jupyter_notebooks/classification/siren_base_multi_sc.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wUIkE03vfdtg","outputId":"fcbe72df-f3cc-4133-fddf-113e580d7c57"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting tensorflow_addons\n","  Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.8/611.8 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (23.2)\n","Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n","  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n","Installing collected packages: typeguard, tensorflow_addons\n","Successfully installed tensorflow_addons-0.23.0 typeguard-2.13.3\n","Collecting keras-tuner\n","  Downloading keras_tuner-1.4.6-py3-none-any.whl (128 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.9/128.9 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.12.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (23.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.31.0)\n","Collecting kt-legacy (from keras-tuner)\n","  Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2024.2.2)\n","Installing collected packages: kt-legacy, keras-tuner\n","Successfully installed keras-tuner-1.4.6 kt-legacy-1.0.5\n","Cloning into 'Siren'...\n","remote: Enumerating objects: 351, done.\u001b[K\n","remote: Counting objects: 100% (54/54), done.\u001b[K\n","remote: Compressing objects: 100% (32/32), done.\u001b[K\n","remote: Total 351 (delta 26), reused 48 (delta 22), pack-reused 297\u001b[K\n","Receiving objects: 100% (351/351), 188.93 MiB | 20.30 MiB/s, done.\n","Resolving deltas: 100% (98/98), done.\n","Updating files: 100% (236/236), done.\n"]}],"source":["!pip install --upgrade keras==2.15.0\n","!pip install --upgrade tensorflow==2.15.0.post1\n","!pip install --upgrade scipy==1.11.4\n","!pip install tensorflow-addons==0.21.0\n","!pip install keras-tuner --upgrade\n","\n","# clone siren repository, refresh folder to see the siren repository\n","# runtime -> change runtime -> choose a gpu/tpu\n","!git clone https://github.com/hubtru/Siren/"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CgLEXSrwfMmq","outputId":"b651a3db-91ab-4478-849d-cb76240119f5"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n","\n","TensorFlow Addons (TFA) has ended development and introduction of new features.\n","TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n","Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n","\n","For more information see: https://github.com/tensorflow/addons/issues/2807 \n","\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.13.0 and strictly below 2.16.0 (nightly versions are not supported). \n"," The versions of TensorFlow you are currently using is 2.12.0 and is not supported. \n","Some things might work, some things might not.\n","If you were to encounter a bug, do not file an issue.\n","If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n","You can find the compatibility matrix in TensorFlow Addon's readme:\n","https://github.com/tensorflow/addons\n","  warnings.warn(\n"]}],"source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import tensorflow_addons as tfa\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from tensorflow import keras as k\n","from keras import layers\n","from sklearn.model_selection import train_test_split\n","import cv2\n","import os\n","os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"3\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xevVUE9FfMms","outputId":"d81cba2d-d141-460c-e61b-2cd769f60ab1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Num GPUs Available:  0\n","GPUs:  []\n"]}],"source":["# ============================== gpu info ==============================\n","print(\"Num GPUs Available: \", len(tf.config.list_physical_devices(\"GPU\")))\n","print(\"GPUs: \", tf.config.list_physical_devices(\"GPU\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"doEsvvuDfMms"},"outputs":[],"source":["# ============================== variables ==============================\n","image_size = 256\n","num_classes = 3\n","\n","in_channel_spec = 9\n","in_channel_chip = 3\n","img_rows, img_cols = image_size, image_size\n","input_shape_spec = (img_rows, img_cols, in_channel_spec)\n","input_shape_chip = (img_rows, img_cols, in_channel_chip)\n","\n","learning_rate = 0.001\n","weight_decay = 0.0001\n","batch_size = 8\n","epochs = 100"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cDokzEIGfMmt"},"outputs":[],"source":["# ============================== paths ==============================\n","# needed for generate_data\n","modality = \"multi_sc\"\n","exp_name = f\"siren\"\n","sub_exp_name = f\"base\"\n","model_name = f\"{exp_name}_{sub_exp_name}_{modality}\"\n","model_save_path = f\"{model_name}.h5\"\n","\n","model_spec_path = f\"/content/Siren/models/classification/{exp_name}_{sub_exp_name}_spec.h5\"\n","model_chip_path = f\"/content/Siren/models/classification/{exp_name}_{sub_exp_name}_chip.h5\"\n","\n","# dataset\n","csv_path = \"/content/Siren/dataset/labels_sample.csv\"\n","spec_path = \"/content/Siren/dataset/spec\"\n","chip_path = \"/content/Siren/dataset/chip\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pvtPEIZZfMmt","outputId":"2f6565e0-1c31-4580-f481-c419865d9c44"},"outputs":[{"name":"stdout","output_type":"stream","text":["size check train/test/val 14 3 3 20\n"]}],"source":["# ============================== load/prepare dataset ==============================\n","df = pd.read_csv(csv_path)\n","\n","# RANDOM DATASET: we switch to toolbased\n","# shuffle, need same state for tool/spec/chip/multi\n","np.random.seed(55)\n","df = df.iloc[np.random.permutation(len(df))]\n","# train/test/val : 70/15/15\n","train_df, test_df = train_test_split(df, test_size=0.3)\n","test_df, val_df = train_test_split(test_df, test_size=0.5)\n","\n","print(\n","    \"size check train/test/val\",\n","    len(train_df),\n","    len(test_df),\n","    len(val_df),\n","    (len(train_df) + len(test_df) + len(val_df)),\n",")\n","\n","train_df[\"spec_x\"] = train_df.id.map(lambda id: f\"{spec_path}/x/{id}.jpg\")\n","test_df[\"spec_x\"] = test_df.id.map(lambda id: f\"{spec_path}/x/{id}.jpg\")\n","val_df[\"spec_x\"] = val_df.id.map(lambda id: f\"{spec_path}/x/{id}.jpg\")\n","\n","train_df[\"spec_y\"] = train_df.id.map(lambda id: f\"{spec_path}/y/{id}.jpg\")\n","test_df[\"spec_y\"] = test_df.id.map(lambda id: f\"{spec_path}/y/{id}.jpg\")\n","val_df[\"spec_y\"] = val_df.id.map(lambda id: f\"{spec_path}/y/{id}.jpg\")\n","\n","train_df[\"spec_z\"] = train_df.id.map(lambda id: f\"{spec_path}/z/{id}.jpg\")\n","test_df[\"spec_z\"] = test_df.id.map(lambda id: f\"{spec_path}/z/{id}.jpg\")\n","val_df[\"spec_z\"] = val_df.id.map(lambda id: f\"{spec_path}/z/{id}.jpg\")\n","\n","train_df[\"chip\"] = train_df.id.map(lambda id: f\"{chip_path}/{id}.jpg\")\n","test_df[\"chip\"] = test_df.id.map(lambda id: f\"{chip_path}/{id}.jpg\")\n","val_df[\"chip\"] = val_df.id.map(lambda id: f\"{chip_path}/{id}.jpg\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":546},"id":"sVNrAdy6fMmt","outputId":"872f5f05-cf74-49a8-8886-df80092ab165"},"outputs":[{"data":{"text/plain":["<Figure size 600x600 with 0 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(6,6))\n","sns.displot(train_df['tool_label'])\n","plt.title('Label Distribution')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eJoZALrSfMmu","outputId":"84def58e-f4d0-4ea5-b9a6-71089b50f74c"},"outputs":[{"name":"stdout","output_type":"stream","text":["check if permutation is same on all models\n","train_head          id image_label  tool_label  \\\n","14   T8R3B4       sharp           1   \n","10   T6R9B3        used           2   \n","0    T1R2B1       sharp           1   \n","3   T2R13B4      dulled           3   \n","1    T1R7B4        used           2   \n","\n","                                       spec_x  \\\n","14   /content/Siren/dataset/spec/x/T8R3B4.jpg   \n","10   /content/Siren/dataset/spec/x/T6R9B3.jpg   \n","0    /content/Siren/dataset/spec/x/T1R2B1.jpg   \n","3   /content/Siren/dataset/spec/x/T2R13B4.jpg   \n","1    /content/Siren/dataset/spec/x/T1R7B4.jpg   \n","\n","                                       spec_y  \\\n","14   /content/Siren/dataset/spec/y/T8R3B4.jpg   \n","10   /content/Siren/dataset/spec/y/T6R9B3.jpg   \n","0    /content/Siren/dataset/spec/y/T1R2B1.jpg   \n","3   /content/Siren/dataset/spec/y/T2R13B4.jpg   \n","1    /content/Siren/dataset/spec/y/T1R7B4.jpg   \n","\n","                                       spec_z  \\\n","14   /content/Siren/dataset/spec/z/T8R3B4.jpg   \n","10   /content/Siren/dataset/spec/z/T6R9B3.jpg   \n","0    /content/Siren/dataset/spec/z/T1R2B1.jpg   \n","3   /content/Siren/dataset/spec/z/T2R13B4.jpg   \n","1    /content/Siren/dataset/spec/z/T1R7B4.jpg   \n","\n","                                       chip  \n","14   /content/Siren/dataset/chip/T8R3B4.jpg  \n","10   /content/Siren/dataset/chip/T6R9B3.jpg  \n","0    /content/Siren/dataset/chip/T1R2B1.jpg  \n","3   /content/Siren/dataset/chip/T2R13B4.jpg  \n","1    /content/Siren/dataset/chip/T1R7B4.jpg  \n","test_head          id image_label  tool_label  \\\n","16   T9R7B4        used           2   \n","18  T10R3B4       sharp           1   \n","9   T5R12B1      dulled           3   \n","\n","                                       spec_x  \\\n","16   /content/Siren/dataset/spec/x/T9R7B4.jpg   \n","18  /content/Siren/dataset/spec/x/T10R3B4.jpg   \n","9   /content/Siren/dataset/spec/x/T5R12B1.jpg   \n","\n","                                       spec_y  \\\n","16   /content/Siren/dataset/spec/y/T9R7B4.jpg   \n","18  /content/Siren/dataset/spec/y/T10R3B4.jpg   \n","9   /content/Siren/dataset/spec/y/T5R12B1.jpg   \n","\n","                                       spec_z  \\\n","16   /content/Siren/dataset/spec/z/T9R7B4.jpg   \n","18  /content/Siren/dataset/spec/z/T10R3B4.jpg   \n","9   /content/Siren/dataset/spec/z/T5R12B1.jpg   \n","\n","                                       chip  \n","16   /content/Siren/dataset/chip/T9R7B4.jpg  \n","18  /content/Siren/dataset/chip/T10R3B4.jpg  \n","9   /content/Siren/dataset/chip/T5R12B1.jpg  \n","val_head          id image_label  tool_label  \\\n","5   T3R10B1      dulled           3   \n","13  T7R14B3      dulled           3   \n","12   T7R8B3        used           2   \n","\n","                                       spec_x  \\\n","5   /content/Siren/dataset/spec/x/T3R10B1.jpg   \n","13  /content/Siren/dataset/spec/x/T7R14B3.jpg   \n","12   /content/Siren/dataset/spec/x/T7R8B3.jpg   \n","\n","                                       spec_y  \\\n","5   /content/Siren/dataset/spec/y/T3R10B1.jpg   \n","13  /content/Siren/dataset/spec/y/T7R14B3.jpg   \n","12   /content/Siren/dataset/spec/y/T7R8B3.jpg   \n","\n","                                       spec_z  \\\n","5   /content/Siren/dataset/spec/z/T3R10B1.jpg   \n","13  /content/Siren/dataset/spec/z/T7R14B3.jpg   \n","12   /content/Siren/dataset/spec/z/T7R8B3.jpg   \n","\n","                                       chip  \n","5   /content/Siren/dataset/chip/T3R10B1.jpg  \n","13  /content/Siren/dataset/chip/T7R14B3.jpg  \n","12   /content/Siren/dataset/chip/T7R8B3.jpg  \n"]}],"source":["print(\"check if permutation is same on all models\")\n","print(\"train_head\", train_df.head())\n","print(\"test_head\", test_df.head())\n","print(\"val_head\", val_df.head())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"99bpKxJHfMmu"},"outputs":[],"source":["def read_tools(file_paths, img_rows, img_cols, channels):\n","    \"\"\"\n","    Reads the spectogram files from disk and normalizes the pixel values\n","      @params:\n","        file_paths - Array of file paths to read from\n","        img_rows - The image height.\n","        img_cols - The image width.\n","        as_grey - Read the image as Greyscale or RGB.\n","        channels - Number of channels.\n","      @returns:\n","        The created and compiled model (Model)\n","    \"\"\"\n","    images = []\n","\n","    for file_path in file_paths:\n","        img = cv2.imread(file_path)\n","        res = cv2.resize(img, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC)\n","        images.append(res)\n","\n","    images = np.asarray(images, dtype=np.float32)\n","\n","    # normalize\n","    images = images / np.max(images)\n","\n","    # reshape to match Keras expectaions\n","    images = images.reshape(images.shape[0], img_rows, img_cols, channels)\n","\n","    return images\n","\n","\n","def read_specs(file_paths_x, file_paths_y, file_paths_z, img_rows, img_cols, channels):\n","    \"\"\"\n","    Reads the spectogram files from disk and normalizes the pixel values\n","      @params:\n","        file_paths - Array of file paths to read from\n","        img_rows - The image height.\n","        img_cols - The image width.\n","        as_grey - Read the image as Greyscale or RGB.\n","        channels - Number of channels.\n","      @returns:\n","        The created and compiled model (Model)\n","    \"\"\"\n","    images = []\n","    for i in range(file_paths_x.size):\n","        img = []\n","        img_x = cv2.imread(file_paths_x[i])\n","        res_x = cv2.resize(\n","            img_x, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC\n","        )\n","        img.append(res_x)\n","\n","        img_y = cv2.imread(file_paths_y[i])\n","        res_y = cv2.resize(\n","            img_y, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC\n","        )\n","        img.append(res_y)\n","\n","        img_z = cv2.imread(file_paths_z[i])\n","        res_z = cv2.resize(\n","            img_z, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC\n","        )\n","        img.append(res_z)\n","        img = np.concatenate(img, axis=2)\n","        images.append(img)\n","\n","    images = np.asarray(images, dtype=np.float32)\n","\n","    # normalize\n","    images = images / 255.0\n","\n","    return images"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oqqVsXXdfMmu"},"outputs":[],"source":["x_train_spec = read_specs(\n","    train_df.spec_x.values,\n","    train_df.spec_y.values,\n","    train_df.spec_z.values,\n","    img_rows,\n","    img_cols,\n","    in_channel_spec,\n",")\n","x_test_spec = read_specs(\n","    test_df.spec_x.values,\n","    test_df.spec_y.values,\n","    test_df.spec_z.values,\n","    img_rows,\n","    img_cols,\n","    in_channel_spec,\n",")\n","x_val_spec = read_specs(\n","    val_df.spec_x.values,\n","    val_df.spec_y.values,\n","    val_df.spec_z.values,\n","    img_rows,\n","    img_cols,\n","    in_channel_spec,\n",")\n","\n","x_train_chip = read_tools(train_df.chip.values, img_rows, img_cols, in_channel_chip)\n","x_test_chip = read_tools(test_df.chip.values, img_rows, img_cols, in_channel_chip)\n","x_val_chip = read_tools(val_df.chip.values, img_rows, img_cols, in_channel_chip)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Dn-9P1uyfMmv","outputId":"815d76f0-26ce-40eb-f8b4-7d36f4e330d0"},"outputs":[{"data":{"text/plain":["(14, 256, 256, 9)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["x_train_spec.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bDQc2ps3fMmv","outputId":"542cb0e9-10e5-420e-89fe-f790ccc93861"},"outputs":[{"data":{"text/plain":["(14, 256, 256, 3)"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["x_train_chip.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273},"id":"zf1ymfa_fMmv","outputId":"3c0691d8-16f2-4830-f029-84fcae8e8dc8"},"outputs":[{"data":{"image/png":"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","text/plain":["<PIL.Image.Image image mode=RGB size=256x256>"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["pil_img = tf.keras.utils.array_to_img(x_train_chip[0])\n","pil_img"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":310},"id":"gBlxklmSF1tI","outputId":"b1877001-d4eb-41a8-d603-5c4d3482aeea"},"outputs":[{"data":{"text/plain":["[]"]},"execution_count":37,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1000x3000 with 3 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Split the last dimension into 3 separate spectrograms\n","spectrogram1 = x_train_spec[0, :, :, 0:3]  # First spectrogram (channels 0-2)\n","spectrogram2 = x_train_spec[0, :, :, 3:6]  # Second spectrogram (channels 3-5)\n","spectrogram3 = x_train_spec[0, :, :, 6:9]  # Third spectrogram (channels 6-8)\n","# Convert arrays to images\n","pil_img1 = tf.keras.utils.array_to_img(spectrogram1)\n","pil_img2 = tf.keras.utils.array_to_img(spectrogram2)\n","pil_img3 = tf.keras.utils.array_to_img(spectrogram3)\n","# Display the images\n","fig, ax = plt.subplots(1,3, figsize=(10,30))\n","ax[0].imshow(pil_img1)\n","ax[1].imshow(pil_img2)\n","ax[2].imshow(pil_img3)\n","plt.plot()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ohjGhrVmfMmw","outputId":"afecc166-9445-422c-e8e1-0fb5f0511a0b"},"outputs":[{"data":{"text/plain":["array([1, 2, 1, 3, 2, 2, 3, 2, 1, 1, 3, 1, 2, 3])"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["labels_train = train_df.tool_label.values\n","labels_test = test_df.tool_label.values\n","labels_val = val_df.tool_label.values\n","\n","labels_train"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jOMWFAPefMmw"},"outputs":[],"source":["labels_train = labels_train - 1\n","labels_test = labels_test - 1\n","labels_val = labels_val - 1\n","\n","labels_train = tf.keras.utils.to_categorical(\n","    labels_train, num_classes=num_classes, dtype=\"float32\"\n",")\n","labels_test = tf.keras.utils.to_categorical(\n","    labels_test, num_classes=num_classes, dtype=\"float32\"\n",")\n","labels_val = tf.keras.utils.to_categorical(\n","    labels_val, num_classes=num_classes, dtype=\"float32\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u3ynsl5WfMmw"},"outputs":[],"source":["# ============================== augment 1/2 ==============================\n","data_augmentation_spec = k.Sequential(\n","    [\n","        k.layers.RandomBrightness(factor=(-0.2, 0.2), value_range=(0, 1)),\n","        k.layers.RandomContrast(factor=0.2),\n","        k.layers.GaussianNoise(stddev=0.2),\n","    ],\n","    name=\"data_augmentation_spec\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J-tjaRqVfMmw"},"outputs":[],"source":["# ============================== augment 2/2 ==============================\n","data_augmentation_chip = k.Sequential(\n","    [\n","        k.layers.RandomBrightness(factor=(-0.2, 0.2), value_range=(0, 1)),\n","        k.layers.RandomContrast(factor=0.2),\n","        k.layers.GaussianNoise(stddev=0.2),\n","    ],\n","    name=\"data_augmentation_chip\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sduW9qyHfMmw"},"outputs":[],"source":["x_train_spec = data_augmentation_spec(x_train_spec)\n","x_train_chip = data_augmentation_chip(x_train_chip)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C3wbCI8RfMmw"},"outputs":[],"source":["train_dataset = tf.data.Dataset.from_tensor_slices(\n","    (\n","        {\n","            \"input_1_spec\": x_train_spec,\n","            \"input_1_chip\": x_train_chip,\n","        },\n","        labels_train,\n","    )\n",")\n","test_dataset = tf.data.Dataset.from_tensor_slices(\n","    (\n","        {\n","            \"input_1_spec\": x_test_spec,\n","            \"input_1_chip\": x_test_chip,\n","        },\n","        labels_test,\n","    )\n",")\n","val_dataset = tf.data.Dataset.from_tensor_slices(\n","    (\n","        {\n","            \"input_1_spec\": x_val_spec,\n","            \"input_1_chip\": x_val_chip,\n","        },\n","        labels_val,\n","    )\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WeA4B-1PfMmx"},"outputs":[],"source":["auto = tf.data.AUTOTUNE\n","inputs2 = k.Input(input_shape_spec)\n","inputs3 = k.Input(input_shape_chip)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8d5XzkLtfMmx"},"outputs":[],"source":["def generate_datasets(images, is_train=False, shuffle=False):\n","    dataset = images\n","    if shuffle:\n","        dataset = dataset.shuffle(batch_size * 10)\n","    dataset = dataset.batch(batch_size)\n","    if is_train:\n","        pass\n","    return dataset.prefetch(auto)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jT49PmwifMmx"},"outputs":[],"source":["train_ds = generate_datasets(train_dataset, is_train=True, shuffle=True)\n","val_ds = generate_datasets(val_dataset)\n","test_ds = generate_datasets(test_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1HSRh0aIfMmx","outputId":"b8558141-3d59-48cc-899d-6cbb77fc646c"},"outputs":[{"data":{"text/plain":["<_PrefetchDataset element_spec=({'input_1_spec': TensorSpec(shape=(None, 256, 256, 9), dtype=tf.float32, name=None), 'input_1_chip': TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None)}, TensorSpec(shape=(None, 3), dtype=tf.float32, name=None))>"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["train_ds"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uWE1LSyRfMmx"},"outputs":[],"source":["model_spec = k.models.load_model(model_spec_path, compile=False)\n","model_chip = k.models.load_model(model_chip_path, compile=False)\n","\n","model_spec.trainable = False\n","model_chip.trainable = False\n","\n","\n","def load_siren(model_spec, model_chip):\n","    inputs2 = model_spec.input\n","    inputs2._name = \"input_spec\"\n","    inputs3 = model_chip.input\n","    inputs3._name = \"input_chip\"\n","\n","    truncated_model_spec = k.Model(\n","        inputs=model_spec.input, outputs=model_spec.layers[-2].output\n","    )\n","    