brain-tumor-detection-using-inceptionresnetv2.ipynb

{"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":377107,"sourceType":"datasetVersion","datasetId":165566}],"dockerImageVersionId":30636,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/mukaffimoin/brain-tumor-detection-using-inceptionresnetv2?scriptVersionId=158715226\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import Input\nfrom tensorflow.keras.applications.densenet import DenseNet121, DenseNet169, DenseNet201 \nfrom tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\nfrom tensorflow.keras.applications import MobileNetV3Small\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.models import Sequential, Model\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\nfrom tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dense, Flatten, Dropout\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, log_loss, jaccard_score\nimport numpy as np\nimport os\nfrom PIL import Image\nfrom shutil import copyfile  # Import the copyfile function\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport shutil\nimport random\nfrom sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:27.861448Z","iopub.execute_input":"2024-01-12T14:05:27.86173Z","iopub.status.idle":"2024-01-12T14:05:40.203221Z","shell.execute_reply.started":"2024-01-12T14:05:27.861682Z","shell.execute_reply":"2024-01-12T14:05:40.202375Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Dataset Describtion**","metadata":{}},{"cell_type":"code","source":"# Define paths\ndataset_path = \"/kaggle/input/brain-mri-images-for-brain-tumor-detection\" # Total Data path pot_image_sets\ntrain_output_path = \"/kaggle/working/Train\" # Train data path \ntest_output_path = \"/kaggle/working/Test\" # test data path \n\n\n# Create output directories if they don't exist\nos.makedirs(train_output_path, exist_ok=True)\nos.makedirs(test_output_path, exist_ok=True)\n\n\n# List all the class directories in the dataset path\nclass_dirs = [d for d in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, d))]\nprint(class_dirs)\n# Remove 'brain_tumor_dataset' if it exists in the list\nif 'brain_tumor_dataset' in class_dirs:\n    class_dirs.remove('brain_tumor_dataset')\n\nprint(\"Updated class_dirs:\", class_dirs)\n# Shuffle the class directories to ensure randomness\nrandom.shuffle(class_dirs)\n\n# Split percentages\ntrain_percentage = 0.7\ntest_percentage = 0.3\n\n\n\n# Loop through each class directory and copy files to appropriate splits\nfor class_dir in class_dirs:\n    files = os.listdir(os.path.join(dataset_path, class_dir))\n    random.shuffle(files)\n    \n    \n    total_samples = len(files)\n    train_samples = int(train_percentage * total_samples)\n    \n    test_samples = int(test_percentage * total_samples)\n    \n\n    \n    \n    train_files = files[:train_samples]\n    \n    test_files = files[train_samples:train_samples + test_samples]\n    \n\n    \n    \n    for filename in train_files:\n        src_path = os.path.join(dataset_path, class_dir, filename)\n        dest_path = os.path.join(train_output_path, class_dir, filename)\n        os.makedirs(os.path.dirname(dest_path), exist_ok=True)\n        shutil.copy(src_path, dest_path)\n    for filename in test_files:\n        src_path = os.path.join(dataset_path, class_dir, filename)\n        dest_path = os.path.join(test_output_path, class_dir, filename)\n        os.makedirs(os.path.dirname(dest_path), exist_ok=True)\n        shutil.copy(src_path, dest_path)\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:40.205257Z","iopub.execute_input":"2024-01-12T14:05:40.206033Z","iopub.status.idle":"2024-01-12T14:05:41.427958Z","shell.execute_reply.started":"2024-01-12T14:05:40.205998Z","shell.execute_reply":"2024-01-12T14:05:41.42709Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"['no', 'yes', 'brain_tumor_dataset']\nUpdated class_dirs: ['no', 'yes']\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\n\nbase_path = '/kaggle/working/Test'\n\n# Rename 'yes' to 'Tumor'\nold_path_yes = os.path.join(base_path, 'yes')\nnew_path_tumor = os.path.join(base_path, 'Tumor')\nos.rename(old_path_yes, new_path_tumor)\n\n# Rename 'no' to 'non-Tumor'\nold_path_no = os.path.join(base_path, 'no')\nnew_path_non_tumor = os.path.join(base_path, 'non-Tumor')\nos.rename(old_path_no, new_path_non_tumor)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.429027Z","iopub.execute_input":"2024-01-12T14:05:41.429299Z","iopub.status.idle":"2024-01-12T14:05:41.435409Z","shell.execute_reply.started":"2024-01-12T14:05:41.429277Z","shell.execute_reply":"2024-01-12T14:05:41.434462Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"import os\n\nbase_path = '/kaggle/working/Train'\n\n# Rename 'yes' to 'Tumor'\nold_path_yes = os.path.join(base_path, 'yes')\nnew_path_tumor = os.path.join(base_path, 'Tumor')\nos.rename(old_path_yes, new_path_tumor)\n\n# Rename 'no' to 'non-Tumor'\nold_path_no = os.path.join(base_path, 'no')\nnew_path_non_tumor = os.path.join(base_path, 'non-Tumor')\nos.rename(old_path_no, new_path_non_tumor)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.437909Z","iopub.execute_input":"2024-01-12T14:05:41.438198Z","iopub.status.idle":"2024-01-12T14:05:41.449744Z","shell.execute_reply.started":"2024-01-12T14:05:41.438175Z","shell.execute_reply":"2024-01-12T14:05:41.44897Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# Define the paths to our train, validation, and test datasets\ntrain_data_dir =  '/kaggle/working/Train'\ntest_data_dir = '/kaggle/working/Test'\nvalidation_data_dir = '/kaggle/working/Test'\n\n# Image dimensions\nIMG_WIDTH, IMG_HEIGHT = 299, 299\ninput_shape = (IMG_WIDTH, IMG_HEIGHT, 3)  # RGB images\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.450938Z","iopub.execute_input":"2024-01-12T14:05:41.451217Z","iopub.status.idle":"2024-01-12T14:05:41.458794Z","shell.execute_reply.started":"2024-01-12T14:05:41.451194Z","shell.execute_reply":"2024-01-12T14:05:41.458001Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"# **Data generators**","metadata":{}},{"cell_type":"code","source":"# Data generators for RGB images\ntrain_datagen = ImageDataGenerator(rescale=1./255)\ntest_datagen = ImageDataGenerator(rescale=1./255)\nvalidation_datagen = ImageDataGenerator(rescale=1./255)\n\n\n\n# Define data generators for RGB images with augmentation\ntrain_datagen_augmented = ImageDataGenerator(\n    rescale=1./255,\n    rotation_range=20,  # Rotation angle range\n    width_shift_range=0.2,  # Horizontal shift range\n    height_shift_range=0.2,  # Vertical shift range\n    shear_range=0.2,  # Shear intensity\n    zoom_range=0.2,  # Random zoom range\n    horizontal_flip=True,  # Randomly flip images horizontally\n    vertical_flip=False,  # Do not flip images vertically\n    fill_mode='nearest'  # Fill mode for newly created pixels\n)\n\n# Generate augmented data for training\ntrain_generator = train_datagen_augmented.flow_from_directory(\n    train_data_dir,\n    target_size=(IMG_WIDTH, IMG_HEIGHT),\n    batch_size=10,\n    class_mode='categorical',\n  \n)\n\n\n# Define data generators for RGB images with augmentation\ntest_datagen_augmented = ImageDataGenerator(\n    rescale=1./255,\n    rotation_range=20,  # Rotation angle range\n    width_shift_range=0.2,  # Horizontal shift range\n    height_shift_range=0.2,  # Vertical shift range\n    shear_range=0.2,  # Shear intensity\n    zoom_range=0.2,  # Random zoom range\n    horizontal_flip=True,  # Randomly flip images horizontally\n    vertical_flip=False,  # Do not flip images vertically\n    fill_mode='nearest'  # Fill mode for newly created pixels\n)\n\ntest_generator = test_datagen.flow_from_directory(\n    test_data_dir,\n    target_size=(IMG_WIDTH, IMG_HEIGHT),\n    batch_size=8,\n    class_mode='categorical',\n    shuffle=False,\n   )\n\n# Define data generators for RGB images with augmentation\nvalidation_datagen_augmented = ImageDataGenerator(\n    rescale=1./255,\n    rotation_range=20,  # Rotation angle range\n    width_shift_range=0.2,  # Horizontal shift range\n    height_shift_range=0.2,  # Vertical shift range\n    shear_range=0.2,  # Shear intensity\n    zoom_range=0.2,  # Random zoom range\n    horizontal_flip=True,  # Randomly flip images horizontally\n    vertical_flip=False,  # Do not flip images vertically\n    fill_mode='nearest'  # Fill mode for newly created pixels\n)\nvalidation_generator = validation_datagen.flow_from_directory(\n    validation_data_dir,\n    target_size=(IMG_WIDTH, IMG_HEIGHT),\n    batch_size=8,\n    class_mode='categorical',\n    shuffle=True,\n   \n)\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.459801Z","iopub.execute_input":"2024-01-12T14:05:41.460095Z","iopub.status.idle":"2024-01-12T14:05:41.490359Z","shell.execute_reply.started":"2024-01-12T14:05:41.460072Z","shell.execute_reply":"2024-01-12T14:05:41.489624Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Found 176 images belonging to 2 classes.\nFound 75 images belonging to 2 classes.\nFound 75 images belonging to 2 classes.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# ***'Tumor': 0, 'non-Tumor': 1***","metadata":{}},{"cell_type":"code","source":"class_indices = train_generator.class_indices\nprint(class_indices)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.491328Z","iopub.execute_input":"2024-01-12T14:05:41.491596Z","iopub.status.idle":"2024-01-12T14:05:41.496006Z","shell.execute_reply.started":"2024-01-12T14:05:41.491574Z","shell.execute_reply":"2024-01-12T14:05:41.495188Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"{'Tumor': 0, 'non-Tumor': 1}\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Number of images for each class in the training dataset**","metadata":{}},{"cell_type":"code","source":"# Count the number of images for each class in the training dataset\nclasses = os.listdir(train_data_dir)\nfor class_name in classes:\n    class_path = os.path.join(train_data_dir, class_name)\n    num_images = len(os.listdir(class_path))\n    print(f\"Class: {class_name}, Number of images: {num_images}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.497071Z","iopub.execute_input":"2024-01-12T14:05:41.497378Z","iopub.status.idle":"2024-01-12T14:05:41.505801Z","shell.execute_reply.started":"2024-01-12T14:05:41.497349Z","shell.execute_reply":"2024-01-12T14:05:41.504968Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Class: Tumor, Number of images: 108\nClass: non-Tumor, Number of images: 68\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Check the shape of the images in Train Generator**","metadata":{}},{"cell_type":"code","source":"# Get a batch of images and labels from the train_generator\nbatch = train_generator.next()\n\n# Iterate through the batch to check image shapes\nfor i in range(len(batch[0])):\n    img = batch[0][i]  # Image data\n    label = batch[1][i]  # Image label\n    \n    # Get image shape and channels\n    height, width, channels = img.shape\n    \n    # Display image shape and channels\n    print(f\"Image {i+1} - Shape: {width}x{height}x{channels}, Label: {label}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.506968Z","iopub.execute_input":"2024-01-12T14:05:41.507232Z","iopub.status.idle":"2024-01-12T14:05:41.712056Z","shell.execute_reply.started":"2024-01-12T14:05:41.507211Z","shell.execute_reply":"2024-01-12T14:05:41.711076Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Image 1 - Shape: 299x299x3, Label: [1. 0.]\nImage 2 - Shape: 299x299x3, Label: [1. 0.]\nImage 3 - Shape: 299x299x3, Label: [0. 1.]\nImage 4 - Shape: 299x299x3, Label: [0. 1.]\nImage 5 - Shape: 299x299x3, Label: [0. 1.]\nImage 6 - Shape: 299x299x3, Label: [0. 1.]\nImage 7 - Shape: 299x299x3, Label: [1. 0.]\nImage 8 - Shape: 299x299x3, Label: [0. 1.]\nImage 9 - Shape: 299x299x3, Label: [1. 0.]\nImage 10 - Shape: 299x299x3, Label: [1. 0.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Number of images for each class in the testing dataset**","metadata":{}},{"cell_type":"code","source":"# Count the number of images for each class in the testing dataset\nclasses = os.listdir(test_data_dir)\nfor class_name in classes:\n    class_path = os.path.join(test_data_dir, class_name)\n    num_images = len(os.listdir(class_path))\n    print(f\"Class: {class_name}, Number of images: {num_images}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.715432Z","iopub.execute_input":"2024-01-12T14:05:41.715759Z","iopub.status.idle":"2024-01-12T14:05:41.721819Z","shell.execute_reply.started":"2024-01-12T14:05:41.715729Z","shell.execute_reply":"2024-01-12T14:05:41.720953Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Class: Tumor, Number of images: 46\nClass: non-Tumor, Number of images: 29\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Check the shape of the images in Test Generator**","metadata":{}},{"cell_type":"code","source":"# Get a batch of images and labels from the test_generator\nbatch = test_generator.next()\n\n# Iterate through the batch to check image shapes\nfor i in range(len(batch[0])):\n    img = batch[0][i]  # Image data\n    label = batch[1][i]  # Image label\n    \n    # Get image shape and channels\n    height, width, channels = img.shape\n    \n    # Display image shape and channels\n    print(f\"Image {i+1} - Shape: {width}x{height}x{channels}, Label: {label}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.723107Z","iopub.execute_input":"2024-01-12T14:05:41.724023Z","iopub.status.idle":"2024-01-12T14:05:41.756743Z","shell.execute_reply.started":"2024-01-12T14:05:41.723995Z","shell.execute_reply":"2024-01-12T14:05:41.755985Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Image 1 - Shape: 299x299x3, Label: [1. 0.]\nImage 2 - Shape: 299x299x3, Label: [1. 0.]\nImage 3 - Shape: 299x299x3, Label: [1. 0.]\nImage 4 - Shape: 299x299x3, Label: [1. 0.]\nImage 5 - Shape: 299x299x3, Label: [1. 0.]\nImage 6 - Shape: 299x299x3, Label: [1. 0.]\nImage 7 - Shape: 299x299x3, Label: [1. 0.]\nImage 8 - Shape: 299x299x3, Label: [1. 0.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Number of images for each class in the validation dataset**","metadata":{}},{"cell_type":"code","source":"# Count the number of images for each class in the testing dataset\nclasses = os.listdir(validation_data_dir)\nfor class_name in classes:\n    class_path = os.path.join(validation_data_dir, class_name)\n    num_images = len(os.listdir(class_path))\n    print(f\"Class: {class_name}, Number of images: {num_images}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.757659Z","iopub.execute_input":"2024-01-12T14:05:41.75793Z","iopub.status.idle":"2024-01-12T14:05:41.763761Z","shell.execute_reply.started":"2024-01-12T14:05:41.757908Z","shell.execute_reply":"2024-01-12T14:05:41.762868Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Class: Tumor, Number of images: 46\nClass: non-Tumor, Number of images: 29\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Check the shape of the images in Validation Generator**","metadata":{}},{"cell_type":"code","source":"# Get a batch of images and labels from the validation_generator\nbatch = validation_generator.next()\n\n# Iterate through the batch to check image shapes\nfor i in range(len(batch[0])):\n    img = batch[0][i]  # Image data\n    label = batch[1][i]  # Image label\n    \n    # Get image shape and channels\n    height, width, channels = img.shape\n    \n    # Display image shape and channels\n    print(f\"Image {i+1} - Shape: {width}x{height}x{channels}, Label: {label}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.764831Z","iopub.execute_input":"2024-01-12T14:05:41.765096Z","iopub.status.idle":"2024-01-12T14:05:41.797405Z","shell.execute_reply.started":"2024-01-12T14:05:41.765074Z","shell.execute_reply":"2024-01-12T14:05:41.796635Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Image 1 - Shape: 299x299x3, Label: [1. 0.]\nImage 2 - Shape: 299x299x3, Label: [1. 0.]\nImage 3 - Shape: 299x299x3, Label: [1. 0.]\nImage 4 - Shape: 299x299x3, Label: [0. 1.]\nImage 5 - Shape: 299x299x3, Label: [1. 0.]\nImage 6 - Shape: 299x299x3, Label: [0. 1.]\nImage 7 - Shape: 299x299x3, Label: [1. 0.]\nImage 8 - Shape: 299x299x3, Label: [1. 0.]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Check for GPU availability**","metadata":{}},{"cell_type":"code","source":"# Check for GPU availability\nprint(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT available\")\n\n# Set TensorFlow to use the GPU device\nif tf.config.list_physical_devices('GPU'):\n    tf.config.experimental.set_memory_growth(tf.config.list_physical_devices('GPU')[0], True)\n    print(\"GPU device configured\")\nelse:\n    print(\"No GPU device found\")","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:41.798326Z","iopub.execute_input":"2024-01-12T14:05:41.798574Z","iopub.status.idle":"2024-01-12T14:05:42.172976Z","shell.execute_reply.started":"2024-01-12T14:05:41.798553Z","shell.execute_reply":"2024-01-12T14:05:42.171944Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"GPU is available\nGPU device configured\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Model checkpoint**","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint\nmodel_dir = '/kaggle/working/Checkpoints_InceptionResNetV2'\n\nif not os.path.exists(model_dir):\n    os.makedirs(model_dir)\n\ncheckpoint_path = model_dir + '/cp.ckpt'\ncheckpoint_dir = os.path.dirname(checkpoint_path)\n\n# Create a callback that saves the model's weights\ncp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n                                                 save_weights_only=True,\n                                                 save_best_only=True,  # Save only the best model\n                                                 monitor=\"val_accuracy\",   # Monitor validation loss\n                                                 mode=\"max\",           # Save the model when validation loss is minimized\n                                                 verbose=1)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:42.174167Z","iopub.execute_input":"2024-01-12T14:05:42.174457Z","iopub.status.idle":"2024-01-12T14:05:42.187427Z","shell.execute_reply.started":"2024-01-12T14:05:42.174433Z","shell.execute_reply":"2024-01-12T14:05:42.186543Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"checkpoint_path","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:42.188633Z","iopub.execute_input":"2024-01-12T14:05:42.189017Z","iopub.status.idle":"2024-01-12T14:05:42.201067Z","shell.execute_reply.started":"2024-01-12T14:05:42.188986Z","shell.execute_reply":"2024-01-12T14:05:42.200129Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"'/kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt'"},"metadata":{}}]},{"cell_type":"markdown","source":"# **InceptionResNet for Feature Extractor** \n* ## InceptionResNetV2\n","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras import models, layers, optimizers","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:42.202155Z","iopub.execute_input":"2024-01-12T14:05:42.202411Z","iopub.status.idle":"2024-01-12T14:05:42.209978Z","shell.execute_reply.started":"2024-01-12T14:05:42.202389Z","shell.execute_reply":"2024-01-12T14:05:42.209066Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"def create_model(summary=True):\n    # apply transfer learning\n    new_input = Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3))\n    base_model = InceptionResNetV2(weights='imagenet', include_top=False, input_tensor=new_input) ##MobileNetV3Small(weights='imagenet', include_top=False, input_tensor=new_input)\n    # add new classifier layers\n    flat1 = Flatten()(base_model.layers[-1].output)\n    output = Dense(2, activation='softmax')(flat1)  \n    # define new model\n    model = Model(inputs=base_model.inputs, outputs=output)\n    # Modify loss function to 'weighted_binary_crossentropy'\n    model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n    if summary:\n        print(model.summary())\n    return model\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:42.211007Z","iopub.execute_input":"2024-01-12T14:05:42.211257Z","iopub.status.idle":"2024-01-12T14:05:42.220692Z","shell.execute_reply.started":"2024-01-12T14:05:42.211235Z","shell.execute_reply":"2024-01-12T14:05:42.219878Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"# **Model summary**","metadata":{}},{"cell_type":"code","source":"model = create_model()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:05:42.221862Z","iopub.execute_input":"2024-01-12T14:05:42.222388Z","iopub.status.idle":"2024-01-12T14:05:51.813282Z","shell.execute_reply.started":"2024-01-12T14:05:42.222356Z","shell.execute_reply":"2024-01-12T14:05:51.812347Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n219055592/219055592 [==============================] - 1s 0us/step\nModel: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_1 (InputLayer)        [(None, 299, 299, 3)]        0         []                            \n                                                                                                  \n conv2d (Conv2D)             (None, 149, 149, 32)         864       ['input_1[0][0]']             \n                                                                                                  \n batch_normalization (Batch  (None, 149, 149, 32)         96        ['conv2d[0][0]']              \n Normalization)                                                                                   \n                                                                                                  \n activation (Activation)     (None, 149, 149, 32)         0         ['batch_normalization[0][0]'] \n                                                                                                  \n conv2d_1 (Conv2D)           (None, 147, 147, 32)         9216      ['activation[0][0]']          \n                                                                                                  \n batch_normalization_1 (Bat  (None, 147, 147, 32)         96        ['conv2d_1[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n activation_1 (Activation)   (None, 147, 147, 32)         0         ['batch_normalization_1[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_2 (Conv2D)           (None, 147, 147, 64)         18432     ['activation_1[0][0]']        \n                                                                                                  \n batch_normalization_2 (Bat  (None, 147, 147, 64)         192       ['conv2d_2[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n activation_2 (Activation)   (None, 147, 147, 64)         0         ['batch_normalization_2[0][0]'\n                                                                    ]                             \n                                                                                                  \n max_pooling2d (MaxPooling2  (None, 73, 73, 64)           0         ['activation_2[0][0]']        \n D)                                                                                               \n                                                                                                  \n conv2d_3 (Conv2D)           (None, 73, 73, 80)           5120      ['max_pooling2d[0][0]']       \n                                                                                                  \n batch_normalization_3 (Bat  (None, 73, 73, 80)           240       ['conv2d_3[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n activation_3 (Activation)   (None, 73, 73, 80)           0         ['batch_normalization_3[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_4 (Conv2D)           (None, 71, 71, 192)          138240    ['activation_3[0][0]']        \n                                                                                                  \n batch_normalization_4 (Bat  (None, 71, 71, 192)          576       ['conv2d_4[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n activation_4 (Activation)   (None, 71, 71, 192)          0         ['batch_normalization_4[0][0]'\n                                                                    ]                             \n                                                                                                  \n max_pooling2d_1 (MaxPoolin  (None, 35, 35, 192)          0         ['activation_4[0][0]']        \n g2D)                                                                                             \n                                                                                                  \n conv2d_8 (Conv2D)           (None, 35, 35, 64)           12288     ['max_pooling2d_1[0][0]']     \n                                                                                                  \n batch_normalization_8 (Bat  (None, 35, 35, 64)           192       ['conv2d_8[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n activation_8 (Activation)   (None, 35, 35, 64)           0         ['batch_normalization_8[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_6 (Conv2D)           (None, 35, 35, 48)           9216      ['max_pooling2d_1[0][0]']     \n                                                                                                  \n conv2d_9 (Conv2D)           (None, 35, 35, 96)           55296     ['activation_8[0][0]']        \n                                                                                                  \n batch_normalization_6 (Bat  (None, 35, 35, 48)           144       ['conv2d_6[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n batch_normalization_9 (Bat  (None, 35, 35, 96)           288       ['conv2d_9[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n activation_6 (Activation)   (None, 35, 35, 48)           0         ['batch_normalization_6[0][0]'\n                                                                    ]                             \n                                                                                                  \n activation_9 (Activation)   (None, 35, 35, 96)           0         ['batch_normalization_9[0][0]'\n                                                                    ]                             \n                                                                                                  \n average_pooling2d (Average  (None, 35, 35, 192)          0         ['max_pooling2d_1[0][0]']     \n Pooling2D)                                                                                       \n                                                                                                  \n conv2d_5 (Conv2D)           (None, 35, 35, 96)           18432     ['max_pooling2d_1[0][0]']     \n                                                                                                  \n conv2d_7 (Conv2D)           (None, 35, 35, 64)           76800     ['activation_6[0][0]']        \n                                                                                                  \n conv2d_10 (Conv2D)          (None, 35, 35, 96)           82944     ['activation_9[0][0]']        \n                                                                                                  \n conv2d_11 (Conv2D)          (None, 35, 35, 64)           12288     ['average_pooling2d[0][0]']   \n                                                                                                  \n batch_normalization_5 (Bat  (None, 35, 35, 96)           288       ['conv2d_5[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n batch_normalization_7 (Bat  (None, 35, 35, 64)           192       ['conv2d_7[0][0]']            \n chNormalization)                                                                                 \n                                                                                                  \n batch_normalization_10 (Ba  (None, 35, 35, 96)           288       ['conv2d_10[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_11 (Ba  (None, 35, 35, 64)           192       ['conv2d_11[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_5 (Activation)   (None, 35, 35, 96)           0         ['batch_normalization_5[0][0]'\n                                                                    ]                             \n                                                                                                  \n activation_7 (Activation)   (None, 35, 35, 64)           0         ['batch_normalization_7[0][0]'\n                                                                    ]                             \n                                                                                                  \n activation_10 (Activation)  (None, 35, 35, 96)           0         ['batch_normalization_10[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_11 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_11[0][0]\n                                                                    ']                            \n                                                                                                  \n mixed_5b (Concatenate)      (None, 35, 35, 320)          0         ['activation_5[0][0]',        \n                                                                     'activation_7[0][0]',        \n                                                                     'activation_10[0][0]',       \n                                                                     'activation_11[0][0]']       \n                                                                                                  \n conv2d_15 (Conv2D)          (None, 35, 35, 32)           10240     ['mixed_5b[0][0]']            \n                                                                                                  \n batch_normalization_15 (Ba  (None, 35, 35, 32)           96        ['conv2d_15[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_15 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_15[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_13 (Conv2D)          (None, 35, 35, 32)           10240     ['mixed_5b[0][0]']            \n                                                                                                  \n conv2d_16 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_15[0][0]']       \n                                                                                                  \n batch_normalization_13 (Ba  (None, 35, 35, 32)           96        ['conv2d_13[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_16 (Ba  (None, 35, 35, 48)           144       ['conv2d_16[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_13 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_13[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_16 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_16[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_12 (Conv2D)          (None, 35, 35, 32)           10240     ['mixed_5b[0][0]']            \n                                                                                                  \n conv2d_14 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_13[0][0]']       \n                                                                                                  \n conv2d_17 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_16[0][0]']       \n                                                                                                  \n batch_normalization_12 (Ba  (None, 35, 35, 32)           96        ['conv2d_12[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_14 (Ba  (None, 35, 35, 32)           96        ['conv2d_14[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_17 (Ba  (None, 35, 35, 64)           192       ['conv2d_17[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_12 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_12[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_14 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_14[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_17 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_17[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_1_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_12[0][0]',       \n te)                                                                 'activation_14[0][0]',       \n                                                                     'activation_17[0][0]']       \n                                                                                                  \n block35_1_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_1_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer (Custom  (None, 35, 35, 320)          0         ['mixed_5b[0][0]',            \n ScaleLayer)                                                         'block35_1_conv[0][0]']      \n                                                                                                  \n block35_1_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer[0][0]']  \n                                                                                                  \n conv2d_21 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_1_ac[0][0]']        \n                                                                                                  \n batch_normalization_21 (Ba  (None, 35, 35, 32)           96        ['conv2d_21[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_21 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_21[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_19 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_1_ac[0][0]']        \n                                                                                                  \n conv2d_22 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_21[0][0]']       \n                                                                                                  \n batch_normalization_19 (Ba  (None, 35, 35, 32)           96        ['conv2d_19[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_22 (Ba  (None, 35, 35, 48)           144       ['conv2d_22[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_19 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_19[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_22 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_22[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_18 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_1_ac[0][0]']        \n                                                                                                  \n conv2d_20 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_19[0][0]']       \n                                                                                                  \n conv2d_23 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_22[0][0]']       \n                                                                                                  \n batch_normalization_18 (Ba  (None, 35, 35, 32)           96        ['conv2d_18[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_20 (Ba  (None, 35, 35, 32)           96        ['conv2d_20[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_23 (Ba  (None, 35, 35, 64)           192       ['conv2d_23[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_18 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_18[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_20 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_20[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_23 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_23[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_2_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_18[0][0]',       \n te)                                                                 'activation_20[0][0]',       \n                                                                     'activation_23[0][0]']       \n                                                                                                  \n block35_2_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_2_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_1 (Cust  (None, 35, 35, 320)          0         ['block35_1_ac[0][0]',        \n omScaleLayer)                                                       'block35_2_conv[0][0]']      \n                                                                                                  \n block35_2_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_1[0][0]']\n                                                                                                  \n conv2d_27 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_2_ac[0][0]']        \n                                                                                                  \n batch_normalization_27 (Ba  (None, 35, 35, 32)           96        ['conv2d_27[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_27 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_27[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_25 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_2_ac[0][0]']        \n                                                                                                  \n conv2d_28 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_27[0][0]']       \n                                                                                                  \n batch_normalization_25 (Ba  (None, 35, 35, 32)           96        ['conv2d_25[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_28 (Ba  (None, 35, 35, 48)           144       ['conv2d_28[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_25 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_25[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_28 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_28[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_24 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_2_ac[0][0]']        \n                                                                                                  \n conv2d_26 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_25[0][0]']       \n                                                                                                  \n conv2d_29 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_28[0][0]']       \n                                                                                                  \n batch_normalization_24 (Ba  (None, 35, 35, 32)           96        ['conv2d_24[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_26 (Ba  (None, 35, 35, 32)           96        ['conv2d_26[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_29 (Ba  (None, 35, 35, 64)           192       ['conv2d_29[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_24 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_24[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_26 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_26[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_29 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_29[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_3_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_24[0][0]',       \n te)                                                                 'activation_26[0][0]',       \n                                                                     'activation_29[0][0]']       \n                                                                                                  \n block35_3_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_3_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_2 (Cust  (None, 35, 35, 320)          0         ['block35_2_ac[0][0]',        \n omScaleLayer)                                                       'block35_3_conv[0][0]']      \n                                                                                                  \n block35_3_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_2[0][0]']\n                                                                                                  \n conv2d_33 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_3_ac[0][0]']        \n                                                                                                  \n batch_normalization_33 (Ba  (None, 35, 35, 32)           96        ['conv2d_33[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_33 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_33[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_31 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_3_ac[0][0]']        \n                                                                                                  \n conv2d_34 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_33[0][0]']       \n                                                                                                  \n batch_normalization_31 (Ba  (None, 35, 35, 32)           96        ['conv2d_31[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_34 (Ba  (None, 35, 35, 48)           144       ['conv2d_34[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_31 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_31[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_34 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_34[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_30 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_3_ac[0][0]']        \n                                                                                                  \n conv2d_32 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_31[0][0]']       \n                                                                                                  \n conv2d_35 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_34[0][0]']       \n                                                                                                  \n batch_normalization_30 (Ba  (None, 35, 35, 32)           96        ['conv2d_30[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_32 (Ba  (None, 35, 35, 32)           96        ['conv2d_32[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_35 (Ba  (None, 35, 35, 64)           192       ['conv2d_35[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_30 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_30[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_32 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_32[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_35 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_35[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_4_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_30[0][0]',       \n te)                                                                 'activation_32[0][0]',       \n                                                                     'activation_35[0][0]']       \n                                                                                                  \n block35_4_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_4_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_3 (Cust  (None, 35, 35, 320)          0         ['block35_3_ac[0][0]',        \n omScaleLayer)                                                       'block35_4_conv[0][0]']      \n                                                                                                  \n block35_4_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_3[0][0]']\n                                                                                                  \n conv2d_39 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_4_ac[0][0]']        \n                                                                                                  \n batch_normalization_39 (Ba  (None, 35, 35, 32)           96        ['conv2d_39[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_39 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_39[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_37 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_4_ac[0][0]']        \n                                                                                                  \n conv2d_40 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_39[0][0]']       \n                                                                                                  \n batch_normalization_37 (Ba  (None, 35, 35, 32)           96        ['conv2d_37[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_40 (Ba  (None, 35, 35, 48)           144       ['conv2d_40[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_37 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_37[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_40 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_40[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_36 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_4_ac[0][0]']        \n                                                                                                  \n conv2d_38 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_37[0][0]']       \n                                                                                                  \n conv2d_41 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_40[0][0]']       \n                                                                                                  \n batch_normalization_36 (Ba  (None, 35, 35, 32)           96        ['conv2d_36[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_38 (Ba  (None, 35, 35, 32)           96        ['conv2d_38[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_41 (Ba  (None, 35, 35, 64)           192       ['conv2d_41[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_36 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_36[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_38 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_38[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_41 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_41[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_5_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_36[0][0]',       \n te)                                                                 'activation_38[0][0]',       \n                                                                     'activation_41[0][0]']       \n                                                                                                  \n block35_5_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_5_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_4 (Cust  (None, 35, 35, 320)          0         ['block35_4_ac[0][0]',        \n omScaleLayer)                                                       'block35_5_conv[0][0]']      \n                                                                                                  \n block35_5_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_4[0][0]']\n                                                                                                  \n conv2d_45 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_5_ac[0][0]']        \n                                                                                                  \n batch_normalization_45 (Ba  (None, 35, 35, 32)           96        ['conv2d_45[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_45 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_45[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_43 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_5_ac[0][0]']        \n                                                                                                  \n conv2d_46 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_45[0][0]']       \n                                                                                                  \n batch_normalization_43 (Ba  (None, 35, 35, 32)           96        ['conv2d_43[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_46 (Ba  (None, 35, 35, 48)           144       ['conv2d_46[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_43 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_43[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_46 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_46[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_42 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_5_ac[0][0]']        \n                                                                                                  \n conv2d_44 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_43[0][0]']       \n                                                                                                  \n conv2d_47 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_46[0][0]']       \n                                                                                                  \n batch_normalization_42 (Ba  (None, 35, 35, 32)           96        ['conv2d_42[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_44 (Ba  (None, 35, 35, 32)           96        ['conv2d_44[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_47 (Ba  (None, 35, 35, 64)           192       ['conv2d_47[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_42 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_42[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_44 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_44[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_47 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_47[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_6_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_42[0][0]',       \n te)                                                                 'activation_44[0][0]',       \n                                                                     'activation_47[0][0]']       \n                                                                                                  \n block35_6_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_6_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_5 (Cust  (None, 35, 35, 320)          0         ['block35_5_ac[0][0]',        \n omScaleLayer)                                                       'block35_6_conv[0][0]']      \n                                                                                                  \n block35_6_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_5[0][0]']\n                                                                                                  \n conv2d_51 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_6_ac[0][0]']        \n                                                                                                  \n batch_normalization_51 (Ba  (None, 35, 35, 32)           96        ['conv2d_51[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_51 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_51[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_49 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_6_ac[0][0]']        \n                                                                                                  \n conv2d_52 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_51[0][0]']       \n                                                                                                  \n batch_normalization_49 (Ba  (None, 35, 35, 32)           96        ['conv2d_49[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_52 (Ba  (None, 35, 35, 48)           144       ['conv2d_52[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_49 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_49[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_52 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_52[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_48 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_6_ac[0][0]']        \n                                                                                                  \n conv2d_50 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_49[0][0]']       \n                                                                                                  \n conv2d_53 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_52[0][0]']       \n                                                                                                  \n batch_normalization_48 (Ba  (None, 35, 35, 32)           96        ['conv2d_48[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_50 (Ba  (None, 35, 35, 32)           96        ['conv2d_50[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_53 (Ba  (None, 35, 35, 64)           192       ['conv2d_53[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_48 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_48[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_50 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_50[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_53 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_53[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_7_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_48[0][0]',       \n te)                                                                 'activation_50[0][0]',       \n                                                                     'activation_53[0][0]']       \n                                                                                                  \n block35_7_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_7_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_6 (Cust  (None, 35, 35, 320)          0         ['block35_6_ac[0][0]',        \n omScaleLayer)                                                       'block35_7_conv[0][0]']      \n                                                                                                  \n block35_7_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_6[0][0]']\n                                                                                                  \n conv2d_57 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_7_ac[0][0]']        \n                                                                                                  \n batch_normalization_57 (Ba  (None, 35, 35, 32)           96        ['conv2d_57[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_57 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_57[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_55 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_7_ac[0][0]']        \n                                                                                                  \n conv2d_58 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_57[0][0]']       \n                                                                                                  \n batch_normalization_55 (Ba  (None, 35, 35, 32)           96        ['conv2d_55[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_58 (Ba  (None, 35, 35, 48)           144       ['conv2d_58[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_55 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_55[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_58 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_58[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_54 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_7_ac[0][0]']        \n                                                                                                  \n conv2d_56 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_55[0][0]']       \n                                                                                                  \n conv2d_59 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_58[0][0]']       \n                                                                                                  \n batch_normalization_54 (Ba  (None, 35, 35, 32)           96        ['conv2d_54[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_56 (Ba  (None, 35, 35, 32)           96        ['conv2d_56[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_59 (Ba  (None, 35, 35, 64)           192       ['conv2d_59[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_54 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_54[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_56 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_56[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_59 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_59[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_8_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_54[0][0]',       \n te)                                                                 'activation_56[0][0]',       \n                                                                     'activation_59[0][0]']       \n                                                                                                  \n block35_8_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_8_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_7 (Cust  (None, 35, 35, 320)          0         ['block35_7_ac[0][0]',        \n omScaleLayer)                                                       'block35_8_conv[0][0]']      \n                                                                                                  \n block35_8_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_7[0][0]']\n                                                                                                  \n conv2d_63 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_8_ac[0][0]']        \n                                                                                                  \n batch_normalization_63 (Ba  (None, 35, 35, 32)           96        ['conv2d_63[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_63 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_63[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_61 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_8_ac[0][0]']        \n                                                                                                  \n conv2d_64 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_63[0][0]']       \n                                                                                                  \n batch_normalization_61 (Ba  (None, 35, 35, 32)           96        ['conv2d_61[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_64 (Ba  (None, 35, 35, 48)           144       ['conv2d_64[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_61 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_61[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_64 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_64[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_60 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_8_ac[0][0]']        \n                                                                                                  \n conv2d_62 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_61[0][0]']       \n                                                                                                  \n conv2d_65 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_64[0][0]']       \n                                                                                                  \n batch_normalization_60 (Ba  (None, 35, 35, 32)           96        ['conv2d_60[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_62 (Ba  (None, 35, 35, 32)           96        ['conv2d_62[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_65 (Ba  (None, 35, 35, 64)           192       ['conv2d_65[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_60 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_60[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_62 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_62[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_65 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_65[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_9_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_60[0][0]',       \n te)                                                                 'activation_62[0][0]',       \n                                                                     'activation_65[0][0]']       \n                                                                                                  \n block35_9_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_9_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_8 (Cust  (None, 35, 35, 320)          0         ['block35_8_ac[0][0]',        \n omScaleLayer)                                                       'block35_9_conv[0][0]']      \n                                                                                                  \n block35_9_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_8[0][0]']\n                                                                                                  \n conv2d_69 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_9_ac[0][0]']        \n                                                                                                  \n batch_normalization_69 (Ba  (None, 35, 35, 32)           96        ['conv2d_69[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_69 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_69[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_67 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_9_ac[0][0]']        \n                                                                                                  \n conv2d_70 (Conv2D)          (None, 35, 35, 48)           13824     ['activation_69[0][0]']       \n                                                                                                  \n batch_normalization_67 (Ba  (None, 35, 35, 32)           96        ['conv2d_67[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_70 (Ba  (None, 35, 35, 48)           144       ['conv2d_70[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_67 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_67[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_70 (Activation)  (None, 35, 35, 48)           0         ['batch_normalization_70[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_66 (Conv2D)          (None, 35, 35, 32)           10240     ['block35_9_ac[0][0]']        \n                                                                                                  \n conv2d_68 (Conv2D)          (None, 35, 35, 32)           9216      ['activation_67[0][0]']       \n                                                                                                  \n conv2d_71 (Conv2D)          (None, 35, 35, 64)           27648     ['activation_70[0][0]']       \n                                                                                                  \n batch_normalization_66 (Ba  (None, 35, 35, 32)           96        ['conv2d_66[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_68 (Ba  (None, 35, 35, 32)           96        ['conv2d_68[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_71 (Ba  (None, 35, 35, 64)           192       ['conv2d_71[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_66 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_66[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_68 (Activation)  (None, 35, 35, 32)           0         ['batch_normalization_68[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_71 (Activation)  (None, 35, 35, 64)           0         ['batch_normalization_71[0][0]\n                                                                    ']                            \n                                                                                                  \n block35_10_mixed (Concaten  (None, 35, 35, 128)          0         ['activation_66[0][0]',       \n ate)                                                                'activation_68[0][0]',       \n                                                                     'activation_71[0][0]']       \n                                                                                                  \n block35_10_conv (Conv2D)    (None, 35, 35, 320)          41280     ['block35_10_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_9 (Cust  (None, 35, 35, 320)          0         ['block35_9_ac[0][0]',        \n omScaleLayer)                                                       'block35_10_conv[0][0]']     \n                                                                                                  \n block35_10_ac (Activation)  (None, 35, 35, 320)          0         ['custom_scale_layer_9[0][0]']\n                                                                                                  \n conv2d_73 (Conv2D)          (None, 35, 35, 256)          81920     ['block35_10_ac[0][0]']       \n                                                                                                  \n batch_normalization_73 (Ba  (None, 35, 35, 256)          768       ['conv2d_73[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_73 (Activation)  (None, 35, 35, 256)          0         ['batch_normalization_73[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_74 (Conv2D)          (None, 35, 35, 256)          589824    ['activation_73[0][0]']       \n                                                                                                  \n batch_normalization_74 (Ba  (None, 35, 35, 256)          768       ['conv2d_74[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_74 (Activation)  (None, 35, 35, 256)          0         ['batch_normalization_74[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_72 (Conv2D)          (None, 17, 17, 384)          1105920   ['block35_10_ac[0][0]']       \n                                                                                                  \n conv2d_75 (Conv2D)          (None, 17, 17, 384)          884736    ['activation_74[0][0]']       \n                                                                                                  \n batch_normalization_72 (Ba  (None, 17, 17, 384)          1152      ['conv2d_72[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_75 (Ba  (None, 17, 17, 384)          1152      ['conv2d_75[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_72 (Activation)  (None, 17, 17, 384)          0         ['batch_normalization_72[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_75 (Activation)  (None, 17, 17, 384)          0         ['batch_normalization_75[0][0]\n                                                                    ']                            \n                                                                                                  \n max_pooling2d_2 (MaxPoolin  (None, 17, 17, 320)          0         ['block35_10_ac[0][0]']       \n g2D)                                                                                             \n                                                                                                  \n mixed_6a (Concatenate)      (None, 17, 17, 1088)         0         ['activation_72[0][0]',       \n                                                                     'activation_75[0][0]',       \n                                                                     'max_pooling2d_2[0][0]']     \n                                                                                                  \n conv2d_77 (Conv2D)          (None, 17, 17, 128)          139264    ['mixed_6a[0][0]']            \n                                                                                                  \n batch_normalization_77 (Ba  (None, 17, 17, 128)          384       ['conv2d_77[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_77 (Activation)  (None, 17, 17, 128)          0         ['batch_normalization_77[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_78 (Conv2D)          (None, 17, 17, 160)          143360    ['activation_77[0][0]']       \n                                                                                                  \n batch_normalization_78 (Ba  (None, 17, 17, 160)          480       ['conv2d_78[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_78 (Activation)  (None, 17, 17, 160)          0         ['batch_normalization_78[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_76 (Conv2D)          (None, 17, 17, 192)          208896    ['mixed_6a[0][0]']            \n                                                                                                  \n conv2d_79 (Conv2D)          (None, 17, 17, 192)          215040    ['activation_78[0][0]']       \n                                                                                                  \n batch_normalization_76 (Ba  (None, 17, 17, 192)          576       ['conv2d_76[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_79 (Ba  (None, 17, 17, 192)          576       ['conv2d_79[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_76 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_76[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_79 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_79[0][0]\n                                                                    ']                            \n                                                                                                  \n block17_1_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_76[0][0]',       \n te)                                                                 'activation_79[0][0]']       \n                                                                                                  \n block17_1_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_1_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_10 (Cus  (None, 17, 17, 1088)         0         ['mixed_6a[0][0]',            \n tomScaleLayer)                                                      'block17_1_conv[0][0]']      \n                                                                                                  \n block17_1_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_10[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_81 (Conv2D)          (None, 17, 17, 128)          139264    ['block17_1_ac[0][0]']        \n                                                                                                  \n batch_normalization_81 (Ba  (None, 17, 17, 128)          384       ['conv2d_81[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_81 (Activation)  (None, 17, 17, 128)          0         ['batch_normalization_81[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_82 (Conv2D)          (None, 17, 17, 160)          143360    ['activation_81[0][0]']       \n                                                                                                  \n batch_normalization_82 (Ba  (None, 17, 17, 160)          480       ['conv2d_82[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_82 (Activation)  (None, 17, 17, 160)          0         ['batch_normalization_82[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_80 (Conv2D)          (None, 17, 17, 192)          208896    ['block17_1_ac[0][0]']        \n                                                                                                  \n conv2d_83 (Conv2D)          (None, 17, 17, 192)          215040    ['activation_82[0][0]']       \n                                                                                                  \n batch_normalization_80 (Ba  (None, 17, 17, 192)          576       ['conv2d_80[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_83 (Ba  (None, 17, 17, 192)          576       ['conv2d_83[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_80 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_80[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_83 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_83[0][0]\n                                                                    ']                            \n                                                                                                  \n block17_2_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_80[0][0]',       \n te)                                                                 'activation_83[0][0]']       \n                                                                                                  \n block17_2_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_2_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_11 (Cus  (None, 17, 17, 1088)         0         ['block17_1_ac[0][0]',        \n tomScaleLayer)                                                      'block17_2_conv[0][0]']      \n                                                                                                  \n block17_2_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_11[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_85 (Conv2D)          (None, 17, 17, 128)          139264    ['block17_2_ac[0][0]']        \n                                                                                                  \n batch_normalization_85 (Ba  (None, 17, 17, 128)          384       ['conv2d_85[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_85 (Activation)  (None, 17, 17, 128)          0         ['batch_normalization_85[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_86 (Conv2D)          (None, 17, 17, 160)          143360    ['activation_85[0][0]']       \n                                                                                                  \n batch_normalization_86 (Ba  (None, 17, 17, 160)          480       ['conv2d_86[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_86 (Activation)  (None, 17, 17, 160)          0         ['batch_normalization_86[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_84 (Conv2D)          (None, 17, 17, 192)          208896    ['block17_2_ac[0][0]']        \n                                                                                                  \n conv2d_87 (Conv2D)          (None, 17, 17, 192)          215040    ['activation_86[0][0]']       \n                                                                                                  \n batch_normalization_84 (Ba  (None, 17, 17, 192)          576       ['conv2d_84[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_87 (Ba  (None, 17, 17, 192)          576       ['conv2d_87[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_84 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_84[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_87 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_87[0][0]\n                                                                    ']                            \n                                                                                                  \n block17_3_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_84[0][0]',       \n te)                                                                 'activation_87[0][0]']       \n                                                                                                  \n block17_3_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_3_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_12 (Cus  (None, 17, 17, 1088)         0         ['block17_2_ac[0][0]',        \n tomScaleLayer)                                                      'block17_3_conv[0][0]']      \n                                                                                                  \n block17_3_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_12[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_89 (Conv2D)          (None, 17, 17, 128)          139264    ['block17_3_ac[0][0]']        \n                                                                                                  \n batch_normalization_89 (Ba  (None, 17, 17, 128)          384       ['conv2d_89[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_89 (Activation)  (None, 17, 17, 128)          0         ['batch_normalization_89[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_90 (Conv2D)          (None, 17, 17, 160)          143360    ['activation_89[0][0]']       \n                                                                                                  \n batch_normalization_90 (Ba  (None, 17, 17, 160)          480       ['conv2d_90[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_90 (Activation)  (None, 17, 17, 160)          0         ['batch_normalization_90[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_88 (Conv2D)          (None, 17, 17, 192)          208896    ['block17_3_ac[0][0]']        \n                                                                                                  \n conv2d_91 (Conv2D)          (None, 17, 17, 192)          215040    ['activation_90[0][0]']       \n                                                                                                  \n batch_normalization_88 (Ba  (None, 17, 17, 192)          576       ['conv2d_88[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_91 (Ba  (None, 17, 17, 192)          576       ['conv2d_91[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_88 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_88[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_91 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_91[0][0]\n                                                                    ']                            \n                                                                                                  \n block17_4_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_88[0][0]',       \n te)                                                                 'activation_91[0][0]']       \n                                                                                                  \n block17_4_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_4_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_13 (Cus  (None, 17, 17, 1088)         0         ['block17_3_ac[0][0]',        \n tomScaleLayer)                                                      'block17_4_conv[0][0]']      \n                                                                                                  \n block17_4_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_13[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_93 (Conv2D)          (None, 17, 17, 128)          139264    ['block17_4_ac[0][0]']        \n                                                                                                  \n batch_normalization_93 (Ba  (None, 17, 17, 128)          384       ['conv2d_93[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_93 (Activation)  (None, 17, 17, 128)          0         ['batch_normalization_93[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_94 (Conv2D)          (None, 17, 17, 160)          143360    ['activation_93[0][0]']       \n                                                                                                  \n batch_normalization_94 (Ba  (None, 17, 17, 160)          480       ['conv2d_94[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_94 (Activation)  (None, 17, 17, 160)          0         ['batch_normalization_94[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_92 (Conv2D)          (None, 17, 17, 192)          208896    ['block17_4_ac[0][0]']        \n                                                                                                  \n conv2d_95 (Conv2D)          (None, 17, 17, 192)          215040    ['activation_94[0][0]']       \n                                                                                                  \n batch_normalization_92 (Ba  (None, 17, 17, 192)          576       ['conv2d_92[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_95 (Ba  (None, 17, 17, 192)          576       ['conv2d_95[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_92 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_92[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_95 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_95[0][0]\n                                                                    ']                            \n                                                                                                  \n block17_5_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_92[0][0]',       \n te)                                                                 'activation_95[0][0]']       \n                                                                                                  \n block17_5_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_5_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_14 (Cus  (None, 17, 17, 1088)         0         ['block17_4_ac[0][0]',        \n tomScaleLayer)                                                      'block17_5_conv[0][0]']      \n                                                                                                  \n block17_5_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_14[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_97 (Conv2D)          (None, 17, 17, 128)          139264    ['block17_5_ac[0][0]']        \n                                                                                                  \n batch_normalization_97 (Ba  (None, 17, 17, 128)          384       ['conv2d_97[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_97 (Activation)  (None, 17, 17, 128)          0         ['batch_normalization_97[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_98 (Conv2D)          (None, 17, 17, 160)          143360    ['activation_97[0][0]']       \n                                                                                                  \n batch_normalization_98 (Ba  (None, 17, 17, 160)          480       ['conv2d_98[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_98 (Activation)  (None, 17, 17, 160)          0         ['batch_normalization_98[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_96 (Conv2D)          (None, 17, 17, 192)          208896    ['block17_5_ac[0][0]']        \n                                                                                                  \n conv2d_99 (Conv2D)          (None, 17, 17, 192)          215040    ['activation_98[0][0]']       \n                                                                                                  \n batch_normalization_96 (Ba  (None, 17, 17, 192)          576       ['conv2d_96[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n batch_normalization_99 (Ba  (None, 17, 17, 192)          576       ['conv2d_99[0][0]']           \n tchNormalization)                                                                                \n                                                                                                  \n activation_96 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_96[0][0]\n                                                                    ']                            \n                                                                                                  \n activation_99 (Activation)  (None, 17, 17, 192)          0         ['batch_normalization_99[0][0]\n                                                                    ']                            \n                                                                                                  \n block17_6_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_96[0][0]',       \n te)                                                                 'activation_99[0][0]']       \n                                                                                                  \n block17_6_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_6_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_15 (Cus  (None, 17, 17, 1088)         0         ['block17_5_ac[0][0]',        \n tomScaleLayer)                                                      'block17_6_conv[0][0]']      \n                                                                                                  \n block17_6_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_15[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_101 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_6_ac[0][0]']        \n                                                                                                  \n batch_normalization_101 (B  (None, 17, 17, 128)          384       ['conv2d_101[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_101 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_101[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_102 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_101[0][0]']      \n                                                                                                  \n batch_normalization_102 (B  (None, 17, 17, 160)          480       ['conv2d_102[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_102 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_102[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_100 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_6_ac[0][0]']        \n                                                                                                  \n conv2d_103 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_102[0][0]']      \n                                                                                                  \n batch_normalization_100 (B  (None, 17, 17, 192)          576       ['conv2d_100[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_103 (B  (None, 17, 17, 192)          576       ['conv2d_103[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_100 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_100[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_103 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_103[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_7_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_100[0][0]',      \n te)                                                                 'activation_103[0][0]']      \n                                                                                                  \n block17_7_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_7_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_16 (Cus  (None, 17, 17, 1088)         0         ['block17_6_ac[0][0]',        \n tomScaleLayer)                                                      'block17_7_conv[0][0]']      \n                                                                                                  \n block17_7_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_16[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_105 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_7_ac[0][0]']        \n                                                                                                  \n batch_normalization_105 (B  (None, 17, 17, 128)          384       ['conv2d_105[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_105 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_105[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_106 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_105[0][0]']      \n                                                                                                  \n batch_normalization_106 (B  (None, 17, 17, 160)          480       ['conv2d_106[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_106 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_106[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_104 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_7_ac[0][0]']        \n                                                                                                  \n conv2d_107 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_106[0][0]']      \n                                                                                                  \n batch_normalization_104 (B  (None, 17, 17, 192)          576       ['conv2d_104[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_107 (B  (None, 17, 17, 192)          576       ['conv2d_107[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_104 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_104[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_107 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_107[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_8_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_104[0][0]',      \n te)                                                                 'activation_107[0][0]']      \n                                                                                                  \n block17_8_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_8_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_17 (Cus  (None, 17, 17, 1088)         0         ['block17_7_ac[0][0]',        \n tomScaleLayer)                                                      'block17_8_conv[0][0]']      \n                                                                                                  \n block17_8_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_17[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_109 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_8_ac[0][0]']        \n                                                                                                  \n batch_normalization_109 (B  (None, 17, 17, 128)          384       ['conv2d_109[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_109 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_109[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_110 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_109[0][0]']      \n                                                                                                  \n batch_normalization_110 (B  (None, 17, 17, 160)          480       ['conv2d_110[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_110 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_110[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_108 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_8_ac[0][0]']        \n                                                                                                  \n conv2d_111 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_110[0][0]']      \n                                                                                                  \n batch_normalization_108 (B  (None, 17, 17, 192)          576       ['conv2d_108[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_111 (B  (None, 17, 17, 192)          576       ['conv2d_111[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_108 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_108[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_111 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_111[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_9_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_108[0][0]',      \n te)                                                                 'activation_111[0][0]']      \n                                                                                                  \n block17_9_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_9_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_18 (Cus  (None, 17, 17, 1088)         0         ['block17_8_ac[0][0]',        \n tomScaleLayer)                                                      'block17_9_conv[0][0]']      \n                                                                                                  \n block17_9_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_18[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_113 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_9_ac[0][0]']        \n                                                                                                  \n batch_normalization_113 (B  (None, 17, 17, 128)          384       ['conv2d_113[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_113 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_113[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_114 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_113[0][0]']      \n                                                                                                  \n batch_normalization_114 (B  (None, 17, 17, 160)          480       ['conv2d_114[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_114 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_114[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_112 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_9_ac[0][0]']        \n                                                                                                  \n conv2d_115 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_114[0][0]']      \n                                                                                                  \n batch_normalization_112 (B  (None, 17, 17, 192)          576       ['conv2d_112[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_115 (B  (None, 17, 17, 192)          576       ['conv2d_115[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_112 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_112[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_115 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_115[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_10_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_112[0][0]',      \n ate)                                                                'activation_115[0][0]']      \n                                                                                                  \n block17_10_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_10_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_19 (Cus  (None, 17, 17, 1088)         0         ['block17_9_ac[0][0]',        \n tomScaleLayer)                                                      'block17_10_conv[0][0]']     \n                                                                                                  \n block17_10_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_19[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_117 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_10_ac[0][0]']       \n                                                                                                  \n batch_normalization_117 (B  (None, 17, 17, 128)          384       ['conv2d_117[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_117 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_117[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_118 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_117[0][0]']      \n                                                                                                  \n batch_normalization_118 (B  (None, 17, 17, 160)          480       ['conv2d_118[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_118 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_118[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_116 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_10_ac[0][0]']       \n                                                                                                  \n conv2d_119 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_118[0][0]']      \n                                                                                                  \n batch_normalization_116 (B  (None, 17, 17, 192)          576       ['conv2d_116[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_119 (B  (None, 17, 17, 192)          576       ['conv2d_119[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_116 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_116[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_119 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_119[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_11_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_116[0][0]',      \n ate)                                                                'activation_119[0][0]']      \n                                                                                                  \n block17_11_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_11_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_20 (Cus  (None, 17, 17, 1088)         0         ['block17_10_ac[0][0]',       \n tomScaleLayer)                                                      'block17_11_conv[0][0]']     \n                                                                                                  \n block17_11_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_20[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_121 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_11_ac[0][0]']       \n                                                                                                  \n batch_normalization_121 (B  (None, 17, 17, 128)          384       ['conv2d_121[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_121 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_121[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_122 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_121[0][0]']      \n                                                                                                  \n batch_normalization_122 (B  (None, 17, 17, 160)          480       ['conv2d_122[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_122 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_122[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_120 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_11_ac[0][0]']       \n                                                                                                  \n conv2d_123 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_122[0][0]']      \n                                                                                                  \n batch_normalization_120 (B  (None, 17, 17, 192)          576       ['conv2d_120[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_123 (B  (None, 17, 17, 192)          576       ['conv2d_123[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_120 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_120[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_123 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_123[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_12_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_120[0][0]',      \n ate)                                                                'activation_123[0][0]']      \n                                                                                                  \n block17_12_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_12_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_21 (Cus  (None, 17, 17, 1088)         0         ['block17_11_ac[0][0]',       \n tomScaleLayer)                                                      'block17_12_conv[0][0]']     \n                                                                                                  \n block17_12_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_21[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_125 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_12_ac[0][0]']       \n                                                                                                  \n batch_normalization_125 (B  (None, 17, 17, 128)          384       ['conv2d_125[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_125 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_125[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_126 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_125[0][0]']      \n                                                                                                  \n batch_normalization_126 (B  (None, 17, 17, 160)          480       ['conv2d_126[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_126 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_126[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_124 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_12_ac[0][0]']       \n                                                                                                  \n conv2d_127 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_126[0][0]']      \n                                                                                                  \n batch_normalization_124 (B  (None, 17, 17, 192)          576       ['conv2d_124[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_127 (B  (None, 17, 17, 192)          576       ['conv2d_127[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_124 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_124[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_127 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_127[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_13_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_124[0][0]',      \n ate)                                                                'activation_127[0][0]']      \n                                                                                                  \n block17_13_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_13_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_22 (Cus  (None, 17, 17, 1088)         0         ['block17_12_ac[0][0]',       \n tomScaleLayer)                                                      'block17_13_conv[0][0]']     \n                                                                                                  \n block17_13_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_22[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_129 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_13_ac[0][0]']       \n                                                                                                  \n batch_normalization_129 (B  (None, 17, 17, 128)          384       ['conv2d_129[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_129 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_129[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_130 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_129[0][0]']      \n                                                                                                  \n batch_normalization_130 (B  (None, 17, 17, 160)          480       ['conv2d_130[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_130 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_130[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_128 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_13_ac[0][0]']       \n                                                                                                  \n conv2d_131 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_130[0][0]']      \n                                                                                                  \n batch_normalization_128 (B  (None, 17, 17, 192)          576       ['conv2d_128[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_131 (B  (None, 17, 17, 192)          576       ['conv2d_131[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_128 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_128[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_131 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_131[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_14_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_128[0][0]',      \n ate)                                                                'activation_131[0][0]']      \n                                                                                                  \n block17_14_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_14_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_23 (Cus  (None, 17, 17, 1088)         0         ['block17_13_ac[0][0]',       \n tomScaleLayer)                                                      'block17_14_conv[0][0]']     \n                                                                                                  \n block17_14_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_23[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_133 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_14_ac[0][0]']       \n                                                                                                  \n batch_normalization_133 (B  (None, 17, 17, 128)          384       ['conv2d_133[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_133 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_133[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_134 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_133[0][0]']      \n                                                                                                  \n batch_normalization_134 (B  (None, 17, 17, 160)          480       ['conv2d_134[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_134 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_134[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_132 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_14_ac[0][0]']       \n                                                                                                  \n conv2d_135 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_134[0][0]']      \n                                                                                                  \n batch_normalization_132 (B  (None, 17, 17, 192)          576       ['conv2d_132[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_135 (B  (None, 17, 17, 192)          576       ['conv2d_135[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_132 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_132[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_135 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_135[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_15_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_132[0][0]',      \n ate)                                                                'activation_135[0][0]']      \n                                                                                                  \n block17_15_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_15_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_24 (Cus  (None, 17, 17, 1088)         0         ['block17_14_ac[0][0]',       \n tomScaleLayer)                                                      'block17_15_conv[0][0]']     \n                                                                                                  \n block17_15_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_24[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_137 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_15_ac[0][0]']       \n                                                                                                  \n batch_normalization_137 (B  (None, 17, 17, 128)          384       ['conv2d_137[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_137 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_137[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_138 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_137[0][0]']      \n                                                                                                  \n batch_normalization_138 (B  (None, 17, 17, 160)          480       ['conv2d_138[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_138 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_138[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_136 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_15_ac[0][0]']       \n                                                                                                  \n conv2d_139 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_138[0][0]']      \n                                                                                                  \n batch_normalization_136 (B  (None, 17, 17, 192)          576       ['conv2d_136[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_139 (B  (None, 17, 17, 192)          576       ['conv2d_139[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_136 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_136[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_139 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_139[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_16_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_136[0][0]',      \n ate)                                                                'activation_139[0][0]']      \n                                                                                                  \n block17_16_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_16_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_25 (Cus  (None, 17, 17, 1088)         0         ['block17_15_ac[0][0]',       \n tomScaleLayer)                                                      'block17_16_conv[0][0]']     \n                                                                                                  \n block17_16_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_25[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_141 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_16_ac[0][0]']       \n                                                                                                  \n batch_normalization_141 (B  (None, 17, 17, 128)          384       ['conv2d_141[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_141 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_141[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_142 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_141[0][0]']      \n                                                                                                  \n batch_normalization_142 (B  (None, 17, 17, 160)          480       ['conv2d_142[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_142 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_142[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_140 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_16_ac[0][0]']       \n                                                                                                  \n conv2d_143 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_142[0][0]']      \n                                                                                                  \n batch_normalization_140 (B  (None, 17, 17, 192)          576       ['conv2d_140[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_143 (B  (None, 17, 17, 192)          576       ['conv2d_143[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_140 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_140[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_143 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_143[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_17_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_140[0][0]',      \n ate)                                                                'activation_143[0][0]']      \n                                                                                                  \n block17_17_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_17_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_26 (Cus  (None, 17, 17, 1088)         0         ['block17_16_ac[0][0]',       \n tomScaleLayer)                                                      'block17_17_conv[0][0]']     \n                                                                                                  \n block17_17_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_26[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_145 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_17_ac[0][0]']       \n                                                                                                  \n batch_normalization_145 (B  (None, 17, 17, 128)          384       ['conv2d_145[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_145 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_145[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_146 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_145[0][0]']      \n                                                                                                  \n batch_normalization_146 (B  (None, 17, 17, 160)          480       ['conv2d_146[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_146 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_146[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_144 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_17_ac[0][0]']       \n                                                                                                  \n conv2d_147 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_146[0][0]']      \n                                                                                                  \n batch_normalization_144 (B  (None, 17, 17, 192)          576       ['conv2d_144[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_147 (B  (None, 17, 17, 192)          576       ['conv2d_147[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_144 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_144[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_147 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_147[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_18_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_144[0][0]',      \n ate)                                                                'activation_147[0][0]']      \n                                                                                                  \n block17_18_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_18_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_27 (Cus  (None, 17, 17, 1088)         0         ['block17_17_ac[0][0]',       \n tomScaleLayer)                                                      'block17_18_conv[0][0]']     \n                                                                                                  \n block17_18_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_27[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_149 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_18_ac[0][0]']       \n                                                                                                  \n batch_normalization_149 (B  (None, 17, 17, 128)          384       ['conv2d_149[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_149 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_149[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_150 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_149[0][0]']      \n                                                                                                  \n batch_normalization_150 (B  (None, 17, 17, 160)          480       ['conv2d_150[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_150 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_150[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_148 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_18_ac[0][0]']       \n                                                                                                  \n conv2d_151 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_150[0][0]']      \n                                                                                                  \n batch_normalization_148 (B  (None, 17, 17, 192)          576       ['conv2d_148[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_151 (B  (None, 17, 17, 192)          576       ['conv2d_151[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_148 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_148[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_151 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_151[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_19_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_148[0][0]',      \n ate)                                                                'activation_151[0][0]']      \n                                                                                                  \n block17_19_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_19_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_28 (Cus  (None, 17, 17, 1088)         0         ['block17_18_ac[0][0]',       \n tomScaleLayer)                                                      'block17_19_conv[0][0]']     \n                                                                                                  \n block17_19_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_28[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_153 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_19_ac[0][0]']       \n                                                                                                  \n batch_normalization_153 (B  (None, 17, 17, 128)          384       ['conv2d_153[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_153 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_153[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_154 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_153[0][0]']      \n                                                                                                  \n batch_normalization_154 (B  (None, 17, 17, 160)          480       ['conv2d_154[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_154 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_154[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_152 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_19_ac[0][0]']       \n                                                                                                  \n conv2d_155 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_154[0][0]']      \n                                                                                                  \n batch_normalization_152 (B  (None, 17, 17, 192)          576       ['conv2d_152[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_155 (B  (None, 17, 17, 192)          576       ['conv2d_155[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_152 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_152[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_155 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_155[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_20_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_152[0][0]',      \n ate)                                                                'activation_155[0][0]']      \n                                                                                                  \n block17_20_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_20_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_29 (Cus  (None, 17, 17, 1088)         0         ['block17_19_ac[0][0]',       \n tomScaleLayer)                                                      'block17_20_conv[0][0]']     \n                                                                                                  \n block17_20_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_29[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_160 (Conv2D)         (None, 17, 17, 256)          278528    ['block17_20_ac[0][0]']       \n                                                                                                  \n batch_normalization_160 (B  (None, 17, 17, 256)          768       ['conv2d_160[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_160 (Activation  (None, 17, 17, 256)          0         ['batch_normalization_160[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_156 (Conv2D)         (None, 17, 17, 256)          278528    ['block17_20_ac[0][0]']       \n                                                                                                  \n conv2d_158 (Conv2D)         (None, 17, 17, 256)          278528    ['block17_20_ac[0][0]']       \n                                                                                                  \n conv2d_161 (Conv2D)         (None, 17, 17, 288)          663552    ['activation_160[0][0]']      \n                                                                                                  \n batch_normalization_156 (B  (None, 17, 17, 256)          768       ['conv2d_156[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_158 (B  (None, 17, 17, 256)          768       ['conv2d_158[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_161 (B  (None, 17, 17, 288)          864       ['conv2d_161[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_156 (Activation  (None, 17, 17, 256)          0         ['batch_normalization_156[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_158 (Activation  (None, 17, 17, 256)          0         ['batch_normalization_158[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_161 (Activation  (None, 17, 17, 288)          0         ['batch_normalization_161[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_157 (Conv2D)         (None, 8, 8, 384)            884736    ['activation_156[0][0]']      \n                                                                                                  \n conv2d_159 (Conv2D)         (None, 8, 8, 288)            663552    ['activation_158[0][0]']      \n                                                                                                  \n conv2d_162 (Conv2D)         (None, 8, 8, 320)            829440    ['activation_161[0][0]']      \n                                                                                                  \n batch_normalization_157 (B  (None, 8, 8, 384)            1152      ['conv2d_157[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_159 (B  (None, 8, 8, 288)            864       ['conv2d_159[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_162 (B  (None, 8, 8, 320)            960       ['conv2d_162[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_157 (Activation  (None, 8, 8, 384)            0         ['batch_normalization_157[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_159 (Activation  (None, 8, 8, 288)            0         ['batch_normalization_159[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_162 (Activation  (None, 8, 8, 320)            0         ['batch_normalization_162[0][0\n )                                                                  ]']                           \n                                                                                                  \n max_pooling2d_3 (MaxPoolin  (None, 8, 8, 1088)           0         ['block17_20_ac[0][0]']       \n g2D)                                                                                             \n                                                                                                  \n mixed_7a (Concatenate)      (None, 8, 8, 2080)           0         ['activation_157[0][0]',      \n                                                                     'activation_159[0][0]',      \n                                                                     'activation_162[0][0]',      \n                                                                     'max_pooling2d_3[0][0]']     \n                                                                                                  \n conv2d_164 (Conv2D)         (None, 8, 8, 192)            399360    ['mixed_7a[0][0]']            \n                                                                                                  \n batch_normalization_164 (B  (None, 8, 8, 192)            576       ['conv2d_164[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_164 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_164[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_165 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_164[0][0]']      \n                                                                                                  \n batch_normalization_165 (B  (None, 8, 8, 224)            672       ['conv2d_165[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_165 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_165[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_163 (Conv2D)         (None, 8, 8, 192)            399360    ['mixed_7a[0][0]']            \n                                                                                                  \n conv2d_166 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_165[0][0]']      \n                                                                                                  \n batch_normalization_163 (B  (None, 8, 8, 192)            576       ['conv2d_163[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_166 (B  (None, 8, 8, 256)            768       ['conv2d_166[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_163 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_163[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_166 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_166[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_1_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_163[0][0]',      \n e)                                                                  'activation_166[0][0]']      \n                                                                                                  \n block8_1_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_1_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_30 (Cus  (None, 8, 8, 2080)           0         ['mixed_7a[0][0]',            \n tomScaleLayer)                                                      'block8_1_conv[0][0]']       \n                                                                                                  \n block8_1_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_30[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_168 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_1_ac[0][0]']         \n                                                                                                  \n batch_normalization_168 (B  (None, 8, 8, 192)            576       ['conv2d_168[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_168 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_168[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_169 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_168[0][0]']      \n                                                                                                  \n batch_normalization_169 (B  (None, 8, 8, 224)            672       ['conv2d_169[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_169 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_169[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_167 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_1_ac[0][0]']         \n                                                                                                  \n conv2d_170 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_169[0][0]']      \n                                                                                                  \n batch_normalization_167 (B  (None, 8, 8, 192)            576       ['conv2d_167[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_170 (B  (None, 8, 8, 256)            768       ['conv2d_170[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_167 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_167[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_170 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_170[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_2_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_167[0][0]',      \n e)                                                                  'activation_170[0][0]']      \n                                                                                                  \n block8_2_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_2_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_31 (Cus  (None, 8, 8, 2080)           0         ['block8_1_ac[0][0]',         \n tomScaleLayer)                                                      'block8_2_conv[0][0]']       \n                                                                                                  \n block8_2_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_31[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_172 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_2_ac[0][0]']         \n                                                                                                  \n batch_normalization_172 (B  (None, 8, 8, 192)            576       ['conv2d_172[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_172 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_172[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_173 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_172[0][0]']      \n                                                                                                  \n batch_normalization_173 (B  (None, 8, 8, 224)            672       ['conv2d_173[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_173 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_173[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_171 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_2_ac[0][0]']         \n                                                                                                  \n conv2d_174 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_173[0][0]']      \n                                                                                                  \n batch_normalization_171 (B  (None, 8, 8, 192)            576       ['conv2d_171[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_174 (B  (None, 8, 8, 256)            768       ['conv2d_174[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_171 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_171[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_174 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_174[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_3_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_171[0][0]',      \n e)                                                                  'activation_174[0][0]']      \n                                                                                                  \n block8_3_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_3_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_32 (Cus  (None, 8, 8, 2080)           0         ['block8_2_ac[0][0]',         \n tomScaleLayer)                                                      'block8_3_conv[0][0]']       \n                                                                                                  \n block8_3_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_32[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_176 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_3_ac[0][0]']         \n                                                                                                  \n batch_normalization_176 (B  (None, 8, 8, 192)            576       ['conv2d_176[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_176 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_176[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_177 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_176[0][0]']      \n                                                                                                  \n batch_normalization_177 (B  (None, 8, 8, 224)            672       ['conv2d_177[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_177 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_177[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_175 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_3_ac[0][0]']         \n                                                                                                  \n conv2d_178 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_177[0][0]']      \n                                                                                                  \n batch_normalization_175 (B  (None, 8, 8, 192)            576       ['conv2d_175[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_178 (B  (None, 8, 8, 256)            768       ['conv2d_178[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_175 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_175[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_178 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_178[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_4_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_175[0][0]',      \n e)                                                                  'activation_178[0][0]']      \n                                                                                                  \n block8_4_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_4_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_33 (Cus  (None, 8, 8, 2080)           0         ['block8_3_ac[0][0]',         \n tomScaleLayer)                                                      'block8_4_conv[0][0]']       \n                                                                                                  \n block8_4_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_33[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_180 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_4_ac[0][0]']         \n                                                                                                  \n batch_normalization_180 (B  (None, 8, 8, 192)            576       ['conv2d_180[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_180 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_180[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_181 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_180[0][0]']      \n                                                                                                  \n batch_normalization_181 (B  (None, 8, 8, 224)            672       ['conv2d_181[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_181 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_181[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_179 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_4_ac[0][0]']         \n                                                                                                  \n conv2d_182 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_181[0][0]']      \n                                                                                                  \n batch_normalization_179 (B  (None, 8, 8, 192)            576       ['conv2d_179[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_182 (B  (None, 8, 8, 256)            768       ['conv2d_182[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_179 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_179[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_182 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_182[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_5_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_179[0][0]',      \n e)                                                                  'activation_182[0][0]']      \n                                                                                                  \n block8_5_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_5_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_34 (Cus  (None, 8, 8, 2080)           0         ['block8_4_ac[0][0]',         \n tomScaleLayer)                                                      'block8_5_conv[0][0]']       \n                                                                                                  \n block8_5_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_34[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_184 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_5_ac[0][0]']         \n                                                                                                  \n batch_normalization_184 (B  (None, 8, 8, 192)            576       ['conv2d_184[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_184 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_184[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_185 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_184[0][0]']      \n                                                                                                  \n batch_normalization_185 (B  (None, 8, 8, 224)            672       ['conv2d_185[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_185 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_185[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_183 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_5_ac[0][0]']         \n                                                                                                  \n conv2d_186 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_185[0][0]']      \n                                                                                                  \n batch_normalization_183 (B  (None, 8, 8, 192)            576       ['conv2d_183[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_186 (B  (None, 8, 8, 256)            768       ['conv2d_186[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_183 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_183[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_186 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_186[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_6_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_183[0][0]',      \n e)                                                                  'activation_186[0][0]']      \n                                                                                                  \n block8_6_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_6_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_35 (Cus  (None, 8, 8, 2080)           0         ['block8_5_ac[0][0]',         \n tomScaleLayer)                                                      'block8_6_conv[0][0]']       \n                                                                                                  \n block8_6_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_35[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_188 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_6_ac[0][0]']         \n                                                                                                  \n batch_normalization_188 (B  (None, 8, 8, 192)            576       ['conv2d_188[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_188 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_188[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_189 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_188[0][0]']      \n                                                                                                  \n batch_normalization_189 (B  (None, 8, 8, 224)            672       ['conv2d_189[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_189 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_189[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_187 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_6_ac[0][0]']         \n                                                                                                  \n conv2d_190 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_189[0][0]']      \n                                                                                                  \n batch_normalization_187 (B  (None, 8, 8, 192)            576       ['conv2d_187[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_190 (B  (None, 8, 8, 256)            768       ['conv2d_190[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_187 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_187[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_190 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_190[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_7_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_187[0][0]',      \n e)                                                                  'activation_190[0][0]']      \n                                                                                                  \n block8_7_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_7_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_36 (Cus  (None, 8, 8, 2080)           0         ['block8_6_ac[0][0]',         \n tomScaleLayer)                                                      'block8_7_conv[0][0]']       \n                                                                                                  \n block8_7_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_36[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_192 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_7_ac[0][0]']         \n                                                                                                  \n batch_normalization_192 (B  (None, 8, 8, 192)            576       ['conv2d_192[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_192 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_192[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_193 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_192[0][0]']      \n                                                                                                  \n batch_normalization_193 (B  (None, 8, 8, 224)            672       ['conv2d_193[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_193 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_193[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_191 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_7_ac[0][0]']         \n                                                                                                  \n conv2d_194 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_193[0][0]']      \n                                                                                                  \n batch_normalization_191 (B  (None, 8, 8, 192)            576       ['conv2d_191[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_194 (B  (None, 8, 8, 256)            768       ['conv2d_194[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_191 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_191[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_194 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_194[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_8_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_191[0][0]',      \n e)                                                                  'activation_194[0][0]']      \n                                                                                                  \n block8_8_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_8_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_37 (Cus  (None, 8, 8, 2080)           0         ['block8_7_ac[0][0]',         \n tomScaleLayer)                                                      'block8_8_conv[0][0]']       \n                                                                                                  \n block8_8_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_37[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_196 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_8_ac[0][0]']         \n                                                                                                  \n batch_normalization_196 (B  (None, 8, 8, 192)            576       ['conv2d_196[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_196 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_196[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_197 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_196[0][0]']      \n                                                                                                  \n batch_normalization_197 (B  (None, 8, 8, 224)            672       ['conv2d_197[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_197 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_197[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_195 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_8_ac[0][0]']         \n                                                                                                  \n conv2d_198 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_197[0][0]']      \n                                                                                                  \n batch_normalization_195 (B  (None, 8, 8, 192)            576       ['conv2d_195[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_198 (B  (None, 8, 8, 256)            768       ['conv2d_198[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_195 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_195[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_198 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_198[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_9_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_195[0][0]',      \n e)                                                                  'activation_198[0][0]']      \n                                                                                                  \n block8_9_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_9_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_38 (Cus  (None, 8, 8, 2080)           0         ['block8_8_ac[0][0]',         \n tomScaleLayer)                                                      'block8_9_conv[0][0]']       \n                                                                                                  \n block8_9_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_38[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_200 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_9_ac[0][0]']         \n                                                                                                  \n batch_normalization_200 (B  (None, 8, 8, 192)            576       ['conv2d_200[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_200 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_200[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_201 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_200[0][0]']      \n                                                                                                  \n batch_normalization_201 (B  (None, 8, 8, 224)            672       ['conv2d_201[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_201 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_201[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_199 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_9_ac[0][0]']         \n                                                                                                  \n conv2d_202 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_201[0][0]']      \n                                                                                                  \n batch_normalization_199 (B  (None, 8, 8, 192)            576       ['conv2d_199[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_202 (B  (None, 8, 8, 256)            768       ['conv2d_202[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_199 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_199[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_202 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_202[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_10_mixed (Concatena  (None, 8, 8, 448)            0         ['activation_199[0][0]',      \n te)                                                                 'activation_202[0][0]']      \n                                                                                                  \n block8_10_conv (Conv2D)     (None, 8, 8, 2080)           933920    ['block8_10_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_39 (Cus  (None, 8, 8, 2080)           0         ['block8_9_ac[0][0]',         \n tomScaleLayer)                                                      'block8_10_conv[0][0]']      \n                                                                                                  \n conv_7b (Conv2D)            (None, 8, 8, 1536)           3194880   ['custom_scale_layer_39[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv_7b_bn (BatchNormaliza  (None, 8, 8, 1536)           4608      ['conv_7b[0][0]']             \n tion)                                                                                            \n                                                                                                  \n conv_7b_ac (Activation)     (None, 8, 8, 1536)           0         ['conv_7b_bn[0][0]']          \n                                                                                                  \n flatten (Flatten)           (None, 98304)                0         ['conv_7b_ac[0][0]']          \n                                                                                                  \n dense (Dense)               (None, 2)                    196610    ['flatten[0][0]']             \n                                                                                                  \n==================================================================================================\nTotal params: 54533346 (208.03 MB)\nTrainable params: 54472802 (207.80 MB)\nNon-trainable params: 60544 (236.50 KB)\n__________________________________________________________________________________________________\nNone\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Training starts here**","metadata":{}},{"cell_type":"code","source":"# Train the model\nhistory = model.fit(\n    train_generator,\n    steps_per_epoch=15,\n    epochs=50,\n    validation_data=validation_generator,\n    validation_steps=10,\n    callbacks=[cp_callback]\n    \n)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:11:36.759188Z","iopub.execute_input":"2024-01-12T14:11:36.75962Z","iopub.status.idle":"2024-01-12T14:15:47.196193Z","shell.execute_reply.started":"2024-01-12T14:11:36.759589Z","shell.execute_reply":"2024-01-12T14:15:47.195275Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Epoch 1/50\n15/15 [==============================] - ETA: 0s - loss: 1.9193 - accuracy: 0.7466\nEpoch 1: val_accuracy did not improve from 0.38667\n15/15 [==============================] - 4s 279ms/step - loss: 1.9193 - accuracy: 0.7466 - val_loss: 28335.9004 - val_accuracy: 0.3867\nEpoch 2/50\n15/15 [==============================] - ETA: 0s - loss: 1.8115 - accuracy: 0.7671\nEpoch 2: val_accuracy improved from 0.38667 to 0.61333, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 7s 501ms/step - loss: 1.8115 - accuracy: 0.7671 - val_loss: 223.1181 - val_accuracy: 0.6133\nEpoch 3/50\n15/15 [==============================] - ETA: 0s - loss: 0.8763 - accuracy: 0.7945\nEpoch 3: val_accuracy did not improve from 0.61333\n15/15 [==============================] - 4s 275ms/step - loss: 0.8763 - accuracy: 0.7945 - val_loss: 41.9699 - val_accuracy: 0.6000\nEpoch 4/50\n15/15 [==============================] - ETA: 0s - loss: 0.8991 - accuracy: 0.7808\nEpoch 4: val_accuracy did not improve from 0.61333\n15/15 [==============================] - 4s 276ms/step - loss: 0.8991 - accuracy: 0.7808 - val_loss: 70.4136 - val_accuracy: 0.4000\nEpoch 5/50\n15/15 [==============================] - ETA: 0s - loss: 0.7212 - accuracy: 0.8333\nEpoch 5: val_accuracy did not improve from 0.61333\n15/15 [==============================] - 4s 280ms/step - loss: 0.7212 - accuracy: 0.8333 - val_loss: 46.9675 - val_accuracy: 0.3733\nEpoch 6/50\n15/15 [==============================] - ETA: 0s - loss: 0.9916 - accuracy: 0.7671\nEpoch 6: val_accuracy did not improve from 0.61333\n15/15 [==============================] - 4s 276ms/step - loss: 0.9916 - accuracy: 0.7671 - val_loss: 47.1595 - val_accuracy: 0.3467\nEpoch 7/50\n15/15 [==============================] - ETA: 0s - loss: 0.5943 - accuracy: 0.8288\nEpoch 7: val_accuracy improved from 0.61333 to 0.64000, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 7s 504ms/step - loss: 0.5943 - accuracy: 0.8288 - val_loss: 20.5553 - val_accuracy: 0.6400\nEpoch 8/50\n15/15 [==============================] - ETA: 0s - loss: 0.5037 - accuracy: 0.7933\nEpoch 8: val_accuracy improved from 0.64000 to 0.73333, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 8s 551ms/step - loss: 0.5037 - accuracy: 0.7933 - val_loss: 1.2445 - val_accuracy: 0.7333\nEpoch 9/50\n15/15 [==============================] - ETA: 0s - loss: 0.6731 - accuracy: 0.8425\nEpoch 9: val_accuracy did not improve from 0.73333\n15/15 [==============================] - 4s 277ms/step - loss: 0.6731 - accuracy: 0.8425 - val_loss: 3.6129 - val_accuracy: 0.5600\nEpoch 10/50\n15/15 [==============================] - ETA: 0s - loss: 0.5510 - accuracy: 0.8288\nEpoch 10: val_accuracy did not improve from 0.73333\n15/15 [==============================] - 4s 278ms/step - loss: 0.5510 - accuracy: 0.8288 - val_loss: 38.5030 - val_accuracy: 0.6133\nEpoch 11/50\n15/15 [==============================] - ETA: 0s - loss: 0.2482 - accuracy: 0.8836\nEpoch 11: val_accuracy did not improve from 0.73333\n15/15 [==============================] - 4s 278ms/step - loss: 0.2482 - accuracy: 0.8836 - val_loss: 16.6060 - val_accuracy: 0.6667\nEpoch 12/50\n15/15 [==============================] - ETA: 0s - loss: 0.4699 - accuracy: 0.8800\nEpoch 12: val_accuracy improved from 0.73333 to 0.77333, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 8s 509ms/step - loss: 0.4699 - accuracy: 0.8800 - val_loss: 3.2118 - val_accuracy: 0.7733\nEpoch 13/50\n15/15 [==============================] - ETA: 0s - loss: 0.6188 - accuracy: 0.8836\nEpoch 13: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 276ms/step - loss: 0.6188 - accuracy: 0.8836 - val_loss: 2.9828 - val_accuracy: 0.5600\nEpoch 14/50\n15/15 [==============================] - ETA: 0s - loss: 0.7592 - accuracy: 0.8425\nEpoch 14: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 276ms/step - loss: 0.7592 - accuracy: 0.8425 - val_loss: 4.9741 - val_accuracy: 0.6267\nEpoch 15/50\n15/15 [==============================] - ETA: 0s - loss: 0.4791 - accuracy: 0.8933\nEpoch 15: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 281ms/step - loss: 0.4791 - accuracy: 0.8933 - val_loss: 12.6543 - val_accuracy: 0.4667\nEpoch 16/50\n15/15 [==============================] - ETA: 0s - loss: 0.2991 - accuracy: 0.9247\nEpoch 16: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 276ms/step - loss: 0.2991 - accuracy: 0.9247 - val_loss: 5.3657 - val_accuracy: 0.6533\nEpoch 17/50\n15/15 [==============================] - ETA: 0s - loss: 0.4354 - accuracy: 0.9178\nEpoch 17: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 275ms/step - loss: 0.4354 - accuracy: 0.9178 - val_loss: 46.7230 - val_accuracy: 0.5200\nEpoch 18/50\n15/15 [==============================] - ETA: 0s - loss: 0.6075 - accuracy: 0.8082\nEpoch 18: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 278ms/step - loss: 0.6075 - accuracy: 0.8082 - val_loss: 73.3672 - val_accuracy: 0.6133\nEpoch 19/50\n15/15 [==============================] - ETA: 0s - loss: 0.3775 - accuracy: 0.8836\nEpoch 19: val_accuracy did not improve from 0.77333\n15/15 [==============================] - 4s 277ms/step - loss: 0.3775 - accuracy: 0.8836 - val_loss: 7.0958 - val_accuracy: 0.6267\nEpoch 20/50\n15/15 [==============================] - ETA: 0s - loss: 0.2689 - accuracy: 0.9041\nEpoch 20: val_accuracy improved from 0.77333 to 0.84000, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 7s 500ms/step - loss: 0.2689 - accuracy: 0.9041 - val_loss: 0.7706 - val_accuracy: 0.8400\nEpoch 21/50\n15/15 [==============================] - ETA: 0s - loss: 0.2683 - accuracy: 0.9110\nEpoch 21: val_accuracy improved from 0.84000 to 0.85333, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 8s 507ms/step - loss: 0.2683 - accuracy: 0.9110 - val_loss: 0.5920 - val_accuracy: 0.8533\nEpoch 22/50\n15/15 [==============================] - ETA: 0s - loss: 0.1701 - accuracy: 0.9247\nEpoch 22: val_accuracy did not improve from 0.85333\n15/15 [==============================] - 4s 278ms/step - loss: 0.1701 - accuracy: 0.9247 - val_loss: 0.3373 - val_accuracy: 0.8533\nEpoch 23/50\n15/15 [==============================] - ETA: 0s - loss: 0.1604 - accuracy: 0.9384\nEpoch 23: val_accuracy improved from 0.85333 to 0.92000, saving model to /kaggle/working/Checkpoints_InceptionResNetV2/cp.ckpt\n15/15 [==============================] - 8s 508ms/step - loss: 0.1604 - accuracy: 0.9384 - val_loss: 0.6892 - val_accuracy: 0.9200\nEpoch 24/50\n15/15 [==============================] - ETA: 0s - loss: 0.0316 - accuracy: 0.9932\nEpoch 24: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.0316 - accuracy: 0.9932 - val_loss: 0.6815 - val_accuracy: 0.8667\nEpoch 25/50\n15/15 [==============================] - ETA: 0s - loss: 0.1026 - accuracy: 0.9795\nEpoch 25: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.1026 - accuracy: 0.9795 - val_loss: 0.4670 - val_accuracy: 0.8000\nEpoch 26/50\n15/15 [==============================] - ETA: 0s - loss: 0.1140 - accuracy: 0.9384\nEpoch 26: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.1140 - accuracy: 0.9384 - val_loss: 0.4738 - val_accuracy: 0.8533\nEpoch 27/50\n15/15 [==============================] - ETA: 0s - loss: 0.1956 - accuracy: 0.9452\nEpoch 27: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.1956 - accuracy: 0.9452 - val_loss: 0.7088 - val_accuracy: 0.7600\nEpoch 28/50\n15/15 [==============================] - ETA: 0s - loss: 0.1157 - accuracy: 0.9589\nEpoch 28: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 277ms/step - loss: 0.1157 - accuracy: 0.9589 - val_loss: 0.4950 - val_accuracy: 0.8267\nEpoch 29/50\n15/15 [==============================] - ETA: 0s - loss: 0.1671 - accuracy: 0.9467\nEpoch 29: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 5s 324ms/step - loss: 0.1671 - accuracy: 0.9467 - val_loss: 1.2884 - val_accuracy: 0.7333\nEpoch 30/50\n15/15 [==============================] - ETA: 0s - loss: 0.1267 - accuracy: 0.9589\nEpoch 30: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.1267 - accuracy: 0.9589 - val_loss: 1.0333 - val_accuracy: 0.8000\nEpoch 31/50\n15/15 [==============================] - ETA: 0s - loss: 0.2492 - accuracy: 0.9200\nEpoch 31: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 278ms/step - loss: 0.2492 - accuracy: 0.9200 - val_loss: 0.7720 - val_accuracy: 0.7867\nEpoch 32/50\n15/15 [==============================] - ETA: 0s - loss: 0.1193 - accuracy: 0.9658\nEpoch 32: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.1193 - accuracy: 0.9658 - val_loss: 0.5148 - val_accuracy: 0.8933\nEpoch 33/50\n15/15 [==============================] - ETA: 0s - loss: 0.2914 - accuracy: 0.8973\nEpoch 33: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.2914 - accuracy: 0.8973 - val_loss: 0.4615 - val_accuracy: 0.8800\nEpoch 34/50\n15/15 [==============================] - ETA: 0s - loss: 0.2116 - accuracy: 0.9315\nEpoch 34: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.2116 - accuracy: 0.9315 - val_loss: 1.5255 - val_accuracy: 0.6667\nEpoch 35/50\n15/15 [==============================] - ETA: 0s - loss: 0.1518 - accuracy: 0.9384\nEpoch 35: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 277ms/step - loss: 0.1518 - accuracy: 0.9384 - val_loss: 0.5614 - val_accuracy: 0.8667\nEpoch 36/50\n15/15 [==============================] - ETA: 0s - loss: 0.1701 - accuracy: 0.9315\nEpoch 36: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 278ms/step - loss: 0.1701 - accuracy: 0.9315 - val_loss: 0.4614 - val_accuracy: 0.8800\nEpoch 37/50\n15/15 [==============================] - ETA: 0s - loss: 0.0987 - accuracy: 0.9589\nEpoch 37: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.0987 - accuracy: 0.9589 - val_loss: 0.6989 - val_accuracy: 0.8667\nEpoch 38/50\n15/15 [==============================] - ETA: 0s - loss: 0.0507 - accuracy: 0.9863\nEpoch 38: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 277ms/step - loss: 0.0507 - accuracy: 0.9863 - val_loss: 0.4484 - val_accuracy: 0.9067\nEpoch 39/50\n15/15 [==============================] - ETA: 0s - loss: 0.4048 - accuracy: 0.9315\nEpoch 39: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 274ms/step - loss: 0.4048 - accuracy: 0.9315 - val_loss: 1.1174 - val_accuracy: 0.6933\nEpoch 40/50\n15/15 [==============================] - ETA: 0s - loss: 0.2089 - accuracy: 0.9315\nEpoch 40: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.2089 - accuracy: 0.9315 - val_loss: 1.9616 - val_accuracy: 0.5067\nEpoch 41/50\n15/15 [==============================] - ETA: 0s - loss: 0.1296 - accuracy: 0.9589\nEpoch 41: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.1296 - accuracy: 0.9589 - val_loss: 0.5531 - val_accuracy: 0.8400\nEpoch 42/50\n15/15 [==============================] - ETA: 0s - loss: 0.1023 - accuracy: 0.9589\nEpoch 42: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.1023 - accuracy: 0.9589 - val_loss: 0.6371 - val_accuracy: 0.8400\nEpoch 43/50\n15/15 [==============================] - ETA: 0s - loss: 0.0674 - accuracy: 0.9726\nEpoch 43: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 277ms/step - loss: 0.0674 - accuracy: 0.9726 - val_loss: 0.9508 - val_accuracy: 0.8000\nEpoch 44/50\n15/15 [==============================] - ETA: 0s - loss: 0.0293 - accuracy: 0.9933\nEpoch 44: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 280ms/step - loss: 0.0293 - accuracy: 0.9933 - val_loss: 1.2869 - val_accuracy: 0.8267\nEpoch 45/50\n15/15 [==============================] - ETA: 0s - loss: 0.0718 - accuracy: 0.9726\nEpoch 45: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.0718 - accuracy: 0.9726 - val_loss: 1.0863 - val_accuracy: 0.8000\nEpoch 46/50\n15/15 [==============================] - ETA: 0s - loss: 0.0733 - accuracy: 0.9795\nEpoch 46: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.0733 - accuracy: 0.9795 - val_loss: 1.3392 - val_accuracy: 0.8667\nEpoch 47/50\n15/15 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 0.9589\nEpoch 47: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.1018 - accuracy: 0.9589 - val_loss: 1.7301 - val_accuracy: 0.8400\nEpoch 48/50\n15/15 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9600\nEpoch 48: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 281ms/step - loss: 0.1083 - accuracy: 0.9600 - val_loss: 1.5054 - val_accuracy: 0.8667\nEpoch 49/50\n15/15 [==============================] - ETA: 0s - loss: 0.1566 - accuracy: 0.9452\nEpoch 49: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 275ms/step - loss: 0.1566 - accuracy: 0.9452 - val_loss: 1.9859 - val_accuracy: 0.8133\nEpoch 50/50\n15/15 [==============================] - ETA: 0s - loss: 0.0844 - accuracy: 0.9589\nEpoch 50: val_accuracy did not improve from 0.92000\n15/15 [==============================] - 4s 276ms/step - loss: 0.0844 - accuracy: 0.9589 - val_loss: 1.1216 - val_accuracy: 0.8667\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Save model history in a CSV file**","metadata":{}},{"cell_type":"markdown","source":"# **Accuracy and loss graph of training and validation**\n## Here, No. of epochs and (xticks & yticks) will be changed","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nfrom matplotlib.legend_handler import HandlerLine2D\nimport numpy as np\n\n# Plot training & validation loss values\nplt.figure(figsize=(10, 6))\n\n# Plot Loss\ntrain_accuracy, = plt.plot(history.history['accuracy'], label='Train Accuracy',  color='green')\nval_accuracy, = plt.plot(history.history['val_accuracy'], label='Validation Accuracy', color='red')\n# Add a title with specified font properties\nplt.title('Model Performance during Training', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12},pad=10)\n# Set x-axis label with specified font properties\nplt.xlabel('No. of Epochs', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n\n\nplt.xticks(np.linspace(0, 150, num=16), fontname='Serif', weight='bold')\n\n\n# Set y-axis ticks font properties\nplt.yticks(np.linspace(0, 1, num=5), fontname='Serif', weight='bold')\n\n# Set the x-axis and y-axis limits\n#plt.xlim(0, len(history.history['loss']))\n\nplt.xlim(0, 50)\nplt.ylim(0, 1)\n\n# Define custom legend lines with desired line properties\nlegend_lines = [\n    \n    Line2D([0], [0], color='green', lw=3),        # Train Accuracy\n    Line2D([0], [0], color='red', lw=3)           # Validation Accuracy\n]\n\n# Place legend outside the graph by adjusting bbox_to_anchor and specifying it to be outside the axes\nplt.legend(legend_lines, [ 'Train Accuracy', 'Validation Accuracy'],\n           loc='lower center', bbox_to_anchor=(0.5, 1.1), ncol=5,\n           prop={'family': 'Serif', 'weight': 'bold', 'size': 8}, frameon=False,\n           handler_map={Line2D: HandlerLine2D(numpoints=5)})\n\n# Adjust padding between x-axis label and x-axis ticks\nplt.gca().xaxis.labelpad = 10  # Change the value as needed to adjust the space\n\n# Remove top and right spines\nplt.gca().spines['top'].set_visible(False)\nplt.gca().spines['right'].set_visible(False)\n# Adjust layout to prevent