U_Net

{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":8089,"databundleVersionId":44321,"sourceType":"competition"}],"dockerImageVersionId":30747,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":5,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/kansalritu/semantic-segmentation?scriptVersionId=193083151\" 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\nimport os\nimport numpy as np\nimport random\nfrom tqdm import tqdm\nfrom skimage.io import imread, imshow\nfrom skimage.transform import resize\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:56:18.730867Z","iopub.execute_input":"2024-08-18T14:56:18.731244Z","iopub.status.idle":"2024-08-18T14:56:18.736561Z","shell.execute_reply.started":"2024-08-18T14:56:18.731203Z","shell.execute_reply":"2024-08-18T14:56:18.73544Z"},"trusted":true},"execution_count":102,"outputs":[]},{"cell_type":"code","source":"import zipfile\nTRAIN_ZIP = '/kaggle/input/data-science-bowl-2018/stage1_train.zip'\nTEST_ZIP = '/kaggle/input/data-science-bowl-2018/stage1_test.zip'\n# Directory where to extract\nTRAIN_PATH = '/kaggle/working/stage1_train/'\nTEST_PATH = '/kaggle/working/stage1_test/'\n\n# Unzip the train data\nwith zipfile.ZipFile(TRAIN_ZIP, 'r') as zip_ref:\n    zip_ref.extractall(TRAIN_PATH)\n\n# Unzip the test data\nwith zipfile.ZipFile(TEST_ZIP, 'r') as zip_ref:\n    zip_ref.extractall(TEST_PATH)\n\nprint(\"Extraction complete!\")","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:38.90857Z","iopub.execute_input":"2024-08-18T13:55:38.909071Z","iopub.status.idle":"2024-08-18T13:55:44.318105Z","shell.execute_reply.started":"2024-08-18T13:55:38.909044Z","shell.execute_reply":"2024-08-18T13:55:44.317126Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Extraction complete!\n","output_type":"stream"}]},{"cell_type":"code","source":"train_ids = next(os.walk(TRAIN_PATH))[1] #gives the folder names\ntest_ids = next(os.walk(TEST_PATH))[1]\n# test_ids","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:56:14.78339Z","iopub.execute_input":"2024-08-18T14:56:14.783785Z","iopub.status.idle":"2024-08-18T14:56:14.789949Z","shell.execute_reply.started":"2024-08-18T14:56:14.783754Z","shell.execute_reply":"2024-08-18T14:56:14.78908Z"},"trusted":true},"execution_count":101,"outputs":[]},{"cell_type":"code","source":"IMG_WIDTH = 128\nIMG_HEIGHT = 128\nIMG_CHANNELS = 3","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.332842Z","iopub.execute_input":"2024-08-18T13:55:44.333407Z","iopub.status.idle":"2024-08-18T13:55:44.336963Z","shell.execute_reply.started":"2024-08-18T13:55:44.333381Z","shell.execute_reply":"2024-08-18T13:55:44.336017Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"# path = TRAIN_PATH + train_ids[0]\n\n# # path\n# img = imread(path + '/images/' + train_ids[0] + '.png')[:,:,:IMG_CHANNELS]\n\n# imshow(img)\n# min_val = img.min()\n# max_val = img.max()\n\n# plt.show()\n# img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n# # After resizing, ensure the data is in uint8 format and the range [0, 255]\n# min_val1 = img.min()\n# max_val1 = img.max()\n\n# print(img.dtype)\n# if img.dtype != np.uint8:\n#     img = np.clip(img, 0, 255)  # Clip values\n#     img = img.astype(np.uint8)  # Convert to uint8\n\n# imshow(img)\n# plt.show()\n# print(min_val, min_val1)\n# print(max_val, max_val1)","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.337874Z","iopub.execute_input":"2024-08-18T13:55:44.338116Z","iopub.status.idle":"2024-08-18T13:55:44.34772Z","shell.execute_reply.started":"2024-08-18T13:55:44.338095Z","shell.execute_reply":"2024-08-18T13:55:44.34696Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\nY_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=bool)","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.348724Z","iopub.execute_input":"2024-08-18T13:55:44.349004Z","iopub.status.idle":"2024-08-18T13:55:44.35893Z","shell.execute_reply.started":"2024-08-18T13:55:44.348981Z","shell.execute_reply":"2024-08-18T13:55:44.358266Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"X_train.shape, Y_train.shape","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.359915Z","iopub.execute_input":"2024-08-18T13:55:44.360163Z","iopub.status.idle":"2024-08-18T13:55:44.377108Z","shell.execute_reply.started":"2024-08-18T13:55:44.360141Z","shell.execute_reply":"2024-08-18T13:55:44.376195Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"((670, 128, 128, 3), (670, 128, 128, 1))"},"metadata":{}}]},{"cell_type":"code","source":"# id_ = train_ids[0]\n# path = TRAIN_PATH + id_\n# mask_file = next(os.walk(path + '/masks/'))[2][0]\n# mask_ = imread(path + '/masks/' + mask_file)\n# imshow(mask_)\n# plt.show()\n# print(mask_.min(), mask_.max())\n# print(mask_.dtype)\n# print(mask_.shape)\n# mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.uint8)\n# print(\"Mask\", mask.shape)\n# mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant',  \n#                                       preserve_range=True), axis=-1)\n# mask_ = mask_.astype(np.uint8)\n\n# imshow(mask_)\n# plt.show()\n# print(mask_.shape)\n# print(mask_.min(), mask_.max())\n# print(mask_.dtype)\n# mask = np.maximum(mask, mask_)\n# print(f\"combined mask shape: {mask.shape}\")\n# # from skimage.filters import unsharp_mask\n\n# # # Apply an unsharp mask to enhance edges\n# # mask_ = unsharp_mask(mask_, radius=1, amount=1)\n\n# # # Ensure the image is still binary\n# # mask_ = (mask_ > 0.5).astype(np.uint8)\n# # imshow(mask_)\n# # plt.show()\n\n# # print(\"After sharpening and thresholding:\")\n# # print(f\"Min value: {mask_.min()}, Max value: {mask_.max()}\")\n# # print(f\"Data type: {mask_.dtype}\")","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.37839Z","iopub.execute_input":"2024-08-18T13:55:44.378962Z","iopub.status.idle":"2024-08-18T13:55:44.386105Z","shell.execute_reply.started":"2024-08-18T13:55:44.37892Z","shell.execute_reply":"2024-08-18T13:55:44.385417Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# id_ = train_ids[308]\n# path = TRAIN_PATH + id_\n# # mask_files = next(os.walk(path + '/masks/'))[2]\n# # Initialize an empty mask with boolean type\n# mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.uint8)\n# print(mask.shape)\n# # Directory containing mask files\n# mask_path = path + '/masks/'\n\n# # Iterate through all mask files in the directory\n# for mask_file in next(os.walk(mask_path))[2]:\n#     mask_file_path = os.path.join(mask_path, mask_file)\n#     mask_ = imread(mask_file_path)\n    \n#     # Debug: Check the dtype and range of the loaded mask\n#     print(f\"Loaded mask file: {mask_file}\")\n#     print(f\"Original mask dtype: {mask_.dtype}, min: {mask_.min()}, max: {mask_.max()}\")\n#     print(f\"Original mask shape: {mask_.shape}\")\n#     # Resize the mask and convert to uint8\n#     mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant',  \n#                                       preserve_range=True), axis=-1)\n#     mask_ = mask_.astype(np.uint8)\n#     # Debug: Check dtype and range after resizing\n#     print(f\"Resized mask dtype: {mask_.dtype}, min: {mask_.min()}, max: {mask_.max()}\")\n#     print(f\"Resized mask shape: {mask_.shape}\")\n\n#     # Combine the mask with the existing mask\n#     mask = np.maximum(mask, mask_)\n#     print(f\"combined mask shape: {mask.shape}\")\n\n# # Debug: Check the final combined mask\n# # Convert the mask to boolean\n# mask = (mask > 0).astype(bool)\n# print(f\"Final combined mask dtype: {mask.dtype}, min: {mask.min()}, max: {mask.max()}\")\n# mask.shape\n# imshow(mask)\n# # Assign the final mask to the Y_train array\n# # Y_train[0] = mask","metadata":{"execution":{"iopub.status.busy":"2024-08-18T15:34:41.415477Z","iopub.execute_input":"2024-08-18T15:34:41.415862Z","iopub.status.idle":"2024-08-18T15:34:41.422549Z","shell.execute_reply.started":"2024-08-18T15:34:41.415832Z","shell.execute_reply":"2024-08-18T15:34:41.421439Z"},"trusted":true},"execution_count":105,"outputs":[]},{"cell_type":"code","source":"seed = 42\nnp.random.seed = seed","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.910021Z","iopub.execute_input":"2024-08-18T13:55:44.910313Z","iopub.status.idle":"2024-08-18T13:55:44.916662Z","shell.execute_reply.started":"2024-08-18T13:55:44.910289Z","shell.execute_reply":"2024-08-18T13:55:44.915713Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):   \n    path = TRAIN_PATH + id_\n    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]  \n    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n    # After resizing, ensure the data is in uint8 format and the range [0, 255]\n    if img.dtype != np.uint8:\n        img = np.clip(img, 0, 255)  # Clip values\n        img = img.astype(np.uint8)  # Convert to uint8\n    X_train[n] = img  #Fill empty X_train with values from img\n    mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=bool)\n    for mask_file in next(os.walk(path + '/masks/'))[2]:\n        mask_ = imread(path + '/masks/' + mask_file)\n        mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant',  \n                                      preserve_range=True), axis=-1)\n        mask_ = mask_.astype(np.uint8)\n        \n        mask = np.maximum(mask, mask_)  \n    # Convert the mask to boolean\n    mask = (mask > 0).astype(bool)    \n    Y_train[n] = mask   ","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:55:44.917891Z","iopub.execute_input":"2024-08-18T13:55:44.918275Z","iopub.status.idle":"2024-08-18T13:59:44.141965Z","shell.execute_reply.started":"2024-08-18T13:55:44.918214Z","shell.execute_reply":"2024-08-18T13:59:44.141207Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stderr","text":"100%|██████████| 670/670 [03:59<00:00,  2.80it/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train[0].shape, Y_train[0].shape","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:17:39.589358Z","iopub.execute_input":"2024-08-18T14:17:39.589777Z","iopub.status.idle":"2024-08-18T14:17:39.597542Z","shell.execute_reply.started":"2024-08-18T14:17:39.589743Z","shell.execute_reply":"2024-08-18T14:17:39.596613Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"((128, 128, 3), (128, 128, 1))"},"metadata":{}}]},{"cell_type":"code","source":"imshow(X_train[0])\nplt.show()\nimshow(Y_train[0])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:17:43.333847Z","iopub.execute_input":"2024-08-18T14:17:43.334763Z","iopub.status.idle":"2024-08-18T14:17:43.924097Z","shell.execute_reply.started":"2024-08-18T14:17:43.334723Z","shell.execute_reply":"2024-08-18T14:17:43.923114Z"},"trusted":true},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"# test images\nX_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\nsizes_test = []\nprint('Resizing test images') \nfor n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n    path = TEST_PATH + id_\n    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n    sizes_test.append([img.shape[0], img.shape[1]])\n    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n    # print(img.dtype)\n    if img.dtype != np.uint8:\n        img = np.clip(img, 0, 255)  # Clip values\n        img = img.astype(np.uint8)  # Convert to uint8\n    X_test[n] = img\n\nprint('Done!')","metadata":{"execution":{"iopub.status.busy":"2024-08-18T13:59:44.763412Z","iopub.execute_input":"2024-08-18T13:59:44.763752Z","iopub.status.idle":"2024-08-18T13:59:46.087775Z","shell.execute_reply.started":"2024-08-18T13:59:44.76372Z","shell.execute_reply":"2024-08-18T13:59:46.086669Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Resizing test images\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 65/65 [00:01<00:00, 49.54it/s]","output_type":"stream"},{"name":"stdout","text":"Done!\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"sizes_test[1]\nimshow(X_test[1])\nX_test[1].max()","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:17:48.353816Z","iopub.execute_input":"2024-08-18T14:17:48.354564Z","iopub.status.idle":"2024-08-18T14:17:48.680508Z","shell.execute_reply.started":"2024-08-18T14:17:48.35453Z","shell.execute_reply":"2024-08-18T14:17:48.679567Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"234"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"image_x = random.randint(0, len(train_ids))\nprint(image_x)\n\nimshow(X_train[image_x])\nplt.show()\nimshow(np.squeeze(Y_train[image_x]))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:17:52.498617Z","iopub.execute_input":"2024-08-18T14:17:52.499321Z","iopub.status.idle":"2024-08-18T14:17:53.075586Z","shell.execute_reply.started":"2024-08-18T14:17:52.499288Z","shell.execute_reply":"2024-08-18T14:17:53.074706Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"418\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"#Build the model\ninputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\ns = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)\n\n#Contraction path\nc1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)\nc1 = tf.keras.layers.Dropout(0.1)(c1)\nc1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)\np1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)\n\nc2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)\nc2 = tf.keras.layers.Dropout(0.1)(c2)\nc2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)\np2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)\n \nc3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)\nc3 = tf.keras.layers.Dropout(0.2)(c3)\nc3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)\np3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)\n \nc4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)\nc4 = tf.keras.layers.Dropout(0.2)(c4)\nc4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)\np4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)\n \nc5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)\nc5 = tf.keras.layers.Dropout(0.3)(c5)\nc5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)\n\n#Expansive path \nu6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)\nu6 = tf.keras.layers.concatenate([u6, c4])\nc6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)\nc6 = tf.keras.layers.Dropout(0.2)(c6)\nc6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)\n \nu7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)\nu7 = tf.keras.layers.concatenate([u7, c3])\nc7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)\nc7 = tf.keras.layers.Dropout(0.2)(c7)\nc7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)\n \nu8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)\nu8 = tf.keras.layers.concatenate([u8, c2])\nc8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)\nc8 = tf.keras.layers.Dropout(0.1)(c8)\nc8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)\n \nu9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)\nu9 = tf.keras.layers.concatenate([u9, c1], axis=3)\nc9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)\nc9 = tf.keras.layers.Dropout(0.1)(c9)\nc9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)\n \noutputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)\n \nmodel = tf.keras.Model(inputs=[inputs], outputs=[outputs])\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmodel.summary()\n","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:17:56.137341Z","iopub.execute_input":"2024-08-18T14:17:56.138054Z","iopub.status.idle":"2024-08-18T14:17:56.425364Z","shell.execute_reply.started":"2024-08-18T14:17:56.138024Z","shell.execute_reply":"2024-08-18T14:17:56.424452Z"},"trusted":true},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_1\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ lambda_1 (\u001b[38;5;33mLambda\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │        \u001b[38;5;34m448\u001b[0m │ lambda_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m2,320\u001b[0m │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │      \u001b[38;5;34m4,640\u001b[0m │ max_pooling2d_4[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_10          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_5     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m18,496\u001b[0m │ max_pooling2d_5[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_11          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m36,928\u001b[0m │ dropout_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_6     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │     \u001b[38;5;34m73,856\u001b[0m │ max_pooling2d_6[\u001b[38;5;34m…\u001b[0m │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_12          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m147,584\u001b[0m │ dropout_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_7     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │    \u001b[38;5;34m295,168\u001b[0m │ max_pooling2d_7[\u001b[38;5;34m…\u001b[0m │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_13          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ conv2d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │    \u001b[38;5;34m590,080\u001b[0m │ dropout_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_4  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m131,200\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m256\u001b[0m)              │            │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m295,040\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_14          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │    \u001b[38;5;34m147,584\u001b[0m │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_5  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m32,832\u001b[0m │ conv2d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_5       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m128\u001b[0m)              │            │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m73,792\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_15          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │     \u001b[38;5;34m36,928\u001b[0m │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_6  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │      \u001b[38;5;34m8,224\u001b[0m │ conv2d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_6       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m64\u001b[0m)               │            │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │     \u001b[38;5;34m18,464\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ conv2d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_7  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m2,064\u001b[0m │ conv2d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)   │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_7       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_transpose… │\n│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m32\u001b[0m)               │            │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m4,624\u001b[0m │ concatenate_7[\u001b[38;5;34m0\u001b[0m]… │\n│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_17          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ conv2d_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│ (\u001b[38;5;33mDropout\u001b[0m)           │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │      \u001b[38;5;34m2,320\u001b[0m │ dropout_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n│                     │ \u001b[38;5;34m16\u001b[0m)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_37 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m17\u001b[0m │ conv2d_36[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n│                     │ \u001b[38;5;34m1\u001b[0m)                │            │                   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n│ input_layer_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ lambda_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Lambda</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ lambda_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ dropout_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │ max_pooling2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_10          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_5     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │ max_pooling2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_11          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ dropout_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_6     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ max_pooling2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_12          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ dropout_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ max_pooling2d_7     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │ max_pooling2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_13          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │                   │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ dropout_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_4  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │ conv2d_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │ conv2d_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,040</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_14          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_29[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ dropout_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_5  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">32,832</span> │ conv2d_30[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_5       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │ conv2d_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_15          