truncated_model_chip = k.Model(\n","        inputs=model_chip.input, outputs=model_chip.layers[-2].output\n","    )\n","    mergedOut = k.layers.Concatenate()(\n","        [\n","            truncated_model_spec.output,\n","            truncated_model_chip.output,\n","        ]\n","    )\n","    output = layers.Dense(num_classes, activation=\"softmax\", name=\"output_mult\")(\n","        mergedOut\n","    )\n","    return k.Model(inputs=[inputs2, inputs3], outputs=output)\n","\n","\n","for layer in model_spec.layers:\n","    layer._name = layer.name + str(\"_spec\")\n","for layer in model_chip.layers:\n","    layer._name = layer.name + str(\"_chip\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2wJgQs8tfMmy"},"outputs":[],"source":["def launch_experiment(model):\n","    optimizer = tfa.optimizers.AdamW(\n","        learning_rate=learning_rate, weight_decay=weight_decay\n","    )\n","\n","    model.compile(\n","        optimizer=optimizer,\n","        loss=\"categorical_crossentropy\",\n","        metrics=[\"accuracy\"],\n","    )\n","\n","    history = model.fit(\n","        train_ds,\n","        validation_data=val_ds,\n","        epochs=epochs,\n","    )\n","\n","    _, accuracy = model.evaluate(test_ds)\n","    print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n","\n","    return history, model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jp46lU4AfMmy","outputId":"e9c92f46-b6f6-4f31-bfe0-2860fe8c3d39"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_2\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1_spec (InputLayer)      [(None, 256, 256, 9  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," input_1_chip (InputLayer)      [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," conv2d_spec (Conv2D)           (None, 16, 16, 256)  590080      ['input_1_spec[0][0]']           \n","                                                                                                  \n"," conv2d_chip (Conv2D)           (None, 16, 16, 256)  196864      ['input_1_chip[0][0]']           \n","                                                                                                  \n"," activation_spec (Activation)   (None, 16, 16, 256)  0           ['conv2d_spec[0][0]']            \n","                                                                                                  \n"," activation_chip (Activation)   (None, 16, 16, 256)  0           ['conv2d_chip[0][0]']            \n","                                                                                                  \n"," batch_normalization_spec (Batc  (None, 16, 16, 256)  1024       ['activation_spec[0][0]']        \n"," hNormalization)                                                                                  \n","                                                                                                  \n"," batch_normalization_chip (Batc  (None, 16, 16, 256)  1024       ['activation_chip[0][0]']        \n"," hNormalization)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_spec (Depthwi  (None, 16, 16, 256)  6656       ['batch_normalization_spec[0][0]'\n"," seConv2D)                                                       ]                                \n","                                                                                                  \n"," depthwise_conv2d_chip (Depthwi  (None, 16, 16, 256)  6656       ['batch_normalization_chip[0][0]'\n"," seConv2D)                                                       ]                                \n","                                                                                                  \n"," activation_1_spec (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_spec[0][0]']  \n","                                                                                                  \n"," activation_1_chip (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_chip[0][0]']  \n","                                                                                                  \n"," batch_normalization_1_spec (Ba  (None, 16, 16, 256)  1024       ['activation_1_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_1_chip (Ba  (None, 16, 16, 256)  1024       ['activation_1_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_spec (Add)                 (None, 16, 16, 256)  0           ['batch_normalization_1_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_spec[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," add_chip (Add)                 (None, 16, 16, 256)  0           ['batch_normalization_1_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_chip[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," conv2d_1_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_spec[0][0]']               \n","                                                                                                  \n"," conv2d_1_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_chip[0][0]']               \n","                                                                                                  \n"," activation_2_spec (Activation)  (None, 16, 16, 256)  0          ['conv2d_1_spec[0][0]']          \n","                                                                                                  \n"," activation_2_chip (Activation)  (None, 16, 16, 256)  0          ['conv2d_1_chip[0][0]']          \n","                                                                                                  \n"," batch_normalization_2_spec (Ba  (None, 16, 16, 256)  1024       ['activation_2_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_2_chip (Ba  (None, 16, 16, 256)  1024       ['activation_2_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," depthwise_conv2d_1_spec (Depth  (None, 16, 16, 256)  6656       ['batch_normalization_2_spec[0][0\n"," wiseConv2D)                                                     ]']                              \n","                                                                                                  \n"," depthwise_conv2d_1_chip (Depth  (None, 16, 16, 256)  6656       ['batch_normalization_2_chip[0][0\n"," wiseConv2D)                                                     ]']                              \n","                                                                                                  \n"," activation_3_spec (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_1_spec[0][0]']\n","                                                                                                  \n"," activation_3_chip (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_1_chip[0][0]']\n","                                                                                                  \n"," batch_normalization_3_spec (Ba  (None, 16, 16, 256)  1024       ['activation_3_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_3_chip (Ba  (None, 16, 16, 256)  1024       ['activation_3_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_1_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_3_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_2_spec[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," add_1_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_3_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_2_chip[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," conv2d_2_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_1_spec[0][0]']             \n","                                                                                                  \n"," conv2d_2_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_1_chip[0][0]']             \n","                                                                                                  \n"," activation_4_spec (Activation)  (None, 16, 16, 256)  0          ['conv2d_2_spec[0][0]']          \n","                                                                                                  \n"," activation_4_chip (Activation)  (None, 16, 16, 256)  0          ['conv2d_2_chip[0][0]']          \n","                                                                                                  \n"," batch_normalization_4_spec (Ba  (None, 16, 16, 256)  1024       ['activation_4_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_4_chip (Ba  (None, 16, 16, 256)  1024       ['activation_4_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_4_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_4_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_spec[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," add_4_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_4_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_chip[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," depthwise_conv2d_4_spec (Depth  (None, 16, 16, 256)  6656       ['add_4_spec[0][0]']             \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_4_chip (Depth  (None, 16, 16, 256)  6656       ['add_4_chip[0][0]']             \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_2_spec (Depth  (None, 16, 16, 256)  6656       ['batch_normalization_4_spec[0][0\n"," wiseConv2D)                                                     ]']                              \n","                                                                                                  \n"," activation_9_spec (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_4_spec[0][0]']\n","                                                                                                  \n"," depthwise_conv2d_2_chip (Depth  (None, 16, 16, 256)  6656       ['batch_normalization_4_chip[0][0\n"," wiseConv2D)                                                     ]']                              \n","                                                                                                  \n"," activation_9_chip (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_4_chip[0][0]']\n","                                                                                                  \n"," activation_5_spec (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_2_spec[0][0]']\n","                                                                                                  \n"," batch_normalization_9_spec (Ba  (None, 16, 16, 256)  1024       ['activation_9_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," activation_5_chip (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_2_chip[0][0]']\n","                                                                                                  \n"," batch_normalization_9_chip (Ba  (None, 16, 16, 256)  1024       ['activation_9_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_5_spec (Ba  (None, 16, 16, 256)  1024       ['activation_5_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_5_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_9_spec[0][0\n","                                                                 ]',                              \n","                                                                  'add_4_spec[0][0]']             \n","                                                                                                  \n"," batch_normalization_5_chip (Ba  (None, 16, 16, 256)  1024       ['activation_5_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_5_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_9_chip[0][0\n","                                                                 ]',                              \n","                                                                  'add_4_chip[0][0]']             \n","                                                                                                  \n"," add_2_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_5_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_4_spec[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," conv2d_5_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_5_spec[0][0]']             \n","                                                                                                  \n"," add_2_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_5_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_4_chip[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," conv2d_5_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_5_chip[0][0]']             \n","                                                                                                  \n"," conv2d_3_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_2_spec[0][0]']             \n","                                                                                                  \n"," activation_10_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_5_spec[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," conv2d_3_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_2_chip[0][0]']             \n","                                                                                                  \n"," activation_10_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_5_chip[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_6_spec (Activation)  (None, 16, 16, 256)  0          ['conv2d_3_spec[0][0]']          \n","                                                                                                  \n"," batch_normalization_10_spec (B  (None, 16, 16, 256)  1024       ['activation_10_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," activation_6_chip (Activation)  (None, 16, 16, 256)  0          ['conv2d_3_chip[0][0]']          \n","                                                                                                  \n"," batch_normalization_10_chip (B  (None, 16, 16, 256)  1024       ['activation_10_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_6_spec (Ba  (None, 16, 16, 256)  1024       ['activation_6_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_6_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_2_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_10_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," batch_normalization_6_chip (Ba  (None, 16, 16, 256)  1024       ['activation_6_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_6_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_2_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_10_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_7_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_6_spec[0][0\n","                                                                 ]',                              \n","                                                                  'add_6_spec[0][0]']             \n","                                                                                                  \n"," add_7_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_6_chip[0][0\n","                                                                 ]',                              \n","                                                                  'add_6_chip[0][0]']             \n","                                                                                                  \n"," depthwise_conv2d_5_spec (Depth  (None, 16, 16, 256)  6656       ['add_7_spec[0][0]']             \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_5_chip (Depth  (None, 16, 16, 256)  6656       ['add_7_chip[0][0]']             \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_3_spec (Depth  (None, 16, 16, 256)  6656       ['batch_normalization_6_spec[0][0\n"," wiseConv2D)                                                     ]']                              \n","                                                                                                  \n"," activation_11_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_5_spec[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," depthwise_conv2d_3_chip (Depth  (None, 16, 16, 256)  6656       ['batch_normalization_6_chip[0][0\n"," wiseConv2D)                                                     ]']                              \n","                                                                                                  \n"," activation_11_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_5_chip[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_7_spec (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_3_spec[0][0]']\n","                                                                                                  \n"," batch_normalization_11_spec (B  (None, 16, 16, 256)  1024       ['activation_11_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," activation_7_chip (Activation)  (None, 16, 16, 256)  0          ['depthwise_conv2d_3_chip[0][0]']\n","                                                                                                  \n"," batch_normalization_11_chip (B  (None, 16, 16, 256)  1024       ['activation_11_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_7_spec (Ba  (None, 16, 16, 256)  1024       ['activation_7_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_8_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_11_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_7_spec[0][0]']             \n","                                                                                                  \n"," batch_normalization_7_chip (Ba  (None, 16, 16, 256)  1024       ['activation_7_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_8_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_11_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_7_chip[0][0]']             \n","                                                                                                  \n"," add_3_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_7_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_6_spec[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," conv2d_6_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_8_spec[0][0]']             \n","                                                                                                  \n"," add_3_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_7_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_6_chip[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," conv2d_6_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_8_chip[0][0]']             \n","                                                                                                  \n"," conv2d_4_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_3_spec[0][0]']             \n","                                                                                                  \n"," activation_12_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_6_spec[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," conv2d_4_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_3_chip[0][0]']             \n","                                                                                                  \n"," activation_12_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_6_chip[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_8_spec (Activation)  (None, 16, 16, 256)  0          ['conv2d_4_spec[0][0]']          \n","                                                                                                  \n"," batch_normalization_12_spec (B  (None, 16, 16, 256)  1024       ['activation_12_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," activation_8_chip (Activation)  (None, 16, 16, 256)  0          ['conv2d_4_chip[0][0]']          \n","                                                                                                  \n"," batch_normalization_12_chip (B  (None, 16, 16, 256)  1024       ['activation_12_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_8_spec (Ba  (None, 16, 16, 256)  1024       ['activation_8_spec[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_9_spec (Add)               (None, 16, 16, 256)  0           ['batch_normalization_4_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_12_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," batch_normalization_8_chip (Ba  (None, 16, 16, 256)  1024       ['activation_8_chip[0][0]']      \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_9_chip (Add)               (None, 16, 16, 256)  0           ['batch_normalization_4_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_12_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_10_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_8_spec[0][0\n","                                                                 ]',                              \n","                                                                  'add_9_spec[0][0]']             \n","                                                                                                  \n"," add_15_spec (Add)              (None, 16, 16, 256)  0           ['add_9_spec[0][0]',             \n","                                                                  'batch_normalization_spec[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," add_10_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_8_chip[0][0\n","                                                                 ]',                              \n","                                                                  'add_9_chip[0][0]']             \n","                                                                                                  \n"," add_15_chip (Add)              (None, 16, 16, 256)  0           ['add_9_chip[0][0]',             \n","                                                                  'batch_normalization_chip[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," depthwise_conv2d_6_spec (Depth  (None, 16, 16, 256)  6656       ['add_10_spec[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_8_spec (Depth  (None, 16, 16, 256)  6656       ['add_15_spec[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_6_chip (Depth  (None, 16, 16, 256)  6656       ['add_10_chip[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_8_chip (Depth  (None, 16, 16, 256)  6656       ['add_15_chip[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," activation_13_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_6_spec[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_17_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_8_spec[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_13_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_6_chip[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_17_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_8_chip[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_13_spec (B  (None, 16, 16, 256)  1024       ['activation_13_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_17_spec (B  (None, 16, 16, 256)  1024       ['activation_17_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_13_chip (B  (None, 16, 16, 256)  1024       ['activation_13_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_17_chip (B  (None, 16, 16, 256)  1024       ['activation_17_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_11_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_13_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_10_spec[0][0]']            \n","                                                                                                  \n"," add_16_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_17_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_15_spec[0][0]']            \n","                                                                                                  \n"," add_11_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_13_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_10_chip[0][0]']            \n","                                                                                                  \n"," add_16_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_17_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_15_chip[0][0]']            \n","                                                                                                  \n"," conv2d_7_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_11_spec[0][0]']            \n","                                                                                                  \n"," conv2d_9_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_16_spec[0][0]']            \n","                                                                                                  \n"," conv2d_7_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_11_chip[0][0]']            \n","                                                                                                  \n"," conv2d_9_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_16_chip[0][0]']            \n","                                                                                                  \n"," activation_14_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_7_spec[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_18_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_9_spec[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_14_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_7_chip[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_18_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_9_chip[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_14_spec (B  (None, 16, 16, 256)  1024       ['activation_14_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_18_spec (B  (None, 16, 16, 256)  1024       ['activation_18_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_14_chip (B  (None, 16, 16, 256)  1024       ['activation_14_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_18_chip (B  (None, 16, 16, 256)  1024       ['activation_18_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_12_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_6_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_14_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_17_spec (Add)              (None, 16, 16, 256)  0           ['add_6_spec[0][0]',             \n","                                                                  'batch_normalization_18_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_12_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_6_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_14_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_17_chip (Add)              (None, 16, 16, 256)  0           ['add_6_chip[0][0]',             \n","                                                                  'batch_normalization_18_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_18_spec (Add)              (None, 16, 16, 256)  0           ['add_12_spec[0][0]',            \n","                                                                  'add_17_spec[0][0]']            \n","                                                                                                  \n"," add_18_chip (Add)              (None, 16, 16, 256)  0           ['add_12_chip[0][0]',            \n","                                                                  'add_17_chip[0][0]']            \n","                                                                                                  \n"," depthwise_conv2d_7_spec (Depth  (None, 16, 16, 256)  6656       ['add_12_spec[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_9_spec (Depth  (None, 16, 16, 256)  6656       ['add_18_spec[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_7_chip (Depth  (None, 16, 16, 256)  6656       ['add_12_chip[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_9_chip (Depth  (None, 16, 16, 256)  6656       ['add_18_chip[0][0]']            \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," activation_15_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_7_spec[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_19_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_9_spec[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_15_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_7_chip[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," activation_19_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_9_chip[0][0]']\n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_15_spec (B  (None, 16, 16, 256)  1024       ['activation_15_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_19_spec (B  (None, 16, 16, 256)  1024       ['activation_19_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_15_chip (B  (None, 16, 16, 256)  1024       ['activation_15_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_19_chip (B  (None, 16, 16, 256)  1024       ['activation_19_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_13_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_15_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_12_spec[0][0]']            \n","                                                                                                  \n"," add_19_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_19_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_18_spec[0][0]']            \n","                                                                                                  \n"," add_13_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_15_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_12_chip[0][0]']            \n","                                                                                                  \n"," add_19_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_19_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_18_chip[0][0]']            \n","                                                                                                  \n"," conv2d_8_spec (Conv2D)         (None, 16, 16, 256)  65792       ['add_13_spec[0][0]']            \n","                                                                                                  \n"," conv2d_10_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_19_spec[0][0]']            \n","                                                                                                  \n"," conv2d_8_chip (Conv2D)         (None, 16, 16, 256)  65792       ['add_13_chip[0][0]']            \n","                                                                                                  \n"," conv2d_10_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_19_chip[0][0]']            \n","                                                                                                  \n"," activation_16_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_8_spec[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_20_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_10_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_16_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_8_chip[0][0]']          \n"," )                                                                                                \n","                                                                                                  \n"," activation_20_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_10_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_16_spec (B  (None, 16, 16, 256)  1024       ['activation_16_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_20_spec (B  (None, 16, 16, 256)  1024       ['activation_20_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_16_chip (B  (None, 16, 16, 256)  1024       ['activation_16_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_20_chip (B  (None, 16, 16, 256)  1024       ['activation_20_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_14_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_8_spec[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_16_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_20_spec (Add)              (None, 16, 16, 256)  0           ['add_9_spec[0][0]',             \n","                                                                  'batch_normalization_20_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_14_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_8_chip[0][0\n","                                                                 ]',                              \n","                                                                  'batch_normalization_16_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_20_chip (Add)              (None, 16, 16, 256)  0           ['add_9_chip[0][0]',             \n","                                                                  'batch_normalization_20_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_21_spec (Add)              (None, 16, 16, 256)  0           ['add_14_spec[0][0]',            \n","                                                                  'add_20_spec[0][0]']            \n","                                                                                                  \n"," add_26_spec (Add)              (None, 16, 16, 256)  0           ['add_20_spec[0][0]',            \n","                                                                  'batch_normalization_spec[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," add_21_chip (Add)              (None, 16, 16, 256)  0           ['add_14_chip[0][0]',            \n","                                                                  'add_20_chip[0][0]']            \n","                                                                                                  \n"," add_26_chip (Add)              (None, 16, 16, 256)  0           ['add_20_chip[0][0]',            \n","                                                                  'batch_normalization_chip[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," depthwise_conv2d_10_spec (Dept  (None, 16, 16, 256)  6656       ['add_21_spec[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_12_spec (Dept  (None, 16, 16, 256)  6656       ['add_26_spec[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_10_chip (Dept  (None, 16, 16, 256)  6656       ['add_21_chip[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_12_chip (Dept  (None, 16, 16, 256)  6656       ['add_26_chip[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," activation_21_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_10_spec[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_25_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_12_spec[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_21_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_10_chip[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_25_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_12_chip[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," batch_normalization_21_spec (B  (None, 16, 16, 256)  1024       ['activation_21_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_25_spec (B  (None, 16, 16, 256)  1024       ['activation_25_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_21_chip (B  (None, 16, 16, 256)  1024       ['activation_21_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_25_chip (B  (None, 16, 16, 256)  1024       ['activation_25_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_22_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_21_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_21_spec[0][0]']            \n","                                                                                                  \n"," add_27_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_25_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_26_spec[0][0]']            \n","                                                                                                  \n"," add_22_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_21_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_21_chip[0][0]']            \n","                                                                                                  \n"," add_27_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_25_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_26_chip[0][0]']            \n","                                                                                                  \n"," conv2d_11_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_22_spec[0][0]']            \n","                                                                                                  \n"," conv2d_13_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_27_spec[0][0]']            \n","                                                                                                  \n"," conv2d_11_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_22_chip[0][0]']            \n","                                                                                                  \n"," conv2d_13_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_27_chip[0][0]']            \n","                                                                                                  \n"," activation_22_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_11_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_26_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_13_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_22_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_11_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_26_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_13_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_22_spec (B  (None, 16, 16, 256)  1024       ['activation_22_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_26_spec (B  (None, 16, 16, 256)  1024       ['activation_26_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_22_chip (B  (None, 16, 16, 256)  1024       ['activation_22_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_26_chip (B  (None, 16, 16, 256)  1024       ['activation_26_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_23_spec (Add)              (None, 16, 16, 256)  0           ['add_12_spec[0][0]',            \n","                                                                  'batch_normalization_22_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_28_spec (Add)              (None, 16, 16, 256)  0           ['add_17_spec[0][0]',            \n","                                                                  'batch_normalization_26_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_23_chip (Add)              (None, 16, 16, 256)  0           ['add_12_chip[0][0]',            \n","                                                                  'batch_normalization_22_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_28_chip (Add)              (None, 16, 16, 256)  0           ['add_17_chip[0][0]',            \n","                                                                  'batch_normalization_26_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_29_spec (Add)              (None, 16, 16, 256)  0           ['add_23_spec[0][0]',            \n","                                                                  'add_28_spec[0][0]']            \n","                                                                                                  \n"," add_29_chip (Add)              (None, 16, 16, 256)  0           ['add_23_chip[0][0]',            \n","                                                                  'add_28_chip[0][0]']            \n","                                                                                                  \n"," depthwise_conv2d_11_spec (Dept  (None, 16, 16, 256)  6656       ['add_23_spec[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_13_spec (Dept  (None, 16, 16, 256)  6656       ['add_29_spec[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_11_chip (Dept  (None, 16, 16, 256)  6656       ['add_23_chip[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_13_chip (Dept  (None, 16, 16, 256)  6656       ['add_29_chip[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," activation_23_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_11_spec[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_27_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_13_spec[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_23_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_11_chip[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_27_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_13_chip[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," batch_normalization_23_spec (B  (None, 16, 16, 256)  1024       ['activation_23_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_27_spec (B  (None, 16, 16, 256)  1024       ['activation_27_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_23_chip (B  (None, 16, 16, 256)  1024       ['activation_23_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_27_chip (B  (None, 16, 16, 256)  1024       ['activation_27_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_24_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_23_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_23_spec[0][0]']            \n","                                                                                                  \n"," add_30_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_27_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_29_spec[0][0]']            \n","                                                                                                  \n"," add_24_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_23_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_23_chip[0][0]']            \n","                                                                                                  \n"," add_30_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_27_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_29_chip[0][0]']            \n","                                                                                                  \n"," conv2d_12_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_24_spec[0][0]']            \n","                                                                                                  \n"," conv2d_14_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_30_spec[0][0]']            \n","                                                                                                  \n"," conv2d_12_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_24_chip[0][0]']            \n","                                                                                                  \n"," conv2d_14_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_30_chip[0][0]']            \n","                                                                                                  \n"," activation_24_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_12_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_28_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_14_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_24_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_12_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_28_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_14_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_24_spec (B  (None, 16, 16, 256)  1024       ['activation_24_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_28_spec (B  (None, 16, 16, 256)  1024       ['activation_28_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_24_chip (B  (None, 16, 16, 256)  1024       ['activation_24_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_28_chip (B  (None, 16, 16, 256)  1024       ['activation_28_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_25_spec (Add)              (None, 16, 16, 256)  0           ['add_14_spec[0][0]',            \n","                                                                  'batch_normalization_24_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_31_spec (Add)              (None, 16, 16, 256)  0           ['add_20_spec[0][0]',            \n","                                                                  'batch_normalization_28_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_25_chip (Add)              (None, 16, 16, 256)  0           ['add_14_chip[0][0]',            \n","                                                                  'batch_normalization_24_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_31_chip (Add)              (None, 16, 16, 256)  0           ['add_20_chip[0][0]',            \n","                                                                  'batch_normalization_28_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_32_spec (Add)              (None, 16, 16, 256)  0           ['add_25_spec[0][0]',            \n","                                                                  'add_31_spec[0][0]']            \n","                                                                                                  \n"," add_32_chip (Add)              (None, 16, 16, 256)  0           ['add_25_chip[0][0]',            \n","                                                                  'add_31_chip[0][0]']            \n","                                                                                                  \n"," depthwise_conv2d_14_spec (Dept  (None, 16, 16, 256)  6656       ['add_32_spec[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_14_chip (Dept  (None, 16, 16, 256)  6656       ['add_32_chip[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," activation_29_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_14_spec[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_29_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_14_chip[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," batch_normalization_29_spec (B  (None, 16, 16, 256)  1024       ['activation_29_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_29_chip (B  (None, 16, 16, 256)  1024       ['activation_29_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_33_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_29_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_32_spec[0][0]']            \n","                                                                                                  \n"," add_33_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_29_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_32_chip[0][0]']            \n","                                                                                                  \n"," conv2d_15_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_33_spec[0][0]']            \n","                                                                                                  \n"," conv2d_15_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_33_chip[0][0]']            \n","                                                                                                  \n"," activation_30_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_15_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_30_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_15_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_30_spec (B  (None, 16, 16, 256)  1024       ['activation_30_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_30_chip (B  (None, 16, 16, 256)  1024       ['activation_30_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_34_spec (Add)              (None, 16, 16, 256)  0           ['add_23_spec[0][0]',            \n","                                                                  'batch_normalization_30_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_34_chip (Add)              (None, 16, 16, 256)  0           ['add_23_chip[0][0]',            \n","                                                                  'batch_normalization_30_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," depthwise_conv2d_15_spec (Dept  (None, 16, 16, 256)  6656       ['add_34_spec[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," depthwise_conv2d_15_chip (Dept  (None, 16, 16, 256)  6656       ['add_34_chip[0][0]']            \n"," hwiseConv2D)                                                                                     \n","                                                                                                  \n"," activation_31_spec (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_15_spec[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," activation_31_chip (Activation  (None, 16, 16, 256)  0          ['depthwise_conv2d_15_chip[0][0]'\n"," )                                                               ]                                \n","                                                                                                  \n"," batch_normalization_31_spec (B  (None, 16, 16, 256)  1024       ['activation_31_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_31_chip (B  (None, 16, 16, 256)  1024       ['activation_31_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_35_spec (Add)              (None, 16, 16, 256)  0           ['batch_normalization_31_spec[0][\n","                                                                 0]',                             \n","                                                                  'add_34_spec[0][0]']            \n","                                                                                                  \n"," add_35_chip (Add)              (None, 16, 16, 256)  0           ['batch_normalization_31_chip[0][\n","                                                                 0]',                             \n","                                                                  'add_34_chip[0][0]']            \n","                                                                                                  \n"," conv2d_16_spec (Conv2D)        (None, 16, 16, 256)  65792       ['add_35_spec[0][0]']            \n","                                                                                                  \n"," conv2d_16_chip (Conv2D)        (None, 16, 16, 256)  65792       ['add_35_chip[0][0]']            \n","                                                                                                  \n"," activation_32_spec (Activation  (None, 16, 16, 256)  0          ['conv2d_16_spec[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," activation_32_chip (Activation  (None, 16, 16, 256)  0          ['conv2d_16_chip[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," batch_normalization_32_spec (B  (None, 16, 16, 256)  1024       ['activation_32_spec[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," batch_normalization_32_chip (B  (None, 16, 16, 256)  1024       ['activation_32_chip[0][0]']     \n"," atchNormalization)                                                                               \n","                                                                                                  \n"," add_36_spec (Add)              (None, 16, 16, 256)  0           ['add_25_spec[0][0]',            \n","                                                                  'batch_normalization_32_spec[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," add_36_chip (Add)              (None, 16, 16, 256)  0           ['add_25_chip[0][0]',            \n","                                                                  'batch_normalization_32_chip[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," global_average_pooling2d_spec   (None, 256)         0           ['add_36_spec[0][0]']            \n"," (GlobalAveragePooling2D)                                                                         \n","                                                                                                  \n"," global_average_pooling2d_chip   (None, 256)         0           ['add_36_chip[0][0]']            \n"," (GlobalAveragePooling2D)                                                                         \n","                                                                                                  \n"," concatenate (Concatenate)      (None, 512)          0           ['global_average_pooling2d_spec[0\n","                                                                 ][0]',                           \n","                                                                  'global_average_pooling2d_chip[0\n","                                                                 ][0]']                           \n","                                                                                                  \n"," output_mult (Dense)            (None, 3)            1539        ['concatenate[0][0]']            \n","                                                                                                  \n","==================================================================================================\n","Total params: 3,174,403\n","Trainable params: 1,539\n","Non-trainable params: 3,172,864\n","__________________________________________________________________________________________________\n"]}],"source":["model = load_siren(model_spec, model_chip)\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oex7vfKifMmy","outputId":"93c7f70c-6096-4887-ae20-4d23da2ab758"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","2/2 [==============================] - 30s 3s/step - loss: 2.9386 - accuracy: 0.4286 - val_loss: 1.0764 - val_accuracy: 0.3333\n","Epoch 2/100\n","2/2 [==============================] - 1s 698ms/step - loss: 1.9793 - accuracy: 0.3571 - val_loss: 0.9586 - val_accuracy: 0.6667\n","Epoch 3/100\n","2/2 [==============================] - 1s 611ms/step - loss: 1.2489 - accuracy: 0.5000 - val_loss: 1.0817 - val_accuracy: 0.3333\n","Epoch 4/100\n","2/2 [==============================] - 1s 784ms/step - loss: 1.2436 - accuracy: 0.5000 - val_loss: 1.2902 - val_accuracy: 0.0000e+00\n","Epoch 5/100\n","2/2 [==============================] - 2s 1s/step - loss: 1.1491 - accuracy: 0.5000 - val_loss: 1.5126 - val_accuracy: 0.0000e+00\n","Epoch 6/100\n","2/2 [==============================] - 5s 3s/step - loss: 1.0297 - accuracy: 0.5000 - val_loss: 1.6551 - val_accuracy: 0.0000e+00\n","Epoch 7/100\n","2/2 [==============================] - 3s 1s/step - loss: 0.9153 - accuracy: 0.5000 - val_loss: 1.6896 - val_accuracy: 0.0000e+00\n","Epoch 8/100\n","2/2 [==============================] - 1s 694ms/step - loss: 0.7822 - accuracy: 0.5714 - val_loss: 1.6695 - val_accuracy: 0.0000e+00\n","Epoch 9/100\n","2/2 [==============================] - 1s 631ms/step - loss: 0.7008 - accuracy: 0.6429 - val_loss: 1.6790 - val_accuracy: 0.3333\n","Epoch 10/100\n","2/2 [==============================] - 1s 607ms/step - loss: 0.7302 - accuracy: 0.6429 - val_loss: 1.7030 - val_accuracy: 0.3333\n","Epoch 11/100\n","2/2 [==============================] - 1s 597ms/step - loss: 0.7126 - accuracy: 0.6429 - val_loss: 1.6736 - val_accuracy: 0.3333\n","Epoch 12/100\n","2/2 [==============================] - 1s 579ms/step - loss: 0.6400 - accuracy: 0.7857 - val_loss: 1.6475 - val_accuracy: 0.3333\n","Epoch 13/100\n","2/2 [==============================] - 1s 585ms/step - loss: 0.5958 - accuracy: 0.7857 - val_loss: 1.6490 - val_accuracy: 0.3333\n","Epoch 14/100\n","2/2 [==============================] - 1s 592ms/step - loss: 0.5795 - accuracy: 0.7857 - val_loss: 1.6860 - val_accuracy: 0.3333\n","Epoch 15/100\n","2/2 [==============================] - 1s 585ms/step - loss: 0.5607 - accuracy: 0.7857 - val_loss: 1.7493 - val_accuracy: 0.3333\n","Epoch 16/100\n","2/2 [==============================] - 2s 995ms/step - loss: 0.5725 - accuracy: 0.7857 - val_loss: 1.8268 - val_accuracy: 0.3333\n","Epoch 17/100\n","2/2 [==============================] - 1s 597ms/step - loss: 0.5454 - accuracy: 0.8571 - val_loss: 1.8712 - val_accuracy: 0.3333\n","Epoch 18/100\n","2/2 [==============================] - 1s 590ms/step - loss: 0.5336 - accuracy: 0.8571 - val_loss: 1.9034 - val_accuracy: 0.3333\n","Epoch 19/100\n","2/2 [==============================] - 1s 692ms/step - loss: 0.5245 - accuracy: 0.8571 - val_loss: 1.9101 - val_accuracy: 0.3333\n","Epoch 20/100\n","2/2 [==============================] - 1s 603ms/step - loss: 0.5051 - accuracy: 0.8571 - val_loss: 1.9000 - val_accuracy: 0.3333\n","Epoch 21/100\n","2/2 [==============================] - 1s 594ms/step - loss: 0.5061 - accuracy: 0.8571 - val_loss: 1.8851 - val_accuracy: 0.3333\n","Epoch 22/100\n","2/2 [==============================] - 1s 599ms/step - loss: 0.4906 - accuracy: 0.8571 - val_loss: 1.8827 - val_accuracy: 0.3333\n","Epoch 23/100\n","2/2 [==============================] - 1s 704ms/step - loss: 0.4825 - accuracy: 0.7857 - val_loss: 1.8649 - val_accuracy: 0.3333\n","Epoch 24/100\n","2/2 [==============================] - 1s 693ms/step - loss: 0.4776 - accuracy: 0.7857 - val_loss: 1.8642 - val_accuracy: 0.3333\n","Epoch 25/100\n","2/2 [==============================] - 2s 1s/step - loss: 0.4675 - accuracy: 0.7857 - val_loss: 1.8848 - val_accuracy: 0.3333\n","Epoch 26/100\n","2/2 [==============================] - 1s 600ms/step - loss: 0.4618 - accuracy: 0.8571 - val_loss: 1.8882 - val_accuracy: 0.0000e+00\n","Epoch 27/100\n","2/2 [==============================] - 1s 607ms/step - loss: 0.4540 - accuracy: 0.8571 - val_loss: 1.9090 - val_accuracy: 0.0000e+00\n","Epoch 28/100\n","2/2 [==============================] - 1s 691ms/step - loss: 0.4538 - accuracy: 0.8571 - val_loss: 1.9382 - val_accuracy: 0.0000e+00\n","Epoch 29/100\n","2/2 [==============================] - 1s 591ms/step - loss: 0.4473 - accuracy: 0.8571 - val_loss: 1.9537 - val_accuracy: 0.0000e+00\n","Epoch 30/100\n","2/2 [==============================] - 1s 695ms/step - loss: 0.4412 - accuracy: 0.8571 - val_loss: 1.9590 - val_accuracy: 0.0000e+00\n","Epoch 31/100\n","2/2 [==============================] - 1s 690ms/step - loss: 0.4349 - accuracy: 0.8571 - val_loss: 1.9586 - val_accuracy: 0.0000e+00\n","Epoch 32/100\n","2/2 [==============================] - 1s 606ms/step - loss: 0.4304 - accuracy: 0.8571 - val_loss: 1.9438 - val_accuracy: 0.0000e+00\n","Epoch 33/100\n","2/2 [==============================] - 1s 586ms/step - loss: 0.4291 - accuracy: 0.8571 - val_loss: 1.9248 - val_accuracy: 0.3333\n","Epoch 34/100\n","2/2 [==============================] - 2s 935ms/step - loss: 0.4239 - accuracy: 0.8571 - val_loss: 1.9318 - val_accuracy: 0.3333\n","Epoch 35/100\n","2/2 [==============================] - 1s 694ms/step - loss: 0.4207 - accuracy: 0.8571 - val_loss: 1.9256 - val_accuracy: 0.3333\n","Epoch 36/100\n","2/2 [==============================] - 1s 611ms/step - loss: 0.4142 - accuracy: 0.8571 - val_loss: 1.9350 - val_accuracy: 0.0000e+00\n","Epoch 37/100\n","2/2 [==============================] - 1s 701ms/step - loss: 0.4153 - accuracy: 0.8571 - val_loss: 1.9427 - val_accuracy: 0.0000e+00\n","Epoch 38/100\n","2/2 [==============================] - 1s 598ms/step - loss: 0.4071 - accuracy: 0.8571 - val_loss: 1.9441 - val_accuracy: 0.0000e+00\n","Epoch 39/100\n","2/2 [==============================] - 1s 582ms/step - loss: 0.4041 - accuracy: 0.9286 - val_loss: 1.9645 - val_accuracy: 0.0000e+00\n","Epoch 40/100\n","2/2 [==============================] - 1s 692ms/step - loss: 0.4048 - accuracy: 0.9286 - val_loss: 1.9770 - val_accuracy: 0.0000e+00\n","Epoch 41/100\n","2/2 [==============================] - 1s 591ms/step - loss: 0.3980 - accuracy: 0.8571 - val_loss: 2.0151 - val_accuracy: 0.0000e+00\n","Epoch 42/100\n","2/2 [==============================] - 1s 714ms/step - loss: 0.3942 - accuracy: 0.8571 - val_loss: 2.0371 - val_accuracy: 0.0000e+00\n","Epoch 43/100\n","2/2 [==============================] - 2s 1s/step - loss: 0.3917 - accuracy: 0.8571 - val_loss: 2.0446 - val_accuracy: 0.0000e+00\n","Epoch 44/100\n","2/2 [==============================] - 1s 578ms/step - loss: 0.3873 - accuracy: 0.8571 - val_loss: 2.0379 - val_accuracy: 0.0000e+00\n","Epoch 45/100\n","2/2 [==============================] - 1s 596ms/step - loss: 0.3823 - accuracy: 0.9286 - val_loss: 2.0401 - val_accuracy: 0.0000e+00\n","Epoch 46/100\n","2/2 [==============================] - 1s 710ms/step - loss: 0.3806 - accuracy: 0.8571 - val_loss: 2.0456 - val_accuracy: 0.0000e+00\n","Epoch 47/100\n","2/2 [==============================] - 1s 716ms/step - loss: 0.3794 - accuracy: 0.8571 - val_loss: 2.0642 - val_accuracy: 0.0000e+00\n","Epoch 48/100\n","2/2 [==============================] - 1s 570ms/step - loss: 0.3772 - accuracy: 0.8571 - val_loss: 2.0840 - val_accuracy: 0.0000e+00\n","Epoch 49/100\n","2/2 [==============================] - 1s 596ms/step - loss: 0.3785 - accuracy: 0.8571 - val_loss: 2.0978 - val_accuracy: 0.0000e+00\n","Epoch 50/100\n","2/2 [==============================] - 1s 588ms/step - loss: 0.3718 - accuracy: 0.8571 - val_loss: 2.1063 - val_accuracy: 0.0000e+00\n","Epoch 51/100\n","2/2 [==============================] - 1s 711ms/step - loss: 0.3691 - accuracy: 0.8571 - val_loss: 2.1130 - val_accuracy: 0.0000e+00\n","Epoch 52/100\n","2/2 [==============================] - 2s 1s/step - loss: 0.3648 - accuracy: 0.8571 - val_loss: 2.1153 - val_accuracy: 0.0000e+00\n","Epoch 53/100\n","2/2 [==============================] - 2s 855ms/step - loss: 0.3614 - accuracy: 0.9286 - val_loss: 2.1271 - val_accuracy: 0.0000e+00\n","Epoch 54/100\n","2/2 [==============================] - 1s 596ms/step - loss: 0.3579 - accuracy: 0.9286 - val_loss: 2.1541 - val_accuracy: 0.0000e+00\n","Epoch 55/100\n","2/2 [==============================] - 1s 585ms/step - loss: 0.3578 - accuracy: 0.9286 - val_loss: 2.1627 - val_accuracy: 0.0000e+00\n","Epoch 56/100\n","2/2 [==============================] - 1s 706ms/step - loss: 0.3558 - accuracy: 0.9286 - val_loss: 2.1558 - val_accuracy: 0.0000e+00\n","Epoch 57/100\n","2/2 [==============================] - 1s 706ms/step - loss: 0.3527 - accuracy: 0.9286 - val_loss: 2.1578 - val_accuracy: 0.0000e+00\n","Epoch 58/100\n","2/2 [==============================] - 1s 870ms/step - loss: 0.3475 - accuracy: 0.9286 - val_loss: 2.1550 - val_accuracy: 0.0000e+00\n","Epoch 59/100\n","2/2 [==============================] - 1s 582ms/step - loss: 0.3484 - accuracy: 0.9286 - val_loss: 2.1276 - val_accuracy: 0.0000e+00\n","Epoch 60/100\n","2/2 [==============================] - 1s 698ms/step - loss: 0.3505 - accuracy: 0.9286 - val_loss: 2.1289 - val_accuracy: 0.0000e+00\n","Epoch 61/100\n","2/2 [==============================] - 2s 1s/step - loss: 0.3426 - accuracy: 0.9286 - val_loss: 2.1587 - val_accuracy: 0.0000e+00\n","Epoch 62/100\n","2/2 [==============================] - 1s 598ms/step - loss: 0.3391 - accuracy: 0.9286 - val_loss: 2.1969 - val_accuracy: 0.0000e+00\n","Epoch 63/100\n","2/2 [==============================] - 1s 725ms/step - loss: 0.3344 - accuracy: 0.9286 - val_loss: 2.2194 - val_accuracy: 0.0000e+00\n","Epoch 64/100\n","2/2 [==============================] - 1s 707ms/step - loss: 0.3338 - accuracy: 0.9286 - val_loss: 2.2438 - val_accuracy: 0.0000e+00\n","Epoch 65/100\n","2/2 [==============================] - 1s 705ms/step - loss: 0.3330 - accuracy: 0.9286 - val_loss: 2.2626 - val_accuracy: 0.0000e+00\n","Epoch 66/100\n","2/2 [==============================] - 1s 686ms/step - loss: 0.3322 - accuracy: 0.9286 - val_loss: 2.2644 - val_accuracy: 0.0000e+00\n","Epoch 67/100\n","2/2 [==============================] - 1s 608ms/step - loss: 0.3274 - accuracy: 0.9286 - val_loss: 2.2734 - val_accuracy: 0.0000e+00\n","Epoch 68/100\n","2/2 [==============================] - 1s 717ms/step - loss: 0.3251 - accuracy: 0.9286 - val_loss: 2.2846 - val_accuracy: 0.0000e+00\n","Epoch 69/100\n","2/2 [==============================] - 1s 598ms/step - loss: 0.3230 - accuracy: 0.9286 - val_loss: 2.2758 - val_accuracy: 0.0000e+00\n","Epoch 70/100\n","2/2 [==============================] - 2s 998ms/step - loss: 0.3204 - accuracy: 0.9286 - val_loss: 2.2802 - val_accuracy: 0.0000e+00\n","Epoch 