENqKOm+ZvGQJq8yArXktZJvqOvqBAAAADDiCNGcIEQDMNZ1WG3all2pjftL9c8jZb2WfErSnLhArUqO0KqUCM2JDZR5PC37HAo2m1SyTzr6DyNQq8ruvmYyS5OWGYFaypVSYJzr6gQAAAAwIgjRnCBEAzCeWG12ZZ6o1eaj5dqcVa6DxfW9rof6euji5HCtSo7QRdPCFejj7qJKRym7XarIMsK0I+9IJ/f3vh4zX5pxlTTjailsmmtqBAAAADCsCNGcIEQDMJ6V17cqPatCm7PKtfV4pRrbOh3XLGaTFiQEa2WKEaqlRPnLZKJLrZeaAulo15LPwh2SevwVGZ4ipVxldKlFp0n82gEAAADjAiGaE4RoACaK9k6bdhdUG6Ha0XIdL2/sdT060EsrkyO0Kjlcy6aGydfTzUWVjlKN5dLRjUaglrdFsvVYNhuY0NWhtk6KXyyZLa6rEwAAAMB5IURzghANwERVVN2s9Kxybc6q0LbsSrV12hzXPCxmLZ4c4thLLSnM14WVjkIttdLxD41ALfufUkdz9zXfcONAghlXS0kXSW4eLisTAAAAwNkjRHOCEA0ApNYOq7bnVin9aLk+ySpXUXVLr+uJoT5alRKhVckRuiApRF7udFo5tDdLOZ8Yyz6zNkmtdd3XPAOk6WuMDrWpqyUPwkgAAABgtCNEc4IQDQB6s9vtyqloUnpWuT45Wq4v8qvVYe3+q8Hb3aJlU8O0qmsvtZggbxdWO8pYO6T8rdKRd41QrbGs+5qblzTlUiNQm75G8glxXZ2ScSqptU3qaJE6W7s+tkmdLZJPmBQ8ybX1AQAAAC5CiOYEIRoAnFlDa4e2ZVc5Tvwsb2jrdT0lyl8rkyN0SUqE5icEyc1idlGlo4zNJp34Qjr6D2PZZ01+9zWzm5S43AjUUq6SfEJ7h1gdrUawdXq4dcbxrq9PhWKdrWcet7Y5LV2SFLdImnuzNOtayTtoOH+lAAAAgFFlWEO0P//5zwoLC9OVV14pSfr+97+vZ599VjNnztSrr76qSZNG7/9mE6IBwODZ7XYdLq3vCtQqtK+wRrYef2sEeLlpxfRwXZIcoYuTwxXm5+m6YkcTu10qO2h0qB35h1R+yNUV9WaySO7eRrecm5fUUCrZrcY1i6eUslZKu1macolk4cAJAAAAjG/DGqIlJyfrmWee0SWXXKLt27dr9erV+s///E+9++67cnNz01tvvXVexQ8nQjQAOHc1Te3actw47fPTYxWqae4+sdJkkubEBuqyWVG6MjVaiRxO0K0qx1jueeQfRrdaTxZPyd1LcvOW3Dx7h1v9jbt3fe3m3XXdaxDjp32/04OxhjLpwN+kjFek8sPd436RUur1Roda5Kzh/3UCAAAAXGBYQzQfHx8dPXpUCQkJ+sEPfqDS0lK99NJLOnTokFauXKmKiorzKn44EaIBwNCw2uzKKKrtOvGzXAeL63tdnx0boKvmxOjK1GjFh/i4qMpRqLVOslmNcMviKZlH0XJYu10qzZQyX5UOvCE1V3Vfi04zutNSr5d8Q11XIwAAADDEhjVEi4iI0AcffKB58+Zp3rx5evjhh3XrrbcqJydHaWlpamxsPK/ihxMhGgAMj7L6Vn1ytFybDpTq85wqWXus+0yLD9K6OdFamxrNwQRjRWe7lP2R0Z127APJ1tV1aHaTpq0xutOmXSa5ebi2TgAAAOA8DWuIdsstt+jo0aOaN2+eXn31VRUWFio0NFTvvPOOfvSjH+ngwYOD+j5btmzRr3/9a+3Zs0elpaXasGGD1q9fP6j7btu2TRdffLFmz56tjIyMQddOiAYAw6+qsU3vHzqpjftLtSO3qtc+agsmBeuqrkAtMsDLdUVi8JqqpINvGoFaaUb3uHdI13LPr0rRc401vQAAAMAYM6whWm1trX7yk5+oqKhI3/jGN3T55ZdLkn72s5/Jw8NDP/7xjwf1fd577z1t27ZNCxYs0LXXXjvoEK22tlYLFizQ1KlTVVZWRogGAKNYeUOr3j94Uu9mluqLgmqd+lvHZJIuSAzRVXOidUVqNIcSjBXlR4wwbf/rUmNZ93j4DCNMm3Oj5B/luvoAAACAszSsIdpwMJlMgw7RbrrpJk2bNk0Wi0Vvv/02IRoAjBEn61q16UCp3t1for2FtY5xs0laOiVUV82J0ZpZUQrxZYngqGftlHI3G4Ha0Y2Stc0YN5mlKZcagVrylcZhBgAAAMAoNtis6JzOrX///ffl5+en5cuXS5KefvppPffcc5o5c6aefvppBQcHn1vVg/DCCy8oNzdXf/3rX/WLX/xiwPltbW1qa2tzfF1fX3+G2QCA4RQV6KU7lyfpzuVJKq5t0ab9RqCWeaJO27KrtC27Sj95+6CWTQ3TVXOitWZmlAJ93F1dNvpjcZOmfcm4tdRKhzYYgdqJXcZeatkfSZ6B0uwvS3NvkeIWsdwTAAAAY9o5HQn2ve99zxFGHThwQN/5zne0du1a5eXl6eGHHx7SAns6fvy4fvjDH+qvf/2r3NwGl/898cQTCgwMdNzi4+OHrT4AwODFBnnrnosm6+/3L9eW763S9y9P1qyYAFltdm05VqHvv7lfC3/5ke588Qu9tfeEGlo7XF0ynPEOkhZ+Tbr7I+n+PdKK70oBcVJbnbTnRelPX5J+t0Da8muptsjV1QIAAADn5JyWc/r5+engwYNKTEzUY489poMHD+rNN9/U3r17tXbtWp08efLsCxlgOafVatWSJUt011136d5775UkPfbYYwMu5+yvEy0+Pp7lnAAwSuVWNGrj/lJtPFCqoycbHOMebmZdPD1cV82J1uoZkfL1PKdmaowUm03K32p0px15R+po7rpgkpJWSGk3SzOvljx8XVomAAAAMKx7ooWEhOizzz7TzJkztXz5ct122236+te/rvz8fM2cOVPNzc0Df5PTCxkgRKutrVVwcLAsFotjzGazyW63y2Kx6MMPP9Qll1wy4OOwJxoAjB3Hyxr0bteSz5yKJse4l7tZl6RE6MrUGF2SEiFvD8sZvgtcrq1BOvyOlPmqEayd4uEnzbxGSvuqNGmZZD6nBvlzY7NJHU1GbW2NXR/rpfbGHmM9vg6IkVJvkILoaAcAABhvhjVEu/rqq9Xe3q5ly5bp5z//ufLy8hQbG6sPP/xQ999/v44dO3bWBQ8UotlsNh0+fLjX2B/+8Ad98sknevPNN5WUlCRf34H/N5sQDQDGHrvdrqyyBr2baQRq+VXd/1nj42HRpTMiddWcaF08PVxe7gRqo1pNgZT5mpT5ilST3z0elCDNuUlKu0kKndL/fe12qb2pR9DV43b62BnnNErtDf0/xhl1ddHNvUWasY4uOgAAgHFiWEO0wsJCffOb31RRUZEeeOAB3XXXXZKkhx56SFarVf/93/89qO/T2Nio7OxsSdK8efP05JNPatWqVQoJCVFCQoIeeeQRFRcX66WXXur3/oNZznk6QjQAGNvsdrsOldQ7OtRO1LQ4rvl5uulLM41Abfm0MHm6EaiNWna7VLjDCNMOvW10fZ0Su1Dy9O8bfrU3Snbb0NZhskiefpJngPGYHn7GR8+ujx7+RlhWtLOfLrr1ximkCReObBfdaGO3S5XHJZ8QyTfM1dUAAACctWEN0YZKenq6Vq1a1Wf89ttv14svvqg77rhD+fn5Sk9P7/f+hGgAMLHZ7XZlnqjTxv0l2ri/VCV1rY5r/l5uWjMrSmtTo3RBUqj82ENt9Gpvlo5uNAK1nM2SBvqniakr9DoVePUMv07/ur+ArMccd+/BnxpaUyDtf13KePm0LrpJxpLUtJukkKRz/EUYYzrbpYLPpKObpKz3pPoTksVDmnertPxBo7MQAABgjBj2EM1qtertt9/WkSNHJEmzZs3S1Vdf3WvPstGIEA0Axiebza59RTX6R2apNh0oVXlD96EyFrNJs2MDtWRyiJYkhWphYrD8vdxdWC2cqi8xgjSz5bRArEdo5u4z+OBrOJzqost42eii67k0dNIyae7Nxl5vnv4uK3FYtNZJxz+SsjZJx/9pnL56itldsnWdoGt2M0LFFQ9LIZNdUysAAMBZGNYQLTs7W2vXrlVxcbGSk5MlSVlZWYqPj9fGjRs1ZYqTvUxGAUI0ABj/bDa7vsiv1rv7S5V+rFxF1S29rptN6grVQrU4KUQLE0MU6E2ohnNwqosu42UpN12OLjp3H2nG1cZyz8SLxu5yz7oTRqfZ0Y1S/mfdQZkk+YZL0y+XUq6UJq+UTuyWtvyHlLfFuG6ySKnXSyu+I4VPd0n5AAAAgzGsIdratWtlt9v18ssvKyQkRJJUVVWlf/mXf5HZbNbGjRvPvfJhRogGABNPcW2LduZWaWdutXbkVamgqvcp0maTNDMmQIuTQrVkcqguSAxRoA+hGs5S3Ymu5Z6vSlXHu8cD46U5Nxodas4OTRgt7Hap7GDXMs2NUmlm7+uh06SUtVLylVLcQqNj8HSFO40wLfufXQMmadZ66aLvSZGzhvsZAAAAnLVhDdF8fX21Y8cOpaam9hrPzMzUsmXL1NjYePYVjxBCNABAaV2LduZWa2delXbkViuvsqnXdZNJmhEVoMWTQxyhWrCvh4uqxZhjtxtdWZmvSAf+r/eyx/jFRpg268uSV6DrauzJ2iEVfG4s08zaJNUW9rhokuIvkJLXGh1nYdMG/32L90hbfmuEcaekXGWEaTFzh6p6AACA8zasIVpISIjeffddXXjhhb3Gt23bpnXr1qm6uvrsKx4hhGgAgNOV1bdqR26VduZVa0dulXIrmvrMSYnydyz/vCApRKF+ni6oFGNOR6sRTGW8IuV83H26qJuXESjN/ao0eVX/HV3Dqa1Byv7YqO3YB1Jrbfc1Ny+jppS1xnJNv4jze6yTB6Qtv5EO/12O5a7TLpMu+r4Uv+j8vjcAAMAQGNYQ7bbbbtPevXv1pz/9SRdccIEkaefOnbrnnnu0YMECvfjii+dc+HAjRAMADKS8odXRqbYzt1rHy/t2WE+P9OsK1UK1eHKIwgjVMJD6UunA34zlnhVHusf9o7uXe4YnD9/jN5w0QrOjm6S8TyVre/c1n1AjMEteK01ZJXn4Dv3jlx+Vtv5WOvhmd5g4eaURpiUuG/rHAwAAGKRhDdFqa2t1++236x//+Ifc3Y09Yzo6OnTNNdfohRdeUFBQ0DkXPtwI0QAAZ6uysU27urrUduZWK6usoc+cqRF+WjI5xBGqRfh7uaBSjAl2u1SyT8p8VTrwhtRS030tdoFxsuXs6ySfkPN/nIqjxqEAWZuM5ZU9hUzuXqYZv3jkuuGqcqTPnpQyX5NsncbYpGXGMs/JK1178ioAAJiQhjVEOyU7O1tHjhj/kzpjxgxNnTr1XL/ViCFEAwCcr+qmdu3q2k9tR26Vjp7sG6pNDvd1LP9cMjlUkQGEauhHZ5uxnDLjFen4h5LdaoxbPKTkK6S5t0hTLpUsboP7ftZOqWhnV8fZRqkmr/f12IXdBwOEJ7s2sKopkLY9Je37a3dXXNwiI0ybdhlhGgAAGDFDHqI9/PDDg37wJ598ctBzRxohGgBgqNU0tWtXfnen2pGT9Tr9b9ekMF9HoHbh1FA61dBXY4XRmZbxilR2oHvcN0Kac4Ox3LO/0y3bm6ScT4xlmsfel1p67E1r8ZQmX2x0nCVfIflHDf/zOFt1xdLn/y3teVHqbDXGotOMMC35Sslsdml5AABg/BvyEG3VqlWDemCTyaRPPvlkcFW6ACEaAGC41TV3aFd+tXbmVmlHXpUOl9TLdtrftnPiArUyOUKrksM1Jy5IFjNdN+ihdL+x3HP/36Tmyu7x6DQp7WZp6qXdJ2rmpneHT5LkHSxNW2N0nE25VPL0G/Hyz0lDmbT9d9IXz0sdXYd7RMyULvquNHP9yB++AAAAJowRWc45FhGiAQBGWl1Lh3bnV2tnXrU+z6nUweL6XtdDfD108fRwrUqJ0EXTwhTk4+GiSjHqWDuk4x9Jma9IWe9Lto7+5wVNMvY2S14rJSwd/PLP0aipStrxtLTzWam9a6l06DQjTJv9lbH93AAAwKhEiOYEIRoAwNXKG1r1aVaF0rMqtOVYhRraOh3XzCZpfkKwVqVEaGVyuGZGB8jE3lCQjHDp4P9JGS9LpRlSzDxjuWPKWqNja7y9TlpqjCBtxx+k1lpjLDhJWvGwNOcmyY2wGQAADA1CNCcI0QAAo0mH1aY9BTXanFWu9KMVfU7+jAzw1KrkCK1MjtDyaWHy86QLBzIOEJgoHVmt9dIX/ytt/73UXGWMBcZLy74tzbtVcmd/QQAAcH4I0ZwgRAMAjGbFtS1KzyrX5qMV2pZdqZYOq+Oau8WkRYkhWpUcoVUp4ZoS7keXGiaO9iZp9wvGIQSNZcaYf7R04QPSgjskDx+XlgcAAMYuQjQnCNEAAGNFa4dVu/KqjS61rArlVTb1uh4f4m0EaskRWjI5VN4ebLyOCaCjVdr3F+mzp6T6E8aYb7i09H5p0V2Sp79LywMAAGMPIZoThGgAgLEqr7JJ6Vnl+uRouXbmVqvdanNc83Qza+mUUK1KjtAlKRGKD6ErB+NcZ7tx4MLWJ6XaAmPMO1hacp+0+OuSV+DI1WK3S7ZO4yAIW4dks3Z/bu0wrtk6pcA4Qj4AAEYhQjQnCNEAAONBc3unPs+u0uascm0+Wq6SutZe16eE+3Yt+4zQosQQebiZXVQpMMysHdKBN6Stv5Wqso0xz0Bp7s1GYGXr7AqzOnuHWo5wy9m1U587u2btPW9QTFJ4shS7QIqdL8XMlyJnc0gCAAAuRojmBCEaAGC8sdvtOlbW6AjUdhfUyGrr/uvd18OiZVPDdEmKcUBBVCAbsWMcslmlQxukLb+RKo64uhpJJsniLpndJbObZJLUWtd3msVDippjhGqxC4xbyBTJTPANAMBIIURzghANADDe1bV0aFt2pTYfLdfmrApVNrb1uj4jOkCrksO1KiVC8+KD5Gbhh3WMIzabdPRdKTddMpm7giyLEWadCrUsbt3h1qnPB32t55jbGa71s0dhQ5lUslcq3isV7zFurbV953kGSjFzuzvWYhdIATHD/AsHAMDERYjmBCEaAGAisdnsOlRSb3SpZZUro6hWPf/mD/By04pp4Vo6JVRLp4RqcpgvJ34CI8Vul2ryeoRqe6XSDKmzte9c/2hj+eepUC1mnuQdNNIVAwAwLhGiOUGIBgCYyKqb2vXpsXJtPlqhT49VqK6lo9f1CH9PLZ0SqiWTQ7V0cqgmhfoQqgEjydohlR/p6ljrCtbKD0t2W9+5oVO7l4DGzJeiUiV3lmsDAHC2CNGcIEQDAMDQabUpo6hW27KrtD23UnsLa9Xe2fsH9ehALy2dHKolU4xQjVM/ARdob5JK9xuh2qlwrSa/7zyzm3FQQc/91cKm97+0FAAAOBCiOUGIBgBA/1o7rNpbWKMdOVXanluljKJadVh7/zMhLtjb0aW2dEqoYoK8XVQtMME1VUkl+7r3ViveIzVX9p3n4SdFz+0K1rrCtcB4iQ5TAAAcCNGcIEQDAGBwWtqt2lNQo+25ldqeU6X9J+rUaev9z4ZJoT6OQG3p5FBFBLCUDHAJu12qK+oRqu0zQraOpr5zfcON5Z8Ji6XZX5GCJ418vQAAjCKEaE4QogEAcG6a2jr1RX61tudWaUdOlQ4U1+m0TE2Tw30dnWpLJocq3N/TNcUCkGxWqSKrx/5qe6SyQ5Kts/e8xBVS2lelmddInn6uqRUAABciRHOCEA0AgKFR39qh3fnV2t61/PNQSb1O/1fFtAg/R5fa4smhCvH1cE2xAAwdrdLJA1LxbinrPSlvi6SuP7juPtKMq6W5X5USL5LMZpeWCgDASCFEc4IQDQCA4VHX3KGdeUagtj2nSkdPNvSZkxLl7zj9c0lSqAJ93F1QKQCH2iJp/2tSxqtSdU73eECclHaT0aEWNtV19QEAMAII0ZwgRAMAYGTUNLUboVpXp9qxssZe100maWZ0gGNPtUVJIQrwIlQDXMJul058IWW8Ih18S2qr674Wd4HRnTbrWsk7yGUlAgAwXAjRnCBEAwDANSob27Sjq0ttR26Vcip6b3huNkmpsYFGl9qUUC1KDJGfp5uLqgUmsI5WKWuTlPmqlP1PyW4zxi2eUspaae4t0uRVkoU/nwCA8YEQzQlCNAAARofy+lbjkIKuYC2/qrnXdYvZpNTYQMfyz4WTguVLqAaMrIaT0v6/GYFa+eHucb9Iac4NUtrNUuRM19UHAMAQIERzghANAIDRqbSuxVj6mVOlHXlVKqpu6XXdzWzSnDijU23plFAtmBQsHw9CNWBE2O1SaaYRph14Q2qu6r4WnWaEaanXS76hrqsRAIBzRIjmBCEaAABjw4maZu3IrXZ0qhXX9g7V3C0mpcUFOUK1+QnB8vawuKhaYALpbJeyPzL2Tzv2gWTrMMbNbtK0NdLcm6Vpl0luY/w03pYayWYjGASACYAQzQlCNAAAxqai6mbH8s8dOVUqqWvtdd3DYtbc+CAtmRyiJV2hmpc7oRowrJqqpINvGoFaaUb3uHeI0Zk296tS9FzjJJHRxm6Xmqul6tx+bjlGiCZJPqFSeIoUntz7o1/k6HxeAICzRojmBCEaAABjn91uV1F1i9Gl1tWpdrL+tFDNzax58d2danPjgwjVgOFUfsQI0/b/TWo82T0ePsMI0+bcKPlHjWxNdrvUVGmEYv2FZa11A38PZ7wCpbDkHuFaV8AWGEe4BgBjDCGaE4RoAACMP3a7XQVVzb1CtfKGtl5zPN3Mmp8Q7AjV0uID5elGqAYMOWunlJsuZbwsHd0oWbv+LJrM0pRLjUAt+UrJ3WtoHs9ulxrLuoOxqp6BWZ7U3nDm+wfESiGTpZCkro9TjI/BiUYYVnlcqjwmVRyVKrKMj9W53aeWns7DTwqb3rd7LWiSZDYPzXMGAAwpQjQnCNEAABj/7Ha78iqbtCO32rEEtOK0UM3L3awFk4K1JMkI1ebEBcnDjR9wgSHVUisd2mAcSFC0s3vcM1Ca/WXjQIL4Cwbu3LLZpIZSJ0svc6WO5jPc2SQFxvcIybpuoVOMoMzd++yfV0er0d3WM1iryJKqsiVbZ//3cfOWwqadFq6lGDVYOCQFAFyJEM0JQjQAACYeu92unIomR6faztwqVTa295rj7W7RwkSjU23J5BDNiQuSu4VQDRgyVTlGmJb5mlRX1D0eMkVK+6o05wajW636tE6yUx87W5x/b5NZCkroHZKdugUnSm6ew/70JEnWDqPe08O1yuPdHXmns3hIodOk8NO610KmjP3DGQBgjCBEc4IQDQAA2O12ZZc3GocUdJ0AWtXUO1Tz8bBowaRgLZ0SqgunhCk1NlAWM/scAefNZpPytxqB2uF3pI6mwd3PZJGCJ3Uvt+x5C0oY3YGTtVOqLegRrnUFbJXHnHfRmSxGt1zPrrWwaZJ3sLFk1MPXCODYfw0AzhshmhOEaAAA4HR2u13Hyxu1Pafr9M/cKtU0d/SaE+jtrmVTQ7ViWriWTw1TfIiPi6oFxpG2RunIO8aBBPlbJbO70TkW2jMo61qGGRgvWdxdXfHQstmMrryeXWunPg60l5tkBG2nAjXHrefXPqd93c/n7qfP8ZXMY3C/SJvNWEprMo2/1wmAYUeI5gQhGgAAGIjNZtex8gZtzzEOKdieW6WG1t77HCWF+WrFtDCtmBauJZND5O/FD23AeWlrNPYnG4sBzlCz26X6kr7BWnWO1NYgdbYO/D3Oh5v3aWGbTz8hXNfXJosRXtmtxkdbp2Tr+fm5fN1z7PSPTu6jrh9rTWYpOk1KulhKukhKWGLUCgBnQIjmBCEaAAA4W51WmzJP1Omz45XaerxC+4pqZbV1/xPKzWzSvIQgrZgWrhXTwjQnLoilnwCGj7XTWAba3tR1a+z7eUdz/+NOb41GEDbemN2luEXS5K5QLXbh6F76C8AlCNGcIEQDAADnq761QztyqrS1K1TLr+q9p1GAl5uWTQ1zhGos/QQw6tntUmdbd6DmCOn6C+JOBXSNkt0mmd2MjjSzm9FJaHbrcRvo61NjlkHMOcPXpq7v0dYgFWyT8rZIuZ9K9Sd6P093H6M77VSnWnQa3Y8ACNGcIUQDAABDrai62RGobcuuVP1pSz8TQ30cgdrSKaEs/QSAkWC3G6el5m3pvjVX9p7jGSglLu/uVAtP4bAGYAIiRHOCEA0AAAynTqtNB4rrHKHa3sLeSz8tZpPmxXct/ZwepjmxgXKzmF1YMQBMEHa7VH64O1DL/0xqq+89xzdCSlrR3akWnEioBkwAhGhOEKIBAICR1NDaoR251dp6vEJbj1cqr7Kp1/UALzddOCVMK6aH6aJp4Sz9BICRYu2USjOlvE+NUK1wh9TZ0ntOYII0+SIjVEtcIQVEu6bW8aaxQspNl3I+MZbfunlKgXHGKbxB8cave2Cc8bl/jGRxc3XFGOcI0ZwgRAMAAK5UVN2sz7KNLrXPjvdd+jkp1Mdx6ufSKaEKYOknAIyMzjbpxO7uUO3EF10nf/YQNt3oUEu62FgG6hPimlrHms42I6TM+cS4ndw/+PuazEaQFhRvhGynwrWeQRsnsI4Ma6dUWyBVHpOq86Ql3xg3nZqEaE4QogEAgNHCarMbSz+PGV1qewtr1Hna0s+58UGOUC0tjqWfADBi2hqloh3dhxSUZkrq+eOzSYpKNUK1ySulhKWSp5+Lih1l7HYjaMn5RMr+2Og26+h9CI+iUqUplxiBpNki1RZJdSekuiKptrDr8xOSrWPgx/MO6QrUEk4L2rpuvmHjJuwZEa11UmW28XtYddz4WHlcqsrp/fvx3WzJL9x1dQ4hQjQnCNEAAMBo1djW2XXqpxGq5Z629NPfy00XTgnV8mnhWpQYrGkR/rKY+aEAAEZES42U33XyZ96nUsXR3tfNblLsgu5OtbhFkruXa2p1heZqKXdzV7fZZqm+uPd13wgjNJtyiTRlleQXMfD3tNmkxrKuQK3wtKCt6/O2uoG/j5t313LRfrrYAuOlgBjJMsE6v2024/TaUwFZZY+wrPGk8/u5eUthU42uzNWPG7+G4wAhmhOEaAAAYKw4UdOsz45XauvxSn2WXam6lt7/G+/n6aa0+EDNTwjWvIQgzYsPVrCvh4uqBYAJpqFMyt/avfyzJr/3dTcvKX6xFLfQCBxCpxnhg1egS8odcp3txpLXnI+N4KwkQ7069Sye0qQLu4OzyFnD0w3WWueki60raDtTIHSKySz5R3d3sQXEGN1rPqGn3UKME13NY6grvL1Zqsru0U12KizL7rsHYE9+UVLYNOO1Gzat+/OAuLH1/AeJEM0JQjQAADAWWW12HSyu09bjFfo8p0qZRbVqarf2mTc5zFdzE4I0PyFY8xOClRxFtxoAjIiagu6TP/O2OA9vxmo4Ybcby/lO7WuWv1Vqb+w9J2KW0WU25RIjQHP3dk2tPXW2GV1xfbrYTt1OSNb2wX8/k8UI0/oEbP0Ebqc+9/Ad3uWkdrvRsVd57LTOsuNGB58zZncpdIrxGgw99ZqcPr7C3kEiRHOCEA0AAIwHVptdx8oatLewRnsLarWvqEa5FU195vl4WJQWF6T5k4K6OtaCFUK3GgAML7vdCDDyPpXKD3cHGoNZJhfaM2CbLoVOlTxcdHJzS42xH9ypJZqnBzI+Yd2h2eRVY/P0UptNaqro3cXWUGosT22u6nGrltobzu0xLJ79h2s9x3p2vnmH9L8UuLNdqs7tHZZVdb222uqdP753sBSW3Du0DZsuBU3i5NMuYyJE27Jli379619rz549Ki0t1YYNG7R+/Xqn89966y0988wzysjIUFtbm2bNmqXHHntMa9asGfRjEqIBAIDxqqapXRlFtdpXWKO9hbXKKKpVY1tnn3mJoT7dS0ATgpUS5c+BBQAwEnpu2O7YtL2fDdtPFxjfu3vtVNDmHzW0HU7WDql4T/eBACV7Jbut+7rFQ0pY0mOJZuro7p4bap1tPcK1yu5wrVfY1iN0a6qUrG3n9lgeft2Bm6e/VFdsLBm29+1Cl2QsSQ1O7A5eHV1l0yXf0HN9xhPGmAjR3nvvPW3btk0LFizQtddeO2CI9uCDDyomJkarVq1SUFCQXnjhBf3mN7/Rzp07NW/evEE9JiEaAACYKKw2u7LLG7u61Wq0r6hW2eWNfeZ5u1s0Jy5Q8ycFO8K1MD9PF1QMABOUtVOqLeixFK/Hx5Zq5/fz8O/RXdSjwyhksuQ2yPfx6tzuTrO8LX07msJTukOzSRcaSxMxOHa7cSrp6eFaf4Fbz69tff8DzGEofs/Rx5gI0XoymUwDhmj9mTVrlm688UY9+uijg5pPiAYAACayuuYO7SsyOtX2FdYoo6hWDa19/7GeEOKj+V2davMTgpUS7S93utUAYOQ1VfXYDL7Hflc1eb27xHoymY2lej2XhZ76aHGX8rZ2Hwhw+oEI3sHG0sxTwVlg7LA/RfRgtxsdiz3DtdZa4+CD4eg+hKTBZ0VjevGrzWZTQ0ODQkJCnM5pa2tTW1t3+2R9/RnWCQMAAIxzgT7uWpkcoZXJEZIkm82u7IpGYwloQa32FtboeHmjCqubVVjdrLczSiRJXu5mzYkN0rxJ3YcWhPvzP94AMOx8Q41bwpLe451tUnVej6WhPU5gbKs3QraaPOn4B2f+/mY3KX5J995m0WmS2TJ8zwdnZjJJ3kHGLXSKq6vBacZ0iPab3/xGjY2NuuGGG5zOeeKJJ/T444+PYFUAAABjh9ls0vRIf02P9NeNixIkSXUtHcosMgK1fV0da/WtndqVX61d+d3LiuKCvR3LPxclhmhGdAAngQLASHHzlCJSjFtPvU5qPN57eeipgwFCp3V3miUuM/bcAjCgMbuc85VXXtE999yjv//971q9erXTef11osXHx7OcEwAAYJBsNrtyKxsdS0D3FtTqWHmDTv9XZICXmy5ICtXSKaFaMjlEM6ICZCZUA4DRo71Zam+S/MJdXQkwqozr5Zyvvfaa7r77br3xxhtnDNAkydPTU56eLDUAAAA4V2azSVMj/DU1wl83LIyXJDW0diizqM44tKCwRrvzjW61fx4p0z+PlEmSAr3dtTgppCtUC1VypD+hGgC4koePcQNwTsZciPbqq6/qzjvv1GuvvaYrr7zS1eUAAABMSP5e7lo+LUzLp4VJkjqtNh0qqdf23CrtyK3SF3nVqmvp0IeHy/ThYSNUC/Zx12JHp1qopkf6ycTmyAAAYIxwaYjW2Nio7Oxsx9d5eXnKyMhQSEiIEhIS9Mgjj6i4uFgvvfSSJGMJ5+23367/+q//0uLFi3Xy5ElJkre3twIDA13yHAAAACC5WcxKiw9SWnyQ7r14ijqsNh0srusK1aq1O79aNc0dev/QSb1/yPg3XKivhxZPDtHSyUaoNjWCUA0AAIxeLt0TLT09XatWreozfvvtt+vFF1/UHXfcofz8fKWnp0uSVq5cqU8//dTp/MEY7DpXAAAADJ0Oq037T9Rpx6lOtfxqtXbYes0J8/PQ4smhjlBtSrgvoRoAABh2g82KRs3BAiOFEA0AAMD12jtt2n+iVttzqrQjr0q782vU1tk7VAv399QSR6gWoqQwQjUAADD0CNGcIEQDAAAYfdo6rcosqjNCtdwq7SmsUftpoVpkQM9QLVSTQn0I1QAAwHkjRHOCEA0AAGD0a+2wKqOo1hGq7SusVbu1d6gWHejVK1SLD/EmVAMAAGeNEM0JQjQAAICxp7XDqr2FNdqRYxxUsK+oRh3W3v+MjQ3y7nVQQVwwoRoAABgYIZoThGgAAABjX0u7Eaqd6lTLKKpVp633P2tDfT2UGheoObGBSo0LUlpcoCICvFxUMQAAGK0I0ZwgRAMAABh/mts7taegRjtyq7Q9p0r7T9T1CdUkY1+11FgjUEuNC9ScuCCF+Hq4oGIAADBaEKI5QYgGAAAw/rV2WHX0ZIMOnKjV/hN12n+iTsfLG9RPrqbYIG+lxQcqNTZIc+ICNTs2UIHe7iNfNAAAcAlCNCcI0QAAACam5vZOHS6pV+aJOiNcK65TbkVTv3OTwnyVGhuoOV3darNiAuTr6TbCFQMAgJFAiOYEIRoAAABOqW/t0MHiOh04Uaf9xXXaf6JWRdUtfeaZTdLUCD9Ht9qcuEDNiA6Ql7vFBVUDAIChRIjmBCEaAAAAzqSmqV0HugK1/SfqdKC4TqV1rX3muZlNmh7p32sp6PRIf3m4mV1QNQAAOFeEaE4QogEAAOBslde3dgVr3eFaVVN7n3kebmbNiA7QnB5LQadG+MliNrmgagAAMBiEaE4QogEAAOB82e12lda19upW23+iTnUtHX3mertbNCsmQIuSQrR8apgWTApmGSgAAKMIIZoThGgAAAAYDna7XYXVzY5QLbOoVgeL69TUbu01z9PNrAu6ArXl08I0IypAZjrVAABwGUI0JwjRAAAAMFJsNrtyK5uUUVSrz3Mq9dnxSpU3tPWaE+rroQunhmn51FAtnxau2CBvF1ULAMDERIjmBCEaAAAAXMVutyu7vFFbj1dqW3alduRW9elUSwrz1fKpYVo2NUxLp4Qq0NvdRdUCADAxEKI5QYgGAACA0aK906aMolp9ll2pz45XKPNEnay27n+em01SWnyQsfRzapjmJQRz+icAAEOMEM0JQjQAAACMVvWtHdqRU6Vt2ZXaml2p3IqmXtd9PCxanBSiZVPDtGJauKZH+slkYj81AADOByGaE4RoAAAAGCtKalu6utSM5Z9VTe29rof7ezqWfi6fGqaoQC8XVQoAwNhFiOYEIRoAAADGIpvNrqMnGxxdarvyqtTaYes1Z2qEn2Pp55IpofLzdHNRtQAAjB2EaE4QogEAAGA8aOu0ak9BjbZ1dartL65Tz3/Zu5lNmpcQ1LX0M0xz4oLkbmE/NQAATkeI5gQhGgAAAMaj2uZ2bc+p0tZsY+lnQVVzr+t+nm5aMjlUy6eGavm0cE0J92U/NQAARIjmFCEaAAAAJoKi6ubu/dRyKlXb3NHreoS/p+bGByktPkhz4gI1JzZIgT7uLqoWAADXIURzghANAAAAE43NZtehknojVMuu0Bf5NWrvtPWZlxTmawRqcUFKiwvUrJhAeXtYXFAxAAAjhxDNCUI0AAAATHStHVbtP1Gn/SdqlXmiTplFtSqsbu4zz2I2aXqkv9JOBWvxgZoe6c/eagCAcYUQzQlCNAAAAKCvmqZ27S82ArVT4VpFQ1ufeZ5uZs2KCdCcuCDN7VoKmhjqK7OZ/dUAAGMTIZoThGgAAADAwOx2u07WtyqzyAjU9p+o1f4TdWpo7ewz19/LTXPiApUWF+ToWIsK8OLgAgDAmECI5gQhGgAAAHBubDa78qqajE61ojplnqjVoZL6fvdXC/f3VFrX3mpz4o2PQT4eLqgaAIAzI0RzghANAAAAGDodVpuyTjZof9feapknanWsrEG2fn7KmBTq4zi0YE5ckGbHBsjHw23kiwYAoAdCNCcI0QAAAIDh1dzeqUMl9V37qxlLQfOr+h5cYDZJ0yP9jaWg8UFaOjlUSWG+LAMFAIwoQjQnCNEAAACAkVfb3N7nRNDyfg4uSAjx0crkcK1MDtfSyWHy9rC4oFoAwERCiOYEIRoAAAAwOpysa1XmCeM00D0FNdpTUKMOa/ePJx5uZi1OCtHK5AitTA7XZLrUAADDgBDNCUI0AAAAYHRqbOvU59mVSj9WoU+zKlRc29LrenyIt1ZOj9CqFLrUAABDhxDNCUI0AAAAYPSz2+3KLm/U5qxypWdV6Iv8arrUAADDghDNCUI0AAAAYOwZbJfayuRwLZ0SyqmfAIBBI0RzghANAAAAGNtOdamlZ1Uo/Vi5duX136V28fRwrUqJoEsNAHBGhGhOEKIBAAAA40tTW6c+z6lSetfST7rUAABngxDNCUI0AAAAYPyy2+3KqejqUsuq0K68arVbbY7rHhazFk82utRWJkdoSjhdagAw0RGiOUGIBgAAAEwcA3WpxQV7a2VyuFZOj9CFU+lSA4CJiBDNCUI0AAAAYGIaTJfaBUkhRqhGlxoATBiEaE4QogEAAACQjC617TlVSj9mdKmdqOndpRYZ4KmUqAClRPkrJdpfyZEBmhLhK083i4sqBgAMB0I0JwjRAAAAAJzO6FJrUnpWuT49VqGdub271E5xM5s0OdxXKVEBSo7y7wrYAhQT6EXXGgCMUYRoThCiAQAAABhIc3unjpTW6+jJBh0tbVDWyQYdOVmvhtbOfuf7e7kpJcq/K1gzutemR/krwMt9hCsHAJwtQjQnCNEAAAAAnAu73a7SulZHoJbVFbDlVDSq09b/j1WxQd7dy0GjAjQjyl9JYb5ys5hHuHoAgDOEaE4QogEAAAAYSu2dNuVUNBqh2skGHe0K2ErrWvud72Exa0qEn2ac6lyLNjrXIvw9WRIKAC4w2KyI85sBAAAA4Dx4uJk1IzpAM6J7/+BV19xhBGplDTpS2qCsrnCtqd2qI6X1OlJa32t+kI+70bXWtRw0Ocpf0yP95evJj20AMBrQiQYAAAAAI8Rms6u4tkVHSruWg5Y16GhpvfIqm9TfilCTSUoI8VFypNGxNjc+UPPigxXs6zHyxQPAOMVyTicI0QAAAACMNq0dVmWXN+roSaNj7WjX0tCKhrZ+5yeF+WpeQpDmJwRrfkKwkqP8ZTGzFBQAzgUhmhOEaAAAAADGiqrGtq6DDBp0uKRe+4pqlFvR1Geej4dFaXFBmj/JCNbmJQQrhG41ABgUQjQnCNEAAAAAjGW1ze3aV1SrfQU12ltYq4yiWjW2dfaZlxjqYwRqk4I1PyFIyZH+nAoKAP0gRHOCEA0AAADAeGK12ZVd3qi9hTXaW1CjvYU1ynHSrTYnLtCxBHReQpBC/TxdUDEAjC6EaE4QogEAAAAY72qb25VRVKu9hbXaV1ijjMJaNfTTrTapq1ttfkKQ5iUEKyWKbjUAE8+YCNG2bNmiX//619qzZ49KS0u1YcMGrV+//oz3SU9P18MPP6xDhw4pPj5eP/nJT3THHXcM+jEJ0QAAAABMNDabXdkVjY5Otb2Ftcoub+wzz9u9q1ttUrDmxQdp/qRghdGtBmCcG2xW5DaCNfXR1NSktLQ03Xnnnbr22msHnJ+Xl6crr7xS9957r15++WV9/PHHuvvuuxUdHa01a9aMQMUAAAAAMPaYzSZNj/TX9Eh/3XRBgiSprqXD6FbrCtYyimrV0NqpnXnV2plX7bhvQoiP5icEdQVrwUqJ9pc73WoAJqBRs5zTZDIN2In2gx/8QBs3btTBgwcdYzfddJNqa2v1/vvvD+px6EQDAAAAgL5sNrtyKk7trVarfUU1Ol7eqNN/YvRyN2tOXJDmJQRpQUKwFkwKZm81AGPamOhEO1vbt2/X6tWre42tWbNGDz74oNP7tLW1qa2tzfF1fX39cJUHAAAAAGOW2WzStEh/TYv0142LurvVMotqtbewRvu69lerb+3Urrxq7erRrTY5zFcLJgVrYaIRqk0J95PJZHLVUwGAYTGmQrSTJ08qMjKy11hkZKTq6+vV0tIib2/vPvd54okn9Pjjj49UiQAAAAAwbgR6u+ui6eG6aHq4JKNbLbeyUXsLjGBtT4HRrZZb2aTcyia9seeEJCnIx93oUksM1sJJIZoTFygvd4srnwoAnLcxFaKdi0ceeUQPP/yw4+v6+nrFx8e7sCIAAAAAGJvMZpOmRvhraoS/blhk/FxV29yuvYU12p1fo90FNcosqlVtc4c+Plquj4+WS5LcLSbNjg3UwknBWjApRAsmBSvcnyWgAMaWMRWiRUVFqaysrNdYWVmZAgIC+u1CkyRPT095evLmDAAAAADDIcjHQ5ekROqSFGPVUHunTYdK6rSnwOhU211Qo4qGtq7loLV6bmueJCkx1EfzJxmdagsTgzU13E9mM0tAAYxeYypEW7p0qTZt2tRr7KOPPtLSpUtdVBEAAAAAoCcPN7PmJQRrXkKw7l4h2e12FVW3aHdBtXYX1GhPfo2OlTcov6pZ+VXNemtvsSRj6ej8hCAtTDQ61dLiguTtwRJQAKOHS0O0xsZGZWdnO77Oy8tTRkaGQkJClJCQoEceeUTFxcV66aWXJEn33nuvfv/73+v73/++7rzzTn3yySf629/+po0bN7rqKQAAAAAAzsBkMikh1EcJoT66dn6cJOPAgr2FRqC2u6BaGUW1qmvp0OasCm3OqpAkuZlNmtW1BHThJGN/tQh/L1c+FQATnMluP/3A4pGTnp6uVatW9Rm//fbb9eKLL+qOO+5Qfn6+0tPTe93noYce0uHDhxUXF6ef/vSnuuOOOwb9mIM9thQAAAAAMDI6rDYdKa3X7vxTS0CrVVbf1mdefIi3FnbtqbYwMVjTI/xZAgrgvA02K3JpiOYKhGgAAAAAMLrZ7XadqGlxBGq782uUVdag03969fdy0/yE7k61ufFB8vEYU7sWARgFCNGcIEQDAAAAgLGnvrVD+wprtSff2Fsto6hWze3WXnMsZpOmhPtqVkygZkYHaFZMgGbGBCjIx8NFVQMYCwjRnCBEAwAAAICxr9Nq09GTDdrdFartKahRaV1rv3Njg7w1M8YI1WbFBGpWTICiA71kMrEUFAAhmlOEaAAAAAAwPp2sa9WhkjodLqnXoZJ6HSqtU1F1S79zg33cu4K1wK5wLUBJYX6ysMcaMOEQojlBiAYAAAAAE0ddS4eOlHaFal0BW3Z5ozptfX8U9nI3KyWqu2NtZkyAUqL85eVucUHlAEYKIZoThGgAAAAAMLG1dlh1vKzRCNW6ArYjpfV99liT2GcNmAgI0ZwgRAMAAAAAnM5qsyu/qql7KWhX11pVU3u/89lnDRg/CNGcIEQDAAAAAAyG3W5XeUObDpXU6VCxEa4dLq1XYXVzv/NP32dtZnSAJoezzxow2hGiOUGIBgAAAAA4Hz33WTvc1bXmbJ81f083LUwM1gVJobogKVipsUHycDO7oGoAzhCiOUGIBgAAAAAYaq0dVmWXG/usHSpxvs+al7tZ8+KDdUFSiBYnhWheQrC8PTi4AHAlQjQnCNEAAAAAACPBarPrSGm9duVVG7f8alWftseam9mk1LhAR6i2YFKIAr3dXVQxMDERojlBiAYAAAAAcAW73a6cikbtyqvRrrwq7cyrVmlda685JpOUEhWgxUkhuiApRIsSQxTu7+miioGJgRDNCUI0AAAAAMBoYLfbdaKmRbvyqvVFvtGtllvZ1Gfe5HBfXZBohGoXJIUoLtjHBdUC4xchmhOEaAAAAACA0aq8oVVfdHWq7cqv0dGT9Tr9p/bYIG9Hl9oFSSGaEu4rk4kTQIFzRYjmBCEaAAAAAGCsqGvu0O4Co0ttZ161DhbX9TkFNNTXo1eoNiM6QBYzoRowWIRoThCiAQAAAADGqub2Tu0rrNXOvGrtyqvSvsJatXXaes3x93TTwsRgXZAUqguSgpUaGyQPN7OLKgZGP0I0JwjRAAAAAADjRVunVQeL67pCtWrtzq9RY1tnrzle7mbNiw92dKulxgYq0IcTQIFTCNGcIEQDAAAAAIxXVptdR0rrtasrVNuVX63qpvY+8xJCfJQaG6jZsYGaHRug1NhABfl4uKBiwPUI0ZwgRAMAAAAATBR2u105FY3amVetL/KqtaewRkXVLf3OjQv27hGsBSo1NlAhvgRrGP8I0ZwgRAMAAAAATGS1ze06VFKvA8V1OlBcp4PFdSqoau53bmyQt2bFGJ1qs+OMYC3Mz3OEKwaGFyGaE4RoAAAAAAD0VtfSoUMlRqB2oLheh4rrlFvZ1O/cqAAvR6daalyAZscGKsLfa4QrBoYOIZoThGgAAAAAAAysobVDh0rqdbD4VLhmBGv9pQgR/p6OpaBGuBaoCH9PmUymkS8cOEuEaE4QogEAAAAAcG4a2zp1uGsp6KGuYC2nolG2fpKFMD9PpXYdWnBqn7XoQC+CNYw6hGhOEKIBAAAAADB0mtuNYO3UUtCDxXU6Xt7Qb7AW6uvh6FabHWssBY0N8iZYg0sRojlBiAYAAAAAwPBqabfqyMmuYO2E0bF2vLxR1n6StXB/Ty2cFKwFk4K1KDFEM2MC5G4xu6BqTFSEaE4QogEAAAAAMPJaO6w6erLBOBG0K1g7VtagztOCNW93i9LiA7VwUogWJgZr/qRgBXi5u6hqTASEaE4QogEAAAAAMDq0dlh1oLhOX+RXa09+jfYU1qi2uaPXHJNJSo70d3SqLZgUrLhgloBi6BCiOUGIBgAAAADA6GSz2ZVT0ajdBTXanV+jPQXVyq9q7jMvMsBTCxNDtHBSsBZOCtGMaH+5sQQU54gQzQlCNAAAAAAAxo7yhlbt7QrVviio0aHiuj5LQH08LJqXEKQFk4xgbV5CkPxZAopBIkRzghANAAAAAICxq6XdqswTtdqdX63dBTXaU1CjhtbOXnPMJiklKkALE7sPLIgJ8nZRxRjtCNGcIEQDAAAAAGD8sNnsOlbe0LX8s0Zf5FfrRE1Ln3kxgV5akBiiRV3BWkpUgCxm9lUDIZpThGgAAAAAAIxvZfWtxvLP/GrtKajR4dJ6WU9bAurn6aZ5CUGOU0DnxgfJ19PNRRXDlQjRnCBEAwAAAABgYmlq61RGUa1259dod0G19hXWqrGt9xJQi9mkGdH+WjgpRHPjgzQvIUgJIT6cAjoBEKI5QYgGAAAAAMDEZrXZdfRkvfZ0HViwO79aJXWtfeaF+HooLS5Qc+ODNTchSHPjghTow4EF4w0hmhOEaAAAAAAA4HQltS3aXVCjvQU1yiiq1eGSerVbbX3mTQ7z1dyEIM2LD9Lc+GClRPvL3WJ2QcUYKoRoThCiAQAAAACAgbR1WnW4pF4ZRbWOW0FVc595nm5mzY4N1Nz4IMctLtibZaBjCCGaE4RoAAAAAADgXFQ3tSuzqFb7Cmu0r6hWmUW1qm/t7DMvzM/Tsa/a3PggzYkLlL8Xy0BHK0I0JwjRAAAAAADAULDZ7MqralJGYXe32pHSenWedhKoySRNDfczOtUSgjQvPljTI/3kxjLQUYEQzQlCNAAAAAAAMFxaO6w6WFynjKJa7SuqVUZhrYprW/rM83a3KDUusGtvNSNciw70dkHFIERzghANAAAAAACMpIqGtq5ONePQgsyiOjW29V0GGhngqXmnTgKND1JqbKB8Pd1cUPHEQojmBCEaAAAAAABwJavNrpyKRmUUdnWrFdUq62S9TlsFKrNJmh7pr9TYQM2KCdDMmEDNiPZnf7UhRojmBCEaAAAAAAAYbZrbO3XgRJ1jb7V9hbU6Wd/a79zEUB/NignUzJgAzYwJ0KyYAEX4e41wxeMHIZoThGgAAAAAAGAsOFnXqoyiWh0uqdOhknodLq1XaV3/wVq4v6dmdQVqM6ONzrWEEB+ZzaYRrnrsIURzghANAAAAAACMVVWNbTpcWq9DJcbtcEmdciub1F+64+fpppnRAb061qZF+MvDjVNBeyJEc4IQDQAAAAAAjCfN7Z06UtqgwyV1joDt6MkGtXfa+sx1t5g0PdJfM6ONUG1WbKBmRAfIbwIfYECI5gQhGgAAAAAAGO86rDblVDTqUHF9V7BmLAltaO17KqjUe581Y1looML9PUe4atcgRHOCEA0AAAAAAExEdrtdJ2paHMtATy0JdXaAQc991mbFGPusxQePv33WCNGcIEQDAAAAAADodvo+a4dK6pTnZJ81f083zYgO0K++MkdJYb4jX+wwGGxWNHEXvAIAAAAAAEChfp5aMS1cK6aFO8Z67rN2KlzLOtmghrZO7cqvVrCPuwsrdg1CNAAAAAAAAPTi4+GmBZOCtWBSsGPs1D5r2eWNCvLxcGF1rkGIBgAAAAAAgAG5W8xKiQpQStTE3B7L7OoCJOnpp59WYmKivLy8tHjxYu3ateuM85966iklJyfL29tb8fHxeuihh9Ta2v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size 1500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history, metric='loss')"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:56:32.486993Z","iopub.status.busy":"2024-04-26T11:56:32.486563Z","iopub.status.idle":"2024-04-26T11:56:59.139960Z","shell.execute_reply":"2024-04-26T11:56:59.139016Z","shell.execute_reply.started":"2024-04-26T11:56:32.486935Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m242/242\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 110ms/step - accuracy: 0.6118 - loss: 1.2363\n","Accuracy: 0.6085999011993408\n","Loss: 1.229475975036621\n"]}],"source":["evaluation = model.evaluate(dataset.test, return_dict=True)\n","print(f'Accuracy: {evaluation[\"accuracy\"]}')\n","print(f'Loss: {evaluation[\"loss\"]}')"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-04-26T11:56:59.141464Z","iopub.status.busy":"2024-04-26T11:56:59.141155Z","iopub.status.idle":"2024-04-26T11:57:23.191785Z","shell.execute_reply":"2024-04-26T11:57:23.190822Z","shell.execute_reply.started":"2024-04-26T11:56:59.141438Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m242/242\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 