cropping\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:15:51.547504Z","iopub.execute_input":"2024-01-12T14:15:51.547888Z","iopub.status.idle":"2024-01-12T14:15:51.932656Z","shell.execute_reply.started":"2024-01-12T14:15:51.547857Z","shell.execute_reply":"2024-01-12T14:15:51.931774Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA94AAAJMCAYAAAAMtH7XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAADYS0lEQVR4nOzddVhU2RsH8O8M3SBgoxgIgmJ3IHYXYifq2i2ru2ugG+ruWmutsWIXttiB3YVIqCg20t0w5/fH+c0MSMMU8H6eh4fL3DvnnLkzw8x7zznvETDGGAghhBBCCCGEECIXQmU3gBBCCCGEEEIIKc0o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhBBCCCGEEELkiAJvQgghhBBCCCFEjijwJoQQQgghhBBC5IgCb0IIIYQQQgghRI4o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhBBCCCGEEELkiAJvQgghhBBCCCFEjijwJoQQQgghhBBC5IgCb0IIIYQQQgghRI4o8CaEEKIwPj4+MDMzw7Jly5TdFFJCLFq0CNra2hAIBJLbxo8fj6pVqyIkJCTb8WvWrIGxsTEEAgHev39fpDrpdUoIIUTWKPAmhBCSIxsbG3To0AEdOnSApqYmBAIBGjZsiA4dOqBixYpFCmri4+MRExODsLCwYrevZ8+eMDAwQGxsbLHLIvIVHx+PRo0aQSAQQCAQYN68eZJ9K1euhKmpKSZMmJDjfX/77TdUrFgxy20hISGIiopCcnJytuPnzp2Lhg0bFrhtu3btgkAggJubW5b2yup1mpeHDx9CIBBgyZIlcq2HEEKI8qkruwGEEEJyJ2IiRCRGyLRMU11TCAX5X3etWLEirl+/LtkOCQnBunXr0KFDB4wdO7ZIdbdq1QoREREwMDAo0v3F3r59iwsXLoAxhl27dmHmzJnFKq9MEImACNm+lmBqCgjzfy3p6+vj9u3bqFq1KqKjo9GjRw/JvpkzZ8LLyws7duwocLWnTp1CUlIS9PX1i9Ts/MjqdZqfjRs3AgC2b9+OxYsXQ0NDQ671EUIIUR7q8SaEEBUWkRiB8n+Xl+lPQQN5cdCdk5UrV6J///4QCATo3bs3RowYgcqVK8PNzQ1r1qxBu3bt0L59e1haWmLhwoVgjCEjIwMtW7aEkZERHB0dAWQdRrxixQq0atUK1apVg5eXV55t27x5M0aOHCnZZoxJ9sXGxmLy5MmoXr06HBwc0LFjRxw5cgQAEBwcjKFDh6JatWro3LkzOnfujGvXruHw4cOoWLEiBAIBrl+/jgMHDmQZrvz8+XPY2NhAIBBg5MiRGDRoEExMTLBr1y64urrCwcEBrVu3Rq1atbB27dp82zJy5EgIBAJoa2vjv//+Q1hYGKytrVG/fn3Ex8cX6PkptIgIoHx52f4UIpDX09OTXLDZtGmT5PZ9+/ZhxIgRaNu2LTp16oRatWrB2dkZ4eHhOZbz33//oUqVKjAwMJC8RgMCAtCuXTtYWlpi/Pjx2e6b23Pk5+eHlStXAuA93x06dMCXL1+yvU4B4MiRI2jQoAFatWqFunXrYsmSJUhLS0NgYKDktTFw4EC4uLjA1NQUQ4YMgUgkyvV8hIeHIzg4GJaWlvj27Rs8PDyy7L9w4QKaNm2KOnXqoGvXrhgxYoRkdMfevXthZ2eHevXqoVOnTpg4cSIAwNHREQKBAB06dAAANG/eHAKBQHLeZ8+eLXm/rVq1Ci1atECNGjXg7e2N9u3bo0uXLqhTpw66d++Od+/e5dmWmzdvSt4zNjY2yMjIwF9//QVTU1PJBQVCCCGZMEIIISorND6UwQ0y/QmNDy10OypUqMAAMC8vL8ltXl5eDACrXLkyi4uLY69fv2YHDx5kO3fuZPHx8Ywxxv744w8GgB07dowxxlhQUBADwBwcHCTlVK9enQFgz58/Z+Hh4QwAa9euXa5tSUhIYK1atWLx8fHMyMiIAWAXL16U7B83bhwDwP755x/GGGPe3t5szJgxjDHGHB0dGQB2+vRpxhhjp06dYkuXLmWMMTZmzJgsj9HBwYEBYEFBQYwxxtzd3RkA1rhxY5aWlsZu377Nrl69yjZv3szS09MZY4z98MMPDAB7/Phxnm0RiUSsbt26DADz8fFhjDE2atQoFh4eXpinpXBCQxkDZPsTWrjX0ps3b5hAIGBqamrs48ePjDHGOnXqxK5fv868vb0ZY4zdvXuXAWAzZsyQ3E/8GhH7/rlq0qQJA8Du37/PUlJSmLm5eZbnLq/nSPy8il8HjGV/nd66dYsJBALWpUsXxhhjq1evznIfcRn9+/dnjDE2ffp0BoBdvXo113Pxxx9/sAMHDrDffvuNAWCtWrWS7Hv9+jXT0tJiVapUYYmJiYwxxgYMGMCCgoLY9evXGQDWrFkzlp6ezjIyMliTJk1ybLf4PSp+/Wc+l+L35PLly9mLFy8k5/Lr168MAOvTp0++bTl48CADwJycnBhjjL18+ZK5urrm+pgJIaQsox5vQgghxdKmTRvo6+vDysoKQ4cORdWqVdG/f380bdoUu3fvBsB7FvNTr149mJqaAgA+f/6c63EHDhyAs7Mz9PT0MGLECADI0sN25swZAEDbtm0BAPb29vjnn38QHx8v6SEV7+vduzfmz59fqMfbqVMnqKuro02bNujYsSNMTEzg6OiI5s2b4+LFiwAAf3//PNsiEAgk9f7555/48uULNDU1JY+/tKpduza6deuGjIwMbNmyBTdv3kTz5s1RpUoV/P7772jUqBHmzJkDoGCvGYCPKnjy5AkAoG7dutDU1ESdOnWyHJPXc1QQp0+fBmMMdnZ2ACD5feLEiSzH1atXDwDyfR1nZGTg+PHjcHJygouLC9TV1XHv3j08e/YMAHDp0iWkpKSgSZMm0NHRAcB7uS0sLODp6QmAD4dXU1ODUCjEtWvXCvxYxLp37w4AWLx4MSpUqIA9e/agcePGGDp0KADp+c+rLc7OzqhRowZOnDiBwMBAbN68GZMnTy50WwghpCygwJsQQkix6OrqSrZTUlLQr18/vHr1Crdu3cLChQsBAOnp6fmWo6amJtnOa4ju9u3bcf36dQwdOlQyHPbs2bPZkr2xTMPPDQ0Nc9wnFAol84TFWbPF+zIyMnKsP/Pj/fDhA0aMGIHU1FTcu3dPMqT3+8ebU1tGjhyJypUr49ChQ/j5558xadKkXB9zaTJ9+nQAwI4dO7B+/XpMmTIFkyZNwpEjR7B3714cOnQIQMFeM98Tn+fM57ugz5EsZH4NA7m/js+cOYP4+HiMHj0ac+bMkSSP27BhQ5bjMj8OPT29LOXn9Joq6GsYyPo6/umnn+Du7o7ly5fjxo0bAPJ+DYvboqamhrlz50IkEmHx4sUIDw9HzZo1c62TEELKMgq8CSFEhZnqmiJ0fqhMf0x15dermpiYiKSkJJiYmEBHRwfBwcEyLf/27dto0qQJTp06hUOHDuH8+fPo3LkzRCIRNm/eDADo06cPAODOnTsAgCtXrmD06NHQ19eXzH0V79uxY4cko3SVKlUA8Lm3qampeP36db7tiYqKgkgkQoUKFaCmppbt8ebWFgDQ1NTErFmzkJaWhoCAADRr1qzI56VATE2B0FDZ/hShh75Hjx6oVasWwsLCwBiDhYUFIv4/V7xKlSqFfs0YGhqiSZMmAABfX1+kpqYiMDBQsj+/50jci5uamooHDx5IAv/M+vbtC4FAAF9fXwDS3vKBAwcWqq1i27Ztw+XLl3Ho0CHJDwAcOnQIkZGR6Nq1K7S0tPD06VMkJSWBMQYHBwe8ffsWvXv3BgDcv38fIpEIqampsLe3R3x8PMqXLw91dXXJHPeXL18WqD15nf+82gIALi4uMDMzw6FDh4qcdJEQQsoEZYxvJ4QQUnJ06dKFaWhoMACsQYMG7MGDBywuLo41aNCAAWAVKlRgv/zyi+T4efPmMT09PTZs2DA2ceJEBoBVr16dXb9+nbVo0YIBYEZGRmz16tXsl19+YVpaWgwAc3V1Za6urgwA09LSYgsXLszSDl9fX2Zpacnq16/PAgICGGOMvXv3jtWuXZsBYPr6+mzFihUsJiaGTZ48mVWvXp21b9+e9e3blwUHBzPG+PzVoUOHsurVqzNHR0c2dOhQyXz0T58+sSZNmjBLS0s2ffp01rBhQwaAdejQgb1584ZZW1tLHsuGDRsYY4yJRCI2ZMgQpq+vzyZNmsScnJwYAGZtbc0CAgLybAtjjMXExDAjIyO2e/duuT6HqkY8R1o8B/rQoUPMzMyMtW3bVjLn2cjIiK1bty7La2Ts2LFsx44dkpwDDRo0YBEREczf35+1adOGVatWjY0dO5bZ2dlJnrv8nqNv374xe3t7Zm5uzpo1a8bu37+f7XXKGGOHDx9m9vb2rGXLlsza2potWrSIpaamZntteHh4SOZRi+vIzNXVlRkaGmZ5fS9ZsoSpq6szAKxevXosODiYnT9/njVp0oRZW1uzjh07si1btkiO37t3L7Ozs2P169dnHTt2ZMePH5fs+/PPP5mRkRHr27evZK55hQoV2KFDh7KcSwcHBxYXF8cYY+z69eusatWqrEGDBmz9+vXZ3oN5tYUxxtzc3FitWrWYSCSS4auEEEJKFwFjmcYOEUIIIUTuoqOjcefOHXTv3h2Ojo64fPkytLS0lN0sQgrl+PHj6NGjB06ePImvX79mWZ+dEEJIVjTUnBBCCFGwlJQUzJ07F82aNYOLiwsF3aRE8vb2RosWLbB9+/Yyk6OAEEKKinq8CSGEEEIIIYQQOaIeb0IIIYQQQgghRI4o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhBBCCCGEEELkiAJvQgghhBBCCCFEjijwJoQQQgghhBBC5IgCb0IIIYQQQgghRI4o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhBBCCCGEEELkiAJvQgghhBBCCCFEjijwJoQQQgghhBBC5IgCb0IIIYQQQgghRI4o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhBBCCCGEEELkiAJvQgghhBBCCCFEjijwJoQQQgghhBBC5IgCb0IIIYQQQgghRI4o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhBBCCCGEEELkiAJvQgghhBBCCCFEjijwJoQQQgghhBBC5IgCb0IIIYQQQgghRI4o8CaEEEIIIYQQQuSIAm9CCCGEEEIIIUSOKPAmhJAyoHfv3tDW1oZAIIBAIMCuXbsKXYa9vT00NTUlZVy/fl3m7ZQ1b29v9O3bFxUrVoSenh7Kly+P7t27K7tZZcqBAwdgaGgoed106NBB7nUuXrwYBgYGGDt2rNzrUmXv3r2DjY0NqlSpggcPHhSrLDqnhBBSPBR4E0KInNy+fRvGxsZQV1eXBB3GxsaIj4/P8fjx48dLjhMIBDA0NMTUqVNl0hZPT08sXLiwWGW8ePECw4cPL/DxU6dOzRJwiR+/UCiErq4u7Ozs8PPPPyMyMrJY7cpNZGQkunTpgjNnzsDCwgLh4eE4ceJEsQMQUjjDhw/HixcvFFrnunXrEB8fj927dyMiIkKhdWc2duxYaGtrw9jYGMbGxjAwMMj2fhD/qKurF+mCWF5OnDiBV69e4evXr9i3b1+xylKVc0oIISUVBd6EECInbdu2RXR0NNq2bQuBQAAAiImJgbu7e7ZjQ0JCcODAgSy3vXjxAps3b1ZIW+Vh8+bN2QKu6OhoxMTEYNSoUfDz88OKFSvQtGlTREVFybz+u3fvIiwsDADQqVMn6OjooE2bNrh3757M6yKqZebMmdDT08OYMWNgamqq1LYsXLgQ0dHRiI6OxuXLl7PsE98u/j8ha/369YOVlRUqV66MkSNHFqssVTqnhBBSElHgTQghCtCzZ0/J9vr16yESibLs37RpEzp27KjoZimFgYEBNmzYAHNzcwBAUFAQtm3bJvN6wsPDJdva2tqSbRsbG5nXRVTL77//jvj4eJn3IBdWhQoVUK5cuQIdW6VKFRgaGsq0/tq1a+P169f48uULWrRoUayyVOWcEkJISUWBNyGEKMCAAQNQo0YNAMDbt29x5swZyb7k5GT8+++/mD17dp5lREVF4ccff4S1tTX09fVhYGCAZs2aYePGjdkC+UePHqFz587Q09ODsbEx+vbti48fP+Za9qVLl9C1a1eYmJhAV1cXVlZW+OWXX5CcnFz0B50HTU1NWFpaSv5+9+4dAD4iwNXVFVZWVpK2d+vWDXfu3JEcu3Llymxzhnfs2AE7OztoaGhAIBBg6tSpWYbpr1y5EsbGxujdu7fktvv372PAgAGoUKECdHV1UaFCBQwYMAD3798vVF0rV66Evr6+5BhnZ2dMnjwZlStXhra2Npo3b47Hjx8jPDwco0ePhqGhISpVqoQZM2ZkOb8hISGYOHEibG1tUbFiRWhpacHCwgLDhw9HQECA5LiPHz9KhuyL67x48SJatGgBbW1tVKtWDb/++isYY1nO+bdv3zB9+nTUqFEDWlpaqFKlCpo0aYJ58+bh1atXkuMK8hzkJS4uDjNmzEDlypWhqakJe3t7HD16NNtx9+/fh7GxseQxiF8PV69ezXI+M88pNjY2zpJn4MCBA3BycoKpqSkEAgHc3NyyTe94//49AD71QVdXV3K7q6srJk2ahMqVK0NfXx8ODg7w9/fP0sa0tDQsXrwYlpaW0NTURJ06dbBt2zZJGUKhEMbGxrm+t1atWoWZM2cW6LxNmTIFLi4uWZ7Xu3fvonXr1tDT05PkVTh//jx69+6N6tWrw9jYGHp6erC3t8evv/6a5fX0/etSfB6L8vpRpXNKCCElFiOEECJXDg4OzN3dna1fv54BYABY+/btJfu3bt3KOnfuzIKDgyX7AbCgoCDJMSEhIaxWrVoMAGvdujWLjY1lb968YaampgwA69evH8vIyGCMMfbixQumo6PDALC2bduyuLg45uvrywwNDSVlu7u7S8revn07EwgEDADz8PBgcXFxzN7engFgnTp1kpTLGGNjxoyRlOHl5ZXvYw8KCsrymMSSk5MlbQfAVq9ezWJiYpidnR0DwJo2bcoSEhLYnj17GACmoaHBrly5kmO5hoaGbPz48SwxMZGdP39eUo+7u7vkmKVLl2Zp1+HDh5mamhoDwNasWcPS09PZunXrGACmpqbGDh8+XKi6vLy8JMdoaGiw+/fvs/j4eMlzVq1aNTZ16lQWGxsruR8AtnLlSkk9jx49Ynp6euzu3buMMcZiYmLYoEGDGABmbGzMPn36lOUxVK9eXVLOnDlzWFpaGluzZo3ktiNHjkiO/fz5M6tSpQoDwCwtLZmPjw8TiUTs+vXrTFtbm61du1ZSZ0Gfg5xkZGQwBwcHBoBpaWmx+/fvs6SkJNanTx9JuxwcHLLcR3x79erVJbdlPp9jxozJcnzm12D16tXZgwcPWEpKCmvfvr3keRa34fv3UebXhLm5OfP392fR0dHMwsKCAWD16tVjIpFIcvzIkSMlx584cYKlpqayKVOm5Njmgrh3716O74fMMj+vHTp0YN++fWNfv35lJiYmzMvLi02bNo116tSJRUREMMYY8/b2ZmZmZgwAGzRoUJay8jqPhXn9qPI5JYSQkoICb0IIkTNx4B0fH89MTEwkXzCfPHnCRCIRs7GxYWfPns0z8J40aZLk9l27dkluHzdunOT2Q4cOMcaYJFgDwPbs2SM5dujQodkC79jYWElArq+vLwmyf/vtN8mxx48fl5Qhi8A7PDw8Szk2NjYsNjaWLVu2THLbqlWrGGOMpaenMy0tLQaA2dvb51iulpYWi4yMZIzxwG/Dhg2MsdwD74SEBFauXDkGgGlqarLU1FTGGGOpqalMU1OTAWDlypVjCQkJBa4rc4DTuXNnSV3Ozs6S2/fu3SupP6cLMElJSezdu3dZzl/mIP3333/Psi9z4BQYGMgYY8zX11dy2+jRoyXHuri4SG5fv359lnJcXFwkgXdhnoOceHp6Su7fsWNHye0XLlyQS+Dt6uoquf3evXvswYMHjLGCBYkjR46U3J75eRI/By9fvpTcVrNmTcmxAQEBCgu8z549K7n96NGj7PPnz+zr168sKioqy32GDBkiuc+XL18ktxc08M7v9cOY6p5TQggpKWioOSGEKIienh4mTZok+Xvt2rU4d+4cGGPo0aNHnvfNPDS9WrVqkm0LC4tsx9y8eTPH/Zm3xe7evYvY2FgAgJmZGYRC/rFQoUIFyTHnz5/P+4EVgrGxMapWrYozZ86gcePGWL58OR4+fAgDA4Ms9YjrV1NTg5mZGQCebO7r16/ZyqxduzZMTEwAAEKhENOnT8+zDXfu3JFkUq9YsSI0NDQAABoaGqhYsSIAnhH97t27RaqrUqVKkm0dHR3JduXKlbPd9uXLF8m2trY2rly5gjZt2sDMzAz6+voYNGiQZH9QUFCuj0n83Orq6uZYduZza2trm+W+mzdvxpQpU7IdV5jnQKwwrz1ZaN68uWS7ZcuWWf7OT+Y25XTeFP1YcpL58Tg5OaFKlSpQV1fHkiVLYG1tDUNDQxgZGeH48eOS4/J6neQmv9dPYcvJrSxVOKeEEKIsFHgTQogCzZw5E5qamgCAw4cPY8mSJZg9e7Yk63luQkNDJduZE4VpaWllOybzUj+Z92f+IiyWOQGZeO6nsbEx5syZAy0tLWhpaSEkJKSgDy9f0dHRSEpKQkREBJ48eSJZG/j7tkydOlXSloiICElbcgoGCpthObdzCeR8Pgtbl/jiBYAsz6v49sy3paenS7bXrFmDH374AXfv3sWECRMQGRkJT09Pyf60tLRc6xS/pjLLXLY4uzvALwBlJj63QNGfA7HCvPZkoTjZtXM6Z4D0vCn6seTk+8cnEonQpUsXbNiwAZ8+fcKlS5cQExOTZZm/vF4nucnv9VOccjKXpQrnlBBClIUCb0IIUaBKlSph2LBhAPgX5Pfv32P06NH53q98+fKS7cwJlFJSUrIdk/nLeub9iYmJ2crNfGyVKlUkSxvFxcUhOTkZycnJOHXqVEEeWrFlbsuGDRskbUlKSpK0pVmzZtnul99Fi+/ldi6BnM9nceoqjMzrLC9YsCDXIKYoxBnkASAhISHX44r6HOR0//xee2JqamrZbst837zI8/koymORte8f38uXL+Ht7Q0A6NKlC1q2bKmwtsiCKpxTQghRFgq8CSFEwebNmyfZnjRpUoF6ezJn486c7TfztvgYBwcHyW2fPn3KcVusVatWkvq/ffuG+Pj4LPt79OiRY0ZqeejcubNk+82bN1n2HT9+HN27d8+Wqbso2rRpIxku/u3bN0kPYVpaGr59+wYAKFeuHFq3bl3sugojIyNDsi3uDYyLi5NJ2ZmnMvj6+mbZ5+TkhGXLlgEo/nNQmNeemHgIfuYLAqqQ0booj0XecnqNALJ7ncibKp5TQghRFAq8CSFEwerXr49bt27h8uXL+PHHHwt0n+XLl0uWI9u2bRvi4uIQGBgomdfdu3dvDB48GACwZMkSyTziHTt2ID4+Hn5+fjnO1TYyMsLvv/8OgAeeixYtQlJSElJSUrB06VL4+voqbH3xuXPnombNmgAAd3d3PH/+HAAPFF1dXdG5c2eZ9HDq6upi06ZNEAqFSE1NxcaNG5GRkYHNmzcjNTUVQqEQmzZtUvjw13bt2km2jx8/jtTUVGzfvl0mZS9fvhxVqlQBwHML+Pv7gzGGI0eO4OrVqxg6dCiA4j8H3bt3lwRXd+7cwaNHj5CcnIxNmzblep8+ffoA4MPcfXx8EBsbC3d392I/5uKytbXFyJEjAfDl7k6fPo20tDSsX79eaW2ysbGRjF64desWwsPD8erVK1y5ckVpbSoMVTynhBCiMMrN7UYIIaXXrVu3mJGREVNTU2M6OjrMyMgo12OnTJmSZbkvAMzAwIBNmTJFckx4eDibN28es7KyYrq6ukxPT481adKErV+/nqWnp2cp7+HDh6xjx45MV1eXGRoash49erCxY8dKytbR0clS9vHjx5mDgwMzMDBgurq6rGbNmmzChAns/fv3kmPq16/PNDQ0JGXo6emxFStW5PmYDAwMsjwmIyOjPM9DaGgomz59OrO0tGQaGhqsYsWKrGXLllkyue/fvz9LuWpqaszIyIjdunVLcsyKFSskS6rh/9nIvz/mzp07rF+/fszMzIxpa2szc3Nz1q9fP3bnzp1C16Wnpyc5RkNDg02ZMoVNmTIl2/kSvybEtwkEAmZkZMQ+fPjAIiIi2ODBg5mRkREzMDBgAwYMYJs2bcpSbv369dmHDx+YkZGRZAk48Xn98OFDjm0V+/btG5sxYwarWbMm09TUZBUrVmTdunWTZAIvzHOQl9jYWDZt2jRWsWJFpqGhwezs7NiqVatybVdUVBQbMWIEK1euHNPX12e9e/dm27Zty/a4Gcv5NdirV68s9Yvfc5nfR/v372dTpkzJ9ppYsWJFjs+T+HWdmprKFi1axKpVq8bU1dVZjRo1siy5ZWlpWaBzwhhjvXr1Yvr6+tneD+L3YW7P6/fvMS8vL9agQQOmpaXFatSowX766acsKxaI25/T67Korx9VPaeEEFKSCBiTwbg9QgghhJAyIC0tTTL/vn79+njx4oWSW1Ty0TklhJQFNNScEEIIISQHw4YNw+HDh7Pc9vr1a8l25ukBpGDonBJCyioKvAkhhBBCcpCUlITly5dLEs0FBwdL8jJUq1YNixYtUmbzSiQ6p4SQsooCb0IIIYSQHDg5OaFKlSpwcHCAkZERatSogaCgIMyfPx9Pnz5FpUqVlN3EEofOKSGkrKI53oQQQgghhBBCiBxRjzchhBBCCCGEECJHFHgTQgghhBBCCCFyRIE3IYQQQgghhBAiR0UKvNPS0vDHH39AR0cHAoEAbm5u+d4nMTERrq6uqFatGiwtLWFtbY3169dnO87Hxwc9e/ZEpUqVULVqVXTu3BmPHz8uSjMJIYQQQgghhBClK3Tg/fnzZzRt2hR3795FcnJyge83ZMgQ/P3331iwYAHev3+Pnj17Yvbs2Vi2bJnkmLdv36Jdu3a4ffs2nj17hoCAALx69QodOnSAr69vYZtKCCGEEEIIIYQoXaED77i4OKxfvx4bN24s8H2uX78OT09PAECvXr0AAL179wYA/PHHHwgPDwcA/Prrr4iJiUHjxo1RsWJF6Ovro0OHDkhISMCSJUsK21RCCCGEEEIIIUTpCh14161bFx06dCjUfcRBNwCYm5sDAMqXLw8ASE1NxaVLlwAAZ8+ezXJM5uPOnTuHjIyMwjaXEEIIIYQQQghRKoUkV3vz5o1kW0dHBwCgq6srue3169eIioqS9HyLj8l8XHJyMj59+qSI5hJCCCGEEEIIITKjrohK4uPjJdtCoTDLb/H+nI7J6bicpKSkICUlRfI3YwypqakwMzODQCAo/gMghBBCCCGEEEKKSCE93vr6+pJtkUiU5bd4f07H5HRcTlasWAEjIyPJj7GxMcqXL4+4uDiZPQZCCCGEEEIIIaQoFBJ4W1lZSbaTkpIA8OXFxOrUqQMTExOYmppmOSbzcdra2rCwsMix/J9++gkxMTGSHxqSTgghRFVEJkXi3JtziE2JVXZTCCGEFABjDF5BXrjy7gpETJT/HQgpALkE3vPmzUPHjh3x5csXANJM5gAQFhaW5bempia6du2a5TjxvszbPXr0gJqaWo71aWlpwdDQMMsPIYQQomyJaYnosKsDeh3ohfJ/lUe/Q/2w78U+xCTHKLtphBBCvsMYw5lXZ9B0e1N03NMRXfZ2QcN/G+KY3zEKwEmxyTzwDg8Px5o1a+Dl5YWDBw8CABwdHdGjRw8AwPnz5wHwLOUAsGDBApiZmQEAFi9eDENDQzx79gwhISFISEjAjRs3oKuri+XLl8u6qYQQQohczTw/Ez6hPlATqCElIwWnX53GqBOjUP7v8uhzsA/2eO9BdHK0sptJCCFlGmMMnq890Wx7M/Q91BdPg59CT0MPhlqG8An1wSCPQWi0tRGO+x+nAJwUmYAxxgpzh9TUVDRu3BhpaWl4/fo1AL78V/ny5bFkyRI4Ozujd+/eePbsGS5fvgw7OzsAkKzFfeTIEairq0NDQwOTJ0/GnDlzsiRA8/b2xoIFC/D8+XOoq6ujTp06WLlyJZo3b17gNsbGxsLIyAgxMTHU+00IIUQp9nrvxeiToyEUCHF51GWU1ysPD18PePh5wD/cX3KchlADXWt1hbOtM/rZ9IOxtrHyGk0Ikbl3Ue8AADVNaiq5JeR7jDGce3MObjfc8PjrYwCAroYupjebjvmt50NNqIZ199dh3f11iEvluaMaVGiApQ5L0c+mH4QChczaJaVEoQPvkoACb0IIIcrkF+aHZtubITEtEcs6LMMShyVZ9vuG+sLDjwfhfmF+kts1hBroUqsLD8Kt+8FEx0TRTSeEyEhyejKWXV+Gv+7+BU01Tdwbfw8NKjZQdrMIeMB9PvA83K674dHXRwCyBtzmeuZZjo9MisTae2ux/sF6SQDesGJDHoBb96NVlEiBUOBNCCGEyFBCagKa72gOvzA/dK7ZGRdGXICaMOccJQAP0sU94b5hvpLb1YXq6FyzM5xtndHfpj/K6ZRTRPMJITLw4PMDjDs1LsvoFqtyVnj8w2MYatF3U2VhjOFC4AW43XDDwy8PAfCAe1qzaZjfej7K65XP8/6RSZFYc28N1j9Yj/hUvsxxw4oN4ebghr7WfSkAJ3miwJsQQgiRobEnx2K3925U0q+E55Of5/tFLjP/MH9JT/jL0JeS29WF6uhUoxOcbZ0xrP4w6GroyqPphJBiSkpLwtLrS7H63mqImAgV9Crgzy5/YtG1RfgU+wlD7IbgoNNBCtAULKeAW0ddB9OaTYNrG9dC/Z8GgIjECKy5twb/PPxHEoA3qtgISx2WyiUAZ4zhW/w3vIt6B7vydjQlqYSiwJsQQgiREfdn7nA57QKhQIhro6/BwdKhyGUFhAfgqN9RePh54EXIC8ntjSs1xuVRl6kHnBAVc+/TPYw7NQ6vIl4BAEbaj8T67utRTqcc7n26h/a72iNdlI7NPTdjSrMpSm5t2cAYw8W3F+F23Q0PvjwAwAPuqc2mwrW1KyroVyhW+RGJEVh9bzU2PNyQJQB36+CGPnX6FDoAZ4zha9xX+IX5wS/MD75hvpLf4kSchlqGmNViFma3nE2fAyUMBd6EEEKIDLwMfYnm25sjKT0Jv3f8HT+3+1lmZb+OeA0PXw+se7AO4YnhaFSxEa6MvkJfughRAYlpiVh8bTHW3l8LBoZK+pWwtfdW9LHuk+W41XdXY/7l+ZL53o0rNVZSi0s/xhguvb0EtxtuuP/5PgAecE9pOgU/tvmx2AH398ITw7H6Lg/AE9ISAPCLpG4Obuhdp3e2AJwxhi9xX3hQHeqbJciOScl5uUmhQIhyOuUQnhgOADDQNMDMFjMxt9Vc+iwoISjwJoQQQoopPjUezbY3Q0B4ALrX7o6zw8/KJdvty9CX6Li7I8ISw9CwYkNcGXUFprqmMq+HEFIwtz/ehsspF7yJfAMAGNNgDNZ2W5tjYkTGGPof7o/Tr06jpklNPP3hKYy0jRTd5FJNxES49PYSlt9Yjnuf7wEAtNW1JQF3Rf2Kcq0/pwC8SaUmmNF8BiKSIniQHc57s2NTYnMsQ02ghtrlasOuvB1szWz5b3Nb1DGtA001TZzwP4HlN5dLRkIZaBpgRvMZmNtqLn0eqDgKvAkhhJBiYIxh1IlR2O+zH1UMquD55Ocw0zWTW32+ob7ouKcjQhNCKfgmREkS0xLxy9VfsP7BejAwVDaojG29t6FXnV553i8qKQqNtjbCh5gPcKrrBA9nD5rvXUwZogzc+XQHHr4eOOZ/DMHxwQAUG3B/LywhDKvvrcbGhxslAfj31IXqsCpnBVtzW9iZ8+BaHGBrqWvlWb6IiXAq4BSW3VgG7xBvAIC+pr4kAJfnZxApOgq8CSGEkGLY/mQ7fvD8AWoCNdwYewNtqrWRe51+YX5w3O2I0IRQNKjQAFdGX6EvWoQoyM0PN+FyygVvo94CAFwaumB1t9UFTnj18MtDtN3ZFmmiNPzT/R/MaDFDjq0tnTJEGbj18RY8fD1wPOA4vsV/k+wz0jLC2IZjsaDNAlQyqKTEVvIA/O+7f8PrvReqG1eXBNh25nawMrWCpppmscoXMRFOvzqNZTeW4fm35wAAPQ09TG8+HfNazcu2LBpRLgq8CSGEkCLy/uaNFjtaICUjBas6r8KPbX5UWN1+YX7ouLsjQhJCYF/BHldHX6Xgu5RLSktCuigdBloGym5KmZSQmoCfrv6EDQ83AACqGlbF9j7b0b1290KXtf7+esy+OBsaQg3ccbmDZlWaybq5pU6GKAM3P9yEh58HjvsfR0hCiGSfsbYx+ln3g7OtMzrX7Jxvj3FpwxiTBODPvj0DwANw8TJpFICrBgq8CSGEkCKITYlF021N8SbyDXpZ9cLpYaflMq87L/5h/nDc7YiQhBDUL18fV0dfpS9YpZRvqC8cdztCS10LT394Ss+zgnkFeWH86fEIig4CAExoNAF/d/27yHO0GWMY5DEIx/2Pw9LYEk9/eJrjvPCyLl2UzoPt//dshyaESvaZaJugv01/DLIdhM41Oxe797g0YIzB87Un3G644WnwUwCFW6ecyBcF3oQQQkghMcYw7NgwHPY9DAtDCzyb9Exp86wDwgPguNsR3+K/oV75erg6+ip9uSplPsV8QuudrfE59jMAYHyj8djRd4eSW1U2xKfGY8HlBdj8eDMAwMLQAjv67kDXWl2LXXZ0cjQab22MoOgg9LPuhxNDTtB8b/Bg+8b7G5Ke7bDEMMk+E20TDLAZAGc7Z3Ss0ZGC7VwwxnD2zVksu7EMj78+BsAD8ClNp8hkGTVSNBR4E0IIIYW05dEWTD03FepCddwadwstq7ZUantehb+C425HBMcHw87cDtfGXCtRwTdjDN/iv8FM1wwaahrKbg4AIDUjFVFJUUr/ghqZFIm2O9vCP9wf1Y2q40PMBwDAXZe7aGXRSqltK+2uvruKCWcm4H30ewDApCaT8GeXP2GoJbvvlk++PkHrna2RmpGKNV3XYE6rOTIrWxESUhPwMeajTMr6FPsJx/yO4XjAccmSWQBQTqccD7ZtebCtKv8jSgLGGM4HnofbdTc8+voIgHRZtRH2IyBiIqSkpyAlI0XyOzUjteC3ZaRAU6iJ3nV6o4dVD5W4EBKVFIVn356hY42Oym5KNhR4E0IIIYXwNPgpWv3XCqkZqVjddTXmtpqr7CYBKHnBN2MML0NfwsPPAx5+HggID4ChliG61+6OXla90KN2D4UPpw5NCMX5N+fh+cYTFwMvIj41Hn91+QvzWs9TaDvEEtMS0XlPZ9z7fA9VDKrg3vh7WHp9Kdyfu6NhxYZ4PPEx1IRqSmlbaRabEosfL/+IrU+2AgCqG1XHjr470LlmZ7nUt/nRZkw7N01lLuQVBGMM+17sw6wLsxCVHCXz8k11TCU9246WjhRsFxNjDBcCL8DthhsefnkolzrK6ZTDYNvBGGE/Aq0tWit06lVkUiROBZyCh58Hrry7AjWhGsJcw6Cvqa+wNhQEBd6EEEJIAcUkx6DxtsZ4F/VOJYeGvo54Dcfdjvga9xW25ra4Nvqa0ntsM2OMwSfUBx6+PNh+FfEq12MFEKBl1ZboXac3etfpjfrl68v8XDPG4B3iDc/Xnjj75iwefH4Ahuxfixa3X4xlHZYp9LlOF6VjwOEB8HztCWNtY9wedxt25e0QmhAK643WiE6OxoYeGzC9+XSFtaksuPz2MiacmSDpxZ3SdApWdV4l14R2qjR1pSC+xH7BJM9JOPvmLAC+jrQsejp1NXTRvXZ3ONs6o4NlBwq25YAxhotvL2LF7RUICA+AlpoWtNS1oKmmKdnO7bbcjgmOC8Zh38OSZdwAwNLYEiPqj8CI+iNQ17yuXB5LRGIETgachIefB64GXUW6KF2yz87cDkecj8DW3FYudRcVBd6EEEJIATDG4OzhjGP+x1Q6GdKbiDdw3O2IL3FfUNesLq6NuabwNWwzEwe3Hr4eOOp/FK8jXkv2aappSr5o97LqhdcRr+H52hOebzwlS+OIWRhaoJdVL/Su0xsda3SEjoZOkdqTmJaIa0HXJMG2eN60WKOKjSTB/pV3V/DLtV8AADObz8Ta7msV0ovDGMOE0xOw8/lOaKtr4/Koy2hbra1kv3iqg5GWEV5Nf6VSF1dKqpjkGLhedsX2p9sBADWMa+C/vv/BsYajQupXhWSN+WGMYbf3bsy+MBsxKTHQVNPEsg7LML/1fKgL1ZXdPKJEGaIMeL33wn6f/TjmdwxxqXGSfY0rNcbI+iMxtN7QYi/vFpEYgRMBJ+Dh54FrQdeyBNv1y9eHs60zBtkOkluwX1wUeBNCCCEFsOHBBsy8MLNELP8TGBmIDrs64EvcF9iY2eDa6GsKXc+WMYbn355LhpEHRgZK9mmpaUmC7T7WfXKdL/s59jPOvTkHz9eeuPLuCpLSkyT7dNR10KlmJ/Sy6oVeVr1gYWSRZ3s+xnzE2ddn4fnGE9eCriE5PTlLWV1qdUFvq97oadUTVQyrZLnvpoebMP0871Ue23AstvfZLvcgY9G1Rfj91u8QCoQ4MeQE+lr3zbI/Q5SBFjta4EnwE4xuMBq7+++Wa3tKuwuBFzDxzETJRZjpzaZjRecVCh+m+vzbc7Tc0VIpyxPm53PsZ/xw5gecDzwPAGhepTnc+7mrXI8iUb7EtESceXUG+33243zgeUlwLBQI0alGJ4y0H4kBNgMKPIokPDEcJ/ylwXYGy5Dss69gD2dbZzjbOsPazFouj0eWKPAmhBBC8vHoyyO02dkGaaI0rO++HjNbzFR2k/IVGBkIx92O+Bz7Gdam1vAa4yXX4JsxhmffnkmGkb+NeivZp6WmhR5WPeBs64zedXoXOjlVUloSvN578d7w1574FPspy/4GFRqgd53e6GXVC82rNAcAPPjyQHK8T6hPluOrGVVDnzp90MuqFzpYdsi393yP9x6MOzUOIiaCU10n7B+4X27rBG98uBEzzs8AAGzvsx0TGk/I8biHXx6i5Y6WYGC4OfYm2lVvJ5f2lGbRydGYd3Eedj7fCQCoaVITO/vuhIOlg9LatO3JNkzynAQ1gRquj72eZaSDMjDGsPPZTsy9NBexKbHQUtPCcsflmNtqLvVyk3yFJ4bjiO8R7PfZj7uf7kpu11HXQT+bfhhRfwS61eqWbVpBWEKYpGfbK8grS7DdsGJDSc92HdM6CnssskCBNyGEEJKHqKQoNNraCB9iPsCprhM8nD1Ual53Xt5GvoXjbkd8iv0kl+BbxER4GvwUR/2OwsPPA++i3kn2aatro6dVT8kwclnNkRUnZRMPSb/36V6WedlmumZgjCEiKUJym1AgRKuqrSRDyO3M7Qr9HJ7wP4Ghx4YiNSMV3Wp1w/Ehx6GroSuTxyR2xPcIhh4dCgaG5R2WY7HD4jyP/+HMD9j+dDvql6+Pp5OeUiBUCGdfn8Ukz0n4EvcFAggws8VM/N7xd+hp6im1XYwxjDwxEgd8DqCKQRU8m/RMaWu2f4z5iIlnJuLS20sAgJZVW8K9nztszGyU0h5Ssr2NfIsDPgewz2dflilHZrpmGGI3BINsB+FV+Ct4+Hng+vvrWYLtRhUbSYJtK1MrZTRfJijwJoQQQnLBGMOAwwNw6tUp1DSpiac/PIWRtpGym1Uo76LeocOuDvgU+wl1TOvAa4wXKhtULlQZIiZCUFQQ/ML84BfmB98wX/iF+cE/3B+JaYmS43TUdaTBdp1eChmqG54YjvNvzuPsm7O4EHgBMSkxAABjbWN0r90dva16o3vt7jJJVnX57WX0P9wfiWmJaFutLTyHecrs9eAV5IXu+7sjNSMVU5tOxcaeG/O9OBCRGIE6G+sgMikSa7utxeyWs2XSltIsKikKcy7OwW5vPjzfqpwVdvbbqfSe5cziU+PRdFtTvIp4hW61uuHciHMKne/NGMP2p9sx/9J8xKXGQVtdG785/obZLWdTFn1SbIwxPAl+gn0v9uHgy4MITQjN8bjGlRpLgu3a5WoruJXyQYE3IYQQkos199Zg3qV50FTTxL3x99C4UmNlN6lI3kW9g+NuR3yM+QirclbwGuOVbS4zwOcOB0XzANs31Bd+4fx3QHhAljnWmelq6Gbp2VZmj2FaRhruf74PgYBnRJdHD/Cdj3fQ60AvxKTEoHGlxrgw4kKxeySff3uO9u7tEZcaB6e6Tjg86HCBA5ztT7bjB88fYKBpgFfTXyl0Ln9Jc+bVGUzynITg+GAIIMCclnPwa8dfZT5yQRZ8QnzQYkcLJKUn4feOv+Pndj8rpN730e8x8cxEXHl3BQDQ2qI1dvbdWSLmz5KSJ12UjqvvrmKfzz6cf3MeNUxqSILtmiY1ld08maPAmxBCCMnB/c/30c69HdJF6djUcxOmNpuq7CYVS1BUEBx3O+JDzAfULlcb+wbsQ3B8cJYe7IDwgCyJxzLTUtOCjZkNbM1tYWtuCztzO9ia26JWuVplbojzs+Bn6LavG8ISw2BjZoMro67keCGjIIKigtB6Z2t8i/8Gh+oOuDDyArTVtQt8fxETodV/rfDwy0MMrz8c+wfuL1I7lE3ERAAgl57dyKRIzLowC/te7AMA1DGtA/d+7mht0VrmdcnSzmc7Mf70eAgFQlwbfU2uc89FTIStj7fixys/Ij41HjrqOvij0x+Y0XwG9XITIiMUeBNCCCH/l5aRhqtBV3HU7yiO+h1FTEoMhtgNwUGngyVmXnde3ke/R4ddHfAh5kOux2ira0sCbHFwbWduhxomNcpcgJ2XgPAAdNnbBZ9jP8PS2BJXRl1BrXK1ClVGaEIo2uxsg8DIQNhXsMfNsTeLNHT9ydcnaLa9GRgYvMZ4oYNlh0KXoSgZogy8i3onudiT+aIPYww2ZjawK28HWzNb/tvcFjVNahb5tXcy4CQme05GSEIIhAIh5rWah2UdlhV5OTpFYoxh7Kmx2OO9B5X0K+HZpGdyWTouKCoI40+Ph9d7LwBA22ptsbPvzhI9l5YQVUSBNyGEkDItNSMVV99dhYefB04GnERUcpRkX8OKDXFj7I1CZ+FWZe+j36Pbvm74GPMRdc3qZgtyahjXoB6uAvoQ/QGd93ZGYGQgKulXwqVRl1CvfL0C3Tc+NR6Oux3x+OtjVDeqjrvj7xZ67n1mU89OxZbHW2Brbovnk55nyxKsaOmidLyNfJsluBYH2CkZKYUqS0tNC9Zm1lkuBtma26J2udq5BuThieGYeX4mDr48CACwMbOBez93tKzastiPTZESUhPQfEdz+IX5oXPNzrgw4oLM3p8iJsKWR1uw4MoCJKQlQFdDFys6rcD05tNVbg1xQkoDCrwJIYSUOakZqbjy7ook2I5OjpbsK69XHk51neBs64z21duXyiCUMQYGRl+uZeBb/Dd02dsFL0NfopxOOVwYcSHfNd5TM1LR52AfXHp7CaY6prjjcqfYc2ijkqJQZ2MdhCeG468uf2F+6/nFKq+g0jLS8DbqLc8JkCnIfhXxCqkZqTneR0ddB3XN62YJou3M7QAgW6DuF+aXa34BDaFGjgG5f5g/pp6bitCEUAgFQri2doVbB7dCDeFXJX5hfmi2vRkS0xLh5uCGJQ5Lil3mu6h3GH96PG58uAEAcKjugP/6/lfoURuEkIKjwJsQQkiZkJKegsvvLsPDzwOnAk5Jsl8DQAW9CjzYtnNGu2rtSmWwTeQnMikSPfb3wMMvD2GgaYAzw87kOh9XxEQYfWI09vvsh66GLrzGeEnWHi8u92fucDntAj0NPQRMD0BVw6oyKTc3+1/sx9RzUxGbEpvjfl0NXUkwnHlUhaWxZYEv+oiYCB+iP2Qbmu4X5pclo35ObM1t4d7PXWbnV5n2eO/BmJNjZF6unoYe/uzyJyY3nUwX4giRMwq8CSGElFop6Sm49PYSPPw8cPrV6SzBdkX9ipKe7bbV2lKwTYolLiUO/Q71g9d7L2ira+PY4GPoadUz23HzL83H6nuroS5Ux5lhZ9C9dneZtUHERGjn3g53P93FYLvBODzosMzK/r6eRdcWYcXtFQB48PZ90j278naoZlRNbsGciInwMeajpKddnIHfL8wPaaI0zG81H0sclkBLXUsu9SvDvIvzsPb+2izr1hdHpxqdsL3PdtQwqSGT8ggheaPAmxBCSKmSnJ6Mi4EXcdT/KE6/Op2lN66yQWVJsN3aojUF20SmktOT4ezhDM/XnlAXqmPfgH0YUm+IZP/fd/+G62VXAMCe/nswqsEombfh+bfnaLKtCURMhMujLqNzzc4yLT8uJQ6jTozCqVenAAAL2yzEbx1/U5n3koiJkCHKUPocd3mJSY5Bmiit2OWoCdRgomMigxYRQgqKAm9CCMmB+zN3LL+5HDrqOtl6cazKWZWqXpTSICktCRffXoSHnwfOvDqDuNQ4yb4qBlUkw8hbW7Sm4ZRErtIy0jD65GgcenkIAgiwrc82TGg8AXu992L0ydEAgD87/wnXNq5ya8PM8zOx4eEG1DGtgxeTX8js/9WH6A/oc7APfEJ9oKmmiR19dsjl4gEhhJRGFHgTQkgmiWmJmHZuGnY935XrMWoCNViZWmWbt2htak0BuQIlpSXhfOB5HPU7ijOvzyA+NV6yr6phVQyqOwjOds5oWbUlBdtEoTJEGZh6diq2Pd0GAJjQaAJ2ee9Cuigdc1rOwequq+W6PF10cjRsNtogJCEEKzqtwMK2C4td5u2PtzHw8ECEJYahgl4FnBx6ssRlCCeEEGWiwJsQQv4vIDwAzh7OeBn6EkKBEG4ObmhepXmWZD6+Yb65JhISCoSoXa52tky91mbWJTabrqpJTEvE+Tfn4eHnAc/XnkhIS5DsszC0wCDbQXC2dUaLqi2KF2ynpgJDhwLq6sC+fYCmpgxaT8oSxhgWXFmAv+7+JblteP3h2Dtgr0IuBIl72HU1dOE/zR/VjKoVuSz3Z+6Y5DkJaaI0NKrYCKeGnoKFkYUMW0sIIaUfBd6EEALgoM9B/OD5A+JT41FBrwIOOh2EYw3HbMcxxvA17qs0w26orySpT+bEXZkJBUL0t+mPo85H5drLVVolpCbgfCAPts++Ppsl2K5mVA3Ots4YZDsIzas0l11As2IF8PPPfHvGDOCff2RTLilTGGP449YfWOy1GN1rd8fJoSehqaaYiziMMTjscsCtj7cwsO5AHBt8rNBlZIgy8OPlH7Hm/hoAgFNdJ+zuvxt6mnqybi4hhJR6FHgTQsq05PRkzLkwB/8++RcA0MGyAw46HURF/YqFKocxhuD4YGkw/v/ecd8wX8ka0Xdd7qKVRStZP4RSKSE1AWffnIWHnwfOvTmXZdmg6kbV4WzrDGc7ZzSr3Ez2FzPevQPs7IDkZOltBw4Aw4bJth5SZoQlhMFM10zhF958QnzQaGsjZLAMnB9xvlAZ1GOSYzDs2DCcDzwPAFjqsBRLHJbQtA1CCCkiCrwJIWXWu6h3cPZwxtPgpwCARe0WYWmHpVAXqsusDsYYxpwcg70v9mJi44nY1mebzMoubeJT43H2tTTYTkpPkuyzNLbkwbatM5pWbiq/AIYxoFcv4Px5wNERaNUK+OMPQFcXePiQB+SElCDzLs7DmvtrUMukFl5OfVmgaS+BkYHoe7Av/MP9oaOug139d2Gw3WAFtJYQQkovCrwJIWXSCf8TGHdqHGJSYmCqY4p9A/fJdD3dzK6/vw7H3Y4w1DJE8Lxg6GroyqWekiK34fpPg58iOV3ay1zTpKYk2G5cqbFiegs9PIDBg/mc7hcvgNq1gW7dgKtXgTp1gEePAPpcISVIbEosbDbaIDg+GMs7LMdih8V5Hu8V5IVBHoMQmRSJygaVcXroaTSp3ERBrSWEkNKLAm9CSJmSmpGKhVcWYu39tQCA1hatccjpkFwTBYmYCLX+qYX30e+xb8A+jLAfIbe6VAljDJ9jP2dJTCfezm0+fC2TWpJh5I0qNlLs0NzYWMDGBggOBpYsAZYt47eHhQGNGwOfPwNOTjw4p7n6pAQ56HMQw48Ph7a6Nvym+qGGSY0cj9v6eCumn5+OdFE6mlVuhpNDT6KyQWUFt5YQQkonCrwJIWXGx5iPGHJ0CO5/vg8AmNdqHlZ0WgENNQ25173s+jK43XBD55qdcXnUZbnXp0iMMXyK/ZRtfrtfmF+W9bQzUxOo8Qzw5e0kS7LZV7BHXbO6yktAN3MmsGED7+X28QG0Mw3JvX8faN8eSEsDVq8G5s5VThsJKQLGGDrt6QSv917oU6cPTg87nWV/uigdcy7MwcZHGwHw7Os7+uyAjoaOMppbuoSEAM7OfBTNpUuAkObIE1JWUeBNCCkTzr05h1EnRiEyKRLG2sbY1W8X+tn0U1j9QVFBqPlPTQggwPvZ74u1tI+quPnhJlbeXolbH29lWUM7MzWBGuqY1pEsrSZeZq2OaR3VWvP88WOgeXM+x/vyZaBz5+zHbNoETJ8OqKkBXl5Au3aKbychReQX5ocG/zZAuigdp4eeRh/rPgCAqKQoDD46GFfeXQEA/NHxDyxsu5BWYJCFL1/4/5KAAP63vz8fVUMIKZMo8CaElGrponQs8VqCFbdXAACaVm6KI4OO5DrUUp467u4Ir/de+NXxVyxqv0jh9cvKjfc34HbDDdffX5fcpi5UzxZg25nbwcrUSmHLJxVZRgYPup8+BYYPB/bvz/k4xoCRI3mG84oV+fGVKim2rYQUw4LLC/Dn3T9Rw7gGfKf64kPMB/Q92BdvIt9AT0MP+wbuQ3+b/spuZunw8SPQsSPw9q30tv37+f8YQkiZRIE3IaTUCo4LxrBjw3Djww0AwLRm07C662ql9bTu8d6DMSfHoJZJLbyZ8abE9Shdf38dbtfdJOdTQ6gBl0YumNpsKmzMbFQ/wM7NP/8As2YBRkbAq1dAhQq5H5uQALRoAfj68h7vq1cBDflPVSBEFuJT41F3U118jv0Mp7pOuPLuCmJSYlDNqBpODz2NBhUbKLuJpcPbt0CnTsCHD0DNmkD9+sCpU8D8+cBffym7dYQQJaGJJoSQUula0DU03NoQNz7cgL6mPg45HcLGnhuVOrzZqa4T9DX18TbqLW5/vK20dhQGYwzXgq7BYZcDHHc74saHG9AQamBK0ykInBmIf3v/C/sK9iU36P7yBVj0/9EHK1fmHXQDgJ4ecOwYYGAA3LoF/Pyz/NtIiIzoa+pjbTeeWPKY/zHEpMSgjUUbPJr4iIJuWXn1CnBw4EF3nTrAjRtA375839Onym0bIUSpKPAmhJQqIibCrzd+Rec9nRGaEIr65evjyQ9PMKTeEGU3DXqaehhsy9fCdX/uruTW5I0xhqvvrqL9rvbotKcTbn64CU01TUxtOhVvZ77F5l6bS8U8dcyeDcTFAS1bAj/8ULD7WFsD7v9//v7+mwfihChKMQcqOtV1Qi+rXgCAcQ3H4eroqyivV14WLSO+vjzo/vIFsLPjQXfVqkCjRnz/06fFfv4IISUXDTUnhJQajDGMPDESB3wOAADGNxqPDT02qFRm3tsfb6Odezvoaejh2/xv0NfUV3aTsmCM4cq7K1h2YxnufLoDANBU08TExhOxsO1CVDWsquQWytC5c0CvXjxZ2pMnQINC9vi5uvLA28CAr+9tbS2fdhICAKmpfJpDUBDQpw8wcCBfY15Xt9BFpWWkITAyEDZmNiVuyovKev4c6NIFCA8HGjbkSRrNzPi+1FRAX5+vihAUBFhaKrGhhBBloR5vQkipsfL2ShzwOQANoQZ29duFHX1VbzmcNhZtULtcbSSkJeCYn+r0lDLGcOntJbTZ2QZd93XFnU93oKWmhenNpuPtzLfY2HNj6Qq6ExOBadP49uzZhQ+6AWDFCr7EWFwcX987IUGmTSQkizNneHAXEwPs28cDb3NzvlTVwYN8HfoC0lDTQF1zJS7dV9o8egQ4OvKgu1kznvtBHHQDfCmxevX4Ng03J6TMosCbEFIqeL72xC/XfgEAbOy5EWMajpHuZExlhvcJBAKMbTAWALDLe5dS2wLwgPtC4AW03tka3fZ1w73P96ClpoWZzWfi3ax32NBzQ+kKuMV+/RV4/x6wsADc3IpWhro6cOgQz3Du68uHqqvI64yUQv/9x3+PGAHMmQNUr84vIB09yjNlm5vznnB3dyAiQrltLUvu3OGJ1KKjgdateU93uXLZj2vcmP9+9kyhzSOEqA4aak4IKfH8w/zRYkcLxKXGYUrTKdjca7N0p/hfnAr17HyK+YTq66qDgeHtzLeoaVJT4W0QB9zLbizDgy8PAADa6tqY1GQSfmzzIyobVFZ4mxTm5Us+5zI9nWcaFic+Kqpbt3hvV0YGsHGjtCedEFn59IkPTxaJgNevASsr/r/t6VOeY+DYMX67mJoaf00OHAgMGMAvDhHZ8/LiFzsSEoAOHfioBP1cpg9t2gRMnw707AmcPavQZhJCVAMF3oSUEBmiDCy8shCBUYEor1se5fVy/imnUw5qQjWZ1ZuUloTQhNCcfxJDkZiWiMlNJqNLrS4yq7MwopOj0Xx7c7yJfIP21dvj8qjLJSLDdte9XXH53WUsdVgKtw5uCq37a9xXOB1xwv3P9wHwgHtK0ylwbe2KSgalfF1qkYgPD79zB+jXDzh5UjblrlkDzJvHlxa7eZMnayNEVn79FViyhL92b9zIvp8xwM+PB+DHjwPe3tJ9AgHviXVy4oF49eqKa3dpdvEi0L8/kJwMdO0KnDiR93z7e/f481CxIhAcrLBmEkJUBwXehJQQy64vg9sNt3yPEwqEMNM1yxqQ5xCo62nqITwxPPeg+v8/calx+dapLlSHh7MH+tv0L/4DLYQMUQZ6H+yNC4EXUM2oGh5NfJQ1Oy9jKtXTndkBnwMYcXwEqhtVx7tZ7yAUKG7mz9CjQ3HY9zB01HV4wN3GFRX1y0iP2I4dwMSJfFkwf38+1FwWGAMGD+bDfqtW5T2R5uayKZuUbSIRUKsWnxqxezcwenT+9wkM5IHgsWPAgwdZ9zVpwoPwQYN4z3lpk54OREXJ9/135gw/f6mpvMf7yBFAWzvv+yQkAIaG/Pn8+hWoVMovchJCsqHAm5AS4Oq7q+iytwsYGOa3mg8DLYMcg+SIJPnM69NU08zeu/7/YP7e53s4EXAC6kJ1HBl0BAPqDpBLG3Ky4PIC/Hn3T+io6+COyx00qtRIulMFh5hnlpSWhEqrKyEmJQbXRl+DYw1HhdT78MtDtNjRAgII8OSHJ1nPWWkXFsYzj0dFAatXA3Pnyrb82FigeXO+jm+nTrxHTE12o09IGXX1KtC5Mw/agoMLn8X80yc+suPYMT4tQiTitwuFfKh0+/Yyb7LCpaQA167xx3jqFE9yZmfHe/idnAB7e9l9Fhw7BgwdygN8JyfgwAGePK0gbG35Bb+zZ/mQc0JImaKu7AYQQvIWHBeM4ceHg4FhfKPx+KvrX7kemy5KL1AvdmhCKOJT42GuZ55nr7j4x1DLMNfst+midIw+MRoHXx7E4KODccjpEJxsneR1OiQO+hzEn3f/BAC493PPHkCqaMAtpqOhg6H1hmLrk61wf+6ukMCbMQbXy64AgNENRpetoBsA5s/nQXeDBsDMmbIv39CQfylv3pwHS0uXAr/9Jvt6SNkiTqo2fHiRlg6DhQUwYwb/CQ3lgem6dXxo+qVLJTfwTkwELlzgQ+vPnMme1d3Xl//8+isfMSAOwps14xcdiuLAAT7iICODPx+7d/MkiwXVuDEPvJ89o8CbkDKIerwJUWHponR02dsF199fR/3y9fFgwgOVWx4L4O0cc3IMDvgcgJpADYcGHcIg20Fyq+/J1ydo694WyenJ+KntT/ij0x/SnSre053Z/c/30eq/VtBR18G3+d9gqCXf/1dnXp1B30N9oaWmhTcz3sDCSEbDrEsCLy+gY0f+urh3j6+HLC8HD/Iv5QAPCHr3ll9dpHSLiuJDklNS+JJVTZvKptySmugrNhbw9OTB9vnzPPgWq1SJJ5IT93CfP8+Pu3CBz8MWq1pVelzbtgUflbJzJzBhAv+MGTcO2L698CNaVq/mFwAHDuQX6QghZQotJ0aIClt2fRmuv78OfU19eDh7qGTQDfA53nv678FI+5HIYBkYenQoPHw95FJXSHwI+h/uj+T0ZPSy6oVfHX/NflAJCLoBoEWVFrAxs0FSepLczpdYuigdP175EQAwu+XsshV0p6QAU6bw7cmT5Rt0A8CwYbx3EQBGjQLevZNvfaT02r+fv37t7fncbFkRL21VEtaUjojgQW/v3nze9ogRPGhNTOSZ3ufO5ckSP3/mFxQ6duRraI8axee5h4XxOdhDh/KM458/Axs28CzklSrxZQAvXuTztXOzZQswfjwPuqdM4bkiijKNhJYUI6RMox5vQlTUpbeX0H1fdzAwHBh4AMPqD1N2k/KVIcrAuFPjsPfFXqgJ1HDA6QAG2w2WWfmpGanotKcTbn+8DWtTazyY8ABG2kYyK18ZVt1ehYVXF6KNRRvcdrktt3q2PdmGSZ6TYKpjircz35b481Yo4ozQFSoAAQGAsbH860xNBRwcgPv3gYYNgbt3AR3VvHBGVBRjfNk7b29g/XrZTo9ISAAMDHgdqpjoKzhYmhzuxg0+tFvM2pr3Vjs58fNTmAutycnAlSvSueBRUdJ9xsY8UdrAgUC3btL369q10nwQs2fzFQyKenE3OhowMeHbkZHSbUJImUCBNyEq6EvsFzTc2hDhieGY1GQS/u39r7KbVGAZogy4nHbBHu89UBOoYf/A/RhSb4hMyp7sORlbn2yFkZYRHkx4AGsza+nOEjTEPLOvcV9hsdYCIibC6+mvYWUq+yzD8anxqP1PbYQkhGBdt3WY1XKWzOtQWW/eAPXr817DAwd4b7SifP7Me7jCwgAXF+lcXUIK4skTPrRcU5MHx6amsi1f1RJ9BQfzaRrHjvHpIJm/njZsKF0OzdZWNvWlpfGg/tgxHuSHhEj36enxc1KhArBxI79t4ULgjz+K/xlTsyYQFMTzQHTsWLyySNnz8SN/XWppKbslXGoq8OULUKOGslsi5eXFL6Q1Ur08NjTUnBAVky5Kx9BjQxGeGI6GFRtiXfd1ym5SoagJ1bCz706MbTgWGSwDw48Px6GXh4pd7r+P/8XWJ1shgAAHnA6UiqAbACobVEa3Wt0AALue75JLHavvrkZIQghqmtTElGZT5FKHSmIMmDaNB91duvChpopUtSoPJIRCPlT2UPHfB6QMEV+oGThQ9kE3oFrDnlNSeNLDefP46BDGgJYtgb/+4kujPXsGLFoku6AbADQ0eLb4LVt44HDrFu/RrlaNjwjw8JAG3cuWySboBlTrvJOS5dgxPr3Cxga4fFnZreHvGXt7fjFp3Tplt4b78IEv9de6NZ+ComIo8CZExSy6tgi3P96GgaYBPJw9oK2ez9qgKkhNqIYdfXZgXMNxEDERRhwfgYM+B4tc3s0PNzHjPJ8zu6LTCvS0+q53RiAokUG32LiG4wAAe17sQYYoI5+jC+db/Df8dZdnwl/RaQU01Qq47E1pcOgQ/3KipQVs3qyc10inTsDixXx79mw+1JSQ/CQl8REaAJ9bLA/i3iBVmOf94gUfGWJgwOdff/rEe73nz+cZyeVNTY0nWlu7lq+X/ugR8NNPPAP6+vV8qoqs/n+o0nknJUdMDE+IyBh/jXbtCowdy6csKFpsLL+o3b49Xz4T4O/VW7cU35bMUlIAZ2d+TurVk21eDBmhwJsQFXL29VmsurMKAPBf3/9Qu1xtJbeo6NSEatjRdwdcGrpAxEQYeWIkDvgcKHQ5H6I/YNCRQXwkQL2h+LHNj1kPKAWzZfpY94GJtgk+x37G1aCrMi3b7bobEtIS0LxKczjbOsu0bJUWHQ3MmcO3f/kFqK3E99JPP/F5qSEhwM8/K68dpOQ4dox/0ba0lN9wZFVKsPboEf/dujUPLqpWVV5bBAI+xP+PP4CHD2W/9CD1eJOi+OUX4Ns3oE4dnrxTIODL2dWty0dnKOq70NmzgJ0dv5gN8Ez/gwfzPAyDB/M2Ksvs2fx/SblywNGjgLbqdVxR4E2IivgY8xGjT44GAExvNh3OdiU/SBIKhNjedzvGNxoPERNh1IlR2PdiX4Hvn5iWiP6H+yMsMQyNKjbCf33/y7qeeCkIugFAW10bw+vz5adkOdzcP8wfO57uAAD81eWvXNdiL5V+/pkHutbWwI8/5n+8PGlp8eGsAPDvv8CDB8ptD1F94mHm48YVfc3p/DRsyH+/f581yZgyPH7Mfzdrptx2KIK4xzsggA9pJyQ/Dx9KA90tW4B//gFu3+ZBd2goD3gHDOC5IOQlLIwvk9m7N89fUrMmz1OwfTufSmVnx4PuIUN4/gRF27OHf74KBHw1iOrVFd+GAqDAmxAVkJaRhqFHhyIyKRJNKzfF313/VnaTZEYoEGJbn22Y0GgCREyEMSfHYK/33nzvxxiDyykXPP/2HOa65jg59CR0NXSzHlTCh5hnNrbhWADAiYATiE6OlkmZC68uRAbLQF/rvmhfvb1MyiwRHjzgH8AA/5KiCkloHB2B0aP5xaJJk4D0dGW3iKiqwEDg+nX+v23sWPnVY2IiTYik7N5XcY93WQi8K1bkWeQZ40PsCclLejpfBpMxYORI6QiY1q35+3bxYkBdnWfpr1sX2LYNEIlkVz9jPJCtW1eas2T+fMDHR9oWPT0+SsfAALh5U/Eju1684OcIAJYuBbp3V2z9haCu7AYQQoCfrv6Ee5/vwUjLCEcGHYGWugoECjIkFAixtc9WHoQ/3YYxJ8eAgWF0g9G53mfVnVU47HsY6kJ1HBt8DNWMqmU9gLFSE3QDQJNKTVCvfD28DH2Jwy8PY1LTScUq7+aHmzj96jTUBGpY1XmVjFopR58+8SzOshjF4ObGyxk9mge8quLvv4EzZ/jyUP/8I12iSBnevuXzWi0tldcGkrOdO/nvrl15oi95atSIZ9h+9kx5GbYTEgA/P77dtKly2qBojRvzIbtPnwKtWim7NUSVbdzI358mJsDq1Vn3aWkBy5fzec3jx/MLWJMm8QB5+/biT7H6+JEHtOfP87/t7flonJzep9bWgLs7T2z299/8dT1wYPHqL4joaF5PUhIPuMU5VVQVK4ViYmIYABYTE6PsphCSr1MBpxjcwOAGdtzvuLKbI1cZogw26cwkBjcwgZuA7Xq2K8fjPF95MoGbgMEN7N9H/2Y/QCTiP6XM33f+ZnADa7G9RbHKEYlErPn25gxuYJPOTJJR6+Tg9WvGVq5krFkzxnioLLsfExPGQkOV/Qiz276dt09Pj7EPH5TThqdPGdPWZszYmLGICOW0geQsLY2xSpX4a8TDQ/71/fYbr2v4cPnXlZubN3kbKldWXhsUbdEi/phdXJTdEqLKPn1iTF+fv1a2bcv72PR0xtasYUxHhx+vrc3YqlX8f0phZWQwtmGDtG5NTf6/IjU1//vOn8/vY2DAWEBA4esuDJGIsX79eH3VqzMWHi7f+mSAerwJUaL30e8x5uQYAMDsFrMxoO4AJbdIvoQCITb32gyhQIgtj7dg3KlxYGCSYdYAEBAegOHHh4OBYXKTyTn3/Jainu7MRtqPxIIrC/DgywP4h/mjrnndIpXj4eeBh18eQk9DD24d3GTbyOJgDHj5Ejh+nA9L8/GR7hMI+HJCurq537+g1NUBV1fA3Lz4Zcmaiwuwaxdf5mTWLL5+sCJFRfH1kJOT+c/27cCCBYptA8ndhQt8PWszM6BvX/nXpwoJ1srSMHMxSrBGCmLWLCA+ng8rz291AzU1nlC0Xz/e633lCv/ffvgw76UW53TIj78/T5h29y7/u00bYMcOvoRZQaxYweek37zJP2sePOBD0eXhzz/5EHtNTZ5MTR7LLsqasiN/eaAeb1ISpKSnSHolm29vzlLSU5TdJIURiURsqudUSc/3f0//Y4wxFpUUxepsqMPgBtZuZ7vs56SU9nRn1udAHwY3sB8v/Vik+6ekp7Ca62syuIG5ebnJuHVFIBIx9vAhYwsWMGZllbVXWk2NsS5dGNuyhbHgYGW3VHFevmRMXZ2fg1OnFFdvRgZjvXrxesW9IlWqFKwXgyhG//78eZkzRzH1BQfz+gQCxuLjFVPn94YO5W347Tfl1K8MQUH8MWtoMJZSdj77SSGcOcNfI+rqjL14Ubj7ikSM7dzJRzWJP2t/+omxpKTc75OSwtivv/LebYD3dm/cyD83CuvrV8YqVpSOppHH97Zr1xgTCnkdW7fKvnw5ocCbECWZdX4WgxuYyUoT9j7qvbKbo3AikYhNOztNEnxve7yN9djXg8ENzGKNBQuJD/n+DqU+6GaMseN+xxncwCr9XYmlZRR+iNi6e+sY3MAq/l2RxaXEyaGFBZCezoePzprFmIVF1mBbS4uxPn0Y27WrbA9zXriQnw8LC8biFPQ8iYcVa2kxdv++9IvR/v2KqZ/kLThYekHm5UvF1Sse2n7njuLqzKxWLV7/xYvKqV8ZRCI+HQbgUz8IySw+nrFq1fjr48eiXYRnjPH/KYMGST9/69Thn83fe/iQsfr1pcf17Fn8qVA3b/KAH+ABvCx9/sxY+fK87LFjS9R3Qwq8CcmFf5g/2/hgI3sd/lrmZR/zOyaZ13064LTMyy8pRCIRm3FuhuRcwA1M5zcd9uTrE2U3TWlS0lOY2Z9mDG5gZ1+fLdR9o5KimOkqUwY3sK2PFXwFODWVsUuXGJs0ibEKFbIG23p6jDk7M3boEGOxsYptl6pKSGDM0pKfn/nz5V/fpUu8VxNg7D8+woT9+iv/u2nTEvXFpdRatYo/Hy2Kl+Oh0Hr2lM+X44KIiJD+nyhrF+I6deKPe8cOZbeEqJoff5TOW5bFSJTjx6UX2ADGJk9mLCaGlz13rrTn2NSUX4iV1efB6tXSkR337smmzJQUxlq35uU2aMA/S0sQmuNNSA6CooLQzr0dwhPDAQD2FezhbOsMZ1tnWJtZF6vsd1Hv4HLKBQAwv9V89LHuU+z2llQCgQDru6+HAAL88/AfAMB/ff9D40qNsx5YyjKY50VTTRMj6o/A+gfrsev5LvS06lng+666vQoRSRGoa1YXLo1c5NjKTG7f5vO/Tp/OuhawsTGfozpwIM/OrKOjmPaUFLq6wKZNQK9ewNq1fJmYBg3kU9enT3z9Vcb43D2X/782Jk8Gfv+dr6F8+zbQrp186if5Y0y6dnd+czllrXFj4Nw55czzFq/fXasWUK6c4utXpkaN+DrIT58q/jmXJW9vPo9XFszNgT59eJ6OssrHB1izhm9v3Cib+dEDBvAVPlxd+ef1v//yFTa0tIB37/gxw4cD69bJNjfKnDnAvXt8/rWzM3+tF7f8H3/k88+NjHiuGFnkhVEgAWOyWLtFtcTGxsLIyAgxMTEwNDRUdnNICRObEovW/7WGb5gvyuuVR2RSJNJF0jV365evz4NwO2fYmBUw2cT/Jacno83ONnga/BStLVrj+pjr0FDTkPVDKHEYY9j1fBcMtAwwyHbQ9zv57zISeAOA9zdvNNzaEJpqmgieF4xyOvl/If0U8wl1NtZBcnoyTg89rZgLOtHRQIUKQGoq/7t8eaB/fx5sOzryhCckb87O/EtJy5Y84ZpQKNvyU1KA9u15spvGjXkd2trS/ZMm8XVfBwzgSe+IcogvfOjp8eRqBgaKq/vECf6ebdRI8cH3H38Av/wCDB3Kl0AqSw4e5MFOq1bSRFYlTWoqD6RiY2VX5oAB/Nxola5lVQtEJALatuXB6sCBPLCUNS8vYOJEvqQkAFStygPxXr1kXxfAXxvNmwOvXgGdO/MEkmpqRSvr8GH+vwLgSdUUkYBSxijwJiSTdFE6+h7si/OB51FJvxIeTXwEbXVtnHp1Ch5+Hrjy7kqWINzO3E4ShNua2+Zb/rSz07D58WaY6pji2aRnsDCykOfDISVYo62N8Pzbc2zssRHTmk/L9/ixJ8dit/dutK/eHtfHXIdAERcqrl0DOnUCKlbkH4ht2hT9A7Ws+vIFqFsXiIvjX34mFW/99mymT+c96yYmfJ30GjWy7vf3B2xt+YWtN294zyNRvHHjeLb7ceOk63gryvv3/HWhocFfh4oMeAYMAE6e5Ov+zpunuHpVQUAAf+/r6vLgpCT+7wwMBKys+GunuIEbYzwoS0kBevbkQWfmi4RlwbZt/DNAX5//b65aVT71JCby91xKCs98Lu9YydeXB9+JifxC22+/Fb4MPz9eRkIC8NNP/KJdSaTUge5yQnO883ft3TX26MsjZTdD5cw8N1Myzzin8xORGMF2Pt3JeuzrwTSWa2SZm2y7yZYt9VrKXobknBTnkM8hybHnXp+T90MpHcrwvNP199czuIE12dok32OfBz+XrHv+4PMDBbTu//76i8+zcnJSXJ2l0fr1/DwaGzP27Zvsyt23Tzqn72we+QK6d+fHzJwpu7pJwcXEMKary5+D27cVX3/mRF9PFJxfo0oVXu+NG4qtVxWkp/P8FwBjfn7Kbk3RXL7M229jI7vyxCsudO6svEz7yhASIs1CvnatslsjewcOSD+Pzpwp3H1jY/lrDGCsY8eirU2uImQ8po2UBCf8T6Djno5o594OgZGBym6Oyvj38b+SecZ7BuxB08pNsx1TTqccxjUah3MjziFkfgh29duFXla9oCHUgF+YH5bdWIZ6W+rBdpMtlngtgU+IDxhjeBPxBhPPTAQALGyzED2seij0sZVIpW8wTqEMrz8cGkINPAl+Ap8QnzyP/fHKj2BgGGI3BM2rNFdQCyFdg7ZRI8XVWRpNmwY0acKH7s+dK5syfXz4cEIAWLyY9yDlRlznzp1ATIxs6icFd/gw7wmytubr9SqaQCB9DytyXengYD7iQyiUrmtdlqipSfM6KHMd9eL48IH/trSUTXniocj6+nwd6p49+SiMsmDePP4Z0KgRH6lU2gwbBsyYwbdHjZLOLc+PODdJQABQpQqfhlCCcwBQ4F3GBIQHYMzJMQD4fOOpZ6eCqUCAkyHKgOdrT0kyM0W78u4Kpp/j/+h+c/wt+zzjHJjomGBMwzHwHO6JUNdQ7O6/G73r9Iammib8w/3x681fYf+vPepuqoueB3oiLjUO7aq1w68df5X3wykdBIIyNa/7e2a6ZpJ52rue78r1uEtvL+HS20vQEGrgj04KHnol/rJYFr80y5KaGh9mLhQCBw4Aly8Xr7yYGMDJCUhK4ontli7N+/jOnYF69YD4eJ54hyiW+JyPH6+8/3ni97AiA8BHj/jvunV5oFUWic+7Ii94yNL79/y3rAJvgOekuHSJD3++eZP/D4uOll35qujqVWDfPv7+37q1RAeWefr7b57PJDpa+hmVn/XrgSNH+Dnx8OC5ZEowCrzLkLiUOAw8PBBxqXFoVrkZtNS0cPndZRx8qfyEJvMvzUefg33QaGsjvAh5odC6A8IDMOjIIGSwDIy0H4mf2/1c6DKMtY0xusFonBl2BqHzQ7F3wF70te4LTTVNvIp4hcDIQJjrmuOg00GoC0vpP1RZEQ9GIhjbYCwAYJ/PPqRlpGXbnyHKwI+XfwQATGs2DTVNaiqucQkJPFkKQD3estC0Ke/5BoCpU4Hk5KKVwxifJ/zmDWBhAezfn//cUYEAmD2bb//zD5CenufhcpGWBri78wzsZcnLlzzxnbo6MHq08tqhjB5vceDdrJni6lQ1yrjgIUviwLt6ddmW26oVD0ZNTID793kukYgI2daRH8aAs2d5DgJ5fidJTgamTOHbU6eW7veDpiYPns3NgefP8+/Zv32bZ2IHeKb3Vq3k3kR5o8C7jGCMweW0C/zD/VHZoDLODDuDRe0XAQDmXJyDqKSofEqQn5sfbmL9g/UAgM+xn9FmZxtcCLygkLojEiPQ+0BvxKTEoLVFa2zvs73YSamMtI0w0n4kTg09hTDXMOwbsA8TGk2A53BPVDGsIqOWl3JluKc7s+61u6OCXgWEJoTifOD5bPv3++yHd4g3jLSMJO9nhfH25l9GKlXiydVI8f32Gz+fgYHAihVFK2P1ap6lWlOTZ0s3MyvY/UaM4F+GPn5UTnbzxYv5MmfDhim+bmUSLyHWpw9fIUBZxAGgtzeQkaGYOinwznrBoyRecJZHj7dY06Y8A7e5Ob8w0bEjEBoq+3py8ukTz5jduzdPANitGxAUJJ+6Vq3iF0orVeLLO5Z2Vavy4eJCIZ/eJP4f+L2QEGDwYH4heNiwUjP8ngLvMuLvu3/jqN9RaAg1cGzwMVTQrwDX1q6oa1YXoQmhWHhloVLalZCagHGnxoGBYVi9YXC0dER8ajx6HeiFLY+2yLXu1IxUOB1xwtuot7A0tsSJISegrS7bDJqGWoYYYT8C2/tuV+zc25KMgm4JDTUNjLQfCQBwf+6eZV9SWhIWXePB9s/tfoaprqliG0fDzGXP0JAPqwOAlSulIwoK6sYNYOH//5evX88zwBaUtjbvbQH4uuKK9PIlv2AA8OXOZLUmsKpLSQH27uXbyl7H2cqKL2WWmAi8fi3/+hijwBvgKwpoavKht+IgtiSR9Rzv7zVoAFy/zoPSFy+ADh2Ar1/lUxfAl/PasgWwswM8PXm2dm1tPv2nXj3+v1GWF6Zev5Zm5163jq9NXRZ06iTNbD5tGl9xI7P0dL5sWHAwf49s21ZqvhtS4F0GXAu6hoVX+Zexf3r8g5ZVWwIAtNS18G/vfwEA255uw52PdxTetp+u/oR3Ue9Q1bAqtvTaggsjL2Bsw7EQMRGmnpuKeRfnIUMk+6vvjDFM8ZyCGx9uwEDTAGeGnUF5vZI9b6TEoyHmORrbcCwAwPO1J8ISwiS3//PgH3yK/QQLQwvMaD5D8Q2jxGryMWgQ0KMHXx93ypSCvye+fgWGDOFfCkeNKtqyZFOm8CDg/n2+jqwiiES8renp/EsuoPjAX1lOn+bDZytX5j1qyqToRF/v3wORkfw5t7eXf32qSlOTB3RAyRtunprKk+MB8gu8AR543bjBe0r9/QEHB/lMSXn1igf2U6fyhG4tW/LPuRcveJ2JiTwRZZs2/GJhcTHG60pN5e9/Z+fil1mSLFjAR/qkpPDPvchI6b5ffuEXXPT1+bJypSgHBAXepdzHmI8YcnQIREyEsQ3HYlKTrF/G2ldvj/GN+JX2SZ6TkJqRqrC2XX9/HRsebgAA/Nf3PxhpG0FTTRM7++7E7x35cJs199fA6YgTElITZFr36nursfP5TggFQhwadAj1yteTafmkkMTBRSm5oilL9crXQ9PKTZEuSsd+n/0AgPDEcPxxm18l/63jb9DR0FF8w6jHWz4EAr7uto4OH2a5b1/+90lL40F3SAhQvz5P1FaU91KFCsBIPsJCYcHvzp3A3bv8i9WJE/y2o0f5kPfSTpxUbexY1UimpMj5xuLe7gYNFLtuuCoqqQnWPn/mF860tOSf8MrKiidaq1GDT8Vp3152Q7/T0vjUngYNgFu3+MiP9ev5/GI7O173tWs86ZmhIR+R07gxT1qZklL0eg8c4PPYtbWBzZvL3vcfoRDYsweoWZNfiBs1ir+eTpwA/vyTH+PuDtjYKLWZMqfc1czkg9bx5pLSklizbc0Y3MAab23MElMTczwuPCGcmf1pxuAG9sfNPxTStriUOFZjXQ0GN7CJpyfmeMxBn4NM61ctyVrGX2O/yqTuUwGnJGser7u3TiZlEiJPmx5uYnADa7ClAWOMsVnnZ0n+zhBlKL5BycmMaWjwMQpBQYqvvyxYsYKfX3NzxiIi8j52zhx+rKEhY69fF6/eFy94WUKh/J/bkBDp+tFr1vDbHB35366u8q1b2d6/Z0wg4I81MFDZreH++4+3x9FR/nXNn8/rmjxZ/nWpus2b+bno0UPZLSmca9d4u+vUUVydHz8yZmXF661albFXr4pX3uPHjDVsKF1fulu3vP/vff7MWN++0uPr1mXs7t3C1xsZyVj58ryM338vcvNLhWfPGNPW5udi0iT+OQYwNneuslsmFxR4l2ITTk1gcAMrt6ocC4oKyvPYPc/3MLiBaf+mzd5GvpV726Z6TmVwA6u2thqLSc79ebrz8Y7kooDFGgvm/c27WPU+D37O9H7XY3ADm3RmEhOJRMUqj8gAPQf5ikiMYJq/ajK4gR31Pco0lmswuIFdCryknAY9ecI/GE1M6PmTl5QUxmxt+XmemPPFScYYY0eOSL8Enjghm7o7d1bMF59Ro3g9DRsylpbGbztzht9mZMRYXJx861cmNzf+ODt0UHZLpJ49420yNpb/+9rBgdf133/yrackuHePn4sKFZTdksLZuZO3u2tXxdb79SsPeAHGKlZkzNe38GUkJPCLe2pqvJxy5Rjbs6dgr3uRiLHDh6WBs0DA2IwZhft/NWmSNHBPSSl8+0sbd3fp5xjAWNu2jKWmKrtVckGBdym17fE2BjcwgZuAXQy8mO/xIpGIddzdkcENrNvebnINSK++u8rgBgY3sMtvL+d7fGBEILPeYM3gBmbwhwE7/+Z8keoNjgtmFmssGNzAOu3uxFLTS+ebukQRiShwK6DBHoMZ3CAJwLvt7aa8xuzYwT8cO3ZUXhvKglu3pF9Ebt/Ovt/fnzF9fb5/wQLZ1XvunLQHXV6fo+LeMoGAsQcPpLdnZPAeNICxf/6RT93KlpHBWPXq/DHu26fs1kilpDCmqcnb9e6d/OpJT5e+bl+8kF89JUVCAh9hAvCgsqRYsoS3+YcfFF93SAhj9va8fjMzxp4/L/h9vbwYq11b+r91yBDGvn0rfBvCwxkbM0ZaTvXqjF24kP/97t6V3ufGjcLXW1r98IP0AlRJeh8UEs3xLoUefnmI6ed52v3fO/6OrrW65nsfgUCALb22QEtNCxffXsRh38NyaVtcShxcTrkAACY3mYzONTvne59a5Wrh3vh76GDZAXGpceh9oHehM54npSWh36F++BT7CXVM68DD2QMaahpFegxEhgSCsjevqYjEa3qnZqRCAAH+7PKn8hpD87sVo21babbryZP5XESx+Hhg4ED+u0MHaYZYWejWjc+ri43lc7BlLSWFPx6AJ3TLnH1dKARmzeLb69crbmkrRbp6lWeDNjLiz6GqUFSir1ev+OtWVxeoW1d+9ZQUurrSeawlKcGavNbwLojy5XkOjCZNgPBwwNFRmjcgN9HRwA8/8GMDA4EqVYBTp4BDh4q2lJ+pKbBrF3DxIk8u9+ED0L07MHp07muOp6VJE1+OG8fnqhNuwwZpzo9KlZTdGrmhwLuUCUsIw6Ajg5CakYr+Nv2xsG3BlwmrY1oHP7f7GQAw+8JsRCdHy7x9rpdd8SHmAyyNLQsVOJjomODiyIsY23AsMlhGoTKeM8Yw7tQ4PPzyECbaJvAc5gkTHZPiPAxSXJTBvNC61uqKygaVAQBjGo6BfQUlZgKmwFtxVq3ia3G/fClNeMYYMHEiz/BbuTL/4ijL5FxCITB7Nt/+5x/ZB7+rVvFldCpWlC6lk9mYMYCJCfD2LXDmjGzrVgXidWtHjOBJ9FSJIhKsiQOkxo1VI6mcKiiJCdbkvZRYfsqV4xexWrUCoqKAzp150JaTU6d4orTt2/nfkycDvr58re7i6toV8PHhFwwFAr5EYN26wOHD2b/nrF/PjzU1lSYQI5ymJr8YUbOmslsiVxR4lyLponQMPTZU0qu7u/9uCArZm7igzQJYm1ojJCEEP135Sabtu/z2MrY+2QoA2Nl3Jwy0DAp1f3HG898cec/OmvtrMMhjUL4Zz5fdWIbDvoehLlTH8SHHYWVqVbQHQGSLeroLRU2oho09NsLZ1hkrO61UXkMyMgBvb75NS4nJn6kp8PfffNvNjfcybdwoDbaPHClab01+Ro3idQcF8S+tsvLmTf7r1urpSXuFStvSYhER0uztyl67Oyfi97Q8A0Bavzs78XkviT3eygq8Af7/4+JFvtxXbCwPgm/ckO4PCQEGDwb69+dLLlpZ8WWqtmyR7ZrZ+vr8/9nduzzADwvj61D368ezvwP8QsXSpXz7r7/4BVVS9ih7rLs8lNU53q6XXBncwPR+12O+oUVINvF/14OuS+Zg3/1YhGyNOYhJjpHMr552dlqxyytoxvODPgclj2XHkx3FrpeQMs/Xl49X0NPjczWJ/IlE0mRUTZowpq7Ot9fJeVWGX37h9bRpI5vyRCJp4rauXfPO7/D5s/RxPn4sm/pVwbp10oRyqkgRib6aN+d1HDggvzpKGi8vfk4sLZXdkoJJS5MmJvvyRdmt4fPku3Th7dHRYeziRZ6wS7xqgpoaYwsXMpaY8+o+MpWSwpMnilf+MDBgbMsWxvr04X+3b0+5bcowCrxLiSMvj0gCzCMvjxS7vHEnxzG4gdXbXE8mScjEGdZrrq/J4lJkk6n2+4znL75lTdJy79M9SXA+7+I8mdRJiomSqZV8+/bxLw+tWyu7JWWLv7/0i5w4IZC830tfv0rrfPiw+OXt38/L0tYu2BJaI0bw40eMKH7dRXH3LmPTpjF29Chj8fHFL08kYqx+ff6YNmwofnnyIO9EXykpjGlp8fLfvJF9+SVVVJT0vZ3f8oGqICiIt1VTkycLVAVJSYz16pU1OzbAWKNGjD19qvj2vHzJWIsWWduioVG0LOyk1KCh5qWAX5gfxp0aBwBwbe0KZzvnYpf5Z5c/YapjipehL7H2fvGG+l0MvIgdz3YA4EPM9TX1i90+AGht0Rr3x9+Htak1PsV+QpudbXAx8CIA4GPMR/Q/1B8pGSnoU6cPVnVeJZM6STGI5zrREPOSjeZ3K4eNDbBgAd+uWxfYsUP+76VKlYBhw/h2cYd8R0UBc+bw7UWLgFq18r+P+PjDh4EvX4pXf2FFRwMDBgCbNgGDBgHm5jwR2r59QExM0cp8/JjP79TS4vO7VZG8E329fMmT6xkbF+w1UFYYG0vntpaEed7i+d3VqvGcEKpAWxs4fpy/b8V/r1wJPHyonGlRdnbAnTt8CLquLr/N1RWwtVV8W4jKUJF3S+mWmpGKjzEfweSQTComOQYDDg9AQloCHC0d8UenHBLVFIGZrhlWd10NAHC77oagqKAit2/CmQkAgJnNZ8LB0kEm7RP7PuN5rwO9sO7+OvQ52AchCSGwr2CPA04HoCZUk2m9pAgog3npQIG38ri5AQcOANeu8TmFiiAOfo8cAT59Kno5P/8MhIbyoG7+/ILdp0kToF07ID2dB8CK9NNPfH5olSpAjRpAUhKfmz1qFA/Ce/bkFz/CwgpepjipmpMTTx6nquSZ6Es8v7tpU/o8+F5JSrCmCvO7c6KpyS/UHTnCk6ctWKDcBH5qajzpmr8/cOwYsHy58tpCVAIF3nJ29d1VWG+0RvV11VF7Q20suLwAj78+lkkQLmIijD01Fq8jXsPC0AKHB/EEYrIyusFodLDsgKT0JEw9N7VIbZ57cS4+x35G7XK1ZXZR4HvijOdjGoxBBsvAnItz8CLkBSroVcCZYWdk1sNOioEymJcOjEm/FFJiNcVTU+M90BUrKq7Ohg35cmUZGTypW1Hcvw9s5Yk18e+/vMe3oObOld4vIe9EmjKTub179/Ls6k+f8p76unX5kkDnz/PM8hUr8uWJNm7Mu1c+IQE4eJBvq2JStczkmeiLEqvlriQlWFPmUmL50dAAnJ1VKzt2tWp8xIwadQKVdRR4y0lsSiwme05G572d8T76PQDgXdQ7/Hn3TzTb3gw1/6kJ10uuePjlYZGD8FW3V+FkwEloqmni2OBjMNczl+Ej4Gt7/9vrX2iqaeJC4AV4+HkU6v7n3pzDzuc7IYAA7v3coaepJ9P2Zaappgn3fu6SjOdaalo4OfQkqhlVk1udpIAo6C49goL4MFtNTRouV5aIg99t2/j6y4WRns4zlDMGjB3Lsw8XRp8+/At0VBSwZ0/h7lsU6el8qSHG+Hq8jo68Z7ZRI+DXXwE/P/7z22+8h1Ik4lmSZ8wAqlblSxv9/Tfw7l3Wco8e5VmXa9TgFzJUmTyXFKPAO3clqcdb2UuJEVJCUeAtB5ffXkb9LfUlS2dNbToV3+Z9w5FBRzDYbjB0NXTxPvo9/r73N1rsaIEa62tg/qX5ePD5QYGD8EtvL2GR1yIAwKaem9Csinw+xKzNrPFTW76s2KwLsxCTXLC5bVFJUZh4ZiIAYHbL2Whbra1c2peZQCDAL+1/waOJj/Bs0jO0rNpS7nWSAqAh5qWH+AthvXo8+CZlQ69efBme6Ghg167C3Xf9euDFC77m7l9/Fb5u8VBNgM8zF4kKX0Zh/PMPXy7PxES6jNv36tYFfvkFePKEB9h//w20bs333b/P53HWqsWD9d9+44G6eJi5i4vqzInNTcOG/PeHD0BkpOzKTUzkw38BCrxzIu7xfvWq8Be4FE1Vh5oTouIETB4Tj5UsNjYWRkZGiImJgaGhocLqjUmOwfxL8yWJxGoY18B/ff+DYw3HLMclpiXi/Jvz8PDzgOdrTySkSYfPWRhaYJDtIDjbOqNF1RYQCrJ/QL+Pfo8m25ogMikSExpNwPa+2+X6uJLTk9Hg3wZ4HfEaU5tOxaZe+c+1G3tyLHZ774ZVOSs8n/wcuhq6cm0jUUGUTK30+eUXvgbzhAnAdvn+3yEqZtMmYPp0oHZtHhgUJHj8+JEHqYmJPPB0cSla3XFxgIUFH21x5gzQu3fRysnPx498JEdCAn99T5hQuPt//QqcPMnnct64wYfnZyYU8mC2alWZNVluatXiFxWuXAE6dZJNmXfuAG3b8uH5X7/SZ0NOqlTh5+b2baBNG2W3Jnc1a/IRULdu8eeUEFIgKn7ZteS4EHgB9bbUkwTdM5rPwIspL7IF3QCgq6ELJ1snHBp0CKGuoTg2+BiG1RsGfU19fIr9hLX316L1ztaovq465lyYg7uf7kLE+FX+pLQkOB1xQmRSJJpWbooNPTfI/bFpq2vj317/AgC2PN6CB58f5Hn8mVdnsNt7NwQQYFf/XRR0l2X0xap0ofndZdfYsbwXODAQ8PQs2H1mzuRBd7t2/P5FZWDA51MDxc+unpeZM3nQ3aZN0S4SVK4MTJ0KXL0KfPvGLzb07MnnnAJA374lI+gG5DPsOfMwc/psyFlJGG6ekSFNtKiKc7wJUWEUeBdTdHI0XE65oMf+Hvgc+xm1TGrh+pjr+KfHPwVK6qWroYuBdQfigNMBhM4PxYkhJzC8/nDoa+rjc+xnrHuwDm12tkG1tdUw6/wsjDk5Bk+Dn8JM1wzHBh+Dtrq2Ah4l4FjDEWMajAEDww+ePyAtIy3H4yKTIjHJcxIAYF6reWht0Voh7SMqiL5YlS6M8aG1AGU0L4v09IAffuDba9bkf/ypU/xHXR3YsqX4w6tnzODDzq9dA54/L15ZOcnc3n//LX57zcx48H72LM98fulS4YfpK5M8En3R/O78lYQEa1+/8lwI6ur8YhMhpMAo8C6Gs6/Pwm6zHdyfu0MAAWa1mAXvyd5FXjJLR0MH/W36Y//A/QhzDcPJIScx0n4kDLUM8SXuC/55+A88/DwgFAhxeNBhhScO+7vr3zDVMcWLkBdY/2B9jsfMujALwfHBsDa1xnJHWjahTGKMEqqVRsHBfDkooRCwt1d2a4gyTJ/Ov2zfuJF3j1x8PA+UAT7f2c6u+HVXq8bX0wb4uriylLm98+bxHAayZGQEdOnCf5cU8uh5ffyY/6bAO3clocdbPL+7WjXK0k1IIVHgXQRRSVEYe3Iseh/sja9xX2FVzgo3x93Euu7rZJa5W1tdG/1s+mHvgL0InR+K00NPY5T9KFgYWmBjj43oWKOjTOopDDNdM/zVhSfHWXp9qSRbu9ipgFPY92IfhAIhdvXfBR0NHYW3kSgZzesuvcRfBG1sAF2aPlImVa0KDB7Mt/Ma8u3mxoei1qjBl+CSFfGa4gcO8AtBsiJur6UlsGSJ7MotyWSd6Cs6Gnj9mm83bVr88kor8Xl/+RJISVFuW3JDidUIKTIKvAvpzKszsNtsJ5nDPLflXDyf/FyuWbu11LXQx7oP9gzYg49zPmJKsylyqys/YxuORfvq7ZGYlojp56ZLsrBHJEZIhpi7tnaljOJlFWUwL73EQx9pmHnZJg5+Dx7kQ06/5+0t7ZHetEm2F2latODLdaWlAZs3y6ZMeba3JKtQgQ8jZoyfo+IST1OxtOTD8EnOqlXjKwCkp0szwKsaVV7DmxAVR4F3AUUmRWLUiVHoe6ivZCj1bZfbWN1tdZlKHiYQCLC191ZoCDVw9s1ZHPc/DgCYcX4GQhJCYGtuC7cObsptJMmfhwcwYADvhZAVGl5eulFiNQLw3sq2bXlgsOm7FS4yMvia3RkZgLMz0KOH7OsXryn+779AUlLxyhKJ+JrdGRmAkxNPhEakxO91WQx7pvndBSMQyHcddVmgNbwJKTIKvAvgZMBJ2G6ylQyjdm3timeTnpXZxGE2ZjaStb1nnJ8B92fuOPjyINQEatjVb5fCEr6RIkpN5Zl3T54EDh2STZkUdJd+1ONNxDIHv4mJ0tu3bwcePOBZyGU9D1usf3/e0xYeDuzbV7yytm3j624bGPD1xklWsgwAKfAuOFVPsEZDzQkpMgq88xCeGI7hx4ZjwOEBCEkIQV2zurjrchd/dvmzzM9f/qndT7AqZ4Xg+GC4nObLrvzY5kc0q0IfqirvzBn+pRWQXQIXGmJeukVESHs5GjZUalOICujbl6/jGxkJ7NnDb/v2DVi4kG///rv8sh2rq/NlvwA+z7yoF/1CQqTt/e03vn4yyUqWib4o8C44VU+wRoE3IUVGgXcO4lLisOLWCthstMHBlwchFAixsM1CPJ30FC2qtlB281SCtro2tvTaIvnbztwOSx2WKrFFpMB27JBuF/eDnTKYlw3i5Ztq1gSMjZXZEqIK1NSkwe+6dXzI9ty5QEwMH4o+dap86x8/nvdS+/sDFy8WrQxxexs3BqZNk237SgtZJfoKCeHJ6wQCoEkT2bStNBOfd29vPg1ClYhEwMePfJvmeBNSaBR4ZxKbEovfb/4Oy/WW+Pnaz4hIioCduR3uj7+PFZ1X0BDq73Sq2Qkzm89Eeb3y2DNgD7TUtZTdJJKfT5+yflF98YInKioO6uku/WiYOfmeiwtgaMizXs+fz5OtCYV8+Lm8lxgyMuLBN5B3dvXcXLnCM6MLhcDWrbQkUm4yJ/p6+bLo5Yh7u21s+AUTkjcrK0Bfn+cwePVK2a3JKjiYf2dQU6NRIoQUAQXe4AH3bzd/g+U6SyzyWoTIpEjUMa2DvQP24vnk5zR8Og/re6xHyPwQNK5EX8hLhF27eA91+/b8C1BKChAQUPTyKOguGyixGvmegQEwcSLfFge/06crrkdz5kweOF+6VLigMDlZ2iM/bRotbZUXgUA2CdZomHnhCIXSKT2qNs9bPMzcwoJP+yCEFEqRAu/jx4+jSZMmsLCwQJUqVTBmzBiEhITkevyuXbsgEAhy/cnvOG1t+fQ0xyTH4Ncbv8JynSUWey1GVHIUrE2tsX/gfvhN9cNI+5FQF9I/FlJKiETAzp18e8KE4iVwoSHmZQv1eJOczJjBgwSAz+n+9VfF1V2jBl+ZAShcIrcVK4A3b4BKlRTb3pJKFgnWHj/mvynwLjhZZpSXJVpKjJBiKXTgffToUQwaNAgaGhp4//49zp49iz179sDR0RHJycmFboDgux4zHR0dmJqaZvkxk/Gaj9HJ0Vh2fRks11tiyfUliEqOgo2ZDQ4MPADfqb4YXn841IQ09IyUMl5e/EPT0JAvnVPUwFsccFNvd9kQHw+8fs23qcebZFa9OjBuHA++N2/m/1sUSbym+L59QGho/se/egWsXMm316/nQ9ZJ3oqb6IsxaY83jS4oOFVdUoyWEiOkWAoVeDPG4OrqCsYYunfvDjU1NTRs2BBVq1aFv78/tm/fnut9XV1dERYWluWnW7du6Pndupk//vgjwsPDs/x8/vy5aI/uO9HJ0XC77gbLdZZwu+GG6ORo2Jrb4qDTQbyc8hLD6g+jgJuUXv/9x38PHw7o6hb9CxVlMC9bvL35l+fKlYEKFZTdGqJq/v0X+PoV6NdP8XW3bs17UVNSgC1b8j6WMT7EPDWVry8+aJBi2ljSFTfR18ePQFgYH5ZMKyIUXOYeb1UaXUYZzQkplkIF3r6+vnj//zedubm55Pby5csDAM6cOZPj/erWrYsOHTrAzMxM8hMdHY3Lly/D1dU1y7EvX77EkCFDULduXdSvXx9z5sxBuHjpoyKKSorCUq+lsFxniWU3liEmJQZ25nY4POgwfKb4YGi9oRRwk9ItMhI4fpxvT5jAf2cOvEWigpWjSl8AiGLQMHOSF3V15V2QEQikvd6bN/P527nZtw+4dg3Q1gY2baKLhwVV3ERf4t7u+vX5uScFY2sLaGryzPtBQcpujRQF3oQUS6EC7zdv3ki2dXSk61jr6uoCAF6LhyN+p0WLFtl6tlevXo2mTZvCwcFBcpumpiYSExOxbds2PHv2DLVq1cK6devQqlUrREdHF6apAIDIpEgsvrYYlustsfzmcsSkxKBe+Xo4MugIXkx5gcF2gyEUUH45Ugbs3897hRo0kAZQNjb8i1B8PPD2bf5lUNBdNlFiNaLKBg0CqlblQ80PHsz5mMhIYN48vr1kCZ8fTgpGKOSfG0DRhj1TYrWi0dDgFysA1RpuTnO8CSmWQkWd8fHx0jsKhdm2M+/PS1hYGHbv3p2tt3v48OE4d+4cjIyMoK2tjcWLFwMAAgMDsXnz5lzLS0lJQWxsbJYfAKi/uT5+u/UbYlNiUb98fRx1Pgrvyd5wtnOmgJuUHYxJh5mPHy/t6VFXB+zt+XZBPthpiHnZRD3eRJVpaPAkbwDPrp7TBcKFC/lwZ1tbaQBOCq44840p8C664s6vl7XMa3hTjzchRVKo6FNfX1+yLco0NFW8nXl/XjZu3IjKlStj4MCBeR5nbW0t2b53716ux61YsQJGRkaSHwsLCwBAfGo87CvY49jgY3g++TmcbJ0o4CZlz9OnfH6elhYwYkTWfQVJsEYZzMuulBTA15dvU483UVUTJ/K8FT4+wNWrWffduQOI88/8+y8fvksKp6gZtkUi4MkTvk2Bd+GpWoK1kBD+mSAU8lEmhJBCK1QUamVlJdlOSkqSbCcmJgIA6tSpk28ZiYmJ2LRpE+bOnZul1xwAvn37hvT0dGnjMu3PyCOpx08//YSYmBjJz6dPnwAA+wbuw7NJzzCw7kAKuEnZJe7tHjAAKFcu676CXlGnnu6y6eVLID2dv26qVVN2awjJmYkJ4OLCt8VrigNAWhoweTLfdnEB2rVTfNtKg8yfE4W5CPv6NRAbC+joAHZ28mlbaZb5wrgqXPwWDzOvWpWPNCGEFFqholE7OztU//+8jrCwMMnt4u3evXsjIyMDo0aNQp8+fXIceu7u7g6BQIBx48Zl2zd06FB4e3tL/n6bad5p4zyGOWppacHQ0DDLDwD0se5DATcp2xITgQMH+Pb48dn3Z76intsHOwXdZVfmYeb0OiCqbNYs/ho9dw7w9+e3rV3LLx6ZmQF//qnc9pVkRU30JV6/u1EjPrWJFI69PaCmxvMXBAcruzU0v5sQGShUVCoQCLBq1SoAwKVLlyASieDj44NPnz7B2toaEydOxLNnz7Bv3z54enriwoULWe6fkZGBNWvWYPr06VmSs2W2efNmiEQiiEQiSV3ly5fHtGnTivL4CCnbjh3jX5YsLYGOHbPvr1ePf7BHRADfL9tHQ8wJJVYjJUXt2kCfPnx7/XoeJLi58b///hswNVVWy0q+oib6ovndxaOjw5OgAqox3JzW8Cak2ArdHTxkyBB4eHggOTkZ1atXR7du3TBy5Eh4eXlBR0cHtra2aNq0KWrXro123w3rOn78OIKDg3MNokeNGoWgoCDY29ujcuXKuHDhAoYMGYJ79+6hUqVKRXuEhJRl4mHmLi58Xtb3tLWlQwAzf7CLA27q5SzbKLEaKUnmzuW/9+zh//OSkgAHB2D0aOW2qzQoSqIvceDdtKns21NWqFKCNVpKjJBiEzBW+rq0YmNjYWRkhJiYGMmwc1KGMEYBIwAEBvI1WAUCfqX6/0kHsxk7Fti9G1i6VNpDREh6OmBoyIOXgAAgU7JLQlQSY0CTJtIgRUMDePFC2mtIim7LFmDqVKB7d+D8+fyPT0vj/z+Sk+n/R3GsW8fXqu/fHzhxQrlt6d4duHiRX9AX51QghBQKTYAmpUtcHO/BdXSkYdI7d/Lf3brlHnQD2TOnlvXzRrhXr3jQra/PL+AQouoEAh6kiC1YQEG3rBQkH0hmvr486DY0pP8fxVHUjPLyIB5qTnO8CSkyCrxJ6eLuzhPrXL/Oe3zLqvR0YNcuvp1TUrXMVC1zKlEN4i96DRrkPE2BEFU0ZAjQqhX/+flnZbem9Chsoq/Mw8zp/0fRNWzIf3/4wHOxKAtjNNScEBmg/4ak9MjI4El1xG7dUl5blO3CBf7lyMwM6Ns372PFH+xfvgBhYTRMn3A0v5uURJqawN27/CeXJK6kCAqb6IsSq8mGkRFQqxbfVmavd2goH8EgEOQ9go4QkicKvEnpcfo08O6d9O+yHHjv2MF/jx7Nv4jmxcBAOhRQFYazEdVAgTchJLPCJPqiwFt2VCHBmri3u3Ll/L9TEEJyRYE3KT3WruW/mzThv8tq4P3tG+DpybfzG2Yupgof7ER1iES0lBghJKvM05LykpQE+PjwbQq8i6+g512eaCkxQmSCAm9SOjx+zANtdXW+lIxAALx9W7C5aKXNnj182H3LloCtbcHu832CNVK2BQUBsbG8Z6OgryFCSOlW0M8Jb2/+GVS+PA1LlgVVuDBO87sJkQkKvEnpIO7tHjKEBwr29vzvstbrzZh07e6C9HYzxn9UKXMqUT7x66B+fb4kEyGEiPOBfPyYd6KvzMPMKWdI8Yk/n1+/5iu3KAMF3oTIBAXepOT78gU4coRvi5eSadeO/y5rgfedO/zDWU+PX4TIiziDuUAg/WAPDARiYuTbRqL6aH43IeR7BU30lTmjOSm+8uWBKlX4Z7a3t3LaIA68aSkxQoqFAm9S8m3cyJfPat9eOr+7rAbe4t7uwYN50rS8CATS3ggzM6BaNb79/LncmkdKCAq8CSE5KciwZ0qsJnvKHm5Oc7wJkQkKvEnJlpAAbN3Kt8W93YA08H7xAoiOVnizlCI2VtrzP2FC3sfmtF43DTcnAH9tiANvSqxGCMksv0RfsbHAq1d8mwJv2VFmgjVaw5sQmaHAm5Rsu3cDUVF8+FufPtLbK1XitzHG13MtCw4dAhIT+VqrrVrlflxOQTdACdYI9/UrX89dTU2aK4EQQoD8e16fPOGfMdWq8SHSRDaU2eMdHs6/WwDSkXGEkCKhwJuUXCIRsG4d3541iwcKmZW14eaZk6rlldAm8xDzzKjHmwDS59/GBtDRUW5bCCGqJb9EXzTMXD7EgbevL5CSoti6xb3dlSoBWlqKrZuQUoYCb1JynT0LvHnDE76MG5d9f1kKvF++BB4+5MupjR6d8zHiDOa5EX+w+/lJr26TsofmdxNCcpNfoi8KvOWjalXA1JTns3n5UrF10/xuQmSGAm9ScomXEPvhB0BfP/t+ceD96BGQnKy4dimDuLe7T5+8h/fl1RNeuTK/r0gE+PjItn2k5BD3eNP8bkJITvIa9kyBt3wIBMqbDkbzuwmRGQq8Scn0/Dng5cWHl8+YkfMxtWsDFSoAqam8N7i0SkkB9uzh23mt3Z3feqqZlxWj4eZlF/V4E0Lykluir7Awae+oeIURIjvKSrBGgTchMkOBNymZxL3dgwYBFhY5HyMQlI3h5qdOAZGRfPhft27Z9+c3xDwzSrBWtkVEAB8/8u2GDZXaFEKIisqtx/vxY/7b2ppPASOypawEa+KLKbSGNyHFRoE3KXmCg4GDB/n23Ll5H1sWAm/xMPOxY/kc75zk19stRj3eZZv4ea9Vi744E0JyJv6c+D7Rl3iYedOmim9TWSA+797efK63olCPNyEyQ4E3KXk2bwbS0oDWrYHmzfM+Vhx4370LZGTIv22K9uEDcPky33ZxyfmYggbdgPSK+osX/ByTsoWGmRNC8mNhkXOiL5rfLV+1a/N8NsnJ0rXS5Y3W8CZEpijwJiVLUhKwZQvfnjMn/+Pt7QFDQ77sSU4ZWEu6Xbv4B6OjI1CzZtZ9BR1enlmNGvx8paYC/v4yaSIpQSixGiEkPzkl+mKMAm95Ewql/5ufPFFMnZGRQHw836Y1vAkpNgq8Scmydy+fh2ppCfTvn//xamq8ZxwofcPNMzKAnTv59vdJ1YoSdANZP9hpnnfZQz3ehJCC+P5z4vNnICSEf+ZSfgj5EV/UuH9fMfWJ53dXqADo6CimTkJKMQq8SckhEgHr1vHtmTNzn8/8vdI6z/vqVZ4Iy8gIGDgw6z6BoHBDzDOjBGtlU1wc8Po136Yeb0JIXr5P9CXu7a5XD9DVVU6byoJWrfhvRQXeNMycEJmiwJuUHBcv8uHPBgZ5L5v1vfbt+e9bt4reE6yKxEnVRo6UXokuTAbz3FCCtbJJPBWjSpW814InhJDvE33RMHPFaNmS/37xAkhIkH99FHgTIlMUeJOSQ7yE2IQJfB5yQTVrBmhpAaGhwJs38mmbokVEACdP8u3vL0IUtadbTNyT8fw5H2VAygYaZk4IKajvE31R4K0YVavyn4wM6TmXJ3HgTUuJESITFHiTkuHlS569Wyjkw8wLQ0tLmv28tAw337ePJ0Br1CjrsODiBt0AX4NVW5snVAkMLH55pGSgxGqEkIISCqVzuZ88ka7hTYG3/ClyuLl4jjf1eBMiExR4k5JB3Ns9cGDRPgBK0zxvxqTDzMW93bIYYi6mrg40aMC3aZ532UE93oSQwhD/r/DwAGJi+EXuevWU26ayQDzc/N49+ddFQ80JkSkKvInqCw0F9u/n2wVZQiwnpSnwfvwY8PHhX3KGD5feLovebrHvE+eQ0i05GfDz49vU400IKQjx58S5c/x3w4aAhobSmlNmiHu8792Tf94aCrwJkSkKvInq27IFSEnhw8XFHziF1bo1Hxr37h3w9ats26doO3bw305OgIkJ35Zl0A3QkmJlzcuXPEGSqSlgYaHs1hBCSgLx54Q4FwgNM1eMxo0BTU0gLAwICpJfPdHRQGws36Y1vAmRCQq8iWpLTgY2beLbc+cWPcA0NJQOny7Jvd4JCcDBg3x7wgT5Xe3OvKRYacoET3KWeZi5rC/iEEJKp7p1+cgrMQq8FUNLS3rRQ57DzcW93ebmgJ6e/OohpAyhwJuotgMH+FVdCwvew1scpWG4+dGjfL3lmjUBBwf51VOvHp/rHRkJfPokv3qIaqDEaoSQwtLQAOrXl/5NgbfiZB5uLi80zJwQmaPAm6guxqRJ1WbM4IFgcZSGwFucVM3FhQ+dl1fvpJYWYGfHt2m4eelHidUIIUUh/p9hYMBXxCCKoYjM5hR4EyJzFHgT1XXlCp97qqcHTJxY/PLEgbePD5+7VNIcO8YvGgiFwJgx8q9P3PtJCdZKt/R04MULvk093oSQwhAv1dm8Of9sIoohzmzu7Q0kJsqnDvFSYrSGNyEyQ/8lieoS93a7uADGxsUvr0IFwMqK96TfuVP88hTp1Stg3Di+PW8eULWq/OvMPM+blF4BATyXgr4+ULu2sltDCClJRo8GVq4ENmxQdkvKFgsLoHJlfuFUvIa6rFGPNyEyR4E3UU3+/sD583wo9axZsiu3JA43j4/n65fHxQHt2wN//KGYeinwLhvEz2+jRtRjRQgpHA0NYMECnmiNKI5AIP/h5hR4EyJz9C2LqKZ16/jvfv2AWrVkV25JC7wZA374ga+xXLEicPhw8ee6F1SDBvzD/etXICREMXUSxaPEaoQQUvKIh5vLK8EaBd6EyBwF3kT1hIcDe/bw7TlzZFu2OPB+9AhI+l97dx4fVXX/f/w9WUnIxhYIixFZIpsgq2wiUn6ooMVK61IoVosrFbXFr7QV3AoVv22VKkVxqX7BWqW0lUWKYiyLKKKAbMoiwUDZkTULJHN/fxzvzAQSyCRzZ309H4885iQzc+ckXJJ53/M55xQH9thOeP55s31YfLz01lsmfAdLWprUtq1pM887erGwGgBEHt+VzQO97efRo961cJjjDQQMwRvh54UXzJzTrl29QTlQLrpIysmRTp+WVq0K7LEDbeVKs3e5JD39dOB/FtURywuslZZK//u/3gVmopHbLa1da9qMeANA5Oja1ZT679sX+L9T9vEaNDAX4QEEBMEb4aW0VHruOdN+8MHAb5flckVGufmBA9IPf2guEIwYId1/f2j6EcvzvF98URo/3pT6R6uvv5aOHTPbxzFHEwAiR0qK1KWLaQe63Jwyc8ARBG+El7/9Tdq716zW+cMfOvMa4R68y8ulm2+Wdu82+6K+8opz+3Wfjx28Y3HEe+lSc/vBB5G5/Vx12P+unTqZkRMAQOTwLTcPJLYSAxxB8Eb4sCzvFmJjx0pJSc68jh28P/rIbMURbiZOlJYsMfuXz50rpaeHri92+fH27dEbPqtirxRbVmZW2I9GzO8GgMjl1MrmjHgDjiB4I3x8+KGZb5qaKt15p3Ov07GjlJlptulat86516mJefO824XNnCm1bx/a/tSv773ibc8FjgW7dpkP27/+Fbq+OIngDQCRy17ZfM2awC4YS/AGHEHwRviwR7tHjzaBzynx8VLfvqYdTuXm27dLo0aZ9s9/bsrNw0EsLrBml+3Z1QYLF5r1B6KJZbGVGABEstxcs9tJWZn02WeBOy7BG3AEwRvhYetWaf580x43zvnXC7d53sXF0g03mC08evc2q2mHi1hcYM0u2/vxj82bmuPHTUVGNNm92yziFx9v5ngDACKLy+VMuTlzvAFHELwRHv75TzMCN2SIWVDMab7BO9D7X/rLsqR77jFl740amf26nZrfXhOxuMCaPeLdp4903XWmHW3l5va/Z7t2ZnVcAEDkscvNA7XA2vHj0qFDpk3wBgKK4I3wsHWrue3VKziv17272ULpwAFpy5bgvGZVXnpJ+stfpLg46c03pebNQ9ufM9llyJs3S0VFoe1LMJSWekv2eveWvv99037nHbPvdbRgfjcARD7flc0DMZBgj3bXq2fWwwEQMARvhIdt28xt69bBeb3kZG/ID2W5+erVZgV3Sfrtb6UrrwxdX6qSkyM1bmxC5/r1oe6N89aulU6dkho2lFq1Mv8maWmmNDuQc+hCjeANAJGvWzcpIUHas0cqLKz98ZjfDTiG4I3wEOzgLYV+nvehQ9KIESbkXXed9NBDoenH+bhc3lHvWJjnbZfrXXaZ+d7r1JGuusp8LZrKzVlYDQAiX2qq1LmzaQei3Jz53YBjCN4IveJi71XaWAnebrc0cqT5A9eqlfTaa6bUPFzF0gJr9hsXu3xP8pabR0vwPnjQ+3+uS5eQdgUAUEu+5ea1xYg34JgwfqePmLFjh7nNyDDlvcHSu7cJuzt2mDLiYHrySWnRIjOa+ve/S1lZwX19fzm9pZjbLT3yiHdLuVCyV4b1Dd5Dh5rVvzdsMNu+RbLdu70/59atzf87AEDkCuTK5gRvwDEEb4Seb5m5yxW8183I8I72BXPUe9Ei6dFHTXvGDG+JWDizR7zXr5dOnw788V991VyMePBBad++wB+/uv77X+mbb8wFmR49vF+vV08aMMC0I3HU++uvzRZ1vXubxfsmTzZf79MntP0CANSevbL5559LJSW1OxbBG3AMwRuhF4r53bZgl5vv3Gn2hrYs6Y47pNGjg/O6tdWypVnd9NQpadOmwB77wIGK89vz8wN7fH/YZXqdOpkF1XwNH25uIyV4b9pkLmZceqmZzjB+vHc0pE8fE8SnTQttHwEAtdeypZSdbS6M13ZKGHO8AccQvBF6sRK8S0vNYmqHD5vtzJ591vnXDBQnF1gbP978TGwffBDY4/ujsjJzm72f9/LlZo50uLEs82/z61+bvbk7dDDl+2vXmjL5K6+Unn/elJqvWCH94hdsFQMA0cDlCsw875MnzcVwiRFvwAEEb4SeHbzbtAn+a9vBe8MG6dtvnX2tcePM9mH160tvv23md0cSJxZY+/BDs7CcyyX95jfma6EM3r4rmp8pN9dMTXC7pfnzg9qtKrnd0kcfmRB90UVmW5nJk6Uvv5QSE6VrrpFeflnau1daskS65x6padNQ9xoAEGj2363azPO2R7szM8N/7RkgAhG8EXqhHPHOzpby8sxo4YoVzr3Oa69JL7xgAubs2ZF5JTnQC6yVlkp33WXad91lRr7j483iZfYf/2A6dcpcGJEqH/GWwqPcvKzMhOh77zXztfv2lf7wBzMvLyVF+sEPzDl24IC0YIF0223BXbQQABB8gRjxpswccBTBuyqWJY0dK91wg/T6686PhsaqU6e8v+hDEbwl76j30qXOHH/dOm/AnDTJuyd0pLFHvNeulcrLa3+8p5+WvvpKatLEjNJmZJgSfCk087zXrTMXA+rXr7r6wt5W7N//loqKgtc325w55uf1ve9J06dLe/aYn9uPf2xWxz940Nzecgtl5AAQS7p3Nxevd+/2bhfpLxZWAxxF8K7K+vVmPuTcuWYBrOxsacgQ6cUXpf37Q9276FFQYMpl69aVGjcOTR+cnOd95Ii5eFNSYgL3I48E/jWCJS/PjKiePOmtUqipbdvMwl+S2drKLmm78kpzG4pyc98y86pW1+/c2YwEFBdL778fvL5JJujfead06JDUoIF0++1mRHv/fmnWLDPSnZoa3D4BAMJD3breXVJqWm5O8AYcRfCuyrp15rZZM6ljR1PeuXixeeObk2O2Fpo2Tdq1K7T9jHSh2krMlx28V68O7Cim220u2mzfbsLarFlmm6pIFR/v/aNem3nelmXmGpeWSoMHSzfe6L1v0CBz+8EH5nHBZAfvqsrMJXOO2qPewS43f/11swhdy5ZmpPull8wc7uTk4PYDABCe7HneNS03J3gDjorgFOAwO3gPH25Gv7/6SpoyxZTyuN2mLHncOKlFC6lXL2nqVBOw4J9Qzu+2XXihucBSViZ98kngjvv009I770hJSaZEuEGDwB07VOxy89rM8/7b36T33jOBcfr0ihdc+vQxP6/du6WtW2vXV3+da0VzX3bwnjcvMCX31eF2S888Y9rjxpmF0wAA8FXbed7M8QYcRfCuyhdfmFt7hK9tW+nhh6VPPzVXBP/4R6lfPxMaVq2S/ud/THjs3Fl6/HFp48bgj9hFIjtchTJ4u1yBLzf/4APpV78y7T/9yTt3OdLVdkuxI0ek++837d/85ux/95QUE74ls4BYsOzda/5fu1xSjx7nfmz//qY0/sCB2i1i44933zUX/zIyzGJpAACcyQ7en39uqsr8xYg34CiCd1XsEe9LLjn7vtxcEx6WLZP++1/pz382JbPx8SawT5pkytMvvtiEr9WrCeFVCYcRbymwwXv3bummm7yl5mPG1P6Y4cJ3S7GanNO/+pW0b5+ZLz5+fOWPCcU8bztAd+xowu25JCZKw4aZdrDKzf/4R3P7s59J6enBeU0AQGS56CKzi8WpU/5XphUXm7/PEsEbcAjBuzJ795oFi1wu80b8XJo0MStWL15snvPqq9K115oy2i1bTHl6jx4maOzeHZz+R5JwC94rV5qS85o6dUr64Q/NaGjnzmeXUke6Dh2khASzyv833/j33E8+kWbMMO0ZM6qem2wH7/x8c/EiGKpbZm6zy83/+U/nL6p98YUZ/Y+Lk+67z9nXAgBELper5uXmdpl5erpUr15g+wVAEsG7cnaZeZs2ZpXI6qpfX7r1VjOv98AB6c03TQhLTTUl1fPmOdLdiHX6tLesKdTBu0MH84fm5MnazV8eP978scvMNPO6o22V6eRk78Uof8rNy8rMBSrLMlUAV1xR9WN79DD/7w4dMusrBIPviubVMWSImYu+bZu0ebNz/ZK8o9033MC8OwDAudnB29+VzX3nd0fTgAEQRgjelTlXmXl1paeb1Zrfess7p/XTT2vdtajyzTcmkNWpIzVtGtq+xMVJffuadk3Lzd9806x0L5kVqEN9McEpNVlgbdo0s/93/fpm0blzSUryViAEo9z89GkzHUSq/oh3errZS1tyttx8717pjTdM+8EHnXsdAEB0qOnK5szvBhxH8K6MHbzthdVqy16sieBdkV1m3qpVeGyzVZt53ps2mfm3kjRhgnTddYHrV7jxd4G1wkJp4kTTnjpVatTo/M8J5jzvL74wc9vq1TOLKFaXb7m5U/78ZzN94bLLqj8aDwCIXT16mPdUhYX+TXEkeAOOC4O0E4bOXNG8tuzgvXGjKWWGES7zu2128F6+3L95u8ePmzLgkydNYHz8cWf6Fy78HfG+7z7zs+nXT/rpT6v3HDt4/+c/tZtzXx32qECvXv5dALr2WnO7apVZZDHQiovNGgGS9MADgT8+ACD6pKV5Kzb9KTe3S80J3oBjCN5nKi31ztmsTam5r2bNpJwcs1BUbeYPR5twC97dupntrA4elL78snrPsSzp9tvN45s1k/76V7P4WDS75BIz/+u//zWl0OfyzjtmRDghwSyoVt1g26WLGYE+flz67LPa9vjc7OBd3TJzW06OdxTaifUbZs825+IFF0g/+EHgjw8AiE41KTe3R7xZSwRwDMH7TJs3mxG2rCzzhjdQKDc/W7gF76QkM+opVb/c/NlnpbffNsHy7bel7Gzn+hcu0tLMKv3SuS8knTghjR1r2r/8pVnArrri470LsDldbu7viua+nCo3tyzvomr33Rf9F3MAAIFTk5XNKTUHHEfwPpNdZm6P6gWKHbztRZwQfsFb8m+e9/Ll3r2o//CHmgW3SFWdcvPHHjNzzC68UHrkEf9fwy43X7LE/+dW1/790tdfm//rPXv6/3w7eH/wgRmdD5TFi826AWlp3rUDAACoDvv9yGefmXVCzqekRNqzx7QJ3oBjCN5nCsSK5pVhxLui8nITeCSzbVu4qG7w3rtX+tGPTHXEzTd7R3ZjxfkWWFu3zjti+/zzNdtWzQ7eK1aYNwVOsEe727c3W8D56+KLzYJsp05JixYFrl/2z+7222vWLwBA7GrdWmrQwEyfXLv2/I8vLDS3qanmeQAcQfA+U6BXNLd1725ut26VjhwJ7LEj0a5dJqwkJUnNm4e6N169e5sy5507vX+IzlRWJt10k7k63L699OKLsbfn5blGvN1us2d3ebk0