71/100\n","2/2 [==============================] - 1s 602ms/step - loss: 0.3221 - accuracy: 0.9286 - val_loss: 2.2886 - val_accuracy: 0.0000e+00\n","Epoch 72/100\n","2/2 [==============================] - 1s 603ms/step - loss: 0.3214 - accuracy: 0.8571 - val_loss: 2.3056 - val_accuracy: 0.0000e+00\n","Epoch 73/100\n","2/2 [==============================] - 1s 714ms/step - loss: 0.3156 - accuracy: 0.8571 - val_loss: 2.3339 - val_accuracy: 0.0000e+00\n","Epoch 74/100\n","2/2 [==============================] - 1s 710ms/step - loss: 0.3128 - accuracy: 0.9286 - val_loss: 2.3459 - val_accuracy: 0.0000e+00\n","Epoch 75/100\n","2/2 [==============================] - 1s 590ms/step - loss: 0.3120 - accuracy: 0.9286 - val_loss: 2.3556 - val_accuracy: 0.0000e+00\n","Epoch 76/100\n","2/2 [==============================] - 1s 575ms/step - loss: 0.3153 - accuracy: 0.9286 - val_loss: 2.3619 - val_accuracy: 0.0000e+00\n","Epoch 77/100\n","2/2 [==============================] - 1s 702ms/step - loss: 0.3091 - accuracy: 1.0000 - val_loss: 2.3414 - val_accuracy: 0.0000e+00\n","Epoch 78/100\n","2/2 [==============================] - 1s 709ms/step - loss: 0.3066 - accuracy: 0.9286 - val_loss: 2.3225 - val_accuracy: 0.0000e+00\n","Epoch 79/100\n","2/2 [==============================] - 2s 988ms/step - loss: 0.3056 - accuracy: 0.9286 - val_loss: 2.3034 - val_accuracy: 0.0000e+00\n","Epoch 80/100\n","2/2 [==============================] - 1s 691ms/step - loss: 0.3021 - accuracy: 0.9286 - val_loss: 2.3089 - val_accuracy: 0.0000e+00\n","Epoch 81/100\n","2/2 [==============================] - 1s 601ms/step - loss: 0.3007 - accuracy: 0.9286 - val_loss: 2.3213 - val_accuracy: 0.0000e+00\n","Epoch 82/100\n","2/2 [==============================] - 1s 710ms/step - loss: 0.3000 - accuracy: 0.9286 - val_loss: 2.3429 - val_accuracy: 0.0000e+00\n","Epoch 83/100\n","2/2 [==============================] - 1s 691ms/step - loss: 0.2973 - accuracy: 0.9286 - val_loss: 2.3534 - val_accuracy: 0.0000e+00\n","Epoch 84/100\n","2/2 [==============================] - 1s 691ms/step - loss: 0.2984 - accuracy: 0.9286 - val_loss: 2.3535 - val_accuracy: 0.0000e+00\n","Epoch 85/100\n","2/2 [==============================] - 1s 623ms/step - loss: 0.2935 - accuracy: 0.9286 - val_loss: 2.3644 - val_accuracy: 0.0000e+00\n","Epoch 86/100\n","2/2 [==============================] - 1s 605ms/step - loss: 0.2907 - accuracy: 0.9286 - val_loss: 2.3598 - val_accuracy: 0.0000e+00\n","Epoch 87/100\n","2/2 [==============================] - 1s 660ms/step - loss: 0.2881 - accuracy: 1.0000 - val_loss: 2.3631 - val_accuracy: 0.0000e+00\n","Epoch 88/100\n","2/2 [==============================] - 2s 973ms/step - loss: 0.2870 - accuracy: 1.0000 - val_loss: 2.3613 - val_accuracy: 0.0000e+00\n","Epoch 89/100\n","2/2 [==============================] - 1s 707ms/step - loss: 0.2869 - accuracy: 1.0000 - val_loss: 2.3574 - val_accuracy: 0.0000e+00\n","Epoch 90/100\n","2/2 [==============================] - 1s 601ms/step - loss: 0.2849 - accuracy: 1.0000 - val_loss: 2.3549 - val_accuracy: 0.0000e+00\n","Epoch 91/100\n","2/2 [==============================] - 1s 592ms/step - loss: 0.2828 - accuracy: 1.0000 - val_loss: 2.3373 - val_accuracy: 0.0000e+00\n","Epoch 92/100\n","2/2 [==============================] - 1s 708ms/step - loss: 0.2822 - accuracy: 1.0000 - val_loss: 2.3396 - val_accuracy: 0.0000e+00\n","Epoch 93/100\n","2/2 [==============================] - 1s 587ms/step - loss: 0.2818 - accuracy: 1.0000 - val_loss: 2.3531 - val_accuracy: 0.0000e+00\n","Epoch 94/100\n","2/2 [==============================] - 1s 698ms/step - loss: 0.2790 - accuracy: 1.0000 - val_loss: 2.3840 - val_accuracy: 0.0000e+00\n","Epoch 95/100\n","2/2 [==============================] - 1s 582ms/step - loss: 0.2754 - accuracy: 1.0000 - val_loss: 2.4080 - val_accuracy: 0.0000e+00\n","Epoch 96/100\n","2/2 [==============================] - 1s 593ms/step - loss: 0.2745 - accuracy: 1.0000 - val_loss: 2.4224 - val_accuracy: 0.0000e+00\n","Epoch 97/100\n","2/2 [==============================] - 2s 1s/step - loss: 0.2763 - accuracy: 1.0000 - val_loss: 2.4065 - val_accuracy: 0.0000e+00\n","Epoch 98/100\n","2/2 [==============================] - 1s 604ms/step - loss: 0.2747 - accuracy: 1.0000 - val_loss: 2.4153 - val_accuracy: 0.0000e+00\n","Epoch 99/100\n","2/2 [==============================] - 1s 693ms/step - loss: 0.2723 - accuracy: 1.0000 - val_loss: 2.3924 - val_accuracy: 0.0000e+00\n","Epoch 100/100\n","2/2 [==============================] - 1s 595ms/step - loss: 0.2685 - accuracy: 1.0000 - val_loss: 2.3811 - val_accuracy: 0.0000e+00\n","1/1 [==============================] - 0s 215ms/step - loss: 1.8302 - accuracy: 0.3333\n","Test accuracy: 33.33%\n"]}],"source":["history, model = launch_experiment(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vQfpZVmKfMmy"},"outputs":[],"source":["model.save(model_save_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r4IUouR7fMmy","outputId":"7e753aa1-4e65-4f3e-a6d0-09175a6076e9"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 2s 2s/step\n"]}],"source":["pred = model.predict(test_ds)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XRbVdrhifMmz"},"outputs":[],"source":["# ============================== generate data ==============================\n","import os\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from scipy import interp\n","from itertools import cycle\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import PredictionErrorDisplay\n","from sklearn import metrics\n","import tensorflow as tf\n","from tensorflow import keras as k"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"eR06cJkwfMmz","outputId":"ec97cc7d-467a-4606-bc38-a44e1f9c5a75"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","plt.plot(history.history[\"loss\"])\n","plt.plot(history.history[\"val_loss\"])\n","plt.title(\"model loss\")\n","plt.ylabel(\"loss\")\n","plt.xlabel(\"epoch\")\n","plt.legend([\"train\", \"val\"], loc=\"upper left\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"i09Zol8FfMmz","outputId":"bf164a14-e6e8-4e06-9140-536123ac3127"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(history.history[\"accuracy\"])\n","plt.plot(history.history[\"val_accuracy\"])\n","plt.title(\"model accuracy\")\n","plt.ylabel(\"accuracy\")\n","plt.xlabel(\"epoch\")\n","plt.legend([\"train\", \"val\"], loc=\"upper left\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"gycE4oHSfMmz","outputId":"dc8b511a-1a13-4579-8094-1753cdca6ab2"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 0s 208ms/step\n"]},{"name":"stderr","output_type":"stream","text":["<ipython-input-34-0227eecc12f4>:27: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n","  mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkIAAAHHCAYAAABTMjf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADbkElEQVR4nOzdd1gUVxcH4N/Sll4EBBREREGRooIFjSKKYomKGoMNxa6xYknsJbaosSZWYgPF2EtsqChWLIiFKIKCBRUQpCN1935/8DFx3aUpMJTzPs8+umfmzpwt7J6duXeugDHGQAghhBBSA8nxnQAhhBBCCF+oECKEEEJIjUWFECGEEEJqLCqECCGEEFJjUSFECCGEkBqLCiFCCCGE1FhUCBFCCCGkxqJCiBBCCCE1FhVChBBCCKmxqBAilVL9+vXh6enJdxo1gqenJ+rXr893GoXq2LEjrK2t+U6j0gkMDIRAIEBgYGCZbG/Pnj0QCAR49epVmWyvopTm/bt48WIIBIIy2zd9TlUPVAjVQAUfeAU3BQUF1K1bF56ennj37h3f6ZFy8P79eyxevBgPHz7kO5UaZcWKFThx4gTfaUiojDmVpU+fPmHx4sVlViCS6o8KoRrs119/ha+vL7Zt24bu3btj3759cHJyQlZWFt+pITw8HN7e3nynUW28f/8eS5YskVkIeXt7Izw8vOKTqgHKs+jo0KEDMjMz0aFDhzLJycPDA5mZmTA1NS2jDPnx6dMnLFmyRGYhNH/+fGRmZlZ8UqRSU+A7AcKf7t27w8HBAQAwevRo6OnpYdWqVTh16hR+/PFHXnMTCoUVvs+srCwoKSlBTq7y/z4oy1wVFRXLIKOqLS8vD2KxGEpKSnynUqzPX3tlZeUy2668vDzk5eXLbHuVkYKCAhQU6GuPSKr8n/ikwrRv3x4AEBkZKRF/9uwZfvjhB9SqVQvKyspwcHDAqVOnpNonJyfDy8sL9evXh1AohLGxMYYNG4aEhARunezsbCxatAgNGzaEUCiEiYkJfv75Z2RnZ0ts6/Nz78HBwRAIBNi7d6/UPv39/SEQCHD69Gku9u7dO4wcORIGBgYQCoVo2rQpdu3aJdGuoH/F33//jfnz56Nu3bpQVVVFampqoc9PRkYGZsyYARMTEwiFQlhaWuL3338HY0xiPYFAgEmTJmH//v2wtLSEsrIy7O3tce3aNaltfmuuiYmJmDlzJmxsbKCurg5NTU10794djx49kmjfsmVLAMCIESO4U6J79uwBIN3H4tWrVxAIBPj999+xY8cOmJubQygUomXLlrh3757UYzh8+DCsrKygrKwMa2trHD9+vFT9Ns6dOwcnJydoaGhAU1MTLVu2hJ+fn9R6T58+hbOzM1RVVVG3bl2sXr1aYnlOTg4WLlwIe3t7aGlpQU1NDe3bt8eVK1ck1vv88W3YsIF7fE+fPi3xNgBALBZj48aNsLGxgbKyMvT19dGtWzcEBwcDyH8fZGRkYO/evdxz/nl/km997WX1EXr+/Dn69+8PQ0NDKCsrw9jYGAMHDkRKSkqxORXWR6ikr8/nCvriREREYOjQodDS0oK+vj4WLFgAxhiio6PRp08faGpqwtDQEGvXrpVoX1guxfWLevXqFfT19QEAS5Ys4R7j4sWLJfIqieJeX1lK8vdY4I8//kDTpk2hqqoKHR0dODg4SDyvaWlpmDZtGvd5Wrt2bXTp0gUhISElyp+UHJXGhFPwoaOjo8PFnjx5gnbt2qFu3bqYPXs21NTUcOjQIbi5ueHo0aPo27cvACA9PR3t27dHWFgYRo4ciRYtWiAhIQGnTp3C27dvoaenB7FYjN69e+PGjRsYO3YsmjRpgtDQUKxfvx4RERGFnkJwcHBAgwYNcOjQIQwfPlxi2cGDB6GjowNXV1cAQFxcHNq0acMVI/r6+jh37hxGjRqF1NRUTJs2TaL90qVLoaSkhJkzZyI7O7vQIwKMMfTu3RtXrlzBqFGj0KxZM/j7+2PWrFl49+4d1q9fL7H+1atXcfDgQUyZMgVCoRBbtmxBt27dcPfuXa7jb1nk+vTpU5w4cQIDBgyAmZkZ4uLisH37djg5OeHp06eoU6cOmjRpgl9//RULFy7E2LFjuYK3bdu2st8I/+fn54e0tDSMGzcOAoEAq1evRr9+/RAVFcUdRTpz5gzc3d1hY2ODlStXIikpCaNGjULdunWL3HaBPXv2YOTIkWjatCnmzJkDbW1tPHjwAOfPn8fgwYO59ZKSktCtWzf069cPP/74I44cOYJffvkFNjY26N69OwAgNTUVf/31FwYNGoQxY8YgLS0NO3fuhKurK+7evYtmzZpJ7Hv37t3IysrC2LFjIRQKUatWrVJtY9SoUdizZw+6d++O0aNHIy8vD9evX8ft27fh4OAAX19fjB49Gq1atcLYsWMBAObm5mX22n8pJycHrq6uyM7OxuTJk2FoaIh3797h9OnTSE5OhpaWVpE5fcvrUxh3d3c0adIEv/32G86cOYNly5ahVq1a2L59Ozp16oRVq1Zh//79mDlzJlq2bFnq03xf0tfXx9atWzFhwgT07dsX/fr1AwDY2tqWelvFvb6yREVFFfv3COSfjp4yZQp++OEHTJ06FVlZWXj8+DHu3LnDPa/jx4/HkSNHMGnSJFhZWeHjx4+4ceMGwsLC0KJFi698hohMjNQ4u3fvZgDYpUuXWHx8PIuOjmZHjhxh+vr6TCgUsujoaG7dzp07MxsbG5aVlcXFxGIxa9u2LWvUqBEXW7hwIQPAjh07JrU/sVjMGGPM19eXycnJsevXr0ss37ZtGwPAbt68ycVMTU3Z8OHDuftz5sxhioqKLDExkYtlZ2czbW1tNnLkSC42atQoZmRkxBISEiT2MXDgQKalpcU+ffrEGGPsypUrDABr0KABFyvKiRMnGAC2bNkyifgPP/zABAIBe/HiBRcDwACw4OBgLvb69WumrKzM+vbtW6a5ZmVlMZFIJBF7+fIlEwqF7Ndff+Vi9+7dYwDY7t27pR7b8OHDmampqUR7AExXV1fi+T558iQDwP755x8uZmNjw4yNjVlaWhoXCwwMZAAktilLcnIy09DQYK1bt2aZmZkSywreM4wx5uTkxAAwHx8fLpadnc0MDQ1Z//79uVheXh7Lzs6W2E5SUhIzMDCQeI8UPD5NTU324cMHifVLuo3Lly8zAGzKlClSj+vz3NXU1CTexwXK4rUvWHblyhXGGGMPHjxgANjhw4el9ve5wnIq+Fx4+fIlY6zkr48sixYtYgDY2LFjuVheXh4zNjZmAoGA/fbbb1w8KSmJqaioSOT0ZS6FPWbGpN+/8fHxDABbtGhRoXkVp6Sv75efUyX9e+zTpw9r2rRpkTloaWmxiRMnFpsr+XZ0aqwGc3Fxgb6+PkxMTPDDDz9ATU0Np06dgrGxMYD8w7yXL1/Gjz/+iLS0NCQkJCAhIQEfP36Eq6srnj9/zo0yO3r0KOzs7LgjRJ8rOBR9+PBhNGnSBI0bN+a2lZCQgE6dOgGAzNMPBdzd3ZGbm4tjx45xsQsXLiA5ORnu7u4A8o/aHD16FL169QJjTGIfrq6uSElJkTqsPHz4cKioqBT7XJ09exby8vKYMmWKRHzGjBlgjOHcuXMScUdHR9jb23P369Wrhz59+sDf3x8ikajMchUKhVw/IZFIhI8fP0JdXR2WlpbffAjd3d1d4uhgwZGkqKgoAPkdsENDQzFs2DCoq6tz6zk5OcHGxqbY7V+8eBFpaWmYPXu2VF+XL09fqKurY+jQodx9JSUltGrVissFyO/jUnCkRCwWIzExEXl5eXBwcJD5XPTv3587jVLabRw9ehQCgQCLFi2S2m5xp17K632qpaUFIP908adPn4pctyRK8/oUZvTo0dz/5eXl4eDgAMYYRo0axcW1tbVhaWkp8Vry7Wtf35L+PWpra+Pt27cyTzV/vs6dO3fw/v37b3gkpCSoEKrBNm/ejIsXL+LIkSPo0aMHEhISJDopv3jxAowxLFiwAPr6+hK3gg+IDx8+AMjvV1TctV6eP3+OJ0+eSG3LwsJCYluy2NnZoXHjxjh48CAXO3jwIPT09LhCKj4+HsnJydixY4fUPkaMGCFzH2ZmZiV6rl6/fo06depAQ0NDIt6kSRNu+ecaNWoktQ0LCwt8+vQJ8fHxZZarWCzG+vXr0ahRIwiFQujp6UFfXx+PHz/m+oV8rXr16kncLyiKkpKSAPz3mBs2bCjVVlbsSwV90UpyjSBjY2OpLyAdHR0ulwJ79+6Fra0tlJWVoaurC319fZw5c0bmc1HYa1+SbURGRqJOnTqoVatWsbl/qbzep2ZmZpg+fTr++usv6OnpwdXVFZs3b/7q90FpXp/CfPke0tLSgrKyMvT09KTiX76WFSExMRGxsbHcreC5+trXt6R/j7/88gvU1dXRqlUrNGrUCBMnTsTNmzcltrV69Wr8+++/MDExQatWrbB48eJKVSxWJ9RHqAZr1aoVd67bzc0N3333HQYPHozw8HCoq6tDLBYDAGbOnMn1wflSSb7wCojFYtjY2GDdunUyl5uYmBTZ3t3dHcuXL0dCQgI0NDRw6tQpDBo0iBsFUpDv0KFDpfoSFfiyr0BJjgaVh7LKdcWKFViwYAFGjhyJpUuXolatWpCTk8O0adO4fXytwkYQsS86h1eEkuSyb98+eHp6ws3NDbNmzULt2rUhLy+PlStXSg0AAGQ/n6Xdxtcoz/fp2rVr4enpiZMnT+LChQuYMmUKVq5cidu3b3NHeiuSrNetJK9lYUddRCJR2ST2f/369cPVq1e5+8OHD+cGEXyNkv49NmnSBOHh4Th9+jTOnz+Po0ePYsuWLVi4cCGWLFkCAPjxxx/Rvn17HD9+HBcuXMCaNWuwatUqHDt2jOsXR8oGFUIEALgPe2dnZ/z555+YPXs2GjRoACB/eLWLi0uR7c3NzfHvv/8Wu86jR4/QuXPnr7q6q7u7O5YsWYKjR4/CwMAAqampGDhwILdcX18fGhoaEIlExeZbWqamprh06RLS0tIkjgo9e/aMW/6558+fS20jIiICqqqq3OmYssj1yJEjcHZ2xs6dOyXiycnJEr+6y/JqugUKHvOLFy+klsmKfamgk+6///5bqoK6MEeOHEGDBg1w7Ngxiccr6/TGt27D3Nwc/v7+SExMLPKogaznvTzfpwBgY2MDGxsbzJ8/H7du3UK7du2wbds2LFu2rNCcZCnr16c0Co4+JicnS8S/PPIqS2ne62vXrpU4ElXQmbmkr++XSvr3CABqampwd3eHu7s7cnJy0K9fPyxfvhxz5szhTkUaGRnhp59+wk8//YQPHz6gRYsWWL58ORVCZYxOjRFOx44d0apVK2zYsAFZWVmoXbs2OnbsiO3btyMmJkZq/fj4eO7//fv3x6NHj3D8+HGp9Qp+6f3444949+6dzAslZmZmIiMjo8j8mjRpAhsbGxw8eBAHDx6EkZGRxCgTeXl59O/fH0ePHpVZlH2eb2n16NEDIpEIf/75p0R8/fr1EAgEUh9MQUFBEn0CoqOjcfLkSXTt2pW7XktZ5CovLy91hObw4cNSVwhXU1MDIP3F8i3q1KkDa2tr+Pj4ID09nYtfvXoVoaGhxbbv2rUrNDQ0sHLlSqmLeH7NUaeCIw2ft71z5w6CgoLKfBv9+/cHY4z79f65z9uqqalJPefl9T5NTU1FXl6eRMzGxgZycnISl6eQlZMsZf36lEZBEfb5JSdEIhF27NhRbFtVVVUAJXuv29vbw8XFhbtZWVkBKPnr+6WS/j1+/PhR4r6SkhKsrKzAGENubi5EIpHUKc3atWujTp06UpcaId+OjggRCbNmzcKAAQOwZ88ejB8/Hps3b8Z3330HGxsbjBkzBg0aNEBcXByCgoLw9u1b7voYs2bNwpEjRzBgwACMHDkS9vb2SExMxKlTp7Bt2zbY2dnBw8MDhw4dwvjx43HlyhW0a9cOIpEIz549w6FDh+Dv71/osNQC7u7uWLhwIZSVlTFq1CipCwr+9ttvuHLlClq3bo0xY8bAysoKiYmJCAkJwaVLl5CYmPhVz0uvXr3g7OyMefPm4dWrV7Czs8OFCxdw8uRJTJs2TWoIsrW1NVxdXSWGzwOQ+GAti1y///57/PrrrxgxYgTatm2L0NBQ7N+/nzuaV8Dc3Bza2trYtm0bNDQ0oKamhtatW5e4j1RhVqxYgT59+qBdu3YYMWIEkpKS8Oeff8La2lqiOJJFU1MT69evx+jRo9GyZUsMHjwYOjo6ePToET59+iTzulFF+f7773Hs2DH07dsXPXv2xMuXL7Ft2zZYWVkVm0tpt+Hs7AwPDw9s2rQJz58/R7du3SAWi3H9+nU4Oztj0qRJAPK/aC9duoR169ahTp06MDMzQ+vWrcvlfXr58mVMmjQJAwYMgIWFBfLy8uDr68sVXgUKy+lLZf36lEbTpk3Rpk0bzJkzhzsq8/fff0sVerKoqKjAysoKBw8ehIWFBWrVqgVra+tS9XUq6ev7pZL+PXbt2hWGhoZo164dDAwMEBYWhj///BM9e/aEhoYGkpOTYWxsjB9++AF2dnZQV1fHpUuXcO/ePalrLpEyUHED1EhlUTA09d69e1LLRCIRMzc3Z+bm5iwvL48xxlhkZCQbNmwYMzQ0ZIqKiqxu3brs+++/Z0eOHJFo+/HjRzZp0iRWt25dpqSkxIyNjdnw4cMlhgjn5OSwVatWsaZNmzKhUMh0dHSYvb09W7JkCUtJSeHW+3JYaoHnz59zw9Nv3Lgh8/HFxcWxiRMnMhMTE6aoqMgMDQ1Z586d2Y4dO7h1CobhFjfU+HNpaWnMy8uL1alThykqKrJGjRqxNWvWSA0lBsAmTpzI9u3bxxo1asSEQiFr3ry5xJDfsso1KyuLzZgxgxkZGTEVFRXWrl07FhQUxJycnJiTk5PEuidPnmRWVlZMQUFBYih9YcPn16xZI7U/yBiW/Pfff7PGjRszoVDIrK2t2alTp1j//v1Z48aNi35C/+/UqVOsbdu2TEVFhWlqarJWrVqxAwcOcMudnJxkDjX+Mm+xWMxWrFjBTE1Nuef89OnTpXp8Jd0GY/nDwdesWcMaN27MlJSUmL6+PuvevTu7f/8+t86zZ89Yhw4dmIqKCgMg8Z7+1tf+y6HkUVFRbOTIkczc3JwpKyuzWrVqMWdnZ3bp0iWJdoXlVNiQ9eJeH1kKhqnHx8dLxIcPH87U1NSk1pf1GkdGRjIXFxcmFAqZgYEBmzt3Lrt48WKxw+cZY+zWrVvM3t6eKSkpSbxnSzp8nrGSvb6yhs+X5O9x+/btrEOHDkxXV5cJhUJmbm7OZs2axX0GZmdns1mzZjE7OzumoaHB1NTUmJ2dHduyZUuJcielI2CMh56PhFRjAoEAEydOlDqNVpM0a9YM+vr6uHjxIt+pEEJIkaiPECHkq+Xm5kqdrggMDMSjR4/QsWNHfpIihJBSoD5ChJCv9u7dO7i4uGDo0KGoU6cOnj17hm3btsHQ0BDjx4/nOz1CCCkWFUKEkK+mo6MDe3t7/PXXX4iPj4eamhp69uyJ3377Dbq6unynRwghxaI+QoQQQgipsaiPECGEEEJqLCqECCGEEFJj1bg+QmKxGO/fv4eGhka5TDtACCGEkLLHGENaWhrq1KkjdTHdb1HjCqH3798XO7knIYQQQiqn6OjoMp1EuMYVQgUTZkZHR0NTU5PnbAghhBBSEqmpqTAxMZGY+Los1LhCqOB0mKamJhVChBBCSBVT1t1aqLM0IYQQQmosKoQIIYQQUmNRIUQIIYSQGosKIUIIIYTUWFQIEUIIIaTGokKIEEIIITUWFUKEEEIIqbGoECKEEEJIjUWFECGEEEJqLCqECCGEEFJj8VoIXbt2Db169UKdOnUgEAhw4sSJYtsEBgaiRYsWEAqFaNiwIfbs2VPueRJCCCGkeuJ1rrGMjAzY2dlh5MiR6NevX7Hrv3z5Ej179sT48eOxf/9+BAQEYPTo0TAyMoKrq2sFZEwIqcwYY0hPzwFj+fc1NJSKnZcoPT0HYjGTiMnLC6CmplRkO7E4f19fEgrlIRQW/dGakyNCVlaeVFxNTRHy8kX/Pv30KRd5eWKJmEAAaGgIi2wHAGlp2dxzU0BRUQ4qKopFthOJxMjIyJWKq6goQFFRvsi22dl5yM4WScXV1ZUgJ1f0a5ORkQORSDJhOTkB1NWLfm0YY0hLk35tlJTkoaxc9GuTmytCZqb0a6OqqggFhaJfm8zMXOTmiqXimppf99ooKMhBVfXrXhtlZQUoKRX92hT2PizJayPrfViS1wYAUlOzpWIleR9+ub+ywmsh1L17d3Tv3r3E62/btg1mZmZYu3YtAKBJkya4ceMG1q9fT4UQITWcj88jzJkTgPfv07hYbu4CKCgU/YHert0uPH4cJxHr1MkMAQHDimz34UMGjIzWSsVXrXLBzz+3K7LtgQOh8PQ8KRV/+HAc7OwMi2w7evQpHDjwr0TMwEANsbEzi2wHAPXrb0RiYqZEbNgwO+zd61ZkuwcPYtGypbdUfP/+fhg82KbItmvW3MKCBVek4gkJs6Crq1pk227d9uPGjTcSMXt7IwQHjy2yXWZmHrS0fpOKz5vXHsuWdSqy7enTEejX75BU/No1T7Rvb1pkWy8vf2zffl8ipiKUxyfPVsDrFOkGPj0B/fznwNZ2G169SpZY7ObWGMet6gAhsZLt6mkC2/O/8yIjk2Bp+afUprdu7YnxJ19K79PNAhhnBwDYvj0YU6acl1ol6sQgmG17JN12aXvAIf/9+eOPh3HmzHOJxY0a1ULEjA7AiQjptucGcP+tVWuVVIE7aVJL/JEjD7xJlWzXwhBY3h4AcOPGa+ntloEqNft8UFAQXFxcJGKurq6YNm0aPwkRQnjHGMOKFdcxf770ly0hvMsWAUdlFAYAIOPokZSIRCBYslBHmvQRIJm+bAdwhUyR0nNkt5VxJEfK21TZbUviyUcgPFEy9v8jTGKxGK9evfi67RZDwNiXB+P4IRAIcPz4cbi5uRW6joWFBUaMGIE5c+ZwsbNnz6Jnz5749OkTVFRUpNpkZ2cjO/u/Fy81NRUmJiYwSkmBnKZmmT4GQkjFy0jPQXJyFgBg0OPbMHmlh1zkfxYUfSwoX2EfgHy0pXwrb9uqlm9J2laVfNNT4uHvOw9vn9+DKC8bKSkp0CzD7+8qdUToa6xcuRJLliyRisfwkAshpByoK3G/Gk3+0UMi6vObDyGkzLx+dBbX/aYhK/1jue2jShVChoaGiIuTPOQWFxcHTU1NmUeDAGDOnDmYPn06d7/giJAAQJ3yTJYQUmHEIjHi4z8hl/3/SBATQV2QUOl+2RbXlvKtvG2rWr4laVvZ8/2UlojAPWORm5Pfr01VoxY+pSVKN/xGVaoQcnR0xNmzZyViFy9ehKOjY6FthEIhhELpHvuGAN6WdYKEEH7Iy+GdSIx1IjEgD2iIP0DjYzwWLnQqdvTL1q33EBeXIREzM9PG8OHNimyXnp6D33+/JRXv0qUB2rWrV2Tbhw9jceLEM6n4+PEOMDRUL7Lt0aNPERr6QSKmrq6EmTPbFtkOAFatuiE1IsrOzgB9+zYpst3792nYseO+VLx//yawsTEosu21a69x+bJ0p92ff25X7IioXbse4M0byU7GdepoYOxY+yLb5eaKsHz5dal4hw6m6NTJrMi2YWHxOHjwiVR8xIhmMDXVLrLt6dMRCA5+LxFTVJTDvHkdgPXBwIUvnofPOktv2HCbO8VboHFjPQwMTSqys/THj5/wxx93pXL5/nsLOCy4KZ3kZ52l79x5i3PnpPvdTO1gBp010tv8vLO0n18oIiIkj9Lo6qpgspJysZ2lf/31qtRIzVat6qLHySipztL2LpMx5uxquLm5YerUhXB2biG97W/Eax+h9PR0vHiR/yI0b94c69atg7OzM2rVqoV69ephzpw5ePfuHXx8fADkD5+3trbGxIkTMXLkSFy+fBlTpkzBmTNnSjxqLDU1FVpaWjBKScF76iNESLUya/NTJAsMoY04rPmp6C93QkjlIhKJkJeXJ3HwgjGGCxcuoGvXrkhLS4OWllaZ9xHi9YKKwcHBaN68OZo3bw4AmD59Opo3b46FCxcCAGJiYvDmzX9DJ83MzHDmzBlcvHgRdnZ2WLt2Lf766y8aOk8IyVfMNYMIIZVTdHQ0XFxcMHOm5GUgBAIBXF1di70e2LeoNKPGKgodESKk6mGMleiDcNaWMCTDgI4IEVKFHDp0COPGjUNycjIA4MyZM+jRo4fUegXf39XqiBAhhBTn/fs0tG79l9SF9QghVVtqaio8PT3h7u7OFUEmJibQ0NCo0DyoECKEVFovXyahffvduHfvPXr29MP9+++Lb0QIqfSCgoLQvHlz7N27l4u5u7vj0aNHaN++fYXmQoUQIaRSevYsAe3b70ZUVBKA/PmJXF334cmTD8W0JIRUVnl5eViyZAnat2+PqKgoAICGhgZ8fHxw4MAB6OjoVHhOVWr4PCGkZnj5MgkdOuxGfPwnifjHj5no0sUX16+PgLl5LZ6yI6SEzr8EvngPQ18V6Fb0EP7q6uPHj+jVqxeCgoK4WNu2bbFv3z6YmfH3nFAhRAipdOrV04KzsxkOHZK+nouOjkqxs1QTUilsDJaed8vBoMYWQtra2lBQyC875OXlsXDhQsydO5eL8YVOjRFCKh15eTn4+vZFz56NJOL29ka4etUTdepUbGdKQsi3k5eXh6+vL1q0aIEbN25g4cKFvBdBABVChJBKSklJHocPD0DHjvUBAN99Vw8BAcOgp6fKb2KEkBK5evUq7t6VvEK1qakpgoOD0aZNG56ykkaFECGk0lJRUcSpUwMxc6Yj/P2HQktLme+UCCHFyMnJwZw5c+Ds7IxBgwYhLS1NYnl5Xhzxa1AhRAip1DQ0hFizpmux81IRQvgXHh4OR0dH/Pbbb2CMISoqClu3buU7rSLxf3KOEEIIqY62dgWyJCe5hXL1/NpljMHb2xvTpk1DZmb+bPGKiopYvnw5ZsyYwXN2RauerwghpFJjjOHXX6+iTh0NjBlT9EzihFRZ9bX4zqBCxMfHY8yYMTh58iQXs7S0hJ+fH1q0KPvZ4ssaFUKEkArFGMPMmRewbt1tCASAuroSBg2y4TstQshX8Pf3h6enJ2JjY7nY+PHjsXbtWqiqVo2BDVQIEUIqjEgkxoQJZ+DtHQIAYAzw8DgONTUl9O5tyXN2hJDSiIuLg5ubG7KysgAAenp62LVrF3r16sVzZqVDnaUJIRVCLGbw8DjOFUEFRCKGH388jICAKJ4yI4R8DQMDA/z2228AAFdXV4SGhla5IgigI0KEkAoiJyeAtXVtmctyc8V4/z5N5jJCSOUgFoshEomgqPjfCM7JkyfD2NgYffv2hZxc1Ty2UjWzJoRUSXPmfIdffmknEVNUlMPBgz/Aw8OOp6wIIcWJiYlB9+7dMX/+fIm4nJwc+vfvX2WLIIAKIUJIBRIIBFi5sjMmTHAAACgrK+DkyYH44QcrnjMjhBTm5MmTsLGxwYULF7BmzRpcvnyZ75TKFJ0aI4RUKIFAgD//7AEAcHdvCien+vwmRAiRKSMjAzNmzMD27du5mIGBAY8ZlQ8qhAghFU5OToAtW3rynQYh5Wt5EBCZLBkz1wbmOfKRTancv38fgwcPRkREBBfr06cP/vrrL+jp6fGYWdmjQogQQggpDzfeAsFxkjGHyn1ERSQS4ffff8f8+fORl5d/VWxVVVVs2LABo0ePrnTzhJUF6iNECCkzT558wIIFl8EY4zsVQkgpJSQkwMXFBbNnz+aKIHt7ezx48ABjxoyplkUQQEeECCFlJDj4PVxd9yExMRN5eWKsXOnCd0qEkFLQ0tJCeno6gPy+fLNnz8bixYuhpKTEc2bli44IEUK+2fXrr9Gp014kJuZPtvjbbzexcuV1nrMihJSGoqIi9u/fjyZNmuDKlStYsWJFtS+CADoiRAj5Rv7+L9C370FkZkrOsj137mVoaAgxaVIrnjIjhGe6KoCRmnSskggKCoKqqirs7P67hpeFhQX+/fffKn1doNKiQogQ8k2ys0XIyRHJXHbsWBgmTHCAvHzN+VAlhLPve74zkCkvLw/Lly/H0qVLYWFhgeDgYIkJUmtSEQTQqTFCyDfq3dsSvr598WU/yp49G+HMmcFUBBFSiURFRaFDhw5YvHgxRCIRwsLCsGXLFr7T4hV9QhFCvtmgQTbYvv2/X78//tgUx465Q0VFsYhWhJCKwhiDj48PmjVrhqCgIACAvLw8fv31V0ybNo3f5HhGp8YIIWVizBh7pKXl4OnTeGzf/j0dCSKkkkhKSsL48eNx6NAhLmZubo59+/ahTZs2PGZWOVAhRAgpM9OnO4IxVm2vN0JIVRMYGAgPDw+8ffuWi40YMQIbN26EhoYGj5lVHlQIEULKFBVBhFQOMTExcHV1RU5ODgBAR0cH27dvx4ABA3jOrHKhY9eEkCLl5Ynx+++3kJmZy3cqhJBSMDIywqJFiwAAzs7OePz4MRVBMtARIUJIoXJyRBg8+CiOHg1DYOArHD/uDkVFeb7TIoTIwBiDWCyGvPx/f6O//PILTExMMGTIkBo3LL6k6FkhhMj06VMu+vT5G0ePhgEAzpx5Dg+P4xCJxDxnRgj5Unx8PPr27Ytly5ZJxOXl5eHh4UFFUBHoiBAhREpqaja+/94P16+/kYgfPPgEamqK8PbuDTk56gtESJH6nQAefDH7fHMD4Jhbme7G398fnp6eiI2NxenTp9G1a1c4OjqW6T6qMyoRCSFS3r9Pw5Mn8TKX/f33E4SHJ1RwRoRUQZm5QPoXtzLsa5eVlQUvLy9069YNsbGxAPI7RKelpZXZPmoCKoQIIVIaN9aDv/9QaGhITrioqSnEhQtD0aSJPk+ZEUIAIDQ0FK1atcKGDRu4mKurK0JDQ9G1a1f+EquCqBAihMjk4FAHZ84MhopK/hl0PT1VBAYOR7t29XjOjJCaSywWY+PGjWjZsiVCQ0MBAEKhEBs3bsTZs2dhaGjIc4ZVDxVChJBCtW9viuPH3WFmpo1r1zzRvLkR3ykRUmN9/PgRPXr0wLRp05CdnQ0AsLGxQXBwMKZMmUIdor8SdZYmhBTJ1bUhnj2bBCUlGjZPSKl0NQMsdSVjpppfvTk1NTW8e/eOu+/l5YUVK1ZAWVn5q7dJqBAihJQAFUGEfAUvhzLdnLKyMvz8/NCnTx9s27aN+gKVESqECKmhrl17DXNzHdSt+/W/UAkh5ef+/ftQU1ND48aNuZiNjQ0iIiKgoEBf32WFTigSUgOdOROBrl190aWLL+LjM/hOhxDyGZFIhFWrVqFNmzYYNGgQ1x+oABVBZYsKIUJqmIMH/4Wb20FkZ4sQFpYAV9d9SE7O4jstQgiA6OhodO7cGbNnz0ZeXh4ePnyILVu28J1WtUaFECE1yM6dIRg06Cjy8v6bJuPBg1j07OmHjIwcHjMjhBw6dAi2tra4evUqAEAgEGDOnDmYOHEiz5lVb1QIEVJD5OSIsHHjHTAmvezWrWhs3Rpc8UkRQpCamgpPT0+4u7sjOTkZAGBiYoIrV65gxYoVUFJSKnoD5JtQIURIDaGkJA9//6Fo2LCW1LLx4+0xfTrNTURIRQsKCkLz5s2xd+9eLubu7o5Hjx7BycmJx8xqDiqECKlBjIw0cOmSB4yN/xspNmtWW2zZ0pMmUSWkgr179w4dO3ZEVFQUAEBDQwM+Pj44cOAAdHR0eM6u5qBCiJAaxtRUGwEBw1C7thqWLXPGqlUuEAioCCKkzD1NAO7FSN6e/jdhcd26dTFz5kwAQNu2bfHo0SN4eHjQ32MFozF4hNRAFha6ePr0J+jqqvKdCiHV14wrQHAcd5cxBjgYQHD+Ry62ePFi1KtXD6NGjaJh8TyhI0KE1FBUBBFScZLEnzAwbTfWvvpHIq6oqIhx48ZREcQjKoQIqUYyMnIQGhpX/IqEkAoTmPMctskrcSjnAeZGHMCDBw/4Tol8hgohQqqJ5OQsdO26D05Oe/D4MRVDhPAtR5yH2Rkn0Sn1D7wVJwMA1BWUERsby29iRAIVQoRUA/HxGXB23otbt6KRlJSFLl18ERHxke+0CKmxwsPD4Xh7HlZlXgJD/sW7nBUb4XG739G9e3eesyOfo0KIkCru3btUdOiwBw8f/vcr88OHDLi4+OD162T+EiOkBmKMYfv27WjevDlCUl8CABQVFLF6+M+4dPA0jJf05DlD8iXqnUVIFbdy5Q08e5YgFY+OTsWQIcdw/foIGo5LSAVITEzEiBEjcOrUKS5maWkJPz8/tGjRgsfMSFHoiBAhVdzatV3RpUsDqbiJiSZ27epDRRAhFUQoFOLZs2fc/QkTJiAkJISKoEqOCiFCqjihUAHHj7ujbVsTLtawYS1cvz4CFha6PGZGSM2ipqaG/fv3o06dOjh16hS2bNkCVVW6TEVlR6fGCKkG1NSUcObMYHTqtBe5uWJcvOgBQ0N1vtMipFoLDQ2FmpoaGjT474isg4MDoqKiIBQKecyMlAYdESKkmtDWVoa//1AEBg6nIoiQciQWi7Fx40a0bNkSQ4YMQV5ensRyKoKqFiqECKlG9PXV6IrRhJSjmJgYdO/eHdOmTUN2djZu376NrVu38p0W+Qa8F0KbN29G/fr1oaysjNatW+Pu3btFrr9hwwZYWlpCRUUFJiYm8PLyQlZWVgVlSwg/Xr1K5jsFQmq8kydPwsbGBhcuXOBiXl5eGDNmDI9ZkW/FayF08OBBTJ8+HYsWLUJISAjs7Ozg6uqKDx8+yFzfz88Ps2fPxqJFixAWFoadO3fi4MGDmDt3bgVnTkjF2bYtGI0a/YGjR5/ynQohNVJGRgbGjx8PNzc3fPyYf6FSIyMj+Pv7Y926dVBWVuY5Q/IteO0svW7dOowZMwYjRowAAGzbtg1nzpzBrl27MHv2bKn1b926hXbt2mHw4MEAgPr162PQoEG4c+dOheZNSEVZs+Ymfv75EgBg0KCj+OcfJbi6NuQ5K0Jqjvv372Pw4MGIiIjgYm5ubvD29oaenl7RjX2fADHpkjEjdcCjaTlkSr4Wb0eEcnJycP/+fbi4uPyXjJwcXFxcEBQUJLNN27Ztcf/+fe70WVRUFM6ePYsePXoUup/s7GykpqZK3Aip7BhjWLjwClcEAUBurhh9+x7E9euvecyMkJojOjoabdu25YogVVVVeHt749ixY8UXQQDg9xRYc0/y5kdHdisb3gqhhIQEiEQiGBgYSMQNDAwKnZBu8ODB+PXXX/Hdd99BUVER5ubm6NixY5GnxlauXAktLS3uZmJiUui6hFQW58+/wNKl16TimZl56NnTD+/eUUFPSHkzMTHBTz/9BACwt7fHgwcPMHr0aLpIaTXDe2fp0ggMDMSKFSuwZcsWhISE4NixYzhz5gyWLl1aaJs5c+YgJSWFu0VHR1dgxoR8nW7dGmLq1NYyl82b1x5162pWcEaE1AyMMYn7K1euxLp163Dr1i1YWFjwlBUpT7z1EdLT04O8vDzi4uIk4nFxcTA0NJTZZsGCBfDw8MDo0aMBADY2NsjIyMDYsWMxb948yMlJ13VCoZCu6UCqHIFAgHXrXJGWlo1dux5y8c2be+Cnn1rylxgh1VRqaiqmTJmCVq1acUeBAEBZWRleXl48ZkbKG29HhJSUlGBvb4+AgAAuJhaLERAQAEdHR5ltPn36JFXsyMvLA5Cu4gmp6uTkBNixoxd+/LEp5OQE8PFxoyKIkHIQFBSEZs2aYe/evZgxYwbCwsL4TolUIF5HjU2fPh3Dhw+Hg4MDWrVqhQ0bNiAjI4MbRTZs2DDUrVsXK1euBAD06tUL69atQ/PmzdG6dWu8ePECCxYsQK9evbiCiJDqRF5eDr6+fTFpUku0b2/KdzqEVCt5eXlYtmwZli1bBpFIBABQVFREZGQkmjRp8u07OOIGiL/4kS5H/YsqG14LIXd3d8THx2PhwoWIjY1Fs2bNcP78ea4D9Zs3bySOAM2fPx8CgQDz58/Hu3fvoK+vj169emH58uV8PQRCyp2SkjwVQYSUsaioKAwdOlRilHLbtm2xb98+mJmZlc1O1BTLZjukXAlYDTunlJqaCi0tLRilpOC9JnU4JfxJTc2GhoYSjUApQ7O2hCEZBtBGHNb8VAa/6Em1wxiDj48PJk2ahPT0/Gv8yMvLY+HChZg7dy4UFGgu8sqq4Ps7JSUFmmX4/V2lRo0RUl28eZMCB4cd+PXXq3ynQkiNkZycjIEDB8LT05Mrgho0aIAbN25g4cKFVATVUFQIEVLBnj//iPbtd+P580QsXnwV69bJvoAoIaRsCQQCiZkIPD098fDhQ7Rp04bHrAjfqBAipAKFhsahffvdePMmhYvNmHEB3t73ecyKkJpBS0sLvr6+0NPTw6FDh7B7925oaGjwnRbhGR0HJKSCpKZmo3NnH8THf5JaNm7caejrq8HNrTEPmRFSPYWHh0NNTQ3GxsZcrH379nj16hXU1NR4zIxUJnREiJAKoqkpxOrVXWQus7U1QNu2NP0LIWWBMYbt27ejefPmGDZsGMRiscRyKoLI56gQIqQCeXo2wx9/dJeIOToa48qV4ahdmz6cCflW8fHxcHNzw/jx45GZmYkrV65gx44dfKdFKjE6NUZIBZs0qRXS0rIxd+5ldOpkhpMnB0JdXYnvtAip8vz9/eHp6Skxcff48eMxbNgwfhLyugw8+ygZa6wLrO/ETz5EJiqECOHBnDntYWysiQEDmkJZmf4MCfkWWVlZmDNnDjZs2MDF9PT0sGvXLvTq1Yu/xJ59BILjil+P8Io+gQnhiYeHHd8pEFLlhYaGYsiQIQgNDeVirq6u2LNnT6ETeBPyOSqECCljeXliKChQ9ztCytvr16/RsmVLZGdnAwCEQiFWr16NSZMmSU3QTUhh6J1CSBlhjGH27Ev48cfDyMsTF9+AEPJNTE1Nuf4/NjY2CA4OxpQpU6gIIqVCR4QIKQNiMcOkSWexdWswAGDkyJPYs8cNcjTTNCHlav369TA1NcWMGTOgrKzMdzqSGuuWLEZ4RYUQId8oL0+MkSNPwtf3MRfz9X0MDQ0l/PlnD5pUlZAykJGRgRkzZqBNmzbw9PTk4mpqapg3bx5/iRWFRodVCXT8kJBvNGbMPxJFUIEtW4Ixd24ADxkRUr3cv38f9vb22L59OyZPnozIyEi+UyLVCBVChHyjsWNbQE1NUSquoCAHOzsatULI1xKJRFi1ahXatGmD8PBwAIBYLMa///7Lc2akOqFCiJBv5OhogpMnB0IolOdiQqE8jh37EQMHWvOYGSFVV3R0NDp37ozZs2cjLy8PAGBvb48HDx6gT58+PGdHqhMqhAgpA507N8DhwwOgoCAHNTVFnD07BL16WfKdFiFV0qFDh2Bra4urV68CAAQCAebMmYNbt27BwsKC5+xIdUOdpQkpI716WcLPrx+MjTXh6EgTqBJSWmlpaZg8eTL27t3LxUxMTODr6wsnJyceMyPVGRVChJShAQOa8p0CIVVWdnY2Lly4wN13d3fH1q1boaOjw2NWpLqjU2OEEEIqBT09Pezduxeamprw8fHBgQMHqAgi5Y4KIUKK8eBBDFxcfJCYmMl3KoRUK1FRUYiLk5yUtEuXLnj9+jU8PDyq/jW4MnKBtBzJW0Yu31mRL1AhREgRgoKi4ey8FwEBL9G9+36kpWXznRIhVR5jDHv37oWdnR1GjhwJxpjEcm1tbX4SK2s/nAAa7JC8/XCC76zIF6gQIqQQAQFR6NLFFykp+cXP3bvv0Lv338jMpF90hHytpKQkDBw4EJ6enkhPT8fZs2exe/duvtMiNRgVQoTIcOFCJHr08EPGF4exAwNfYcCAw8jJEfGUGSFVV2BgIGxtbXHo0CEu5unpiQEDBvCYFanpqBAiRAYrK30YGanLXJaXJ6bZ5QkphZycHMyePRudOnXC27dvAQA6Ojo4dOgQdu/eDQ0NDZ4zJDUZFUKEyGBsrImAgGFSxVD//k1w8uRAqKpKT6lBCJH27NkzODo6YtWqVVxfIGdnZzx+/JiOBJFKga4jREghzM1r4eJFD3TosAeJiZkYNswOO3f2hoIC/X4gpCSioqLQokULZGbmj7hUVFTE8uXLMWPGDMjJ1YC/o8FWgHM9yVghR5oJf6gQIqQITZvWhr//UBw8+C9WreoCObkqPpyXkArUoEED9OvXD/v374elpSX8/PzQokULvtOqOB50gdWqgAohQorh4FAHDg51+E6DkCpp8+bNMDU1xbx586Cqqsp3OoRI+aZjk1lZWWWVByGEkCosKysLXl5eOHz4sERcS0sLy5cvpyKIVFqlLoTEYjGWLl2KunXrQl1dHVFRUQCABQsWYOfOnWWeICHlITdXhJ9+OoOnT+P5ToWQKi80NBStWrXChg0bMHbsWERHR/OdEiElVupCaNmyZdizZw9Wr14NJSUlLm5tbY2//vqrTJMjpDxkZeWhf/9D2Lo1GF26+CIqKonvlAipksRiMTZu3IiWLVsiNDQUAJCZmYng4GCeMyOk5EpdCPn4+GDHjh0YMmQI5OXlubidnR2ePXtWpskRUtbS03PQs6cf/vknAgDw/n0aXFx88O5dKs+ZEVK1xMTEoEePHpg2bRqys/Ovvm5jY4Pg4GD07duX5+wIKblSF0Lv3r1Dw4YNpeJisRi5uTT1AKm8kpOz0KWLLy5ffikRf/kyGS4uvoiPz+ApM0KqlpMnT8LW1hb+/v5czMvLC3fv3oW1tTWPmRFSeqUuhKysrHD9+nWp+JEjR9C8efMySYqQ8iAUykMolJe57N27VERG0ikyQoqSkZGB8ePHw83NDQkJCQAAIyMj+Pv7Y926dVBWVuY5Q0JKr9TD5xcuXIjhw4fj3bt3EIvFOHbsGMLDw+Hj44PTp0+XR46ElAkVFUWcOjUILi4+uHfvPRfX0VGGv/9QtGxZl8fsCKn8UlNTcfToUe6+m5sbvL29oaenx2NWldi1aCApWzKmIwQ6mPCTD5Gp1EeE+vTpg3/++QeXLl2CmpoaFi5ciLCwMPzzzz/o0qVLeeRISJnR1BTi3LkhsLauDQAwMFDD1aueVAQRUgJGRkb466+/oKqqCm9vbxw7doyKoKKsvA2MPi95W3mb76zIF77qgort27fHxYsXyzoXQiqErq4qLl70gIfHcWzZ0gONGunynRIpD69SgAkXpONTHYBuZkW3PRYBeD+Sjvv0BPSLuR7OvOtASKxkrJ4msN216HYA0P2wdMzNAhhnV3S74FhggXSXBSxtDzgYFt12+yPgRIR0/NwAREdHQ01NDbVq1eLCffr0wcuXL1G7dm1gnD/w5ouBBi0MgeXti95n/Cdg2Bnp+Bg7oJ9F0W3PvwQ2yhiVtrUrUF+r6LbLg4AbbyVjuirAvu+LbgcA/U4AmV/0g+1qBng5FN+WVGqlLoQaNGiAe/fuQVdX8ssjOTkZLVq04K4rREhlZmiojosXPfhOg5SnrDwgOE46Hv+p+LZxGbLb5oqLbxuRKN02rYQDSWTts7hCBgBSs2W3Tc2Wjn3pbarMtocOHcK4cePg4uKCQ4cOQSD4b3qZ2rXzj6jiyUcgPFGyoboSipUrlp1v7xIMWIj/JLttVl7xbSOTpdsaqRXfDgAexAHpX7yOliX4EVVLGUikiw9XZqU+Nfbq1SuIRCKpeHZ2Nt69e1cmSRFCCOFHqjgTnmm+cHd3R3JyMo4cOQI/Pz++06qaupoBu7rxnQUpRomPCJ06dYr7v7+/P7S0/jsEKRKJEBAQgPr165dpcoSUlr//C+TmivH998UcXieESAnKfYkhaXvxUvyRi7m7u6NHjx48ZlWFeTkA92IkY6bFnL4jFa7EhZCbmxsAQCAQYPjw4RLLFBUVUb9+faxdu7ZMkyOkNI4dC8PAgUcgJyfAuXND4OxcTD8QQggAII+JsDzTH0s/+UOE/NN/Ghoa2Lx5M4YOHSpxWox8AwU5YHATvrMgXyhxISQW5/9xmJmZ4d69ezRSgFQqPj6PMGLESYjFDADQq9cBXLo0DG3aGPOcGeGNsgLgYCAdL66zMwAYqMluq1iC3gQWtYD0HMlYPc3i2wGy92lcgraaQtltNYXFNo0SpmKoaAuCPv3XYbqttiX2hZyDmVkxPyaa6gIaipIxi1qy1/2copzsfA1K0F9HX1V2W+USfJ2Za0u31VUpvh0ANDeQ7ixtWoLXRk0xf5+mWvlFEA2dr3QEjDHGdxIVKTU1FVpaWjBKScF7zRJ+OJFKbcuWe5g48axUXFtbGVevesLWVsaHJqmWZm0JQzIMoI04rPmJfnkX58WLF2jRogXS0tIAAPLy8li4cCHmzp0LBYWvGlRMSLkp+P5OSUmBZhl+f3/VOz0jIwNXr17FmzdvkJMj+ctnypQpZZIYISX1778fZMaTk7Pg6/sIa9Z0reCMCKkazM3N0blzZ5w4cQINGjTA/v370aZNG77TIqRClboQevDgAXr06IFPnz4hIyMDtWrVQkJCAlRVVVG7dm0qhEiF+/PPHkhLy8G+fY8l4tOmtcbq1XSRT0IKIxAI4O3tDVNTUyxduhQaGhp8p0RIhSv18HkvLy/06tULSUlJUFFRwe3bt/H69WvY29vj999/L48cCSmSnJwAu3f3gZtbYy62cGEHrFvnSp08a4rlQcDIc8D/+4hBzPJjhJOTk4PZs2fjzBnJCxnq6elhw4YNVASRGqvUhdDDhw8xY8YMyMnJQV5eHtnZ2TAxMcHq1asxd+7c8siRkGIpKMjh77/7o0uXBlizpguWLHGmIqgmufEW+CcSKOjyyJj0FYRrsPDwcDg6OmLVqlUYOXIk4uJkXJCQkBqq1IWQoqIi5OTym9WuXRtv3rwBAGhpaSE6OrpssyOkFIRCBZw7NwQzZ7blOxVCKgXGGLZv347mzZsjJCQEAJCUlISbN2/ynBkhlUep+wg1b94c9+7dQ6NGjeDk5ISFCxciISEBvr6+sLa2Lo8cCSkxeflS1/aEVEvx8fEYPXq0xMVwLS0t4efnhxYtWvCYGSGVS6m/NVasWAEjIyMAwPLly6Gjo4MJEyYgPj4e27dvL/MECUlNzYafXyjfaRBSZfj7+8PW1laiCJowYQJCQkKoCCLkC6U+IuTg8N9Mu7Vr18b58+fLNCFCPvfx4yd0774f9+69R3JyFn76qSXfKZHKSFfl/5NnFvQLE5T8QnnVSFZWFubMmYMNGzZwMT09PezatQu9evXiLzFCKrEyO48QEhKC77//vqw2RwhiYtLQseNe3Lv3HgAwceJZ+Pg84jkrUint+x54PAKQ/38hJC/Ij9UwHz58wO7du7n73bp1Q2hoKBVBhBShVIWQv78/Zs6ciblz5yIqKgoA8OzZM7i5uaFly5bcNByEfKvXr5PRocMeqYsljhhxEkePPuUpK0Iqt3r16mHr1q0QCoXYtGkTzp49C0NDQ77TIqRSK3EhtHPnTnTv3h179uzBqlWr0KZNG+zbtw+Ojo4wNDTEv//+i7Nnpac5IORrXL36Gi9eJErFxWKGmTMvIjs7j4esCKlcYmJikJqaKhEbNGgQnj9/jsmTJ9MlJAgpgRIXQhs3bsSqVauQkJCAQ4cOISEhAVu2bEFoaCi2bduGJk1oXh9SdoYNs8OaNdJXhTYz00ZAwDAIhTQPEqnZTp48CVtbW5lX8zcxoYk9CSmpEhdCkZGRGDBgAACgX79+UFBQwJo1a2BsTLN7k/Ixc2ZbLFzYgbvfpIkerl8fgQYNdHjMihB+ZWRkYPz48XBzc0NCQgL27t2Lo0eP8p0WIVVWiX9WZ2ZmQlVVFUD+/DRCoZAbRk9IeVm8uCNSU7Nx9epr+PsPhb6+Gt8pEcKb+/fvY/DgwYiIiOBibm5ucHJy4jErQqq2Up1f+Ouvv6Curg4AyMvLw549e6CnpyexDk26SsqSQCDAunWuyMjIhbq6Et/pEMILkUiE33//HfPnz0deXn7/OFVVVWzcuBGjRo2ivkCEfIMSF0L16tWDt7c3d9/Q0BC+vr4S6wgEglIXQps3b8aaNWsQGxsLOzs7/PHHH2jVqlWh6ycnJ2PevHk4duwYEhMTYWpqig0bNqBHjx6l2i+pOgQCARVBpMaKjo6Gh4cHrl69ysXs7e3h5+cHCwsLHjMjpHoocSH06tWrMt/5wYMHMX36dGzbtg2tW7fGhg0b4OrqivDwcNSuXVtq/ZycHHTp0gW1a9fGkSNHULduXbx+/Rra2tplnhspf5cvv4STkylNi0FIISIiItC6dWskJycDyP9RMHv2bCxevBhKSvTjgJCywOs30Lp16zBmzBiMGDECVlZW2LZtG1RVVbFr1y6Z6+/atQuJiYk4ceIE2rVrh/r168PJyQl2dnYVnDn5Vhs23Ebnzj4YP/40WMGM4YR8rX4nALPtgOj/1zITifNjVVzDhg3RunVrAPkjwa5cuYIVK1ZQEURIGeKtEMrJycH9+/fh4uLyXzJycnBxcUFQUJDMNqdOnYKjoyMmTpwIAwMDWFtbY8WKFRCJRBWVNvlGjDEsXXoVXl7+AIC//nqAGTMuUDFEvk1mLpCeKx2r4uTk5LB7926MHTsWjx49ok7RhJQD3gqhhIQEiEQiGBgYSMQNDAwQGxsrs01UVBSOHDkCkUiEs2fPYsGCBVi7di2WLVtW6H6ys7ORmpoqcSP8YIzh558vYuHCQIn4+vW3sWTJVdmNCKkh8vLysGTJEly+fFkibmRkhO3bt0NHhy4bQUh5qFKdM8RiMWrXro0dO3bA3t4e7u7umDdvHrZt21Zom5UrV0JLS4u70YXG+PP8eSI2b74nc9mSJVdx+/bbCs6IkMohKioKHTp0wOLFizFs2DAkJkpfVZ0QUj54K4T09PQgLy+PuLg4iXhcXFyhc+MYGRnBwsIC8vLyXKxJkyaIjY1FTk6OzDZz5sxBSkoKd4uOji67B0FKxcJCF8eOuUNRUfptt2GDK9q0oYtzkpqFMQYfHx80a9aM6xIQGxuLK1eu8JwZITXHVxVCkZGRmD9/PgYNGoQPH/InxTx37hyePHlS4m0oKSnB3t4eAQEBXEwsFiMgIACOjo4y27Rr1w4vXryQmNw1IiICRkZGhXYeFAqF0NTUlLgR/nTr1hAHDvSHnFz+dU/k5ATYubM3pk5tw3NmpErragYMsQIKrqcjEOTHKrGkpCQMHDgQw4cPR1paGgCgQYMGuHHjBvr3789zdoTUHKUuhK5evQobGxvcuXMHx44dQ3p6OgDg0aNHWLRoUam2NX36dHh7e2Pv3r0ICwvDhAkTkJGRgREjRgAAhg0bhjlz5nDrT5gwAYmJiZg6dSoiIiJw5swZrFixAhMnTiztwyA86t/fCrt29YaSkjwOHOiPkSOb850Sqeq8HIANnYD/F9iQE+THKqnAwEDY2tri0KFDXMzT0xMPHz5Emzb0o4CQilTqmStnz56NZcuWYfr06dDQ0ODinTp1wp9//lmqbbm7uyM+Ph4LFy5EbGwsmjVrhvPnz3MdqN+8eQM5uf9qNRMTE/j7+8PLywu2traoW7cupk6dil9++aW0D4PwbPjwZujUyQwmJlp8p0JIhcnJycGiRYuwatUqbqSktrY2duzYwc3lSAipWAJWynHL6urqCA0NhZmZGTQ0NPDo0SM0aNAAr169QuPGjZGVlVVeuZaJ1NRUaGlpwSglBe/pNBkh1cqsLWFIhgG0EYc1PzXhOx0pUVFRsLW1RUZGBgCgY8eO8PHxoUEchJRAwfd3SkpKmXZzKfWpMW1tbcTExEjFHzx4gLp165ZJUqTqevcuFSkplbsYJoQvDRo0wMaNG6GoqIjVq1cjICCAiiBCeFbqQmjgwIH45ZdfEBsbC4FAALFYjJs3b2LmzJkYNmxYeeRIqoioqCS0b78bPXv6ISND9ig+QmqShIQEfPr0SSI2cuRIPH36FLNmzZI49U8I4Uep/wpXrFiBxo0bw8TEBOnp6bCyskKHDh3Qtm1bzJ8/vzxyJFXA06fx+O67XXj5Mhk3b0ajb9+DyM7O4zstQnjj7+8PGxsbzJo1SyIuEAjQsGFDnrIihHyp1IWQkpISvL29ERkZidOnT2Pfvn149uwZfH19Ja7vQ2qOkJAYODntQUxMOhe7eDEKAwceRV6euIiWhFQ/WVlZ8PLyQrdu3RAbG4stW7bgzJkzfKdFCClEqUeN3bhxA9999x3q1auHevXqlUdOpAoRixmGDz+BhIRPUstOnHiGJUsCsXRpJx4yI6TihYaGYsiQIQgNDeVi3bp1g729PY9ZEUKKUuojQp06dYKZmRnmzp2Lp0+flkdOpAqRkxPgyJEB0NdXlVrWsmUdeHnJvjgmIWXqaQJwLwYoGATLWH6sgojFYmzcuBEtW7bkiiChUIhNmzbh7NmzhV4tnxDCv1IXQu/fv8eMGTNw9epVWFtbo1mzZlizZg3evqV5omoqS0s9XLzoAW1tZS7WoYMpLl0ahlq1VHjMjNQYM64APY4C4v8XQmKWH6sAMTEx6NGjB6ZNm4bs7GwAgI2NDYKDgzF58mQICq52TQiplEpdCOnp6WHSpEm4efMmIiMjMWDAAOzduxf169dHp050CqSmsrMzxLlzQ6Cmpoju3Rvi3Lkh0NQU8p0WIeUqPDwctra28Pf352JeXl64e/curK2tecyMEFJSpe4j9DkzMzPMnj0bdnZ2WLBgAa5evVpWeZEqqE0bY9y4MRJWVvpQUqKO86T6a9iwIaysrHDt2jUYGRlhz5496Nq1K99pEUJK4asvYnHz5k389NNPMDIywuDBg2FtbU0jIwiaNTOkIojUGPLy8vD19YWHhwceP35MRRAhVVCpC6E5c+bAzMwMnTp1wps3b7Bx40bExsbC19cX3bp1K48cCc8YYzJHhRFSk4hEIqxatQq3bt2SiNerVw8+Pj7Q09PjKTNCyLco9amxa9euYdasWfjxxx/pD78GEIsZvLzO48SJcFy/PgL16tEkqaQSmtMGSMoG3v7/iuZygvxYGYmOjoaHhweuXr0KMzMzPHz4sEznOiKE8KfUR4QKTolREVT9iURijBlzCps23cWbNylwcfFBXFx68Q0JqWgdTIA+DYGCEVoCQX6sDBw6dAi2trZcH8hXr17hwoULZbJtQgj/SnRE6NSpU+jevTsUFRVx6tSpItft3bt3mSRG+JWTI8LQocdw+PB/14p6/jwRXbr4IjDQk4bFk2ovNTUVU6ZMwd69e7mYiYkJfH194eTkxGNmhJCyVKJCyM3NDbGxsahduzbc3NwKXU8gEEAkEpVVboRHixZdkSiCCoSGfkCvXgdw/foIyMnR9VFI9RQUFIShQ4ciKiqKi7m7u2Pr1q3Q0dHhMTNCSFkr0akxsViM2rVrc/8v7EZFUPXxyy/foVkz6avhKirKYcYMRyqCSLWUl5eHJUuWoH379lwRpKGhAR8fHxw4cICKIEKqoVL3EfLx8eGunvq5nJwc+Pj4lElShH/a2sq4cGEoGjf+ry+YiooC/vlnEPr1a8JjZoSUn8jISKxcuZL7Ude2bVs8evQIHh4edIVoQqqpUhdCI0aMQEpKilQ8LS0NI0aMKJOkSOWgr6+Gixc9UL++NjQ1hfD3HwpX14Z8p0VIubG0tMTq1ashLy+PJUuWcKPECCHVV6mHzzPGZP4yevv2LbS0aGh1dWNsrIlLlzyQnJwFe/s6fKdDSJlKSkqCqqoqhML/poOZPHkyOnXqRFNkEFJDlLgQat68OQQCAQQCATp37gwFhf+aikQivHz5ki6oWE2Zm9fiOwVCylxgYCA8PDwwcOBArFmzhosLBAIqggipQUpcCBWMFnv48CFcXV2hrq7OLVNSUkL9+vXRv3//Mk+QlJ/s7DwIhd803RwhlYPvEyAmHVDVyD/hL2b5MY+mUqvm5ORg0aJFWLVqFRhj+P3339GtWzd07ty54vMmhPCuxN+CixYtAgDUr18f7u7uUFZWLrekSPm7e/cd+vc/hAMH+uO77+rxnQ4h38bvKRAcByx0zr/PWH7si0IoPDwcgwcPRkhICBdzdnaGpaVlRWZLCKlESt1Zevjw4VQEVXFXr75C584+ePs2FT17+uH+/fd8p0RIuWKMYfv27WjevDlXBCkqKmL16tW4dOkSjI2Nec6QEMKXEh0RqlWrFiIiIqCnpwcdHZ0ih5EmJiaWWXKk7J079xz9+h1CVlYeACA1NRuurvtw7doIWFnp85wdIWUvPj4eo0ePlrgqvqWlJfz8/NCiRQseMyOEVAYlKoTWr18PDQ0N7v90PY2q6e7dd+jT52/k5ool4h8/ZsLFxQdBQaNgaqrNT3KElIPw8HB07NgRsbGxXGzChAn4/fffoaqqymNmhJDKokSF0PDhw7n/e3p6llcupJzZ2xvBza2xzKkzWrasCwMDdRmtCKm6GjRoABMTE8TGxkJPTw+7du1Cr169+E6LEFKJlLqPUEhICEJDQ7n7J0+ehJubG+bOnYucnJwyTY6ULXl5Oezb1w/du0teFHHQIGscOTIAyso0goxUUUfcgKixgPz/P9Lk5YAjblBUVMT+/fvRr18/hIaGUhFECJFS6kJo3LhxiIiIAABERUXB3d0dqqqqOHz4MH7++ecyT5CULSUleRw9+iOcnEwBAGPGtICvb18oKsrznBkh30BNEWI1BYRc8UVC9GMuBgCNGjXC0aNHYWgoPXceIYSUuhCKiIhAs2bNAACHDx+Gk5MT/Pz8sGfPHhw9erSs8yPlQEVFEadODcLGjd2wffv3kJcv9duAkEolJiYGPXr0wOUjK3Fl91jk5mTynRIhpIoo9TcgYwxicX5n20uXLqFHjx4AABMTEyQkJJRtdqTcaGoKMWVKa+r4Tqq8kydPwtbWFv7+/gCAlLjnePnkOs9ZEUKqilIXQg4ODli2bBl8fX1x9epV9OzZEwDw8uVLGBgYlHmCpHQYY3ynQEiFyMjIwPjx4+Hm5sb9CFPT0ke3SUdg0bwrz9kRQqqKUhdCGzZsQEhICCZNmoR58+ahYcP8jrdHjhxB27ZtyzxBUnLZ2Xn44YfD2LkzpPiVCanC7t+/jxYtWmD79u1czM3NDcPnnoBxE2ceMyOEVDWlHiZka2srMWqswJo1ayAvTx1u+fLpUy769j2ICxcicfx4GNTVleDuThNHkupFJBJhzZo1WLBgAfLy8i8Kqqqqig0bNmD06NH4eesz0NhVQkhpfPV46fv37yMsLAwAYGVlRVdo5VFKSha+//4Abtx4AyB/mqWhQ49DTU0J339vwXN2hJSdZ8+eSRRB9vb28PPzg4UFvc8JIV+n1KfGPnz4AGdnZ7Rs2RJTpkzBlClT4ODggM6dOyM+Pr48ciRFyM7OQ+fOPlwRVCAvT4wffjiEy5df8pQZIWWvadOmWLp0KQQCAebMmYNbt25REUQI+SalLoQmT56M9PR0PHnyBImJiUhMTMS///6L1NRUTJkypTxyJEUQChXg5tZY5jJlZQWoqNBFEknVlZaWxh39KTBr1izcvXsXK1asgJKSUn7Q6zLQ/TAg/v9gATHLjxFCSDFKXQidP38eW7ZsQZMmTbiYlZUVNm/ejHPnzpVpcqRk5s1rj59/luyorq+visBATzg6mvCUFSHfJigoCM2aNcOyZcsk4vLy8nBwcJBc+dlHIDgu/7wwkP/vs48VlCkhpCordSEkFouhqKgoFVdUVOSuL0QqlkAgwG+/uWDChPwvB2NjTVy7NgLNmtGVdEnVk5eXhyVLlqB9+/aIiorC0qVLcevWLb7TIoRUU6U+b9KpUydMnToVBw4cQJ06dQAA7969g5eXFzp37lzmCZKSEQgE+PPPHtDUFGL8eAfUr6/Nd0qElFpUVBSGDh2KoKAgLtamTRsYGRnxmBUhpDor9RGhP//8E6mpqahfvz7Mzc1hbm4OMzMzpKam4o8//iiPHEkJycnlHxmiIohUNYwx+Pj4oFmzZlwRJC8vjyVLluDq1aswMzPjOUNCSHVV6iNCJiYmCAkJQUBAADd8vkmTJnBxcSnz5Agh1V9SUhImTJiAgwcPcrEGDRpg//79aNOmTck20lg3/9+CKWMEgv9ihBBShFIVQgcPHsSpU6eQk5ODzp07Y/LkyeWVF/nMP/+E48qVV1i7tivNDUaqlfDwcHTp0gXR0dFczNPTE5s2bYKGhkbJN7S+U/6/W/J/nEFO8F+MEEKKUOJCaOvWrZg4cSIaNWoEFRUVHDt2DJGRkVizZk155lfjHTgQCg+P4xCJGJSVFbBiBfXDItWHqakptLW1ER0dDR0dHWzfvh0DBgzgOy1CSA1S4j5Cf/75JxYtWoTw8HA8fPgQe/fuxZYtW8oztxrP2/s+hgw5BpEof0jwypU3sHIlzapNqg9lZWX4+fmhR48eePz4MRVBhJAKV+JCKCoqCsOHD+fuDx48GHl5eYiJiSmXxGq6jRtvY+zY0/hyMvm5cy9j8+a7/CRFyDdgjGHHjh14+vSpRNza2hpnzpyBsbExT5kRQmqyEhdC2dnZUFNT+6+hnByUlJSQmZlZLonVdGZmOpCXl90f6NmzhArOhpBvEx8fDzc3N4wbNw6DBw9GdnY23ykRQgiAUnaWXrBgAVRVVbn7OTk5WL58ObS0tLjYunXryi67Gqx3b0v4+PTF0KHHJI4KzZ7djvoJkSrF398fnp6eiI2NBQA8evQIp0+fRv/+/XnOjBBCSlEIdejQAeHh4RKxtm3bIioqirtPI5rK1uDBNkhPz8G4cacBACtWdMKcOe15zoqQksnKysLs2bOxceNGLqanp4ddu3ahV69ePGZGCCH/KXEhFBgYWI5pkMKMHWuPtLRsCIUKmDSpFd/pEFIioaGhGDx4MP79918u5urqij179sDQkKZ+IYRUHjQ1eRUwY0bb4lcipBIQi8X4448/8Msvv3D9gIRCIVavXo1JkyZBTq7UF7MvmYzc/2ae/zymJj0vIiGEfK6cPpUIITVRaGgopk+fzhVBNjY2CA4OxpQpU8qvCAKAH04ADXYAov9P/CwS58cIIaQYVAjxJCkpE9On+yMzM5fvVAgpM3Z2dpg7dy4AwMvLC3fv3oW1tTXPWRFCSOHo1BgPPnzIQNeuvnj0KA4RER9x/Lg7FBXl+U6LkFL79OkTlJWVJY72LFy4EF27dkX79tSxnxBS+dERoQoWHZ2CDh1249GjOADAmTPP/z+FhpjnzAgpnfv376N58+ZYu3atRFxRUZGKIEJIlfFVhdD169cxdOhQODo64t27dwAAX19f3Lhxo0yTq24iIxPRvv1uhId/lIgfPPgE48adhvjLzp6EVEIikQirVq1CmzZtEBERgXnz5iEkJITvtAgh5KuUuhA6evQoXF1doaKiggcPHnCdIlNSUrBixYoyT7A6yczMQ1pajsxlFy9GIT4+o4IzIqR0oqOj0blzZ8yePRt5eXkAAFtbW6irq/Ob2GArYFZLoOBaZgJBfowQQopR6kJo2bJl2LZtG7y9vaGo+N/Q1Hbt2tGvwmJYW9fG+fNDoKGhJBG3sNDFjRsjYGDA85cJIUU4dOgQbG1tcfXqVQD5F1CdM2cObt26BQsLC36T82gK/NwakPt/ISQnyI8RQkgxSl0IhYeHo0OHDlJxLS0tJCcnl0VO1VrLlnVx+vRgqKjk91O3samNa9c8YWKiVUxLQviRmpoKT09PuLu7c3/jJiYmuHLlClasWAElJaWiN0AIIZVYqQshQ0NDvHjxQip+48YNNGjQoEySqu46dDDFsWPuaN++HgIDPelIEKm0wsPD0bx5c+zdu5eLubu74/Hjx3BycuIxM0IIKRulLoTGjBmDqVOn4s6dOxAIBHj//j3279+PmTNnYsKECeWRY7XUrVtDXL3qiVq1VPhOhZBCGRsbQ0Eh/+ilhoYGfHx8cODAAWhra/ObGCGElJFSF0KzZ8/G4MGD0blzZ6Snp6NDhw4YPXo0xo0bh8mTJ39VEps3b0b9+vWhrKyM1q1b4+7duyVq9/fff0MgEMDNze2r9ss3mqSWVHZqamrw8/NDx44d8ejRI3h4eND7lhBSrZS6EBIIBJg3bx4SExPx77//4vbt24iPj8fSpUu/KoGDBw9i+vTpWLRoEUJCQmBnZwdXV1d8+PChyHavXr3CzJkzK931SvbufYh371L5ToOQUmOMwcfHB5GRkRJxe3t7XL58GWZmZjxlRggh5eerL6iopKQEKysrtGrV6puGzq5btw5jxozBiBEjYGVlhW3btkFVVRW7du0qtI1IJMKQIUOwZMmSStUvadWqG/D0PIkuXXyRkPCJ73QIKbGkpCQMHDgQw4cPx5AhQ5CbKzn1Cx0FIoRUV6WeYsPZ2bnID8XLly+XeFs5OTm4f/8+5syZw8Xk5OTg4uKCoKCgQtv9+uuvqF27NkaNGoXr168XuY/s7GzuWkdA/giYssYYw/z5l7FiRf4FJcPCEuDqug+XLw+DlpZyme+PkLIUGBgIDw8PvH37FgBw584dnD59Gn379uU5M0IIKX+lPiLUrFkz2NnZcTcrKyvk5OQgJCQENjY2pdpWQkICRCIRDAwMJOIGBgaIjY2V2ebGjRvYuXMnvL29S7SPlStXQktLi7uZmJiUKsfiiMUMU6ac44qgAiEhMejZ0w8ZGbIvoEgI33JycjB79mx06tSJK4J0dHRw+PDhqlcEXYsGTr4A2P+vzs5YfowQQopR6iNC69evlxlfvHgx0tPTvzmhoqSlpcHDwwPe3t7Q09MrUZs5c+Zg+vTp3P3U1NQyLYbS03MQGPha5rKbN6Nx9uxzDBhAF3YjlUt4eDgGDx4scRFUZ2dn+Pj4wNjYmMfMvtLK20BwHLDQGZAHIGb5sQ5l+8OHEFL9lNmkq0OHDi2yX48senp6kJeXR1xcnEQ8Li4OhoaGUutHRkbi1atX6NWrFxQUFKCgoAAfHx+cOnUKCgoKUp08AUAoFEJTU1PiVpY0NYW4cGEoGjasJREXCIBt23pSEUQqFcYYtm/fjubNm3NFkKKiIlavXo1Lly5VzSKIEEK+QZkVQkFBQVBWLl1/GCUlJdjb2yMgIICLicViBAQEwNHRUWr9xo0bIzQ0FA8fPuRuvXv3hrOzMx4+fFjmp71KyshIA5cuecDYOL/IkpcXwNe3L8aNc+AlH0IK8+DBA4wfPx6ZmZkAAEtLS9y+fRuzZs2CnFyZfRwQQkiVUepTY/369ZO4zxhDTEwMgoODsWDBglInMH36dAwfPhwODg5o1aoVNmzYgIyMDIwYMQIAMGzYMNStWxcrV66EsrIyrK2tJdoXXNjty3hFMzXVRkDAMHTp4otNm7qhT5/GvOZDiCwtWrTA9OnTsW7dOkyYMAG///47VFVV+U6LEEJ4U+pCSEtLck4sOTk5WFpa4tdff0XXrl1LnYC7uzvi4+OxcOFCxMbGolmzZjh//jzXgfrNmzdV5peqhYUuwsMnQVm51E8rIeUiOzsbSkpKEiM9V6xYgW7duqFLly48ZkYIIZWDgLGCYRbFE4lEuHnzJmxsbKCjo1OeeZWb1NRUaGlpwSglBe/LuL8QIZVJaGgoBg8ejAkTJuCnn37iO53y9TQByMjFrLtJSBYYQpvFYo1zbcCqZIMqCCGVX8H3d0pKSpn29y3VoRZ5eXl07dq1xs0y//p1MoKD3/OdBiElIhaLsXHjRrRs2RL//vsvZsyYgadPn/KdVvmy0gNaGuWPUgDy/6UiiBBSAqU+52RtbY2oqKjyyKVSioj4iO++242uXX3x+HFc8Q0I4VFMTAx69OiBadOmcRcSbdSoEc9ZEUJI5VXqQmjZsmWYOXMmTp8+jZiYGKSmpkrcqpPHj+PQvv1uvH2biqSkLHTt6ouIiI98p0WITCdPnoStrS38/f25mJeXF+7evQsrKyseMyOEkMqrxIXQr7/+ioyMDPTo0QOPHj1C7969YWxsDB0dHejo6EBbW7vK9huS5c6dt3By2oMPHzK4WFxcBlxcfPDmTQqPmREiKSMjA+PHj4ebmxsSEhIAAEZGRvD398e6detKfVkLQgipSUo8vGnJkiUYP348rly5Up75VBqbNt1FcnKWVDw6OhUzZ17AoUMDeMiKEEkRERHo1asXIiIiuJibm1uprr5OCCE1WYkLoYLBZU5OTuWWTGWyc2dvxMWlIyDgpUS8XTsTeHv34ikrQiQZGBggJyd/PjtVVVVs3LgRo0aNotniCSGkhErVR6gmfbgqKyvgxImBcHT8b8qBLl0awN9/KM0oTyoNLS0t7Nu3D61bt8aDBw8wevToGvV3Sggh36pUV/6zsLAo9kM2MTHxmxKqTNTVlXD27BA4O+9F/fra+Pvv/hAK6WKJhD+HDx9GmzZtJKaTadeuHYKCgqgAIoSQr1Cqb/UlS5ZIXVm6utPWVkZAwDBoaChBUVGe73RIDZWamoopU6Zg79696NixIy5dugR5+f/ejzW+CFofDLxOBRoa5h/nFrP8mBfN90cIKVqpCqGBAweidu3a5ZVLpVWrlgrfKZAaLCgoCEOHDuWu3xUYGIjTp0+jT58+PGdWiVx4CQTHAQvzp+YBY/kxKoQIIcUocR+haveLkzHcv09XiyaVV15eHpYsWYL27dtzRZCGhgZ8fHzQu3dvnrMjhJDqocSFUCmmJKsSUpKz0Lr1Xzh+PIzvVAiREhUVhQ4dOmDx4sUQiUQAgLZt2+LRo0fw8PCofj9MCCGEJyUuhMRicbU6LfbpUy5EIoaBA4/iwoVIvtMhBED+Dw4fHx80a9YMQUFBAPLn+FuyZAmuXr0KMzMznjMkhJDqpcYPgcrJEcGt+35c2Ngd301qWfTKvk8APxmTVx5xA9QUi27rdRl49sX0HI11gfWdim6XkQv8cEI6PtgK8GhadNtr0cDK29Lxtc7FT0i5Pji/j8XnVBSBY25FtwOAoaeBj5mSse+MgXmORbd7lQJMuCAdn+oAdCumADgWAXg/ko779AT0VYtuO+86EBIrGaunCWx3LbodAHQ/LB1zswDG2RXdLjgWWHBdOjxQA8M9h3P3GzRogP3796NNmzb/rbT9EXAiQqotzpXgIp/j/IE3X0yF08IQWN6+6Hbxn4BhZ6TjY+yAfhZFtz3/EtgYLB3f2hWoX8zgi+VBwI23kjFdFWDf95IxFUVAXVE6RgghxajxhRAAZIoZ3OYG4OVwW2hoCAtfMSY9v0Pml8QlOG347KPstsURM9ntnOsV3zYpW3bbjNzi275OlW775RdNYR7HAzEZkjEj9eLbZeXJzjf+U/Ft4zJkt80VF982IlG6bVoJniNA9j4dDItvlyr7tWk5qxXGjRuH7du3w9PTE5s2bYKGhobkSm9lvDYl9eQjEP7FJS7UlYpvlyuWvc/eGdKxL8V/kt02K6/4tpHJ0m2N1KTXKyjQt/z/VLe8XMmKdkJIjUeFEAAhgL1THYsugggpB7lMBAXISfT5Wbt2LXr06EEdogkhpAKUevb56kZdAJzT1ERPh7p8p0JqmPC8OLRJXou92Xck4mpqalQEEUJIBamxR4SU5eWgIhDgnKYGWitSXwJScfLy8rAv8Dh+0/obWVpyWCO4C0dNO5jK6QByeUCW9GS/EtQFgLGMaV6KawcAtZWAjC/a1lIovm1etux9KrPi2yqJZbcV5xTfVlteuq2+sNB2GspiADnQgBhZJXk+CCGVipKSEuTkKvYYTY0thHQU5HD1uwZopPb/02E6JTgtZqQOOBhIx+VKMJS5sW7JYrK2LWufJelzoyOU3ba4jt0AYKop3baknU9t9YG6X+Rnrl18O2UF2fkW19kZAAzUZLdVLMEflEUtID1HMlZPs/h2gOx9GstuyxhDZGQkXr9+DQNHS2xo+0d+igJ5ZClp4qWcPKCVCbx8KbM9x1kDaNNMOl5cOwCY3gAQfdFvSkm++LYiMbBBxj7VFYpv2wiy24oSgZfJRbf9sTbQp5ZkTE6u0H26WIsgxkfIQYSXJXk+CCGVipycHMzMzKCkVIK+i2VEwKrbBYKKkZqaCi0tLRilpOC9Zgm/7AgpA9euXYNIJJLo/KyjowNDQ0OJ6TLI14v5mAUxFCCHPBjp0uTIhFQlYrEY79+/h6KiIurVqyd1vbSC7++UlBRoluH3d409IkRIRcnKysLChQvRrl071K2b3xdNQUEB9evXh7a2Nr/JVTMKigwiKEIeuVBWpkKIkKpGX18f79+/R15eHhQrqNsKFUKElKMXL16gX79+SElJgbOzMwBAU1MTZmZmFfZHTgghVUXBKTGRSESFECHVgY6ODj5+/AhFRUXIycnB0NAQdevWpSkyCCFEBj4+G2v88HlCypOuri727NmDxo0bw9DQEHp6elQEEUJIJUKFECFl6J9//kFsrOR0HV26dMHRo0crdBQEnwIDAyEQCJCcnMx3KoQUa8GCBRg7dizfaVQ758+fR7NmzSAWl+Dq/jyruYVQclb+fFqElIGMjAyMHz8evXv3xsiRI/HlYMyaNCqsbdu2iImJgZZWMfOIkTIjEAi4m6amJlq2bImTJ09KrZeZmYlFixbBwsICQqEQenp6GDBgAJ48eSK1bmpqKubNm4fGjRtDWVkZhoaGcHFxwbFjx6Te31VVbGwsNm7ciHnz5vGdSrlJTEzEkCFDoKmpCW1tbYwaNQrp6elFtunYsaPEe0ogEGD8+PFS6+3Zswe2trZQVlZG7dq1MXHiRG5Zt27doKioiP3795f5YyprNbcQysyTnlSUkK9w//59tGjRAtu3bwcAnDt3DqdPn+Y5K/4oKSnB0NDwq08B5uTkFL9SJcIYQ15eCeZNK2e7d+9GTEwMgoOD0a5dO/zwww8IDQ3llmdnZ8PFxQW7du3CsmXLEBERgbNnzyIvLw+tW7fG7dv/TdCcnJyMtm3bwsfHB3PmzEFISAiuXbsGd3d3/Pzzz0hJSamwx5WbW8J5/77CX3/9hbZt28LU1PSbtlOeOX6rIUOG4MmTJ7h48SJOnz6Na9eulegI2JgxYxATE8PdVq9eLbF83bp1mDdvHmbPno0nT57g0qVLcHWVnKi6YL7ESo/VMCkpKQwAM3r2jrFuh/hOh1RheXl57LfffmMKCgoMAAPAVFVVmbe3NxOLxRLrZmZmsqdPn7LMzEzJjXQ7JH3b9rD4nd+Lkd32XkwZPkLGnJyc2KRJk9jUqVOZtrY2q127NtuxYwdLT09nnp6eTF1dnZmbm7OzZ89yba5cucIAsKSkJC5248YN5uTkxFRUVJi2tjbr2rUrS0xM5PYxceJENnXqVKarq8s6duzIGGMsMDCQtWzZkikpKTFDQ0P2yy+/sNzc3CLz/ef8NdauQ2emU0uXaWpqsg4dOrD79+9zywcNGsR+/PFHiTY5OTlMV1eX7d27lzHGmEgkYitWrGD169dnysrKzNbWlh0+fFjq8Z09e5a1aNGCKSoqsitXrrAXL16w3r17s9q1azM1NTXm4ODALl68KLGv9+/fsx49ejBlZWVWv359tn//fmZqasrWr1/PrZOUlMRGjRrF9PT0mIaGBnN2dmYPHxb9ngDAjh8/zt1PTU1lANjGjRu52G+//cYEAoHUtkQiEXNwcGBWVlbc+3bChAlMTU2NvXv3TmpfaWlpRb4Op06dYg4ODkwoFDJdXV3m5uZWaJ6MMaalpcV2797NGGPs5cuXDAD7+++/WYcOHZhQKGQbN25kysrKEu8xxhg7duwYU1dXZxkZGYwxxt68ecMGDBjAtLS0mI6ODuvduzd7+fJloXkyxljTpk3Zn3/+KRE7d+4ca9euHdPS0mK1atViPXv2ZC9evOCWy8qxIH9vb2/WuHFjJhQKmaWlJdu8ebPEtn/++WfWqFEjpqKiwszMzNj8+fNZTk5OkTl+i6dPnzIA7N69exKPTyAQyHxtCzg5ObGpU6cWujwxMZGpqKiwS5cuFbn/169fMwASz19xCv2sZP99f6ekpJR4eyVRc48IEfINoqOj0blzZ8yePZs7GmBvb48HDx5g9OjRJT8aEhwnfXubWny7gtnrv7ylZn/Do5Jt79690NPTw927dzF58mRMmDABAwYMQNu2bRESEoKuXbvCw8MDnz59ktn+4cOH6Ny5M6ysrBAUFIQbN26gV69eEIlEEvtQUlLCzZs3sW3bNrx79w49evRAy5Yt8ejRI2zduhU7d+7EsmXLisw1PT0d/d09cPTUJdy+fRuNGjVCjx49kJaWBiD/1/E///wjcWrA398fnz59Qt++fQEAK1euhI+PD7Zt24YnT57Ay8sLQ4cOxdWrVyX2NXv2bPz2228ICwuDra0t0tPT0aNHDwQEBODBgwfo1q0bevXqhTdv3nBthg0bhvfv3yMwMBBHjx7Fjh078OHDB4ntDhgwAB8+fMC5c+e4o42dO3dGYmJiCV6t/Clcdu7cCQAS/dL8/PzQpUsX2NnZSawvJycHLy8vPH36FI8ePYJYLMbff/+NIUOGoE6dOlLbV1dXh4KC7AHHZ86cQd++fdGjRw88ePAAAQEBaNWqVYny/tzs2bMxdepUhIWFYcCAAfj+++/h5+cnsc7+/fvh5uYGVVVV5ObmwtXVFRoaGrh+/Tpu3rwJdXV1dOvWrdAjjImJiXj69CkcHBwk4hkZGZg+fTqCg4MREBAAOTk59O3bV6qvy+c5urq6Yv/+/Vi4cCGWL1+OsLAwrFixAgsWLMDevXu5NhoaGtizZw+ePn2KjRs3wtvbG+vXry/yuWjatCnU1dULvXXv3r3QtkFBQdDW1pZ4jC4uLpCTk8OdO3cKbQfkP796enqwtrbGnDlzJP6+L168CLFYjHfv3qFJkyYwNjbGjz/+iOjoaIlt1KtXDwYGBrh+/XqR++JdmZZVVQAdESLf6u+//2ba2trcUSCBQMDmzJnDsrOzC21T6K8cvT+kb/OvFZ9EwCvZbQNefeOjk+Tk5MS+++477n5eXh5TU1NjHh4eXCwmJoYBYEFBQYwx6SNCgwYNYu3atStyH82bN5eIzZ07l1laWkocWdu8eTNTV1dnIpGo0G29ifvEXsblsjdxnxhj+Uc7NDQ02D///MMYYyw3N5fp6ekxHx8frs2gQYOYu7s7Y4yxrKwspqqqym7duiWx3VGjRrFBgwZJPL4TJ04UmkeBpk2bsj/++IMxxlhYWJjUr/Pnz58zANwRoevXrzNNTU2WlZUlsR1zc3O2ffv2QvcDgCkrKzM1NTUmJyfHALD69euzjx8/cusoKysX+is/JCSEAWAHDx5kcXFxDABbt25dsY/vS46OjmzIkCFF5lmSI0IbNmyQWOf48eMSR39SUlKYsrIyO3fuHGOMMV9fX6n3S3Z2NlNRUWH+/v4yc3nw4AEDwN68eVPkY4qPj2cAWGhoaJE5mpubMz8/P4nY0qVLmaOjY6HbXrNmDbO3ty9y/69evWLPnz8v9Pb27dtC2y5fvpxZWFhIxfX19dmWLVsKbbd9+3Z2/vx59vjxY7Zv3z5Wt25d1rdvX275ypUrmaKiIrO0tGTnz59nQUFBrHPnzszS0lLqc7B58+Zs8eLFRT7Gz/FxRKjmXkdIgJLPnUXI/92+fRsDBw7k7puYmMDX1xdOTk48ZlW+bG1tuf/Ly8tDV1cXNjY2XMzAIH++tS+PbBR4+PAhBgwYUOQ+7O3tJe6HhYXB0dFR4shau3btkJ6ejrdv3wIArKysuGVz587F3LlzEf8hDqt/W4o7t64i8WM8RCIRPn36xB2VUVBQwI8//oj9+/fDw8MDGRkZOHnyJP7++28A+RfA/PTpE7p06SKRT05ODpo3by4R+/JIQnp6OhYvXowzZ84gJiYGeXl5yMzM5PYdHh4OBQUFtGjRgmvTsGFD6OjocPcfPXqE9PR06OpKzkOYmZmJyMjIIp/D9evXw8XFBVFRUfDy8sKmTZtQq5bkPG2sBJ2cS7JOYR4+fIgxY8Z8dfsCXz63PXr0gKKiIk6dOoWBAwfi6NGj0NTUhIuLC4D85+3FixcS09cA+Vd1L+x5y8zMBACpK5A/f/4cCxcuxJ07d5CQkMAdCXrz5g2sra1l5piRkYHIyEiMGjVK4vHn5eVJDBo4ePAgNm3ahMjISKSnpyMvL6/YqSK+tf/S1/i8D5GNjQ2MjIzQuXNnREZGwtzcHGKxGLm5udi0aRO6du0KADhw4AAMDQ1x5coVib5CKioqhR4trixqbiFkqA4cc+M7C1LFtGnTBh4eHvD19YW7uzu2bt0q8UVWHX15dVeBQCARKyhWChsmq6KiUuw+1NTUSpVTnTp18PDhQ+5+wRf+9CljkJSUhMXL1sDBLn9klKOjo8TpkSFDhsDJyQkfPnzAxYsXoaKigm7dugEAd8rszJkz3HQoBYRCyYmZv8x55syZuHjxIn7//Xc0bNgQKioq+OGHH0rV+Ts9PR1GRkYIDAyUWlbcdCyGhoZo2LAhGjZsiN27d6NHjx54+vQpateuDQCwsLBAWFiYzLYFcQsLC+jr60NbWxvPnj0rcd4FinutBQKBVKElq6Pxl8+tkpISfvjhB/j5+WHgwIHw8/ODu7s7d4ouPT0d9vb2Mkco6evry8xFT08PAJCUlCSxTq9evWBqagpvb2/UqVMHYrEY1tbWUq/j5zkWvG+8vb3RunVrifUKRowGBQVhyJAhWLJkCVxdXaGlpYW///4ba9eulZlfgaZNm+L169eFLm/fvj3OnTsnc5mhoaHUD5S8vDwkJibC0NCwyP1+ruAxvXjxAubm5jAyMgIg+WNEX18fenp6EqeCgfxTkIW9BpVFzS2ECCkBsVgMOTnJrnR//vknevbsiR9//PHbL45YitnrJWgKZbfVFErHeGZra4uAgAAsWbKkxG2aNGmCo0ePgjHGPcc3b96EhoYGjI2NIScnh4YNG0q1C757G7+u+gOdXLrBpLYKoqOjkZCQILFO27ZtYWJigoMHD+LcuXMYMGAAV9hZWVlBKBTizZs3pT7Kd/PmTXh6enJ9jdLT0/Hq1StuuaWlJfLy8vDgwQPuCNiLFy+QlJTErdOiRQvExsZyc9F9rVatWsHe3h7Lly/Hxo0bAQADBw7EvHnz8OjRI4l+QmKxGOvXr4eVlRXs7OwgEAgwcOBA+Pr6YtGiRVL9hNLT06GsrCyzn1DBaz1ixAiZeenr6yMmJoa7//z58xIfLRgyZAi6dOmCJ0+e4PLlyxL9xVq0aIGDBw+idu3aJZ6M09zcHJqamnj69CksLCwAAB8/fkR4eDi8vb3Rvn17AMCNGzeK3ZaBgQHq1KmDqKgoDBkyROY6t27dgqmpqcRQ/aIKnAJnz54tclRaUcWno6MjkpOTcf/+fe49d/nyZYjFYqmCrSgFPzoKCqB27doByD/KaWxsDCC/4ElISJA4glVwRO7Lo6mVTpmeaKsCuD5CZXyOkVQ/kZGRzNHRkR08ePCbt1XUee/KTNbokS9HOTEm2ffjyz5C4eHhTElJiU2YMIE9evSIhYWFsS1btrD4+PhC9/H27VumqqrKJk6cyMLCwtiJEyeYnp4eW7RoUZH5WtvYse+cXFjA9RB2+/Zt1r59e6aioiKV77x585iVlRVTUFBg169fl1qmq6vL9uzZw168eMHu37/PNm3axPbs2SPz8RXo27cva9asGXvw4AF7+PAh69WrF9PQ0JB4bC4uLqxFixbszp07LCQkhDk7OzMVFRWuv4lYLGbfffcds7OzY/7+/uzly5fs5s2bbO7cuRJ9i74EGX1vzp49y4RCIdeHJDMzk7Vu3ZqZmJiwQ4cOsdevX7O7d+8yNzc3pqamxvXxYoyxjx8/ssaNGzNjY2O2d+9e9uTJExYREcF27tzJGjZsKPXYC1y5coXJycmxhQsXsqdPn7LHjx+z3377jVs+cOBA1qRJExYSEsLu3bvHOnXqxBQVFaX6CD148EBq22KxmJmYmDA7Oztmbm4usSwjI4M1atSIdezYkV27do1FRUWxK1eusMmTJ7Po6OhCn7d+/fqxGTNmcPdFIhHT1dVlQ4cOZc+fP2cBAQGsZcuWEs9vYTl6e3szFRUVtnHjRhYeHs4eP37Mdu3axdauXcsYY+zkyZNMQUGBHThwgL148YJt3LiR1apVi2lpaRWaX1no1q0ba968Obtz5w67ceMGa9SoEdffjbH8vzVLS0t2584dxhhjL168YL/++isLDg5mL1++ZCdPnmQNGjRgHTp0kNhunz59WNOmTdnNmzdZaGgo+/7775mVlZXEKLgrV65I9O0qCT76CFEhRMgXxGIx27t3L9PQ0GAAmLa2drEdKotTkwshxvKHwrdt25YJhUKmra3NXF1dueWFDdX9muHzZy/dYrbN7JlQWZk1atSIHT58WGa+BcOKTU1NpS51IBaL2YYNG5ilpSVTVFRk+vr6zNXVlV29erXQx8dY/hdkQWFjYmLC/vzzT6nH9v79e9a9e3cmFAqZqakp8/PzY7Vr12bbtm3j1klNTWWTJ09mderUYYqKiszExIQNGTKkyPegrEJILBazxo0bswkTJnCxjIwMNm/ePNawYUOmqKjIatWqxfr37891BP5ccnIymz17NmvUqBFTUlJiBgYGzMXFhR0/flzqOfvc0aNHWbNmzZiSkhLT09Nj/fr145a9e/eOde3alampqbFGjRqxs2fPyuwsLasQYix/+DkAtnDhQqllMTExbNiwYUxPT48JhULWoEEDNmbMmCK/NM+ePcvq1q0r0QH/4sWLrEmTJkwoFDJbW1sWGBhYokKIMcb279/PPXYdHR3WoUMHduzYMW75rFmzmK6uLlNXV2fu7u5s/fr15V4Iffz4kQ0aNIipq6szTU1NNmLECJaWlsYtL3g8V65cYYzlX4agQ4cOrFatWkwoFLKGDRuyWbNmST2PKSkpbOTIkUxbW5vVqlWL9e3bV+o9OnbsWDZu3LhS5ctHISRgrJpcIrSEUlNToaWlBaOUFLwv4SFUUnMkJSVh/PjxOHToEBdr0KABjh49imbNmn31drOysvDy5UuYmZlJdc4kZSf6QyZEUIQ8cmFSu/i+SXx7+/YtTExMcOnSJXTu3JnvdGocxhhat24NLy8vDBo0iO90qpWEhARYWloiODgYZmZmJW5X1Gdlwfd3SkpKiU+BlgRdR4iQ/wsMDIStra1EEeTp6YmHDx9+UxFESIHLly/j1KlTePnyJW7duoWBAweifv366NChA9+p1UgCgQA7duyoFFcGr25evXqFLVu2lKoI4gt1liY1Xk5ODhYuXIjVq1dzI1q0tbWxY8eOYod9E1Iaubm5mDt3LqKioqChoYG2bdti//79UiPzSMVp1qwZ/dApBw4ODlKXQaisqBAiNVpUVBQGDBiAkJAQLtaxY0f4+PjAxMSEx8xIdeTq6io1HxMhhF90aozUaCoqKtx1LxQVFbF69WoEBARQEUQIITVEzS2EPmQAQ2vuDOEkn5GREXbu3InGjRvj9u3bmDVrltR1gwghhFRfNfcTX8SAj5l8Z0Eq2KVLl/Dx40eJWO/evfH48WOJqQ8IIYTUDDW3ECI1SlZWFry8vNClSxeMGzdO6jL/1FmVEEJqJiqESLUXGhqKVq1aYcOGDQCAo0eP4vz58/wmRQghpFKgQohUW2KxGBs3bkTLli0RGhoKIH/izE2bNnGTbBJCCKnZam4hJFQAvjPmOwtSTmJiYtCjRw9MmzYN2dnZAAAbGxsEBwdj8uTJ3z5ZKiGk2urQoQP8/Pz4TqPamT17NiZPnsx3GlJqbiFUSxmY58h3FqQcnDp1Cra2tvD39+diXl5euHv3LqytrXnMjJCKERgYCIFAwN309fXRo0cP7sjo56KjozFy5EjUqVMHSkpKMDU1xdSpU6UGFQDAixcvMGLECBgbG0MoFMLMzAyDBg1CcHBwRTysCnHq1CnExcVh4MCBfKdSbh4/foz27dtDWVkZJiYmWL16dbFt7t27h86dO0NbWxs6OjpwdXXFo0ePuOXh4eFwdnaGgYEBlJWV0aBBA8yfPx+5ubncOjNnzsTevXsRFRVVLo/ra9XcQohUSzdv3kSfPn2QkJAAADA0NIS/vz/WrVtHc3xVYZ9/mFYFOTk5fKcAIP/LKSYmBv7+/sjOzkbPnj0lcouKioKDgwOeP3+OAwcO4MWLF9i2bRsCAgLg6OiIxMREbt3g4GDY29sjIiIC27dvx9OnT3H8+HE0btwYM2bMqLDHJBKJIBaLy237mzZtwogRI77pMhrlneO3SE1NRdeuXWFqaor79+9jzZo1WLx4MXbs2FFom/T0dHTr1g316tXDnTt3cOPGDWhoaMDV1ZX721RUVMSwYcNw4cIFhIeHY8OGDfD29saiRYu47ejp6cHV1RVbt24t98dZKmU6hWsVQLPPV29isZj17duXAWB9+vRh8fHxfKfEGCt8RuU2bf6Sum3YEFTs9oKComW2DQqKLtO8nZyc2KRJk9jUqVOZtrY2q127NtuxYwdLT09nnp6eTF1dnZmbm7OzZ89ybfLy8tjIkSNZ/fr1mbKyMrOwsGAbNmyQ2vbOnTuZlZUVN7v8xIkTuWUA2JYtW1ivXr2YqqoqW7RoEWOMsS1btrAGDRowRUVFZmFhwXx8fCS2+SbuE3sZl8vexH3iYj4+Psze3p6pq6szAwMDNmjQIBYXF8cYY0wkErG6deuyLVu2SGwnJCSECQQC9urVK8YYY0lJSWzUqFFMT0+PaWhoMGdnZ/bw4UNu/UWLFjE7Ozvm7e3N6tevzwQCAWOMsXPnzrF27doxLS0tVqtWLdazZ0/24sULiX3dvHmT2dnZMaFQyOzt7dnx48elZjcPDQ1l3bp1Y2pqaqx27dps6NChRb63r1y5wgCwpKQkLnbq1CkGgD169IiLdevWjRkbG7NPnz5JtI+JiWGqqqps/PjxjLH8v6umTZsye3t7iZnaC3y+ny+JRCK2atUqZm5uzpSUlJiJiQlbtmxZoXk+ePCAAWAvX75kjDG2e/dupqWlxU6ePMmaNGnC5OXl2fbt25lQKJTa75QpU5izszN3//r16+y7775jysrKzNjYmE2ePJmlp6cXmuuHDx+YQCBg//77r0R87dq1zNramqmqqjJjY2M2YcIEiRncZeX48uVLlpWVxWbMmMHq1KnDVFVVWatWrbhZ3hljLCEhgQ0cOJDVqVOHqaioMGtra+bn51dofmVhy5YtTEdHh2VnZ3OxX375hVlaWhba5t69ewyAxOzyjx8/ZgDY8+fPC23n5eXFvvvuO4nY3r17mbGxcaFt+Jh9ngohUqWJxWKpWEJCAtu9e7fMZXwp7I8bWCx18/I6X+z2zp9/LrPt+fOFfyh9DScnJ6ahocGWLl3KIiIi2NKlS5m8vDzr3r0727FjB4uIiGATJkxgurq6LCMjgzHGWE5ODlu4cCG7d+8ei4qKYvv27WOqqqrs4MGD3Ha3bNnClJWV2YYNG1h4eDi7e/cuW79+/WfPC1jt2rXZrl27WGRkJHv9+jU7duwYU1RUZJs3b2bh4eFs7dq1TF5enl2+fJlrJ6sQ2rlzJzt79iyLjIxkQUFBzNHRkXXv3p1bPnPmTKkP6xkzZkjEXFxcWK9evdi9e/dYREQEmzFjBtPV1WUfP35kjOUXQmpqaqxbt24sJCSEKzaOHDnCjh49yp4/f84ePHjAevXqxWxsbLhiIiUlhdWqVYsNHTqUPXnyhJ09e5ZZWFhIFEJJSUlMX1+fzZkzh4WFhbGQkBDWpUsXiS/8L31ZYCQnJ7PBgwczACwsLIwxxtjHjx+ZQCBgK1askLmNMWPGMB0dHSYWi1lISAgD8FVf0j///DPT0dFhe/bsYS9evGDXr19n3t7eMvNkTHYhpKioyNq2bctu3rzJnj17xtLT05mBgQH766+/uHZ5eXkSsRcvXjA1NTW2fv16FhERwW7evMmaN2/OPD09C8312LFjTE1NTarYW79+Pbt8+TJ7+fIlCwgIYJaWlmzChAncclk5ZmRksNGjR7O2bduya9eusRcvXrA1a9YwoVDIIiIiGGOMvX37lq1Zs4Y9ePCARUZGsk2bNjF5eXl2586dQnN8/fo1U1NTK/K2fPnyQtt7eHiwPn36SMQuX77MALDExESZbVJTU5muri5btGgRy87OZp8+fWJTp05lTZo0Ybm5uTLbPH/+nDVp0oTNmzdPIh4WFibx+n6JCqEKQIVQ9fHmzRvWsWNH9s8///CdSrGqciH0eUGQl5fH1NTUmIeHBxeLiYlhAFhQUOFHsiZOnMj69+/P3a9Tp47UB+TnALBp06ZJxNq2bcvGjBkjERswYADr0aMHd19WIfSlgl+3Bb/oHzx4wAQCAXv9+jVj7L+jRFu3bmWM5R9V0NTUZFlZWRLbMTc3Z9u3b2eM5RdCioqK7MOHD4XulzHG4uPjGQAWGhrKGGNs69atTFdXV+J94e3tLVEILV26lHXt2lViO9HR0QwACw8Pl7mfggKj4IsRAAPAevfuza1z+/ZtBoAdP35c5jbWrVvHALC4uDh28OBBBoCFhIQU+fi+lJqayoRCIVf4FJZncYUQAIkjcIwxNnXqVNapUyfuvr+/v8RRolGjRrGxY8dKtLl+/TqTk5OT+SXLWH7B06BBg2If1+HDh5muri53X1aOr1+/ZvLy8uzdu3cSbTt37szmzJlT6LZ79uzJZsyYUejy3Nxc9vz58yJvBQW6LF26dJF6Xp48ecIAsKdPnxbaLjQ0lJmbmzM5OTkmJyfHLC0tuSOmn3N0dGRCoZABYGPHjpUqKgu+gwMDA2Xuh49CiPoIkSrp0KFDsLW1RWBgIEaOHInY2Fi+U6q2bG1tuf/Ly8tDV1cXNjY2XMzAwAAA8OHDBy62efNm2NvbQ19fH+rq6tixYwc3p9uHDx/w/v17dO7cucj9fjlzdVhYGNq1aycRa9euHcLCwgAA+/fvR2MzfTQ100ZjM31cv34dAHD//n306tUL9erVg4aGBpycnACAy6dZs2Zo0qQJN0ro6tWr+PDhAwYMGAAAePToEdLT06Grqwt1dXXu9vLlS0RGRnK5mJqaQl9fXyK/58+fY9CgQWjQoAE0NTVRv359iX2Hh4fD1tZWov9aq1atJLbx6NEjXLlyRWLfjRs3BgCJ/cty/fp13L9/H3v27IGFhQW2bdsmtQ774uKispRkHVnCwsKQnZ1d7GtdHCUlJYn3IQAMGTIEgYGBeP/+PYD8179nz57Q1tYGkP+87dmzR+J5c3V1hVgsxsuXL2XuJzMzU2ZfwkuXLqFz586oW7cuNDQ04OHhgY8fP+LTp0+F5hgaGgqRSAQLCwuJHK5evcq9biKRCEuXLoWNjQ1q1aoFdXV1+Pv7c+8PWRQUFNCwYcMib7Vq1SrZE1tCmZmZGDVqFNq1a4fbt2/j5s2bsLa2Rs+ePZGZKTlDw8GDBxESEgI/Pz+cOXMGv//+u8RyFRUVAJB47vhGs8+TKiU1NRVTpkzB3r17uZiysjLev38PQ0NDHjOrvr686rZAIJCIFVyKoKBz6N9//42ZM2di7dq1cHR0hIaGBtasWYM7d+4A+O+DsDhqamqlyrN3796o19AOIihAHnlwsDVHRkYGN+P7/v37oa+vjzdv3sDV1VWi0/CQIUPg5+eH2bNnw8/PD926dYOuri6A/I6iRkZGCAwMlNpnwZduYfn26tULpqam8Pb2Rp06dSAWi2FtbV2qztTp6eno1asXVq1aJbXMyMioyLZmZmbQ1taGpaUlPnz4AHd3d1y7dg0A0LBhQwgEAoSFhaFv375SbcPCwqCjowN9fX1YWFgAAJ49e4bmzZuXOPfiXuuCDsmfF1qyOsarqKhIXfKiZcuWMDc3x99//40JEybg+PHj2LNnD7c8PT0d48aNw5QpU6S2V69ePZn56OnpISkpSSL26tUrfP/995gwYQKWL1+OWrVq4caNGxg1ahRycnKgqqoqM8f09HTIy8vj/v37kJeXl9imuro6AGDNmjXYuHEjNmzYABsbG6ipqWHatGlFvj/evHkDKyurQpcDwNy5czF37lyZywwNDREXFycRK7hf2Geon58fXr16haCgIO418/Pzg46ODk6ePCkxwq5gwmorKyuIRCKMHTsWM2bM4J6Dgg74X/5o4BMVQqTKCAoKwpAhQyR+zbm7u2Pr1q3Q0dHhMbOv16aN9LWsTE21im2npaUss62WFv8j427evIm2bdvip59+4mKfH7nQ0NBA/fr1ERAQAGdn5xJvt0mTJrh58yaGDx8usa+CLwUNDQ3UNzOHCIqQRy5UVFRw//59fPz4Eb/99hv3AS1rqPfgwYMxf/583L9/H0eOHJE4ctKiRQvExsZCQUGBO6JTEh8/fkR4eDi8vb3Rvn17AMCNGzck1rG0tMS+ffuQnZ0NoVAIIH+Y8udatGiBo0ePon79+lBQ+PqP7IkTJ2LlypU4fvw4+vbtC11dXXTp0gVbtmyBl5eXRNESGxuL/fv3Y9iwYRAIBGjWrBmsrKywdu1auLu7S42oSk5OligKCzRq1AgqKioICAjA6NGjpZYXfBnGxMRwf8MPHz4s8WMaMmQI9u/fD2NjY8jJyaFnz57cshYtWuDp06do2LBhibfXvHlzxMbGIikpicvn/v37EIvFWLt2Lfe4Dx06VKJtiUQifPjwgXv9v1QwynXo0KEA8n9MREREFFno1KlTp9jnqKgjQo6Ojpg3bx5yc3O5HzQXL16EpaVloZ+jnz59gpycnEShV3C/qNFxYrEYubm5EIvFXCH077//QlFREU2bNi3yMVSoMj3RVgVwfYQSEhl7mcx3OqQEcnNz2aJFi5i8vDzX10FDQ4P5+PhUqg7RRSnqvHdl5uTkxKZOnSoRMzU1lejYzBiT6GuyceNGpqmpyc6fP8/Cw8PZ/PnzmaamJrOzs+PW37NnD1NWVmYbN25kERER7P79+2zTpk0yt1fg+PHjTFFRkW3ZsoVFRERwnaU/H4XzZR+hDx8+MCUlJTZr1iwWGRnJTp48KdUZuUC7du2YnZ0d09DQkBhFJRaL2Xfffcfs7OyYv78/e/nyJbt58yabO3cuu3fvHmPsv1FjnxOJRExXV5cNHTqUPX/+nAUEBLCWLVtKPLaCztLDhg1jT58+ZefPn2eNGzeW6G/y7t07pq+vz3744Qd29+5d9uLFC3b+/Hnm6enJ8vLyZL5usvreMJbfcdnGxob7u4mIiGB6enqsffv27OrVq+zNmzfs3LlzzNramjVq1Eiir8mdO3eYhoYGa9u2LTtz5gyLjIxk/2vvzuNqTP//gb861TmddlJaVEqKUJJlypgs0WHGmGTLVoZB9rGNkVEYyxhZMtaZIRGS+SBkKWQqCS1MSpQSo2UslUr7+/eHX/fXcTop0qGu5+NxHg/nuq/7ut/3udV5d93XdV83b96kn3/+mb744osa4yAi8vb2phYtWtC+ffsoNTWVoqOjuQHNZWVlZGhoSCNHjqS7d+/SqVOnyMLCosZZYzW5d+8eASArKyuaPHmy2LabN2+SUCikmTNnUnx8PN29e5eOHz8uNjvxTRUVFaStrS027jAhIYEA0ObNmyktLY38/f3JwMBA7POVFuO4ceOobdu29Ndff9H9+/cpJiaG1qxZQ6dOnSKiV7OqDA0NKSoqipKSkmjKlCmkrq4uMZi5IeXl5VHr1q1pwoQJlJiYSIcPHyZlZWVuvBvRq0Hjr88iS05OJoFAQB4eHpSUlESJiYk0fvx40tDQoMePHxMR0YEDBygwMJCSkpIoLS2NAgMDSV9fn8aNGyd2fC8vL7GxXW9ig6UbAZcI3fmXSHRE1uEwb5Genk52dnZcAgSA7O3t6f79+7IOrV6aUyJUUlJC7u7upKGhQZqamuTh4UFLliyRSBR27txJFhYWpKioSHp6ejR79uwa23vdu0yfP3jwILVt25YEAgHZ2dlx08jfTIS2b99OAGjixIkSxy0oKKDZs2eTvr4+KSoqkqGhIY0bN46bTlxTIkREFBoaSh07diSBQEBWVlYUHh4ucW5RUVFkZWVFfD6fbG1t6eDBgwSA7ty5w9W5e/cuOTs7k6amJgmFQurQoQPNmzdP6h8C0hKhzMxMUlBQEJvBl5GRQW5ubtS6dWvu3GbPnk1PnjyRaDclJYUmTpxI+vr6xOfzydjYmFxdXWsdRF1ZWUk///wzGRsbk6KiIhkZGYnNVIuMjKQuXbqQkpIS9enTh4KCguqcCBER9ezZkwCIzR6sdu3aNRo4cCCpqqqSiooKWVlZ1TqjiuhVsjhmzBixso0bN5Kenh4JhUJycnIif3//OiVC1TMo27Zty/0/d3Z2plu3bhHRq5l7w4YNI1VVVdLR0aFly5bRxIkTP2giRPQqSfz8889JIBCQgYEBrVu3Tmx79eDv150/f557FESLFi2of//+YhMkDh8+TN26deM+a0tLS1qzZo3E7zwLCws6dOiQ1NhkkQjJEb3jKLhPVEFBATQ0NKB35188nhcFnBkp65CYWmRmZsLKygr5+fmQl5fH8uXLsXTp0ve6RSALJSUlSE9Ph4mJCXuw4wf0MPcld2vMUKduY5E+NgEBAZg0aRLy8/PrPJ6KaTjZ2dno1KkT4uLiYGxsLOtwmpQzZ85gwYIFuHXrltTf4bX9rqz+/s7Pz4e6unqDxcVmjTEfNSMjI+zcuROmpqaIjIzE8uXLP7kkiGFq4+/vj8jISKSnp+P48eP44YcfMGrUKJYEyYiuri7+/PPPWmduMe+mqKgIe/fu/eh+h39c0TDNXkREBKytrcWy/TFjxuCbb75hPSlMk5SdnY3ly5cjOzsbenp6GDlyJFavXi3rsJq1b775RtYhNEkjRoyQdQg1+ih6hLZt24a2bdtCSUkJvXr1wrVr16TWrZ6B0aJFC7Ro0QKOjo611mc+DWVlZViyZAkcHBxqXJ2YJUFMU7V48WJkZGRwtwQ2bdrETclmGObDk3kiFBgYiPnz58PLywtxcXGwtraGk5OT2MPZXhceHg5XV1dcunQJ0dHRMDQ0xKBBg/Dvv//W78DqAmBu97fXYz64lJQU2NnZ4ZdffgERwd/fH+fPn5d1WAzDMEwzIPNEaOPGjfjuu+8wadIkWFpaYufOnVBWVsaePXtqrB8QEIAZM2aga9eu6NChA/744w9UVVXhwoUL9TuwiiIgMmmAM2DeFRFh165dsLGxQVxcHIBXD+9bv349HB0dZRwdwzAM0xzIdIxQWVkZYmNj8eOPP3JlPB4Pjo6OiI6OrlMbxcXFKC8vl/oAqdLSUpSWlnLvCwoK3i9opkH8999/mDJlCoKDg7kyCwsLHDx4EN26dZNhZAzDMExzItMeoSdPnqCyspJbq6ha69at67x21A8//AB9fX2pPQhr166FhoYG96p+uiwjO+fOnYOVlZVYEuTh4YG4uDiWBDEMwzCNSua3xt7HunXrcPjwYRw7dkzqYNoff/wR+fn53Ovhw4eNHCXzuoiICIhEIi7RbdWqFYKDg7F9+3Y2QJRhGIZpdDJNhFq1agV5efkaF4B72wKaGzZswLp163D+/HmJVYlfJxAIoK6uLvZiZOfzzz+HSCQCAIhEIvzzzz8YOnSojKNiGIZhmiuZJkJ8Ph+2trZiA52rBz7b2dlJ3W/9+vVYtWoVzp49i+7d2cyvT4mcnBz27t2L7du3IyQkhK0YzzRbx48fh5mZGeTl5TFv3rx67+/n51fjQqcfuz///BODBg2SdRhNTlJSEtq0aYOioiJZh/LJkfmtsfnz5+P333/Hvn37kJycDA8PDxQVFWHSpEkAgIkTJ4oNpv7ll1/w008/Yc+ePWjbti2ys7ORnZ2NwsJCWZ0CI0V2dja+/PJLiRl9urq68PDwEFvJmPk4ubu7Q05ODnJyclBUVISJiQkWL16MkpISibqnTp2Cg4MD1NTUoKysjB49esDPz6/Gdv/66y/07dsXGhoaUFVVhZWVFVauXIlnz5594DP6eEybNg0jRozAw4cPsWrVKlmHU2+ZmZn48ssvoaysDB0dHSxatAgVFRW17lNSUoKffvoJXl5ejRRl49u9ezf69u0LdXV1yMnJIS8vr077ve15eiUlJZg5cya0tLSgqqoKFxcXsbsplpaW+Oyzz7Bx48aGPJ3moUFXLntHW7duJSMjI+Lz+dSzZ0+6evUqt83BwYHc3Ny498bGxmILcFa/vLy86nQsbtHVBl60jRF34sQJatWqFQEgAwODGhdwbE4+1UVX3dzcSCQSUVZWFmVmZtKxY8dIXV2dFi9eLFbP19eXeDwe/fjjj3T79m26d+8ebdiwgQQCAS1YsECs7tKlS0leXp4WLlxIUVFRlJ6eTufPn6fhw4fT5s2b3yvemhZdlaa0tPS9jvU+Xrx4IXWh0Lp622KkH1JFRQV17tyZHB0dKT4+nkJCQqhVq1b0448/1rrf/v37xVY1f1dlZWXv3caHsmnTJlq7di2tXbu2xoVva3L48GHi8/m0Z88eun37Nn333XekqalJOTk5XJ3p06eToaEhXbhwgW7cuEGfffYZ2dvbi7Vz6tQp0tPTo/Ly8oY+rUbDVp9vBFwi9O9/RH+lyDqcJqewsJCmTZsmlqTq6enRjRs3ZB2aTH3KidCbK2EPHz6cbGxsuPeZmZmkqKhI8+fPl9jf19eXAHB/3MTExBAAqQlPbV8aDx8+pDFjxlCLFi1IWVmZbG1tuXar43w9EZo7dy45ODhw+zs4ONDMmTNp7ty5pKWlRX379iVXV1caNWqU2HHKyspIS0uL9u3bR0SvVk9fs2YNtW3blpSUlMjKyoqCgoKkxklE9OzZM5owYQK3WrxIJKK7d+8S0f+tDP/669KlS1I/j6lTp5KOjg4JBALq1KkTnTx5kogkE6HU1FT6+uuvSUdHh1RUVKh79+4UGhoq1t62bdvIzMyMBAIB6ejokIuLC7ctKCiIOnfuTEpKStSyZUsaMGAAFRYW1hhXSEgI8Xg8ys7O5sp27NhB6urqtSaYX375JS1cuFCs7Nq1a+To6EhaWlqkrq5OX3zxBcXGxorVAUDbt2+noUOHkrKyMveH7/Hjx8nGxoYEAgGZmJiQt7e3WBLg4+NDnTt3JmVlZWrTpg15eHjQixcvpMbXkKqvc10SoZ49e9LMmTO595WVlaSvr09r164lIqK8vDxSVFQU+3+XnJxMAMRWgC8tLSWBQEBhYWENdyKNTBaJkMxvjcnMizLg95uyjqJJiY2Nha2tLXbt2sWVffPNN7h16xZsbW1lGNnHqTuANjJ4vc+ousTERFy5cgV8Pp8rO3r0KMrLy7Fw4UKJ+tOmTYOqqioOHToE4NUDUVVVVTFjxowa25c25qWwsBAODg74999/ERwcjJs3b2Lx4sWoqqqqV/z79u0Dn89HVFQUdu7ciXHjxuHkyZNit9bPnTuH4uJiODs7A3j1CA5/f3/s3LkTt2/fxvfff4/x48fj8uXLUo/j7u6OGzduIDg4GNHR0SAiDBkyBOXl5bC3t0dKSgqAV7cIs7KyYG9vL9FGVVUVBg8ejKioKBw4cABJSUlYt24d5OXlpX5GQ4YMwYULFxAfHw+RSIShQ4dyi4feuHEDc+bMwcqVK5GSkoKzZ8/iiy++AABkZWXB1dUV3377LZKTkxEeHo7hw4eDiGo8VnR0NLp06SL26BMnJycUFBTg9u3bUj+XyMhIiXGdL168gJubGyIjI3H16lW0b98eQ4YMwYsXL8TqeXt7w9nZGf/88w++/fZbREREYOLEiZg7dy6SkpKwa9cu+Pn5ia3TxuPx4Ovri9u3b2Pfvn24ePEiFi9eLDU+ABg8eDBUVVWlvjp16lTr/vVV/Ty91x8B8+bz9GJjY1FeXi5Wp0OHDjAyMhJ75h6fz0fXrl0RERHRoDE2eQ2aVn0CuB6hO/8SiY7IOpwmoaKigtatW0cKCgrcX7jKysr0+++/U1VVlazD+yjU9FeOAb3qkm3sl0E94nZzcyN5eXlSUVEhgUBAAIjH49HRo0e5OtOnT6/1Fo2VlRUNHjyYiIgGDx5MVlZW9YjglV27dpGamho9ffpUapx16RF6vSeLiKi8vJxatWpF/v7+XJmrqyuNHj2aiIhKSkpIWVmZrly5Irbf5MmTydXVtcZY7t69SwAoKiqKK3vy5AkJhUI6cuTV75znz5/X2hNERHTu3Dni8XiUklJzz3Vdbo116tSJtm7dSkREf/31F6mrq1NBQYFEvdjYWAJAGRkZtbZX7bvvvqNBgwaJlRUVFREACgkJqXGf6nP++++/a227srKS1NTUuJ4volc9QvPmzROrN2DAAFqzZo1Y2f79+0lPT09q20FBQaSlpVXr8R89ekT37t2T+qrrZ1TXHqF///2XAEj8H1u0aBH17NmTiIgCAgKIz+dL7NujRw+J29TOzs7k7u5epxg/RrLoEWKrzzPv5dGjR5gwYQLCw8O5MltbWxw8eBDm5uayC+wTIKv5cvU9br9+/bBjxw4UFRVh06ZNUFBQgIuLyzsdm6T0MLxNQkICbGxspD5Bvq7e7JlUUFDAqFGjEBAQgAkTJqCoqAgnTpzA4cOHAQCpqakoLi7GwIEDxfYrKyuDjY1NjcdITk6GgoICevXqxZVpaWnBwsICycnJdY41ISEBbdq0qfPPUWFhIby9vXH69GlkZWWhoqICL1++5HqEBg4cCGNjY5iamkIkEkEkEsHZ2RnKysqwtrbGgAED0KVLFzg5OWHQoEEYMWIEWrRoUed43+bly5cAJBdQzsnJwbJlyxAeHo7c3FxUVlaiuLiYi7vamz1JN2/eRFRUlFgPUGVlJUpKSlBcXAxlZWWEhYVh7dq1uHPnDgoKClBRUSG2vSYGBgYNcboyIxQKUVxcLOswPiksEWLey8uXL3H9+nUAr6bGL1myBN7e3mK3Tpia3ZB1AHWkoqICMzMzAMCePXtgbW2NP//8E5MnTwYAmJubIz8/H48fP4a+vr7YvmVlZUhLS0O/fv24upGRkSgvL4eiomKdYxAKhbVu5/F4EklWeXl5jefypnHjxsHBwQG5ubkIDQ2FUCjknnVVfcvs9OnTEl+QAoGgzvG/i7ed85sWLlyI0NBQbNiwAWZmZhAKhRgxYgTKysoAAGpqaoiLi0N4eDjOnz+P5cuXw9vbG9evX4empiZCQ0Nx5coVnD9/Hlu3boWnpydiYmJgYiK5JqOurq7ErKbqGUzSHomhpaUFOTk5PH/+XKzczc0NT58+xZYtW2BsbAyBQAA7Ozsu7mpvXrvCwkKsWLECw4cPlziWkpISMjIy8NVXX8HDwwOrV69Gy5YtERkZicmTJ6OsrExqIjR48OBaby0ZGxvXevuvvuryPD1dXV2UlZUhLy9P7PZxTc/ce/bsGdq1a9dg8TUHzXeMENMg2rdvD19fXxgaGuLSpUtYs2YNS4KaMB6Ph6VLl2LZsmXcX/guLi5QVFSEj4+PRP2dO3eiqKgIrq6uAICxY8eisLAQ27dvr7F9aVONrayskJCQIHV6vba2NrKyssTKEhIS6nRO9vb2MDQ0RGBgIAICAjBy5EguSbO0tIRAIEBmZibMzMzEXtKW6+nYsSMqKioQExPDlT19+hQpKSmwtLSsU0zAq3N+9OgR7t69W6f6UVFRcHd3h7OzM7p06QJdXV1kZGSI1VFQUICjoyPWr1+PW7duISMjAxcvXgTw6g+Z3r17Y8WKFYiPjwefz8exY8dqPJadnR3++ecf5ObmcmWhoaFQV1eXeo58Ph+WlpZISkqSiHvOnDkYMmQIOnXqBIFAgCdPnrz1fLt164aUlBSJ62JmZgYej4fY2FhUVVXBx8cHn332GczNzfH48eO3tvvHH38gISFB6iskJOStbdRHXZ6nZ2trC0VFRbE6KSkpyMzMlHjmXmJiotTeSkaKBr3R9gngxgg9e06UWyTrcD45MTExVFQk/rlVVVU12kyMT1VTmjVWXl5OBgYG9Ouvv3JlmzZtIh6PR0uXLqXk5GRKTU0lHx+fGqfPL168mOTl5WnRokV05coVysjIoLCwMBoxYoTU2WSlpaVkbm5Offr0ocjISEpLS6OjR49y4yrOnj1LcnJytHHr73QxOonmLviR1NXVJcYIzZ07t8b2PT09ydLSkhQUFCgiIkJim5aWFvn5+VFqairFxsaSr68v+fn5Sf3chg0bRpaWlhQREUEJCQkkEonIzMyMm/ZdlzFCRER9+/alzp070/nz5+n+/fsUEhJCZ86cISLJMULOzs7UtWtXio+Pp4SEBBo6dCipqalx53zy5EnasmULxcfHU0ZGBm3fvp14PB4lJibS1atXafXq1XT9+nV68OABHTlyhPh8vtTxPtXT5wcNGkQJCQl09uxZ0tbWfuv0+fnz54vNVCMisrGxoYEDB1JSUhJdvXqV+vTpQ0KhkDZt2sTVAUDHjh0T2+/s2bOkoKBA3t7elJiYSElJSXTo0CHy9PQkIqKEhARuhmJaWhr5+/uTgYFBnWdyvausrCyKj4+n33//nRsTFR8fLza+rX///tzYLaJX0+cFAgH5+flRUlISTZ06lTQ1NcVm5U2fPp2MjIzo4sWLdOPGDbKzsyM7OzuxY6enp5OcnFydxzF9jNj0+UbAniP0bsrLy8nb25vk5eXJw8ND1uF8cppSIkREtHbtWtLW1habXn3ixAnq06cPqaiokJKSEtna2tKePXtqbDcwMJC++OILUlNTIxUVFbKysqKVK1fW+gWVkZFBLi4upK6uTsrKytS9e3eKiYnhti9fvpy0tXVITV2DpkybTbNmzapzIpSUlEQAyNjYWGKAf1VVFW3evJksLCxIUVGRtLW1ycnJiS5fviw11urp8xoaGiQUCsnJyYmbPk9U90To6dOnNGnSJNLS0iIlJSXq3LkznTp1iogkE6H09HTq168fCYVCMjQ0pN9++03snCMiIsjBwYFatGhBQqGQrKysKDAwkDt/Jycn0tbWJoFAQObm5mJf1DXJyMigwYMHk1AopFatWtGCBQve+vya27dvk1AopLy8PK4sLi6OunfvTkpKStS+fXsKCgoiY2PjtyZCRK+SIXt7exIKhaSurk49e/ak3bt3c9s3btxIenp63DXw9/f/4ImQl5dXjc+627t3L1fH2NhY4tl3tT1Pj+jV75AZM2Zwj49wdnamrKwssTpr1qwhJyenD3VqjUIWiZAc0TuOXvxEFRQUQENDA3r5+XjM1h2rk/v372P8+PFi0zQvXrzIjftg3q6kpATp6ekwMTGRukAw8/4e5r5EJRQhj3IY6tRvjA3TOEaOHIlu3bqJrRjAvL+ysjK0b98eBw8eRO/evWUdzjur7Xdl9fd3fn5+g64bysYIMVIREfz9/dG1a1cuCZKXl8eKFSvQp08fGUfHMMyn6Ndff4Wqqqqsw2hyMjMzsXTp0k86CZIVNmuMqdHz58/h4eGBwMBArszU1BQBAQH47LPPZBgZwzCfsrZt22L27NmyDqPJqR4oztQf6xFiJFy+fBnW1tZiSZC7uzsSEhJYEsQwDMM0KaxHiBFz+fJl9OvXj3smS4sWLbBr1y6MHDlSxpExDMMwTMNjPUKMmM8//5xbf6hfv364desWS4IYhmGYJov1CDFi5OXlsX//fgQFBWHevHng8ViuzDAMwzRdzfdb7tlLwLN5r9D733//wcXFBVFRUWLlhoaGmD9/PkuCGIZhmCav+fYIlVYCcdmyjkJmzp07B3d3d2RnZyMuLg43b95s0OcyMAzDMMyngP3J38yUlJRg3rx5EIlEyM5+lQgWFhbWeT0jhmEYhmlKWCLUjPzzzz/o0aMHtmzZwpWJRCL8888/6N69uwwjY5jm5/jx4zAzM4O8vDzmzZtX7/39/PzEViL/VFy4cAEdO3ZEZWWlrENpUpKSktCmTRsUFRXJOpRPDkuEmoGqqips2bIFPXr0QGJiIgBAIBDA19cXISEh0NXVlXGEzMfK3d0dcnJykJOTg6KiIkxMTLB48WKUlJRI1D116hQcHBygpqYGZWVl9OjRA35+fjW2+9dff6Fv377Q0NCAqqoqrKyssHLlSqmryzdF06ZNw4gRI/Dw4UOsWrVK1uHU25w5c2BrawuBQICuXbvWeb/Fixdj2bJlkJeX/3DByRARYfny5dDT04NQKISjoyPu3btX6z7e3t7cz1n1q0OHDmJ1SkpKMHPmTGhpaUFVVRUuLi7IycnhtltaWuKzzz7Dxo0bP8h5NWXNNxFS4AFGTX9MTFZWFoYMGYJ58+ahtLQUANClSxfcuHEDs2fPhpycnIwjZD52IpEIWVlZuH//PjZt2oRdu3bBy8tLrM7WrVsxbNgw9O7dGzExMbh16xbGjBmD6dOnY+HChWJ1PT09MXr0aPTo0QNnzpxBYmIifHx8cPPmTezfv7/RzqusrKzRjvWmwsJC5ObmwsnJCfr6+lBTU5NZLO/j22+/xejRo+tcPzIyEmlpaXBxcXmv48ry2r3N+vXr4evri507dyImJgYqKipwcnKq8Y+H13Xq1AlZWVncKzIyUmz7999/j5MnTyIoKAiXL1/G48ePMXz4cLE6kyZNwo4dO1BRUdHg59WkNegSrp+A5rb6fGJiIgkEAm4F5O+///6TWwG9KWhKq88PHz6cbGxsuPeZmZmkqKhI8+fPl9jf19eXAHAracfExBAA2rx5c43Hq21V8IcPH9KYMWO41bdtbW25dqvjzMwppvSccsrMKaa5c+dKrD4/c+ZMmjt3LmlpaVHfvn3J1dWVRo0aJXacsrIy0tLSon379hERUWVlJa1Zs4batm1LSkpKZGVlRUFBQVLjJPq/1ec1NTVJKBSSSCTiVp+/dOmSxMrk0lahf/78OU2dOpV0dHRIIBBQp06d6OTJk0Qkufp8amoqff3116Sjo0MqKirUvXt3Cg0NFWtv27ZtZGZmRgKBgHR0dMjFxYXbFhQURJ07dyYlJSVq2bIlDRgwgAoLC2s9T6JXq61bW1u/tR4R0cyZM2nEiBFiZXWJ29jYmFauXEkTJkwgNTU1cnNzIyKiiIgI+vzzz0lJSYnatGlDs2fPFovZ39+fbG1tSVVVlVq3bk2urq6Uk5NTp1jfRVVVFenq6tKvv/7KleXl5ZFAIKBDhw5J3e9tn2FeXh4pKiqK/b9LTk4mABQdHc2VlZaWkkAgoLCwsPc7ERmSxerzzXfWWDPRqVMn/Prrr1izZg327duHQYMGyTokptqB7kCRDGYuqugC42+8066JiYm4cuUKjI2NubKjR4+ivLxcoucHeHX7Z+nSpTh06BB69eqFgIAAqKqqYsaMGTW2L23MS2FhIRwcHGBgYIDg4GDo6uoiLi4OVVVV9Yp/37598PDw4B4ZkZqaipEjR6KwsJBbCPTcuXMoLi6Gs7MzAGDt2rU4cOAAdu7cifbt2+Pvv//G+PHjoa2tDQcHhxqP4+7ujnv37iE4OBjq6ur44YcfMGTIECQlJcHe3h4pKSmwsLDAX3/9BXt7e7Rs2VKijaqqKgwePBgvXrzAgQMH0K5dOyQlJUm9pVRYWIghQ4Zg9erVEAgE8Pf3x9ChQ5GSkgIjIyPcuHEDc+bMwf79+2Fvb49nz54hIuLVI0SysrLg6uqK9evXw9nZGS9evEBERAT3hPmGEhERgbFjx9Yr7mobNmzA8uXLud7ItLQ0iEQi/Pzzz9izZw/+++8/zJo1C7NmzcLevXsBAOXl5Vi1ahUsLCyQm5uL+fPnw93dHSEhIVJjnD59Og4cOFDreRQWFtZYnp6ejuzsbDg6OnJlGhoa6NWrF6KjozFmzBipbd67dw/6+vpQUlKCnZ0d1q5dy51/bGwsysvLxdrt0KEDjIyMEB0dzS19xOfz0bVrV0RERGDAgAG1ngPzf1gi1MTcvHkTHTp0gEAg4MpmzZqF8ePHo0WLFjKMjJFQlA0U/ivrKN7q1KlTUFVVRUVFBUpLS8Hj8fDbb79x2+/evQsNDQ3o6elJ7Mvn82FqasrNSrx37x5MTU2hqKhYrxgOHjyI//77D9evX+eShndZYLJ9+/ZYv349975du3ZQUVHBsWPHMGHCBO5YX3/9NdTU1FBaWoo1a9YgLCwMdnZ2AF4tPhwZGYldu3bVmAhVJ0BRUVGwt7cHAAQEBMDQ0BDHjx/HyJEjoaOjAwBo2bKl1DF6YWFhuHbtGpKTk2Fubs4dWxpra2tYW1tz71etWoVjx44hODgYs2bNQmZmJlRUVPDVV19BTU0NxsbGsLGxAfAqEaqoqMDw4cO5JLdLly51+1Dr4cGDB9DX169X3NX69++PBQsWcO+nTJmCcePGcQPN27dvD19fXzg4OGDHjh1QUlLCt99+y9U3NTWFr68vevToIZb4vmnlypU1JvV1UT0Tt3Xr1mLlrVu35rbVpFevXvDz84OFhQWysrKwYsUK9OnTB4mJiVBTU0N2djb4fL7EHwo1tauvr48HDx68U/zNFUuEmojKykps2LABy5Ytw9y5c7FhwwZum5ycHEuCPkYqMhqkXs/j9uvXDzt27EBRURE2bdoEBQWFdx7j8a49DAkJCbCxsamx56Q+bG1txd4rKChg1KhRCAgIwIQJE1BUVIQTJ07g8OHDAF71GBUXF2PgwIFi+5WVlXFJxJuSk5OhoKCAXr16cWVaWlqwsLBAcnJynWNNSEhAmzZtuCTobQoLC+Ht7Y3Tp09zic3Lly+RmZkJABg4cCCMjY1hamoKkUgEkUgEZ2dnKCsrw9raGgMGDECXLl3g5OSEQYMGYcSIEQ3+e+Ply5dQUlKqV9zV3pzZevPmTdy6dQsBAQFcGRGhqqoK6enp6NixI2JjY+Ht7Y2bN2/i+fPnXA9iZmYmLC0ta4xRR0eHS1Qby+DBg7l/W1lZoVevXjA2NsaRI0cwefLkerUlFApRXFzc0CE2aSwRagIePnyICRMm4PLlywAAHx8ffPPNN/j8889lHBlTq3e8PdXYVFRUuN6XPXv2wNraGn/++Sf3C9rc3Bz5+fl4/PixxF/7ZWVlSEtLQ79+/bi6kZGRKC8vr1evkFAorHU7j8eTSLLKy8trPJc3jRs3Dg4ODsjNzUVoaCiEQiFEIhGA/7sFcvr0aRgYGIjt93qv64fwtnN+08KFCxEaGooNGzbAzMwMQqEQI0aM4AYWq6mpIS4uDuHh4Th//jyWL18Ob29vXL9+HZqamggNDcWVK1dw/vx5bN26FZ6enoiJiYGJiUmDnVOrVq3w/PnzesVd7c1rV1hYiGnTpmHOnDkSxzEyMkJRURGcnJzg5OSEgIAAaGtrIzMzE05OTrUOtn6fW2PVvXs5OTliPaQ5OTn1mlmnqakJc3NzpKamcu2WlZUhLy9PrFcoJydHokfx2bNnaNeuXZ2PxTTnWWNNxJEjR2BlZcUlQXJycvjxxx/Rs2dPGUfGNEU8Hg9Lly7FsmXL8PLlSwCAi4sLFBUV4ePjI1F/586dKCoqgqurKwBg7NixKCwsxPbt22tsPy8vr8ZyKysrJCQkSJ1er62tjaysLLGyhISEOp2Tvb09DA0NERgYiICAAIwcOZJL0iwtLSEQCJCZmQkzMzOxl6GhYY3tdezYERUVFYiJieHKnj59ipSUFKm9EDWxsrLCo0eP6vyw06ioKLi7u8PZ2RldunSBrq4uMjIyxOooKCjA0dER69evx61bt5CRkYGLFy8CePW7o3fv3lixYgXi4+PB5/Nx7NixOsdbFzY2NkhKSqp33DXp1q0bkpKSJK6LmZkZ+Hw+7ty5g6dPn2LdunXo06cPOnTogNzc3Le2u3LlSiQkJNT6ksbExAS6urq4cOECV1ZQUICYmBju1mpdFBYWIi0tjUumbG1toaioKNZuSkoKMjMzJdpNTEyU2lvJSNGgQ68/AU1l1lh+fj65ubmJzTwxNDSk8PBwWYfG1KApzRorLy8nAwMDsZkxmzZtIh6PR0uXLqXk5GRKTU0lHx8fEggEtGDBArH9Fy9eTPLy8rRo0SK6cuUKZWRkUFhYGI0YMULqbLLS0lIyNzenPn36UGRkJKWlpdHRo0fpypUrRER09uxZkpOTo41bf6eL0Uk0d8GPpK6uLjFrbO7cuTW27+npSZaWlqSgoEARERES27S0tMjPz49SU1MpNjaWfH19yc/PT+rnNmzYMLK0tKSIiAhKSEggkUhEZmZmVFZWRkSvZoOhltli1fr27UudO3em8+fP0/379ykkJITOnDlDRJKzxpydnalr164UHx9PCQkJNHToUFJTU+PO+eTJk7RlyxaKj4+njIwM2r59O/F4PEpMTKSrV6/S6tWr6fr16/TgwQM6cuQI8fl8CgkJkRrbvXv3KD4+nqZNm0bm5uYUHx9P8fHxVFpaKnUfX19fsrW1FSt7W9xEr2aNbdq0SWy/mzdvklAopJkzZ1J8fDzdvXuXjh8/TjNnziQiotzcXOLz+bRo0SJKS0ujEydOkLm5OQGg+Pj4Wj/397Fu3TrS1NSkEydO0K1bt2jYsGFkYmIi9rPfv39/2rp1K/d+wYIFFB4eTunp6RQVFUWOjo7UqlUrys3N5epMnz6djIyM6OLFi3Tjxg2ys7MjOzs7sWOnp6eTnJwcZWRkfLDz+9BkMWuMJUKfoCtXrpCpqalYEjR69Gh69uyZrENjpGhKiRAR0dq1a0lbW1tsqvKJEyeoT58+pKKiQkpKSmRra0t79uypsd3AwED64osvSE1NjVRUVMjKyopWrlxZ6/T5jIwMcnFxIXV1dVJWVqbu3btTTEwMt3358uWkra1DauoaNGXabJo1a1adE6GkpCQCQMbGxlRVVSW2raqqijZv3kwWFhakqKhI2tra5OTkRJcvX5Yaa/X0eQ0NDRIKheTk5MRNnyeqeyL09OlTmjRpEmlpaZGSkhJ17tyZTp06RUSSiVB6ejr169ePhEIhGRoa0m+//SZ2zhEREeTg4EAtWrQgoVBIVlZWFBgYyJ2/k5MTaWtrk0AgIHNzc7Ev6po4ODhIPAYAAKWnp9d6PkpKSnTnzp06x01UcyJERHTt2jUaOHAgqaqqcv+PVq9ezW0/ePAgtW3blgQCAdnZ2VFwcPAHT4Sqqqrop59+otatW5NAIKABAwZQSkqKWB1jY2Py8vLi3o8ePZr09PSIz+eTgYEBjR49mlJTU8X2efnyJc2YMYN7fISzszNlZWWJ1VmzZg05OTl9sHNrDLJIhOSIGnh+5EeuoKDg1QyX/Hw8/gQXGQ0PD4ejoyP3eHo1NTVs27YN48ePZw9H/IiVlJQgPT0dJiYmEoNFmYbzMPclKqEIeZTDUKd+Y2yYxrFo0SIUFBRg165dsg6lSSkrK0P79u1x8OBB9O7dW9bhvLPafldWf3/n5+c36CLhzXeMUFYhMDhI1lHUW+/evbmZL/b29rh58yYmTJjAkiCGYT4Jnp6eMDY2rvczoJjaZWZmYunSpZ90EiQrbNbYJ0ZRUREBAQEIDAzEDz/8AAUFdgkZhvl0aGpqYunSpbIOo8mpHijO1F/z7RH6BDx//hzjxo1DbGysWLmZmRk8PT1ZEsQwDMMw74l9k36kwsPDMWHCBDx69AixsbGIi4uDsrKyrMNiGIZhmCaF9Qh9ZMrKyrBkyRL0798fjx49AgDk5ubi9u3bMo6MYRiGYZqe5tsjpKwIfFO3R9c3lpSUFIwdOxZxcXFcWb9+/eDv7482bdrIMDKGYRiGaZqab4+QhgCYZv32eo2AiLBr1y7Y2NhwSZCioiLWr1+PsLAwlgQxDMMwzAfSfHuEPhL//fcfpkyZguDgYK7MwsICBw8eRLdu3WQYGcMwDMM0fc23R+gj8fDhQ4SEhHDvPTw8EBcXx5IghmEYhmkELBGSsW7duuHnn39Gq1atEBwcjO3bt7PZYQzTDBw/fhxmZmaQl5fHvHnz6r2/n5+f2Erkn4o///wTgwYNknUYTc7Zs2fRtWtX9qDKd8ASoUZ2584dlJeXi5UtXLgQt2/fxtChQ2UUFcPUzN3dHXJycpCTk4OioiJMTEywePFilJSUSNQ9deoUHBwcoKamBmVlZfTo0QN+fn41tvvXX3+hb9++0NDQgKqqKqysrLBy5Uqpq8s3RdOmTcOIESPw8OFDrFq1Stbh1MvNmzfh6uoKQ0NDCIVCdOzYEVu2bHnrfiUlJfjpp5/g5eXVCFHKRklJCWbOnAktLS2oqqrCxcUFOTk5te7z+s9Z9UskEonVefbsGcaNGwd1dXVoampi8uTJKCws5LaLRCLugbtM/bBEqJFUVVVhy5Yt6Nq1K37++WexbfLy8tDR0ZFRZAxTO5FIhKysLNy/fx+bNm3Crl27JL7Itm7dimHDhqF3796IiYnBrVu3MGbMGEyfPh0LFy4Uq+vp6YnRo0ejR48eOHPmDBITE+Hj44ObN29i//79jXZeZWVljXasNxUWFiI3NxdOTk7Q19eHmpqazGJ5F7GxsdDR0cGBAwdw+/ZteHp64scff8Rvv/1W635Hjx6Furr6ey8D8eYfkx+T77//HidPnkRQUBAuX76Mx48fY/jw4W/dr/rnrPp16NAhse3jxo3D7du3ERoailOnTuHvv//G1KlTxeq4u7vD19e3Qc+nWWjQJVw/AbJYff7x48fk5OTErc7M4/HEVs1mmr6mtPr88OHDycbGhnufmZlJioqKNH/+fIn9fX19CQBdvXqViIhiYmIIAG3evLnG49W2+vzDhw9pzJgx3Orbtra2XLvVcWbmFFN6Tjll5hTT3LlzJVafnzlzJs2dO5e0tLSob9++5OrqSqNGjRI7TllZGWlpadG+ffuIiKiyspLWrFlDbdu2JSUlJbKysqKgoCCpcRL93+rzmpqaJBQKSSQScavPX7p0SWLFdmmr0D9//pymTp1KOjo6JBAIqFOnTnTy5Ekiklx9PjU1lb7++mvS0dEhFRUV6t69O4WGhoq1t23bNjIzMyOBQEA6Ojrk4uLCbQsKCqLOnTuTkpIStWzZkgYMGECFhYW1nufrZsyYQf369au1zpdffkkLFy4UK7t27Ro5OjqSlpYWqaur0xdffEGxsbFidQDQ9u3baejQoaSsrMyt3H78+HGysbEhgUBAJiYm5O3tTeXl5dx+Pj4+1LlzZ1JWVqY2bdqQh4cHvXjxos7nVF95eXmkqKgo9v8jOTmZAFB0dLTU/Wr6OXtdUlISAaDr169zZWfOnCE5OTn6999/ubIHDx4QAImV6z8lslh9ns0a+8BOnDiBKVOm4MmTJ1zZnDlzYGVlJcOomI/Bz0H5yC9u/Pv5Gso8LBup8U77JiYm4sqVKzA2NubKjh49ivLycomeH+DV7Z+lS5fi0KFD6NWrFwICAqCqqooZM2bU2L60MS+FhYVwcHCAgYEBgoODoauri7i4uHqPh9i3bx88PDwQFRUFAEhNTcXIkSNRWFgIVVVVAMC5c+dQXFwMZ2dnAMDatWtx4MAB7Ny5E+3bt8fff/+N8ePHQ1tbGw4ODjUex93dHffu3UNwcDDU1dXxww8/YMiQIUhKSoK9vT1SUlJgYWGBv/76C/b29mjZsqVEG1VVVRg8eDBevHiBAwcOoF27dkhKSoK8vLzUz2jIkCFYvXo1BAIB/P39MXToUKSkpMDIyAg3btzAnDlzsH//ftjb2+PZs2eIiIgAAGRlZcHV1RXr16+Hs7MzXrx4gYiICBBRnT/b/Pz8Gs/jdZGRkZgwYYJY2YsXL+Dm5oatW7eCiODj44MhQ4bg3r17Yj1l3t7eWLduHTZv3gwFBQVERERg4sSJ8PX1RZ8+fZCWlsb1kFT3WPJ4PPj6+sLExAT379/HjBkzsHjxYmzfvl1qjIMHD+Y+l5oYGxtLfcBtbGwsysvL4ejoyJV16NABRkZGiI6OxmeffSa13fDwcOjo6KBFixbo378/fv75Z2hpaQEAoqOjoampie7du3P1HR0dwePxEBMTw/1fNTIyQuvWrREREYF27dpJPRYjrvkmQiUVwN1soLvuB2m+qKgICxYswK5du7gyXV1d7Nu3jw0UZAAA+cVVyCuq+xdNw6lf8nDq1CmoqqqioqICpaWl4PF4YrdA7t69Cw0NDejp6Unsy+fzYWpqirt37wIA7t27B1NTUygqKtYrhoMHD+K///7D9evXuS/bd1lgsn379li/fj33vl27dlBRUcGxY8e4L+iDBw/i66+/hpqaGkpLS7FmzRqEhYXBzs4OAGBqaorIyEjs2rWrxkSoOgGKioqCvb09ACAgIACGhoY4fvw4Ro4cyd0Kb9myJXR1a/4dFBYWhmvXriE5ORnm5ubcsaWxtraGtfX/PRtt1apVOHbsGIKDgzFr1ixkZmZCRUUFX331FdTU1GBsbAwbGxsArxKhiooKDB8+nEtyu3TpUrcPFcCVK1cQGBiI06dPS62Tl5eH/Px86Ovri5X3799f7P3u3buhqamJy5cv46uvvuLKx44di0mTJnHvv/32WyxZsgRubm4AXn02q1atwuLFi7lE6PVB6G3btsXPP/+M6dOn15oI/fHHH3j58qXU7bX9383Ozgafz5dI6Fu3bo3s7Gyp+4lEIgwfPhwmJiZIS0vD0qVLMXjwYERHR0NeXh7Z2dkSwycUFBTQsmVLiXb19fXx4MEDqcdiJDXfROh5CfBTFHBmZIM3HRsbi7Fjx3K//AFg2LBh+OOPP9CqVasGPx7zadJQ5qG+SUnDHbfu+vXrhx07dqCoqAibNm2CgoICXFxc3unY9elheF1CQgJsbGze2uPwNra2tmLvFRQUMGrUKAQEBGDChAkoKirCiRMncPjwYQCveoyKi4sxcOBAsf3Kysq4JOJNycnJUFBQQK9evbgyLS0tWFhYIDk5uc6xJiQkoE2bNlwS9DaFhYXw9vbG6dOnucTm5cuXyMzMBAAMHDgQxsbGMDU1hUgkgkgkgrOzM5SVlWFtbY0BAwagS5cucHJywqBBgzBixAi0aNHircdNTEzEsGHD4OXlVesfedXJhZKSklh5Tk4Oli1bhvDwcOTm5qKyshLFxcVc3NVe7w0BXg3YjoqKwurVq7myyspKlJSUoLi4GMrKyggLC8PatWtx584dFBQUoKKiQmx7TQwMDN56zg1tzJgx3L+7dOkCKysrtGvXDuHh4RgwYEC92hIKhSguLm7oEJu05psIfSAXL16Ek5MTKioqAADKysrYvHkzpkyZAjk5ORlHx3xM3vX2VGNTUVHhel/27NkDa2tr/Pnnn5g8eTIAwNzcHPn5+Xj8+LHEX/tlZWVIS0tDv379uLqRkZEoLy+vV6+QUCisdTuPx5NIsmoaUKuioiJRNm7cODg4OCA3NxehoaEQCoXcjJ3qWTmnT5+W+IIUCAR1jv9dvO2c37Rw4UKEhoZiw4YNMDMzg1AoxIgRI7hB4WpqaoiLi0N4eDjOnz+P5cuXw9vbG9evX4empiZCQ0Nx5coVnD9/Hlu3boWnpydiYmJgYmIi9ZhJSUkYMGAApk6dimXLltUan5aWFuTk5PD8+XOxcjc3Nzx9+hRbtmyBsbExBAIB7OzsJAazv3ntCgsLsWLFihoHIispKSEjIwNfffUVPDw8sHr1arRs2RKRkZGYPHkyysrKpCZC73NrTFdXF2VlZcjLyxPrFcrJyZHa81cTU1NTtGrVCqmpqRgwYAB0dXWRm5srVqeiogLPnj2TaPfZs2fQ1tau87EYNmuswfXu3RuWlpYAXv31GR8fj++++44lQUyTwOPxsHTpUixbtoz7C9/FxQWKiorw8fGRqL9z504UFRXB1dUVwKvbG4WFhVJvTeTl5dVYbmVlhYSEBKnT67W1tZGVlSVWlpCQUKdzsre3h6GhIQIDAxEQEICRI0dySZqlpSUEAgEyMzNhZmYm9jI0NKyxvY4dO6KiogIxMTFc2dOnT5GSksL9bqgLKysrPHr0SKxnuTZRUVFwd3eHs7MzunTpAl1dXWRkZIjVUVBQgKOjI9avX49bt24hIyMDFy9eBADIycmhd+/eWLFiBeLj48Hn83Hs2DGpx7t9+zb69esHNzc3sV4Zafh8PiwtLZGUlCQR95w5czBkyBB06tQJAoFAbEylNN26dUNKSorEdTEzMwOPx0NsbCyqqqrg4+ODzz77DObm5nj8+PFb2/3jjz+QkJAg9fX6A3DfZGtrC0VFRVy4cIErS0lJQWZmJndrtS4ePXqEp0+fcreb7ezskJeXh9jYWK7OxYsXUVVVJdbzWFJSgrS0NKm9lYwUDTr0+hPAzRq78y+R6MgHOUZiYiJ5enpSaWnpB2mf+fQ0pVlj5eXlZGBgQL/++itXtmnTJuLxeLR06VJKTk6m1NRU8vHxIYFAQAsWLBDbf/HixSQvL0+LFi2iK1euUEZGBoWFhdGIESOkziYrLS0lc3Nz6tOnD0VGRlJaWhodPXqUrly5QkREZ8+eJTk5Odq49Xe6GJ1Ecxf8SOrq6hKzxubOnVtj+56enmRpaUkKCgoUEREhsU1LS4v8/PwoNTWVYmNjydfXl/z8/KR+bsOGDSNLS0uKiIighIQEEolEZGZmRmVlZUT0ajYYapktVq1v377UuXNnOn/+PN2/f59CQkLozJkzRCQ5a8zZ2Zm6du1K8fHxlJCQQEOHDiU1NTXunE+ePElbtmyh+Ph4ysjIoO3btxOPx6PExES6evUqrV69mq5fv04PHjygI0eOEJ/Pp5CQkBrj+ueff0hbW5vGjx9PWVlZ3Cs3N7fW85k/f77YTDUiIhsbGxo4cCAlJSXR1atXqU+fPiQUCmnTpk1cHQB07Ngxsf3Onj1LCgoK5O3tTYmJiZSUlESHDh0iT09PIiJKSEjgZiimpaWRv78/GRgYEIBaZye+r+nTp5ORkRFdvHiRbty4QXZ2dmRnZydWx8LCgv73v/8REdGLFy9o4cKFFB0dTenp6RQWFkbdunWj9u3bU0lJCbePSCQiGxsbiomJocjISGrfvj25urqKtXvp0iVSVVWloqKiD3Z+H5osZo2xROg925oyZQolJiY2UHRMU9WUEiEiorVr15K2trbY9OoTJ05Qnz59SEVFhZSUlMjW1pb27NlTY7uBgYH0xRdfkJqaGqmoqJCVlRWtXLmy1i+ojIwMcnFxIXV1dVJWVqbu3buLPYZi+fLlpK2tQ2rqGjRl2myaNWtWnROh6unJxsbGVFVVJbatqqqKNm/eTBYWFqSoqEja2trk5OREly9flhpr9fR5DQ0NEgqF5OTkxE2fJ6p7IvT06VOaNGkSaWlpkZKSEnXu3JlOnTpFRJKJUHp6OvXr14+EQiEZGhrSb7/9JnbOERER5ODgQC1atCChUEhWVlYUGBjInb+TkxNpa2uTQCAgc3Nz2rp1q9S4vLy8JB4BUP351eb27dskFAopLy+PK4uLi6Pu3buTkpIStW/fnoKCgsjY2PitiRDRq2TI3t6ehEIhqaurU8+ePWn37t3c9o0bN5Kenh53Dfz9/T94IvTy5UuaMWMG95gHZ2dnysrKEqsDgPbu3UtERMXFxTRo0CDS1tYmRUVFMjY2pu+++46ys7PF9nn69Cm5urqSqqoqqaur06RJkyQeBTB16lSaNm3aBzu3xiCLREiO6B1HL36iCgoKXs1wyXmKx5ll7zxrLDo6GuPHj8f9+/dhZWWFa9euffAxA8ynq6SkBOnp6TAxMZEYLMo0nIe5L1EJRcijHIY69RtjwzSOkSNHolu3bvjxxx9lHUqT8uTJE1hYWODGjRu1juv62NX2u7L6+zs/Px/q6uoNdszmO0ZISeGdkqCKigqsWLECffr0wf379wEA6enpuHXrVkNHyDAM0+T8+uuv3DObmIaTkZGB7du3f9JJkKywWWP1cP/+fYwfPx7R0dFcmb29PQ4cOMD+8zEMw9RB27ZtMXv2bFmH0eR0795d4hEDTN003x6heiAi+Pv7o2vXrlwSJC8vjxUrVuDy5cssCWIYhmGYTxTrEXqL58+fw8PDA4GBgVyZqakpAgICan1cOsMwDMMwHz/WI/QWycnJCAoK4t67u7sjISGBJUHMO2lmcxMYhmHqRRa/I1ki9Bb29vbw9PSEpqYmjhw5gr1794otBMgwdVH9gD726HuGYRjpqp8oLm1x4Q+h+U6fz8/H4xqm36Wnp8PIyEjsIpSXlyM3N1cma9AwTUdWVhby8vKgo6MDZWVl9rTxDyDraQmqoAAeKqCnxR5TwDCfkqqqKjx+/BiKioowMjKS+B35oabPszFC/x8RYffu3fj+++/h5eWFH374gdumqKjIkiDmvVWvCfTmmkFMw3n+ohxVkAcPlSgpqN8K9wzDyB6Px6sxCfqQmm+PUGYuHoc8BqZZ47///sOUKVMQHBwM4NV6PNeuXWPrtTAfRGVlZY0LgjLvb/3BNLyAFtTwFIvHtpN1OAzD1BOfzwePV/OonSbdI7Rt2zb8+uuvyM7OhrW1NbZu3YqePXtKrR8UFISffvoJGRkZaN++PX755RcMGTKkfgctLgeO38W5ttlwd3dHdnY2t2nKlCmwsLB419NhmFrJy8s36v3v5uRFCQ954APgsSd4MwxTJzIfLB0YGIj58+fDy8sLcXFxsLa2hpOTk9TbB1euXIGrqysmT56M+Ph4fPPNN/jmm2+QmJhYr+NSSSnmJftBJBJxSVCrVq0QHByMHTt2QFlZ+b3PjWEYhmGYj5vMb4316tULPXr0wG+//Qbg1WApQ0NDzJ49G0uWLJGoP3r0aBQVFeHUqVNc2WeffYauXbti586dbz1eddeafHsLVN5L4cpFIhH27t3LjeNgGObTs2h7MvLQGprIwa8zOso6HIZhGlCTXGusrKwMsbGxcHR05Mp4PB4cHR3FlrF4XXR0tFh9AHBycpJaX5rqJEggEMDX1xchISEsCWIYhmGYZkamY4SePHmCyspKtG7dWqy8devWuHPnTo37ZGdn11j/9TE+rystLUVpaSn3Pj8/n/t3S912cBr/M+6VtcPcjTfe9TQYhvlIFJAWSK4ApShEQUGBrMNhGKYBVf9MN/SNrI9isPSHtHbtWqxYsaLGbc+y03Bog2sjR8QwTGPYulDWETAM8yE8ffoUGhoaDdaeTBOhVq1aQV5eHjk5OWLlOTk5Um9T6erq1qv+jz/+iPnz53Pv8/LyYGxsjMzMzAb9IJn6KygogKGhIR4+fNig93uZd8Oux8eDXYuPB7sWH4/8/HwYGRmhZcuWDdquTBMhPp8PW1tbXLhwAd988w2AV4OlL1y4gFmzZtW4j52dHS5cuIB58+ZxZaGhobCzs6uxvkAggEAgkCjX0NBg/6k/Eurq6uxafETY9fh4sGvx8WDX4uMh7TlD70rmt8bmz58PNzc3dO/eHT179sTmzZtRVFSESZMmAQAmTpwIAwMDrF27FgAwd+5cODg4wMfHB19++SUOHz6MGzduYPfu3bI8DYZhGIZhPkEyT4RGjx6N//77D8uXL0d2dja6du2Ks2fPcgOiMzMzxbI/e3t7HDx4EMuWLcPSpUvRvn17HD9+HJ07d5bVKTAMwzAM84mSeSIEALNmzZJ6Kyw8PFyibOTIkRg5cuQ7HUsgEMDLy6vG22VM42LX4uPCrsfHg12Ljwe7Fh+PD3UtZP5ARYZhGIZhGFmR+RIbDMMwDMMwssISIYZhGIZhmi2WCDEMwzAM02yxRIhhGIZhmGarSSZC27ZtQ9u2baGkpIRevXrh2rVrtdYPCgpChw4doKSkhC5duiAkJKSRIm366nMtfv/9d/Tp0wctWrRAixYt4Ojo+NZrx9RPfX82qh0+fBhycnLcg0+Z91ffa5GXl4eZM2dCT08PAoEA5ubm7HdVA6nvtdi8eTMsLCwgFAphaGiI77//HiUlJY0UbdP1999/Y+jQodDX14ecnByOHz/+1n3Cw8PRrVs3CAQCmJmZwc/Pr/4Hpibm8OHDxOfzac+ePXT79m367rvvSFNTk3JycmqsHxUVRfLy8rR+/XpKSkqiZcuWkaKiIv3zzz+NHHnTU99rMXbsWNq2bRvFx8dTcnIyubu7k4aGBj169KiRI2+a6ns9qqWnp5OBgQH16dOHhg0b1jjBNnH1vRalpaXUvXt3GjJkCEVGRlJ6ejqFh4dTQkJCI0fe9NT3WgQEBJBAIKCAgABKT0+nc+fOkZ6eHn3//feNHHnTExISQp6envS///2PANCxY8dqrX///n1SVlam+fPnU1JSEm3dupXk5eXp7Nmz9Tpuk0uEevbsSTNnzuTeV1ZWkr6+Pq1du7bG+qNGjaIvv/xSrKxXr140bdq0Dxpnc1Dfa/GmiooKUlNTo3379n2oEJuVd7keFRUVZG9vT3/88Qe5ubmxRKiB1Pda7Nixg0xNTamsrKyxQmw26nstZs6cSf379xcrmz9/PvXu3fuDxtnc1CURWrx4MXXq1EmsbPTo0eTk5FSvYzWpW2NlZWWIjY2Fo6MjV8bj8eDo6Ijo6Oga94mOjharDwBOTk5S6zN18y7X4k3FxcUoLy9v8AX2mqN3vR4rV66Ejo4OJk+e3BhhNgvvci2Cg4NhZ2eHmTNnonXr1ujcuTPWrFmDysrKxgq7SXqXa2Fvb4/Y2Fju9tn9+/cREhKCIUOGNErMzP9pqO/vj+LJ0g3lyZMnqKys5JbnqNa6dWvcuXOnxn2ys7NrrJ+dnf3B4mwO3uVavOmHH36Avr6+xH90pv7e5XpERkbizz//REJCQiNE2Hy8y7W4f/8+Ll68iHHjxiEkJASpqamYMWMGysvL4eXl1RhhN0nvci3Gjh2LJ0+e4PPPPwcRoaKiAtOnT8fSpUsbI2TmNdK+vwsKCvDy5UsIhcI6tdOkeoSYpmPdunU4fPgwjh07BiUlJVmH0+y8ePECEyZMwO+//45WrVrJOpxmr6qqCjo6Oti9ezdsbW0xevRoeHp6YufOnbIOrdkJDw/HmjVrsH37dsTFxeF///sfTp8+jVWrVsk6NOYdNakeoVatWkFeXh45OTli5Tk5OdDV1a1xH11d3XrVZ+rmXa5FtQ0bNmDdunUICwuDlZXVhwyz2ajv9UhLS0NGRgaGDh3KlVVVVQEAFBQUkJKSgnbt2n3YoJuod/nZ0NPTg6KiIuTl5bmyjh07Ijs7G2VlZeDz+R805qbqXa7FTz/9hAkTJmDKlCkAgC5duqCoqAhTp06Fp6en2CLhzIcl7ftbXV29zr1BQBPrEeLz+bC1tcWFCxe4sqqqKly4cAF2dnY17mNnZydWHwBCQ0Ol1mfq5l2uBQCsX78eq1atwtmzZ9G9e/fGCLVZqO/16NChA/755x8kJCRwr6+//hr9+vVDQkICDA0NGzP8JuVdfjZ69+6N1NRULhkFgLt370JPT48lQe/hXa5FcXGxRLJTnaASW7qzUTXY93f9xnF//A4fPkwCgYD8/PwoKSmJpk6dSpqampSdnU1ERBMmTKAlS5Zw9aOiokhBQYE2bNhAycnJ5OXlxabPN5D6Xot169YRn8+no0ePUlZWFvd68eKFrE6hSanv9XgTmzXWcOp7LTIzM0lNTY1mzZpFKSkpdOrUKdLR0aGff/5ZVqfQZNT3Wnh5eZGamhodOnSI7t+/T+fPn6d27drRqFGjZHUKTcaLFy8oPj6e4uPjCQBt3LiR4uPj6cGDB0REtGTJEpowYQJXv3r6/KJFiyg5OZm2bdvGps9X27p1KxkZGRGfz6eePXvS1atXuW0ODg7k5uYmVv/IkSNkbm5OfD6fOnXqRKdPn27kiJuu+lwLY2NjAiDx8vLyavzAm6j6/my8jiVCDau+1+LKlSvUq1cvEggEZGpqSqtXr6aKiopGjrppqs+1KC8vJ29vb2rXrh0pKSmRoaEhzZgxg54/f974gTcxly5dqvE7oPrzd3NzIwcHB4l9unbtSnw+n0xNTWnv3r31Pq4cEevLYxiGYRimeWpSY4QYhmEYhmHqgyVCDMMwDMM0WywRYhiGYRim2WKJEMMwDMMwzRZLhBiGYRiGabZYIsQwDMMwTLPFEiGGYRiGYZotlggxDCPGz88Pmpqasg7jncnJyeH48eO11nF3d8c333zTKPEwDPNxY4kQwzRB7u7ukJOTk3ilpqbKOjT4+flx8fB4PLRp0waTJk1Cbm5ug7SflZWFwYMHAwAyMjIgJyeHhIQEsTpbtmyBn59fgxxPGm9vb+485eXlYWhoiKlTp+LZs2f1aoclbQzzYTWp1ecZhvk/IpEIe/fuFSvT1taWUTTi1NXVkZKSgqqqKty8eROTJk3C48ePce7cufduW9qq4a/T0NB47+PURadOnRAWFobKykokJyfj22+/RX5+PgIDAxvl+AzDvB3rEWKYJkogEEBXV1fsJS8vj40bN6JLly5QUVGBoaEhZsyYgcLCQqnt3Lx5E/369YOamhrU1dVha2uLGzducNsjIyPRp08fCIVCGBoaYs6cOSgqKqo1Njk5Oejq6kJfXx+DBw/GnDlzEBYWhpcvX6KqqgorV65EmzZtIBAI0LVrV5w9e5bbt6ysDLNmzYKenh6UlJRgbGyMtWvXirVdfWvMxMQEAGBjYwM5OTn07dsXgHgvy+7du6Gvry+2sjsADBs2DN9++y33/sSJE+jWrRuUlJRgamqKFStWoKKiotbzVFBQgK6uLgwMDODo6IiRI0ciNDSU215ZWYnJkyfDxMQEQqEQFhYW2LJlC7fd29sb+/btw4kTJ7jepfDwcADAw4cPMWrUKGhqaqJly5YYNmwYMjIyao2HYRhJLBFimGaGx+PB19cXt2/fxr59+3Dx4kUsXrxYav1x48ahTZs2uH79OmJjY7FkyRIoKioCANLS0iASieDi4oJbt24hMDAQkZGRmDVrVr1iEgqFqKqqQkVFBbZs2QIfHx9s2LABt27dgpOTE77++mvcu3cPAODr64vg4GAcOXIEKSkpCAgIQNu2bWts99q1awCAsLAwZGVl4X//+59EnZEjR+Lp06e4dOkSV/bs2TOcPXsW48aNAwBERERg4sSJmDt3LpKSkrBr1y74+flh9erVdT7HjIwMnDt3Dnw+nyurqqpCmzZtEBQUhKSkJCxfvhxLly7FkSNHAAALFy7EqFGjIBKJkJWVhaysLNjb26O8vBxOTk5QU1NDREQEoqKioKqqCpFIhLKysjrHxDAM0CRXn2eY5s7NzY3k5eVJRUWFe40YMaLGukFBQaSlpcW937t3L2loaHDv1dTUyM/Pr8Z9J0+eTFOnThUri4iIIB6PRy9fvqxxnzfbv3v3Lpmbm1P37t2JiEhfX59Wr14ttk+PHj1oxowZREQ0e/Zs6t+/P1VVVdXYPgA6duwYERGlp6cTAIqPjxer4+bmRsOGDePeDxs2jL799lvu/a5du0hfX58qKyuJiGjAgAG0Zs0asTb2799Penp6NcZAROTl5UU8Ho9UVFRISUmJW0l748aNUvchIpo5cya5uLhIjbX62BYWFmKfQWlpKQmFQjp37lyt7TMMI46NEWKYJqpfv37YsWMH915FRQXAq96RtWvX4s6dOygoKEBFRQVKSkpQXFwMZWVliXbmz5+PKVOmYP/+/dztnXbt2gF4ddvs1q1bCAgI4OoTEaqqqpCeno6OHTvWGFt+fj5UVVVRVVWFkpISfP755/jjjz9QUFCAx48fo3fv3mL1e/fujZs3bwJ4dVtr4MCBsLCwgEgkwldffYVBgwa912c1btw4fPfdd9i+fTsEAgECAgIwZswY8Hg87jyjoqLEeoAqKytr/dwAwMLCAsHBwSgpKcGBAweQkJCA2bNni9XZtm0b9uzZg8zMTLx8+RJlZWXo2rVrrfHevHkTqampUFNTEysvKSlBWlraO3wCDNN8sUSIYZooFRUVmJmZiZVlZGTgq6++goeHB1avXo2WLVsiMjISkydPRllZWY1f6N7e3hg7dixOnz6NM2fOwMvLC4cPH4azszMKCwsxbdo0zJkzR2I/IyMjqbGpqakhLi4OPB4Penp6EAqFAICCgoK3nle3bt2Qnp6OM2fOICwsDKNGjYKjoyOOHj361n2lGTp0KIgIp0+fRo8ePRAREYFNmzZx2wsLC7FixQoMHz5cYl8lJSWp7fL5fO4arFu3Dl9++SVWrFiBVatWAQAOHz6MhQsXwsfHB3Z2dlBTU8Ovv/6KmJiYWuMtLCyEra2tWAJa7WMZEM8wnwqWCDFMMxIbG4uqqir4+PhwvR3V41FqY25uDnNzc3z//fdwdXXF3r174ezsjG7duiEpKUki4XobHo9X4z7q6urQ19dHVFQUHBwcuPKoqCj07NlTrN7o0aMxevRojBgxAiKRCM+ePUPLli3F2qsej1NZWVlrPEpKShg+fDgCAgKQmpoKCwsLdOvWjdverVs3pKSk1Ps837Rs2TL0798fHh4e3Hna29tjxowZXJ03e3T4fL5E/N26dUNgYCB0dHSgrq7+XjExTHPHBkszTDNiZmaG8vJybN26Fffv38f+/fuxc+dOqfVfvnyJWbNmITw8HA8ePEBUVBSuX7/O3fL64YcfcOXKFcyaNQsJCQm4d+8eTpw4Ue/B0q9btGgRfvnlFwQGBiIlJQVLlixBQkIC5s6dCwDYuHEjDh06hDt37uDu3bsICgqCrq5ujQ+B1NHRgVAoxNmzZ5GTk4P8/Hypxx03bhxOnz6NPXv2cIOkqy1fvhz+/v5YsWIFbt++jeTkZBw+fBjLli2r17nZ2dnBysoKa9asAQC0b98eN27cwLlz53D37l389NNPuH79utg+bdu2xa1bt5CSkoInT56gvLwc48aNQ6tWrTBs2DBEREQgPT0d4eHhmDNnDh49elSvmBim2ZP1ICWGYRpeTQNsq23cuJH09PRIKBSSk5MT+fv7EwB6/vw5EYkPZi4tLaUxY8aQoaEh8fl80tfXp1mzZokNhL527RoNHDiQVFVVSUVFhaysrCQGO7/uzcHSb6qsrCRvb28yMDAgRUVFsra2pjNnznDbd+/eTV27diUVFRVSV1enAQMGUFxcHLcdrw2WJiL6/fffydDQkHg8Hjk4OEj9fCorK0lPT48AUFpamkRcZ8+eJXt7exIKhaSurk49e/ak3bt3Sz0PLy8vsra2lig/dOgQCQQCyszMpJKSEnJ3dycNDQ3S1NQkDw8PWrJkidh+ubm53OcLgC5dukRERFlZWTRx4kRq1aoVCQQCMjU1pe+++47y8/OlxsQwjCQ5IiLZpmIMwzAMwzCywW6NMQzDMAzTbLFEiGEYhmGYZoslQgzDMAzDNFssEWIYhmEYptliiRDDMAzDMM0WS4QYhmEYhmm2WCLEMAzDMEyzxRIhhmEYhmGaLZYIMQzDMAzTbLFEiGEYhmGYZoslQgzDMAzDNFssEWIYhmEYptn6f+y9M+xh7AQcAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["x_test = test_ds\n","y_test = labels_test\n","y_score = model.predict(x_test)\n","y_test = y_test\n","\n","n_classes = num_classes\n","# Plot linewidth.\n","lw = 2\n","# Compute ROC curve and ROC area for each class\n","fpr = dict()\n","tpr = dict()\n","roc_auc = dict()\n","for i in range(n_classes):\n","    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n","    roc_auc[i] = auc(fpr[i], tpr[i])\n","\n","# Compute micro-average ROC curve and ROC area\n","fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n","roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n","\n","# First aggregate all false positive rates\n","all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n","\n","# Then interpolate all ROC curves at this points\n","mean_tpr = np.zeros_like(all_fpr)\n","for i in range(n_classes):\n","    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n","\n","# Finally average it and compute AUC\n","mean_tpr /= n_classes\n","\n","fpr[\"macro\"] = all_fpr\n","tpr[\"macro\"] = mean_tpr\n","roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n","\n","# Plot all ROC curves\n","plt.figure(1)\n","plt.plot(\n","    fpr[\"micro\"],\n","    tpr[\"micro\"],\n","    label=\"micro-average ROC curve (area = {0:0.2f})\" \"\".format(roc_auc[\"micro\"]),\n","    color=\"deeppink\",\n","    linestyle=\":\",\n","    linewidth=4,\n",")\n","\n","plt.plot(\n","    fpr[\"macro\"],\n","    tpr[\"macro\"],\n","    label=\"macro-average ROC curve (area = {0:0.2f})\" \"\".format(roc_auc[\"macro\"]),\n","    color=\"navy\",\n","    linestyle=\":\",\n","    linewidth=4,\n",")\n","\n","colors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\n","for i, color in zip(range(n_classes), colors):\n","    plt.plot(\n","        fpr[i],\n","        tpr[i],\n","        color=color,\n","        lw=lw,\n","        label=\"ROC curve of class {0} (area = {1:0.2f})\" \"\".format(i, roc_auc[i]),\n","    )\n","\n","plt.plot([0, 1], [0, 1], \"k--\", lw=lw)\n","plt.xlim([0.0, 1.0])\n","plt.ylim([0.0, 1.05])\n","plt.xlabel(\"False Positive Rate\")\n","plt.ylabel(\"True Positive Rate\")\n","plt.title(\"Receiver operating characteristic multi-class\")\n","plt.legend(loc=\"lower right\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e4ru8n2UfMmz","outputId":"cc58fbbf-bc66-45f5-e3d2-409e6b1f369d"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[1 0 0]\n"," [1 0 0]\n"," [0 1 0]]\n","              precision    recall  f1-score   support\n","\n","           0       0.50      1.00      0.67         1\n","           1       0.00      0.00      0.00         1\n","           2       0.00      0.00      0.00         1\n","\n","    accuracy                           0.33         3\n","   macro avg       0.17      0.33      0.22         3\n","weighted avg       0.17      0.33      0.22         3\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]}],"source":["y_classes = np.argmax(y_score, axis=1)\n","labels_test_f1 = np.argmax(y_test, axis=1)\n","\n","print(metrics.confusion_matrix(labels_test_f1, y_classes))\n","print(classification_report(labels_test_f1, y_classes))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mh0Jy3kHfMm0","outputId":"8672b047-e59c-418e-e992-a3d8eb06a389"},"outputs":[{"name":"stdout","output_type":"stream","text":["AUC:  1.0\n"]}],"source":["fpr, tpr, thresholds = metrics.roc_curve(labels_test_f1, y_classes, pos_label=2)\n","print(\"AUC: \", metrics.auc(fpr, tpr))"]}],"metadata":{"accelerator":"TPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.9.17"}},"nbformat":4,"nbformat_minor":0}