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iEBAQgGvXrgkdUelu3bqFpKQk6OjowNXVFVZWVkJHUgqpVIoVK1bg22+/xddff4358+cDqCievZ6ZJUavXr2Cjo4OEhMT4eLiInQclQsPD3/nc8XWYZbobSyeEVGNYGBggMTERNjZ2cHExATnzp1D06ZNkZSUhAEDBiAzM1PoiEqTl5eHPXv2ID09HTNmzECdOnWQkJCAevXqoWHDhkLHIwUqLS3F4sWLERwcjEePHuHWrVuwtbXFnDlzYG1tDT8/P6EjKkVGRgZKSkrg4OAgN3779m1oaWmJupCkjoyNjXH58mU4OjrC2NgYFy9ehJOTEy5fvgwfHx+kpqYKHVEp4uPjcerUKTx+/LhSIUFMMwvfZGJigoSEBNjY2MDOzg4hISHw8PBAeno6XF1dRb2HpbqRSqV4+PChbKluhw4dsG3bNjx//lz0M89sbW2xb98+NGvWTOgoRCQg7nlG74W399AQm2nTpr3zuWK9ANfT05Pti9OgQQOkp6ejadOmAICnT58KGU2pkpOT0aNHDxgZGSEzMxNjx45FnTp1EBkZiaysLERERAgdkRRo0aJFCA8Px7JlyzB27FjZuIuLC1avXi3a4pmvry/GjBlTqXh2+fJlhISE4PTp08IEU4G8vDzExcVVWVAZNWqUQKmUS0tLC1KpFEDFpupZWVlwcnKCkZERsrOzBU6nHIsXL8a3334LR0dH1KtXr9LejmLl4uKCpKQk2NjYoE2bNli2bBm0tbWxadMm2NraCh1P4aZNm4aFCxdCT0/vL6/dxHa99vr/47Zt2+Ly5cvw8vJC+/btq2yKIjazZ8/Gf/7zH2zbtk3W9EidlJaWYv/+/fj1118BAE2bNoWXl5foGx0RvY3FM6rRIiIisHz5cty+fRsA8MEHH2DGjBkYOXKkwMkU612XNYj5Arxt27Y4d+4cnJyc4OnpiYCAAKSkpCAyMlLUnUanTZsGX19fLFu2TK7rpKenJz777DMBkynXo0ePMH36dJw8eRKPHz/G25OgxXoHOyIiAps2bUL37t3h7+8vG2/WrJloZ+MAFa9xHTp0qDTetm1bTJw4UYBEqnHo0CEMHz4c+fn5MDQ0rFRQEWvxrEWLFoiPj4eDgwO6dOmCuXPn4unTp9i2bZtolz19//332Lp1K3x9fYWOolLffvstCgoKAACBgYHo168fOnXqBFNTU+zcuVPgdIp37do1WefBP7t2E+P12pvv040bN8aFCxcwfPhwfPTRRwKmUo1169YhLS0NFhYWsLKyqrTXl9iaO70pLS0Nnp6euHfvHhwdHQEA3333HSwtLXHkyBHY2dkJnJBIdVg8oxpr5cqVmDNnDiZOnCj70HXu3Dn4+/vj6dOnomoFferUKaEjCG7lypXIz88HULEvVn5+Pnbu3AkHBwfR3b19U3x8PDZu3FhpvGHDhnj48KEAiVTD19cXWVlZmDNnjqzzpDq4d+8e7O3tK42XlZVV2QpeLCQSCf74449K48+ePRNtoRQAAgICMGbMGCxevBi6urpCx1GZxYsXy57vRYsWYdSoURg/fjwcHBywZcsWgdMph1QqrbJALHa9evWS/d3e3h6pqanIzc2FiYmJKF/X37xeU7drt3nz5kFfX1/2WFdXF/v27cO8efNw5swZAZMp34ABA0T5//O7mDx5Muzs7HDp0iXZrLucnByMGDECkydPxpEjRwROSKQ63POMaiwbGxssWLCg0p358PBwzJ8/HxkZGQIlU53ffvsNAGQd+Uh8zM3NERUVhRYtWsgaJdja2uLEiRMYM2aMaJc4GRgY4OzZs1W2exezDz/8EFOnTsWIESPknu/AwECcOHECZ8+eFTqiUvTv3x86OjrYvn27bJlHaWkphg4dioKCAhw7dkzghMqhp6eHlJQUUS5fI3nLli3D/fv3sXr1aqGjqNTrAvjbS9lyc3Ohqakp1xmdiN4/enp6uHTpElxdXeXGk5KS0KFDB9mNbyJ1wJlnVGM9ePAA7du3rzTevn17PHjwQIBEqlFWVob//ve/CAoKkr0hGRgYICAgALNnz5btIyNWxcXFVe4N1LhxY4ESKZeXlxcCAwOxa9cuABUzdLKysjBz5ky5VvBiY2lpWWmppjqYO3cufHx8cO/ePZSVlSEyMhI3b95EREQEDh8+LHQ8pVm6dCk6d+4MR0dHdOrUCQBw9uxZPH/+HDExMQKnU55evXrhypUralc869atGyIjI2FsbCw3/vz5cwwcOFCUz/n06dPRt29f2NnZwdnZGVpaWnLHIyMjBUqmXMOGDUP//v0xYcIEufFdu3bh4MGDOHr0qEDJlK+goABLliyRbT/w9nWLWDvB37hxA1lZWbJ9aoGKa5f+/fsLmEq5bG1tER8fD1NTU7nxvLw8uLu7i/a5BoBatWpVOXM8Pz8f2traAiQiEg6LZ1Rj2dvbY9euXfjPf/4jN/56KZ9YzZ49G1u2bMGSJUvklqvOnz8fL1++xKJFiwROqBy3bt2Cn58fLly4IDdeXl4OiUQi2qVdQUFB+OSTT2Bubo4XL16gS5cuePjwIdq1ayfa5xoAVq9ejVmzZmHjxo1q1WlxwIABOHToEAIDA6Gnp4e5c+fC3d0dhw4dEvW+Mc7OzkhOTsa6deuQlJQEHR0djBo1ChMnThT15st9+/bFjBkzcOPGDbi6ulYqqHh5eQmUTLlOnz4t98H6tZcvX4p2duXkyZNx6tQpeHh4wNTUVG2WeF2+fLnKrRW6du2K2bNnC5BIdT7//HPExsZi5MiRarH9wOsumykpKZBIJJWaeYn1Og0AMjMzq/z5ioqKZKtExKpfv34YN24ctmzZgtatWwOo+L339/cX7XsYUXW4bJNqrL1792Lo0KHo0aOHrIh0/vx5nDx5Ert27cKgQYMETqgcFhYWCA4OrvSGdODAAUyYMAH37t0TKJlydejQAZqampg1a1aVF6Fibw9+/vx5JCUlIT8/H+7u7ujRo4escChGJiYmKCwsRElJCXR1dSsVFXJzcwVKRqQ4fzZTWIw3BZKTkwEAzZs3R0xMjFxhtLS0FMePH8fGjRuRmZkpUELlMTAwwI4dO9C3b1+ho6hUdUu6UlJS0KZNGxQWFgqUTPmMjY1x5MgRtdnrrn///tDQ0EBISAhsbGwQFxeHnJwcBAQEYMWKFbJZxWJy8OBBAMDAgQMRHh4OIyMj2bHS0lKcPHkSJ06cwM2bN4WKqHR5eXnw9fXFoUOHoKlZMe+mpKQEXl5eCAsLk/s3IRI7Fs+oRrt69SpWrVola43s5OSEgIAAtGjRQuBkylO7dm0kJyfjgw8+kBu/efMmmjdvjhcvXgiUTLn09PRw9epVNGnSROgoKrV8+XLMmDGj0nhpaSlGjBiB7du3C5BK+cLDw//0uI+Pj4qSkKrk5eVhy5Ytcq3ux4wZwwtvEZFKpbKCf1WXlzo6Oli7di3GjBmj6mhKZ2VlhaioKLV7D/Pw8ICLiwvWrl0rN/7ll18iOTlZtDMNgYq9eY8ePQonJyeho6iEmZkZYmJi4ObmBiMjI8TFxcHR0RExMTEICAh4587x75PXN0DenGn3mpaWFqytrREUFIR+/foJEU+pysrKsHz5chw8eBDFxcVo3LgxfHx8IJFI4OTkVGXzIyKxY/GMqIZp06YN2rRpgzVr1siNT5o0CfHx8bh06ZJAyZSrVatWWLVqFTp27Ch0FJUyNzfHd999Bz8/P9lYaWkphg0bhuvXr8sKDSQObxYXqiK2mUivXblyBb169YKOjo5s2Ud8fDxevHiBn3/+Ge7u7gInJEW4e/cuysvLYWtri7i4ONStW1d2TFtbG+bm5rKGEWITGhqK48ePIzQ0VK06q54/fx49evRAq1at0L17dwDAyZMnER8fj59//lmUs5Fe++GHH3DgwAGEh4erxXNuYmKChIQE2NjYwM7ODiEhIfDw8EB6ejpcXV1FPcvQxsYG8fHxMDMzEzqKyixcuBDz589Hjx49oKOjg6ioKHz66afYunWr0NGIBMPiGdU4z58/f6fzxNrBKTY2Fn379kXjxo3Rrl07AMDFixeRnZ2No0ePivZCNCYmBt9++y0WL15c5d5AYn2+4+Pj0bNnT2zevBmffPIJSkpKMGTIEKSmpiImJgb169cXOqJKJSQkYO7cuaLdPP/AgQNyj1+9eoVr164hPDwcCxYskCuiikmnTp1gb2+PzZs3yy37+Pzzz3Hnzh2cOXNG4ISKs2bNGowbNw61a9eudBPkbZMnT1ZRKlK2Fi1aID09HeXl5bC2tq70HpaQkCBQMuVLTEzE8uXLkZiYCB0dHbi5ueGbb74R9f60gPo95506dUJAQAAGDhyIzz77DL///ju+/fZbbNq0CVevXsX169eFjkgK5ODggOnTp+OLL74AAERHR6Nv37548eKF6JuXEVWHxTOqcf5qZoZYN5C/c+cObGxsIJFIcP/+ffzvf/+TW646YcIEWFhYCJxSed6cGv8msT7fb4qJicHAgQPxww8/YMuWLUhLS0NMTAzq1asndDSliIqKwokTJ6CtrY3PP/8ctra2SE1NxaxZs3Do0CH06tVL1B3aqvLTTz9h586dlYprYqGjo4Nr165VWtJ248YNtGzZUlQzFmxsbHDlyhWYmprCxsam2vMkEomoO7Tdvn0bp06dqrIL4dy5cwVKpTwLFiz40+Pz5s1TURJSFXV7zqOiolBQUABvb2/cvn0b/fv3x61bt2BqaoodO3bIZh6K1cmTJ6vtrCrG2Vi1atVCWloaLC0tZWO1a9dGWloaGjVqJGAyIuGweEY1TmxsrOzv5eXl8PT0REhICBo2bCh3XpcuXVQdTak0NDTw4MEDmJubAwCGDh2KNWvWiLaA8rY3n/eqiO35ftv+/fsxePBgODk5ISYmRrRLA7Zs2YKxY8eiTp06+P3332FqaoqVK1di0qRJGDp0KKZMmaI2+8e86c6dO3Bzc0N+fr7QUZSiXr162LZtG3r27Ck3HhUVhVGjRuHRo0cCJSNl2Lx5M8aPHw8zMzPUr19f7qaIRCIR3YwcdfP8+XPZbPC/Wi0g1lnjVCE3NxcmJiaibW702oIFCxAYGIiWLVtW2dRq3759AiVTHg0NDTx8+FBu+b2BgQGSk5P/9MYQkZixeEY1noGBAZKSkmBrayt0FKWSSqV4+PChrHhmaGiIxMRE0f/c6sjb27vK8UuXLsHe3l6ucBYZGamqWCrh5uaGkSNHYsaMGdi7dy8GDx6Mtm3bYteuXWp7J/PFixf45ptvcOzYMdF27Jo8eTL27duHFStWoH379gAq9kqaPn06Pv74Y3z//fcCJyRFsrKywoQJEzBz5kyho5ASvHmzr7rVAuowa/y1q1evyjVCEVtTq+quWd6kqamJ+vXr46OPPkL//v1VkEq1GjRogGXLlmHkyJFCR1EZqVSKPn36oFatWrKxQ4cOoVu3btDT05ONie06lejPaAodgIiqpo51bXXpxlfdz9OrVy8VJ1G99PR0DB48GEDFBbmmpiaWL1+uNoWzt+/Ql5eX448//oCuri5++OEHAZMp14oVKyCRSDBq1CiUlJSgvLwc2tramDBhAhYtWiR0PKUpLS1FWFhYtUt9YmJiBEqmXL///rvs91zM6tSpg1u3bsHMzOwvZ9/k5uaqMJlyxcTEoE6dOgCAU6dOCZxGOI8fP8awYcNw+vRpGBsbA6i4jvHw8MCOHTvkZuy8z97lGqysrAy3b99GSEgIpk+fjsDAQBUkU53i4mLZjR91UVXX8xEjRgiQhKjm4MwzqvHUZebZ29Oj1W1qNLvxqYe3Z1iqy+/3a2FhYXIfsKVSKerWrYs2bdrAxMREwGSqUVhYiPT0dACAnZ0dNmzYgOXLl+Phw4cCJ1OOiRMnIiwsDH379q1yqc+qVasESqZcfn5+aNWqFfz9/YWOolTh4eEYNmwYatWqhfDw8D89t6oPovR+Gzp0KO7cuYOIiAjZdgM3btyAj48P7O3tsX37doETqt7hw4cxYcIEZGVlCR1FoWbOnAl9fX3MmTNH6ChEJCDOPKP3gtj3UgAqZqD4+vrKpke/fPkS/v7+clOjAfFOj546dSq8vLyq7Mb31VdfiaobX1WePHkiW7Ln6OgomjvWVQkJCYG+vj6Aiuc4LCys0h5vYu1C6OvrK3QElSoqKsL8+fNx4sQJ1KpVCzNmzMDAgQMRGhqK3r17Q0NDA1OnThU6ptLs2LEDu3btgqenp9BRVMre3h5z5szBpUuXquyeLJbfbx8fH3Tr1g2RkZFqWxw7fvw49PX10bFjRwDA+vXrsXnzZjg7O2P9+vWivilw/PhxREdHy+3T+frnfnt/R3XRsWNHtGzZUugYCvfy5Uts2rQJ0dHRcHNzq/SatnLlSoGSEZEqceYZ1Thv761Q1fp6QHxFpNGjR7/TeaGhoUpOIgx16sb3poKCAkyaNAkRERGyJV0aGhoYNWoU1q5dC11dXYETKpa1tfVfFsPF1oUwOTn5nc91c3NTYhLVmzlzJjZu3IgePXrgwoULePLkCUaPHo1Lly7hP//5DwYPHgwNDQ2hYyqNhYUFTp8+jQ8++EDoKCqlTl1G355Nq25cXV2xdOlSeHp6IiUlBS1btkRAQABOnTqFJk2aiPaaBaiYOX327Fk0b95cbvzatWvo0qXLXzZToPeHh4dHtcckEolol+ATkTzOPKMa5+29FdRlfb2YLzDfhaGhIbKysioVz7Kzs2FgYCBQKuWbNm0aYmNjcejQIXTo0AEAcO7cOUyePBkBAQHYsGGDwAkVKzMzU+gIKte8eXNIJJK/3MdQjJtr7969GxEREfDy8sL169fh5uaGkpISJCUlqcWM4oCAAHz//fdYt26dWvy8r2VkZAgdgVQkIyMDzs7OAIC9e/eif//+WLx4MRISEkQ/47Jbt26YMmUKtm/fDgsLCwDAvXv3MHXqVHTv3l3gdKRI6ry3HxH9P848I6IaobpufDNmzMDHH3+M1atXCxtQSczMzLBnzx507dpVbvzUqVMYMmQInjx5IkwwFfrtt99gYWEBqVQqdBSluHv37jufa2VlpcQkqqetrY2MjAw0bNgQQMUM07i4OLi6ugqcTDUGDRqEU6dOoU6dOmjatGmlpT5im0H9tuLiYmRkZMDOzk62HF9spFKp3Ab61RHbrNLX6tSpg3PnzsHZ2RkdO3bEqFGjMG7cOGRmZsLZ2Vm0s8aBipt7Xl5e+OWXX2BpaSkbc3FxwcGDB9WmEQ4RkboQ55UMic727dvh5eVVaekmicfb3fgAQEtLC+PHj8eSJUsETqc8hYWFqFevXqVxc3NzUX/oeJOzszMSExNF2zTgzYJYTk4OTE1NAVR8yNq8eTNevHgBLy8vdOrUSaiISlNaWgptbW3ZY01NTdl+d+rA2NgYgwYNEjqGyhUWFmLSpEmyTfRv3boFW1tbTJo0CQ0bNsSsWbMETqhY3bt3r3Jm6esZp2KcVfpax44dMW3aNHTo0AFxcXHYuXMngIrnXOzFI0tLSyQkJCA6OhqpqakAACcnJ/To0UPgZKRoHh4efzp7mMs2idQDZ57Re8HQ0FDUH67p/73djU9se369rXv37jA1NUVERARq164NAHjx4gV8fHyQm5uL6OhogRMqnzp03ExJSUH//v2RnZ0NBwcH7NixA71790ZBQQGkUikKCgqwZ88eDBw4UOioCiWVStGnTx9ZIxR12cNS3U2ZMgXnz5/H6tWr0bt3byQnJ8PW1hYHDhzA/Pnzce3aNaEjKoxUKkVcXNxfNnkR26zS17KysjBhwgRkZ2dj8uTJ8PPzA1DRBKi0tBRr1qwROKFyvHr1Cjo6OkhMTISLi4vQcUjJ3m5s8+rVKyQmJuL69evw8fHB999/L1AyIlIlFs/ovaAOH65JPV2/fh29evVCUVERmjVrBgBISkpC7dq1ERUVhaZNmwqcUPnU4fe7T58+0NTUxKxZs7Bt2zYcPnwYvXr1wubNmwEAkyZNwtWrV3Hp0iWBkyqWujdCASo6yp4+fRrp6en47LPPYGBggPv378PQ0FC0s/CsrKywc+dOtG3bVu73Oy0tDe7u7qLaSF3dGwaoM1tbW+zbt0/23k3qZ/78+cjPz8eKFSuEjkJEKsDiGb0X1OHDtTry9vZGWFgYDA0NK3VZfZuYZ6YUFhbixx9/lFv2MXz4cOjo6AicTDW+++47jB8/HsbGxkJHURozMzPExMTAzc0N+fn5MDQ0RHx8PD788EMAQGpqKtq2bYu8vDxhg5JC3b17F71790ZWVhaKiopkyxenTJmCoqIiBAcHCx1RKXR1dXH9+nXY2trKvX8nJSWhc+fOePbsmdARFUbdi2cJCQnQ0tKS7WN44MABhIaGwtnZGfPnz5dbti02W7ZsQWRkJLZt2/aXe96ROKWlpaF169bIzc0VOgoRqQD3PKP3wrFjx2QbTpN4GBkZyfaQMDQ0VKtudG/S1dXF2LFjhY4hmG+++UboCEqXm5uL+vXrAwD09fWhp6cHExMT2XETExP88ccfQsUjJZkyZQpatmyJpKQk2V53QEUjATH/zrds2RJHjhzBpEmTAED22h4SEoJ27doJGU3hunTpUqlA5OrqiqNHj8o2kRezL774ArNmzYKrqyvu3LmDYcOGYdCgQdi9ezcKCwtF2+wHANatW4e0tDRYWFjAysqq0nL0hIQEgZKRqly8eFG25QYRiR+LZ1TjPX78GOXl5YiLi4Ojo6Pa3t0VozeXaoWFhQkXRGA3b97E2rVr8euvvwKomHk2ceJENGnSROBkijVt2rR3PnflypVKTCKMt4vD6losVidnz57FhQsXKhVXrK2tce/ePYFSKd/ixYvRp08f3LhxAyUlJfj+++9x48YNXLhwAbGxsULHU6hTp05VGsvMzMSrV68ESKN6t27dQvPmzQEAu3fvRufOnfHTTz/h/PnzGDZsmKiLZwMGDODruJp4e3VEeXk5Hjx4gCtXrmDOnDkCpSIiVWPxjGqsP/74AxMmTMCOHTtkXao0NDQwdOhQrF+/HkZGRgInJEXq1q0bIiMjKy3de/78OQYOHCjaTkZ79+7FsGHD0LJlS9mMjEuXLsHV1RU7duzAxx9/LHBCxXnXTcLF+mHE19dXtnH+y5cv4e/vL5upUFRUJGQ0UpKysrIquyz+9ttvMDAwECCRanTs2BGJiYlYsmQJXF1d8fPPP8Pd3R0XL16ULe8jcSgvL0dZWRkAIDo6Gv369QNQ0Yny6dOnQkZTuvnz5wsdgVTk7c8cUqkUjo6OCAwMRM+ePQVKRUSqxj3PqMYaOnQorl27hrVr18qKChcvXsSUKVPQvHlz7NixQ+CEpEjV7Rvz+PFjNGzYULR38e3s7DB8+HAEBgbKjc+bNw8//PCDrPMovd+4cb56Gjp0KIyMjLBp0yYYGBggOTkZdevWxYABA9C4cWM+3yLl6emJLVu2oEGDBkJHUbpu3brB0tISPXr0gJ+fH27cuAF7e3vExsbCx8cHmZmZQkdUGltbW8THx8styQaAvLw8uLu7486dOwIlIyIiZWDxjGosPT09REVFoWPHjnLjZ8+eRe/evVFQUCBQMlKk5ORkAEDz5s0RExMjt+luaWkpjh8/jo0bN4r2AlxXVxfJycmwt7eXG799+zaaNWuGwsJCgZIR0b/122+/oVevXigvL8ft27fRsmVL3L59G2ZmZjhz5ozotyF4/PgxHj9+LJuZ9Jqbm5tAiUjRkpOTMXz4cGRlZWHatGmYN28egIoOwjk5Ofjpp58ETqg81d30e/ToESwtLVFcXCxQMlKWq1evyrbYaNq0KVq0aCFwIiJSJS7bpBrL1NS0yqWZRkZGchtt0/utefPmkEgkkEgk6NatW6XjOjo6WLt2rQDJVKNr1644e/ZspeLZuXPn0KlTJ4FSKZ+Hh8efLs8U6zJdUi+NGjVCUlISdu7ciaSkJOTn58PPz0/03XSvXr0KHx8f/Prrr3j7Hq1EIqlyKev76ODBg+98rpeXlxKTCMfNzQ0pKSmVxpcvXw4NDQ0BEinfm897VFSU3LVqaWkpTp48CRsbGyGikZI8fvwYw4YNw+nTp2Xbi+Tl5cHDwwM7duxA3bp1hQ1IRCrBmWdUY23atAm7d+/Gtm3bZF3qHj58CB8fH3h7e+OLL74QOCEpwt27d1FeXg5bW1vExcXJXYBoa2vD3NxctBfgABAcHIy5c+diyJAhaNu2LYCKPc92796NBQsWwMLCQnaumD58TZ06Ve7xq1evkJiYiOvXr8PHxwfff/+9QMmIFOfMmTNo3749NDXl71WWlJTgwoUL6Ny5s0DJlKtZs2aws7PDzJkzUa9evUqFcisrK4GSKZZUKpV7LJFI5IqFb/7cYikY0v8/728/3wCgpaUFa2trBAUFyfZ/o/ff0KFDcefOHURERMDJyQkAcOPGDfj4+MDe3h7bt28XOCERqQKLZ1RjtWjRAmlpaSgqKkLjxo0BAFlZWahVqxYcHBzkzmU7cHpfvf3hqzpimq3xZ+bPn4/8/HysWLFC6ChE/5qGhgYePHhQaVlXTk4OzM3NRfs7bWBggGvXrlWaUStm0dHRmDlzJhYvXiy3T+u3336LxYsX46OPPhI4oXKUlpZi1apV2LVrF7KysiotVczNzRUomfLZ2NggPj4eZmZmQkchJTMyMkJ0dDRatWolNx4XF4eePXsiLy9PmGBEpFJctkk11sCBA4WOQAK4ceNGlRfgYpp19aa39wJSdyNGjEDr1q1ZPCNRKC8vr3J5ck5OjqzTqhh1794dSUlJalU8++qrrxAcHCy3T2uvXr2gq6uLcePGyfZJEpsFCxYgJCQEAQEB+PbbbzF79mxkZmZi//79mDt3rtDxlCojI0PoCKQiZWVl0NLSqjSupaXF6zgiNcKZZ0RUI9y5cweDBg1CSkqK3FKI1x88xTZD4+LFi8jJyZFb1hEREYF58+ahoKAAAwcOxNq1a1GrVi0BU6retm3bMHPmTNy/f1/oKET/mLe3NwDgwIED6N27t9zvcWlpKZKTk+Ho6Ijjx48LFVGpnj59Ch8fH7Ru3RouLi6VPnSK8WaIjo4O4uPj4eLiIjeenJyMNm3a4MWLFwIlUy47OzusWbMGffv2hYGBARITE2Vjly5dEnXDgMmTJ8Pe3h6TJ0+WG1+3bh3S0tKwevVqYYKRwg0YMAB5eXnYvn27bDuNe/fuYfjw4TAxMcG+ffsETkhEqsCZZ0RUI0yZMgU2NjayjXbj4uKQk5ODgIAAUc5CCgwMRNeuXWXFs5SUFPj5+cHX1xdOTk5Yvnw5LCwsMH/+fGGDKsnr4sJr5eXlePDgAa5cuYI5c+YIlIpIMV5vIF5eXg4DAwO55gDa2tpo27Ytxo4dK1Q8pbt48SLOnz+PY8eOVTom1iXorVq1wrRp07Bt2zbUq1cPQEXXxRkzZqB169YCp1Oehw8fwtXVFQCgr6+PZ8+eAQD69esn+tfyvXv3Vtk0on379liyZAmLZyKybt06eHl5wdraGpaWlgCA7OxsuLi44IcffhA4HRGpCotnVKPUqVMHt27dgpmZGUxMTP60G5+Y99FQRxcvXkRMTAzMzMwglUohlUrRsWNHfPfdd5g8eTKuXbsmdESFSkxMxMKFC2WPd+zYgTZt2mDz5s0AAEtLS8ybN0+0xbO3O+lKpVI4OjoiMDAQPXv2FCgVkWKEhoYCAKytrTF9+nRRL9GsyqRJkzBixAjMmTNHVkgSu61bt2LQoEFo3Lix3IdrBwcH7N+/X9hwStSoUSM8ePAAjRs3hp2dHX7++We4u7sjPj5e9DOnc3JyquwKb2hoiKdPnwqQiJTF0tISCQkJiI6ORmpqKgDAyckJPXr0EDgZEakSi2dUo6xatQoGBgayv/9Z8YzEpbS0VPbcm5mZ4f79+3B0dISVlRVu3rwpcDrF+/333+U+VMbGxqJPnz6yx61atUJ2drYQ0VTidXGBSMy+/vpruW58d+/exb59++Ds7CzqInFOTg6mTp2qNoUzALC3t0dycjJOnDhR6cO1mK9lBg0ahJMnT6JNmzayoumWLVuQlZVVqauy2Njb2+P48eOYOHGi3PixY8dga2srUCpSpJiYGEycOBGXLl2CoaEhPvroI1nzj2fPnqFp06YIDg5Gp06dBE5KRKrA4hnVKD4+Pnj+/DmKiooqLesicXNxcUFSUhJsbGzQpk0bLFu2DNra2ti0aZMoL0Lr1auHjIwMWFpaori4GAkJCViwYIHs+B9//FHl5rRic/XqVdlG2k2bNkWLFi0ETkSkOAMGDIC3tzf8/f2Rl5eH1q1bQ1tbG0+fPsXKlSsxfvx4oSMqhbe3N06dOgU7Ozuho6iURCJBz549RV0YfduSJUtkfx86dCgaN26MixcvwsHBAf379xcwmfJNmzYNEydOxJMnT9CtWzcAwMmTJxEUFMQlmyKxevVqjB07FoaGhpWOGRkZ4YsvvsDKlStZPCNSE2wYQDWOVCp9p7u0YtwzRZ1FRUWhoKAA3t7eSEtLQ79+/XDr1i2Ymppi586dsgtTsRg/fjySkpKwdOlS7N+/H+Hh4bh//z60tbUBAD/++CNWr16N+Ph4gZMqx+PHjzFs2DCcPn0axsbGAIC8vDx4eHhgx44dqFu3rrABiRTAzMwMsbGxaNq0KUJCQrB27Vpcu3YNe/fuxdy5c0XbgXHRokVYvXo1+vbtC1dX10o3At7eYP19tWbNmnc+Vyw/M8nbsGEDFi1aJGtyY21tjfnz52PUqFECJyNFsLKywvHjx+Hk5FTl8dTUVPTs2RNZWVkqTkZEQmDxjGqc2NhY2d/Ly8vh6emJkJAQNGzYUO68Ll26qDoaqVhubu5f7n33vnr69Cm8vb1x7tw56OvrIzw8HIMGDZId7969O9q2bYtFixYJmFJ5hg4dijt37iAiIkJ2UXrjxg34+PjA3t4e27dvFzgh0b+nq6uL1NRUNG7cGEOGDEHTpk0xb948ZGdnw9HREYWFhUJHVAobG5tqj0kkEty5c0eFaZTnz37ON4npZwZQ5Sb51RFjZ9WqPHnyBDo6OtDX1xc6CilQ7dq1cf36ddjb21d5PC0tDa6urqLtpktE8lg8oxrPwMAASUlJoly6R/Ts2TPo6+tDQ0NDbjw3Nxf6+vqymWhiY2RkhOjoaLRq1UpuPC4uDj179kReXp4wwYgUyM3NDZ9//jkGDRoEFxcXHD9+HO3atcPVq1fRt29fPHz4UOiIpGBPnjyBRCKBmZmZ0FGURiqVvtN5Yu2s+qaSkhKcPn0a6enp+Oyzz2BgYID79+/D0NCQhTQRsLOzQ1BQEAYOHFjl8cjISEyfPl1UxXEiqt67vfsRESlZQUEB5syZg/bt28Pe3h62trZyf8TKyMioUuEMqOg8K9bCGQCUlZVVuaeblpYWysrKBEhEpHhz587F9OnTYW1tjTZt2qBdu3YAgJ9//pn7+4lIXl4evvzyS5iZmaF+/fqoV68ezMzMMHHiRDx79kzoeApXVlb2Tn/EXji7e/cuXF1dMWDAAHz55Zd48uQJAGDp0qWYPn26wOlIETw9PTFnzhy8fPmy0rEXL15g3rx56NevnwDJiEgInHlGNR5nnqmHTz/9FLGxsRg5ciQaNGhQaanmlClTBEpGyjBgwADk5eVh+/btsLCwAADcu3cPw4cPh4mJCfbt2ydwQiLFePjwIR48eIBmzZrJZuzExcXB0NAQTZo0ETidcowZM+ZPj2/dulVFSZQvNzcX7dq1k71+vbkM/aeffoKlpSUuXLgAExMTgZMq1ttdCN/07NkztG/fXvRdCAcOHAgDAwNs2bIFpqamsmvV06dPY+zYsbh9+7bQEelfevToEdzd3aGhoYGJEyfC0dERQMVeZ+vXr0dpaSkSEhLUqrMwkTpj8YxqPAMDAyQnJ7/z3iL0fjI2NsaRI0fQoUMHoaOQCmRnZ8PLywu//PILLC0tAQBZWVlwdXXFwYMH0ahRI4ETEtE/9eb+jQDw6tUrXL9+HXl5eejWrRsiIyMFSqZ4X331FU6ePIno6OhKH6AfPnyInj17onv37li1apVACZXDy8sLHh4emDp1apXH16xZg1OnTon6RoipqSkuXLgAR0dHuRu9mZmZcHZ2Fu2ehurm7t27GD9+PKKiovD6Y7NEIkGvXr2wfv16fj4hUiOaQgcgepu3t7fc45cvX8Lf3x96enpy42K6+CbAxMQEderUEToGqYilpSUSEhIQHR2N1NRUAICzszO6d+8ucDKif8fb2xthYWEwNDSs9H72NrG+j1VVMCkrK8P48eNhZ2cnQCLl2b9/PzZu3FjlzJP69etj2bJl8Pf3F13x7HW36Or07NkTK1asUGEi1atuaepvv/0GAwMDARKRMlhZWeHo0aP4/fffkZaWhvLycjg4OIhuNikR/TXueUY1jpGRkdyfESNGwMLCotI4icvChQsxd+5c3qkVuYsXL+Lw4cMAKu7cfvTRRzA0NERQUBA+/fRTjBs3DkVFRQKnJPrnjIyMZMvO337fUuf3MalUimnTpomuiPTgwQM0bdq02uMuLi6ibAzx6NGjKvetfE1TU1O2B5hY9ezZE6tXr5Y9lkgkyM/Px7x58+Dp6SlcMFIKExMTtGrVCq1bt2bhjEhNceYZ1TihoaFCRyABBAUFIT09HfXq1YO1tXWli/KEhASBkpEiBQYGomvXrrINdlNSUjB27Fj4+PjAyckJy5cvh4WFBebPny9sUKJ/6M33ML6fyUtPT0dJSYnQMRTKzMwMmZmZ1S41z8jIEOWs6oYNG+L69euwt7ev8nhycjIaNGig4lSqFRQUhF69esHZ2RkvX77EZ599htu3b8PMzAzbt28XOh4RESkYi2dEVCNU1wacxCUxMRELFy6UPd6xYwdat26NzZs3A6hYzjlv3jwWz0jUkpOT0bJlSxQXFwsdRSmmTZsm97i8vBwPHjzAkSNH4OPjI1Aq5ejVqxdmz56NEydOVOqQXFRUhDlz5qB3794CpVOe110Ie/fujdq1a8sdU5cuhI0aNUJSUhJ27NiB5ORk5Ofnw8/PD8OHD4eOjo7Q8YiISMHYMICIBFdSUoLFixdjzJgx3Che5GrXro3bt2/LmgR07NgRffr0wezZswEAmZmZcHV1xR9//CFkTCKlSkpKQosWLVBWViZ0FKXw8PCQeyyVSlG3bl1069YNY8aMgaameO7d/vbbb2jZsiVq1aqFL7/8Ek2aNEF5eTl+/fVX/O9//0NRURGuXLkie80TC3YhJCIidcPiGRHVCAYGBkhJSYG1tbXQUUiJrKyssG3bNnTu3BnFxcUwNjbGoUOHZI0CUlJS0KVLF+Tm5gqclEh5kpKS4O7uXuVm4++78vJyZGdno27dumoz+yYjIwMTJkzAzz//LNeN76OPPsK6deuqXdr4vlPHLoQHDx5853O9vLyUmISIiFRNPLf+iOi91q1bN8TGxrJ4JnKenp6YNWsWli5div3790NXVxedOnWSHU9OThZdNz4idVJeXg57e3v88ssvcHBwEDqOStjY2ODYsWP4/fffcfv2bQCAvb29KPc6e5M6diF81y0mJBKJKIvjRETqjMUzIqoR+vTpg1mzZiElJQUffvgh9PT05I7zDq44LFy4EN7e3ujSpQv09fURHh4ut0/Q1q1b0bNnTwETEv17z58//9PjYl6WLJVK4eDggJycHLUpnr1mYmKC1q1bCx1D5V53IVQHYl1qTUREf43LNomoRpBKpdUe4x1c8Xn27Bn09fWhoaEhN56bmwt9ff1KG28TvU+kUikkEkm1x8vLy0X9unbo0CEsW7YMGzZsgIuLi9BxiBTK09MT27dvh5GREQBgyZIl8Pf3h7GxMQAgJycHnTp1wo0bNwRMSUREisbiGREREZECxcbGvtN5Xbp0UXISYZiYmKCwsBAlJSXQ1tautPcZ9zSk95lUKsXDhw9hbm4OADA0NERiYiJsbW0BVDRTsLCwEG1xnIhIXXHZJhHVOC9fvkTt2rWFjkFE9I+ItSj2rlatWvWnM++IxITzEIiI1AOLZ0RUI5SWlmLx4sUIDg7Go0ePcOvWLdja2mLOnDmwtraGn5+f0BGJiP6xvn37IiQkBA0aNBA6itL5+voKHYGIiIhIoarfZIiISIUWLVqEsLAwLFu2TG6/KxcXF4SEhAiYjIjo3ztz5gxevHghdAyV0NDQwOPHjyuN5+TkVNrnkOh9I5FIKs2s5ExLIiLx48wzIqoRIiIisGnTJnTv3h3+/v6y8WbNmiE1NVXAZERE9HdUt4ytqKiIzUDovVdeXg5fX1/UqlULQMVWE/7+/rIu4UVFRULGIyIiJWHxjIhqhHv37sHe3r7SeFlZGV69eiVAIiIixbGysoKWlpbQMZRqzZo1ACpm4YSEhEBfX192rLS0FGfOnEGTJk2EikekED4+PnKPR4wYUemcUaNGqSoOERGpCItnRFQjODs74+zZs7CyspIb37NnD1q0aCFQKiKif+7OnTuyDnzXr18XOI3yrVq1CkDFzJzg4GC5JZra2tqwtrZGcHCwUPGIFCI0NFToCEREJAAWz4ioRpg7dy58fHxw7949lJWVITIyEjdv3kRERAQOHz4sdDwior/N3t4eXbp0gZ+fHz755BPRdxHOyMgAAHh4eCAyMhImJiYCJyIiIiJSDEk5+ysTUQ1x9uxZBAYGIikpCfn5+XB3d8fcuXPRs2dPoaMREf1tiYmJCA0Nxfbt21FcXIyhQ4fCz88PrVu3FjqaSpWWliIlJQVWVlYsqBEREdF7icUzIiIiIiUqKSnBwYMHERYWhuPHj+ODDz7AmDFjMHLkSNStW1foeAr31VdfwdXVFX5+figtLUXnzp1x8eJF6Orq4vDhw+jatavQEYmIiIj+FqnQAYiIAMDW1hY5OTmVxvPy8mR7BhERvY80NTXh7e2N3bt3Y+nSpUhLS8P06dNhaWmJUaNG4cGDB0JHVKjdu3ejWbNmAIBDhw4hMzMTqampmDp1KmbPni1wOiIiIqK/j8UzIqoRMjMzUVpaWmm8qKgI9+7dEyAREZFiXLlyBRMmTECDBg2wcuVKTJ8+Henp6Thx4gTu37+PAQMGCB1RoXJyclC/fn0AwNGjRzF48GDZbLuUlBSB0xERERH9fWwYQESCOnjwoOzvUVFRMDIykj0uLS3FyZMnYW1tLUAyIqJ/Z+XKlQgNDcXNmzfh6emJiIgIeHp6QiqtuHdpY2ODsLAw0b3G1atXDzdu3ECDBg1w/PhxbNiwAQBQWFgo14GTiIiI6H3B4hkRCWrgwIEAAIlEAh8fH7ljWlpasLa2RlBQkADJiIj+nQ0bNmDMmDHw9fVFgwYNqjzH3NwcW7ZsUXEy5Ro9ejSGDBmCBg0aQCKRoEePHgCAy5cvo0mTJgKnIyIiIvr72DCAiGoEGxsbxMfHw8zMTOgoRET0L+3ZswfZ2dkYPHgwGjVqBAAIDw+HsbGx6JapEhERkfixeEZERESkBMePH4e+vj46duwIAFi/fj02b94MZ2dnrF+/HiYmJgInJCIiIqJ3weIZEdUYJ0+exMmTJ/H48WOUlZXJHdu6datAqYiI/hlXV1csXboUnp6eSElJQatWrTBt2jScOnUKTZo0QWhoqNARlYav50RERCQm3POMiGqEBQsWIDAwEC1btpTtk0NE9D7LyMiAs7MzAGDv3r3o168fFi9ejISEBHh6egqcTnn4ek5ERERiw+IZEdUIwcHBCAsLw8iRI4WOQkSkENra2igsLAQAREdHY9SoUQCAOnXq4Pnz50JGUyq+nhMREZHYsHhGRDVCcXEx2rdvL3QMIiKF6dixI6ZNm4YOHTogLi4OO3fuBADcunVLtom+GPH1nIiIiMRGKnQAIiIA+Pzzz/HTTz8JHYOISGHWrVsHTU1N7NmzBxs2bEDDhg0BAMeOHUPv3r0FTqc8fD0nIiIisWHDACKqEaZMmYKIiAi4ubnBzc0NWlpacsdXrlwpUDIiIvo7+HpOREREYsPiGRHVCB4eHn96/NSpUypKQkSkeC9fvkRxcbHcmKGhoUBplIuv50RERCQ2LJ4RERERKUFBQQFmzpyJXbt2IScnp9Lx0tJSAVIRERER0d/FhgFEJChvb++/PEcikWDv3r0qSENEpDhff/01Tp06hQ0bNmDkyJFYv3497t27h40bN2LJkiVCx1M4vp4TERGRWLF4RkSCMjIyEjoCEZFSHDp0CBEREejatStGjx6NTp06wd7eHlZWVvjxxx8xfPhwoSMqFF/PiYiISKy4bJOIiIhICfT19XHjxg00btwYjRo1QmRkJFq3bo2MjAy4uroiPz9f6IhERERE9A6kQgcgIiIiEiNbW1tkZGQAAJo0aYJdu3YBqJiRZmxsLGAyIiIiIvo7OPOMiIiISAlWrVoFDQ0NTJ48GdHR0ejfvz/Ky8vx6tUrBAUF4auvvhI6IhERERG9AxbPiIiIiFTg7t27uHr1Kuzt7eHm5iZ0HCIiIiJ6R1y2SURERKRAnp6eePbsmezxkiVLkJeXBysrK3h7e6Nhw4ZwdnYWMCERERER/R2ceUZERESkQBoaGnjw4AHMzc0BAIaGhkhMTIStrS0A4NGjR7CwsEBpaamQMYmIiIjoHXHmGREREZECvX1fkvcpiYiIiN5vLJ4RERERERERERFVg8UzIiIiIgWSSCSQSCSVxoiIiIjo/aQpdAAiIiIiMSkvL4evry9q1aoFAHj58iX8/f2hp6cHACgqKhIyHhERERH9TWwYQERERKRAo0ePfqfzQkNDlZyEiIiIiBSBxTMiIiIiIiIiIqJqcM8zIiIiIiIiIiKiarB4RkREREREREREVA0Wz4iIiIiIiIiIiKrB4hkREREREREREVE1WDwjIiIitebr64uBAwfKHnft2hVfffWVynOcPn0aEokEeXl5Svseb/+s/4QqchIRERHVJCyeERERUY3j6+sLiUQCiUQCbW1t2NvbIzAwECUlJUr/3pGRkVi4cOE7navqQpK1tTVWr16tku9FRERERBU0hQ5AREREVJXevXsjNDQURUVFOHr0KL788ktoaWnhm2++qXRucXExtLW1FfJ969Spo5CvQ0RERETiwJlnREREVCPVqlUL9evXh5WVFcaPH48ePXrg4MGDAP5/+eGiRYtgYWEBR0dHAEB2djaGDBkCY2Nj1KlTBwMGDEBmZqbsa5aWlmLatGkwNjaGqakpvv76a5SXl8t937eXbRYVFWHmzJmwtLRErVq1YG9vjy1btiAzMxMeHh4AABMTE0gkEvj6+gIAysrK8N1338HGxgY6Ojpo1qwZ9uzZI/d9jh49ig8++AA6Ojrw8PCQy/lPlJaWws/PT/Y9HR0d8f3331d57oIFC1C3bl0YGhrC398fxcXFsmPvkp2IiIhInXDmGREREb0XdHR0kJOTI3t88uRJGBoa4sSJEwCAV69eoVevXmjXrh3Onj0LTU1N/Pe//0Xv3r2RnJwMbW1tBAUFISwsDFu3boWTkxOCgoKwb98+dOvWrdrvO2rUKFy8eBFr1qxBs2bNkJGRgadPn8LS0hJ79+7Fxx9/jJs3b8LQ0BA6OjoAgO+++w4//PADgoOD4eDggDNnzmDEiBGoW7cuunTpguzsbHh7e+PLL7/EuHHjcOXKFQQEBPyrf5+ysjI0atQIu3fvhqmpKS5cuIBx48ahQYMGGDJkiNy/W+3atXH69GlkZmZi9OjRMDU1xaJFi94pOxEREZG6YfGMiIiIarTy8nKcPHkSUVFRmDRpkmxcT08PISEhsuWaP/zwA8rKyhASEgKJRAIACA0NhbGxMU6fPo2ePXti9erV+Oabb+Dt7Q0ACA4ORlRUVLXf+9atW9i1axdOnDiBHj16AABsbW1lx18v8TQ3N4exsTGAiplqixcvRnR0NNq1ayf7b86dO4eNGzeiS5cu2LBhA+zs7BAUFAQAcHR0REpKCpYuXfqP/520tLSwYMEC2WMbGxtcvHgRu3btkiueaWtrY+vWrdDV1UXTpk0RGBiIGTNmYOHChXj16tVfZiciIiJSNyyeERERUY10+PBh6Ovr49WrVygrK8Nnn32G+fPny467urrK7XOWlJSEtLQ0GBgYyH2dly9fIj09Hc+ePcODBw/Qpk0b2TFNTU20bNmy0tLN1xITE6GhofG3ikZpaWkoLCzERx99JDdeXFyMFi1aAAB+/fVXuRwAZMWqf2P9+vXYunUrsrKy8OLFCxQXF6N58+Zy5zRr1gy6urpy3zc/Px/Z2dnIz8//y+xERERE6obFMyIiIqqRPDw8sGHDBmhra8PCwgKamvKXLXp6enKP8/Pz8eGHH+LHH3+s9LXq1q37jzK8Xob5d+Tn5wMAjhw5goYNG8odq1Wr1j/K8S527NiB6dOnIygoCO3atYOBgQGWL1+Oy5cvv/PXECo7ERERUU3G4hkRERHVSHp6erC3t3/n893d3bFz506Ym5vD0NCwynMaNGiAy5cvo3PnzgCAkpISXL16Fe7u7lWe7+rqirKyMsTGxsqWbb7p9cy30tJS2ZizszNq1aqFrKysamesOTk5yZofvHbp0qW//iH/xPnz59G+fXtMmDBBNpaenl7pvKSkJLx48UJWGLx06RL09fVhaWmJOnXq/GV2IiIiInXDbptEREQkCsOHD4eZmRkGDBiAs2fPIiMjA6dPn8bkyZPx22+/AQCmTJmCJUuWYP/+/UhNTcWECROQl5dX7de0traGj48PxowZg/3798u+5q5duwAAVlZWkEgkOHz4MJ48eYL8/HwYGBhg+vTpmDp1KsLDw5Geno6EhASsXbsW4eHhAAB/f3/cvn0bM2bMwM2bN/HTTz8hLCzsnX7Oe/fuITExUe7P77//DgcHB1y5cgVRUVG4desW5syZg/j4+Er/fXFxMfz8/HDjxg0cPXoU8+bNw8SJEyGVSt8pOxEREZG6YfGMiIiIREFXVxdnzpxB48aN4e3tDScnJ/j5+eHly5eymWgBAQEYOXIkfHx8ZEsbBw0a9Kdfd8OGDfjkk08wYcIENGnSBGPHjkVBQQEAoGHDhliwYAFmzZqFevXqYeLEiQCAhQsXYs6cOfjuu+/g5OSE3r1748iRI7CxsQEANG7cGHv37sX+/fvRrFkzBAcHY/Hixe/0c65YsQItWrSQ+3PkyBF88cUX8Pb2xtChQ9GmTRvk5OTIzUJ7rXv37nBwcEDnzp0xdOhQeHl5ye0l91fZiYiIiNSNpLy6HXKJiIiIiIiIiIjUHGeeERERERERERERVYPFMyIiIiIiIiIiomqweEZERERERERERFQNFs+IiIiIiIiIiIiqweIZERERERERERFRNVg8IyIiIiIiIiIiqgaLZ0RERERERERERNVg8YyIiIiIiIiIiKgaLJ4RERERERERERFVg8UzIiIiIiIiIiKiarB4RkREREREREREVI3/AyE97WL1qIauAAAAAElFTkSuQmCC","text/plain":["<Figure size 1500x1500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.63      0.76      0.69       750\n","           1       0.54      0.66      0.60       750\n","           2       0.67      0.43      0.52       750\n","           3       0.74      0.59      0.66       142\n","           4       0.56      0.41      0.48       185\n","           5       0.54      0.31      0.40        89\n","           6       0.70      0.63      0.66       750\n","           7       0.60      0.36      0.45        25\n","           8       0.59      0.65      0.62       750\n","           9       0.97      0.98      0.98       612\n","          10       0.83      0.80      0.81       743\n","          11       0.53      0.50      0.52       750\n","          12       0.53      0.30      0.38       461\n","          13       0.40      0.63      0.49       214\n","          14       0.33      0.43      0.37       750\n","\n","    accuracy                           0.61      7721\n","   macro avg       0.61      0.56      0.57      7721\n","weighted avg       0.62      0.61      0.61      7721\n","\n"]}],"source":["y_pred = model.predict(dataset.test)\n","y_pred = np.argmax(y_pred, axis=1)\n","y_true = tf.concat([y for x, y in dataset.test], axis=0).numpy().argmax(axis=1)\n","\n","show_confusion_matrix(y_true, y_pred, labels=dataset.labels)\n","print(classification_report(y_true, y_pred))"]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":4852849,"sourceId":8209559,"sourceType":"datasetVersion"}],"dockerImageVersionId":30673,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.13"}},"nbformat":4,"nbformat_minor":4}