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_31[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ dropout_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_6  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,224</span> │ conv2d_32[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_6       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │ conv2d_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,464</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_33[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_transpose_7  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,064</span> │ conv2d_34[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ concatenate_7       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose… │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │ conv2d_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │ concatenate_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ dropout_17          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_35[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ dropout_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)               │            │                   │\n├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n│ conv2d_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span> │ conv2d_36[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                │            │                   │\n└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,941,105\u001b[0m (7.40 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,941,105</span> (7.40 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,941,105\u001b[0m (7.40 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,941,105</span> (7.40 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}}]},{"cell_type":"code","source":"import datetime","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:18:01.193897Z","iopub.execute_input":"2024-08-18T14:18:01.194278Z","iopub.status.idle":"2024-08-18T14:18:01.198557Z","shell.execute_reply.started":"2024-08-18T14:18:01.194227Z","shell.execute_reply":"2024-08-18T14:18:01.197436Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\ntensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n#Defining callbacks\n# checkpointer = tf.keras.callbacks.ModelCheckpoint('model_for_nuclei.h5', verbose=1, save_best_only=True)\ncallbacks = [\n    tf.keras.callbacks.ModelCheckpoint(filepath='model_for_nuclei.keras', verbose=1, save_best_only=True),\n    tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),\n#     tf.keras.callbacks.TensorBoard(log_dir='logs')\n    tensorboard_callback\n]","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:18:04.52377Z","iopub.execute_input":"2024-08-18T14:18:04.524132Z","iopub.status.idle":"2024-08-18T14:18:04.530455Z","shell.execute_reply.started":"2024-08-18T14:18:04.524103Z","shell.execute_reply":"2024-08-18T14:18:04.529547Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"# Clear any logs from previous runs\n!rm -rf /kaggle/working/logs","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:18:09.328811Z","iopub.execute_input":"2024-08-18T14:18:09.329636Z","iopub.status.idle":"2024-08-18T14:18:10.377557Z","shell.execute_reply.started":"2024-08-18T14:18:09.329602Z","shell.execute_reply":"2024-08-18T14:18:10.376304Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=16, epochs=25, callbacks=callbacks)","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:18:21.694735Z","iopub.execute_input":"2024-08-18T14:18:21.695446Z","iopub.status.idle":"2024-08-18T14:19:36.398753Z","shell.execute_reply.started":"2024-08-18T14:18:21.695406Z","shell.execute_reply":"2024-08-18T14:19:36.397928Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"Epoch 1/25\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 457ms/step - accuracy: 0.7137 - loss: 0.6548\nEpoch 1: val_loss improved from inf to 0.46969, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 535ms/step - accuracy: 0.7155 - loss: 0.6536 - val_accuracy: 0.8271 - val_loss: 0.4697\nEpoch 2/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.8260 - loss: 0.4161\nEpoch 2: val_loss improved from 0.46969 to 0.24704, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - accuracy: 0.8271 - loss: 0.4125 - val_accuracy: 0.8987 - val_loss: 0.2470\nEpoch 3/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.9068 - loss: 0.2066\nEpoch 3: val_loss improved from 0.24704 to 0.15471, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - accuracy: 0.9077 - loss: 0.2050 - val_accuracy: 0.9367 - val_loss: 0.1547\nEpoch 4/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.9457 - loss: 0.1350\nEpoch 4: val_loss improved from 0.15471 to 0.13696, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 55ms/step - accuracy: 0.9458 - loss: 0.1347 - val_accuracy: 0.9452 - val_loss: 0.1370\nEpoch 5/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.9533 - loss: 0.1198\nEpoch 5: val_loss improved from 0.13696 to 0.13405, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - accuracy: 0.9534 - loss: 0.1196 - val_accuracy: 0.9467 - val_loss: 0.1341\nEpoch 6/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9557 - loss: 0.1132\nEpoch 6: val_loss improved from 0.13405 to 0.13026, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - accuracy: 0.9558 - loss: 0.1130 - val_accuracy: 0.9475 - val_loss: 0.1303\nEpoch 7/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9554 - loss: 0.1140\nEpoch 7: val_loss improved from 0.13026 to 0.10650, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - accuracy: 0.9555 - loss: 0.1136 - val_accuracy: 0.9556 - val_loss: 0.1065\nEpoch 8/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.9639 - loss: 0.0955\nEpoch 8: val_loss improved from 0.10650 to 0.10568, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - accuracy: 0.9638 - loss: 0.0957 - val_accuracy: 0.9547 - val_loss: 0.1057\nEpoch 9/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9620 - loss: 0.0966\nEpoch 9: val_loss improved from 0.10568 to 0.10364, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 56ms/step - accuracy: 0.9620 - loss: 0.0966 - val_accuracy: 0.9574 - val_loss: 0.1036\nEpoch 10/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9634 - loss: 0.0933\nEpoch 10: val_loss improved from 0.10364 to 0.09981, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - accuracy: 0.9634 - loss: 0.0934 - val_accuracy: 0.9573 - val_loss: 0.0998\nEpoch 11/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9665 - loss: 0.0880\nEpoch 11: val_loss improved from 0.09981 to 0.09405, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - accuracy: 0.9663 - loss: 0.0884 - val_accuracy: 0.9601 - val_loss: 0.0940\nEpoch 12/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9597 - loss: 0.1030\nEpoch 12: val_loss did not improve from 0.09405\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - accuracy: 0.9599 - loss: 0.1026 - val_accuracy: 0.9549 - val_loss: 0.1021\nEpoch 13/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9639 - loss: 0.0934\nEpoch 13: val_loss improved from 0.09405 to 0.09357, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - accuracy: 0.9639 - loss: 0.0933 - val_accuracy: 0.9601 - val_loss: 0.0936\nEpoch 