YoR0zTU1e4127aTGjU3o9nc/0uqq6fxum8sV+HLzjRulf//bHPu++wJzTABA7HC5/Jvn7VtmHmvvZ4AgInj7sizngneDBlLLlqZNubm3zPyii0zQDRdpad7R3KpGvX/1K7Padlqa9Pe/m9tY06WLuf36a+nbbyveN3OmCcrp6dIzz9T8NVwu57cVs9+Q1GarLjt4L1xo9gSvLftnNny4+f8BAIC/7AvK1blwzfxuICgI3r727jWrCMfF+bcQVHVRbu4VjvO7becqN587V3r6adN+9VVTahyL6tf3/oH2LWPbt096+GHTfvJJs9J7bTgZvMvKvP8XazM//7LLzKJ6R45IS5fWrk8HDkj/93+m/eCDtTsWACB21XTEG4BjCN6+7NHuNm1qNif1fAjeXpEYvLdskW691bQffNCUUceyysrNf/ELE0C7dZPuvbf2r2EH708+MaukB9L69VJRkZlDXZsLKPHx3j29a1tuPmOGmZPXvbvUt2/tjgUAiF09e5qBpJ07vQunVcXew5utxABHEbx92SuaB7rM3Ebw9grn4N2vn7nduFE6dMi0T56UbrjBrFzdr5/0u9+Frn/h4swF1t5/3+w9HRcnvfBCYKYQXHSRuQJfVmZWkQ8kexSgV6/q7y9eleHDze2//mWmrNREaalZiE6SHniAeXYAgJpLT5c6djTt85WbM+INBAXB25dTK5rbunY1b6Z37TJl7bEsnIN3o0beEdAVK0yQuusuacMGs9jXW29JiYmh7WM48B3xLimR7rnHfH7vvWbEO1CcKjevzf7dZxo0yFTJFBZWbwXZyvz1r6ZUv1kz6Yc/rH2fAACxrbrl5gRvICgI3r6cWljNlp5uVmqWYnuBNbdb2r7dtMMxeEsVy81nzJBmzTIjuG+9JeXkhLZv4cIe8f7yS2niRLNif06OmdsdSE4F79quaO4rJUUaMsS0a1JublneLcR+/nMu7AAAas/++3au4H3qlPTf/5o2wRtwFMHbVlpqAoTkXPCWKDeXzC/44mIpISF85xPZwftvf5PGjTPt3/1Ouvzy0PUp3OTkSE2amAsp9oJzzz4rZWQE9nUGDjS3n38uHT4cmGMeOOCtuujZMzDH9C0399cHH5ipLqmp0h13BKY/AIDYZgfv1aur3nWjsNBc/E1JMRV/ABxD8LZt2mT2Ha5Xz9l9pQne3sBz4YUmfIcjO3gXFpo/Vj/4gVk4DBXZo96SdPXVziw417SpKf23LLONWyB88om5bdfO/J8PhKFDTVXEunXesr3qske7f/rTwPUHABDb2rQxf1NKSrxVnWey/17l5rK2COAwgrfNd363k794fIN3TRdhinThPL/blpvrvQDTpo3ZOow/SGez53nXqWMWBnPqZxTocvNAlpnbGjTwLsznz6j3V19JCxaYn51dXQEAQG3FxZ1/njfzu4GgIXjbnF7R3HbJJWaU9+BB7/YNsSYSgrfLJY0fb86HuXMDXz4dLUaONKPG06dLLVs69zpOBW/7DUmg1KTc/JlnzO2wYeYiDwAAgWJfYK5qZXP7vSjBG3Acwdvm9MJqtjp1vKumx2q5eSQEb0m67z6zQrW9HQfOdvHFZprGT3/q7OtccYW53bSp9jsClJdLq1aZdiBHvCXp+983t0uXVm8++qFD0muvmfaDDwa2LwAAVHfEO1zX3AGiCMFbMiXfTm8l5ivW53lHSvBG+GjQQOrSxbTz82t3rA0bzL7sGRlS+/a17loFLVtKnTqZcL9gwfkf/+KLZqHBLl2kAQMC2xcAAHr1MlV8O3aYLSvPRKk5EDQEb0nas8eMPMXFSR06OP96sRy8LYvgjZoZNMjc1rbc3L7q37On+T8faNUtNz91SvrTn0z7wQdZQwAAEHgZGd73tpWVmxO8gaAheEve0e68PLOdgtPs4P3ZZ2Yrpliyb58ZbYyLc3ZOMKJPoOZ52288Al1mbrPLzRctMivJVuWtt8xFv5wc6cYbnekLAABVlZufPi3t3m3alJoDjiN4S8EtM5dMeWtKinT8uLRlS3BeM1zYo925uVJSUmj7gsjSv7/Zruvrr/3frsuXUwur2bp2NSvinzwpLVlS+WMsy7uF2L338n8BAOAc+0LzmcF71y4zAJScLDVuHPx+ATGG4C0Fb0VzW0KCdxumWCs3p8wcNZWebsrDpZqPeh865L3Y5VTwdrm8o95VlZsvXSp9/rm5AHfnnc70AwAAyRu8P/1UKivzft13YTUnpl4BqID/ZVLwVjT3FavzvAneqI3alpt/8om5zcuT6tcPTJ8qYwfvd96pfDqJPdr9k59IDRs61w8AAPLypKwss5inPdgkMb8bCDKCd0mJ9NVXph2sUnNJ6t7d3BK8gerzDd6W5f/znS4ztw0YYBa02bfPG/Zt27aZQC5J99/vbD8AAIiLM6ubSxXLze09vJnfDQQFwXvTJrP1T/36UrNmwXtde8R77VqzuEWsIHijNnr3NnPR9uzxXjDzh/2Gw6mF1WxJSdLQoaZ9Zrn5s8+aiwbXXGP2QQcAwGn23z3flc0Z8QaCiuDtW2YezO18WreWMjPNiPuGDcF73VBiKzHUVkqK1KePaftbbl5eLq1aZdpOB2+p8nneR45Ir75q2g884HwfAACQKl/ZnOANBBXBO9grmtvi4mKv3PzQIenoUXOB46KLQt0bRKqazvPetMnsJJCW5t3T1ElXXy0lJkpffukdnZ8506x23qmTd19yAACcZpeab98u7d9v2napOcEbCAqCd7BXNPcVawus2aPdzZtLdeqEti+IXHZgzc+vfOGyqthX+Xv2NNuSOS0jw3uR4F//MlNKpk0znz/wQHArbAAAsS0ry2xnK5m1R8rKpMJC8zlzvIGgiO3gbVmhWdHcFqvBmzJz1Eb37mbU+vDhiquzno89ry0YZeY233Lzv//d7JmanS3dfHPw+gAAgFSx3Hz3bjMFKzFRyskJbb+AGBHbwXv3bvPmPT7eexUwmOzgvWGD2eIh2hG8EQiJidLll5v2kiXVf16wVjT3dd113td+/HHTvuceKj4AAMFnX3heuZI9vIEQiO3/afZoWV5eaN4IN28uNW5srjiuXRv81w+2rVvNLcEbteXvPO/Dh81caym4wbtZM3OBzbKkzZvNiux33x281wcAwGYH708/NXO9JeZ3A0EU28E7lGXmkpnjGUvl5ox4I1Ds4L10afW247NXM2/TRmrY0Ll+VcYuN5ekkSNNqTkAAMHWrp1Zf+TkSWn+fPM15ncDQUPwloK/ormvWFrZnOCNQOncWapXTzpxQlq9+vyPD0WZuW34cG/7/vuD//oAAEimpNxe3XzhQnPLiDcQNLEdvEO5orktVka8Dx82H5LUqlVo+4LIFxcnDRxo2tUpN7eDdzAXVrN16CD98Y/SjBlSx47Bf30AAGz238HSUnNL8AaCJnaDd3Gxd2/dcAjeX31l9riOVvZcopwcqW7d0PYF0aG687zdbrN1ihSa4C2Zke477wzNawMAYDuz8otScyBoYjd4b9xo3pA3aBDabRQaNfL+0vvss9D1w2l2mXmbNqHtB6KHHbxXrJBKSqp+3ObN0rFj5oIPI84AgFh2ZvBmxBsImhoF77lz56pbt25q0aKFmjVrptGjR2vfvn3nfI7L5ar0480336zwuKVLl+ryyy9Xs2bN1LRpU11//fXaZoe2QPItM3e5An98f8RCuTnzuxFoF19sLpqVlnpLyStj39ejh5SQEJy+AQAQjurVM38/JfM3sWnT0PYHiCF+B+85c+ZoxIgRSkxMVEFBgRYsWKDXX39dAwcOVMk5Rp3i4uLUoEGDsz6Sk5M9j1m5cqUGDx6swsJCbdmyRatXr9Z7772nfv36nTfY+y3UK5r7soN3dRaJilQEbwSay1W9cvOPPza3oSozBwAgnNij3hdcIMXHh7YvQAzxK3hblqXx48fLsixdddVVio+PV5cuXdS8eXNt3rxZM2fOrPK5LVq00MGDB8/6uP766z2PmTBhgk6dOqUBAwaobt26atq0qS699FLt27dPU6dOrfl3WZlwWNHcxog3UDN28F6ypOrHhHJFcwAAwk3//ua2bdvQ9gOIMX4F740bN6qgoECS1KhRI8/Xs7/bl3bevHlVPrekpEQPP/yw+vTpo5YtW+r73/++li1b5rn/+PHjWrp0aY2O7TfLCo8VzW3dupnbnTulAwdC2xenELzhBDt4r1olHT9+9v1HjkibNpk2wRsAAOknP5GmTpX+939D3RMgpvgVvLdu3eppp6SkeNqpqamSpC1btlT5XJfLpeHDh+ujjz7SlClT9M477+iKK67QO++8I0natm2bLMuq8tjbt2+X2+32p7tV27VL+vZbU17Tvn1gjlkbGRlSXp5pR+Oo97Fj0v79ps1WYgikCy+UWraUysslnwt5HqtWmdtWraTvLuIBABDTEhKk8ePNdpcAgsav4H3ixAnvE+Pizmr73n+mPXv26LLvRpxuuukmdezYUW63W7/85S+rdWy3262ioqJKj11aWqpjx45V+Dgne7T74oslnznmIRXN5eb2VmLZ2eYiAxBI55rnTZk5AAAAwoBfwTstLc3T9h19ttu+959P3ncjvFu3btWhQ4fOe+y4uDjP6PeZpkyZoszMTM9HixYtzv3i4bSwmi2agzdl5nBSdYI3C6sBAAAghPwK3m189mAuLi72tO2R6LZVLNJw4sQJHTlypOIL+4xql5eXq3Xr1nJ9t61XZcdu1apVhef4mjBhgo4ePer5KCwsPPc3Eu7B+7uS+6hB8IaTBg40t2vXSocOeb/udkuffGLaBG8AAACEkF/Bu0OHDsrNzZUkHfBZBMxuDxs2TOXl5Ro1apSuvfZaT/n4nDlz9Mgjj1Q41vbvyo+bNWum7Oxspaenq/93qyxWdeyqJCcnKyMjo8LHOdml5uGwormtSxcz52b/ful8Fw4iDcEbTsrJkdq1Mxes/vMf79e/+sosrpaSInXqFLLuAQAAAH4Fb5fLpaeeekqStHjxYrndbq1fv16FhYXKy8vTmDFjtGbNGs2aNUvz58/XokWLPM+dO3eu9uzZI0n697//rc8//1yS9Pjjj3seM3nyZCUmJmrp0qUqKirS3r17tWbNGmVnZ+uhhx6q9TcrSSouluxF4MJpxDslRerY0bSjrdyc4A2nDRpkbn3Lze0y8x49pMTE4PcJAAAA+I5fwVuSbrzxRr399tsqKSlRbm6uhgwZopEjRyo/P18pKSlq3769unfvrtatW3tGsLt166YBAwZo0KBBateuna6//nr17NlTb731lm677TbPsfv27avFixeradOmatOmjbp27aorr7xSy5YtU5MmTQLzHW/YYEpQGzWSAnXMQLHLzVevDm0/Ao3gDadVtp/3xx+bW8rMAQAAEGIuy4q2CcXSsWPHlJmZqaNHj55ddv7yy9LPfiZ973vSe++FpoNVmTlTuuMOM3r3/vuh7k1gnDwp2QvnHT4s1asX2v4gOh0+LDVsaMrNd++WmjY15eUbNkj/+Ic0fHioewgAAIAY5veId8SzF1YLp/ndNt8R70DtWR5q9lZi9esTuuGc+vWlSy817fx86ehRaeNG8zkj3gAAAAix2A3e4TS/29ahg1SnjgkNdnl2pKPMHMHiu62YvTtAy5ZS48ah7RcAAABiXmwFb8vyrmgejsE7MdGsbi5FzwJrBG8Ei2/wthdWu+yy0PUHAAAA+E5sBe/CQrO9UEKCdPHFoe5N5Xz3844GdvD22QMecES/fub/dkGB9Oab5muUmQMAACAMxFbwtsvM27WTkpND25eqRGvwZsQbTktPl3r2NO1Nm8wtwRsAAABhILaCdziXmdvs4L1mjVRWFtq+BALBG8Fkl5tLZr2EcFxEEQAAADEntoJ3OK9obmvbVsrIkIqLvasyR6riYlPeLxG8ERyDBnnb3btLSUmh6wsAAADwndgM3uE84h0XJ3XrZtqrV4e2L7W1Y4e5zcyUGjQIbV8QGy67zIx0S5SZAwAAIGzETvAuKpK2bjXtcA7eUvTM8/YtM3e5QtsXxIY6daSrrjLtIUNC2xcAAADgO7ETvDdsMNuJZWeH/76+0Ri8gWB55RXp448rlp0DAAAAIRQ7wTsSysxt3bub2y++kEpKQtuX2iB4IxTq1ZN69Qp1LwAAAACP2AnekbCiuS03V2rY0Kxqbl8wiEQEbwAAAACIoeAdCSua21yu6Cg3J3gDAAAAQIwEb8uKrBFvKfKD96lT0s6dpk3wBgAAABDDYiN4f/ONdPSolJgoXXxxqHtTPZEevAsKJLdbqls3/BezAwAAAAAHxUbwtsvM27WTkpJC25fqsoP3l19Kx4+Hti81wVZiAAAAACAp1oJ3pJSZS2aUuEULUyb/+eeh7o3/mN8NAAAAAJJiJXhH2vxuWySXmxO8AQAAAEBSrATvSFrR3Je9n3ckBu+tW80twRsAAABAjIv+4H3ypHf0lRHv4LF/5m3ahLYfAAAAABBi0R+8N2ww86SbNJGys0PdG//YI947dkgHD4a2L/44fdqsai4x4g0AAAAg5kV/8I7UMnNJysryjhivXh3Srvjlm2+ksjIpJUXKyQl1bwAAAAAgpGIneEdambktEsvN7TLzVq2kuOg/xQAAAADgXKI/FUXqiua2SA7elJkDAAAAQJQHb8vyBu9ILDWXvME7kkrNCd4AAAAA4BHdwXvnTunYMSkpSbr44lD3pmYuvVSKj5f27JF27w51b6qH4A0AAAAAHtEdvDduNLft20uJiaHtS02lpkodOph2pJSbE7wBAAAAwCO6g/f69eY2UsvMbfa2YpEQvMvLpa+/Nm2CNwAAAABEefDesMHcRurCarZIWmBt1y7p1ClT3t+8eah7AwAAAAAhF93B2y41j5bgvXq1WTAunNll5hddZOamAwAAAECMi+7gbZc8R3qpeadOZgT522+l7dtD3ZtzY343AAAAAFQQ3cFbknJypEaNQt2L2klKkrp0Me1wLzcneAMAAABABdEfvCO9zNwWKfO8Cd4AAAAAUEH0B+9ILzO3+c7zDmcEbwAAAACoIPqDd7SNeH/+udmyKxy53d456ARvAAAAAJBE8I4ceXlS3brSyZPS5s2h7k3l9uyRioulhAQpNzfUvQEAAACAsBDdwTsx0QTWaBAfL3XrZtrhOs9761Zz27KlCd8AAAAAgCgP3u3aRVcAtMvNp02TvvwytH2pDPO7AQAAAOAs0R28O3YMdQ8C69ZbpbQ0ae1aU0L/299Kp0+HuldeBG8AAAAAOEt0B+9OnULdg8Dq2FHauFG6+mrp1CnpN7+RuncPn5XOCd4AAAAAcJboDt4dOoS6B4F3wQXSggXSrFlSgwbSF19IvXpJv/ylVFQU2r4RvAEAAADgLNEdvKNtxNvmckk//rFZ3fyWW8w2Xr//vfl+P/ggNH2yLII3AAAAAFQiuoN3/fqh7oGzGjWSZs+W5s+XmjeXvv5aGjRI+tnPpCNHgtuXffvMVmdxcdKFFwb3tQEAAAAgjEV38I4VQ4eaud/33GM+f/lls6L73LnB64M92p2bKyUlBe91AQAAACDMEbyjRUaG9Pzz0rJlZu/yvXulG26QRowwbadRZg4AAAAAlSJ4R5t+/cx2Y7/+tdnD/O9/N6Pfr7xi5mE7heANAAAAAJUieEejOnWkJ58024x162bme99+uzR4sJkH7gSCNwAAAABUiuAdzTp3lj7+WHr6aSklRVqyxOwF/oc/SOXlgX0tgjcAAAAAVIrgHe0SEswe3+vXSwMHSsXF0i9+IfXubfYADwS2EgMAAACAKhG8Y0WrVmbE+6WXpMxM6dNPTRn6I49IpaW1O/ahQ9LRo2Z/8YsuCkx/AQAAACBKELxjictl5npv2iRdf71UVmbmgnfpIq1YUfPj2qPdzZub+eUAAAAAAA+Cdyxq2tTs8T1njtS4sfTll1L//tLPfy4dP+7/8ezg3aZNYPsJAAAAAFGA4B3LbrhB2rxZuu02M0/7ueekDh2kd9/17zjM7wYAAACAKhG8Y129etLLL0vvvSe1bCkVFkrXXCONHCkdPFi9YxC8AQAAAKBKBG8Y3/ueWfn8wQeluDhp9mypXTvpr381o+HnsnWruSV4AwAAAMBZCN7wqltX+v3vpZUrzX7fBw9Kt9wiXXutGQmvCiPeAAAAAFAlgjfO1rOn9Nln0uOPS0lJ0oIFZu739OmS213xsYcPmw+JrcQAAAAAoBIEb1QuKcns8b1mjdS7t1nt/N57pQEDzCrotu3bzW3TpmbEHAAAAABQAcEb59a+vbRsmTRtmgnWy5dLnTtLkydLp09TZg4AAAAA50HwxvnFx5s9vjdulK66Sjp1Svr1r6UePaR33jGPIXgDAAAAQKUI3qi+3Fxp4ULp9del+vWldeukN9809xG8AQAAAKBSBG/4x+WSRo2SNm+WbrrJ+/WLLw5dnwAAAAAgjCWEugOIUNnZZo/v0aOlTz+Vhg4NdY8AAAAAICy5LMuyQt2JQDt27JgyMzN19OhRZWRkhLo7AAAAAIAYRqk5AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADiJ4AwAAAADgIII3AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADiJ4AwAAAADgIII3AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADiJ4AwAAAADgIII3AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADiJ4AwAAAADgIII3AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADiJ4AwAAAADgIII3AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADqpR8J47d666deumFi1aqFmzZho9erT27dtX5eOXLl2qa6+9Vu3atVPr1q3VuHFjXXnllVq4cGGFx/3lL3+Ry+U666NOnTo16SYAAAAAACHnd/CeM2eORowYocTERBUUFGjBggV6/fXXNXDgQJWUlFT6nI8++kgFBQVauXKltm3bpgkTJig/P19Dhw7V//3f/1V4bEpKiho0aFDho2HDhjX77gAAAAAACDG/grdlWRo/frwsy9JVV12l+Ph4denSRc2bN9fmzZs1c+bMSp/XoEED/eIXv1BWVpYkaezYsUpOTpYkPf/88xUe+9BDD+ngwYMVPnbt2lWDbw0AAAAAgNDzK3hv3LhRBQUFkqRGjRp5vp6dnS1JmjdvXqXPGzNmjG699VbP5wkJCZ4QXlRUVOGxGzZs0I033qh27dqpU6dOeuCBB3Tw4EF/ugkAAAAAQNjwK3hv3brV005JSfG0U1NTJUlbtmyp1nFOnDih/fv3S5KuuOIKz9eTkpJUVFSkF198UWvWrFGrVq30zDPPqHfv3jpy5EiVxystLdWxY8cqfAAAAAAAEA78Ct4nTpzwPjEu7qy27/3n8tJLL8myLLVp00YTJ070fP2WW27RwoULlZmZqTp16uiRRx6RJG3btk3Tp0+v8nhTpkxRZmam56NFixb+fFsAAAAAADjGr+Cdlpbmabvd7rPavvdXZfny5fr1r3+tgQMHasWKFedcOC0vL8/TXrlyZZWPmzBhgo4ePer5KCwsPG8/AAAAAAAIBr+Cd5s2bTzt4uJiT9uep922bdtzPv/NN9/UsGHD9Mgjj+j9999Xo0aNtGnTJs/9e/fuVVlZmbdzPqPq5eXlVR43OTlZGRkZFT4AAAAAAAgHfgXvDh06KDc3V5J04MABz9ft9rBhw1ReXq5Ro0bp2muv9ZSel5aWauzYsXriiSeUn5+vhx9+2BOqr7nmGs9xbrrpJq1bt87z+fbt2z3trl27+vu9AQAAAAAQcn4Fb5fLpaeeekqStHjxYrndbq1fv16FhYXKy8vTmDFjtGbNGs2aNUvz58/XokWLJEmTJk3S888/r127dmnw4MFq2LCh5+NM06dPl9vtltvt9rxWdna27r333tp+rwAAAAAABF2Cv0+48cYbFR8fr8mTJys3N1fl5eUaOXKkpk6dqpSUFLVv317du3fXkSNH1L9/f0lSSUmJJFW62rjvvPBRo0Zp9uzZuuSSS3Tw4EGVlZXpxhtv1OTJk5WTk1PT7xEAAAAAgJBxWZZlhboTgXbs2DFlZmbq6NGjzPcGAAAAAISUX6XmAAAAAADAPwRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBBG8AAAAAABxE8AYAAAAAwEEEbwAAAAAAHETwBgAAAADAQQRvAAAAAAAcRPAGAAAAAMBBNQrec+fOVbdu3dSiRQs1a9ZMo0eP1r59+875nEOHDmnMmDFq2rSpcnNz1aVLF73xxhtnPW7p0qW6/PLL1axZMzVt2lTXX3+9tm3bVpNuAgAAAAAQcn4H7zlz5mjEiBFKTExUQUGBFixYoNdff10DBw5USUlJpc85ffq0Bg8erJdeekkzZ87Uzp071bJlS/34xz/Wa6+95nncypUrNXjwYBUWFmrLli1avXq13nvvPfXr1++8wR4AAAAAgHDkV/C2LEvjx4+XZVm66qqrFB8fry5duqh58+bavHmzZs6cWenzZs+erTVr1ig+Pl5XXXWVJGnYsGGSpIceekhlZWWSpAkTJujUqVMaMGCA6tatq6ZNm+rSSy/Vvn37NHXq1Np8nwAAAAAAhIRfwXvjxo0qKCiQJDVq1Mjz9ezsbEnSvHnzKn3e/PnzJUn16tVTfHx8hefs379fq1at0vHjx7V06VK/jw0AAAAAQDhL8OfBW7du9bRTUlI87dTUVEnSli1bzvm8yp5jPy8lJUWWZVX5uO3bt8vtdisu7uxrBaWlpSotLfV8fvToUUnSsWPHqvmdAQAAAADglZ6eLpfLFZBj+RW8T5w44Wn7BmC77Xt/Zc+r7Dn2/ec7ttvtVlFRkdLS0s46/pQpU/TYY4+d9fUWLVqc+xsCAAAAAKAS+/fvr1CNXRt+BW/f0Ot2u89qVxaKfb9e2XPs+8937Li4uAqj5L4mTJigBx980PP5kSNHlJubq2+++UaZmZnn/8aACHHs2DG1aNFChYWFysjICHV3gIDh3EY04/xGtOLcRrSyz+2kpKSAHdOv4N2mTRtPu7i42NMuKiqSJLVt27bK533xxReVPsd+XuvWreVyuWRZVqWPa9WqVaVl5pKUnJys5OTks76emZnJLwFEpYyMDM5tRCXObUQzzm9EK85tRKtAlZlLfi6u1qFDB+Xm5kqSDhw44Pm63R42bJjKy8s1atQoXXvttZ7y8aFDh0qSvv32W5WXl1d4TnZ2tnr27Kn09HT179//nMcGAAAAACDS+BW8XS6XnnrqKUnS4sWL5Xa7tX79ehUWFiovL09jxozRmjVrNGvWLM2fP1+LFi2SJI0cOVKdO3dWeXm5Fi9eLElauHChJDM/OyHBDLxPnjxZiYmJWrp0qYqKirR3716tWbNG2dnZeuihhwL2TQMAAAAAECx+BW9JuvHGG/X222+rpKREubm5GjJkiEaOHKn8/HylpKSoffv26t69u1q3bu0ZwU5MTNT777+v22+/Xbfddptyc3O1ZcsWvf7667rttts8x+7bt68WL16spk2bqk2bNuratauuvPJKLVu2TE2aNKl2H5OTkzVp0qRKy8+BSMa5jWjFuY1oxvmNaMW5jWjlxLntsuw9vAAAAAAAQMD5PeINAAAAAACqj+ANAAAAAICDCN4AAAAAADgoqoL33Llz1a1bN7Vo0ULNmjXT6NGjtW/fvlB3C/Db6dOnNXnyZKWkpMjlcunRRx+tcH95ebkmT56sVq1a6cILL1TLli01ceJEnT59OjQdBqrp/vvv16WXXqouXbqofv36ysvL0/jx43Xw4EHPY4qKijR+/HhdcMEFuvDCC5WXl6dnn302hL0Gzu+xxx5Tr1691L17dzVu3FhNmjTRsGHDtHLlSs9jOLcR6WbMmCGXy3XWexPelyASPfroo57z2ffj4osv9jwmoL+3rSjx9ttvWy6Xy+rVq5dVVlZmrVmzxpJktWvXziouLg5194BqKywstC655BJr6NChliRLkjVp0qQKjxk7dqwlyfrlL39pWZZlPfPMM5Yk69Zbbw1Bj4HqS05OtmbOnGlZlmXt37/fat26tSXJat++vVVSUmJZlmUNGzbMkmQ999xzlmVZ1v33329Jsh599NGQ9Rs4n7y8POvVV1+1LMuySktLreuuu86SZKWmplp79uyxLItzG5Ht4MGDVoMGDSp9b8L7EkSiSZMmWWlpaVaDBg0qfFx22WWexwTy93ZUBG+3221deOGFZ/0SaN68uSXJmjZtWug6B/hp06ZNVn5+vrVjx45K/7ht377dcrlcliQrPz/fsizL2rZtm+ex69atC03HgWro27ev5Xa7PZ8/8cQTnnP33XfftfLz8z2f79ixw7Isy3r//fctSVZSUpJ14MCBEPUcOLd//OMfVllZmefz5557znMuL1myhHMbEe/OO++0RowYcdZ7E96XIFJNmjTJc8G0MoH+vR0VpeYbN25UQUGBJKlRo0aer2dnZ0uS5s2bF4puATXSrl07XXHFFVXev3DhQlnf7QJon+/2uS5J8+fPd7R/QG0sX75cLpfL83nDhg097aKiogrn75nn96lTp7R48eIg9RTwz/DhwxUfHy9JOnLkiObMmSNJysvLU8+ePTm3EdE+//xzzZ8/XxMnTjzrPt6XIJItX75cw4cPV+vWrdW1a1dNnDhRRUVFkhTw39tREby3bt3qaaekpHjaqampkqQtW7YEvU+AUyo73+1zXeJ8R2TZvn27JCk5OVm9e/fm/EbEu/POO9WoUSN9+OGH6tevn5YsWaK0tDTObUQsy7I0duxYTZ48Wenp6Wfdz7mNSFWnTh2Vl5frb3/7m1avXq3ExEQ98cQT+t73vqeysrKAn9tREbxPnDjhacfFxZ3V9r0fiHSVne++5z3nOyJFUVGRZs+eLUl66qmnlJOTw/mNiPfCCy9oz549uvLKK7V8+XL1799f+/fv59xGxHrttdckSaNGjar0fs5tRKqHH35Yr776qpKTk5WVlaWHHnpIkrRy5Uq99dZbAT+3oyJ4p6Wledput/ustu/9QKSr7Hz3Pe853xEJysrKNGrUKB09elSvvfaaxo0bJ4nzG9GhYcOG+uMf/yhJ2rFjh6ZPn865jYh09OhR/epXv9Kf/vSnCtOEfHFuI1rk5eV52itXrgz4uR0VwbtNmzaednFxsadt1+e3bds26H0CnFLZ+W6f6xLnO8LfoUOHdPXVV2vnzp367LPP9JOf/ER79+7V4cOHOb8Rkdxut3bu3Fnha77n6ldffcW5jYj03nvvKS4uTrfffru6dOmia665xnPfjBkz1KVLlwrrK3FuI5Ls2rWrwue+o9nl5eUB/70dFcG7Q4cOys3NlSQdOHDA83W7PWzYsJD0C3DC1Vdf7bnqbJ/jvuf90KFDQ9IvoDo++ugj9ejRQz179tTKlSs9e2XOmDFD77zzToXz98zzOykpSf/v//2/4HcaOI9jx46pf//+Fb62e/duTzs7O5tzGxFpxIgR2rVrl9auXau1a9dq4cKFnvvuuusurV27VjfffDPvSxCR+vXrp0OHDnk+t9edkaSuXbsG/Pd2VARvl8ulp556SpK0ePFiud1urV+/XoWFhcrLy9OYMWNC3EMgcFq1aqW77rpLkvTuu+9KkucP4ahRo9S5c+eQ9Q04l+PHj2vAgAEqLCzUCy+8oJycHDVs2FANGzbU1KlTJUkDBw7U1VdfLens8/t//ud/KqyCDoSTwsJCvfzyy5Kk06dPe1Z/Tk1N1R133MG5jajF+xJEsueee06SVFpa6pkilJeXp1tuuSXgv7ddlr3+fxSYM2eOJk+erAMHDqi8vFyDBg3S1KlTlZOTE+quAdV26tQpde3aVadPn/asltioUSNlZ2dr4sSJ+tGPfqTy8nJNmTJFr7zyimeuyciRIzVx4kQlJSWFsvtAlY4cOaJ69epVef+rr76qW2+9VSdPntTEiRP11ltvKSEhQYmJibrrrrv0wAMPVDnHEAil0tJSjR8/XitWrFBZWZn27NmjtLQ09e3bVxMmTFD79u0liXMbEW3ChAmaO3fuWe9NNmzYwPsSRKSnnnpK77zzjk6cOKHCwkIlJydr2LBhmjx5smcKRSB/b0dV8AYAAAAAINxERak5AAAAAADhiuANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAAAAAAOIjgDQAAAACAgwjeAAAAAAA4iOANAAAAAICDCN4AAAAAADiI4A0AAAAAgIMI3gAARLiZM2eqS5cuSk9PV1ZWllq2bKnp06eHulsBM2HCBKWlpcnlcsnlcunWW28NdZcAAPALwRsAEJOWL1+urKwsJSQkyOVyqWXLltq/f/9Zjxs2bJgyMjLkcrmUlZWl3/3udyHobdVmzZqlO+64Q+vWrdOkSZP07bffavDgwdqyZUuljx84cGCFEBsXF6esrKyzPlwulwoKCoL7zVRhypQpmj9/fqi7AQBAjSWEugMAAIRCv379dOTIEV1xxRX6z3/+o4KCAl133XXKz89XSkqK53Hz589XQUGBWrZsqSNHjoSuw1X45z//6WkPGzZMLpdLTz/9tIqLiyt9fH5+vj788EMNHDhQknTBBRdUGrBdLpcT3QUAICYx4g0AwHc++eQTjRw5UpZlhbor1Xbw4EFPu06dOpKkzMxMNWnSpFbHnTRpkrKysmp1DAAAYBC8AQAxr1u3bp723LlzNX78+Go9b/PmzRo1apSaN2+u1NRUNWjQQEOGDNGiRYtq1Z+SkhI9+eSTuuSSS5SWlqa0tDRdcsklevLJJ1VSUuJ5XFZWlpYvX+75/JJLLlFWVpbeeOONGr+2XWL+6KOPekrrfUvTv//97+vmm29Ww4YNlZKSor59+yo/P/+s47zxxhu6/PLLlZmZqdTUVLVs2VJ33XWXdu3aVeFxlmXpxRdfVM+ePZWWlqasrCy1bdtWN910k+bMmVNpH0+ePKl77rlHLVq0UEpKirp3764VK1ZUeMyXX36pH/zgB2rSpInq1Kmj7OxstWvXTqNGjdKXX35Z458PAAA1YgEAEMMGDBhgvfrqq9bPfvYzS5Ln489//rPnMTt27LDO/JO5bNkyKyUlxZJkPfDAA9bp06etuXPnep7/+9//vkb9OXnypNWrVy9LktW6dWtrz5491r59+6w2bdpYkqxevXpZRUVFFfpvv+aOHTuq9Rr5+fme5+Tm5la4r7Lj+D4+Pj7eWrp0qVVcXGwNHz7ckmQlJCRYH374oefxY8eOtSRZSUlJ1ooVK6ySkhLrRz/6kSXJatiwofXVV195Hnvrrbd6jj1jxgyrtLTU2r9/v9W/f3+rc+fOlfYhISHBWr58uVVaWmr16NHDkmRlZ2dbJ06csCzLskpLS63mzZtbkqyf/OQnVnFxseV2u61FixZZqamp1j/+8Y9q/ZwAAAgURrwBAJD05z//WYMHD/Z8PnbsWL377rtVPn7MmDGeedTjxo1TQkKCrr/+erVs2VKS9PDDD6uwsNDvfkybNk2ffPKJJGnkyJFq0qSJsrOzNXLkSEmmHH7atGl+H7cq33zzTYVF1c6nR48e6t+/v+rUqaO7775bklRWVqaJEydKkj7++GM999xzkqQ+ffqoT58+Sk5O1r333ivJlMaPGzdOkrRs2TL95S9/kSRdeumluvPOO5WUlKRGjRrpt7/9bZV9uOyyy9S3b18lJSXpiiuukCTt379fq1atkiRt2rTJM7KekZGh5ORkuVwuDRkyRI888ohycnL8+yEBAFBLBG8AACQlJCTo7bffVocOHSRJ5eXluvHGG7Vu3bqzHrt161ZPubLL5VKLFi0899nt06dP69///rff/Zg3b56nfcEFF5x13DMfU1sXXHCBjhw54vmozuMr69NHH32k06dPV6v/77//vkpKSipc2Gjfvn2F1+nXr5+WLVtWaR98j5Wamupp7969W5LUsGFDxcWZtzjPPfecmjVrplGjRmn27Nm677771KtXr/N+nwAABBLBGwCA72RmZmrBggVq3LixJOn48eMaOnSoJ9DZfLcdS0xM9IQ8SUpOTq70cdXl+xx7sbRAHLe6LMvShRdeWOX9viu++/aprKxMx44dq1b/y8rKdPjwYR04cMDztbp161Z4HZfLpfT09Er7kJSUVOnXy8rKJEnNmzfX73//e89r7tmzR7NmzdLIkSPVtm1bbd26tcrvDwAAJxC8AQDwkZubq3nz5nlGUnfv3q2bb765wmOys7M97dOnT8vtdns+Ly0trfRx1eX7HN+F1Gp7XH9t375dRUVFZ33dd5sy3z4lJCQoIyOjWv1PSEhQ/fr11ahRI8/XTp48GbC+S9L999+vvXv3avbs2frpT3+qBg0aSDL/nlOmTAnoawEAcD4EbwAAztCjRw/NmjXLM5J95lztNm3aKC8vT5IZIfa9/5tvvpFkRsKHDBkiyZSGN2zYUL169TpvOfewYcPOOtaZbd/HOGXQoEGeOdO+fPvhu0J5nz59lJiYWK3+Dxo0SHXq1NHVV1/t+drGjRsrvM5bb72lTp06qby83O++b9q0Sf3791dWVpZuueUWvfLKK1qzZo3n/r179/p9TAAAaoPgDQBAJa6//npNnTq1yvtfeOEFTyn1s88+q7KyMv3zn/9UQUGBJOm3v/2tZy7yiy++qEOHDmnVqlVasmTJOV933Lhx6t69uyRp1qxZ2rdvn/bv36/Zs2dLkrp376777ruvtt9ejX366adatmyZSktLNWPGDElmBPvxxx+XJPXu3duz6NpHH32klStXqrS0VNOnT5ck1a9fX88884wkqX///ho9erQkae3atZo5c6bKyspUUFCgxx57TLfccovi4+P97uOpU6e0fPlyPfHEE54Ret+LCEOHDq3ZNw8AQE2Fell1AABCYdmyZVZmZqYVHx9vpaSkWJmZmZU+7u677/ZsY3WmDRs2WLfccouVk5Nj1alTx8rKyrIGDx5szZ8/v8Lj/vWvf1n169e3evbsaR0+fPi8fSsqKrIee+wxq0OHDlZqaqqVmppqdejQwXrssccqbCXWqVMnKz4+3tO/9PR0q1OnTuc8drdu3Spsm3auj/z8fMuyKm7ldd1111kjRoyw6tWrZyUnJ1u9e/e2lixZctbrvP7661a/fv2s9PR0KyUlxcrNzbXuuOMOa+fOnRUe53a7rRdffNG67LLLrLS0NCsjI8Pq0KGDNW3aNM9jpkyZYtWtW9fTh8TEROvuu++2pkyZYiUnJ3u+npKSYt19993Wnj17rJEjR1odO3a0GjRoYKWmplrp6elWz549renTp5/35w8AQKC5LMuyghv1AQBAJPnwww81cOBASdLo0aM9W4ABAIDqodQcAAAAAAAHEbwBAAAAAHAQpeYAAKBKv/vd7/Tkk096tvtKTExUamrqeVdnBwAAXgRvAAAAAAAcRKk5AAAAAAAOIngDAAAAAOAggjcAAAAAAA4ieAMAAAAA4CCCNwAAAAAADiJ4AwAAAADgIII3AAAAAAAOIngDAAAAAOAggjcAAAAAAA76//rwrbMed782AAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"markdown","source":"# **Testing starts here**","metadata":{}},{"cell_type":"markdown","source":"## **Load the best checkpoint file**","metadata":{}},{"cell_type":"code","source":"# Load the latest checkpoint file\nlatest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\nprint(checkpoint_dir)\nif latest_checkpoint is not None:\n    # Create a new model instance\n    loaded_model = create_model(summary=True)\n\n    # Load the previously saved weights and silence the warnings\n    status = loaded_model.load_weights(latest_checkpoint)\n    status.expect_partial()  # Ignore unrestored variables\nelse:\n    print(\"No checkpoint file found in the specified directory.\")","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:16:20.402465Z","iopub.execute_input":"2024-01-12T14:16:20.403195Z","iopub.status.idle":"2024-01-12T14:16:39.542037Z","shell.execute_reply.started":"2024-01-12T14:16:20.403163Z","shell.execute_reply":"2024-01-12T14:16:39.541119Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"/kaggle/working/Checkpoints_InceptionResNetV2\nModel: \"model_2\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_3 (InputLayer)        [(None, 299, 299, 3)]        0         []                            \n                                                                                                  \n conv2d_406 (Conv2D)         (None, 149, 149, 32)         864       ['input_3[0][0]']             \n                                                                                                  \n batch_normalization_406 (B  (None, 149, 149, 32)         96        ['conv2d_406[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_406 (Activation  (None, 149, 149, 32)         0         ['batch_normalization_406[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_407 (Conv2D)         (None, 147, 147, 32)         9216      ['activation_406[0][0]']      \n                                                                                                  \n batch_normalization_407 (B  (None, 147, 147, 32)         96        ['conv2d_407[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_407 (Activation  (None, 147, 147, 32)         0         ['batch_normalization_407[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_408 (Conv2D)         (None, 147, 147, 64)         18432     ['activation_407[0][0]']      \n                                                                                                  \n batch_normalization_408 (B  (None, 147, 147, 64)         192       ['conv2d_408[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_408 (Activation  (None, 147, 147, 64)         0         ['batch_normalization_408[0][0\n )                                                                  ]']                           \n                                                                                                  \n max_pooling2d_8 (MaxPoolin  (None, 73, 73, 64)           0         ['activation_408[0][0]']      \n g2D)                                                                                             \n                                                                                                  \n conv2d_409 (Conv2D)         (None, 73, 73, 80)           5120      ['max_pooling2d_8[0][0]']     \n                                                                                                  \n batch_normalization_409 (B  (None, 73, 73, 80)           240       ['conv2d_409[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_409 (Activation  (None, 73, 73, 80)           0         ['batch_normalization_409[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_410 (Conv2D)         (None, 71, 71, 192)          138240    ['activation_409[0][0]']      \n                                                                                                  \n batch_normalization_410 (B  (None, 71, 71, 192)          576       ['conv2d_410[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_410 (Activation  (None, 71, 71, 192)          0         ['batch_normalization_410[0][0\n )                                                                  ]']                           \n                                                                                                  \n max_pooling2d_9 (MaxPoolin  (None, 35, 35, 192)          0         ['activation_410[0][0]']      \n g2D)                                                                                             \n                                                                                                  \n conv2d_414 (Conv2D)         (None, 35, 35, 64)           12288     ['max_pooling2d_9[0][0]']     \n                                                                                                  \n batch_normalization_414 (B  (None, 35, 35, 64)           192       ['conv2d_414[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_414 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_414[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_412 (Conv2D)         (None, 35, 35, 48)           9216      ['max_pooling2d_9[0][0]']     \n                                                                                                  \n conv2d_415 (Conv2D)         (None, 35, 35, 96)           55296     ['activation_414[0][0]']      \n                                                                                                  \n batch_normalization_412 (B  (None, 35, 35, 48)           144       ['conv2d_412[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_415 (B  (None, 35, 35, 96)           288       ['conv2d_415[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_412 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_412[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_415 (Activation  (None, 35, 35, 96)           0         ['batch_normalization_415[0][0\n )                                                                  ]']                           \n                                                                                                  \n average_pooling2d_2 (Avera  (None, 35, 35, 192)          0         ['max_pooling2d_9[0][0]']     \n gePooling2D)                                                                                     \n                                                                                                  \n conv2d_411 (Conv2D)         (None, 35, 35, 96)           18432     ['max_pooling2d_9[0][0]']     \n                                                                                                  \n conv2d_413 (Conv2D)         (None, 35, 35, 64)           76800     ['activation_412[0][0]']      \n                                                                                                  \n conv2d_416 (Conv2D)         (None, 35, 35, 96)           82944     ['activation_415[0][0]']      \n                                                                                                  \n conv2d_417 (Conv2D)         (None, 35, 35, 64)           12288     ['average_pooling2d_2[0][0]'] \n                                                                                                  \n batch_normalization_411 (B  (None, 35, 35, 96)           288       ['conv2d_411[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_413 (B  (None, 35, 35, 64)           192       ['conv2d_413[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_416 (B  (None, 35, 35, 96)           288       ['conv2d_416[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_417 (B  (None, 35, 35, 64)           192       ['conv2d_417[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_411 (Activation  (None, 35, 35, 96)           0         ['batch_normalization_411[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_413 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_413[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_416 (Activation  (None, 35, 35, 96)           0         ['batch_normalization_416[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_417 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_417[0][0\n )                                                                  ]']                           \n                                                                                                  \n mixed_5b (Concatenate)      (None, 35, 35, 320)          0         ['activation_411[0][0]',      \n                                                                     'activation_413[0][0]',      \n                                                                     'activation_416[0][0]',      \n                                                                     'activation_417[0][0]']      \n                                                                                                  \n conv2d_421 (Conv2D)         (None, 35, 35, 32)           10240     ['mixed_5b[0][0]']            \n                                                                                                  \n batch_normalization_421 (B  (None, 35, 35, 32)           96        ['conv2d_421[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_421 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_421[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_419 (Conv2D)         (None, 35, 35, 32)           10240     ['mixed_5b[0][0]']            \n                                                                                                  \n conv2d_422 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_421[0][0]']      \n                                                                                                  \n batch_normalization_419 (B  (None, 35, 35, 32)           96        ['conv2d_419[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_422 (B  (None, 35, 35, 48)           144       ['conv2d_422[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_419 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_419[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_422 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_422[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_418 (Conv2D)         (None, 35, 35, 32)           10240     ['mixed_5b[0][0]']            \n                                                                                                  \n conv2d_420 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_419[0][0]']      \n                                                                                                  \n conv2d_423 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_422[0][0]']      \n                                                                                                  \n batch_normalization_418 (B  (None, 35, 35, 32)           96        ['conv2d_418[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_420 (B  (None, 35, 35, 32)           96        ['conv2d_420[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_423 (B  (None, 35, 35, 64)           192       ['conv2d_423[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_418 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_418[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_420 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_420[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_423 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_423[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_1_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_418[0][0]',      \n te)                                                                 'activation_420[0][0]',      \n                                                                     'activation_423[0][0]']      \n                                                                                                  \n block35_1_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_1_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_80 (Cus  (None, 35, 35, 320)          0         ['mixed_5b[0][0]',            \n tomScaleLayer)                                                      'block35_1_conv[0][0]']      \n                                                                                                  \n block35_1_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_80[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_427 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_1_ac[0][0]']        \n                                                                                                  \n batch_normalization_427 (B  (None, 35, 35, 32)           96        ['conv2d_427[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_427 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_427[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_425 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_1_ac[0][0]']        \n                                                                                                  \n conv2d_428 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_427[0][0]']      \n                                                                                                  \n batch_normalization_425 (B  (None, 35, 35, 32)           96        ['conv2d_425[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_428 (B  (None, 35, 35, 48)           144       ['conv2d_428[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_425 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_425[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_428 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_428[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_424 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_1_ac[0][0]']        \n                                                                                                  \n conv2d_426 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_425[0][0]']      \n                                                                                                  \n conv2d_429 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_428[0][0]']      \n                                                                                                  \n batch_normalization_424 (B  (None, 35, 35, 32)           96        ['conv2d_424[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_426 (B  (None, 35, 35, 32)           96        ['conv2d_426[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_429 (B  (None, 35, 35, 64)           192       ['conv2d_429[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_424 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_424[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_426 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_426[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_429 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_429[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_2_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_424[0][0]',      \n te)                                                                 'activation_426[0][0]',      \n                                                                     'activation_429[0][0]']      \n                                                                                                  \n block35_2_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_2_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_81 (Cus  (None, 35, 35, 320)          0         ['block35_1_ac[0][0]',        \n tomScaleLayer)                                                      'block35_2_conv[0][0]']      \n                                                                                                  \n block35_2_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_81[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_433 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_2_ac[0][0]']        \n                                                                                                  \n batch_normalization_433 (B  (None, 35, 35, 32)           96        ['conv2d_433[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_433 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_433[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_431 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_2_ac[0][0]']        \n                                                                                                  \n conv2d_434 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_433[0][0]']      \n                                                                                                  \n batch_normalization_431 (B  (None, 35, 35, 32)           96        ['conv2d_431[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_434 (B  (None, 35, 35, 48)           144       ['conv2d_434[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_431 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_431[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_434 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_434[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_430 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_2_ac[0][0]']        \n                                                                                                  \n conv2d_432 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_431[0][0]']      \n                                                                                                  \n conv2d_435 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_434[0][0]']      \n                                                                                                  \n batch_normalization_430 (B  (None, 35, 35, 32)           96        ['conv2d_430[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_432 (B  (None, 35, 35, 32)           96        ['conv2d_432[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_435 (B  (None, 35, 35, 64)           192       ['conv2d_435[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_430 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_430[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_432 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_432[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_435 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_435[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_3_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_430[0][0]',      \n te)                                                                 'activation_432[0][0]',      \n                                                                     'activation_435[0][0]']      \n                                                                                                  \n block35_3_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_3_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_82 (Cus  (None, 35, 35, 320)          0         ['block35_2_ac[0][0]',        \n tomScaleLayer)                                                      'block35_3_conv[0][0]']      \n                                                                                                  \n block35_3_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_82[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_439 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_3_ac[0][0]']        \n                                                                                                  \n batch_normalization_439 (B  (None, 35, 35, 32)           96        ['conv2d_439[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_439 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_439[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_437 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_3_ac[0][0]']        \n                                                                                                  \n conv2d_440 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_439[0][0]']      \n                                                                                                  \n batch_normalization_437 (B  (None, 35, 35, 32)           96        ['conv2d_437[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_440 (B  (None, 35, 35, 48)           144       ['conv2d_440[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_437 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_437[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_440 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_440[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_436 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_3_ac[0][0]']        \n                                                                                                  \n conv2d_438 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_437[0][0]']      \n                                                                                                  \n conv2d_441 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_440[0][0]']      \n                                                                                                  \n batch_normalization_436 (B  (None, 35, 35, 32)           96        ['conv2d_436[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_438 (B  (None, 35, 35, 32)           96        ['conv2d_438[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_441 (B  (None, 35, 35, 64)           192       ['conv2d_441[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_436 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_436[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_438 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_438[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_441 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_441[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_4_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_436[0][0]',      \n te)                                                                 'activation_438[0][0]',      \n                                                                     'activation_441[0][0]']      \n                                                                                                  \n block35_4_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_4_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_83 (Cus  (None, 35, 35, 320)          0         ['block35_3_ac[0][0]',        \n tomScaleLayer)                                                      'block35_4_conv[0][0]']      \n                                                                                                  \n block35_4_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_83[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_445 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_4_ac[0][0]']        \n                                                                                                  \n batch_normalization_445 (B  (None, 35, 35, 32)           96        ['conv2d_445[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_445 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_445[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_443 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_4_ac[0][0]']        \n                                                                                                  \n conv2d_446 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_445[0][0]']      \n                                                                                                  \n batch_normalization_443 (B  (None, 35, 35, 32)           96        ['conv2d_443[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_446 (B  (None, 35, 35, 48)           144       ['conv2d_446[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_443 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_443[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_446 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_446[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_442 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_4_ac[0][0]']        \n                                                                                                  \n conv2d_444 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_443[0][0]']      \n                                                                                                  \n conv2d_447 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_446[0][0]']      \n                                                                                                  \n batch_normalization_442 (B  (None, 35, 35, 32)           96        ['conv2d_442[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_444 (B  (None, 35, 35, 32)           96        ['conv2d_444[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_447 (B  (None, 35, 35, 64)           192       ['conv2d_447[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_442 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_442[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_444 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_444[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_447 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_447[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_5_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_442[0][0]',      \n te)                                                                 'activation_444[0][0]',      \n                                                                     'activation_447[0][0]']      \n                                                                                                  \n block35_5_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_5_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_84 (Cus  (None, 35, 35, 320)          0         ['block35_4_ac[0][0]',        \n tomScaleLayer)                                                      'block35_5_conv[0][0]']      \n                                                                                                  \n block35_5_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_84[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_451 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_5_ac[0][0]']        \n                                                                                                  \n batch_normalization_451 (B  (None, 35, 35, 32)           96        ['conv2d_451[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_451 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_451[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_449 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_5_ac[0][0]']        \n                                                                                                  \n conv2d_452 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_451[0][0]']      \n                                                                                                  \n batch_normalization_449 (B  (None, 35, 35, 32)           96        ['conv2d_449[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_452 (B  (None, 35, 35, 48)           144       ['conv2d_452[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_449 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_449[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_452 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_452[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_448 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_5_ac[0][0]']        \n                                                                                                  \n conv2d_450 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_449[0][0]']      \n                                                                                                  \n conv2d_453 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_452[0][0]']      \n                                                                                                  \n batch_normalization_448 (B  (None, 35, 35, 32)           96        ['conv2d_448[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_450 (B  (None, 35, 35, 32)           96        ['conv2d_450[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_453 (B  (None, 35, 35, 64)           192       ['conv2d_453[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_448 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_448[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_450 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_450[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_453 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_453[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_6_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_448[0][0]',      \n te)                                                                 'activation_450[0][0]',      \n                                                                     'activation_453[0][0]']      \n                                                                                                  \n block35_6_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_6_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_85 (Cus  (None, 35, 35, 320)          0         ['block35_5_ac[0][0]',        \n tomScaleLayer)                                                      'block35_6_conv[0][0]']      \n                                                                                                  \n block35_6_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_85[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_457 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_6_ac[0][0]']        \n                                                                                                  \n batch_normalization_457 (B  (None, 35, 35, 32)           96        ['conv2d_457[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_457 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_457[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_455 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_6_ac[0][0]']        \n                                                                                                  \n conv2d_458 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_457[0][0]']      \n                                                                                                  \n batch_normalization_455 (B  (None, 35, 35, 32)           96        ['conv2d_455[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_458 (B  (None, 35, 35, 48)           144       ['conv2d_458[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_455 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_455[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_458 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_458[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_454 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_6_ac[0][0]']        \n                                                                                                  \n conv2d_456 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_455[0][0]']      \n                                                                                                  \n conv2d_459 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_458[0][0]']      \n                                                                                                  \n batch_normalization_454 (B  (None, 35, 35, 32)           96        ['conv2d_454[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_456 (B  (None, 35, 35, 32)           96        ['conv2d_456[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_459 (B  (None, 35, 35, 64)           192       ['conv2d_459[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_454 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_454[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_456 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_456[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_459 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_459[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_7_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_454[0][0]',      \n te)                                                                 'activation_456[0][0]',      \n                                                                     'activation_459[0][0]']      \n                                                                                                  \n block35_7_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_7_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_86 (Cus  (None, 35, 35, 320)          0         ['block35_6_ac[0][0]',        \n tomScaleLayer)                                                      'block35_7_conv[0][0]']      \n                                                                                                  \n block35_7_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_86[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_463 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_7_ac[0][0]']        \n                                                                                                  \n batch_normalization_463 (B  (None, 35, 35, 32)           96        ['conv2d_463[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_463 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_463[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_461 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_7_ac[0][0]']        \n                                                                                                  \n conv2d_464 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_463[0][0]']      \n                                                                                                  \n batch_normalization_461 (B  (None, 35, 35, 32)           96        ['conv2d_461[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_464 (B  (None, 35, 35, 48)           144       ['conv2d_464[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_461 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_461[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_464 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_464[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_460 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_7_ac[0][0]']        \n                                                                                                  \n conv2d_462 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_461[0][0]']      \n                                                                                                  \n conv2d_465 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_464[0][0]']      \n                                                                                                  \n batch_normalization_460 (B  (None, 35, 35, 32)           96        ['conv2d_460[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_462 (B  (None, 35, 35, 32)           96        ['conv2d_462[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_465 (B  (None, 35, 35, 64)           192       ['conv2d_465[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_460 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_460[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_462 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_462[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_465 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_465[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_8_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_460[0][0]',      \n te)                                                                 'activation_462[0][0]',      \n                                                                     'activation_465[0][0]']      \n                                                                                                  \n block35_8_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_8_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_87 (Cus  (None, 35, 35, 320)          0         ['block35_7_ac[0][0]',        \n tomScaleLayer)                                                      'block35_8_conv[0][0]']      \n                                                                                                  \n block35_8_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_87[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_469 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_8_ac[0][0]']        \n                                                                                                  \n batch_normalization_469 (B  (None, 35, 35, 32)           96        ['conv2d_469[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_469 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_469[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_467 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_8_ac[0][0]']        \n                                                                                                  \n conv2d_470 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_469[0][0]']      \n                                                                                                  \n batch_normalization_467 (B  (None, 35, 35, 32)           96        ['conv2d_467[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_470 (B  (None, 35, 35, 48)           144       ['conv2d_470[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_467 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_467[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_470 