14/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9635 - loss: 0.0920\nEpoch 14: val_loss improved from 0.09357 to 0.09251, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - accuracy: 0.9636 - loss: 0.0920 - val_accuracy: 0.9590 - val_loss: 0.0925\nEpoch 15/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9639 - loss: 0.0905\nEpoch 15: val_loss improved from 0.09251 to 0.08418, saving model to model_for_nuclei.keras\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - accuracy: 0.9640 - loss: 0.0904 - val_accuracy: 0.9664 - val_loss: 0.0842\nEpoch 16/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9655 - loss: 0.0887\nEpoch 16: val_loss did not improve from 0.08418\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - accuracy: 0.9655 - loss: 0.0888 - val_accuracy: 0.9616 - val_loss: 0.0889\nEpoch 17/25\n\u001b[1m37/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.9682 - loss: 0.0806\nEpoch 17: val_loss did not improve from 0.08418\n\u001b[1m38/38\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - accuracy: 0.9682 - loss: 0.0808 - val_accuracy: 0.9654 - val_loss: 0.0883\n","output_type":"stream"}]},{"cell_type":"code","source":"# type(results)\nresults.history","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:30:05.989123Z","iopub.execute_input":"2024-08-18T14:30:05.989971Z","iopub.status.idle":"2024-08-18T14:30:05.996537Z","shell.execute_reply.started":"2024-08-18T14:30:05.989935Z","shell.execute_reply":"2024-08-18T14:30:05.995619Z"},"trusted":true},"execution_count":70,"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"{'accuracy': [0.784195065498352,\n  0.8479863405227661,\n  0.9251365661621094,\n  0.9488483667373657,\n  0.955160915851593,\n  0.9574512839317322,\n  0.958954930305481,\n  0.9614565372467041,\n  0.9618232846260071,\n  0.9628497362136841,\n  0.9629354476928711,\n  0.9622888565063477,\n  0.964347243309021,\n  0.9640823006629944,\n  0.9649112820625305,\n  0.9653699994087219,\n  0.9669433236122131],\n 'loss': [0.6093277931213379,\n  0.3454512655735016,\n  0.17679463326931,\n  0.12925772368907928,\n  0.11603794246912003,\n  0.10956774652004242,\n  0.1048390120267868,\n  0.09971911460161209,\n  0.09706314653158188,\n  0.09585324674844742,\n  0.09538379311561584,\n  0.0964227244257927,\n  0.09139639139175415,\n  0.09158390760421753,\n  0.08931180834770203,\n  0.08924997597932816,\n  0.08479070663452148],\n 'val_accuracy': [0.827081024646759,\n  0.8987326622009277,\n  0.9366673827171326,\n  0.945182204246521,\n  0.9467463493347168,\n  0.9475170373916626,\n  0.9556310772895813,\n  0.954710066318512,\n  0.9574010968208313,\n  0.9573455452919006,\n  0.9601166844367981,\n  0.9548695087432861,\n  0.9600738883018494,\n  0.9590153694152832,\n  0.9664315581321716,\n  0.9616343975067139,\n  0.965399444103241],\n 'val_loss': [0.46968698501586914,\n  0.24703985452651978,\n  0.1547146737575531,\n  0.13695865869522095,\n  0.1340540200471878,\n  0.13026371598243713,\n  0.10650420188903809,\n  0.10567501932382584,\n  0.10364365577697754,\n  0.09981002658605576,\n  0.09404551237821579,\n  0.10209165513515472,\n  0.09356748312711716,\n  0.09250841289758682,\n  0.08417877554893494,\n  0.08885642141103745,\n  0.08834371715784073]}"},"metadata":{}}]},{"cell_type":"code","source":"history = results.history\nplt.plot(history['loss'], label='Training Loss')\nplt.plot(history['val_loss'], label='Validation Loss')\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:29:16.013883Z","iopub.execute_input":"2024-08-18T14:29:16.014695Z","iopub.status.idle":"2024-08-18T14:29:16.277193Z","shell.execute_reply.started":"2024-08-18T14:29:16.014662Z","shell.execute_reply":"2024-08-18T14:29:16.276272Z"},"trusted":true},"execution_count":67,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"history = results.history\nplt.plot(history['accuracy'], label='Accuracy')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:30:54.784051Z","iopub.execute_input":"2024-08-18T14:30:54.784888Z","iopub.status.idle":"2024-08-18T14:30:55.030144Z","shell.execute_reply.started":"2024-08-18T14:30:54.784852Z","shell.execute_reply":"2024-08-18T14:30:55.029274Z"},"trusted":true},"execution_count":71,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model.input_shape","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:46:42.5781Z","iopub.execute_input":"2024-08-18T14:46:42.578779Z","iopub.status.idle":"2024-08-18T14:46:42.584669Z","shell.execute_reply.started":"2024-08-18T14:46:42.578745Z","shell.execute_reply":"2024-08-18T14:46:42.583805Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"(None, 128, 128, 3)"},"metadata":{}}]},{"cell_type":"code","source":"idx = random.randint(0, len(X_train))\ntrue_image = X_train[idx]\ntrue_mask = np.squeeze(Y_train[idx])\n# Add batch dimension\nsingle_sample = np.expand_dims(X_train[idx], axis=0)\n# Make prediction\npredicted_mask = model.predict(single_sample)[0]\n# print(predicted_mask)\n\npredicted_mask = np.squeeze(predicted_mask)\n# print(predicted_mask.shape)","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:59:39.10907Z","iopub.execute_input":"2024-08-18T14:59:39.109744Z","iopub.status.idle":"2024-08-18T14:59:39.179369Z","shell.execute_reply.started":"2024-08-18T14:59:39.109709Z","shell.execute_reply":"2024-08-18T14:59:39.178604Z"},"trusted":true},"execution_count":103,"outputs":[{"name":"stdout","text":"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"# Plotting\nfig, axes = plt.subplots(1, 3, figsize=(15, 5))\n\n# True Image\naxes[0].imshow(true_image)\naxes[0].set_title('True Image')\naxes[0].axis('off')\n\n# True Mask\naxes[1].imshow(true_mask, cmap='gray')\naxes[1].set_title('True Mask')\naxes[1].axis('off')\n\n# Predicted Mask\naxes[2].imshow(predicted_mask, cmap='gray')\naxes[2].set_title('Predicted Mask')\naxes[2].axis('off')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-18T14:59:39.384282Z","iopub.execute_input":"2024-08-18T14:59:39.385059Z","iopub.status.idle":"2024-08-18T14:59:39.730114Z","shell.execute_reply.started":"2024-08-18T14:59:39.385027Z","shell.execute_reply":"2024-08-18T14:59:39.729134Z"},"trusted":true},"execution_count":104,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x500 with 3 