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_470[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_466 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_8_ac[0][0]']        \n                                                                                                  \n conv2d_468 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_467[0][0]']      \n                                                                                                  \n conv2d_471 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_470[0][0]']      \n                                                                                                  \n batch_normalization_466 (B  (None, 35, 35, 32)           96        ['conv2d_466[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_468 (B  (None, 35, 35, 32)           96        ['conv2d_468[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_471 (B  (None, 35, 35, 64)           192       ['conv2d_471[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_466 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_466[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_468 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_468[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_471 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_471[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_9_mixed (Concatena  (None, 35, 35, 128)          0         ['activation_466[0][0]',      \n te)                                                                 'activation_468[0][0]',      \n                                                                     'activation_471[0][0]']      \n                                                                                                  \n block35_9_conv (Conv2D)     (None, 35, 35, 320)          41280     ['block35_9_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_88 (Cus  (None, 35, 35, 320)          0         ['block35_8_ac[0][0]',        \n tomScaleLayer)                                                      'block35_9_conv[0][0]']      \n                                                                                                  \n block35_9_ac (Activation)   (None, 35, 35, 320)          0         ['custom_scale_layer_88[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_475 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_9_ac[0][0]']        \n                                                                                                  \n batch_normalization_475 (B  (None, 35, 35, 32)           96        ['conv2d_475[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_475 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_475[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_473 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_9_ac[0][0]']        \n                                                                                                  \n conv2d_476 (Conv2D)         (None, 35, 35, 48)           13824     ['activation_475[0][0]']      \n                                                                                                  \n batch_normalization_473 (B  (None, 35, 35, 32)           96        ['conv2d_473[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_476 (B  (None, 35, 35, 48)           144       ['conv2d_476[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_473 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_473[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_476 (Activation  (None, 35, 35, 48)           0         ['batch_normalization_476[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_472 (Conv2D)         (None, 35, 35, 32)           10240     ['block35_9_ac[0][0]']        \n                                                                                                  \n conv2d_474 (Conv2D)         (None, 35, 35, 32)           9216      ['activation_473[0][0]']      \n                                                                                                  \n conv2d_477 (Conv2D)         (None, 35, 35, 64)           27648     ['activation_476[0][0]']      \n                                                                                                  \n batch_normalization_472 (B  (None, 35, 35, 32)           96        ['conv2d_472[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_474 (B  (None, 35, 35, 32)           96        ['conv2d_474[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_477 (B  (None, 35, 35, 64)           192       ['conv2d_477[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_472 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_472[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_474 (Activation  (None, 35, 35, 32)           0         ['batch_normalization_474[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_477 (Activation  (None, 35, 35, 64)           0         ['batch_normalization_477[0][0\n )                                                                  ]']                           \n                                                                                                  \n block35_10_mixed (Concaten  (None, 35, 35, 128)          0         ['activation_472[0][0]',      \n ate)                                                                'activation_474[0][0]',      \n                                                                     'activation_477[0][0]']      \n                                                                                                  \n block35_10_conv (Conv2D)    (None, 35, 35, 320)          41280     ['block35_10_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_89 (Cus  (None, 35, 35, 320)          0         ['block35_9_ac[0][0]',        \n tomScaleLayer)                                                      'block35_10_conv[0][0]']     \n                                                                                                  \n block35_10_ac (Activation)  (None, 35, 35, 320)          0         ['custom_scale_layer_89[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_479 (Conv2D)         (None, 35, 35, 256)          81920     ['block35_10_ac[0][0]']       \n                                                                                                  \n batch_normalization_479 (B  (None, 35, 35, 256)          768       ['conv2d_479[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_479 (Activation  (None, 35, 35, 256)          0         ['batch_normalization_479[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_480 (Conv2D)         (None, 35, 35, 256)          589824    ['activation_479[0][0]']      \n                                                                                                  \n batch_normalization_480 (B  (None, 35, 35, 256)          768       ['conv2d_480[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_480 (Activation  (None, 35, 35, 256)          0         ['batch_normalization_480[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_478 (Conv2D)         (None, 17, 17, 384)          1105920   ['block35_10_ac[0][0]']       \n                                                                                                  \n conv2d_481 (Conv2D)         (None, 17, 17, 384)          884736    ['activation_480[0][0]']      \n                                                                                                  \n batch_normalization_478 (B  (None, 17, 17, 384)          1152      ['conv2d_478[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_481 (B  (None, 17, 17, 384)          1152      ['conv2d_481[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_478 (Activation  (None, 17, 17, 384)          0         ['batch_normalization_478[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_481 (Activation  (None, 17, 17, 384)          0         ['batch_normalization_481[0][0\n )                                                                  ]']                           \n                                                                                                  \n max_pooling2d_10 (MaxPooli  (None, 17, 17, 320)          0         ['block35_10_ac[0][0]']       \n ng2D)                                                                                            \n                                                                                                  \n mixed_6a (Concatenate)      (None, 17, 17, 1088)         0         ['activation_478[0][0]',      \n                                                                     'activation_481[0][0]',      \n                                                                     'max_pooling2d_10[0][0]']    \n                                                                                                  \n conv2d_483 (Conv2D)         (None, 17, 17, 128)          139264    ['mixed_6a[0][0]']            \n                                                                                                  \n batch_normalization_483 (B  (None, 17, 17, 128)          384       ['conv2d_483[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_483 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_483[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_484 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_483[0][0]']      \n                                                                                                  \n batch_normalization_484 (B  (None, 17, 17, 160)          480       ['conv2d_484[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_484 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_484[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_482 (Conv2D)         (None, 17, 17, 192)          208896    ['mixed_6a[0][0]']            \n                                                                                                  \n conv2d_485 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_484[0][0]']      \n                                                                                                  \n batch_normalization_482 (B  (None, 17, 17, 192)          576       ['conv2d_482[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_485 (B  (None, 17, 17, 192)          576       ['conv2d_485[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_482 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_482[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_485 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_485[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_1_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_482[0][0]',      \n te)                                                                 'activation_485[0][0]']      \n                                                                                                  \n block17_1_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_1_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_90 (Cus  (None, 17, 17, 1088)         0         ['mixed_6a[0][0]',            \n tomScaleLayer)                                                      'block17_1_conv[0][0]']      \n                                                                                                  \n block17_1_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_90[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_487 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_1_ac[0][0]']        \n                                                                                                  \n batch_normalization_487 (B  (None, 17, 17, 128)          384       ['conv2d_487[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_487 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_487[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_488 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_487[0][0]']      \n                                                                                                  \n batch_normalization_488 (B  (None, 17, 17, 160)          480       ['conv2d_488[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_488 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_488[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_486 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_1_ac[0][0]']        \n                                                                                                  \n conv2d_489 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_488[0][0]']      \n                                                                                                  \n batch_normalization_486 (B  (None, 17, 17, 192)          576       ['conv2d_486[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_489 (B  (None, 17, 17, 192)          576       ['conv2d_489[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_486 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_486[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_489 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_489[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_2_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_486[0][0]',      \n te)                                                                 'activation_489[0][0]']      \n                                                                                                  \n block17_2_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_2_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_91 (Cus  (None, 17, 17, 1088)         0         ['block17_1_ac[0][0]',        \n tomScaleLayer)                                                      'block17_2_conv[0][0]']      \n                                                                                                  \n block17_2_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_91[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_491 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_2_ac[0][0]']        \n                                                                                                  \n batch_normalization_491 (B  (None, 17, 17, 128)          384       ['conv2d_491[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_491 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_491[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_492 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_491[0][0]']      \n                                                                                                  \n batch_normalization_492 (B  (None, 17, 17, 160)          480       ['conv2d_492[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_492 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_492[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_490 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_2_ac[0][0]']        \n                                                                                                  \n conv2d_493 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_492[0][0]']      \n                                                                                                  \n batch_normalization_490 (B  (None, 17, 17, 192)          576       ['conv2d_490[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_493 (B  (None, 17, 17, 192)          576       ['conv2d_493[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_490 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_490[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_493 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_493[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_3_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_490[0][0]',      \n te)                                                                 'activation_493[0][0]']      \n                                                                                                  \n block17_3_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_3_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_92 (Cus  (None, 17, 17, 1088)         0         ['block17_2_ac[0][0]',        \n tomScaleLayer)                                                      'block17_3_conv[0][0]']      \n                                                                                                  \n block17_3_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_92[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_495 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_3_ac[0][0]']        \n                                                                                                  \n batch_normalization_495 (B  (None, 17, 17, 128)          384       ['conv2d_495[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_495 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_495[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_496 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_495[0][0]']      \n                                                                                                  \n batch_normalization_496 (B  (None, 17, 17, 160)          480       ['conv2d_496[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_496 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_496[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_494 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_3_ac[0][0]']        \n                                                                                                  \n conv2d_497 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_496[0][0]']      \n                                                                                                  \n batch_normalization_494 (B  (None, 17, 17, 192)          576       ['conv2d_494[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_497 (B  (None, 17, 17, 192)          576       ['conv2d_497[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_494 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_494[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_497 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_497[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_4_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_494[0][0]',      \n te)                                                                 'activation_497[0][0]']      \n                                                                                                  \n block17_4_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_4_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_93 (Cus  (None, 17, 17, 1088)         0         ['block17_3_ac[0][0]',        \n tomScaleLayer)                                                      'block17_4_conv[0][0]']      \n                                                                                                  \n block17_4_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_93[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_499 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_4_ac[0][0]']        \n                                                                                                  \n batch_normalization_499 (B  (None, 17, 17, 128)          384       ['conv2d_499[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_499 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_499[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_500 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_499[0][0]']      \n                                                                                                  \n batch_normalization_500 (B  (None, 17, 17, 160)          480       ['conv2d_500[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_500 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_500[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_498 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_4_ac[0][0]']        \n                                                                                                  \n conv2d_501 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_500[0][0]']      \n                                                                                                  \n batch_normalization_498 (B  (None, 17, 17, 192)          576       ['conv2d_498[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_501 (B  (None, 17, 17, 192)          576       ['conv2d_501[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_498 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_498[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_501 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_501[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_5_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_498[0][0]',      \n te)                                                                 'activation_501[0][0]']      \n                                                                                                  \n block17_5_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_5_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_94 (Cus  (None, 17, 17, 1088)         0         ['block17_4_ac[0][0]',        \n tomScaleLayer)                                                      'block17_5_conv[0][0]']      \n                                                                                                  \n block17_5_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_94[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_503 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_5_ac[0][0]']        \n                                                                                                  \n batch_normalization_503 (B  (None, 17, 17, 128)          384       ['conv2d_503[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_503 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_503[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_504 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_503[0][0]']      \n                                                                                                  \n batch_normalization_504 (B  (None, 17, 17, 160)          480       ['conv2d_504[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_504 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_504[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_502 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_5_ac[0][0]']        \n                                                                                                  \n conv2d_505 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_504[0][0]']      \n                                                                                                  \n batch_normalization_502 (B  (None, 17, 17, 192)          576       ['conv2d_502[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_505 (B  (None, 17, 17, 192)          576       ['conv2d_505[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_502 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_502[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_505 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_505[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_6_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_502[0][0]',      \n te)                                                                 'activation_505[0][0]']      \n                                                                                                  \n block17_6_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_6_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_95 (Cus  (None, 17, 17, 1088)         0         ['block17_5_ac[0][0]',        \n tomScaleLayer)                                                      'block17_6_conv[0][0]']      \n                                                                                                  \n block17_6_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_95[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_507 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_6_ac[0][0]']        \n                                                                                                  \n batch_normalization_507 (B  (None, 17, 17, 128)          384       ['conv2d_507[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_507 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_507[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_508 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_507[0][0]']      \n                                                                                                  \n batch_normalization_508 (B  (None, 17, 17, 160)          480       ['conv2d_508[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_508 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_508[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_506 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_6_ac[0][0]']        \n                                                                                                  \n conv2d_509 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_508[0][0]']      \n                                                                                                  \n batch_normalization_506 (B  (None, 17, 17, 192)          576       ['conv2d_506[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_509 (B  (None, 17, 17, 192)          576       ['conv2d_509[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_506 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_506[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_509 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_509[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_7_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_506[0][0]',      \n te)                                                                 'activation_509[0][0]']      \n                                                                                                  \n block17_7_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_7_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_96 (Cus  (None, 17, 17, 1088)         0         ['block17_6_ac[0][0]',        \n tomScaleLayer)                                                      'block17_7_conv[0][0]']      \n                                                                                                  \n block17_7_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_96[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_511 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_7_ac[0][0]']        \n                                                                                                  \n batch_normalization_511 (B  (None, 17, 17, 128)          384       ['conv2d_511[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_511 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_511[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_512 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_511[0][0]']      \n                                                                                                  \n batch_normalization_512 (B  (None, 17, 17, 160)          480       ['conv2d_512[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_512 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_512[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_510 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_7_ac[0][0]']        \n                                                                                                  \n conv2d_513 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_512[0][0]']      \n                                                                                                  \n batch_normalization_510 (B  (None, 17, 17, 192)          576       ['conv2d_510[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_513 (B  (None, 17, 17, 192)          576       ['conv2d_513[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_510 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_510[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_513 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_513[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_8_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_510[0][0]',      \n te)                                                                 'activation_513[0][0]']      \n                                                                                                  \n block17_8_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_8_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_97 (Cus  (None, 17, 17, 1088)         0         ['block17_7_ac[0][0]',        \n tomScaleLayer)                                                      'block17_8_conv[0][0]']      \n                                                                                                  \n block17_8_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_97[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_515 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_8_ac[0][0]']        \n                                                                                                  \n batch_normalization_515 (B  (None, 17, 17, 128)          384       ['conv2d_515[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_515 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_515[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_516 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_515[0][0]']      \n                                                                                                  \n batch_normalization_516 (B  (None, 17, 17, 160)          480       ['conv2d_516[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_516 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_516[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_514 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_8_ac[0][0]']        \n                                                                                                  \n conv2d_517 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_516[0][0]']      \n                                                                                                  \n batch_normalization_514 (B  (None, 17, 17, 192)          576       ['conv2d_514[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_517 (B  (None, 17, 17, 192)          576       ['conv2d_517[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_514 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_514[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_517 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_517[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_9_mixed (Concatena  (None, 17, 17, 384)          0         ['activation_514[0][0]',      \n te)                                                                 'activation_517[0][0]']      \n                                                                                                  \n block17_9_conv (Conv2D)     (None, 17, 17, 1088)         418880    ['block17_9_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_98 (Cus  (None, 17, 17, 1088)         0         ['block17_8_ac[0][0]',        \n tomScaleLayer)                                                      'block17_9_conv[0][0]']      \n                                                                                                  \n block17_9_ac (Activation)   (None, 17, 17, 1088)         0         ['custom_scale_layer_98[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_519 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_9_ac[0][0]']        \n                                                                                                  \n batch_normalization_519 (B  (None, 17, 17, 128)          384       ['conv2d_519[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_519 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_519[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_520 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_519[0][0]']      \n                                                                                                  \n batch_normalization_520 (B  (None, 17, 17, 160)          480       ['conv2d_520[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_520 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_520[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_518 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_9_ac[0][0]']        \n                                                                                                  \n conv2d_521 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_520[0][0]']      \n                                                                                                  \n batch_normalization_518 (B  (None, 17, 17, 192)          576       ['conv2d_518[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_521 (B  (None, 17, 17, 192)          576       ['conv2d_521[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_518 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_518[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_521 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_521[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_10_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_518[0][0]',      \n ate)                                                                'activation_521[0][0]']      \n                                                                                                  \n block17_10_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_10_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_99 (Cus  (None, 17, 17, 1088)         0         ['block17_9_ac[0][0]',        \n tomScaleLayer)                                                      'block17_10_conv[0][0]']     \n                                                                                                  \n block17_10_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_99[0][0]'\n                                                                    ]                             \n                                                                                                  \n conv2d_523 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_10_ac[0][0]']       \n                                                                                                  \n batch_normalization_523 (B  (None, 17, 17, 128)          384       ['conv2d_523[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_523 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_523[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_524 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_523[0][0]']      \n                                                                                                  \n batch_normalization_524 (B  (None, 17, 17, 160)          480       ['conv2d_524[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_524 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_524[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_522 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_10_ac[0][0]']       \n                                                                                                  \n conv2d_525 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_524[0][0]']      \n                                                                                                  \n batch_normalization_522 (B  (None, 17, 17, 192)          576       ['conv2d_522[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_525 (B  (None, 17, 17, 192)          576       ['conv2d_525[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_522 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_522[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_525 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_525[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_11_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_522[0][0]',      \n ate)                                                                'activation_525[0][0]']      \n                                                                                                  \n block17_11_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_11_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_100 (Cu  (None, 17, 17, 1088)         0         ['block17_10_ac[0][0]',       \n stomScaleLayer)                                                     'block17_11_conv[0][0]']     \n                                                                                                  \n block17_11_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_100[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_527 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_11_ac[0][0]']       \n                                                                                                  \n batch_normalization_527 (B  (None, 17, 17, 128)          384       ['conv2d_527[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_527 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_527[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_528 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_527[0][0]']      \n                                                                                                  \n batch_normalization_528 (B  (None, 17, 17, 160)          480       ['conv2d_528[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_528 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_528[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_526 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_11_ac[0][0]']       \n                                                                                                  \n conv2d_529 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_528[0][0]']      \n                                                                                                  \n batch_normalization_526 (B  (None, 17, 17, 192)          576       ['conv2d_526[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_529 (B  (None, 17, 17, 192)          576       ['conv2d_529[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_526 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_526[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_529 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_529[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_12_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_526[0][0]',      \n ate)                                                                'activation_529[0][0]']      \n                                                                                                  \n block17_12_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_12_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_101 (Cu  (None, 17, 17, 1088)         0         ['block17_11_ac[0][0]',       \n stomScaleLayer)                                                     'block17_12_conv[0][0]']     \n                                                                                                  \n block17_12_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_101[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_531 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_12_ac[0][0]']       \n                                                                                                  \n batch_normalization_531 (B  (None, 17, 17, 128)          384       ['conv2d_531[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_531 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_531[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_532 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_531[0][0]']      \n                                                                                                  \n batch_normalization_532 (B  (None, 17, 17, 160)          480       ['conv2d_532[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_532 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_532[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_530 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_12_ac[0][0]']       \n                                                                                                  \n conv2d_533 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_532[0][0]']      \n                                                                                                  \n batch_normalization_530 (B  (None, 17, 17, 192)          576       ['conv2d_530[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_533 (B  (None, 17, 17, 192)          576       ['conv2d_533[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_530 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_530[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_533 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_533[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_13_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_530[0][0]',      \n ate)                                                                'activation_533[0][0]']      \n                                                                                                  \n block17_13_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_13_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_102 (Cu  (None, 17, 17, 1088)         0         ['block17_12_ac[0][0]',       \n stomScaleLayer)                                                     'block17_13_conv[0][0]']     \n                                                                                                  \n block17_13_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_102[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_535 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_13_ac[0][0]']       \n                                                                                                  \n batch_normalization_535 (B  (None, 17, 17, 128)          384       ['conv2d_535[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_535 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_535[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_536 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_535[0][0]']      \n                                                                                                  \n batch_normalization_536 (B  (None, 17, 17, 160)          480       ['conv2d_536[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_536 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_536[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_534 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_13_ac[0][0]']       \n                                                                                                  \n conv2d_537 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_536[0][0]']      \n                                                                                                  \n batch_normalization_534 (B  (None, 17, 17, 192)          576       ['conv2d_534[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_537 (B  (None, 17, 17, 192)          576       ['conv2d_537[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_534 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_534[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_537 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_537[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_14_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_534[0][0]',      \n ate)                                                                'activation_537[0][0]']      \n                                                                                                  \n block17_14_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_14_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_103 (Cu  (None, 17, 17, 1088)         0         ['block17_13_ac[0][0]',       \n stomScaleLayer)                                                     'block17_14_conv[0][0]']     \n                                                                                                  \n block17_14_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_103[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_539 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_14_ac[0][0]']       \n                                                                                                  \n batch_normalization_539 (B  (None, 17, 17, 128)          384       ['conv2d_539[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_539 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_539[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_540 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_539[0][0]']      \n                                                                                                  \n batch_normalization_540 (B  (None, 17, 17, 160)          480       ['conv2d_540[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_540 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_540[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_538 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_14_ac[0][0]']       \n                                                                                                  \n conv2d_541 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_540[0][0]']      \n                                                                                                  \n batch_normalization_538 (B  (None, 17, 17, 192)          576       ['conv2d_538[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_541 (B  (None, 17, 17, 192)          576       ['conv2d_541[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_538 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_538[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_541 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_541[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_15_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_538[0][0]',      \n ate)                                                                'activation_541[0][0]']      \n                                                                                                  \n block17_15_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_15_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_104 (Cu  (None, 17, 17, 1088)         0         ['block17_14_ac[0][0]',       \n stomScaleLayer)                                                     'block17_15_conv[0][0]']     \n                                                                                                  \n block17_15_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_104[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_543 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_15_ac[0][0]']       \n                                                                                                  \n batch_normalization_543 (B  (None, 17, 17, 128)          384       ['conv2d_543[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_543 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_543[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_544 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_543[0][0]']      \n                                                                                                  \n batch_normalization_544 (B  (None, 17, 17, 160)          480       ['conv2d_544[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_544 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_544[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_542 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_15_ac[0][0]']       \n                                                                                                  \n conv2d_545 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_544[0][0]']      \n                                                                                                  \n batch_normalization_542 (B  (None, 17, 17, 192)          576       ['conv2d_542[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_545 (B  (None, 17, 17, 192)          576       ['conv2d_545[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_542 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_542[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_545 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_545[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_16_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_542[0][0]',      \n ate)                                                                'activation_545[0][0]']      \n                                                                                                  \n block17_16_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_16_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_105 (Cu  (None, 17, 17, 1088)         0         ['block17_15_ac[0][0]',       \n stomScaleLayer)                                                     'block17_16_conv[0][0]']     \n                                                                                                  \n block17_16_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_105[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_547 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_16_ac[0][0]']       \n                                                                                                  \n batch_normalization_547 (B  (None, 17, 17, 128)          384       ['conv2d_547[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_547 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_547[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_548 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_547[0][0]']      \n                                                                                                  \n batch_normalization_548 (B  (None, 17, 17, 160)          480       ['conv2d_548[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_548 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_548[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_546 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_16_ac[0][0]']       \n                                                                                                  \n conv2d_549 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_548[0][0]']      \n                                                                                                  \n batch_normalization_546 (B  (None, 17, 17, 192)          576       ['conv2d_546[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_549 (B  (None, 17, 17, 192)          576       ['conv2d_549[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_546 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_546[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_549 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_549[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_17_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_546[0][0]',      \n ate)                                                                'activation_549[0][0]']      \n                                                                                                  \n block17_17_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_17_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_106 (Cu  (None, 17, 17, 1088)         0         ['block17_16_ac[0][0]',       \n stomScaleLayer)                                                     'block17_17_conv[0][0]']     \n                                                                                                  \n block17_17_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_106[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_551 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_17_ac[0][0]']       \n                                                                                                  \n batch_normalization_551 (B  (None, 17, 17, 128)          384       ['conv2d_551[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_551 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_551[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_552 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_551[0][0]']      \n                                                                                                  \n batch_normalization_552 (B  (None, 17, 17, 160)          480       ['conv2d_552[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_552 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_552[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_550 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_17_ac[0][0]']       \n                                                                                                  \n conv2d_553 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_552[0][0]']      \n                                                                                                  \n batch_normalization_550 (B  (None, 17, 17, 192)          576       ['conv2d_550[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_553 (B  (None, 17, 17, 192)          576       ['conv2d_553[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_550 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_550[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_553 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_553[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_18_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_550[0][0]',      \n ate)                                                                'activation_553[0][0]']      \n                                                                                                  \n block17_18_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_18_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_107 (Cu  (None, 17, 17, 1088)         0         ['block17_17_ac[0][0]',       \n stomScaleLayer)                                                     'block17_18_conv[0][0]']     \n                                                                                                  \n block17_18_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_107[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_555 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_18_ac[0][0]']       \n                                                                                                  \n batch_normalization_555 (B  (None, 17, 17, 128)          384       ['conv2d_555[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_555 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_555[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_556 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_555[0][0]']      \n                                                                                                  \n batch_normalization_556 (B  (None, 17, 17, 160)          480       ['conv2d_556[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_556 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_556[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_554 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_18_ac[0][0]']       \n                                                                                                  \n conv2d_557 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_556[0][0]']      \n                                                                                                  \n batch_normalization_554 (B  (None, 17, 17, 192)          576       ['conv2d_554[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_557 (B  (None, 17, 17, 192)          576       ['conv2d_557[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_554 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_554[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_557 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_557[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_19_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_554[0][0]',      \n ate)                                                                'activation_557[0][0]']      \n                                                                                                  \n block17_19_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_19_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_108 (Cu  (None, 17, 17, 1088)         0         ['block17_18_ac[0][0]',       \n stomScaleLayer)                                                     'block17_19_conv[0][0]']     \n                                                                                                  \n block17_19_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_108[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_559 (Conv2D)         (None, 17, 17, 128)          139264    ['block17_19_ac[0][0]']       \n                                                                                                  \n batch_normalization_559 (B  (None, 17, 17, 128)          384       ['conv2d_559[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_559 (Activation  (None, 17, 17, 128)          0         ['batch_normalization_559[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_560 (Conv2D)         (None, 17, 17, 160)          143360    ['activation_559[0][0]']      \n                                                                                                  \n batch_normalization_560 (B  (None, 17, 17, 160)          480       ['conv2d_560[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_560 (Activation  (None, 17, 17, 160)          0         ['batch_normalization_560[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_558 (Conv2D)         (None, 17, 17, 192)          208896    ['block17_19_ac[0][0]']       \n                                                                                                  \n conv2d_561 (Conv2D)         (None, 17, 17, 192)          215040    ['activation_560[0][0]']      \n                                                                                                  \n batch_normalization_558 (B  (None, 17, 17, 192)          576       ['conv2d_558[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_561 (B  (None, 17, 17, 192)          576       ['conv2d_561[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_558 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_558[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_561 (Activation  (None, 17, 17, 192)          0         ['batch_normalization_561[0][0\n )                                                                  ]']                           \n                                                                                                  \n block17_20_mixed (Concaten  (None, 17, 17, 384)          0         ['activation_558[0][0]',      \n ate)                                                                'activation_561[0][0]']      \n                                                                                                  \n block17_20_conv (Conv2D)    (None, 17, 17, 1088)         418880    ['block17_20_mixed[0][0]']    \n                                                                                                  \n custom_scale_layer_109 (Cu  (None, 17, 17, 1088)         0         ['block17_19_ac[0][0]',       \n stomScaleLayer)                                                     'block17_20_conv[0][0]']     \n                                                                                                  \n block17_20_ac (Activation)  (None, 17, 17, 1088)         0         ['custom_scale_layer_109[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_566 (Conv2D)         (None, 17, 17, 256)          278528    ['block17_20_ac[0][0]']       \n                                                                                                  \n batch_normalization_566 (B  (None, 17, 17, 256)          768       ['conv2d_566[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_566 (Activation  (None, 17, 17, 256)          0         ['batch_normalization_566[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_562 (Conv2D)         (None, 17, 17, 256)          278528    ['block17_20_ac[0][0]']       \n                                                                                                  \n conv2d_564 (Conv2D)         (None, 17, 17, 256)          278528    ['block17_20_ac[0][0]']       \n                                                                                                  \n conv2d_567 (Conv2D)         (None, 17, 17, 288)          663552    ['activation_566[0][0]']      \n                                                                                                  \n batch_normalization_562 (B  (None, 17, 17, 256)          768       ['conv2d_562[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_564 (B  (None, 17, 17, 256)          768       ['conv2d_564[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_567 (B  (None, 17, 17, 288)          864       ['conv2d_567[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_562 (Activation  (None, 17, 17, 256)          0         ['batch_normalization_562[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_564 (Activation  (None, 17, 17, 256)          0         ['batch_normalization_564[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_567 (Activation  (None, 17, 17, 288)          0         ['batch_normalization_567[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_563 (Conv2D)         (None, 8, 8, 384)            884736    ['activation_562[0][0]']      \n                                                                                                  \n conv2d_565 (Conv2D)         (None, 8, 8, 288)            663552    ['activation_564[0][0]']      \n                                                                                                  \n conv2d_568 (Conv2D)         (None, 8, 8, 320)            829440    ['activation_567[0][0]']      \n                                                                                                  \n batch_normalization_563 (B  (None, 8, 8, 384)            1152      ['conv2d_563[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_565 (B  (None, 8, 8, 288)            864       ['conv2d_565[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_568 (B  (None, 8, 8, 320)            960       ['conv2d_568[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_563 (Activation  (None, 8, 8, 384)            0         ['batch_normalization_563[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_565 (Activation  (None, 8, 8, 288)            0         ['batch_normalization_565[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_568 (Activation  (None, 8, 8, 320)            0         ['batch_normalization_568[0][0\n )                                                                  ]']                           \n                                                                                                  \n max_pooling2d_11 (MaxPooli  (None, 8, 8, 1088)           0         ['block17_20_ac[0][0]']       \n ng2D)                                                                                            \n                                                                                                  \n mixed_7a (Concatenate)      (None, 8, 8, 2080)           0         ['activation_563[0][0]',      \n                                                                     'activation_565[0][0]',      \n                                                                     'activation_568[0][0]',      \n                                                                     'max_pooling2d_11[0][0]']    \n                                                                                                  \n conv2d_570 (Conv2D)         (None, 8, 8, 192)            399360    ['mixed_7a[0][0]']            \n                                                                                                  \n batch_normalization_570 (B  (None, 8, 8, 192)            576       ['conv2d_570[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_570 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_570[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_571 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_570[0][0]']      \n                                                                                                  \n batch_normalization_571 (B  (None, 8, 8, 224)            672       ['conv2d_571[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_571 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_571[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_569 (Conv2D)         (None, 8, 8, 192)            399360    ['mixed_7a[0][0]']            \n                                                                                                  \n conv2d_572 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_571[0][0]']      \n                                                                                                  \n batch_normalization_569 (B  (None, 8, 8, 192)            576       ['conv2d_569[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_572 (B  (None, 8, 8, 256)            768       ['conv2d_572[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_569 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_569[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_572 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_572[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_1_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_569[0][0]',      \n e)                                                                  'activation_572[0][0]']      \n                                                                                                  \n block8_1_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_1_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_110 (Cu  (None, 8, 8, 2080)           0         ['mixed_7a[0][0]',            \n stomScaleLayer)                                                     'block8_1_conv[0][0]']       \n                                                                                                  \n block8_1_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_110[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_574 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_1_ac[0][0]']         \n                                                                                                  \n batch_normalization_574 (B  (None, 8, 8, 192)            576       ['conv2d_574[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_574 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_574[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_575 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_574[0][0]']      \n                                                                                                  \n batch_normalization_575 (B  (None, 8, 8, 224)            672       ['conv2d_575[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_575 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_575[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_573 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_1_ac[0][0]']         \n                                                                                                  \n conv2d_576 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_575[0][0]']      \n                                                                                                  \n batch_normalization_573 (B  (None, 8, 8, 192)            576       ['conv2d_573[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_576 (B  (None, 8, 8, 256)            768       ['conv2d_576[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_573 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_573[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_576 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_576[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_2_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_573[0][0]',      \n e)                                                                  'activation_576[0][0]']      \n                                                                                                  \n block8_2_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_2_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_111 (Cu  (None, 8, 8, 2080)           0         ['block8_1_ac[0][0]',         \n stomScaleLayer)                                                     'block8_2_conv[0][0]']       \n                                                                                                  \n block8_2_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_111[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_578 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_2_ac[0][0]']         \n                                                                                                  \n batch_normalization_578 (B  (None, 8, 8, 192)            576       ['conv2d_578[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_578 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_578[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_579 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_578[0][0]']      \n                                                                                                  \n batch_normalization_579 (B  (None, 8, 8, 224)            672       ['conv2d_579[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_579 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_579[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_577 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_2_ac[0][0]']         \n                                                                                                  \n conv2d_580 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_579[0][0]']      \n                                                                                                  \n batch_normalization_577 (B  (None, 8, 8, 192)            576       ['conv2d_577[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_580 (B  (None, 8, 8, 256)            768       ['conv2d_580[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_577 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_577[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_580 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_580[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_3_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_577[0][0]',      \n e)                                                                  'activation_580[0][0]']      \n                                                                                                  \n block8_3_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_3_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_112 (Cu  (None, 8, 8, 2080)           0         ['block8_2_ac[0][0]',         \n stomScaleLayer)                                                     'block8_3_conv[0][0]']       \n                                                                                                  \n block8_3_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_112[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_582 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_3_ac[0][0]']         \n                                                                                                  \n batch_normalization_582 (B  (None, 8, 8, 192)            576       ['conv2d_582[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_582 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_582[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_583 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_582[0][0]']      \n                                                                                                  \n batch_normalization_583 (B  (None, 8, 8, 224)            672       ['conv2d_583[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_583 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_583[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_581 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_3_ac[0][0]']         \n                                                                                                  \n conv2d_584 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_583[0][0]']      \n                                                                                                  \n batch_normalization_581 (B  (None, 8, 8, 192)            576       ['conv2d_581[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_584 (B  (None, 8, 8, 256)            768       ['conv2d_584[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_581 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_581[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_584 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_584[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_4_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_581[0][0]',      \n e)                                                                  'activation_584[0][0]']      \n                                                                                                  \n block8_4_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_4_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_113 (Cu  (None, 8, 8, 2080)           0         ['block8_3_ac[0][0]',         \n stomScaleLayer)                                                     'block8_4_conv[0][0]']       \n                                                                                                  \n block8_4_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_113[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_586 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_4_ac[0][0]']         \n                                                                                                  \n batch_normalization_586 (B  (None, 8, 8, 192)            576       ['conv2d_586[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_586 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_586[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_587 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_586[0][0]']      \n                                                                                                  \n batch_normalization_587 (B  (None, 8, 8, 224)            672       ['conv2d_587[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_587 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_587[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_585 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_4_ac[0][0]']         \n                                                                                                  \n conv2d_588 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_587[0][0]']      \n                                                                                                  \n batch_normalization_585 (B  (None, 8, 8, 192)            576       ['conv2d_585[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_588 (B  (None, 8, 8, 256)            768       ['conv2d_588[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_585 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_585[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_588 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_588[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_5_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_585[0][0]',      \n e)                                                                  'activation_588[0][0]']      \n                                                                                                  \n block8_5_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_5_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_114 (Cu  (None, 8, 8, 2080)           0         ['block8_4_ac[0][0]',         \n stomScaleLayer)                                                     'block8_5_conv[0][0]']       \n                                                                                                  \n block8_5_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_114[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_590 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_5_ac[0][0]']         \n                                                                                                  \n batch_normalization_590 (B  (None, 8, 8, 192)            576       ['conv2d_590[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_590 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_590[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_591 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_590[0][0]']      \n                                                                                                  \n batch_normalization_591 (B  (None, 8, 8, 224)            672       ['conv2d_591[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_591 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_591[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_589 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_5_ac[0][0]']         \n                                                                                                  \n conv2d_592 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_591[0][0]']      \n                                                                                                  \n batch_normalization_589 (B  (None, 8, 8, 192)            576       ['conv2d_589[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_592 (B  (None, 8, 8, 256)            768       ['conv2d_592[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_589 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_589[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_592 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_592[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_6_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_589[0][0]',      \n e)                                                                  'activation_592[0][0]']      \n                                                                                                  \n block8_6_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_6_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_115 (Cu  (None, 8, 8, 2080)           0         ['block8_5_ac[0][0]',         \n stomScaleLayer)                                                     'block8_6_conv[0][0]']       \n                                                                                                  \n block8_6_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_115[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_594 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_6_ac[0][0]']         \n                                                                                                  \n batch_normalization_594 (B  (None, 8, 8, 192)            576       ['conv2d_594[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_594 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_594[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_595 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_594[0][0]']      \n                                                                                                  \n batch_normalization_595 (B  (None, 8, 8, 224)            672       ['conv2d_595[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_595 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_595[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_593 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_6_ac[0][0]']         \n                                                                                                  \n conv2d_596 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_595[0][0]']      \n                                                                                                  \n batch_normalization_593 (B  (None, 8, 8, 192)            576       ['conv2d_593[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_596 (B  (None, 8, 8, 256)            768       ['conv2d_596[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_593 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_593[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_596 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_596[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_7_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_593[0][0]',      \n e)                                                                  'activation_596[0][0]']      \n                                                                                                  \n block8_7_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_7_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_116 (Cu  (None, 8, 8, 2080)           0         ['block8_6_ac[0][0]',         \n stomScaleLayer)                                                     'block8_7_conv[0][0]']       \n                                                                                                  \n block8_7_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_116[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_598 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_7_ac[0][0]']         \n                                                                                                  \n batch_normalization_598 (B  (None, 8, 8, 192)            576       ['conv2d_598[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_598 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_598[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_599 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_598[0][0]']      \n                                                                                                  \n batch_normalization_599 (B  (None, 8, 8, 224)            672       ['conv2d_599[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_599 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_599[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_597 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_7_ac[0][0]']         \n                                                                                                  \n conv2d_600 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_599[0][0]']      \n                                                                                                  \n batch_normalization_597 (B  (None, 8, 8, 192)            576       ['conv2d_597[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_600 (B  (None, 8, 8, 256)            768       ['conv2d_600[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_597 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_597[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_600 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_600[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_8_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_597[0][0]',      \n e)                                                                  'activation_600[0][0]']      \n                                                                                                  \n block8_8_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_8_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_117 (Cu  (None, 8, 8, 2080)           0         ['block8_7_ac[0][0]',         \n stomScaleLayer)                                                     'block8_8_conv[0][0]']       \n                                                                                                  \n block8_8_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_117[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_602 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_8_ac[0][0]']         \n                                                                                                  \n batch_normalization_602 (B  (None, 8, 8, 192)            576       ['conv2d_602[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_602 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_602[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_603 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_602[0][0]']      \n                                                                                                  \n batch_normalization_603 (B  (None, 8, 8, 224)            672       ['conv2d_603[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_603 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_603[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_601 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_8_ac[0][0]']         \n                                                                                                  \n conv2d_604 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_603[0][0]']      \n                                                                                                  \n batch_normalization_601 (B  (None, 8, 8, 192)            576       ['conv2d_601[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_604 (B  (None, 8, 8, 256)            768       ['conv2d_604[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_601 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_601[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_604 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_604[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_9_mixed (Concatenat  (None, 8, 8, 448)            0         ['activation_601[0][0]',      \n e)                                                                  'activation_604[0][0]']      \n                                                                                                  \n block8_9_conv (Conv2D)      (None, 8, 8, 2080)           933920    ['block8_9_mixed[0][0]']      \n                                                                                                  \n custom_scale_layer_118 (Cu  (None, 8, 8, 2080)           0         ['block8_8_ac[0][0]',         \n stomScaleLayer)                                                     'block8_9_conv[0][0]']       \n                                                                                                  \n block8_9_ac (Activation)    (None, 8, 8, 2080)           0         ['custom_scale_layer_118[0][0]\n                                                                    ']                            \n                                                                                                  \n conv2d_606 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_9_ac[0][0]']         \n                                                                                                  \n batch_normalization_606 (B  (None, 8, 8, 192)            576       ['conv2d_606[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_606 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_606[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_607 (Conv2D)         (None, 8, 8, 224)            129024    ['activation_606[0][0]']      \n                                                                                                  \n batch_normalization_607 (B  (None, 8, 8, 224)            672       ['conv2d_607[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_607 (Activation  (None, 8, 8, 224)            0         ['batch_normalization_607[0][0\n )                                                                  ]']                           \n                                                                                                  \n conv2d_605 (Conv2D)         (None, 8, 8, 192)            399360    ['block8_9_ac[0][0]']         \n                                                                                                  \n conv2d_608 (Conv2D)         (None, 8, 8, 256)            172032    ['activation_607[0][0]']      \n                                                                                                  \n batch_normalization_605 (B  (None, 8, 8, 192)            576       ['conv2d_605[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n batch_normalization_608 (B  (None, 8, 8, 256)            768       ['conv2d_608[0][0]']          \n atchNormalization)                                                                               \n                                                                                                  \n activation_605 (Activation  (None, 8, 8, 192)            0         ['batch_normalization_605[0][0\n )                                                                  ]']                           \n                                                                                                  \n activation_608 (Activation  (None, 8, 8, 256)            0         ['batch_normalization_608[0][0\n )                                                                  ]']                           \n                                                                                                  \n block8_10_mixed (Concatena  (None, 8, 8, 448)            0         ['activation_605[0][0]',      \n te)                                                                 'activation_608[0][0]']      \n                                                                                                  \n block8_10_conv (Conv2D)     (None, 8, 8, 2080)           933920    ['block8_10_mixed[0][0]']     \n                                                                                                  \n custom_scale_layer_119 (Cu  (None, 8, 8, 2080)           0         ['block8_9_ac[0][0]',         \n stomScaleLayer)                                                     'block8_10_conv[0][0]']      \n                                                                                                  \n conv_7b (Conv2D)            (None, 8, 8, 1536)           3194880   ['custom_scale_layer_119[0][0]\n                                                                    ']                            \n                                                                                                  \n conv_7b_bn (BatchNormaliza  (None, 8, 8, 1536)           4608      ['conv_7b[0][0]']             \n tion)                                                                                            \n                                                                                                  \n conv_7b_ac (Activation)     (None, 8, 8, 1536)           0         ['conv_7b_bn[0][0]']          \n                                                                                                  \n flatten_2 (Flatten)         (None, 98304)                0         ['conv_7b_ac[0][0]']          \n                                                                                                  \n dense_2 (Dense)             (None, 2)                    196610    ['flatten_2[0][0]']           \n                                                                                                  \n==================================================================================================\nTotal params: 54533346 (208.03 MB)\nTrainable params: 54472802 (207.80 MB)\nNon-trainable params: 60544 (236.50 KB)\n__________________________________________________________________________________________________\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"loaded_model.compile(optimizer=Adam(learning_rate=1e-3), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:13.76329Z","iopub.execute_input":"2024-01-12T14:17:13.763645Z","iopub.status.idle":"2024-01-12T14:17:13.795723Z","shell.execute_reply.started":"2024-01-12T14:17:13.76362Z","shell.execute_reply":"2024-01-12T14:17:13.79484Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"markdown","source":"# **Evaluate the model on the test set**","metadata":{}},{"cell_type":"code","source":"test_loss, test_acc = loaded_model.evaluate(test_generator)\nprint(f\"Test Accuracy: {test_acc}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:15.543231Z","iopub.execute_input":"2024-01-12T14:17:15.543613Z","iopub.status.idle":"2024-01-12T14:17:21.734251Z","shell.execute_reply.started":"2024-01-12T14:17:15.543582Z","shell.execute_reply":"2024-01-12T14:17:21.733314Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"10/10 [==============================] - 6s 69ms/step - loss: 0.6892 - accuracy: 0.9200\nTest Accuracy: 0.9200000166893005\n","output_type":"stream"}]},{"cell_type":"code","source":"# Predict labels for the test set\npredictions = loaded_model.predict(test_generator)\npredicted_classes = np.argmax(predictions, axis=1)  \ntrue_classes = test_generator.classes\n\n\nprint(\"Predicted Classes:\", predicted_classes[-10:])  \nprint(\"True Classes:\", true_classes[-10:])  \n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:24.217545Z","iopub.execute_input":"2024-01-12T14:17:24.217934Z","iopub.status.idle":"2024-01-12T14:17:28.114057Z","shell.execute_reply.started":"2024-01-12T14:17:24.217904Z","shell.execute_reply":"2024-01-12T14:17:28.113181Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"10/10 [==============================] - 4s 64ms/step\nPredicted Classes: [1 0 1 1 1 1 1 1 1 1]\nTrue Classes: [1 1 1 1 1 1 1 1 1 1]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Evaluation Metircs**","metadata":{}},{"cell_type":"code","source":"print(f\"Accuracy: {accuracy_score(true_classes, predicted_classes)}\")\nprint(f\"Precision: {precision_score(true_classes, predicted_classes)}\")\nprint(f\"Recall: {recall_score(true_classes, predicted_classes)}\")\nprint(f\"F1 Score: {f1_score(true_classes, predicted_classes)}\")\nprint(f\"Log Loss: {log_loss(true_classes, predicted_classes)}\")\nprint(f\"Jaccard Score: {jaccard_score(true_classes, predicted_classes)}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:29.512828Z","iopub.execute_input":"2024-01-12T14:17:29.51374Z","iopub.status.idle":"2024-01-12T14:17:29.531584Z","shell.execute_reply.started":"2024-01-12T14:17:29.513684Z","shell.execute_reply":"2024-01-12T14:17:29.53058Z"},"trusted":true},"execution_count":37,"outputs":[{"name":"stdout","text":"Accuracy: 0.92\nPrecision: 0.96\nRecall: 0.8275862068965517\nF1 Score: 0.888888888888889\nLog Loss: 2.8834922711293722\nJaccard Score: 0.8\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Classification Report**","metadata":{}},{"cell_type":"code","source":"print(\"\\nClassification Report:\")\nprint(classification_report(true_classes, predicted_classes,digits=4))","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:31.375555Z","iopub.execute_input":"2024-01-12T14:17:31.375939Z","iopub.status.idle":"2024-01-12T14:17:31.390354Z","shell.execute_reply.started":"2024-01-12T14:17:31.375907Z","shell.execute_reply":"2024-01-12T14:17:31.389378Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"\nClassification Report:\n              precision    recall  f1-score   support\n\n           0     0.9000    0.9783    0.9375        46\n           1     0.9600    0.8276    0.8889        29\n\n    accuracy                         0.9200        75\n   macro avg     0.9300    0.9029    0.9132        75\nweighted avg     0.9232    0.9200    0.9187        75\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# **Confusion Matrix**","metadata":{}},{"cell_type":"code","source":"# Generate confusion matrix\nconf_matrix = confusion_matrix(true_classes, predicted_classes)\n\n# Plot confusion matrix\nplt.figure(figsize=(6, 4.5))\n# Define the custom palette\ncustom_palette = sns.color_palette(\"flare\", as_cmap=True)# Modify the number based on number of classes in the dataset\n# Define custom font dictionary for title and labels\nfont = {'family': 'Serif', 'weight': 'bold', 'size': 12}\n\n# Create heatmap with annotations and colormap\nheatmap = sns.heatmap(conf_matrix, annot=True, fmt='d', cmap=custom_palette,vmin=0,vmax=50,\n                      xticklabels=['Tumor', 'Non_Tumor'], yticklabels=['Tumor', 'Non_Tumor'],annot_kws={\"family\": \"Serif\",'weight': 'bold', 'size': 12})\n\n# Set x and y labels with the custom font dictionary\nheatmap.set_xlabel('Predicted Labels', fontdict=font)\nheatmap.set_ylabel('True Labels', fontdict=font)\nheatmap.set_title('Brain Tumor Detection', fontdict=font, pad=12)\n\n# Set font properties for tick labels on both axes\nheatmap.set_xticklabels(heatmap.get_xticklabels(), fontname='Serif', fontsize=12)\nheatmap.set_yticklabels(heatmap.get_yticklabels(), fontname='Serif', fontsize=12)\n\n# Create a color bar to indicate the scale\ncbar = heatmap.collections[0].colorbar\ncbar.set_label('Count', fontdict=font)\ncbar.ax.tick_params(labelsize=10)\n# Adjust padding between x-axis label and x-axis ticks\nplt.gca().xaxis.labelpad = 10  # Change the value as needed to adjust the space\n# Adjust layout to prevent cropping\nplt.tight_layout()\nplt.savefig('/kaggle/working/InceptionResNetV2_cm.pdf')  # Save as pdf format\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:33.552452Z","iopub.execute_input":"2024-01-12T14:17:33.553209Z","iopub.status.idle":"2024-01-12T14:17:34.145918Z","shell.execute_reply.started":"2024-01-12T14:17:33.553174Z","shell.execute_reply":"2024-01-12T14:17:34.145004Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x450 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# **ROC curve**","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, roc_auc_score\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\n\n\n\npositive_class_prob = predictions[:, 0]  # Adjust the index based on the column containing the probabilities\n\n\n# Calculate ROC curve and AUC score\nfpr, tpr, thresholds = roc_curve(true_classes, positive_class_prob)\nroc_auc = roc_auc_score(true_classes, positive_class_prob)\n\n\n# Define custom font dictionary for title and labels\nfont = {'family': 'Serif', 'weight': 'bold', 'size': 12}\n\n# Plot ROC curve\nplt.figure(figsize=(8, 6))\nplt.plot(fpr, tpr, color='blue', lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='red', lw=2, linestyle='--', label='Baseline')\nplt.xlabel('False Positive Rate', fontdict=font)\nplt.ylabel('True Positive Rate', fontdict=font)\nplt.title('Tumor Vs Non_Tumor', fontdict=font, pad=12)\n\n# Create legend entries with bbox\nlegend_handles = [\n    Patch(facecolor='blue',  label='Tumor Vs Non_Tumor (AUC = %0.2f)' % roc_auc),\n    Patch(facecolor='red',  label='Baseline (AUC = 0.5)')\n]\n\n# Combine loss and accuracy labels with custom legend lines and bbox\nplt.legend(handles=legend_handles, loc='lower center', ncol=1, prop={'family': 'Serif', 'weight': 'bold', 'size': 10})\n\n# Enable grid with customized properties\nplt.grid(True, linestyle='--', linewidth=0.7, color='gray')\n\n# Set x-axis ticks font properties\nplt.xticks(fontname='Serif', weight='bold')\n\n# Set y-axis ticks font properties\nplt.yticks(fontname='Serif', weight='bold')\n\n# Adjust padding between x-axis label and x-axis ticks\nplt.gca().xaxis.labelpad = 10  # Change the value as needed to adjust the space\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:43.377251Z","iopub.execute_input":"2024-01-12T14:17:43.378026Z","iopub.status.idle":"2024-01-12T14:17:43.686819Z","shell.execute_reply.started":"2024-01-12T14:17:43.377982Z","shell.execute_reply":"2024-01-12T14:17:43.685847Z"},"trusted":true},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAArgAAAI2CAYAAAC7evuHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC+x0lEQVR4nOzdeZyN5f/H8dcYsxhjjH0bDULWImuyFdmXkuxbpLJE8hUtv1TKGknhqyJL1qSEUMqaJYUQkq8lsm8zMcyYmfv3x23umckMY86cuc89834+HufhnOtsn5n3nONz7nPd1+1lGIaBiIiIiEgGkcXuAkRERERE0pIaXBERERHJUNTgioiIiEiGogZXRERERDIUNbgiIiIikqGowRURERGRDEUNroiIiIhkKGpwRURERCRDUYMrIiIiIhmKGlwRERERyVDU4IpImilWrBiBgYEEBwcTHBxMtmzZ8PLywsvLi6xZs1rjwcHBeHl5cfToUbtLdovg4GC8vb2tnz1btmz07ds30W3mzZtHcHAwWbJkIUeOHGzatCnVz/fXX38RHByMr68vXl5e5M6dmz/++OOW2/Xt29f63QcFBd1SU3o7evQoXl5eif4ufHx8rN+bv7+/NR4YGEixYsVsrVdEnEMNroikqeXLl3P58mUuX77M8OHDrfHatWtb45cvX7avwHRw+fJlunTpYl1u0qQJU6ZMSXSbTp06sWXLFnLkyMHJkyepXbt2qp/vnnvu4fLly3Tq1AmAS5cu0axZM86dO5fodlOmTLF+97t3776lJrsk/Lto0KCBNT5s2DBrfPny5TZWKCJOowZXRNJMsWLFyJYtW4pue++99+Lj4+Pmiuzz/PPPW+eXLVvGiRMnbrnNlClT6NKlCzly5Ejz5z98+DCtW7fm+vXraf7YacXHx4d77703RbfNli2btuCKSIqpwRWRNLNu3Tpq1KiRotsGBQVRvHhx6+vodevWAXDfffeRJUsWazzO6NGjCQwMtMafeuopnn/+eQoXLoy/vz/Vq1fnl19+4fz583Tr1o2goCAKFSrECy+8cEuTd/36dd555x3uv/9+AgMDCQwM5P777+edd95JdNuEX/t7eXkxb948nnzySfLkyYOXlxdvvvlmsj/fQw89xAMPPABATEwM06ZNS3T9lStXmD17dqJpAgcOHKBNmzYULFgQf39/8ufPT9myZenatSsHDhxI0e/1wQcftH5vW7ZsoVu3bhiGccf7nThxgj59+lC8eHECAgLImTMnderUYd68edZt4qZCJMxn9erV1KhRA39/f+655x5GjBiRoucDKFKkCIcOHUrRbYsWLcquXbtu+dv4888/CQoKssbq169v3adFixb4+/tb173++uu0atWKnDlzkiNHDp544gnOnz/Pzp07eeSRRwgICKBkyZL897//TdXvZ9OmTbf8fjZv3kytWrXInj17or9zEXEzQ0TETUaNGmUABmDUq1fvluu7d+9uXb927VprPDQ01BpPaO3atda4j4+PsXXrVuPKlSvGvffeawDGPffcY/Tt29cIDw83Vq5cad129OjR1mNcvXrVqFGjhgEYJUuWNE6dOmWcOXPGKFWqlAEYNWrUMCIiIpKsMTQ01Ni2bZsRGRlp1K1b1xg+fPhtf/6pU6da9y1YsKARFRVlXTdlyhSjfv361uXIyEgjJCTEAIxu3boZ165dM2JjY41Vq1YZAQEBxldffXXH33f37t2N4cOHG++88471vIDx8ssvJ7odYBw5csS6fODAASNv3rwGYLRt29a4du2asXXrVsPX19cAjBdeeCHR/RPmM2jQIOPGjRvGhAkTrLFFixbdsdbkNG7c2HqcpH6/Sf1tHDlyJNm/s+HDh1vXlSpVyjh9+rSxb98+a6x+/frGG2+8Ydy4ccMYNmyYNb5169Y0+f3Ur1/fOH36tHHy5EkjV65cif7ORcR9tAVXRBypXr161KhRg+zZs/Pggw8C5hbGhx56iBw5clC3bl3rtt9++611ftKkSWzbtg2ALl26ULBgQfLnz2/Nmd22bRuTJk1K8jnbtWtH9erV8fX1ZcyYMTRr1uy2NSacfnD69GmWLFliXTdlypREW2/37dtnTWMICgrCz88PLy8vGjduzP/93/9RqFChFP9uXnvtNZ5++mnr8tixY/nkk0+Svf3AgQM5f/48AP3798ff358aNWrw8MMPA/Dhhx9av7N/69evH1mzZqVx48bWmKfOl+3evTsFChSgbNmy5MuXDzC/dejduzdZs2alTp061m0T/s248vsZMmQIBQoUoFChQnzyySeUKlXKXT+eiCSgBldEHClhw5dw3m/hwoVvGfv777+t88uWLbPO33PPPdb5okWLJnmbhKpXr26dr1mzZqLLSQkMDKRz587W5bidutavX8/Fixd54oknrOvy5s1LlizmW/JHH31EkSJF6Nq1K3PnzmXAgAEpnvoRZ9q0aTz66KPW5b59+/Ldd9/dcrtr166xZs0a6/Ld/k7ibhMQEGCNJfx9e5LU/M24+vtJ+Dfy5JNPUqRIkdSWLyJ3QQ2uiDhSXDMIJJqrGzeecCw6Oto6f/bsWeu8v7+/dd7Pzy/J2ySUJ0+eu66zT58+1vkNGzbw+++/M3nyZJ599lmyZs1qXRcSEsL48eOtOk6dOsXnn39Oly5dKF26NH/++eddPa+Pjw9ffvklZcuWBczfwVNPPcWePXsS3e7ixYvExMRYl+/2d+Lr63vLWMLftydJzd+Mq7+f1PzNiIjr1OCKiG0SNngJRUZGuu058+fPb51PuENZwudMeJuEEjZAKXX//ffz0EMPWZffeOMNli9fzrPPPnvLbV988UVOnz7N3Llzefrpp63m6O+//2bUqFF3/dzBwcGsWLHC+nnCw8Np3rx5otvkzp0bb29v6/Ld/k7SW1J/M+78e3H195OavxkRcZ0aXBGxTdxXwwBXr14FICIiwprv6A4tWrSwzv/1119Jnk94m7SQcCvukiVLaNGixS1zavft20edOnUIDg6mU6dOzJgxg507d1rXnz59OlXPXbx4cZYuXWpteTx+/Hii67Nly5Zo7dn0+p2kVlJ/MwnrTGtO+/2IiEkNrojYJmFTsHbtWmJjY/nwww9TvMxUagwcOJCqVasC8Pnnn3PmzBnOnj3L3LlzAahatSoDBgxI0+d86qmnEn1V3a9fv1tuExUVxaZNmxgxYgTXrl0D4Oeff7au//eW17tRs2ZN5syZk+zWxIkTJ5I7d27AnP8bGRnJtm3b2Lx5M2DO361Zs2aqnz8ttWzZ0jr/448/cv369SSX9UpLTvr9iMhNdi/jICIZU86cOQ1/f39ruSRvb28jZ86cxty5cxPdbtq0aca9995r+Pr6GhUqVDBmz56daJmlnDlzGhs3bjRGjRplZM+ePdEyYX369DH69Olj+Pj4WOPZs2c3Nm7caOTMmdMa8/LyMnLmzGkcO3bMMAzDiIiIMN566y2jfPnyRkBAgBEQEGCUL1/eeOuttxItEVaxYsVbHrt58+ap+n0MHjzYAIzy5csnef2pU6eMLl26GBUqVDDy5MljBAQEGDly5DCqV69uTJky5baPfezYMSNnzpyGj4+P4efnl+hnTWjMmDHWz5JwmbC4x3j22WeN0NBQI1u2bEaOHDmM2rVrG7Nnz77leby8vBLlc+zYMSNHjhy3ZH034jLLmjWr9ThJ/SzXr183XnjhBaNAgQJGtmzZjPr16xsLFy5M8rmbN29u+Pn5Wddly5bNmDt3rlGxYsVbfoaNGzfe8vdVsWLFNPn9jBo16q5+FyLiOi/DcOOmEhERERGRdKYpCiIiIiKSoSS9C7OIiIiLXnnlFb766qtkr69evTqzZ89Ox4pEJLNQgysiIm5x6tQp/vjjj2SvL1iwYDpWIyKZiebgioiIiEiGojm4IiIiIpKhqMEVERERkQxFDa6IiIiIZChqcEVEREQkQ1GDKyIiIiIZihpcEREREclQ1OCKiIiISIaiBldEREREMhQ1uCIiIiKSoajBFREREZEMJavdBXiK2NhYTp48SY4cOfDy8rK7HBERERH5F8Mw+OeffyhcuDBZsiS/nVYN7k0nT56kaNGidpchIiIiIndw/PhxQkJCkr1eDe5NOXLkAMxfWFBQULo859ixY3n55ZfT5bkk7Sk/51OGzqcMnU8ZOlt65xceHk7RokWtvi05anBvipuWEBQUlG4Nrr+/f7o9l6Q95ed8ytD5lKHzKUNnsyu/O00n1U5mNgoMDLS7BHGB8nM+Zeh8ytD5lKGzeWp+XoZhGHYX4QnCw8PJmTMnYWFh+iQpIiIi4oFS2q9pC66N1qxZY3cJ4gLl53zK0PmUofMpQ2fz1PzU4Npo69atdpcgLlB+zqcMnU8ZOp8ydDZPzU8NroiIiIhkKGpwRURERCRD0U5mN9mxk1lkZCR+fn7p8lyS9pSf8ylD51OGzqcMnS2989NOZg6wb98+u0sQFyg/51OGzqcMnU8ZOpun5qcG10YrVqywuwRxgfJzPmXofMrQ+ZShs3lqfmpwRURERCRDUYMrIiIiIhmKdjK7yY6dzE6ePEnhwoXT5bkk7Sk/51OGzqcMnU8ZOlt656edzBwgOjra7hLEBcrP+ZSh8ylD51OGzuap+XlUg3vjxg1GjhxJtmzZ8PLy4s0337zjfSIiIhgyZAj33HMPxYoV47777uODDz5wf7FpYPbs2XaXIC5Qfs6nDJ1PGTqfMnQ2T83PYxrcEydOULVqVTZv3sz169dTfL/27dvz3nvvMXToUI4ePUqzZs148cUXeeutt9xYrYiIiIh4Ko9pcP/55x8++OADPvrooxTfZ926dSxfvhyA5s2bA9CiRQsARo4cyfnz59O+UBERERHxaB7T4JYtW5b69evf1X3imluAfPnyAZA/f34AoqKi+O6779KsPndo3bq13SWIC5Sf8ylD51OGzqcMnc1T88tqdwGu+PPPP63z2bJlAyAgIMAaO3jwYLL3jYyMJDIy0rocHh7uhgpvr0SJEun+nJJ2lJ/zKUPnU4bOpwyd6eqx8+QuloMoKtKgAaxZY3dFiTm6wb1y5Yp1PkuWLIn+/ff1/zZq1Kgk5+mOHTsWf39/AAYOHMixY8f4+uuvreu7du2Kr68v06dPt8aaNWtGhQoVGDt2rDVWo0YNHnvsMSZNmmQ1zyVLlqRDhw7MnTuXI0eOEBMTQ968eenXrx8rV67k119/te7/6quv8ssvvyTaCv3cc88RHh7O/PnzrbG2bdtSsGDBRFM7HnnkER5++GFGjRpFTEwMAPfffz+tWrXi448/5uzZswCEhITQo0cPvvzyS/bv3w+YHxQGDx7MunXr2LRpk/WYgwYN4vDhwyxdutQa69atG1mzZmXGjBnWWPPmzSlXrhzjxo2zxmrWrEnDhg2ZOHGilUnp0qVp164dc+bM4dixYwDkyZOHPn36sGLFCnbu3Gnd//XXX2fr1q2sSfDq6dOnDxcvXmThwoXWWLt27cibNy9Tpkyxxho0aMBDDz3EyJEjiY2NBaBSpUq0aNGCadOmce7cOQCKFi1K9+7dWbx4MQcOHADMD0svvfQSP/74I5s3b7Yec/DgwRw8eJCvv/4ab29vAHr06AHAzJkzrdu1bNmS0qVLM378eGusVq1aPProo0yYMIGIiAgAypQpQ9u2bZk1axbHjx8HzG8knnvuOZYvX86uXbsA82/71VdfZcuWLfzwww/WY/bt25fz58+zaNEia6x9+/bkzp2bqVOnWmMNGzakZs2avPPOO9ZY5cqVad68OVOnTuXChQsAhIaG0rVrVxYtWmR9SAwMDOTFF19kzZo1bN261br/kCFD2LdvX6Ij2fTs2ZPo6OhEOx60bt2aEiVK8P7771tjtWvXpn79+owfP55r164B5jc5Tz75JDNnzuTEiROA+a3Ms88+yzfffMPu3bsB8Pb25pVXXuGnn35i7dq11mP279+f06dPs3jxYmusY8eOBAUFMW3aNGusUaNGVK1alffee8/KsEqVKjRt2pTJkydz6dIlAIoXL07nzp1ZsGABhw4dAiAoKIgBAwbw/fffs23bNusxX375Zfbu3cu3335rjfXq1YuoqCjmzJljjT3++OOEhoYm2hm2bt261K1bl3HjxlkfvMuXL88TTzzBjBkzOHnyJAAFCxbkmWeeYenSpezZswcAHx8fhg4dysaNG1m/fr31mAMGDODEiRMsWbLEGuvcuTMBAQF88skn1liTJk2oVKkSo0ePtsaqVatG48aN+eijj7h8+TJgNiGdOnVi3rx5HD58GIDg4GD69+/P6tWr2b59u3X/YcOGsWvXLlatWmWN9e7dm4iICObOnWuNtWnThpCQECZNmmSN1atXjzp16jBmzBhu3LgBQMWKFWndujWffvopp0+fBqBw4cL07NmT8ePHW+/7fn5+DBkyhA0bNrBhwwbrMd39Xg6QK1cuvZen8r18ypQpeHl5WWPp/V6+bNkya0zv5Sl7L/9y4EAeX/QFY+nBi5jvZen1Xp7ihQQMD3PkyBEDMABj+PDht71tq1atrNvGxMQYhmEYhw4dStH9r1+/boSFhVmn48ePG4ARFhaWhj/N7Y0YMSLdnkvSnvJzPmXofMrQ+ZShg0RHG8aIEYaRJYthgGGA0YqvjQYN0q+EsLCwFPVrjt6CW6pUKev8tWvXyJ49u/VJCsxPlsnx8/PDz8/PrfWJiIiIZAhnzkCXLonmIvzII/xMdcrbWFZyPGYns5QYPHgwjz76KH///TcQv3ICYH09Efevr68vjRo1Sv8i70Lt2rXtLkFcoPycTxk6nzJ0PmXoAD/+CJUqxTe3WbIQ9dpbPMb3nKaQraUlxzEN7vnz55kwYQJr16615i098sgjNG3aFICVK1cCWPPfhg4dSt68ee0pNoXudtUI8SzKz/mUofMpQ+dThh4sJgaGD4eGDeHm/HcKFoQffuDGK28Qi7e99d2GxzS4UVFRVKhQgcaNG1tjU6ZMoUKFCixatIg8efLQrFkzChUqZDW1AF988QUvvfQSI0eOpHjx4nzzzTeMHz/eEQd6SDhhXZxH+TmfMnQ+Zeh8ytBDXboEjz0Gb79tzrYFaNQIfvsNHPChxGPm4Pr6+rJ3797b3ibh3n1xsmfPzvjx4x35Aonb21CcSfk5nzJ0PmXofMrQQ+XIAVFR5vksWWDECBg2zDzvAM6oUkRERETST9asMH8+PPAArFsHr77qmOYWPGgLbmZUtmxZu0sQFyg/51OGzqcMnU8ZeogTJ+DyZahQIX6saFHYuRMSrFPsFF6GETexInMLDw8nZ86chIWFERQUZHc5IiIiIunj22+hWzcIDoYdOyAFfdDVqxAYaJ5PzyOZpbRfc8625gwo4ZFSxHmUn/MpQ+dThs6nDG104wa8/DI0bw4XLsD//gevvWZ3VWlCUxRsFHf4OnEm5ed8ytD5lKHzKUObHDsGHTpAgkP20qoVOGAVqpTQFlwRERGRzGTpUqhcOb659fGBiRPh668hd247K0sz2oJro/z589tdgrhA+TmfMnQ+Zeh8yjAdRUXB0KFmMxuneHFYuBCqVbOtLHfQTmY3aSczERERybBiY6FuXfjpp/ixJ5+ETz81dy67S9rJTJL1zTff2F2CuED5OZ8ydD5l6HzKMJ1kyQJPPWWe9/WFyZPhiy9S1dw6gRpcG+3evdvuEsQFys/5lKHzKUPnU4bpaMAAGDjQnHvbt68j17dNKTW4IiIiIhnNoUMwbVriMS8vc/5t5cq2lJSetJOZjby9ve0uQVyg/JxPGTqfMnQ+ZegGCxbAs8/ClStQogQ89pjdFaU77WR2k3YyExEREUe7dg1efBE+/jh+rG5dWLcuzacjaCczSdZPCfdkFMdRfs6nDJ1PGTqfMkwjBw5AjRqJm9uuXWHFigw91zY5anBttHbtWrtLEBcoP+dThs6nDJ1PGaaBOXOgalXYs8e8nC0bzJgBs2bFb2bNZDQHV0RERMSJrl6F/v1h5sz4sXLlzOW/ypWzrSxPoC24IiIiIk7Uu3fi5rZnT9i+PdM3t6CdzCx27GR2+fJlgjPoAsuZgfJzPmXofMrQ+ZShC/78Ex58EAwD/vtf6NIl3Z5aO5lJsk6fPm13CeIC5ed8ytD5lKHzKUMXlCoFCxfCr7+ma3PrBGpwbbR48WK7SxAXKD/nU4bOpwydTxmm0O7d8OSTEBGReLxZM7jvPntq8mDayUxERETEUxmGufTXwIEQGQm5c8Mnn9hdlcfTFlwRERERTxQeDh07wvPPm80tmNMRrlyxty4HUINro44dO9pdgrhA+TmfMnQ+Zeh8yjAZO3ZAlSrmHNs4/fvD5s2Zdm3bu6EG10Y6JLCzKT/nU4bOpwydTxn+i2HARx/BQw/BoUPmWM6csHgxfPgh+PvbW59DqMG10bRp0+wuQVyg/JxPGTqfMnQ+ZZjA5cvQti288AJERZlj1arBzp3mDmaSYmpwRURERDzBzJmwZEn85UGDYNMmKF7ctpKcSg2uiIiIiCd44QXzqAm5csHSpTBhAvj62l2VI2mZMBs1atTI7hLEBcrP+ZSh8ylD58vUGd64AT4+8Ze9vWHuXLh+HUJD7asrA9AWXBtVrVrV7hLEBcrP+ZSh8ylD58u0GW7ZAmXLmqsiJFSggJrbNKAG10YjR460uwRxgfJzPmXofMrQ+TJdhrGxMHYs1KkD//sfdOgAFy7YXVWGoykKIiIiIunh/Hno1g1WrowfCw2NXzFB0oy24IqIiIi428aNUKlSfHPr5QWvvQZr10KhQraWlhFpC66NqlSpYncJ4gLl53zK0PmUofNl+AxjY2HUKHjjDfM8QL585s5kjz1mb20ZmJdhGIbdRXiC8PBwcubMSVhYmI6qIiIiIq47cwa6doXvv48fe+QRs7l1+Fbbq1fjjxjcoAGsWZM+z5vSfk1TFGw0efJku0sQFyg/51OGzqcMnS9DZ3j2rDk1AcwpCW++aTa7Dm9unUANro0uXbpkdwniAuXnfMrQ+ZSh82XoDCtWhA8+gIIF4YcfYPhwc61bcTs1uCIiIiJp4fTpW1dE6N0b9u83pyZIulGDa6PiOra0oyk/51OGzqcMnS/DZPj99/DAAzBsWOJxLy8IDralpMxMO5ndpJ3MRERE5K5FR5tza0eOhLiWatkyaNHC1rLcTTuZSbIWLFhgdwniAuXnfMrQ+ZSh8zk6wxMn4NFH4d1345vbpk2hRg176xI1uHY6dOiQ3SWIC5Sf8ylD51OGzufYDL/91jxwQ9wqCd7e5iF4ly8317kVW+lADyIiIiIpdeOGeQSycePix4oWhYUL4aGH7KtLElGDayPN9XU25ed8ytD5lKHzOSrDc+egdWvYsiV+rFUr+OwzyJ3bvrrkFtrJ7CbtZCYiIiK3deMG1K0LW7eCjw+MGQMvvmiulJDJaCczSdb3CQ/dJ46j/JxPGTqfMnQ+R2Xo4wMLFkCVKrBpEwwalCmbWydQg2ujbdu22V2CuED5OZ8ydD5l6HweneGRI7B3b+Kx0FDYvh2qV7enJkkRNbgiIiIi/7ZkCVSuDG3awD//JL5OW209nhpcERERkTiRkfDCC/DkkxAWBn/+aR7IQRxFO5ndZMdOZlFRUfj6+qbLc0naU37OpwydTxk6n0dleOgQtG8PO3bEj7VvDx9/DNoBPRHtZCbJ2vvveT3iKMrP+ZSh8ylD5/OYDBcuhAcfjG9u/fzgv/+F+fPV3DqQGlwbffvtt3aXIC5Qfs6nDJ1PGTqf7RleuwbPPw8dOsTPtS1dGrZtg+ee03xbh9KBHkRERCRzio4217X95Zf4sS5dYOrU+O/fxZG0BVdEREQyp6xZzS23ANmywfTpMHu2mtsMQDuZ3WTHTmanTp2iUKFC6fJckvaUn/MpQ+dThs5ne4aGAS+9BM88A+XL21eHw2gnM0lWVFSU3SWIC5Sf8ylD51OGzpeuGe7bB9OmJR7z8oL331dzm8GowbXRnDlz7C5BXKD8nE8ZOp8ydL50y3DmTKhaFfr0gR9+SJ/nFNuowRUREZGM68oV6N4dnn7aXDHBMGDMGLurEjdTgysiIiIZ0+7dUK2aueNYnGefhaVL7atJ0oUaXBs9/vjjdpcgLlB+zqcMnU8ZOp9bMjQM8+hjNWrAgQPmWGCgedCGadPMFRMkQ9M6uDYKDQ21uwRxgfJzPmXofMrQ+dI8w/Bw8wANCxbEj1WqBIsWQalSaftc4rG0BddGH3zwgd0liAuUn/MpQ+dThs6X5hk+/XTi5rZfP9iyRc1tJqMGV0RERDKOkSMhe3YICoIvvoCPPgJ/f7urknSmKQoiIiKScdx3nzkdoUwZKFHC7mrEJtqCa6O6devaXYK4QPk5nzJ0PmXofC5luH07PPGEufxXQs2aqbnN5HSo3pvsOFSviIiIpIJhwAcfwMsvw40b8PzzMHWq3VVlKjpUryRr3LhxdpcgLlB+zqcMnU8ZOt9dZ3jxornVdtAgs7kF2LXr1q24kqmpwbVRZGSk3SWIC5Sf8ylD51OGzndXGW7dCpUrJz5Qw5AhsGGD1raVRNTgioiIiGeLjYVx46BOHfjrL3MsTx5YvhzGjgUfH3vrE4+jVRRsVL58ebtLEBcoP+dThs6nDJ3vjhmePw89esCKFfFjtWubRyULCXFrbeJc2snsJu1kJiIi4oHGjTN3Jovzyivw9tuQVdvo7KSdzCRZM2bMsLsEcYHycz5l6HzK0PnumOFLL0H9+pAvH6xaZR7IQc2t3IH+Qmx08uRJu0sQFyg/51OGzqcMne+WDKOiwNc3/rK3tzkdITYWChdO3+LEsTxqC+6SJUuoUqUKRYsWpUiRInTv3p0zZ87c9j4rV67k0UcfpUiRIpQuXZrixYvTt29fzp49m05Vi4iISJpYtw5KlYJt2xKPFyyo5lbuisc0uIsXL6Zt27b4+Phw9OhRVqxYwezZs3nkkUe4fv16kvfZtWsXLVu2ZO3atbzxxhscPHiQ0qVLM3XqVJ588sl0/gnuXsGCBe0uQVyg/JxPGTqfMnS+ggULQkwMvPWWOZnzr7+gfXu4dMnu0sTBPKLBNQyDIUOGYBgGTZo0wdvbm0qVKhESEsL+/fv55JNPkrzfr7/+SkxMDAD33nsvACVLlgRg06ZNXL58OV3qT61nnnnG7hLEBcrP+ZSh8ylD53umRQto1AjefNOchgDmVtzoaFvrEmfziAb3999/5+jRowDky5fPGs+fPz8Ay5YtS/J+jz32GHny5LEewzAM9u3bB0BgYCABAQFurNp1SxMuVC2Oo/ycTxk6nzJ0uDVruF6mDPz4o3k5SxZ45x1zZ7IE/YDI3fKIBvfPP/+0zmdLcCSSuAb14MGDSd7vnnvuYcOGDZQrV45BgwZRsGBB1q1bR65cuZg+fTq+CSep/0tkZCTh4eGJTultz5496f6cknaUn/MpQ+dThg4VHQ2vvw6NGuEfFmaOFS4Ma9fCa6+ZO5aJuMAjVlG4cuWKdT5Lliy3nE94fUJ//fUXjz32GCdPnuTLL7/kiSeeoEePHpw+fdqaspCcUaNG8dZbb90yPnbsWPz9/QEYOHAgx44d4+uvv7au79q1K76+vkyfPt0aa9asGRUqVGDs2LHWWI0aNXjssceYNGmS1TyXLFmSDh06MHfuXI4cOUJMTAyTJ0+mX79+rFy5kl9//dW6/6uvvsovv/zCd999Z40999xzhIeHM3/+fGusbdu2FCxYkI8++sgae+SRR3j44YcZNWqUNYXj/vvvp1WrVnz88cfWDnghISH06NGDL7/8kv379wPmB4zBgwezbt06Nm3aZD3moEGDOHz4cKKtJd26dSNr1qyJlnhp3rw55cqVS3Rs8Zo1a9KwYUMmTpxoZVm6dGnatWvHnDlzOHbsGAB58uShT58+rFixgp07d1r3f/3119m6dStrEiyy16dPHy5evMjChQutsXbt2pE3b16mTJlijTVo0ICHHnqIkSNHEnvzq69KlSrRokULpk2bxrlz5wAoWrQo3bt3Z/HixRw4cAAwP2C99NJL/Pjjj2zevNl6zMGDB3Pw4EFiYmJ45513AOjRowcAM2fOtG7XsmVLSpcuzfjx462xWrVq8eijjzJhwgQiIiIAKFOmDG3btmXWrFkcP34cML/JeO6551i+fDm7du0CzNfDq6++ypYtW/jhhx+sx+zbty/nz59n0aJF1lj79u3JnTs3U6dOtcYaNmxIzZo1rZoBKleuTPPmzZk6dSoXLlwAIDQ0lK5du7Jo0SLrw2VgYCAvvvgia9asYevWrdb9hwwZwr59+1iRYAH2nj17Eh0dzezZs62x1q1bU6JECd5//31rrHbt2tSvX5/x48dz7eYx5MuWLcuTTz7JzJkzOXHiBGB+k/Pss8/yzTffsHv3bgC8vb155ZVX+Omnn1i7dq31mP379+f06dMsXrzYGuvYsSNBQUFMmzbNGmvUqBFVq1ZNlGGVKlVo2rQpkydP5tLNuX/Fixenc+fOLFiwgEOHDgEQFBTEgAED+P7779mWYEeYl19+mb179/Ltt99aY7169SIqKoo5c+ZYY48//jihoaF88MEH1ljdunWpW7cu48aNsw5bWr58eZ544glmzJhh7WVesGBBnnnmGZYuXWo1dj4+PgwdOpSNGzeyfv166zEHDBjAiRMnWLJkiTXWuXNnAgICEk37atKkCZUqVWL06NHWWLVq1WjcuDEfffSRNdWrRIkSdOrUiXnz5nH48GEAgoOD6d+/P6tXr2b79u3W/YcNG8auXbtYtWqVNda7d28iIiKYO3euNdamTRtCQkKYNGmSNVavXj3q1KnDmDFjuHHjBgAVK1akdevWfPrpp5w+fRqAwoUL07NnT2JjY60M/fz8GDJkCBs2bGDDhg3WY7r7vRwgV65cei9P4Xv5tiVLePyLLwi9+c0twKFSpfjmySeJ2LCBdgULput7ecJvifVenvL38qgoH2CodV16vZcnfO+8LcMDLFmyxAAMwJg+fbo1Xrt2bQMwQkNDk7zfiy++aACGl5eXER0dbRiGYXz55ZcGYAQGBhqnTp1K9jmvX79uhIWFWafjx48bgBEWFpamP9vtjB49Ot2eS9Ke8nM+Zeh8ytCBdu40DD8/wwDD8PY21jZpYhgxMXZXJXfpyhUzQjCMBg3S73nDwsJS1K95xBSFUqVKWefjun/A+lRUunTpJO8X90k1e/bseN/8OiPuqBZXrlxJ9In53/z8/AgKCkp0Sm9Dhw69843EYyk/51OGzqcMHahSJXj/fShaFDZsoP7KlebcW5E05BF/UeXLlyc0NBTA+poh4fkWLVoQExND165dadmypfXVSNwOaREREdZXFv/88491f8PDj0K8ceNGu0sQFyg/51OGzqcMHeDvv+HmdBPL88/D3r1Qq5YyFLfwiAbXy8uLMWPGAPDdd98RGxvLnj17OH78OPfddx+9e/dm586dfP755yxfvtya1/X888+TNWtWYmNjrfmZcdflzZuX5s2b2/MDpVDC+XLiPMrP+ZSh8ylDD7dsGVSsaO44lpCXF9z85lQZijt4RIML5kTqL774guvXrxMaGkrjxo3p0qULa9euJVu2bJQrV46qVatSsmRJ6tSpA8DDDz/Mxo0bad68Oc8++yylSpXiu+++4+mnn2br1q3kzZvX5p9KREQkE4qKgsGDoVUr84AN48bB6tV2VyWZiEesohCnbdu2tG3bNsnrAgICEu2lG6dmzZosX77c3aWJiIhIShw5Ah06wM8/x4898QTUqGFfTZLpeBmePlE1nYSHh5MzZ07CwsLSbYez8PBwW3Zuk7Sh/JxPGTqfMvQwS5ZAz54Qt7atry+MHw/9+pnTEpKgDJ3p6lUIDDTPN2gACVbydKuU9mseM0UhM4pbG06cSfk5nzJ0PmXoISIj4YUX4Mkn45vbe++FzZuhf/9km1tQhuIeanBtlHABdnEe5ed8ytD5lKEHOHUKatWCBAepoF072LEDqlS5492VobiDGlwRERFJvTx54g+t6+cH//0vLFhgrZIgYgc1uCIiIpJ6vr6wcCFUrw7btsFzz912SoJIevCoVRQym86dO9tdgrhA+TmfMnQ+ZWiDgwfNAzeULx8/Vrw4bN2aqsZWGYo7aAuujQICAuwuQVyg/JxPGTqfMkxnc+fCgw+aO5PdPKqoJZVbbZWhuIMaXBt98skndpcgLlB+zqcMnU8ZppOICHjmGejSxVwf6o8/4N130+ShlaG4g6YoiIiISPL27TNXRfj99/ixHj3g9ddtK0nkTrQFV0RERJI2cyZUqxbf3AYEwKxZ8NlnkD27raWJ3I624NqoSZMmdpcgLlB+zqcMnU8ZusmVK+bRx2bPjh+rUAG++ALKlEnTp1KG4g5qcG1UqVIlu0sQFyg/51OGzqcM3SAqCh56CPbujR/r3Rs++ACyZUvzp1OG4g6aomCj0aNH212CuED5OZ8ydD5l6Aa+vubOZACBgTBvHnz8sVuaW1CG4h7agisiIiKJDRkCZ8/C889DqVJ2VyNy17QFV0REJDPbtQumTUs8liULjB+v5lYcS1twbVStWjW7SxAXKD/nU4bOpwxdYBgwdSq89JJ5ZLIyZaBevXQvQxmKO3gZhmHYXYQnCA8PJ2fOnISFhREUFGR3OSIiIu4TFmYeuGHx4vixli3hm2/sq0kc5epVc4o2QIMGsGZN+jxvSvs1TVGw0UcffWR3CeIC5ed8ytD5lGEq/PKLebjdhM3twIHmEmA2UIbiDmpwbXT58mW7SxAXKD/nU4bOpwzvgmGYS33VqgWHD5tjwcHw1VcwcSL4+dlSljIUd9AcXBERkYzu0iXo2RO+/jp+rEYNWLgQQkNtK0vEXbQF10YlSpSwuwRxgfJzPmXofMowhbp2Tdzc/uc/sHGjRzS3ylDcQTuZ3aSdzEREJMPavx+qVgV/f/Pwu82b212ROJx2MpNkzZs3z+4SxAXKz/mUofMpwxQqW9bciWzXLo9rbpWhuIMaXBsdjpvkL46k/JxPGTqfMkzCTz9Bq1Zw/Xri8WbNoGhRe2q6DWUo7qAGV0REJCOIjYVRo8yDNSxbZs6zFcmktIqCjYKDg+0uQVyg/JxPGTqfMrzp7Fno1g1Wr44f27sXIiNtW/4rpZShuIN2MrtJO5mJiIgjrV8PHTvCqVPmZS8v+L//M09ZtR1L3EM7mUmyVif8pC2Oo/ycTxk6X6bOMCYG3n4bHn00vrktUAC+/x7eessxzW2mzlDcRg2ujbZv3253CeIC5ed8ytD5Mm2Gp09D48YwfLg59xbMzWi7dpn/OkimzVDcSg2uiIiI00yfDj/8YJ7PkgVGjDDn3xYsaG9dIh7CGd9fiIiISLyhQ82G9n//g3nzzJUTRMSincxusmMns+joaLI6ZI6U3Er5OZ8ydL5Mk2FSqyGcOmXOs82Xz56a0kimyTCD0U5mkqxdu3bZXYK4QPk5nzJ0vkyR4apVcO+98O+5qoUKOb65hUySoaQ7Nbg2WrVqld0liAuUn/MpQ+fL0BlGR8Mrr0DTpvD339C+PVy+bHdVaS5DZyi20XcCIiIinub4cXNt259+ih8rXz5+xQQRuS1twRUREfEky5dDpUrxzW3WrDB+PHzzDeTObWtpIk7h8k5mhmFw4cIF8ubNm1Y12cKOnczOnDlDgQIF0uW5JO0pP+dThs6XoTKMijKnJEyYED8WGgoLF0KNGvbV5WYZKsNMJMPuZLZ//36eeOIJcuTIQWhoKABPP/00X375ZWofMtOJiIiwuwRxgfJzPmXofBkmw6NHoW7dxM3t44/Dzp0ZurmFDJSheJRUNbj79u2jZs2afPPNN0RERBC3Ebhq1ar06dOHb7/9Nk2LzKjmzp1rdwniAuXnfMrQ+TJMhhcumM0sgI8PfPABLFkCuXLZW1c6yDAZikdJVYP7xhtv8M8///Dv2Q39+vXjq6++YkLCT6AiIiJye1WqwHvvQYkSsHkzDBgAXl52VyXiWKlqcNevX8+IESM4ceIEUVFR+Pv7W9c9/PDDHDx4MM0KFBERyXCOHoUbNxKP9e8Pv/0GVavaUpJIRpKqBjciIoKhQ4dSuHDhW44+cuTIEc6cOZMmxWV0bdq0sbsEcYHycz5l6HyOzHDRIrj/fnjjjcTjXl7xe+1kIo7MUDxeqtbBzZcvHx06dKBDhw4UKFCAmJgY1q9fz8GDB5k4cSL58+dP6zozpJCQELtLEBcoP+dThs7nqAyvX4dBg+C//zUvjx4NDRuau6BnYo7KUBwjVVtwmzRpwpIlS2jfvj3169fnypUrPProozz//PMcOHCAxx57LK3rzJAmTZpkdwniAuXnfMrQ+RyT4cGDULNmfHML0KkTVK9uX00ewjEZiqOkqsEdPnw4BQsWxDAMa0ezuPO5c+dm+PDhaVqkiIiIY82bZ+5E9ttv5mV/f/jkE/j8c8iRw97aRDKoVDW4hQoVYvv27fTo0YOCBQvi7e1NgQIF6Ny5M9u2bbPWxRUREcm0IiKgd2/o3BmuXDHHypSBn3+GZ57RKgkibpSqObgARYoUYcaMGYnGzp07R3R0tMtFZRb16tWzuwRxgfJzPmXofB6b4YkT0LQp7N0bP9a9O0yeDNmz21eXB/LYDMXRUrUFt3///kmOr169mhIlSmg+TQrVqVPH7hLEBcrP+ZSh83lshvnygZ+feT4gAGbONE9qbm/hsRmKo6WqwZ03b16S4126dOGPP/7gww8/dKmozGLMmDF2lyAuUH7Opwydz2Mz9PODhQvh4Ydh+3Zz660kyWMzFEdL8RSFv/76i6NHjwIQHR3Nxo0bbzmSWWxsLMeOHePEiRNpWmRGdePfi3yLoyg/51OGzucxGe7ZA97eUK5c/Ni998LGjZprewcek6FkKClucD/77DPefvtt63L9+vWTvW3OnDldKkpERMQRDAOmT4cXXjAPs/vzz4mnIai5FbHFXU1RSGpZsKROdevWdUuxGU3FihXtLkFcoPycTxk6n60Z/vMPdOlirpRw/Trs2wfjxtlXj0PpdSju4GX8e55BMpYuXcrXX38NwPz58+nYseMtt/Hz86N06dL07t2bHA5b2y88PJycOXMSFhZGUFCQ3eWIiIgn27UL2rWDP/+MH+vTByZMMNe5Fcngrl6NP7J0gwawZk36PG9K+7UUT1Fo3bo1rVu3BmDHjh189tlnrleZyX366ac888wzdpchqaT8nE8ZOl+6Z2gY5tHIBg2CyEhzLCjIPHBDu3bpV0cGotehuEOqVlH4Le5oLMlYsGBBqorJbE6fPm13CeIC5ed8ytD50jXDsDBo3x769o1vbqtUgR071Ny6QK9DcYdUH+gBICYmhrNnzybaA9IwDJ5//nk6dOjgcnEiIiIeITISqleHgwfjxwYMgLFj49e7FRGPkaoG1zAMhg0bxpQpU4iIiEjrmjKNwoUL212CuED5OZ8ydL50y9DPz1zL9rXXIDgYZsyAJ55In+fO4PQ6FHdI8U5mCX300UcMGDAg+Qf18iImJsalwtKbdjITEZHbio2FoUOhXz8oVszuakRs5ek7maVqDu7s2bPx8/OjRo0aeHt7U69ePerVq8eDDz6It7e3DruXQl999ZXdJYgLlJ/zKUPnc1uGW7fCtGmJx7JkMZcBU3ObpvQ6FHdIVYP7xx9/sGrVKrZs2UJgYCBr165l7dq1/PLLL6xdu5ZWrVqldZ0Z0u+//253CeIC5ed8ytD50jzD2Fh47z2oU8fcUrtpU9o+vtxCr0Nxh1Q1uAD16tUDzMP2/plgHcAqVaowffp01ysTERFJTxcuQKtWMGQIREdDTAx8+KHdVYlIKqRqJ7OcOXNy/fp1/P39yZ8/P4888ghPPfUUfn5+rF69msOHD6d1nRmSn/a8dTTl53zK0PnSLMOffoIOHeDEifixYcMgwSHqxT30OhR3SNVOZo899hghISFMmTKFvn37MmvWLLwSHG+7VKlSHDhwIE0LdTftZCYikgnFxppLfb3+urnFFiBvXpgzB5o0sbc2EQ+WIXcy69GjB+fPn+fChQsMHz6cQoUKYRgGhmHg6+vLO++8k+rCM5MNGzbYXYK4QPk5nzJ0PpcyPHcOmjeHV16Jb27r1jUPw6vmNt3odSjukKoGt3PnzixbtoyQkBCKFSvGH3/8wdKlS1myZAmHDh2ibdu2aV1nhqQXtbMpP+dThs7nUoadO8OqVeZ5Ly9zK+4PP0CRImlTnKSIXofiDi4dySxOYGAgLVu2tC43btyY1atXp8VDi4iIuMf770O1ahAUBJ9/Dg0b2l2RiKSRNGlwE9q8eTMbN25M64cVERFJW+XLw5dfQuXKULCg3dWISBq6qykKH3/8MaVLl6ZQoUL07NmTK1euWNft2bOHVq1aUadOHSIjI9O80Ixo4MCBdpcgLlB+zqcMnS/FGf7wA7RoAf/+/6lpUzW3NtPrUNwhxQ3u999/z/PPP8///vc/zpw5w6xZsxgyZAiRkZH06tWLypUrs2LFClKxKEOmdezYMbtLEBcoP+dThs53xwyjo+GNN+Cxx2DFCvNQu+JR9DoUd0hxg/v+++8DWA2sYRjMnTuXIUOG8NlnnxEbG4thGBQqVIj33nvPPdVmMF9//bXdJYgLlJ/zKUPnu22GJ0+a6xeNGAFxG1/++ANu3EiX2iRl9DoUd0hxg7t3714qVKjAwoULmTdvHmXKlOHKlSt88sknAISGhjJlyhSOHDnCoEGD3FawiIjIHa1eDQ88AHF76Ht7w6hR5lZcHx97axMRt0vxTmanTp1i9erVlC1bFoDSpUtTtWpVcuXKxbvvvku3bt3w9vZ2W6EiIiJ3FB0N//d/MHp0/FhICMyfD7Vr21eXiKSrFG/B9fX1tZpbgMqVK+Pt7c369et5+umnEzW3lSpVSlUxS5YsoUqVKhQtWpQiRYrQvXt3zpw5c8f7ffXVV9SpU4eQkBAqVKhA6dKl6d27d6pqSE9du3a1uwRxgfJzPmXofIkyPH4c6tdP3Nw2b24euEHNrcfS61DcIcVbcLNkycLGjRsT7UTm7+/P6dOnOXXqlDVmGAaHDx++60IWL15Mu3btqF69OkePHmXPnj1UrlyZ7du3s2PHDvz9/ZO839SpU+nbty8vvPAC69evJ0uWLGzevJkePXrcdQ3pzdfX1+4SxAXKz/mUofMlynD6dPjpJ/N81qxmoztoEGRJ1TGNJJ3odShuYaSQl5eXkSVLlhSf7kZsbKxRrFgxAzCGDx9ujYeEhBiAMWnSpCTvd+nSJSMgIMAAjIsXL97Vc/5bWFiYARhhYWEuPc7dGDFiRLo9l6Q95ed8ytD5EmUYFWUYtWoZRmioYWzZYltNcnf0OnSmK1cMw9x70zAaNEi/501pv3bXH2sNw7BO/76ccPxu/P777xw9ehSAfPnyWeP58+cHYNmyZUneb/ny5URERBAQEMB7771H9erVuffee3n22We5ePHiXdchIiIOcu1a4ss+PvDFF7BzJ9SsaU9NIuIRUjxFwcvLi3vuuSdFtz1+/PhdFfHnn39a57Nly2adDwgIAODgwYNJ3m/Pnj0AREREEBUVxdatW3nqqaf45JNPOHz4MGvWrEn2OSMjIxMdkCI8PPyuahYRERt99RX06UPBNm0SjxcubE89IuJRUtzgBgUFceTIkRTdNleuXHdVRMIjomVJMFcq7nzC6xNKuJW2bdu2ZMmShbZt27JkyRJ++OEHfvnlF6pWrZrkfUeNGsVbb711y/jYsWOt+b4DBw7k2LFjidbo69q1K76+vkyfPt0aa9asGRUqVGDs2LHWWI0aNXjssceYNGmS1TyXLFmSDh06MHfuXI4cOYJhGEyePJl+/fqxcuVKfv31V+v+r776Kr/88gvfffedNfbcc88RHh7O/PnzE/3cBQsW5KOPPrLGHnnkER5++GFGjRpFTEwMAPfffz+tWrXi448/5uzZswCEhITQo0cPvvzyS/bv3w+YHzAGDx7MunXr2LRpk/WYgwYN4vDhwyxdutQa69atG1mzZmXGjBnWWPPmzSlXrhzjxo2zxmrWrEnDhg2ZOHGilWXp0qVp164dc+bMsRb5zpMnD3369GHFihXs3LnTuv/rr7/O1q1bE31g6dOnDxcvXmThwoXWWLt27cibNy9Tpkyxxho0aMBDDz3EyJEjiY2NBcydIFu0aMG0adM4d+4cAEWLFqV79+4sXryYAwcOAOYHrJdeeokff/yRzZs3W485ePBgDh48iGEYvPPOOwDWnO+ZM2dat2vZsiWlS5dm/Pjx1litWrV49NFHmTBhAhEREQCUKVOGtm3bMmvWLOvDYb58+XjuuedYvnw5u3btAszXw6uvvsqWLVv44YcfrMfs27cv58+fZ9GiRdZY+/btyZ07N1OnTrXGGjZsSM2aNa2awdxZtHnz5kydOpULFy4A5pJ/Xbt2ZdGiRdaHy8DAQF588UXWrFnD1q1brfsPGTKEffv2sWLFCmusZ8+eREdHM3v2bGusdevWlChRwlpPG6B27drUr1+f8ePHc+3mlriyZcvy5JNPMnPmTE6cOAGY3+Q8++yzfPPNN+zevRsAb29vXnnlFX766SfWrl1rPWb//v05ffo0ixcvtsY6duxIUFAQ06ZNs8YaNWpE1apVE2VYpUoVmjZtyuTJk7l06RIAxYsXp3PnzixYsIBDhw4B5nvhgAED+P7779m2bZv1mC+//DJ79+7l22+/tcZ69epFVFQUc+bMscYef/xxQkND+eCDD6yxunXrUrduXcaNG2d98C5fvjxPPPEEM2bM4OTJkwAULFiQZ555hqVLl1of8H18fBg6dCgbN25k/fr11mMOGDCAEydOsGTJEmusc+fOBAQEWMs7AjRp0oRKlSoxOsGOWdWqVaNx48Z89NFHXL58GYASJUrQqVMn5s2bZ+1nERwcTP/+/Vm9ejXbt2+37j9s2DB27drFqlWrrLHevXsTERHB3LlzrbE2bdoQEhLCpEmTrLF69epRp04dxowZw42b69VWrFiR1q1b8+mnn3LuxAkarF5N9Zt/h+0WL2Zs7txE+fvj5+fHkCFD2LBhAxvilgfD/e/lYP6/p/fy1L2XV6lSJdH7Unq/lyf8lljv5Sl/L4+K8gHiD5ySXu/lCd87byulcx7mzZuX4vkRd3NbwzCMJUuWGIABGNOnT7fGa9eubQBGaGhokvd7/vnnrfsdOnTIMAzD+P77762xWbNmJfuc169fN8LCwqzT8ePH030ObmRkZLo9l6Q95ed8ytBhDh0yjCpV4if+gRHTpo1hXL5sd2XiAr0OnSnDzMHt2LFjSm96V7cFKFWqlHX+WoI5VXGfikqXLp3k/UJDQ63zXl5eif6FxFuD/83Pz4+goKBEp/SWcCuBOI/ycz5l6CBffAEPPghxW0f9/GDKFEZWqgQ5c9pamrhGr0NxB49YO6V8+fJWsxr3NUPC8y1atCAmJoauXbvSsmVL66uRRo0aWbeNm64Qt2k+7nFFRMTBrl+Hvn2hXTuI21eiVCnYuhX69IEEGzVEROJ4RIPr5eXFmDFjAPjuu++IjY1lz549HD9+nPvuu4/evXuzc+dOPv/8c5YvX27N63rwwQdp164dgDXvbfny5YA5f6hy5co2/DQiIpImDh40V0NIMP+Qjh3NrbipPKCQiGQOKd7JzN3at2+Pt7c3I0eOJDQ0lJiYGLp06cLYsWPJli0b5cqVo2rVqly+fJk6depY95szZw6lS5dm1qxZTJ8+ncjISJ577jmrYfZkNWrUsLsEcYHycz5l6OHCwmDfPvO8vz98+CH06pVoq60ydD5lKO7gZRipWLg2AwoPDydnzpyEhYXZMh9XRESSMHEiTJsGixZBxYp2VyMiN129CoGB5vkGDeA2K7OmqZT2ax4xRSGzSrg0jjiP8nM+ZehhDh2C6OjEYwMHmlMSkmlulaHzKUNxB5ca3OjoaLZu3Wqtsxi36oGkjA4u4WzKz/mUoQeZNQseeAD+vT65lxfcPOhPUpSh8ylDcYdUN7hz5swhJCSEhx9+mK5duwLw2GOPMXDgwDQrTkREMrirV6FHD/MUEQHvvgsJDlghIpIaqWpwly9fTo8ePTh37hyGYRA3jXf8+PFs3bpVXzekUMmSJe0uQVyg/JxPGdpszx6oWtXcehunZ0+oVi3FD6EMnU8ZijukaiezunXrsmnTJkqWLEmBAgXYuXOntTbtsWPHaNOmTaJDFTqBdjITEUknhgHTp8MLL5jr3AJkz27uTNa5s721iUiKZMidzHbt2sWGDRs4ePAgGzduxNfX17ouNDSUv//+OzUPm+kkPCa7OI/ycz5laIN//oEuXaB37/jm9oEHYMeOVDW3ytD5lKG4Q6rWwTUMgwoVKiR53bp16wgLC3OpqMziyJEjdpcgLlB+zqcM09mRI9C4Mfz5Z/zY88/DhAmQLVsqH1IZOp0yFHdIVYNbrFgxKleuTPPmzSlQoACRkZG8+eab/PnnnyxdupTixYundZ0iIuJ0hQqZUxEAcuSATz81D8ErIpLGUtXgtm/fnjfeeIOpCQ6fOGLECOv8k08+6XplmUCuXLnsLkFcoPycTxmmM39/84ANzz5rNrf33uvyQypD51OG4g6p2sksKiqKJk2asG7duluuq1GjBj/++CPZUvl1k120k5mISBr79VdzDduyZe2uRETSWIbcyczX15fvv/+emTNn0qFDBxo2bEj79u355JNPWL9+veOaW7usXLnS7hLEBcrP+ZShmxgGfPgh1KplTkFw40GAlKHzKUNxh1Q1uLlz58bb25tu3boxb948vvvuO+bPn0+vXr0Sraggt+e0pdQkMeXnfMrQDS5dgiefhAEDICoK9u6FiRPd9nTK0PmUobhDqhrcy5cvU61aNaZMmaIVE0RExLRtG1SuDF99FT82eDD85z/21SQimVKqGtygoCDeeOMNfvjhB0JDQ+nQoQOrVq0iFdN5RUTE6QwDxo+H2rXh2DFzLHdu+OYbeO890Dd7IpLOUrWT2a+//kqVKlUAuHDhAnPnzmXWrFmcOXOG7t270717d0qXLp3mxbqTHTuZxcbGkiVLqj5jiAdQfs6nDNPAhQvQowcsXx4/VqsWzJ8P99zj9qdXhs6nDJ0pQ+5kFtfcAuTJk4cXXniBt99+mzx58jB69GjKlSuXmofNdH755Re7SxAXKD/nU4YuioiAqlUTN7dDh8K6denS3IIyzAiUobhDqhrc/v37A/Dnn3/yyiuvULRoUVq1asXevXsxDENTFVLou+++s7sEcYHycz5l6KKAAOjZ0zyfNy+sXAmjR4OPT7qVoAydTxmKO6TqQA+ffvopu3btYsuWLQCJGtoiRYrQvXv3tKlOREQ826uvwj//wMCBUKSI3dWIiACpbHCjoqLYsmWL1dj6+vrSsmVLevbsSePGjTWXRkQkI9qwAfbvh+eeix/z9oaxY+2rSUQkCanaySyuga1QoQI9e/aka9eu5MmTJ82LS0927GR27tw58uXLly7PJWlP+TmfMkyhmBgYNQqGDwcvL7PRrVXL7qoAZZgRKENn8vSdzFK1BTdbtmxs2LAh0c5mcvfCw8P1onYw5ed8yjAFTp+GLl3ghx/ix6ZN85gGVxk6nzIUd0jVXIJPP/30ts3tWH1dlSLz58+3uwRxgfJzPmV4Bz/8AJUqxTe3WbLAW2/BjBm2lpWQMnQ+ZSjukKItuNHR0ezZs4eKFSuSNWtWihQpwoYNG5K9/TvvvMPLL7+cZkWKiEg6iomBt9+GESPMgzgAFCoE8+ZB/fq2liYikhIpanBbtWrF6tWradKkCStWrKB+/fp4eXm5uzYREUlvJ09Cp06wfn38WKNGMGcO5M9vX10iInchRVMUfvrpJwzD4KeffrLG4ta7TeokKdO2bVu7SxAXKD/nU4ZJ6Ngxvrn19oaRI831bT20uVWGzqcMxR1StAV37NixTJ48mX79+gHg4+NDp06dkrytYRgsWLAg7SrMwAoWLGh3CeIC5ed8yjAJH34INWpAnjywYAHUrm13RbelDJ1PGYo7pKjBfe6553guwbqHZcqU4bPPPkv29jt37nS9skzgo48+4vXXX7e7DEkl5ed8yjAJ998PX31lHoI3b167q7kjZeh8ylDcIVWrKPz2229Jjh85coStW7cme72IiHiQFSugWTOIiko83qSJI5pbEZHkpKrBbdWqVZLju3fv5vHHH+eVV15xqSgREXGjGzdgyBBo0cKcXztsmN0ViYikqVQ1uJs2bUpyvHXr1hw8eFBzcFPokUcesbsEcYHyc75MmeGxY1CnDrz3XvzYkSPm0mAOlCkzzGCUobhDio9ktnv3bnbt2gVAVFQUc+bMuWXFhNjYWI4dO8bJkyfTtMiM6uGHH7a7BHGB8nO+TJfh11/D00/D5cvmZR8fGDcOBgwwD8HrQJkuwwxIGYo7pLjB/eqrr3j77betyz169Ej2tnny5HGpqMxi1KhRms7hYMrP+TJNhpGRMHQofPBB/Fjx4rBwIVSrZl9daSDTZJiBKUNxhxQ3uIC1xdbLy+u26902b97ctaoyiRiHfiUoJuXnfJkiw8OHoV07+PXX+LEnn4RPP4XgYNvKSiuZIsMMThmKO6S4wa2f4PCMo0ePZlgSOyX4+flRunRpHn/88bSoTUREXDVjRnxz6+sL778Pffo4dkqCiEhKpLjBrVevHvXq1QPgl19+Yfjw4W4rKrO4//777S5BXKD8nC9TZDh8OPzwA5w/D4sWQeXKdleUpjJFhhmcMhR38DLccGzdsWPH8vLLL6f1w7pVeHg4OXPmJCwsjKCgILvLERFJnYgICAhIPHbyJAQGgt7bRCSNXL1qvq0ANGgAa9akz/OmtF9L0TJh0dHR7Ny5k+joaAA2bNiQ7Gn9+vW88847afNTZHAff/yx3SWIC5Sf82W4DOfPh9BQuLnijaVw4Qzb3Ga4DDMhZSjukKIpCq1atWL16tU0adKEFStWUL9+fbw0f8tlZ8+etbsEcYHyc74Mk+G1azBwIHzyiXk5bqeyHDnsrSsdZJgMMzFlKO6Qogb3p59+wjAMfvrpJ2vsdjMb1PyKiKSTAwfMhnbPnvixmjW1E5mIZGopanDHjh3L5MmT6devHwA+Pj506tQp2dvPnz8/barL4EJCQuwuQVyg/JzP8RnOnm2uiBARYV4OCIDJk+E265RnNI7PUJShuEWqdjJ74IEH+O2331J9vSfSTmYi4hhXr0L//jBzZvxY+fLmKgnlytlWlohkHhliJ7N/u1Pz6rTm1i5ffvml3SWIC5Sf8zkyw99/h+rVEze3vXrBzz9nyubWkRlKIspQ3CFVDe62bduYNGkSK1asAGDr1q00bNiQChUqMGzYMGJjY9O0yIxq//79dpcgLlB+zufIDK9cgYMHzfPZs8Pnn5tHJfv30mCZhCMzlESUobjDXR2qN86ECRPYuHEjI0eOJCwsjGbNmhEWFoZhGOzfv5/g4OAkj3QmIiIuqlEDRo82598uWgT33Wd3RSIiHidVW3B37NjBsmXL6NGjB1988QWXL18GIEeOHGTNmpVFixalZY0ZVrZs2ewuQVyg/JzPERn+8QfExCQee+kl2LZNzS0OyVBuSxmKO6RqJ7MCBQpw5swZANq1a8fixYsJCQnhwIED7N69m8cee4x//vknzYt1J+1kJiIexTDg44/N9W2HDYM337S7IhERS4bcySwsLIyrV68SExPDjz/+iJeXF61atSIgIICaNWuSJUuqHjbTWbdund0liAuUn/N5bIbh4dChAzz/PERGwttvw+bNdlflkTw2Q0kxZSjukKpONF++fLRp04Z27dpx8eJFAB577DHA7Kz9/f3TrsIMbNOmTXaXIC5Qfs7nkRnu2AEPPmjOr43Tvz9UqWJfTR7MIzOUu6IMxR1StZPZo48+ypw5c6wjlgUFBdG4cWOio6N55ZVXKFq0aJoWKSKS4RkGfPQR/Oc/EBVljuXMCTNmQJs29tYmIuIwqdqCO3r0aKpWrYphGGTPnp2PP/4Yf39/KlasyNSpU6lcuXJa1ykiknFdvgxt28KAAfHNbfXqsHOnmlsRkVRI1U5mcS5fvkxgYCBZs5obgv/55x9iY2Px9/fHz88vzYpMD3bsZHb16lWyZ8+eLs8laU/5OZ9HZPjnn9CoERw9Gj/20kswahT4+tpWllN4RIbiEmXoTBlyJ7M4V69eZfbs2YwePZoZM2ZYT+q05tYuhw8ftrsEcYHycz6PyDAkBOLepHPlgm++gfHj1dymkEdkKC5RhuIOqW5w33//fe6991569+7Na6+9Ru/evbn33nuZMGFCWtaXoS1dutTuEsQFys/5PCLDbNnMHcoaNYJdu6BlS7srchSPyFBcogzFHVLV4K5evZrBgwcTFRWFYRjWKSoqiiFDhrBq1aq0rlNEJGPYvNk8eENC990Hq1fDPffYU5OISAaTqlUU3n//fQCKFy9O0aJF8fPzIzIykr/++oujR48yceJEmjRpkqaFiog4WmwsjBsHr70G5cvD1q3m1lsREUlzqWpwf/nlF7766itat259y3Vff/01vXr1crmwzKBbt252lyAuUH7Ol24ZnjsH3bvDypXm5d27YcoUGDw4fZ4/A9Pr0PmUobhDqhrc69ev0zKZeWItW7bk+vXrLhWVWcStPiHOpPycL10y3LABOnaEkyfNy15e5lbcgQPd/9yZgF6HzqcMxR1SNQc3V65cDB06lOPHjycaP378OEOHDiVXrlxpUlxGN2PGDLtLEBcoP+dza4YxMfDOO/DII/HNbf785lzbESNA/6mnCb0OnU8Zijuk6h22fv36TJgwgQkTJuDl5YWPjw83btwgbkndTp06pWmRIiKOcuYMdOmSeGHIRx6BuXOhUCH76hIRySRStQX3tddew9/fH8MwiI2NJTIyktjYWAzDwN/fn1dffTWt6xQRcYYrV6BKlfjmNksWeOst+P57NbciIukkVQ1umTJlWL16Nffdd1+i8fvuu4+VK1dStmzZNCkuo2vevLndJYgLlJ/zuSXDwEB45hnzfMGC8MMP8MYb4O2d9s8leh1mAMpQ3OGupyicOXOGo0ePUqZMGfbv38+hQ4c4e/Ys+fPnp2TJku6oMcMqV66c3SWIC5Sf87ktw//7P7hxw9yRLH9+9zyHAHodZgTKUNzhrrbgDh48mCJFilCrVi0KFy7MG2+8QcmSJalVq5aa21QYN26c3SWIC5Sf86VJht99Bx9/nHjM2xvefVfNbTrQ69D5lKG4Q4ob3MWLF/P+++9bRy2Ljo7m3XffZWXcuo4iIplJdDS8+io0bgz9+sG2bXZXJCIiN6W4wZ00aRKAtVJC3PkPPvgg7asSEfFkJ06YqyKMGmVejo6Gzz6ztyYREbGkuMH9448/KF26NF999RW///47ixYtokSJEhw4cMCd9WVoNWvWtLsEcYHyc75UZfjtt1CpEmzaZF7OmtU8BO+UKWlam6SMXofOpwzFHbyMhJtkbyNLlizs2bOH8uXLW2O//PIL9erV4+rVq4lu279/fz766KO0rdTNwsPDyZkzJ2FhYQQFBdldjoh4mhs3zCOQJZwveM89sGABPPSQfXWJiNjg6lVz0RiABg0SL/vtTint11K8BTcwMDBRcwtQtWpVfHx8brntvHnz7qLUzGvixIl2lyAuUH7Ol+IMjx2DunUTN7etW8POnWpubabXofMpQ3GHFC8TFhkZSc+ePW8Zv379+i3jERERrleWCVy5csXuEsQFys/5UpShYUCHDrB1q3nZx8dsdAcMAC8v9xYod6TXofMpQ3GHFDe4N27cYNasWUlel9y4iIjjeXnB1KlQsyYULgwLF0K1anZXJSIit3FXB3pI4XRdvLRVI0VKly5tdwniAuXnfMlmaBiJt85WqgRLl0KNGhAcnB6lSQrpdeh8ylDcIcU7mWXLlo1hw4bd8XaGYTB27FjHTVPQTmYiAsCXX8Knn8I335jTEURE5BaevpNZirfg+vv7M3z48BTdNrVr4y5ZsoR3332Xs2fPEhsbS8OGDRk7diwFChS4432vX79OuXLlOHLkCJDyrc12mjNnDl27drW7DEkl5ed8iTK8fh3+8x+YPNm8/NprMHasfcVJiuh16HzKUNwhxQ3upUuXUvygd3PbOIsXL6Zdu3ZUr16do0ePsmfPHipXrsz27dvZsWMH/v7+t73/6NGjrebWKY4dO2Z3CeIC5ed8VoZ//gnt25urIsQ5cQJiYyHLXR3RXNKZXofOpwzFHTzindswDIYMGYJhGDRp0gRvb28qVapESEgI+/fv55NPPrnt/Y8ePcrUqVNp3LhxOlUsIhnGggXw4IPxza2/P3z8Mcydq+ZWRMShPOLd+/fff+fo0aMA5MuXzxrPnz8/AMuWLbvt/V988UWGDBlCwYIF3VajO+TJk8fuEsQFys/hrl3jidWroWNHiFum6L77YNs26N1bS4A5hF6HzqcMxR08osH9888/rfPZsmWzzgcEBABw8ODBZO+7evVqDhw4wMCBA+/qOSMjIwkPD090Sm99+vRJ9+eUtKP8HOzAAahRg/Jxh9sF6NoVfvkF7r/fvrrkrul16HzKUNzhrpYJc5eEizxnSfCVYNz55BaBjoqKYsCAAUyaNCnJI6rdzqhRo3jrrbduGR87dqw133fgwIEcO3aMr7/+2rq+a9eu+Pr6Mn36dGusWbNmVKhQgbEJdkipUaMGjz32GJMmTbKa55IlS9KhQwfmzp3LkSNHiI2NJU+ePPTr14+VK1fy66+/Wvd/9dVX+eWXX/juu++sseeee47w8HDmz59vjbVt25aCBQsmOjTyI488wsMPP8yoUaOIiYkB4P7776dVq1Z8/PHHnD17FoCQkBB69OjBl19+yf79+wHzA8bgwYNZt24dmxL85z9o0CAOHz7M0qVLrbFu3bqRNWtWZsyYYY01b96ccuXKMS7BEZ9q1qxJw4YNmThxopVl6dKladeuHXPmzLHmX+XJk4c+ffqwYsUKdiaYC/n666+zdetW1iTYRbNPnz5cvHiRhQsXWmPt2rUjb968TJkyxRpr0KABDz30ECNHjiQ2NhaASpUq0aJFC6ZNm8a5c+cAKFq0KN27d2fx4sUcOHAAMD9gvfTSS/z4449s3rzZeszBgwdz8OBBli5dav2N9ujRA4CZM2dat2vZsiWlS5dm/Pjx1litWrV49NFHmTBhgrXSSJkyZWjbti2zZs3i+PHjgPlNxnPPPcfy5cvZtWsXYL4eXn31VbZs2cIPP/xgPWbfvn05f/48ixYtssbat29P7ty5mTp1qjXWsGFDatasyTvvvGONVa5cmebNmzN16lQuXLgAQGhoKF27dmXRokXWh8vAwEBefPFF1qxZw9a4Ax4AQ4YMYd++faxYscIa69mzJ9HR0cyePdsaa926NSVKlOD999+3xmrXrk39+vUZP348165dA6Bs2bI8+eSTzJw5kxMnTgDmNznPPvss33zzDbt37wbA29ubV155hZ9++om1a9daj9m/f39Onz7N4sWLrbGOHTsSFBTEtGnTrLFef/xBoT17ALjh48OqFi3I2qEDTQMDmTx5srUfQfHixencuTMLFizg0KFDAAQFBTFgwAC+//57tm3bZj3myy+/zN69e/n222/jn6dXL6KiopgzZ4419vjjjxMaGppoZ9y6detSt25dxo0bR2RkJADly5fniSeeYMaMGZw8eRKAggUL8swzz7B06VL23Kzfx8eHoUOHsnHjRtavX2895oABAzhx4gRLliyxxjp37kxAQECiaV9NmjShUqVKjB492hqrVq0ajRs35qOPPuLy5csAlChRgk6dOjFv3jwOHz4MQHBwMP3792f16tVs377duv+wYcPYtWsXq1atssZ69+5NREQEc+fOtcbatGlDSEgIkyZNssbq1atHnTp1GDNmDDdu3ACgYsWKtG7dmk8//ZTTp08DULhwYXr27JmoRj8/P4YMGcKGDRvYsGGD9Zjufi8HyJUrl97LU/levnr1autvCtL/vTzht8R6L0/5e3lUlA8w1Louvd7LU7yQgeEBlixZYgAGYEyfPt0ar127tgEYoaGhSd5v1KhRRqtWrazL3bt3tx7nTq5fv26EhYVZp+PHjxuAERYW5vLPk1IjRoxIt+eStKf8HCwy0jCqVTPO5M9vGL//bnc14gK9Dp1PGTrTlSuGYS4abhgNGqTf84aFhaWoX/OILbilSpWyzsd1/xB/yN/kFoH++uuvuXz5MpUqVQLgr7/+sq6rVKkSNWvW5L///W+S9/Xz88PPz8/V0kXECa5ciV+wEcDXF77+mhn//S/DypWzry4REXELl+bgLl26lOeff542bdoA5jq2cV8V3Y3y5csTGhoKYH3NkPB8ixYtiImJoWvXrrRs2dL6amTr1q0cOHCAXbt2sWvXLlq1amXdd9euXck2tyKSSRgGzJgBoaFw8ysxS+HCRPv62lOXiIi4VYqPZJZQZGQkLVu2tOaO+Pn5ERERQdOmTdm/fz8bN26kaNGid/WYCxcupEOHDjz00ENs2rSJ33//nfvvv5/77ruPnTt38vvvv1Pt5vHfv/jiC9q2bXvLY/To0YNZs2YBd3+gBx3JTCSDuXIF+vSBzz83L993n7kTWcItuSIikiqefiSzVG3BHTFiBGvWrMEwjESN5MqVK+nevXuiic8p1b59e7744guuX79OaGgojRs3pkuXLqxdu5Zs2bJRrlw5qlatSsmSJalTp06i+x4/fpwKFSokmjRfoUKFRBPUPVHCCd7iPMrPg+3eDVWqxDe3APXrg7d3opspQ+dThs6nDMUdUjUHd9GiRdx///20a9eO/PnzM3jwYOu61157jfLly6eqmLZt2ya5ZRbMvSAT7qWbUNGiRdm7d2+qntNOa9asoWbNmnaXIamk/DyQYZgHaRg4EG6uRkCOHOZYhw633FwZOp8ydD5lKO6Qqgb35MmTnD171lqn9uWXX7aui42NtZZxERFJN+Hh8OyzkGDpOB580LxcsqR9dYmISLpL1RQFX19fVq9efct4REQEr7zyCt7/+hpQRMStdu6Mb2bj9O8PmzeruRURyYRStQW3WrVqtG3blsDAQPLmzcs///xDsWLFOH36NDdu3KBevXppXWeGpKO3OJvy8yDXrsHNw32TMydMnw5PPnnHuylD51OGzqcMxR1StQU3bs7tlStXOHr0KLGxsRw/fpyoqCiAuz5sbmZ18eJFu0sQFyg/D1KrFrz7LlSrZm7NTUFzC8owI1CGzqcMxR1S1eA2atSIadOmkSNHDmslBcMwCAgIYMKECbRu3Tqt68yQEh5mVpxH+dno99/h5qFLLUOGwKZNULx4ih9GGTqfMnQ+ZSjukOojmT3zzDN06NCBzZs3c/78efLkycNDDz2kNWRFxH0MAz74AF5+Gf7v/8xTnCxZzCOUiYhIpufSoXoDAwNp1KjRLeMLFiygQxJL8oiIpNrFi/D00/DNN+blN9+Exo2henVbyxIREc+Tqgb3r7/+SvY6wzB4/vnn1eCmQLt27ewuQVyg/NLRli3Qvj0cPx4/9p//QOXKLj2sMnQ+Zeh8ylDcIVUNbrFixfDy8krrWjKdvHnz2l2CuED5pYPYWHjvPXj11fg5t3nywOzZ0KyZyw+vDJ1PGTqfMhR3SNVOZkCincv+fZKU8fRDCcvtKT83O3cOWrSAoUPjm9vatWHXrjRpbkEZZgTK0PmUobhDqrbgent7U7t27URjkZGRHD9+nDNnztxynYjIXdm3Dx57DE6eNC97eZlbcd98E7K6tOuAiIhkAqn6n+KRRx7hu+++S/K6+fPnc+rUKZeKEpFMrlgxyJ3bbHDz54fPPzcbXhERkRRI1RSF5JpbgA4dOvDxxx+nuqDMpEGDBnaXIC5Qfm4UEACLFplTFHbtcltzqwydTxk6nzIUd/AyUjFpdvbs2UmOX716lfXr17N06VKuXbvmcnHpKTw8nJw5cxIWFqa1fEXS29q1EBICpUrZXYmIiKTA1asQGGieb9AA1qxJn+dNab+WqikKPXr0uO0qCmXLlk3Nw2Y6I0eO5NVXX7W7DEkl5ZcGYmLgnXfg7bfh/vvN5cD8/dPt6ZWh8ylD51OG4g5pvoqCr68vo0aNSssaM6zY2Fi7SxAXKD8XnTplTj14801zObBdu+CTT9K1BGXofMrQ+ZShuEOqtuD6+fkxbNiwxA+UNSuFCxemUaNGFClSJE2KE5EM6vvvoUsXOHvWvJwli7kVt18/e+sSEZEMIVUNbtOmTRk+fHha15LpVKpUye4SxAXKLxWio80ttiNHQtz0/yJFYP58qFMn3ctRhs6nDJ1PGYo7pGonsyxZsuDl5cWHH35I37593VFXutNOZiJuduIEdOoEGzfGjzVtah6VTEcyEhFxFE/fySxVc3CzZs3Kt99+S58+fVJdoMC0adPsLkFcoPzuwuXLUKVKfHPr7Q1jx8Ly5bY2t8rQ+ZSh8ylDcYdUNbilS5emfv36ya6ksHnzZpeKyizOnTtndwniAuV3F4KDoXdv83zRomajO2SIOffWRsrQ+ZSh8ylDcYdU/e8yaNAgXnjhBcLCwpK8vkWLFi4VJSIZ0Jtvwuuvm6slPPSQ3dWIiEgGlqqdzD7//HN27drF559/TsmSJcmTJ0+irblXrlxJswIzsqJFi9pdgrhA+d3GN9/A6dPw7LPxY1mzwogR9tWUBGXofMrQ+ZShuINLO5kZhnHLNIW4sZiYmDQrMj1oJzORNBAVBcOGwfvvg48P/PQTVKtmd1UiIpLGMuROZmA2snH/JjxJyi1evNjuEsQFyu9fjhyB2rXN5hbgxg2YO9femu5AGTqfMnQ+ZSjukOIpCn/99Zd5h6xZyZEjB7t3707ydoZh8MADD6RNdRncgQMH7C5BXKD8EliyBHr2hLh5+b6+MH68xx+4QRk6nzJ0PmUo7pDiBrdYsWJ4eXlx77338uqrrxIaGprsbf/73/+mSXEi4uEiI+E//4GPPoofu/deWLQIHnzQvrpERCRTS3GDmzNnTi5dupSi23bs2DHVBWUmAQEBdpcgLsj0+R06BO3bw44d8WPt28PHH4ND5rFn+gwzAGXofMpQ3CHFO5nlzp2bixcvpuhBFyxYQIcOHVwqLL1pJzORu2AY5oEbdu40L/v5waRJ5lq3yayPLSIiGUeG3cnsdjLK4Xvd7ccff7S7BHFBps7Pyws++cSca1u6NPz8s7kkmMOa20ydYQahDJ1PGYo7pHiKwj///MOjjz6aottqHdyU2bx5c4p/p+J5Ml1+hpG4ga1SBZYtg1q14j/GO0ymyzADUobOpwzFHVLc4MbExLB+/fo73i6ptXFFxOE+/9w8LVtmrm8bp1Ej+2oSERFJxl1NUfj3mrdJnUQkA4mIgF69oGtXWL0a3njD7opERETuKMU7mQUEBDB8+PA73s4wDEaMGMHVq1ddLi492bGT2bVr18iWLVu6PJekvQyf37590K4d/P57/FjPnvDpp46ba5ucDJ9hJqAMnU8ZOpOn72SW4ikK/v7+DB06NEW3HTNmTEofNlM7ePCgDorhYBk2P8OAmTPNgzRcu2aOZc8OU6eaW3IzkAybYSaiDJ1PGYo7uGUVhZSul5vZLVu2zO4SxAUZMr8rV6B7d3NLbVxzW7Ei/PJLhmtuIYNmmMkoQ+dThuIOKW5wb9y4wcaNG9m+fbs76xERu+zeDdWqwZw58WPPPgvbtkGZMvbVJSIicpdSPEUhT548dOvWjeLFi2vNOpGMaOZMiDsmfGCguc6tww7YIiIiAnexk1lGZ8dOZidOnCAkJCRdnkvSXobLLzISHn4YYmJg0SIoVcruitwuw2WYCSlD51OGzpRhdjITkQwmPBwSvjn4+Znr3ObKBf7+9tUlIiLiIrfsZCYpM3PmTLtLEBc4Nj/DgMmToVixxEuAARQqlKmaW8dmKBZl6HzKUNxBDa5IZnL5srm2bf/+cOmSed5ha1aLiIjciaYoiGQW27dD+/Zw5Ej8WKNGiQ+9KyIikgFoC66NWrZsaXcJ4gLH5GcYMHGiuQNZXHMbHAxffw3vvw++vjYWZy/HZCjJUobOpwzFHbQF10alS5e2uwRxgSPyu3jRPGjD0qXxYzVrwoIFEBpqX10ewhEZym0pQ+dThuIO2oJro/Hjx9tdgrjA4/P7+WeoXDlxcztkCGzYoOb2Jo/PUO5IGTqfMhR30BZckYwqMhL+/ts8nycPzJoFzZvbW5OIiEg60BZckYyqTh14+22oXRt27VJzKyIimYYaXBvVqlXL7hLEBR6X32+/QWxs4rFhw2DtWtBRgpLkcRnKXVOGzqcMxR3U4Nro0UcftbsEcYHH5BcbCyNHQpUqMGZM4uuyZIGsmomUHI/JUFJNGTqfMhR3UINrowkTJthdgrjAI/I7exaaNoXXXoOYGPi//4MdO+yuyjE8IkNxiTJ0PmUo7qBNOzaKiIiwuwRxge35rVsHnTrBqVPmZS8veP11eOABW8tyEtszFJcpQ+dThuIOanBFnCYmBt55x9yBLG7ObcGCMHcu6Ks+ERERNbh2KlOmjN0liAtsye/0aejcGX78MX6sYUP4/HMoUCD963E4vQadTxk6nzIUd/AyDMOwuwhPEB4eTs6cOQkLCyMoKMjuckRutXs3PPaYOe8WzB3I3n4bXnnFPC8iIpJOrl6FwEDzfIMGsGZN+jxvSvs1/a9oo1mzZtldgrgg3fO7917zgA0AhQuby3+99pqaWxfoNeh8ytD5lKG4g/5ntNHx48ftLkFckO75Zc8OixZBmzbmgRvq1k3f58+A9Bp0PmXofMpQ3EENroinWrUK/ve/xGMVKsCXX0K+fPbUJCIi4gBqcG2UT02Ko7ktvxs3YOhQc33b9u0hMtI9zyN6DWYAytD5lKG4g3Yyu0k7mYlH+Osv6NgRNm+OH/v4Y+jd276aRERE/kU7mUmyli9fbncJ4oI0z2/ZMqhUKb65zZoVJkyAZ55J2+cRi16DzqcMnU8ZijuowbXRrl277C5BXJBm+UVFweDB0KoVXLpkjhUrBj/9BIMGmUcoE7fQa9D5lKHzKUNxBx3oQcROR45Ahw7w88/xY23awPTpEBxsW1kiIiJOpgbXRlm0fqmjuZzfhQtQpUr8VltfXxg/Hvr101bbdKLXoPMpQ+dThuIO2snsJu1kJrYYNgzGjDEP4rBwodnwioiIeDjtZCbJ2rJli90liAvSJL8RI2D4cNixQ82tDfQadD5l6HzKUNxBDa6NfvjhB7tLEBfcdX6LFsGnnyYe8/GBN98EfWtgC70GnU8ZOp8yFHfQHFwRd7t2DV56Cf77X3Oe7YMPmicRERFxC23BFXGnP/6AmjXN5hbMJcG++MLemkRERDI47WR2kx07mV28eJHcuXOny3NJ2rtjfnPnwnPPmTPxAbJlg48+gqef1ioJHkKvQedThs6nDJ1JO5lJss6fP293CeKCZPOLiDCPPtalS3xzW7asudZtz55qbj2IXoPOpwydTxmKO6jBtdGiRYvsLkFckGR++/ZB9ermgRri9OgB27dDhQrpVpukjF6DzqcMnU8Zijt4VIO7ZMkSqlSpQtGiRSlSpAjdu3fnzJkzyd5+w4YNtGzZkrJly1KyZEkKFCjAo48+yrfffpuOVYvcFBsL7drB77+blwMCYNYs+OwzyJ7d3tpEREQyEY9ZRWHx4sW0a9eO6tWrc/ToUfbs2UPlypXZvn07O3bswN/f/5b7bN68maNHj7JlyxaCg4OZOHEigwYNYu3atcyePZuuXbva8JNIppUlC8yYAbVrQ5ky5rJgZcrYXZWIiMgdxcRA9+7w448pu31srHvrcZVHbME1DIMhQ4ZgGAZNmjTB29ubSpUqERISwv79+/nkk0+SvF+ePHkYPHgwwcHBAPTv3x8/Pz8AJk+enF7lp1r79u3tLkFc0L59e/j3PprVq8O338K2bWpuHUCvQedThs6nDD3Dxo3mvtGnTqXslPAL9hw57Ks7OR6xBff333/n6NGjAOTLl88az58/PydOnGDZsmW88MILt9yvd+/eiS5nzZqV4OBgzpw5Q0REhFtrTgvaa9TBDINCy5fDd9/BsmWQNcFLqWFD++qSu6LXoPMpQ+dThp7hypX480FBkDNnyu6XO3c0Q4Z4RDuZiEdswf3zzz+t89myZbPOBwQEAHDw4MEUPc6VK1c4e/YsAPXr17/tbSMjIwkPD090Sm9Tp05N9+eUNPDPP9C5M4EvvQSrVsFbb9ldkaSSXoPOpwydTxl6npdfhr/+StmpbdvR1Kpld8W38oiW+0qCjw1ZsmS55XzC62/n008/xTAMSpUqxRtvvHHb244aNYq3kmhMxo4da833HThwIMeOHePrr7+2ru/atSu+vr5MT7CXfLNmzahQoQJjx461xmrUqMFjjz3GpEmTrOa5ZMmSdOjQgblz53LkyBFiYmKYPHky/fr1Y+XKlfz666/W/V999VV++eUXvvvuO2vsueeeIzw8nPnz51tjbdu2pWDBgnz00UfW2COPPMLDDz/MqFGjiImJAeD++++nVatWfPzxx9aHgJCQEHr06MGXX37J/v37AfMDxuDBg1m3bh2bNm2yHnPQoEEcPnyYpUuXWmPdunUja9aszJgxwxpr3rw55cqVY9y4cdZYzZo1adiwIRMnTrSyLF26NO3atWPOnDkcO3YMMKec9OnThxUrVrBz507r/q+//jpbt25lTYJF9vr06cPFixdZuHChNdauXTvy5s3LlClTrLEGDRrw0EMPMXLkSGJvThiqVKkSLVq0YNq0aZw7dw6AokWL0r17dxYvXsyBAwcA8wPWSy+9xI8//sjmzZsBKHDyJL2++44s//uf9Rzbv/+eQs8+C15ezJw50xpv2bIlpUuXZvz48dZYrVq1ePTRR5kwYYL1LUOZMmVo27Yts2bN4vjx44D5TcZzzz3H8uXL2bVrF2C+Hl599VW2bNmS6NCWffv25fz584n2RG7fvj25c+dO9B9Hw4YNqVmzJu+88441VrlyZZo3b87UqVO5cOECAKGhoXTt2pVFixZZHy4DAwN58cUXWbNmDVu3brXuP2TIEPbt28eKFSussZ49exIdHc3s2bOtsdatW1OiRAnef/99a6x27drUr1+f8ePHc+3aNQDKli3Lk08+ycyZMzlx4gRgfpPz7LPP8s0337B7924AvL29eeWVV/jpp59Yu3at9Zj9+/fn9OnTLF682Brr2LEjQUFBTJs2zRpr1KgRVatWJSYmxvp9VKlShaZNmzJ58mQuXboEQPHixencuTMLFizg0KFDAAQFBTFgwAC+//57tm3bZj3myy+/zN69exPt5NqrVy+ioqKYM2eONfb4448TGhrKBx98YI3VrVuXunXrMm7cOCIjIwEoX748TzzxBDNmzODkyZMAFCxYkGeeeYalS5eyZ88eAHx8fBg6dCgbN25k/fr11mMOGDCAEydOsGTJEmusc+fOBAQEJJr21aRJEypVqsTo0aOtsWrVqtG4cWM++ugjLl++DECJEiXo1KkT8+bN4/DhwwAEBwfTv39/Vq9ezfbt2637Dxs2jF27drFq1SprrHfv3kRERDB37lxrrE2bNoSEhDBp0iRrrF69etSpU4cxY8Zw48YNACpWrEjr1q359NNPOX36NACFCxemZ8+exMbGWhn6+fkxZMgQNmzYwIYNG6zHdPd7OUCuXLn0Xp7K93LDMBK9L6XHeznA4MGDOXjwIMuWLbPGevToAZAp38v/978TwL0ArF27lqefvi9F7+VxeaTXe3nC987bMjzAkiVLDMAAjOnTp1vjtWvXNgAjNDT0jo+xceNGIyAgwHjkkUeMs2fP3vH2169fN8LCwqzT8ePHDcAICwtz5Ue5KyNGjEi35xIXxcYaxuTJhuHnZxjmzFvjmp+fYSxaZHdl4gK9Bp1PGTqfMvQMy5ZZ/70Z77yT8vuld35hYWEp6tc8YopCqVKlrPNx3T9gfSoqXbr0be+/YMECWrRowf/93/+xZs0a8uXLx759+257Hz8/P4KCghKd0ltDzdV0hrAwc/mvfv3g5tY1qlZl/9y58NRT9tYmLtFr0PmUofMpQ2fz1Pw8YopC+fLlCQ0N5dixY9bXDIB1vkWLFsTExNCjRw8uX77M/PnzCQwMJDIyksGDB7N27VrWrl1L5cqVrfs2a9bM2nHNU9WsWdPuEuROfvnFbG5vfg0JwMCBMGYMlW+u2CHOpdeg8ylD51OGzuap+XnEFlwvLy/GjBkDwHfffUdsbCx79uzh+PHj3HffffTu3ZudO3fy+eefs3z5cmte1/Dhw5k8eTInTpzgscceI2/evNbJCRLOnxEPNWtWfHMbHAxffQUTJ4Kfn/LLAJSh8ylD51OGzuap+XnEFlwwJ1J7e3szcuRIQkNDiYmJoUuXLowdO5Zs2bJRrlw5qlatyuXLl6lTpw4A169fB0hyBYTAwMB0rV8yqHHjYNMm8PODhQshNNTuikREROQOPKbBBXMv0rZt2yZ5XUBAQKK9dAEmTpzIxIkT06EyyTQuXza31Mbx9zcP3JA3L/j42FWViIiI3AWPmKKQWSWcMyw2i42F8eOheHG4ucyOpVChJJtb5ed8ytD5lKHzKUNn89T8vAzj38cazZzCw8PJmTMnYWFhtqyoIDa6cME8AHfcWq4VKpiH2r15oBEREZGMbvlyaNnSPP/OO/Daa/bWk5yU9mvagmsjHb3FA2zaBJUqxTe3YL7CfX3veFfl53zK0PmUofMpQ2fz1PzU4Noo7mgjYoPYWBg1CurXh5tHWSFfPvPQuyNHQtY7T09Xfs6nDJ1PGTqfMnQ2T83Po3YyE0kXZ89Ct26wenX8WL16MG8eFC5sX10iIiKSJrQF10ahWnIq/W3caE5JiGtuvbzgjTdgzZq7bm6Vn/MpQ+dThs6nDJ3NU/PTTmY3aSezTGLdOmjQwJyiUKAAzJ1rXhYREcnEtJOZpJlFixbZXULmU78+DB9uNrW7drnU3Co/51OGzqcMnU8ZOpun5qcG10YHDx60u4SM79dfza21Cb32mjlFoWBBlx5a+TmfMnQ+Zeh8ytDZPDU/NbiSMUVHm3Nrq1WD995LfJ23t3kSERGRDEkNro0CAwPtLiFjOnnSnHowYgQYBrz6KuzeneZPo/ycTxk6nzJ0PmXobJ6an3Yyu0k7mWUQq1ZB165w/rx52dvbnC3/8suQRZ/nREREkqKdzCTNrFmzxu4SMo4bN+CVV6Bp0/jmNiQE1q+HYcPc0twqP+dThs6nDJ1PGTqbp+anBtdGW7dutbuEjOH4cXN1hNGj48datDBXSXj4Ybc9rfJzPmXofMrQ+ZShs3lqfmpwxdl++cU8cMPmzeblrFlh/Hj45hvIk8fW0kRERMQeOlSvOFuZMpAvH1y8CKGhsHAh1Khhd1UiIiJiI+1kdpMdO5lFRkbi5+eXLs+Voe3eDaNGwZQpkCtXuj2t8nM+Zeh8ytD5lKFnSO1OZumdn3Yyc4B9+/bZXYLzLF0KR44kHrv/fpg/P12bW1B+GYEydD5l6HzK0Nk8NT9NUbDRihUrqFy5st1lOENkpLnU16RJUL06bNwIvr62lqT8nE8ZOp8ydL7UZPjWW+aXdjduuKmoTCgqKnX389TXoBpc8Xz/+x+0b28edhfg55/NLbbdu9tbl4iIpLurV83j+MTE2F1JxpUzp90VuE4Nrni2L76AZ56B8HDzsp8fvP8+dOtmb10iImKLqKj45jYgwFzyXNJO+fLQsaPdVbhOO5ndZMdOZidPnqRw4cLp8lyOc/06vPQSTJ0aP1aqFCxaZC4L5gGUn/MpQ+dThs53txleugS5c5vnmzaFb791U2GSIun9Gkxpv6YtuDaKjo62uwTPdPAgtGsHv/0WP9apE/z3v5Ajh311/Yvycz5l6HzK0PmUobN5an5aRcFGs2fPtrsEz3PmDFStGt/c+vvDp5/C5597VHMLyi8jUIbOpwydTxk6m6fmpwZXPEuBAvDcc+b5MmVg+3bo1Qu8vOytS0RERBxDUxTE84wcae7COWgQZM9udzUiIiLiMGpwbdS6dWu7S7Df7NnmQoa9esWP+fjA66/bV1MKKT/nU4bOpwydTxk6m6fmpwbXRiVKlLC7BPtcvQr9+sGsWebSX1WrwgMP2F3VXcnU+WUQytD5lKHzKUNn89T8NAfXRu+//77dJdhj716oVs1sbsE8StnSpfbWlAqZNr8MRBk6nzJ0PmXobJ6an7bgSvoxDJg+HV54wVznFiAwEKZNM5cBExEREUkDanAlffzzDzz/PMybFz/2wAPmgRtKl7avLhEREclwNEXBRrVr17a7hPSxa5c5xzZhc9unD2zd6ujmNtPkl4EpQ+dThs6nDJ3NU/PToXpvsuNQvZlCTIx5YOs//jAvBwXBJ5+YRyoTERGPdOUKnDwZfzp71nw79wQREfDGG+Z5Hao389Gheh1g/PjxDB482O4y3MvbG2bOhDp1zCkJCxfCvffaXVWayBT5ZXDK0PmU4d25dg1OnUrcvCZ1+ucfuysVp/DU16AaXBtdu3bN7hLcwzASH3msZk1YvRoefthcEiyDyLD5ZSLK0PmUoSkyEk6fvnPjevmy3ZWmrTp17K5APPU1qAZX0o5hwKRJsGoVLF9ubr2N8+ij9tUlIuJQN27AmTPxDWpyW1/Pn0+b5wsMhMKFbz0VKAC+vmnzHP+2ePFi2rZte9f3K1jQ3G4ikhQ1uDYqW7as3SWknUuXoGdP+Ppr8/I778Dw4baW5G4ZKr9MShk6n1MzjImBc+fuvMX17Flz24GrsmVLunFNeCpUCHLkcP257paXlxdPPpn+zytpw1Nfg9rJ7CbtZOaCbdugfXs4dix+bMgQGDvWvppERGwQGwsXLty+aT11ypxOkBY7bfn63tqkJtW85syZeOaYiFNpJzMHmDlzJj169LC7jNQzDJgwAYYNg+hocyx3bvMIZS1a2FtbOnB8fqIMM4D0ytAwzC+q7rSD1qlT5rQCV3l7J9+sJjzlzu38xlWvQ2fz1PzU4NroxIkTdpeQehcuQI8e5lzbOLVqwYIFULSobWWlJ0fnJ4AyzAhczdAwzBUD7jRV4ORJc0cuV2XJYs5nvdNUgXz5zNtmBnodOpun5qcGV+7eTz9Bx45w/Hj82LBh8Pbb4ONjX10iIglcvZr8VtaEl69eTZvny5//zltd8+eHrPqfV8Tt9DKzUf78+e0uIXXmzo1vbvPmhTlzoEkTe2uygWPzE4sydKbr1+Ob1OPHa/DBB0k3suHhafN8uXPfeaqAO1cZyOj0OnQ2T81PO5ndpJ3M7sK1a+batsHB5uF3ixSxuyIRyQCiou68luupU3DxYto8X86cyU8RSHje3z9tnk9EXKedzBzgm2++oVWrVnaXcWcXL5qbMOJky2YeuCFv3kz9XZtj8pNkKcP0ER2dsrVcz51Lm+fLnj1lS2Jlz542zyeu0evQ2Tw1v8zbnXiA3bt3e+QfhSUmBt5911wp4eefoXTp+OsKFrSvLg+RkvxiYmK4kRa7VItb/O9//+P69et2l+FYsbHm59+zZ289nTsXf/78+Tuv5RoQAKGht7+Nn585hzXh6X//20zr1rWsy/nymY1rSlYWUPTu5+Pjg3fCg/4kweP/L5Tb8tT81OBK0k6fhi5d4IcfzMvt2sHWrfquLoUMw+D06dNczmjHxcxgHnroIY4cOWJ3GR4pJubWU3T0rWMJxTWZqeHtbX4h5O196yluPKlVBcLCAsiZMz7Dc+fSbkuwpI3g4GAKFiyIl9PXMxNHUYNrozt9qrXNDz9A587md4pg/q/y5JNaIeFfbpdfXHObP39+AgIC9Mbuoc6ePeuxO0i4g2HEN6rR0eZ6rTduJL4cdz7hFlcvL7PJTM2MpKxZzbeOuPv7+CS+HHdK7Usks2XoJIZhEBERwdmzZwEoVKhQkrfz2P8LJUU8NT/tZHaTdjLD/J/vrbfMw+zG/VkUKgTz50O9evbW5iAxMTEcPHiQ/PnzkydPHrvLkUwiJsZsUKOi4hvXf1+Oikqbw75CfKPq42OuHpDUZVcaV8k4Lly4wNmzZyldurTHNkPiHNrJzAF++uknHn74YbvLMJ08CZ06wfr18WONG8Ps2an/zjGDSy6/uDm3AQEB6V2S3KUrV64QGBhodxm3Fde4JtWwJhyLjU2b50u4lTVh4/rvJtZTGlcnZJjZxb0X3rhxI8kG16P+L5S75qn5qcG10dq1az3jj+KHH8wDN8RNXPP2Nrfivvxy5jmUTircKT9NS/B84eHhtjVHsbHJN60JL/97nmtqeXsnv6U14clpL3k7M5SUudN7ocf8Xyip4qn5qcEV0/nz5r8hIeaUhNq17a1HxKFiY805rLdrWuPmuqYFb++UTRdwWuOaGgcOHKBMmTJ2l5EpHD58mKJFi+KjfTPEQ2WCtzy5owYN4P/+D1q0gF271NxmQsWKFaNChQpUqFCBkiVL4uXlhZeXF/nz57fGs2fPztGjR+0u9a498cQT+Pn54eXlRZYsWahfv36i6zt06ECuXLm455572L17d7KPYxhmk3r1Kly+bH7h8fff8MwzL1GwYAheXl7cc08pNmwIY/duOHAA/vc/GDx4CA8+GMIDD+Siffs6XLuWsuY2SxZzWazAQHMZ6gIFzM+fxYvDffdBhQpQubJ5qlDBHCtRwrxNgQLmffLk8aVKlQrcf38FQkNDrVxDQkKsXH0zwOG3wsPD6dGjB926dbvlusuXL5MtWzby5MlDVFRUouuOHDli/Q68vLzIli0bFSpUsFY/WbJkCffddx++vr5UqFCBQ4cOWffdtWsXXbp0oWjRopQqVYpKlSpRv359xo4dmy4rcyxZsoQqVapQtGhRihQpQvfu3TkTt2PwbVy4cIHevXtTuHBhQkNDqVSpEvPmzUt0m/r161t/K/8+9ejRAzC/li5Xrhw//fSTO348EdcZYhiGYYSFhRmAERYWlm7PeenSpXR7rkS2bDGM2NjEY9HRt47JbSWX37Vr14x9+/YZ165dSzRutkjpd7oboaGh1vmdO3cagAEYw4cPt8br1atnHDly5O4e2EN0797d+pl27dpljd+4ccOIjTWMMmXKGn/88Zdx+bJhnD1rGH//bRhHjxrGwYOG8fvvhrFrl2Fs35786Y03PrMev27dVsbPP8fecn3z5t2N7dsN45dfDGP3bsPYv98wDh0yjGPHDOPUKcM4f94wwsIMIyIi7V6OCXP96quvrBo/++yzJG/jRBcuXDAqVapkhISEGBcvXrzl+qlTp1o/9+LFi5N8jNDQUAMwHnjggVuuO3LkyC2/o1mzZhlZs2Y1cuTIYaxfv94anz9/vuHt7W10797dlR/pjr744gvDy8vLqFGjhhEdHW29ZsuWLXvL+05CUVFRRuXKlQ3AWL58uWEYhvH4448bgDFz5kzrdvXq1bN+Z/8+Pf3009btBg8ebPj5+RkrV668bb3JvSfGse3/QkkT6Z1fSvs1bcG10enTp9P3CaOiYPBgeOgheP/9xNd5e3vOXiMOke75uVGHDh3ueJvmzZuTI0eOdKgmbRiGuaU0IgLatu1hjX/44SwOHYL9++H337Mwc+YWgoKKEB5elD//hGPH4o+qFRZm3v9ujtWxYcM3zJ8/kvz5zaNYFytmblHNlQsqVYIHH4SKFaFMGbj3XrjnHvO4KXnyQFCQeaDAtHo5piTXlNzGk/Xv359du3YxbNgwcuXKdcv1M2fOtHZsmjlzpsvPd+DAAZ555hmio6MZOnQodevWta7r0KEDvXv3dvk5bscwDIYMGYJhGDRp0gRvb28qVapESEgI+/fv55NPPkn2vnPnzmXnzp14e3vTpEkTAFq0aAHAyy+/THSCrxZ27NjBuXPnrNPvv/+Oj48PzZo1s27z2muvkSVLFjp16sSFCxdS/TNlpPfSzMhT81ODa6PFixen35MdPQp165pHJQMYOtT8H15SLV3zc7PRo0ff8TZnz561pi/Efc3/yiuvkD179kRfXS5ZsoSQkBDrK8333nuP+vXrExAQQOvWrQkLC2PcuHGUKVOGAgUK8OabbyZ6nqtXrzJ48GBCQ0MpVqwYRYoU4ZlnnuHczZ0gP/10eqLH//TThVSr9jD+/v48/fSLHDgAu3fDjh3mjJt9+6BAgXoULlwcgK++msv589FcvQo3bmRh2bKZtGjRw3r+1avn06lTJVq0uIdOnSrRqdMDfPjhfwgIgOBg80hZhQubR90qVQrKlTOb1MaNG1Pw5hH+PvjgDf7443sKFTKPaB0QEH/AgrjGdceOHTRv3pwCBQpQtGhRKlSowNSpU606unXrZv1u27Rpw7PPPktoaCgFChRgxIgRaZbr8ePHb8mwW7du1tf2cfl8+OGH5M+fHy8vL4oWLcorr7xCjRo1CAwMpFevXkRERPDaa69RtGhRQkJC+PjjjxM9z9mzZ3nmmWcoXLgwxYoVIzQ0lMGDB3P16lUARowYYT1+sWLFmDRpEtWrV8fb25uJEycmWfvRo0dZsGABEN+oJXTgwAGuX79OmzZtAFi1alWKvsa/nSlTplgrpbRs2fKW61944QW3HtXp999/t6YK5cuXzxqPWwt42bJlyd53+fLlAOTKlctq+uPud/bsWX7++WfAnNYTGhpK3rx5rdOCBQu45557rN9l3OPUqFGDS5cuMWXKlFT/TBnpvTQz8tT81OBmBl9/bU7U27bNvOzjA+PHm5uQRFJo3LhxtG7dOtHYqFGjqFatWqKxNm3a8M4771iX8+XLx7p16yhfvjzffPMNderU4YknnmDr1q1cvHiRt956i203/zajow2aNm3OhAkTaNLkcbZuPcozz/yH6dOnU7NmXbZvv8aDD/aiV6/4x1+1ah1Tp/7Eyy9P5fp1uHLl1vVevby8aN7cnJ958eJZtmxZBcCNG1fZunUVjz/ehrx54cqVfbzxRhdiYq6yf/8h9u7dxYcfjmP9+sWUKwclS5qNbeHCZqObM2d881qwYEEWLlxI1qxZiY2NpVOnTvz1119J/i737t1LnTp1+Pbbb1m5ciVHjhzBz8+Pvn37Mnz4cABmz55t/W7XrVvHu+++y/bt27l48SJvvPGG1Yy4au7cubdkOHv2bAoXLpxo7IUXXqBv374AnDhxgiZNmrBp0yaCgoKYMWMGjRs35qWXXmLlypX8/fff9OvXjxMnTgAQERFBvXr1mD59OkOGDOHo0aM8/vjjTJgwgebNm2MYBv/3f/9nPf7ff/9NeHg4P//8M6+//nqyta9cuRLDMPDz8yM0ieP8zpw5k27dutG1a1cAoqOj+fzzz1P/ywK2bNlinb/33ntvub5cuXKJmsCkJDe/9d/zXJPy559/WuezZctmnY9biuvgwYN3vG9S90t434EDB5I7d25rPCIigilTpjB48GCy/GtvxdI3D+G+YsWKZJ9XxA5qcDOyyEgYOBCeeMLcKwbMvVA2b4YBAzQlQdJFmzZtiI2FEiVKAZAliw85cpTkypVgcuc2t0B9/fU2duyAjz/+kY0bzbWY77+/BSdOwP33Nwfg8OEDfPPNvFvWe33qqf4ANG7ckaeffhUwt5Rmy2Z+5Z83r3m8kt69u1vLFW3aNIsHH4SdO2fRvHkjKlbMRrFicO7c78TGxnL69El++OEboqKu0LhxIybEffNxB3Xr1rW2mp4/f562bdsSGRl5y+3GjBlDREQEhQsX5sEHHyRr1qw0btwYgLFjx1pbNeM8/PDD5MuXj/z581OgQAEAtm7dmqKa3CF37tzUq1cPHx8fihUrBmAd2CSu4YmOjmbHjh0AzJ8/nwMHDgDxW1qbNzdzXb9+PT/++GOixzcMgwEDBgDwn//8h+7duydZR9xjJmzG4sTExLBgwQI6d+5M06ZNra2ds2bNSvXPDSQ6/HbCRvFu3Hfffbc9JXfELzDX/Y2TsNmMO5/w+uTum9T9bnffGTNmJNt4x/3u47IQ8RRaJsxGHTt2dN+D/+9/0L49/Ppr/FjbtvDpp+ZmJ3GZW/NziNjY+AMMREWZR3e+cSN+SWWAQ4dyEBMDV6+ae+t7e+fg+HFunjfHLl68RGwsHDgQ//caFGT+x5kzZ/zR4P74Ywf+/r1I2FfUrHkvQUHg4+N/85T0klhFihSnbt26rF+/nhUrlhEWdomvvvoq0RSJatWq4efnR0REBE899RQ+Pj40btw4xVMCAAYPHsyWLVv48ssv2b59OwMGDOChhx5KdJtfb74uEzZmcUe9u379Ovv27Uu0VTXh7bLePF7upUuXUlxTWks4FztuFYa4sYSrMsTV+GuC96G4nyXhUf527NhBgwYNrMt58+a1jlB0u3nfcR8Eklqq6vvvv6dChQrWB4KOHTsyadIk9uzZw6+//kqVKlWs28Z9XW8kcZg3wzASHZwg4Tzfa9eukT179mTrS44rzWDCNX9jE3zaizt/uzWB465L6n7J3TcmJoYJEybQv3//JBv6uN/97RrrO9F7qbN5an7agmsjtx0SePNmc0+WuP9U/PxgyhRYtEjNbRrKDId0joqCS5fg7FlzSayrV80toFevmvNbd+yAK1fMpiA8HI4fh9OnzWkCcVJ2oAIDf3/zTzVOgQLmFw4lS8aP5ctnLol1c6orAKGh/uTKZS6n5ed3+/Ve47ZARUZG8t5773H8+PFEC5QXK1aMLVu20LFjR4KDg7lx4wbLly+nfv36ibbc3clnn31mbcn8+OOPmTNnTorvm5SkFspPqhnztMdP7WP4+/un6HZxze+NJPYCnDlzJj///DPFihWjWLFifPHFF4muSyg4OBiAf/7555bHCQ8PT9TUJvywcvjw4Vtu//fff7Nr167b1l2mTJnbnl555ZVk71uqVCnr/LVr16zzERERQPyUgdvdN6n7JXffL7/8kjNnztCvX78kHzNu6TVXdkDNDO+lGZmn5qcG10bTpk1zzwNXqBB/eN1SpWDrVujTR1MS0pjb8nOjpNZyPXnS3Afxzz/NHbL++CP+9ufPm18G/PUXnDoFPj7BANy4EW2t5RoWdvu9p+PWco1rXv384tdyjdvwVqiQ+WfbtGlV635eXhfJnRuiouIfP+FWt9Ro27attcVt9OjRt8wp/u233zh79izz5s3j3LlzjB8//ubPGJZoDdQ7yZEjB0uWLLGe699fwVetav6cFy9etMbi9kLPli0b5cqVu8ufzDVxDV7CvegT1uaquJ834eMm3Os+tbmWLVvWeqyEzfTly5fZtGkTx48f5+jRoxw9epSTJ09SoUIFwJwykXBN3Ljn/+uvvwgLC0v0HHv27KFy5crW5T59+lhbLeN22kqoV69eTJo06bZ1//HHH7c9nTp1Ktn7li9f3ppvfC7BVyVx5+OmgMTExNC1a1datmxpbV2NmxZy6dIlYm5+8oy7X/78+alevfotzzdu3Diefvpp8ubNm2Q9cXm68jfrxPdSieep+anBzYiCgsyttd27m1txK1WyuyKxwcmT5pJXhw6Zjetvv5l/Drt3mwtoHDoUvyTW+fPxS2Ld7iAEFSs+dPOxj+DldYNz5/7g6FFzNY7s2c3GtXRpc3ms+PuY+zPGbeDx9TWXxMqR49bPXI888oi1QkNc8/Dtt98C5lYvV78KCwwMpG3bttblhOfB3AlnyJAhXLp0iaxZs1oNUY4cOW67ZSwp5cuXv2UlgThDhw4lICCAkydPsmPHDqKjo/n+++8Bc7mm1Hzt7Yq4rZJ//vknhmHw/fffu/SV87917NjROsLYv3OtX78+jzzySKoet1mzZmTJkoWoqKhEB1dYsGABTz75JH4JvxIAay7vhQsXEq020K9fP3x8fIiJiWHw4MHWFuG///6b8ePHJ9p6WaZMGT755BOyZs3K6NGj2bhxo3Xd1KlT2b59+223wIK5Zft2p9stZ+bl5cWYMWMA+O6774iNjWXPnj0cP36c++67z1qmbOfOnXz++ecsX76cVavMnSq7dOnCAw88QExMDN999x0Qn8OoUaOs6S9x1q1bx86dO3nppZeSreePm5+Ik1rFQsRWbliD15HsONDDiBEj0uaB5s83V6WXdJVcfp5yoIfbHZggudOLL75nFC16r7Woe548+YwmTR43zp0zjMuXDSM8PNp4/vm+RnBwsFG2bFnjP//5j1GrVi0DMIKDg43//Oc/xpdffmkUKVLEeoxGjRoZkydPNoKDgw3ACAgIMIYNG2b06NHD8PHxMQAjX758xvvvv28YhmH8888/xqBBg4yiRYsaoaGhRqFChYxevXoZZ8+eNQzDuOXxy5cvb/zwww8pzm3t2rUGYDRs2ND4+++/E13366+/Gg0aNDBKlChhlC1b1ihQoIDx6KOPGhs2bEj28QYNGmQUKVLECA4ONurVq3fL9f379zeAWxb///XXX42mTZsa+fLlM0JCQozy5csbkydPtq7v2rWrERAQYP1u33//faNr165J/s7uZNCgQcY999xj/c6KFCli9O3b17r+ypUrRvv27Y3AwEDjwQcfNN59913r4AdxzzNp0iQjX758BmD4+PgYPXr0MF577bVENU6aNMlo1KhRoudZuHChYRiGcebMGaNXr15GoUKFjNDQUOOee+4xXnrpJePKlSuGYRi3PH758uWNAwcO3PFn69ixowEYEyZMMAzDMKZNm2YEBwcbISEhiX4/Bw4cMIoXL27VljdvXuO9996zrt+4caPRtGlTI3fu3NbvoVmzZsbGjRuTfN4dO3YYnTp1MgoXLmyULFnSeOCBB4y2bdsae/fuTVEmrvriiy+MypUrGyEhIUahQoWMLl26GCdPnrSuv3r1qlG1alWjZMmSxunTp63xc+fOGb169TIKFixo3HPPPUbFihWN2bNnJ/kczZo1M5566qlkazh37pzh5+dn5M2b97aL/d/pQA9p9n+h2CK980tpv+ZlGGk4kcvBwsPDyZkzJ2FhYek2n+Tnn39O8iuhFIuIMFdJ+PRTqFkTNmyI/85X3C65/K5fv86RI0coXrx4snMJDcOcm3rjRuJTVNStl9PqFerjE3/y9U36fMJ1WjODq1evpvvWUklbZ86coUWLFhw/fpzffvvN2qlM3K9Pnz7MmjWLZcuWJdpJ8N/u9J7o8v+FYqv0zi+l/ZpWUbBRwnlpd23/fmjXDvbuNS9v3QpffgkOPyqRkySXX9yRr65e5ebBBJJuYP+93FVqZc2auEn9d9Mad8pMjWtKJVwDVJwpf/78rFu3jv/85z80btz4jjt4Sdr49NNP2bhxI+vXr79lHeW75dL/hWI7T81PDa6NRo4cedtFzJM1axb07Wt2UmCuND95sppbN7t2zdzR6uRJ8zRz5hoqVmxkXY475coF//2v2cy6ImvW2zetvr7mbW63aoDc3qlTp245oIGTnDx5kkaNGiV5XZEiRVi9enU6V5T+4jKcOnUqx+PWnxO3a9q0KT179rzlwA+pker/C8UjeGp+anCd5OpVs7GdPTt+rHx5c4eydN7rOiOJijKXtvp3o/rv063LjjZi5cpbHy/BikJJ8va+fdMad16Nq9xJ4cKF2Rv3LY5QtGhRu0vINIok3JNUxAOpwXWKPXvMKQkJFwjv1QsmTTK34MotoqPNAw/crmk9dSrxQQlckT27uXrAAw+Y5/PkMY+m9e8mNsGa8SIiIuIGanBtlOK1H//+G2rUML8jB7N7mjYNOnd2X3EeLCYmfv3WhFMG/n06cyZtdtDy94fChW89nTv3G02bPmBdjlsG6/p1OHLEXNs1hevVi020g5nzKUPnc3V9a7GXp+anBtdGTZs2TdkNixSB556DiRPNzYOLFpmLjWYwhgEXLtx5qsDp0yk9Otbt+fgk3bj++5QzZ3I7aD3gehFiq5w6sp/jKUPnS/H/heKRPDU/Nbg2mjx5crKHP7zFmDHmsUtffNFxmwUNwzxqVnJTBBKed3XHLDCnABQqlLhJ/fflwoXNKQSurCxwV/mJRzp79iz54476J46kDJ1P76XO5qn5qcG10aVb91oyu8Fp08yJmz17xo//f3v3HRbFtf4B/LuUBZa2dJEqgoIQ0IBdg91EsES9Rr0gmvw0sVwbT6JXo1iSmMRYsCcm6jWK8WLXmFiIRkWJxkcTY/cqooKCoIIidd/fH+ue7EpfYGHx/TzPPgwzc2bOzAvDy5kzZ6RSYPp03VWuknJyKm5xTU1V3ravLolEmeNX1OJqb6+bfq6lxo/plaLyXtvG9ALHUP/xtVS/1df4cYJbnzx5AowZo+yCYGoKtG6tfM9pHcjNrVzi+uxZzezPwaHshFXV+urkpBwWi7H64MqVK+L1s0w7GRkZMDQ0hK2tbV1XhTHWwNSrgYh27NiB4OBguLm5wcXFBVFRUXjw4EG5ZTIzMzF69Gg0btwYHh4eaNmyJeLi4nRU4+pp0qTJ39/8/jvw+uvK5BZQNnmWNgZVNakegEpMBOLjlYMwTJ8OjBgB9OihHG1MLlc+x+bjA4SGAsOGAdHRwKJFwJYtwK+/AtevVy65tbUFAgKAnj2BqCjg3/8Gli9XvpPi1Cng9m0gPx9ITwfOnwf271e+mG3ePOCDD4B+/ZR5votL/UtuNeKn544dO4aAgABIJBJIJBJYWVnBz88PlpaWaNSoEfr06YMzZ87ovF49evSAVCqFRCLBnDlzxPzPPvsMVlZW2LRpU7W2b2JiUuUyaWlpCA8Px6xZs0osu3LlCiQSCV4r5R/TxMTEEudY/e0/y5cvR5MmTWBubo6AgAA8Vz1UCuDo0aN4++234ezsDD8/PwQFBaF3795YsWIF7t+/X+VjqKqvv/4a/v7+8PDwgJubGyZOnIicnJxyyyQnJ4tjffmTlJQEQNny4+/vj7Vr12pdN21iyOqXhnQtfRXV2/jp5MXBlRAfH08SiYTatm1LRUVFdO7cOQJAfn5+Zb6/uqCggFq1akUAaN++fURENGDAAAJAGzZsqNL+K/tu4xqnUBDFxhIZGxMpOygQWVsTbd9epc0UFBClpBAlJRHt2EG0YgXRjBlEI0cS9epFFBBAZGv79y6q+7GyIvL1JerWjSgiguijj4iWLiX673+JTpwgunmTKDe3dk5ZfVfme9dr6uRX9qMFAASA+vfvT0TK99mHh4cTAJLL5RrvutcVDw8PAkAxMTFinr+/PwGgsLAwndYlJSWF3N3d6fXXX6e8vLwSy6dNmybO4e+//17qNl4+x+qOHDlCoaGhGvM+/fRTAkAuLi504cIFIiJSKBT01VdflTgvtUG1n3/84x9ERLRr1y4CQF27diWFQlFmuVu3bpGRkRHZ2dmV+Kifm7179xIAmj17dq0eB6s7ZV4TGdNCZfO1epHgKhQK8vT0LHGxdnV1JQC0bNmyUsutX7+eAJChoSEVFRUREdG3335LAMjR0ZEKCwsrXYe6SHC3rV1L9PbbmklJmzbK7PCFwkKie/eIzpwh2r2baPVqolmziN57j+itt4iCgogcHWsuJ5LJiHx8iEJDiYYNI4qOJlq0iCgujujoUaJr14hycnR2iuq1LVu2lDq/ISW4REQ7d+4U8+Pi4rTabnWUluBu3LiRWrduTYcOHarWtjMzM6u0fufOnTX+oVZXVFRE7u7u4lxNmDCh1G1UJcE9fPiwWH/Tpk0l1n/zzTdrNcHNyckhc3NzAkDr168nImXDgoGBAQGgPXv2lFn21q1bJZL1srRr144A0OHDh6tcx6rGkOleRQluWddSph90Hb/K5mv1oovCxYsXkZycDABwcHAQ81VPxu7du7fUcvv27QMA2NjYwPDFU0WqMunp6Th9+nRtVbnaFKd+Q7foj4CdO8W8U+2nYnzgcfSb1AQhIcp+pyYmytvzrVsD/fsDY8cC8+cD332n7MHwxx/K2/sVMTEBmjQBOnYE/vEPYNIk5cAM338PJCQAly8ruwA/fQpcuwYcPQrExQFffQVMnarsphAaquy2YGFRe+dFn9y4caOuq6AThYWFYlryYtiJvLw8DB06FM2bN0eLFi1gY2OD0NBQHD16VKNsbGws/Pz84OnpiZYtWyIkJAQLFy4Uy+/evYt//vOfcHFxQdOmTREcHIytW7eWW5+pU6di4sSJOHPmDD755BMAyu5Nrq6u4hb42rVr0bFjR5ibm+PNN99E+ku/JOvXr0dgYCCCgoLQtGnTSnWH+vXXX3H8+HFIpVL07NmzxPJDhw6hZcuWaN26NQBgy5YtKKjmsCCxsbFiOjw8vMTyWbNm4Y033qjWPspz9OhRPHvRF0l1bTY2NoZcLgdQ9rVZJSsrC+PHj0dISAi8vb0xfPhwXLhwocR6b775JgBl15OqyquJJ1hZnXpVrqUNVX2NX73o1Xj9+nUxbWZmJqZlL97Qde3atXLLlVZGVa5Dhw6lls3Pz0d+fr74Pjs7W4uaa+ftvkX4at9wNMUjAEAWbBCF/2Dfqb7Aqapty9i49CGwXv7I5dUbEou9mjIyMrB06VIAQGBgIPr16wdAmVQkJCTgwoULaNSoEU6dOoWOHTuiX79+uHTpElxdXfHzzz9j8uTJCA0NxZEjRyCRSPDNN98gLi4OH374IZ4+fYrQ0FDcvHkT27ZtQ79+/dCiRQsMHToUhoaGGDx4cKl1Wrx4MbKysvCf//xHzBs4cCCys7MxatQoAMpEPDExEf3798eePXswZ84crFq1CgCwatUqjB8/HkFBQTh58iQSEhIwatQo/PHHHzh79qz4Z/ll+/fvBwB4enpCKpWWWL5hwwZERUXh3r17OHPmDDIzM7F3714MGjRIu5MP4NQp5QXB3t6+1PFey7q+qSQnJ1fYPy4mJkajf7O68q7NWVlZZV6bAcDAwAB5eXmYNGkSvL29sXDhQkyfPh27du3CsWPHEBISItZt9mJcb1VCzS9vYIxVV71IcJ8+fSqmDQwMSkyrLy+tXGllyisHAAsWLMDcuXNLzP/yyy9h+mKc2UmTJuH27dvYtWuXWB4ZGQmpVIrvvvtOzOvTpw8CAgLw5Zdfinlt27ZFz549sWzZMpE8e3t7Y+jQobj3IBUjsBG/IhS/oS2GYQvuwF2jHoaGBFvbQkilGbC0fAorqxx07eoHa+tnuHw5AZaWObCyeorIyD5o3LgRVqxYIcq+8UZXdOzYEQsWLEDxizciqJKTb775RrRmubq6YuTIkdi+fTsuX74MQPlHLDo6GkePHsWJEyfENqdMmYKbN29i9+7dYt6IESNgZGSEdevWiXlhYWFo0aKFRitdu3bt0KNHDyxdulTEpFmzZhgyZAi+//573L59GwBgZ2eHsWPH4scff8S5c+dE+Y8//hhJSUk4fPiwmDd27FhkZWVptPYNGTIE9vb2IpEBgO7du6N9+/b47LPPoFAoAAAtW7ZEeHg4vv76a2S8eE+vm5sboqKisG3bNlx58TpkmUyGqVOn4pdffsHJkyfFNqOjo3Ht2jUUFxeLFsSRI0cCUCY55ubmaN++PZ4/fw6pVCoeAmoM3SoqKtJoubSysoKFhQVSU1PFPJlMBrlcjvT0dI3hlo4cOQJfX1/cuHEDCoUCPXr0wJo1a1BUVITU1FQoFAr89NNPcHR0RG5uLjw8PODg4ID09HT89NNPGDFiBBITEwEoH7zavXs3evbsiZ49e8LU1BSpqanYtm0bbt68CQDw8vJCRkYGAgICcOPGDSxYsEAkb6q45eXlibqrHsAqLi4W89SHqmnXrh1SU1Ph6ekJQJkoqtabN28eAKBNmzYwNDQUCeAff/yBgwcPIjg4WJwLExMT2NnZISsrC+fPnxfnEVD+U6z6eX7y5AlOnDiB1atX4969ezA2NkZhYSHWrVuH8PBwZGZmloiP+vFYWlrC0tISWVlZKCgoQGpqKszMzPD48WMAgFQqRWpqKoyNjeHg4IDHjx8jNzcXgDKZd3Z2Rk5OjsaDX05OTigqKkLTpk3FPFXyXqz2phRLS0sQEdLS0sQ8c3NzWFtba7Rqq7admZkJevGKQFX9njx5Ilp6AcDZ2Rl2dnaiRf/+/fv417/+hS+++AKPHj1CdHQ0tmzZAhsbG0ilUrE9hUKB8+fPo2PHjkhLSxPzVT+nGRkZ4o6CVCqFvb09JBKJOI8GBgZo1KhRqeeioKBA42fEzs4OEokEDx8+FPOsra1hZmam8eCehYUFrKys8ODBA3HeTE1NYWtri8zMTNFQYmRkBEdHx1LPRW5uLp48eSLmOTg4oLi4GFlZWWKejY0NjI2NS/2drey5ePTokfjdKO9c5Ofni9iVdS7kcjlMTU2rdS5UP6dFRUXi+Eu7lpuZmYlrKaD7a7n6nQj1a7lK37590axZMyxatEjM69ChA7p164bFixeL30VfX18MHjwY//nPf3Dnzh0Ayli///772Ldvn7iGGBgYYMaMGTh16hQSEhLENseNG4eHDx/iv6qHzQG88847sLW1xerVq8W8Hj16oF27dhrnrFWrVggLC8Pq1avF9cbDwwORkZH473//K/4ZtbCwwOTJk3H48GHxsCcAfPjhh7h06RJ+/PFHMe/dd99FUVERNm7cKOb1798fXl5eWLJkiZhnbGwMAFi0aJH4+fPz88OgQYOwYcMG3L17F4DyDvuYMWOwZ88e/PnnnwCU16R///vfSExMxJEjR8Q2J0yYgPv372Pbtm1i3rBhw2BlZaVxZ6tctd9bomI7duwQ/cy+++47Mb9Tp04EgDw8PEotFxgYSADIzc1NzFPvs6bqM1aavLw8evLkifjcuXNHZ31wP/6YqFUrountjtAH7xVQTAzR118T7d1LdPYsUVoa0YsuxUwPNaQ+uLm5uTRlyhQCQCYmJvTDDz+IdTds2EBdunQhb29vCgoKImNjYwJAc+fOJSKixMREkkgkYpsymYyGDRtGN1/0MR87dqxYFhAQQEFBQeTp6UlOTk7UvHlzsZ/S+uBGRUURAI0+nqo++QDEw08xMTEEgDw9PYmI6MGDB2Ide3t7CgoKooCAAHJyciInJydau3Ztmeeme/fuBIDeeOONEstWr15N48ePF9/369ePAJCRkRHdv39fY11V/9V+/fqV2M4vv/xC3bp1E987ODiIutaFxYsXi/OVkJAg5quej6hsH1uV4OBgAkAWFhYa8xMSEsR+jh8/XhNVZ/UIP2TGalJl++DWixZcHx8fMa0+NI7qv6JmZbyW1sfHB3/++WepZcorByhbZupqeJn585WfQ4cK0bOncZ3UgVXfoUOHSu2L2ZCYmZlh4cKFWLNmDZ4/f46PPvoI77zzDrZt24aRI0fCwsICf/zxB7y8vODp6Ynbt2+L1pUOHTogISEBsbGxOHToEHJzc7FlyxYkJSXhf//7n8Yt74MHD8LZ2bnG6i15qT8OvWj9Ut/nwIEDsXDhQtEiWxFLS0sAmn2SVTZs2ICUlBTxXIDqOlRUVIRNmzYhOjparCuXy5GVlVXqMFvZ2dmwsbER37dv3x579uzBw4cPkZ2dXaKuN2/eRH5+Pvz8/Eqt871799C9e/dyj2vChAmYMGFCqcu0vTYDytZdIyMjWKh12lfdYSt+6V3b6n2VVee5sko7L0y/vArX0oasvsavXjxkphpfEYC4zaA+HR4ejuLiYkRGRqJv377itmBYWBgA5a1J1QVTVcbR0VFjjMn66LfffqvrKrBqeFXiZ2hoKG5BqW7xqm49BwQEwMvLC0DJpOXIkSOwsLDArl27kJmZiSlTpgAAbt26hczMTPj7+4t1VbfzAGDPnj2YOHFirRyLpaUl3NzcxD7VuzGFh4fjr7/+KrOsKolUv40LKLtgPHv2DKmpqUhOTkZycjLu3r0rXl6g3lcYAIKDgwEoH65VJd4qFy5cQKtWrcT36udB/dYhoEzaw8LCEB8fX2adCwsLcfXq1XI/Lx+Pui5duoj+sKpra2Fhobi9rXrwLTs7G+Hh4YiKihI/B0uXLsXXX38ttqVQKMTDxK+//rrGflS36g0NDctNmktTXlc0ph9elWtpQ1Vf41cvElyJRIIvvvgCgLIlR6FQ4MKFC7hz5w6aN2+O0aNH49y5c9i0aRP27duHn3/+GQAQERGBoKAgFBcX4+DBgwD+fhBkwYIFMKpvbwZgTM8QEZYsWSL6kQ8dOhQARJKYnJyMvLw8XLlyRaMPJwCcPXsWM2fOxPPnz2Fqaire+uXp6Ql7e3sMHz5c9H9ds2YNAGWysmDBglodGWDmzJkAlEm66unfbdu24c6dO2jRokWZ5fr27QtAeczqD6hu2LAB76q/VhvKPpHDhg0DoExaz549K5ZNmjQJgHKkl7lz54ok99KlS4iLixMPygHKfoeqfnYfffQRLl68CED5z8TMmTORn5+PcePGlVlnT09PkHI4yDI/ZT1gBij7682ePRsA8NOLF88cOHAACoUCoaGh4pwcPHgQP/74IzZu3KjRf/77778XfS+//fZbZGRkwMDAADExMRr7uXr1qjhe9VZ2xhjTWu31kqi6+Ph4atWqFbm6upKzszNFRESIgeWfPXtGISEh5O3trdGnLSMjg9577z1q1KgRubu702uvvUYbN26s8r7rYhzc+fPn62xfrOaVFT997IP766+/ipcnACBLS0tq0aIF2dnZkbW1NbVu3Zq++OILKigoICLl70t4eDiZm5tT+/btKTY2VvTLdHBwoCVLltCBAweoc+fO1LRpU/L19SUnJyfq27eveFkBEdGNGzdo4MCBJJfLycPDg9q3b6/RD7979+6ib69qu1OmTCG5XC769Q4aNIi2b99OLi4uov6hoaG0fft20YfV2NhYo7/o8uXLyc/Pj8zMzKh169Y0dOhQSklJqfA8qfrh7tixg4iIZs+eTTKZjLy8vGjr1q1ivYSEBHE+AJCzs7PGGMK7d++m0NBQsrKyIrlcTiEhITRo0CCNc6Pul19+oQEDBpCTkxP5+vpSYGAgjRo1im7fvl25AFfTqlWryM/Pj9zd3cnFxYXGjx+vca1MTU2lpk2bUps2bSj3xRtefv31V3r77bfJz8+PmjZtSubm5tSlSxeNvrwqwcHBJJFI6NixY1Wu271797Q/MKYTFfXB5b+F+k3X8atsviYheuke2SsqOzsb1tbWePLkic76cxUUFJQ63BDTD2XFLy8vD7du3UKTJk3EiBysflIoFBojr1QkLS0NnTp1gkwmw6lTpzT6lzLtbN26FUOHDsWnn36KGTNmVLl8VWPIdK+iayL/LdRvuo5fZfM1virUofL6+7H6j+On/9QfnKoMZ2dnJCUlwdfXF8OHD6+lWr06Ll26hClTpmDjxo1aJbdA1WPI6h++luq3+ho/TnDrkKq/MNNPHD/9pz42aWU5ODggPj4eK1eurIUavVqcnZ1x/fp1REZGar0NbWLI6he+luq3+ho/fgqLMca0oHrQjmlPfUg0xhirSdyCyxhjjDHGGhROcOvQe++9V9dVYNXA8dN/9vb2dV0FVk0cQ/3H11L9Vl/jxwluHVJ/ew/TPxXFT/VGL1Z/8SAy+o9jWP9VdC3kv4X6rb7Gj/vg1qHvv/8eH3/8cV1Xg2mprPhJpVIYGBggNTUVDg4OkEqlJV4dy+qH9PR0ODo61nU1WDVwDOsvIkJBQYF4wUdZQ0nx30L9Vl/jxwkuYzXMwMAATZo0QVpaGlJTU+u6OqwcT548wbNnz+q6GqwaOIb1n0wmg7u7O49XzHSKE1zGaoFUKoW7uzuKiopQXFxc19VhZVi9ejXGjh1b19Vg1cAxrN8MDQ1hZGTEd7GYznGCW4cGDBhQ11Vg1VBR/CQSCYyNjWFsbKybCrEq69mzJ79tTs9xDPUf/y3Ub/U1fny/oA55eHjUdRVYNXD89B/HUP9xDPUfx1C/1df4cYJbh2JjY+u6CqwaOH76j2Oo/ziG+o9jqN/qa/w4wWWMMcYYYw0K98F9QTWWYnZ2ts72mZeXp9P9sZrF8dN/HEP9xzHUfxxD/abr+Kn2VdEY2BLiUbIBAHfv3uV3yzPGGGOM6YE7d+7A1dW1zOWc4L6gUCiQmpoKS0tLnQxnkp2dDTc3N9y5cwdWVla1vj9Wszh++o9jqP84hvqPY6jf6iJ+RIScnBw0bty43LGVuYvCCwYGBuX+J1BbrKys+Jdaj3H89B/HUP9xDPUfx1C/6Tp+1tbWFa7DD5kxxhhjjLEGhRNcxhhjjDHWoHCCW0dMTEwQExMDExOTuq4K0wLHT/9xDPUfx1D/cQz1W32OHz9kxhhjjDHGGhRuwWWMMcYYYw0KJ7iMMcYYY6xB4QSXMcYYY4w1KJzg1oIdO3YgODgYbm5ucHFxQVRUFB48eFBumczMTIwePRqNGzeGh4cHWrZsibi4OB3VmL2sqjE8duwY+vbtCz8/P3h7e8PJyQndunXD/v37dVhrpk6b30OVvLw8eHl5QSKR6OTFL6wkbeO3c+dOdO7cGa6urggICECzZs0wevRoHdSYvUybGP7000/o1q0bXFxc0KxZMzRp0gTjxo1Denq6jmrN1BUWFuKzzz6DmZkZJBIJ5syZU2GZ3NxcfPjhh3B3d4enpyeaN2+O2NjY2q/sy4jVqPj4eJJIJNS2bVsqKiqic+fOEQDy8/Oj58+fl1qmoKCAWrVqRQBo3759REQ0YMAAAkAbNmzQZfUZaRfDBQsWUEBAAD169IiIiJYsWUIACABt3LhRh7VnRNrFUF1MTIyIH18mdU/b+K1atYoA0L/+9S8qLi4mIqLExETy8fHRVdXZC9rE8Ny5c2RoaEgAaM2aNURE1KtXLwJAnTp10mX1GRHduXOHAgMDKSwsTFwLY2JiKiwXHh5OAGjFihVERDR58mQCQHPmzKnlGmviK3cNUigU5OnpWeKHwNXVlQDQsmXLSi23fv16AkCGhoZUVFRERETffvstASBHR0cqLCzURfUZaR/Db775htavXy++LywsJBMTEwJAbdu2reVaM3XaxlDl1q1b5OjoSL179+YEtw5oG79Hjx6RTCYjAJSVlaWj2rLSaBtD1d89AHTo0CEiIho3bpyYp2pAYLpx6dIlOnLkCN26davSCe6RI0fEurdu3SIiosOHDxMAkkqllJGRUfsVf4G7KNSgixcvIjk5GQDg4OAg5js6OgIA9u7dW2q5ffv2AQBsbGxgaGioUSY9PR2nT5+urSqzl2gbw9GjR2PkyJHieyMjI8jlcgDK2zVMd7SNocrkyZPx4YcfolGjRrVWR1a26lxHc3NzIZPJ8NVXX6FNmzZo2rQpxowZg6ysrFqvN/ubtjHs2bMn7OzsxDaICJcuXQIAWFhYQCaT1WKt2cv8/PzQpUuXKpVR5TPA37FXxb2goAAHDx6ssfpVhBPcGnT9+nUxbWZmJqZVv5TXrl0rt1xpZcorx2qetjF82dOnT0WfsapeIFj1VCeGBw4cwJUrVzBp0qTaqyArl7bxu3DhAgDlP5QFBQVISkpCy5YtsXbtWgwZMqQWa8xepm0M3d3dcezYMbRo0QJTpkxBo0aNcPToUdjY2OC7776DVCqt3Yqzaist9nWVz3CCW4OePn0qpg0MDEpMqy8vrVxpZcorx2qetjF82bfffgsigo+PD2bPnl2zlWTl0jaGBQUFmDhxImJjY2FsbFy7lWRl0jZ+6q20gwcPhoGBAQYPHgwASEhIwO+//14b1WWl0DaGKSkp6NmzJy5duoRt27bh/v37GDFiBFq3bo2mTZvWbqVZjSgt9nWVz3CCW4MsLCzEtEKhKDGtvry0cqWVKa8cq3naxlDdiRMnMHPmTHTt2hWJiYmwt7ev+YqyMmkbw8WLF8PX1xe9e/eu3QqycmkbPyMjIzGt+p1Tvz2uutXNap+2MVyyZAlSU1MhkUjQv39/8fXgwYPo0qUL7t+/X7sVZ9VWWuzrKp/hBLcG+fj4iOnnz5+LaVUfzGbNmpVbrrQy5ZVjNU/bGKr88MMPCA8Px6xZs3D48GE4ODjwH1Yd0zaGu3btwtWrV9GyZUu0bNkSe/bsEctatmyJDz74oJZqzNRpGz8PDw8xrRraTX2IN/VWJFa7tI3h5cuXAQDm5ubieRQrKysAypY/XfbfZNopLfZ1lc/wb3wN8vf3FxfZjIwMMV81HR4ejuLiYkRGRqJv376iqT4sLAwA8OjRIxQXF2uUcXR0RJs2bXR2DK86bWOYn5+PCRMmYP78+Thy5AimT58u/qD26dNHx0fxatM2hklJSbhy5QrOnz+P8+fPo1+/fqLs+fPnsWbNGh0exatL2/j16tVLrKvqrpCZmamxXaYb2sZQ1eKem5srWv1ycnJEeSLSSf1Z5UVHR6Nbt264d+8egL/zGeDveKu+SqVSjd/TWqez8RpeET/88AMBoPbt21NxcTH9+eefBICaN29Oubm5dObMGTGERnx8PBEpx8ENCgoiALR//34iIho0aBABoO+++64uD+eVpE0Mp02bRgDIysqK7OzsND4eHh51e0CvIG1i+LKoqCgeJqyOaBu/IUOGEACaO3cuERFFRkYSAAoLC6urQ3llaRPDEydOkJGREQGgAwcOEBHRmDFjCADZ29vrdIgp9reyhgnLyMgQ8xcuXCjmv/XWWwSAVq1aRURE0dHRBIBmzZql03rzlbsWxMfHU6tWrcjV1ZWcnZ0pIiKCUlNTiYjo2bNnFBISQt7e3nT//n1RJiMjg9577z1q1KgRubu702uvvcYvCKhDVY3hpEmTNF4MoP7hBLduaPN7SESUkpJC/v7+JJfLRQz9/f1p5cqVdXEYryxt4pefn08ff/wxeXl5kbu7Ozk5OdH7779Pjx8/rqvDeKVpE8NTp05RWFgYeXh4kLe3N3l6etKoUaPoxo0bdXUYr6z8/Hzy9/enZs2aiWuhg4MD+fv709atW0mhUFCfPn3I2dmZ/vrrL1Hu6dOnNHXqVHJ1dSVPT0/y8fGhRYsWkUKh0Gn9JUTc5s8YY4wxxhoO7oPLGGOMMcYaFE5wGWOMMcZYg8IJLmOMMcYYa1A4wWWMMcYYYw0KJ7iMMcYYY6xB4QSXMcYYY4w1KJzgMsYYY4yxBoUTXMYYY4wx1qBwgssYY4wxxhoUTnAZY4wxxliDwgkuY4wxxhhrUDjBZYwxxhhjDQonuIwxxhhjrEHhBJcxxhhjjDUonOAyxhhjjLEGhRNcxhhjjDHWoHCCyxhrkMLDw2FqagqJRAKJRIINGzbUdZV07ubNm/D19YWLiwt+++23Wi/HGGP1BSe4jLE6deLECcjlchgZGYlk1MzMDHK5XOMjlUoxZ86cSm933759mD59eu1VvALjxo2DlZWVOCaJRAK5XA4DAwPIZDL4+/tjxowZyMrKqrU67Ny5E1evXkVqaio2bdok5j9+/Bhz5szBnDlzSk38yyqnK3K5XOOfE6lUCmtra0gkEtja2qJVq1ZYuHAhnj17pvU+kpOTxTnYtWtXzVWeMVY/EGOM1QOhoaEEgADQggULSiyPioqimJiYKm0zJiZGbHP9+vU1U9EquHXrlti/6nKbnZ1NY8aMEfOaNGlCWVlZtbL/69evk4+PDzVu3JiSkpJKrVdoaGily+mSeuyioqKIiOjJkyc0adIkMb9Dhw5UXFys1faPHDlSYvuMsYaDW3AZY3phwIAB6NKlS11Xo9osLS2xfPlyODg4AABu3bqFb775plb25e3tjWvXruHevXto27ZtrZerbVZWVli8eDHMzc0BACdPnsTJkyfruFaMsfqIE1zGWL02Z84cjBw5UiS4S5cuRadOneDq6gpzc3PY2Nigc+fOVbqVfuXKFQwcOBCNGjWCqakpHB0d4efnh8jISFy5ckWsl5qaig8++AAeHh6QyWSwt7fHoEGDcPHixWodk1Qqhaenp/j+5s2bAIC8vDx88sknCAwMhIWFBSwsLBAYGIhPPvkEeXl5Gtv46quvEBQUBJlMBmtrazRu3BjdunXD4sWLAQCff/45LCwsxG3+kSNHAgDi4uIQGBgotqPqIiKXy5GSklJmOblcXqK7hUqbNm1gbGwMmUyGuLg4AMDBgwfRq1cv2NjYQCaTwcfHBzNnzixxHFVFRCAi8X1OTo6Yvnr1KiIiIuDj4wNHR0eYmprCy8sLY8aMwb1798R6n3/+OcLDw8X3cXFx4hyoKBQKrFy5EsHBwbC0tIS5uTmCg4Nfyb7cjOmlum5CZowxorK7KMTExGjcQvb396dp06ZRfn4+KRQK2rJliyi3YsUKjW2W1kUhPz+fXF1dCQCNGDGCnj9/TgqFgn7++WeSyWS0c+dOIiK6c+cOOTs7EwAaMGAA5efn0/z58wkAWVhY0F9//VXhMZXWRYGIKC8vj+zs7MT8RYsW0bNnz6ht27YEgLy9vSktLY0ePHhAPj4+BIDatm1Lubm5RES0cuVKAkAmJiZ07tw5IiLKzMykwYMHU1BQkNhPWbfhK+qiUFY5f39/Mf/q1atifmFhITk6OtKff/5JRERr164liURCACg+Pp5ycnIoMDCQAFD37t0r1a2gtC4K6enp9MEHH4j5tra29PDhQ1EmPj6eGjduTJcvXyYiogcPHlCnTp0IAHl6etLTp08rPEaViIgIAkCWlpb0v//9jy5fvkwymYwA0Ny5cyusP2OsbnELLmOs3pk7d65oUfv88881lu3atQvz58+HVCqFRCLB0KFD4eTkBACIjY2tcNuXLl3C3bt3AShveZuYmEAikaB3796YNWsWnJ2dAQCzZ89GWloaAGDYsGGQSqUYNWoUAODp06eYMWOGVseWmZmJ999/H5mZmQAAX19fjB49GsuWLRMjFkRERKBRo0ZwdHREREQEAOC3337DsmXLAChbRwHA0NAQNjY2AABbW1ssXLgQXbt21apelaE6fgBYv369mN6/fz/c3Nzw2muvIScnB9HR0SAiWFhYYODAgbCwsMCQIUMAAAkJCdi9e3eV9hsXFwczMzM4OjpizZo1kEgkaNeuHfbv3w87OzuxXq9evXD69Gn4+voCABwdHfHPf/4TgPKhssru99ixY+KOQKdOneDl5QVfX1906NABAPDpp5/iwYMHVToGxphucYLLGKt3YmJi8PjxYzx+/LjESAjPnj3D8OHD4e7uDgsLC8jlcqSnpwNQ9metiL29PQwMlJe+FStWwMXFBZGRkdi8eTMmTpwo+pz+9NNPoowqgVZ9BYBDhw6huLi4Sscll8vh6uqKvXv34vXXX8e8efNw+vRpWFpaYu/evWI9d3d3Me3m5iamVeuo6pGbm4umTZuiU6dOiImJQU5ODpYsWVKlOlVFZGQkjI2NAQAbN24Ux79hwwaR/J48eRLZ2dkANM+1+rlTP7eVMXz4cGRmZopktW3btli9enWJ/sEymQybN29GSEgIbGxsYGlpialTp4rllfn5eLl+6vVWTRcUFCAhIaFKx8AY0y1OcBlj9Zr6UFYpKSno1KkTtm3bBmtra1y7dg2PHz8WCWFRUVGF23N1dcWiRYtgYmICAEhLS8OmTZsQERGBZs2a4fr16wCAhw8fijLh4eGQy+Wwt7eHiYkJTExMoFAoRCtsZT1+/BjPnz9HZmYmzp49i1mzZsHS0hIARJIOAKampmJaVU/1dWbNmoXg4GAAQHFxMRITEzFv3jwEBgbio48+qlKdqsLR0VH0XU1NTcWBAweQmZmJw4cPY/jw4QA0z1tKSopoiZ8yZYo4d9q0fspkMixfvhxWVlZISkpCz549S5z/qVOnYtq0aTh79iw++eQTPH78GKtWrRLLCwsLK7Uv9WNQ75+7c+dOcQyq1n3GWP3ECS5jTG/s2rULT58+BQC8++67aNy4sVbbmTx5Mu7fv4/Nmzdj1KhR4jb3vXv3sGDBAgDQuPW9e/du0aKcl5cnPo6OjtU8or+pb0v9Qaz8/PwS67i6uuL333/HmTNnMG/ePHTs2FGss3DhQty+fbvG6vWyd999V0yvW7cOmzdvxptvvim6SqifNxcXF3HecnJyxHmrahcFFRsbG0yYMAGAMgn97LPPNJaruhVYW1tj/PjxMDQ01Go/6sfwzjvviGN49uyZOIbo6Gitts0Y0w1OcBljekO9S4B6y6b6k/QVuXTpEjp37gy5XI7hw4dj3bp1OHfunFh+//59AECPHj3EPFWrrsrKlSvF6AI1Rf2p/pSUlFKnVeuMGTMG27dvR0hICGbNmoUTJ05oJJ4VtZCqnzuFQgEAuHz5Mvbv319hPd966y3RT3nv3r1YtWqVRt/c9u3bQyaTAVCeS9U/JOrlt23bVuF+yjJlyhSx/VWrVon+1MDfPx9SqVTMK+tno7RzcPr0aRw/frzc2N++fRutW7eudHcHxljd4ASXMaY3OnXqJKb37t2LoqIixMXF4dGjR5XeRkFBAU6cOIH58+fj+fPnAJSJjUpYWBgA5YNutra2AIClS5eKobwSExMRExODPn36VPt41E2aNAkhISEAlC2RDx48QHp6OjZv3gwACAkJwcSJEwEouyrMmDEDf/31FwDg0aNHIhFzcXFBUFBQuftycnKClZUVAGWrdXFxMWJjYyv1Ri9DQ0OMGDECgPJc5uTkoFevXmK5tbU1Pv30UwDKLgEff/wxnj9/jvz8fMTExODixYvo1q1bZU9LCfb29nj//fcBKFu6586dK5Z17twZAJCRkYETJ04gOzsb33//fanb8fLyEi28t2/fhkKhwJw5c0SCO2DAAABAUlIStm7diuLiYmRkZGD8+PFwcnJCkyZNtD4GxpgO1PUwDoyxV9vx48fJ2tqaDA0NxbBNpqamZG1tTZs3by6x/vLly8nd3Z2kUim1atWK1q1bRx4eHqKstbU1HT9+nMLCwsjExETMNzMzo7Fjx1JaWhpFRERQQEAA2dnZkUwmI0tLS2rTpg2tWrVKY183b96kqKgoaty4MRkZGZGLiwt17dqV9uzZU+FxjR07liwtLTWGCbO2tiZra+syy+Tm5tLcuXPJ39+fZDIZyWQy8vf3p7lz54ohwoiUw3B1796dXFxcSC6Xk1QqJTc3N4qIiKAbN24QEdGCBQvI3Nxc7NvY2Jhee+01sY24uDjy9vYmIyMjsrOzo27dulFycnKF5YiIrl69KpZPmzat1GPZsWMHhYaGkqWlJclkMvLy8qL/+7//o+Tk5ArPnbW1tUbsjI2NRVyJiFJTU8VyiURC1tbWdO3aNUpOTqbevXuTubk52draUmRkpBjaDS+GVQsLCxP7Wbx4Mbm5uZGhoSE5OjpS//796dGjR0SkHPrsyy+/pMDAQDI1NSUbGxtq0aIFxcTEaMSCMVY/SYjURsxmjDHGGGNMz3EXBcYYY4wx1qBwgssYY4wxxhoUTnAZY4wxxliDwgkuY4wxxhhrUDjBZYwxxhhjDQonuIwxxhhjrEHhBJcxxhhjjDUonOAyxhhjjLEGhRNcxhhjjDHWoHCCyxhjjDHGGhROcBljjDHGWIPCCS5jjDHGGGtQOMFljDHGGGMNyv8DWJY9bQLl2xIAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"markdown","source":"# **Checking model predictions for random images from test dataset**","metadata":{}},{"cell_type":"code","source":"import random\nimport matplotlib.pyplot as plt\nimport cv2\n\n# Get random indices for three images\nrandom_indices = random.sample(range(len(true_classes)), 6)\n\nplt.figure(figsize=(10, 5))\nfor i, idx in enumerate(random_indices, 1):\n    # Load the image\n    img_path = test_generator.filepaths[idx]\n    img = Image.open(img_path)\n    \n    # Display the image\n    plt.subplot(2, 3, i)\n    plt.imshow(img)\n    plt.axis('off')\n    \n    # Display the true and predicted labels\n    true_label = \"Tumor\" if true_classes[idx] == 0 else \"non-Tumor\"\n    predicted_label = \"Tumor\" if predicted_classes[idx] == 0 else \"non-Tumor\"\n    \n    plt.title(f\"True: {true_label}\\nPredicted: {predicted_label}\",fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:19:08.099172Z","iopub.execute_input":"2024-01-12T14:19:08.099565Z","iopub.status.idle":"2024-01-12T14:19:08.743655Z","shell.execute_reply.started":"2024-01-12T14:19:08.099537Z","shell.execute_reply":"2024-01-12T14:19:08.74277Z"},"trusted":true},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 6 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}