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAABccAAAH/CAYAAACSDGXwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9eZRlVXn3/5zhjjVXdfU80PQAdDeTIAoiIIMoQ0icgsREiSBqHEhinH5vIiYmxLyvbzSDGhMTCSHmDRqRqCAQQRFFJAwy0zQ9jzWPdzzn/P7I8uzn+3T3raru6u7qru9nrV5r797nnrOHZ+9776m7P8dLkiQRQgghhBBCCCGEEEIIIWQW4R/pChBCCCGEEEIIIYQQQgghhxveHCeEEEIIIYQQQgghhBAy6+DNcUIIIYQQQgghhBBCCCGzDt4cJ4QQQgghhBBCCCGEEDLr4M1xQgghhBBCCCGEEEIIIbMO3hwnhBBCCCGEEEIIIYQQMuvgzXFCCCGEEEIIIYQQQgghsw7eHCeEEEIIIYQQQgghhBAy6+DNcUIIIYQQQgghhBBCCCGzDt4cJ4QQQgghZJbzrne9S5qbm490NQghhJApcdxxx8m73vWuNP/AAw+I53nywAMPHLE6WWwdZzLHHXecXHHFFUe6GoQcVnhznJB94HnepP4d6TfcCy64QNatW3dE60AIIYTMVI6m93PP82TVqlX7LL/33nvTun7jG984zLUjhBBC9s3XvvY1eD/N5/OyevVq+cAHPiC7d+8+0tWbEt/73vfkpptuOqJ1+GU/Xnfddfss///+v/8vPaa3t/cw146QY5fwSFeAkJnIrbfeCvl//ud/lnvvvXev/z/ppJMOZ7UIIYQQMgWOpvfzfD4vL730kjzyyCNy1llnQdltt90m+XxeyuXyEaodIYQQsn/++I//WJYvXy7lcll+/OMfy5e+9CX53ve+J08//bQUi8XDWpfzzjtPSqWSZLPZKb3ue9/7nvzt3/7tEb9Bns/n5Zvf/KZ88Ytf3KsNX//61/l5gJBDAG+OE7IP3vGOd0D+4YcflnvvvXev/7eMj48f9jd/QgghhOybo+n9fMWKFVKv1+XrX/863Bwvl8vyrW99Sy6//HL55je/eVjrRAghhEyGN77xjXLmmWeKiMh1110nXV1d8n//7/+Vb3/72/L2t799n68ZGxuTpqamaa+L7/uSz+en/byHize84Q1y5513yl133SVXXXVV+v8/+clPZOPGjfLmN7+ZnwcImWaoVSHkAPml0uS///u/5bzzzpNisSif/OQnReR/tkPt6y/O+3KNDQ4Oyo033ihLliyRXC4nK1eulM9+9rMSx/EB1cvzPPnABz4gt99+u6xZs0YKhYKcffbZ8tRTT4mIyN/93d/JypUrJZ/PywUXXCCbNm2C1z/44IPy1re+VZYuXSq5XE6WLFkiv/u7vyulUmmva/3yGvl8XtatWyff+ta35F3vepccd9xxcFwcx/L5z39e1q5dK/l8XubNmyc33HCDDAwMHFAbCSGEkOliJr2fv/3tb5f/9//+H7zmP//zP2V8fFze9ra37XX85s2b5f3vf7+ccMIJUigUpKurS9761rfu9d5eq9Xk05/+tKxatUry+bx0dXXJueeeK/fee2/D+jzxxBPS3d0tF1xwgYyOjk66HYQQQmY3F154oYiIbNy4UUTccy02bNggl112mbS0tMhv/MZviMjkvysmSSKf+cxnZPHixVIsFuV1r3udPPPMM3tde3/O8Z/97Gdy2WWXSUdHhzQ1Nckpp5wiX/jCF9L6/e3f/q2IoJLtl0x3HRuxaNEiOe+88+Rf//Vf4f9vu+02Ofnkk/epVZ3sd/hdu3bJtddeK4sXL5ZcLicLFiyQq666aq/PDZZbbrlFwjCUP/iDP5hSWwg5WuAvxwk5CPr6+uSNb3yjXH311fKOd7xD5s2bN6XXj4+Py/nnny/bt2+XG264QZYuXSo/+clP5BOf+ITs3LlTPv/5zx9QvR588EG588475Xd+53dEROTmm2+WK664Qj760Y/KF7/4RXn/+98vAwMD8hd/8Rfy27/92/KDH/wgfe3tt98u4+Pj8r73vU+6urrkkUcekb/+67+Wbdu2ye23354e993vfld+/dd/XU4++WS5+eabZWBgQN797nfLokWL9qrPDTfcIF/72tfk2muvlQ996EOyceNG+Zu/+Rt5/PHH5aGHHpJMJnNA7SSEEEKmg5nyfn7NNdfITTfdJA888EB6Y+Ff//Vf5aKLLpK5c+fudfzPf/5z+clPfiJXX321LF68WDZt2iRf+tKX5IILLpBnn302/fX7TTfdJDfffLNcd911ctZZZ8nw8LA8+uij8thjj8kll1yyz7r8/Oc/l0svvVTOPPNM+fa3vy2FQmFKfUIIIWT2smHDBhER6erqSv+vXq/LpZdeKueee678n//zf9L3qMl+V/yjP/oj+cxnPiOXXXaZXHbZZfLYY4/J61//eqlWqxPW595775UrrrhCFixYIB/+8Idl/vz58txzz8l3vvMd+fCHPyw33HCD7NixY5/qtcNVR80111wjH/7wh2V0dFSam5ulXq/L7bffLr/3e7+3T6XKZL/Dv/nNb5ZnnnlGPvjBD8pxxx0ne/bskXvvvVe2bNmy1w/cfslXvvIVee973yuf/OQn5TOf+cyU2kHIUUNCCJmQ3/md30nsdDn//PMTEUm+/OUv73W8iCSf+tSn9vr/ZcuWJe985zvT/J/8yZ8kTU1NyYsvvgjHffzjH0+CIEi2bNnSsF7nn39+snbt2r2uncvlko0bN6b/93d/93eJiCTz589PhoeH0///xCc+kYgIHDs+Pr7XdW6++ebE87xk8+bN6f+dfPLJyeLFi5ORkZH0/x544IFERJJly5al//fggw8mIpLcdtttcM677757n/9PCCGEHCqOhvfzM888M3n3u9+dJEmSDAwMJNlsNrnllluS+++/PxGR5Pbbb09ft6/37J/+9KeJiCT//M//nP7fqaeemlx++eUN6/DOd74zaWpqSpIkSX784x8nra2tyeWXX56Uy+WGryOEEDJ7+ad/+qdERJL77rsv6enpSbZu3Zr827/9W9LV1ZUUCoVk27ZtSZL8z3uMiCQf//jH4fWT/a64Z8+eJJvNJpdffnkSx3F63Cc/+clEROA9+Zfvl/fff3+SJElSr9eT5cuXJ8uWLUsGBgbgOvpc+/qMcKjquD9EJPmd3/mdpL+/P8lms8mtt96aJEmSfPe73008z0s2bdqUfOpTn0pEJOnp6UlfN5nv8AMDA4mIJP/7f//vhnVYtmxZ+pnhC1/4QuJ5XvInf/InE9adkKMZalUIOQhyuZxce+21B/z622+/XV772tdKR0eH9Pb2pv8uvvhiiaJIfvSjHx3QeS+66CL4y++rXvUqEfmfvxS3tLTs9f8vv/xy+n/6l2FjY2PS29sr55xzjiRJIo8//riIiOzYsUOeeuop+a3f+i1pbm5Ojz///PPl5JNP3quNbW1tcskll0AbzzjjDGlubpb777//gNpICCGETBcz6f38mmuukf/4j/+QarUq3/jGNyQIAvm1X/u1fR6r37NrtZr09fXJypUrpb29XR577LG0rL29XZ555hlZv379hNe///775dJLL5WLLrpI/uM//kNyudyk604IIWR2cvHFF0t3d7csWbJErr76amlubpZvfetbe+0qft/73gf5yX5XvO+++6RarcoHP/hB0J3ceOONE9bt8ccfl40bN8qNN94o7e3tUKbPtT8ORx0tHR0d8oY3vEG+/vWvi8j/7CI755xzZNmyZfs8fjLf4QuFgmSzWXnggQcmpTf9i7/4C/nwhz8sn/3sZ+V//a//NeU2EHI0Qa0KIQfBokWLpvwUbM369evlF7/4hXR3d++zfM+ePQd03qVLl0K+ra1NRESWLFmyz//Xb45btmyRP/qjP5I777xzrzfNoaEhEfkfx6mIyMqVK/e69sqVK+EL+fr162VoaGif28FFDryNhBBCyHQxk97Pr776avnIRz4id911l9x2221yxRVXwB+2NaVSSW6++Wb5p3/6J9m+fbskSZKW/fI9W0Tkj//4j+Wqq66S1atXy7p16+QNb3iD/OZv/qaccsopcL5yuSyXX365nHHGGfLv//7vEob8qkAIIWRi/vZv/1ZWr14tYRjKvHnz5IQTThDfx99ihmEoixcvhv+b7HfFX37/XLVqFZR3d3dLR0dHw7r9UvGyL1f3ZDgcddwX11xzjfzmb/6mbNmyRe644w75i7/4i/0eO5nv8LlcTj772c/K7//+78u8efPk1a9+tVxxxRXyW7/1WzJ//nx4zQ9/+EP57ne/Kx/72MfoGSezAn7iJeQgmKp/M4oiyMdxLJdccol89KMf3efxq1evPqB6BUEwpf//5ZfpKIrkkksukf7+fvnYxz4mJ554ojQ1Ncn27dvlXe961wE9JDSOY5k7d67cdttt+yzf340EQggh5HAxk97PFyxYIBdccIF87nOfk4ceeki++c1v7vfYD37wg/JP//RPcuONN8rZZ58tbW1t4nmeXH311fCefd5558mGDRvk29/+ttxzzz3yD//wD/KXf/mX8uUvf1muu+669LhcLieXXXaZfPvb35a7775brrjiiknXmxBCyOzlrLPOkjPPPLPhMblcbq8b5kfDd8UjVcdf+ZVfkVwuJ+985zulUqns88HcIlP7Dn/jjTfKlVdeKXfccYd8//vflz/8wz+Um2++WX7wgx/I6aefnh63du1aGRwclFtvvVVuuOEGWb58+SFpIyEzBd4cJ+QQ0NHRIYODg/B/1WpVdu7cCf+3YsUKGR0dlYsvvvgw1m7/PPXUU/Liiy/KLbfcIr/1W7+V/v+9994Lx/1yO9dLL7201zns/61YsULuu+8+ec1rXsOHeRFCCDmqOFLv59dcc41cd9110t7eLpdddtl+j/vGN74h73znO+Vzn/tc+n/lcnmvOouIdHZ2yrXXXivXXnutjI6OynnnnSc33XQT3Bz3PE9uu+02ueqqq+Stb32r3HXXXXLBBRdMS5sIIYQQy2S/K/7y++f69evl+OOPT/+/p6dnQkXIihUrRETk6aefbvg+vT/FyuGo474oFAryq7/6q/Iv//Iv8sY3vlHmzJmzz+Mm+x1et+f3f//35fd///dl/fr1ctppp8nnPvc5+Zd/+Zf0mDlz5sg3vvENOffcc+Wiiy6SH//4x7Jw4cIpt4GQowU6xwk5BKxYsWIvv+hXvvKVvX5p9ra3vU1++tOfyve///29zjE4OCj1ev2Q1tPyy1+W623ZSZLIF77wBThu4cKFsm7dOvnnf/5nGR0dTf//hz/8oTz11FNw7Nve9jaJokj+5E/+ZK/r1ev1fX6BJ4QQQmYCR+r9/C1veYt86lOfki9+8YsNdS9BEMB7tojIX//1X+9Vv76+Psg3NzfLypUrpVKp7HXObDYr//Ef/yGvfOUr5corr5RHHnlkSnUnhBBCJstkvytefPHFkslk5K//+q/hfe/zn//8hNd4xSteIcuXL5fPf/7ze3331OdqamoSEdnrmMNRx/3xkY98RD71qU/JH/7hH+73mMl+hx8fH5dyuQz/t2LFCmlpadnn54HFixfLfffdJ6VSSS655JK9PksQcizBX44Tcgi47rrr5L3vfa+8+c1vlksuuUSefPJJ+f73v7/XX3v/4A/+QO6880654oor5F3vepecccYZMjY2Jk899ZR84xvfkE2bNu33L8SHghNPPFFWrFghH/nIR2T79u3S2toq3/zmN/f5l+4/+7M/k6uuukpe85rXyLXXXisDAwPyN3/zN7Ju3Tq4YX7++efLDTfcIDfffLM88cQT8vrXv14ymYysX79ebr/9dvnCF74gb3nLWw5bGwkhhJDJcqTez9va2uSmm26a8LgrrrhCbr31Vmlra5M1a9bIT3/6U7nvvvukq6sLjluzZo1ccMEFcsYZZ0hnZ6c8+uij8o1vfEM+8IEP7PO8hUJBvvOd78iFF14ob3zjG+WHP/zhAbtaCSGEkP0x2e+K3d3d8pGPfERuvvlmueKKK+Syyy6Txx9/XO66664J319935cvfelLcuWVV8ppp50m1157rSxYsECef/55eeaZZ9I/bJ9xxhkiIvKhD31ILr30UgmCQK6++urDUsf9ceqpp8qpp57a8JjJfod/8cUX5aKLLpK3ve1tsmbNGgnDUL71rW/J7t275eqrr97nuVeuXCn33HOPXHDBBXLppZfKD37wA2ltbT2gthAyk+HNcUIOAddff71s3LhRvvrVr8rdd98tr33ta+Xee++Viy66CI4rFovywx/+UP7sz/5Mbr/9dvnnf/5naW1tldWrV8unP/3p9IGZh4tMJiP/+Z//KR/60Ifk5ptvlnw+L7/2a78mH/jAB/Z6U77yyivl61//utx0003y8Y9/XFatWiVf+9rX5JZbbpFnnnkGjv3yl78sZ5xxhvzd3/2dfPKTn5QwDOW4446Td7zjHfKa17zmcDaREEIImTQz/f38C1/4ggRBILfddpuUy2V5zWteI/fdd59ceumlcNyHPvQhufPOO+Wee+6RSqUiy5Ytk8985jMNH7LV2toq3//+9+W8886TSy65RB588MF9PoibEEIIORgm+13xM5/5jOTzefnyl78s999/v7zqVa+Se+65Ry6//PIJr3HppZfK/fffL5/+9Kflc5/7nMRxLCtWrJDrr78+PeZNb3qTfPCDH5R/+7d/k3/5l3+RJEnSm8aHo44HymS/wy9ZskTe/va3y3/913/JrbfeKmEYyoknnij//u//Lm9+85v3e/6TTz5Z7rrrLrn44ovlyiuvlLvvvpu6VHLM4SV2LyYhhBwEp512mnR3d+/XcUYIIYQQQgghhBBCyEyAznFCyAFRq9X2cqg+8MAD8uSTT/LhXYQQQgghhBBCCCFkxsNfjhNCDohNmzbJxRdfLO94xztk4cKF8vzzz8uXv/xlaWtrk6effnov3ykhhBBCCCGEEEIIITMJOscJIQdER0eHnHHGGfIP//AP0tPTI01NTXL55ZfLn//5n/PGOCGEEEIIIYQQQgiZ8fCX44QQQgghhBBCCCGEEEJmHXSOE0IIIYQQQgghhBBCCJl18OY4IYQQQgghhBBCCCGEkFkHb44TQgghhBBCCCGEEEIImXVM+oGcD977A8hnm4I0XQ89KMvU8J57XVnNaz4eO9pTgPzm+/rT9OD6ASgbKZUgXxutuPrUx6Esn8WmxZJP00kFiiRXyEPeK2bSdOvqIpTNP7UJ8s++5Oq0+4GX8ZoDeyA/VhlL09U4grLQz0K+uXlOmm5fPQfK1r35JMivXuFeGwSokC8FVcjXxB1bKeOxA5uwf7c80A/5ytaRNN3dnYOyxa/IQL55TXOaznfhGBeyAeRrsRurKKlDmWSxjhhqOG5JguctR+7gehXjoSh43pqMuvOUsR/MSyXIu/NmY7xmlGB8J6pbwgDHODOMbR0dd/FeKWLbMnWMl7zU3DVzph9CvE7Bc/MxiHEsKnWs/+CYi9FoB9av3IMTZ2yni63R3lEo8+oYd8XQzZtsk+mHAq4X1djl6/UylOXzNcgX57u2N89vgTK/iHO1GLoxD3MxHttk1qxQDVwN15axQcwPx25eSDtes6m1HfLNdXfdUtn0b4xxl1VVyHg4xmEZ+2GwNAL5St69uCWPc7MuGEuxmgu5GPslzLVB3vPcZAhLOIcqZqwyoVsjfA/PW/XxtUHB1bHo44SrRthPvlo7qyPYD+XxMcgnaj7WPYz1ahb7O5N3a32+gvO4pYCxFWRcHStmyapVMPaTomt7uY79cMm558mhxvO8iQ86APi4koNnusZmOsfiUMULIYQcKxyO97+lS5dCfmhoKE2XzPfhRuRy+H1tzhz8Tnn66aen6Ve+8pVQtm7dOsgvWrQoTc+dOxfKCgX8fpHJuM91sflsGUX4ObS/333frNXwc121ip+p5s+fn6bzefPZOMTPj76//98AVir4nWZ01H2PeeGFF6Ds7//+7yH/4IMPpunxcfxOYCkW3WdL3X8iIr/+678OeVuu6//DH/4Qyu69917I79y5M03bPrP93YhGnwGCAD9H2/7VbbXnscfqfrNjbuur55ude/Y6uvxIfZ7RbbV1aGrC74l63qxatQrK1q5dC/nLLrssTbe3t0OZ7cPt27en6ZdeegnKdKyIiAwODqbpnp4eKNu9ezfk+/r60rRdh+yc0mNh14CpjNtU1lv7Wn3dic6ry22ZjV9bfqAxejAcrvNOtsyWH0w/NOp/uw7Z9zj9vnDiiSdC2QMPPLDfa6bXmvAIQgghhBBCCCGEEEIIIeQYgzfHCSGEEEIIIYQQQgghhMw6Jq1VKTbhdqlsm3ppgj9nD80280ht24/N1vZsDatQ73bb4vMjHVDm9+OxI3X3s3m/arZz5fCn+17stndlQtQMZPO41SPX5LbQ543eonUutrV52NVpKNMMZWIUEIFybNQT/LuEVW4UC51purOjC8q6WlF1UFCKGz+Lbckk2Fat/RgV3HbV52H/hkaP01R0dVx0UieUdZ+Mr21V2pVMM6ppAg/7sF5z9U98oyQwWyf0qPoxtq1klDJxr0tXe43WIYvxEba6c+WKeGyzmNeq6vtmG6FWmIiIeKFrewHDQSpGvxBl3HUKebNtxWQLnjtZ4hfNsZjP+a5t2chsazNOiPHI9eGoj5oMsdsi1XWsNqPJbHFp6mh1dW8zjQnw2ExJbckJsb7NrZCVooqzfCu2O8ybeFZzLMJpLWHRrEtqzSrHqIwJmjHuWkM373NF1G/kWsyWT6XHMXYhaYrNOOZc23Ox0USZfFLHrWxxztUxLOJ5q1ns02zk4jCom/jNYv19NXc9q0aJcVwD9dqsiR3JYeOTwNXXj039YpwnvlJD1e07WH4QsvWa6+/QKKaCIq7XRRX7RcExLpj3v0Cpiwqmz8Z8jJeo6M7rx1SRHCkO1fbDo52ZrlGZaJxmev1nOuxfQmYmVtehtQn1On52b7R9vZFmQAS3oLe04GfYZcuWQb6zs3OfaRHUqIjg1nd7TZvXW+atHsLWX+sk7Pb6qaxXVsmilSC2763CQusj7LGNdAtWzbBy5UrIW42G7qdNmzZJI6YSHweKHTfb/1rn0tra2vBY3W8TxcdU9AuNyg7X50Bd/0Y6GZHG88TOPx0fdr41asuePaj5tbGk62RVKQMDqDgeHh5O01bfM5Vxs0xl7k5FnTNdY2xVP7P1O8VU+ns6+0j3vx0LG3d6LdfKoMnCX44TQgghhBBCCCGEEEIImXXw5jghhBBCCCGEEEIIIYSQWcektSqR4BadsOq2I9lfycceHutn1ZN5zcGZNtw+Nf9Ut329uACr17IdX7t7u3tt/y6zld3oIsK60mY043kLZstLoeDyxVbUnRSKuEWgo9Vth+lvxi1a9QrqIrRJpeAZXUEW69R8nFOpLDt5MZS1tmAdaolrayB2O5p5QrVSHyRWV1BDjUYxg33Yvsg1oPkkPG+2Ddtazriteb5RyPgVs83CV9eJTEj6RseQuPLYtLVexS1bo70qPl4wW8xMt7Qud3Wau8j4LrL42qjujg0Ss6UvMCoPVV6tNN5OkleqBslYnYzZquTnVBq3Vvk2BlT/x0arktSNMqbP1XFwJ6o6atux04Z7XLyUB8x2o3kYD/PaXXvCAta3Fps5Ju66QR77Iew08aH0M7HRZnih2UrquzrGEY5pCV8qWf20ZfM3xLiIbfVDtb4ZXU9gXusrfUg2wa11ZaMFKmfcGhFXzBZUO26+6SelBal4ZqurmY+eumxQMx2RwRjwVXwHZruUF+B1tDqqJthneaMfilV7InOsZ96mfNWn9dhu6cP6emptT4xqKwwwHw2reWNUYfatUve25+N8K5g1oFJ1sVatYf2OZmbrlkJy6JlKLE1le+1sYTrnIvuXkCOD1S9oVcZEc1yXW8WG3Wbe19eXpidSNYTh/m8b2K3ter2wqhSrY9i9e/d+61s0akCt67DKikZr1ETrly63/TAyMgJ5PTa2vhZ97Ogo3quwfZbN4vehctl9zxoaGoIyq7+YSnwcKPa8tv+PO+64NH3ddddB2bx58yB/xx13pOn/+q//grL+/v5J18mOq87b+tn66/63YzFd2GtavcySJUvS9FVXXQVlV199NeS7uty9IXterfoRQUWSXUtsLG3dujVN79ixA8q0RkUE5/J09tmBxuyR+u4xW7/zHOjn8+nExp19f9HvcQdSB/5ynBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6eHOcEEIIIYQQQgghhBBCyKxj0s7xJEEPlhc7Z1LiofslCtDXmvXcsb6HnrG8cfTW5zt/b6aAx2ZbICsL5ju/UvNOLCvvQG9tMu6aOud4dIm15wqQD5SfJr8U250t4mu725zHaXcXesSjCF9bbHPetCBogrIgj/XtOrEzTS9e3gxlGR+HLUmckywwHreqb7y6ovIJetJyxtfbNRev07HcjWvbfDPGxg2ufeb1Ovri4sA46cE/bNxhxukd60N94w6rjEG+useVV7Yav3PZePQid52oE9uWyWLbAnHlodFt1es4joNDrsKVYWxbwXiiW5Wz3o+xLAmNT1t5mWtmjP3YeK4TV142yuPRPhyLoQ2uD0fW4zXHezDfr1x40Qj6njqX4JgX210+k8e4Gqvja3Vc5oo4FtUi1reuPN1hYpzXRi/ohe46iYcdUY2wDtozHpu/IeYzuF54yhWfNWor3zje/axyYGexbbUE6xSp2KpG1u+MYxHksTyXcy+u2b+BxhijYc291jN1CMIOyPuqzqGpk1fDcY2rzocYZzGeffN+Emh/mG+859YpGauxKuNzEuKyiaWim1Oh8ZxXjZtfqu5cieBaXo7NmquaHpp57Jv1IkxUfqyxm/JoRnseZ6uLbyIOlbN5Kv73Y9kbbdt9LLfVwjlHyLFFFOFntQP1+9q1wTqy9+zZk6at69m6trXHOJezz2ZBdH21U1xEZNeuXZB/4okn0nRPTw+U2XX8TW96U5pevnw5lDU1me/Wyu88kX9ae5lt/Z588knIH6j/vbe3F8q+853vQN6Ose7/O++8E8q0K34y9ZgO7FgsXLgQ8p/5zGfS9IUXXghl1rV90UUXpelPf/rTUKZ95CLoEC4U8DuYdtCLiFx66aVpOp/Hz/K2z+6+++40PTAwAGV2nkzlc5XO6zkjInL66adD/hOf+ESaPvPMM6HMtk3HsI0VW1+9fjz66KNQ9txzz0FezznrJ7c+50PlZifkQLFzUz/TwsbzZOAvxwkhhBBCCCGEEEIIIYTMOnhznBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6Ju0crxmXUVXLln30EQWC7l9fOYTjAC9ZMY7s7WXnaXpxN3qLq/14nYx6qXXudq2GrCxUVSouQwdVtgX9t77qloxxjCc+9kObU4PL4rV4nnhhG+Sbupw7vBZg2/JGVtzU5BxVcRW9c17Z/E0j5/LVvKmv8erWlAs8SNAbNacbXxvk0ScXLnDt84tY/yA0Ljflkq95xuccWH+9Grs6xocfVEze1T9JsH5+xfjVK+61cRXP49Wwv2sDrk5jg+h4z/pY3xHlA88OYzwMDKHvbsuzrnx4CI/tmo/1XbnOpee3Y3/WPBybqpIeJxF63MxUlbDmzlXehoW7NmC/7FKe8aFdODdLo3hsacy58IpmJWnuwv/Idbr6FzM4bp7xfUfKDW39zTWjkI1UeZLD89TNXA0z6lwe9llGjENNOb2DANeLTGh82WpovAjjKoqMEzt28zqTRw9dWDXzWjU2rhhfdgbrm20yfarqmLFu7brx+KtuqoTot2vKYB0D5QqPvBKUReaZBXqsAsFrinHzeyq+ffM32yAx7y+xq0PFPMMiMPER6xgwhV6CMVoPXR3HK3jNYoxtTXzX37UE6xvHeJ3AU/Fj+uxYZSLX83S5MWeiU3om1Gkm1OFoZyoxeqj6mx5xQmYvh+p90jqQr7/++jStPdAiIl1dXZAPQ/e5yTqkR0dHIb9169Y0/fWvfx3KHn/88f0ea7GOae2Gfstb3gJly5Ytg3xHh3tuTjZrvnuaftH1f/HFF6HMemunMjb6WO3OFhH53ve+B/n7778f8tojbV3s1gV9ON4vrLd96dKlkD/11FPTtHXS29fq+PnkJz8JZa9+9ashXyq5z84nnHAClJ100kmQ155uHa8ie/uytQv/t3/7t6Hsxz/+MeT1WNjYsTF62mmnpekrr7wSyt761rdCfu7cuWl6Io9/o88atmzjxo1p+oUXXoCynTvxQX26f+2zDvg5ZHLwuUszBz3PbTxPBv5ynBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6Jq1VMTtRQJUR2l0eRutQVcqNJEE9xJYh3Bb02Atua8foz/qgLNw1APmq57YtFPLmPv+absgef4rbZtPdZR0QWKdYaRQ8owOoj2GdvND9XL/tBFRqRP24hSujdBxhHevr13BLTl1dplTC7Rm+0SQUQlce+di2WgEHzlOKggzaIkQWok4kF2P9RfVLZJQVcYht931XJ9/DLQ2hYHsC1RWJ1VuYrB6NmomzemwUFs3uusEc3KqUHcE6ZJXKozKKJ+7biR1VHlCxj6Ejfbv6IT+43m2DGxlFBcRQJ/a3H85J0y2n47Fi+rAUuY4pl7Bt5Z3YacGIyw9vwm2PvZuGsU5qPo6PYz8EFaxTrurK27tx/Nu6jbqoQ8Vl1ig16jgWXuw6NfZR1SFmrdG2mVoG52po1CN+xo2xZzQ1gTlxELv6JgFuQc34WN9QnSsWM25GhxL5rv+9LPZZ3izHtaprTzkyWqA89m89j7FUV04Tr2TURL6JD7U+53NmUfDMuqrGvG4mYFLFfK1Z1cHqcMxro8DVKTaaEs/Uoa6ODbNmzOs4F6p6rMybWOjhelEpuGMz5ti8aYCv3sd8c2wcmLap1/qRWbSOUQ7VlsLp1FdQPXLsc7i2turrHExczcStuJwnhBwZrLak0TrTaO3IZPDzrlWRaM2DVlKI7K3CaHRNrWYQERkedt8vHn74YSiz2pKxsbE0bdtm6/+Tn/wkTVvVyLnnngv5Cy+8ME3btjU14Wdwrc1ob2+HMqvn0P1iVR0WPY62PysV/Hw+NDQEea0EqBtn5pF4v7DXXL9+PeR/8IMfpOk3v/nNUGa1Nhqr+rnqqqsgr2PCKkxsnzZ6z7JzasGCBWn67//+76FM64ZERHbs2JGm58yZA2XnnHMO5LVKZfVq9PzaONR1Opj3W6vZueuuu9J0I42KCMbZTPwcMhOZiuLGwj4+fEy0Pu8L/nKcEEIIIYQQQgghhBBCyKyDN8cJIYQQQgghhBBCCCGEzDp4c5wQQgghhBBCCCGEEELIrGPSzvHAQ5dRXfmosxF6pPwET1uNnQe2v4zn+e8X0T+8+UHnRcq/vA3KMkPoS06UdzmbwWsOh+iYGVjRmaYXCnp1E890Q8a5gGoxOr7KJTx2ZNCdq2wUQmXjl82Purb6VeyzBJsmpYpygxeMs9u6lQvubxz1InqOahW8TqnmKpmE2A8dphsKRfR6hXV33SQxPmoPvWmxcvIGVeN098ch74OjF6/pRcbNHqk6m7HJGBd70xzVTwN43iCHvuFcm+tjO+bj60cgH4+415Z8HJvB7TiQ5V7nII+q6KOuDA9CftdTbp4sW2acZHmM52GlpRvtMT7qDcZ7Per6pa8P2z3cjx7/WLnwvXFzHqNLzinPW3M7Oq+b2zG2AtX9SRH7zDf+fa/m2hMZP5wf4msDz/VZLBhnkVXmq8sEnjmvmbsZ1diacdnXquZviqpxvnlGgX0+QE25tusRtkXMcwfEc2OeC7AOYtawaBTrVNrj1oHaMPZLtgOv2zLHHRt66OyWGh5b1U67BJ11ZtmXvIoPz/jVqvZhAiXVL8YvabTiEio/vB/gepc3a1qsu7+OYyF2DWhS72k+9oMfYv/q2htFutTN+0lcd30YxceOv5fOPEKmDucNmSwTxQp98Mc21o+svcATjb32MJ988slQ9ru/+7uQ7+rq2u95Gl3HxmexWIR8R0dHmq6a7z/j4/g90PqSNdYZq13m1jfdyBWeN8/qyeXM5zx1rjVr1kCZ7cPdu3enads2e97ly5en6cWLF0OZdYw/8cQTkNf9MhPeO+xYWOf7V77ylTTd3Y3Pflu7di3kOzvdfRnrdLd92MjLfTDroB7zuXPnQtknPvEJyOu2nnLKKVCm2yIi0tbWts9riOzd1gPFxoOdU4899th+yyLz/XMmxNbRho077dS3Y2zXN71msO+nH92ndI4TQgghhBBCCCGEEEIIIZOAN8cJIYQQQgghhBBCCCGEzDp4c5wQQgghhBBCCCGEEELIrGPS4qOqcf0WY+dzCSvoy0kyxtms/Nkv70Q3146f7cAK/eL5NF0fQ6dtKcLXZpWPOptHP5XXi57o3YOu/sd56B3LGF1VonzDdePWRruzyHife/HOfjy2PoR91lnXfYZtqQ6hj8hTp8q1oTy5ycjBy8rZXDde6P5hdNZt3eTypTp6eJYcjx1x0nFYPkd53CsJnjeOjFd83LV9uIZtrZv+bm1y58KREYkDE1vKFZ34WD8vj87mpjmuX/LGBR1G2Idh3tW/dze2JRgfhHx9zEXBSB3HeHTQePFLY2k6NsdGRuI9st35v/u3Yzwn49jW3s2uT+MhrG9HjNeJVdSWRsegTMzY+Oo6+Qo6mcvG050JVf0zOHJeiMd6yg1dM858z8dxSzw35n4G210IcNw85RmvCwaWb0TiWg8WJnjeamDiV3mj6z6uQ4GJpXzi5m5sltQoQRd7WFErSISrifX4R9rZbdyTOeNpr5XxtQMbXfvGe7BfmpbidXNz3PqSZPG8RetMz6rYis2zG0Ls73zd9WnNx1jyBWNUr7NxDetXtW9TSm6+1193fTw2o6TjsYnfyBwb5JvcabK45noVIz6vu5iom/PERr4e62dPhEevW+5Y8+IdaHuOlOd3KvU92l3Euv5HW9wdbfUlMwvGD/klGfP8Fe0JtnFi84WC+1z9nve8B8oWLFiw32tO5HPW12nk3BVBD/O8efOg7Pnnn5fJYvtB+55/67d+C8pOO+00yLe2uuc32fNYp7uuv+3PCy+8EPLPPfdcmi6X8fPtpZdeCvnf+Z3fSdNLliyBMvvaP/uzP4P81772tTQ9OmoeTmbQ42HbZtGxdCBO3n2dRwSd6TfccAOUveIVr4D8ddddl6bPPPNMKLPxojlUn29sn9lY0uV6fu0L6xlvxIG2x8aojY/+fvfMMzvGfJ85eOy46bXm+OOPh7Jt2/AZijt3uucr2jlEppcDiXX+cpwQQgghhBBCCCGEEELIrIM3xwkhhBBCCCGEEEIIIYTMOiatVYlKuH1Ab9AoGR2AeLi1aljpO7ZuHIKy5LktkM8ODabpsRoqIKoxKiCCxG3rH7NahLFxyI+PuK36UR1VAcbcIF7VtadUxZ/jjxgrxfhO9/eFynrUAUTDeHBfi6tvxsNjvUGsbz5x5y2G7VAWmO0xvvobx55xHNKNT+J2jaFHnbqjalQMY+s6Id9iVDUtc92WIs8oLMo17KfBIZff1YfXCXGoRBYW06Sfw/MGvuknT7XdN1uRskXMZl3/ZwtGqeCh7sJTmor8ENY324avrdVVHA5jLEWRmQuq+/NiNB9GqxJGrrw8hMcOvITzZnC924oXJuZvXF0tkK0rHc74GNY3WzVtzbo+DXM4UGHNakpcrGWzGGdJ1mzFDFwdM4Ln9RKc18OJe23k41qSzWIdMok7V8aoO5LYBJrW8MRYv4xnNCVKI5UNUR+S9Yzapa7y2L1SN3O1XnF5r45tiY3qJfDUtkejIcnERsGS4IVDtU0rF2H8FsXoiLSGp27WsAD7JVBtTzyrsTHaoMSNx4jphyajIomUAmcvXZKPba2Kihej2Ql9XLMysWt71W4BtjqtyG2TDO1WYtNnYeTism40RkmMGp7EUwqko9t2cVRztG0jPZj62tcezZqVqdR9JoxxIw0BIYRMlra2NsgXi+47TrVqvg8bJYTWVLz2ta+FsjA0n2GnsMY2OtbWQdd3xYoVUPbjH/8Y8lpDYeu3bt06yH/4wx9O06985SuhzOoudH0naqdWT9g6WB2KVm4sW7Zsv/UTEZk7d+5+66BVDCIiN910E+Q3btyYpu+//34oszoGrSKxapempibI33vvvWlaK2L2dd5G2Pe3ilJH7tq1C8ps/bdu3Zqm//AP/xDKLrvsMshPRVMyFRppgnT82jpMpK1pxMF8HtP1taqU7du3Q16vEfwcMv3YcdRrT3Mz3mOy8+9o/kx+tEGtCiGEEEIIIYQQQgghhBAyCXhznBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6Ju0cf3kTOlvmNDtfTnsrunPyTej8Ko8771H/9n4oS4b6IF9SLt2K8SnFCXqw6so969fQ2V2roSfYVz7noIb1tV7gWOl7+3dAkQy+iMeWt7jrVofR11vpRfdsrc85eIumj1ojfG0Sur9beAmex/PRQ1dVHuAtm7Ftex7ZA/ns1p40HcTozBqvYB9uXYjhsbTJOcmbi3idkvGX79k9nKb7NqF7PTB/k0kqzt+bXYbO4Gbj6Q5DlTeeeeuCjrSr2BybN6FfC9y4FudhW5pXYixJm3NHVYcxHrJDGKPVsgumjPlTVDaP/d++zNXRb8Lzjo7ieUvjKiasTylG/3Qup2K/MgplSWxe67u4zJhmZ4xXPCq4+ZfpxrZki5gPlCve+uzGjKM5VvWPzPMARLBSXlZ5z82RkfmfunJ8xT622zdO6UDFXd70UcasH3Hk8oHxsgcRtq1cdm2PyhiDGePx1+5tcO2LSBKa5yY04bwpzHP9FOZxbWmZj2OTUZ78eAyPrWdx3mi9fc64tiPj4IvUuhqUTb8Ucf3TfvWyGZuqcQ1mVT9Zx7ifwT6NVayFZr3L2mcWqOaE1p9unO+VQDnSzWMG6tbprqt/EJ5CMjUOlWPxUPm8Z6ITcip1mgkORfq+D56ZMI6EEJFzzjkH8osWLUrTmQx+NhscHIT8ySefnKbb29sbXqeRd/lg0F7mjo4OKLP1135ke+zVV18N+Ve84hVpOp/H7wSNXNBTeX+o1/GDnXZpi2D/Wj92d3c35LWreqI6WFfxRz7ykTS9ZQs+o218HL+z33DDDWna9pmNgQ9+8INp+td+7deg7Omnn4b8VBzkGtu2Ugm/D73wwgtp+rOf/SyUaX+6iMipp56apm0f2T7VeVsHm9fjbNtpvfNTGcdGbnB77FR86rqOIyMjUPbd734X8uWyu+d0uD4LNRqLiWhUx5n4Wc6O65497n7bwMAAlNn1w76WHDoO5D2NvxwnhBBCCCGEEEIIIYQQMuvgzXFCCCGEEEIIIYQQQgghsw7eHCeEEEIIIYQQQgghhBAy65i0c3zLI8OQj7qcz7eyCB3YnUvNRSrOjxv2otPJrxmXq/KKB8bzGli5q6JunLy5sAj5QpNzzmRC44YyTuGxuvM0RX3o9q3uMk7eqnM4G6WQjBkPd600lKbjMnrSWluaIJ/Nufp6BfwbRmR8zoMl1/bep9BBFm1Fp3u57OqbxOhk9k1be57EMe9f3pqm8wvxtdEY1qnWqzpj9xCU1cs4rkOx8waPtcyFsnA+tr0curZaXXZoFE6JEiSHxucc+VhfTzmPwzacFp1r8LxNo86H11wyLuUEKzH4srtutmjcyfPxOoV1rn/HjY+vXsD6Jll13RpesylXhXzrQhdr+QI6mssVfG2p1/nh834LlGVaMB+1uT7rWoTx21wwDm9V/UjQQV/z0INeL7i218yUz1SxD/2Mi0MvRI+1VZSVItWncRnKgrrxtCvZeRIbN6JgpRJ9rHHWxRUTH2p6jhu/t5fgdWLlU/fz2G6vGWMnyOJ60tzt2pO047FN5hkR1cjFRCTYD551s3suH2VwjCPT/zn1TIiwCa+ZMc8HqKv5GZt+KETGzR66/vcirF9k5nmm7s4VC86LuodrmJdRQRpifwfWB6887UlgvISmbYF62IBfOTB348FwtHmjZxOHy6M4FafsgdbpULnYD4ZGdZiJDktCZsK8ITMD67J+1atelaatH9k6bbWP2Pq9p8t3O5HPWXvEt23bBmU2zpua3HeIM888E8re8IY3QL5YdN/vrWP8YOaP7tOhIfzeOjyM34eXL1+eppctWwZl1iHdqE62zHqu161bl6ZPP/10KBsbw+9SF110UZqeOxe/S9s6LVy4ME3/8R//MZS9+93vhrz22dvYaeTenug9VsfHU089BWXveMc7IP/e9743Tb/vfe+DsmwWP8vrPtTXENl73ujyQgG/w9ixaDSOtl+0X93Gkp2PU4lnPRaPPvoolN11112Qt2tCo/Pq+LDttv2i+9se29raCvkFCxakaeuRt+P22GOPpemNGzdCmfXVT2UNO1SfA20ddH/buJtorSSHDjrHCSGEEEIIIYQQQgghhJBJwJvjhBBCCCGEEEIIIYQQQmYdk9aqlF7sh3x1nttmUSnhT9bLHm4ZCVuUskJwK0EtxG0Vgefu1wc+Vi/yUB3gidt+EmRw20dTZyfkF3S78qQJt9V4oVFu1N1WiVINt7iMjxpVitu5Jv4IbqPwy7jlKa4qXYvZpu+3N0O+pdWpDvJtqMIIs/g3jf4e156xjQNQloyjsiKpuDpljM4iDLAO1R2DkO/Z7XQibe3Yh5HRc/ixUiqYrSd17BaJ97hOHBk0Opw2jCVlkJGxOtYhMfliwfVxV8boLUIcK1GxFvgYZ7l2rHBzqztXMTaqlGIH5JOTXXvqRo+zM4Ox//Kgq+/u9RhnpX6sQ73qtmkVixgfmZO6IL9wjWtP81xUoyRG/VPd5q7jxWYrVXs71kG1p70T6yB5s9VObWux88324dCY69/hXVhW8s1WzAVqG1MG67BtDMe8Z8jN5ZpR+4Qxbj87vsMF2uo5uA7lzd8Ua1V3bNW4fjyjgYmVtcSLcJtYFBhdi1rvPMFYyUZGs1PGfomLrj0ZoyUZj0yfqm2GXmS2eJr1OqPmkR9hf/umX7zYba/LZcy2prrR7ijNkTGw7KUpifXaacpC0/9J6M6bS7DP4sD8bThQyhiztdEqvXQI+6aPqgmOVVJ1+SRCnc9MYyaqMWY67LOjk0bbwQ/2XIfjPNO1LXc64/VA6zQTx4KQI83atWshrxUF9Tp+Junp6YG83uJv9QpW66DVCFa/0Qg7T2s1o+ZUdbLaDPtarVW54IILoEwrQERQPXEw891qEcbHnffw5ZdfhrJNmzZB/rzzzkvTuRx+Fp7ONUiP1Zo16Pi0dWprQ8WtptG46raIiFx99dWQf/jhh/f72haj29SqjJ///OdQNjqK9xx0vNjY2bFjB+T/8i//Mk3/13/9F5RdfPHFkNf6GdtuPYdERNrV91qrD2mEjV87H3fv3p2mb7nlFiizapdG6hGrWbn77rvT9Pe+9z0os2uArZPGxqhWu9j595a3vAXyJ510Upq249/RgfdA9Nyw88SiVVC33norlP3Jn/wJ5HUsTZcm6mCZilKIHFp0fNs5NBn4y3FCCCGEEEIIIYQQQgghsw7eHCeEEEIIIYQQQgghhBAy6+DNcUIIIYQQQgghhBBCCCGzjkk7x+P+PZAvh84nmzGe2iBEZ3PHSc4HtGAJ+ljHt6PnejTjHEmZMfQTRcbhkwmdH6rQgY7x5SfPh3zXfHeu2PhvPeOtrZfc3wwG96AXeHAI896Ic5R51XEoC/dy5zp3WF2MM7iIfRbMcf3S1Ip9Vsvg3zR27HZ1igaGoCyqoqs6W3XHellsd2IcvONGV1UeUW2vojMr9rGOTcrbPerhieoJ9lNW9Wncjy7wMdRXycvqpRt3oKPMH8PXtsxzdVi5COu3bA62NaNU54FvfOQGrS/KmL8vNS/GeA7muLgbr2C7e17EPtz5oJtjlQ29UBb3o0ssVONYS9Azt0Ww0+Y1uX4qdmLcNQXoWGvuch2Ry6P/PTTHRsqDHfjYllpgPLwq1PwAfYdBHa8TDziXWOUl7LOxMYyl3q2uPRtN//ZvGYG8t8ed1y+jg7Gaw3EcOcGtJ7lXY30XtRjPdU1NdOOm9moYd7E6tGCuGQXm+QvqsoFnvP1VrFNSx7lQi135MBZJJcY1LPFcH/oBtq3FzN2qeiZEGJt138RAoNzgNbPeRb5xjivnpVnexIuxDr5ypkfm+Ra2//3AXSdJ8NiccYVHkSv3Tdti4yuPlCc/Mb566/mLE+cZj2vY9zOdmeDNO5YcwjOhP2cCU+mHwzX+B3Od6RrXqfjrOS8mPtex1Edk9mEdyNoNbj279tiXXnppn68TEek0z+TSXmbrBba+Vj2n7LzVzm4RkYceeihNWx+y9S4vWbIkTV944YVQZuuk63swz2mwddB1tL7ssTH8Lq370PbvwWDbo/N2jPN5vG9QrTb+7ro/bP2vvfZayL/pTW9K03qcRNDZbeu0ceNGKPuHf/gHyH/lK19J09aLbz9H9/a678Q/+tGPoOyJJ56AvHavr1ixAsre/e53Q1771m0/TMW/b+ejru9///d/Q9lzzz0Hee3atjFq+0W72ff6rtHAvW3jys4pPeduvvlmKLNjrsfY9pGt/1T6UPvq3/e+90HZhg0bIK+d5HbdafR5Yjo/a9g+1Wulfa5DI9+6HUc7j3X5TPGrzzQarZs6riYLfzlOCCGEEEIIIYQQQgghZNbBm+OEEEIIIYQQQgghhBBCZh28OU4IIYQQQgghhBBCCCFk1jF5URZqj2Rs0Pm3IuMcjzvQrdOdcf6nE05ohbJMy0LI79re5c67AZ3BlX70g/kdTWm683j0LHef3gT5JOdcQHEV61v3jOtqxJVXjHO8NIB1SsquHwp1rF/e+HvFV9e1bqtO9BNllri/WxS60Pm2W/DY3ducb6k+ivW1jttYOXmjGM8TV4yPLTEeaeXwiTJYp2IV81Xlgw8z2L+ZGNvu1ZxDKRhDQXLvbnQxPfeca8+eZ/qhLDdWhvyuNhdrvSfOhbLBV3VBft3x7rqtGeP9FPS6JYGrQ06MYzo2/ZBzfThcwXb3bBqFfPTiNpfp3YVlFWxboKZuMID9UHoenVS7Fs5J04W5C6AsU8S5WlDK9LAN25IVM2+0+8y4n70IxzH2XT/5Cf5Nrl5D5/h4r/NtjW/H85SN139zvyvfsw2fixD07sQ6KGd+UMc6BAGO8UC/m9frixgPLadj7HSpeZEz3qu68dfnVAjEWeNRtM8hCFXeOK8rxq09No51TAZd3A2Z5wMkxl+uJfpNIda3ZuZ13KzdlHhesyxJoFzhdTHu9Sz2f6jWRt/Dstg46qNYH2vee7AK4qk1zveNn8/HPqsl6ryRmdemv0U1fdhoHsdKeGygfHG1mjnPUYR1uk2Xu49e4GOTQ+XhthyJ+Dlc7nj6sw+OqTjcjzUaxehs6oejGeuU1k5b6/K1buLt27enaes8nj8fn8n12te+Nk0vWrSoYR30da2ze3h4GPJPP/30fo9taWmB/OrVq9P0nDlzoGwq3uJG2DlRKuH3Y+3I3rx5M5RZT/vcue475XTOJ1tH7RseGsJniu3ahd8TBwcH9/k6kb3d8Rrr2j7uuOMgr5291qVsX6v74sQTT4SyP/qjP4L8s88+m6YfeOABKLP11/2ivdsie/eLfu2yZcugzPqzm5vdl95GbbH5id5bmpqa9nusdoyL4Lyx88S+9kA/e9j62Xn+nve8J03b8bdj3sj5P1G+ETpGraP7ox/9KOTvuuuuNL1lyxYoO1Sfz2xbbLwsXOjuZ+o1VUTk5JNPhrxe4+zabef1Pffck6bXr18PZY1i/1hD9799T7Drm46fri68ZzMZ+MtxQgghhBBCCCGEEEIIIbMO3hwnhBBCCCGEEEIIIYQQMuuYvFalbpQbo07zUArwHntLiAqI9qJSpXTg9vr5zW2Qr3e6jfE9PuoWovW4lSZUxUEet31US1jf4T51nqzZtuJj3htx+bCOG/XjMm5ZqKkt9E0xXtM36oAodufystj1uRbMty5yW87itgKU7dhs6rB7wJ23iqqOIMLtGrGqg1836os6HhuYLSTNenddYLQ1CfZTRjfHaBFqNdwu48VOlTFewjrs3o3nHXjOtXV8E26lqRtlQbTLaTbGelA9ktROgPzcZqdgaZmH4+YFWAdf6UVis7XDC/HYRGlWRo1+YXhgDPK1kps3kdXhmLGp+m4rWFPV1K8Ht2xVR9yxfhmPjWK8jldUg2y2qXiC8R0o7U5ixjiJcIyjupv3NVPf8TLG4cigO1elgn00VMJj+/T2tC3boKxSwS2e9arrwyA2W79MPPt7XB32vIDrUHUFbtHJdKqt7xmzpAZmbam5YxMfj02MbibxXFurVhhSMVqVERyr8X7X1tKImZue2aan9CfVAPtloIBbamtNro5dc/G8nZ1YJ1+prKoBluWNfihWTbeqlLppe6iO9fYSqRg1jc6YdpumSqDcYXUT61Fk5k3ZrVnDo3jNPVvMe8aIig/szqMabs0nhJDp5XAoe2aiqmgmcLh0SZOlWsUvDVq5YLfPW0XICy+8kKY3bNgAZVqTISLS3d2dpq3SxOoNNDZOCgX8rqo1A1pDIrJ32044wX0n06oLkb230E8lPvWYWmWF1pCIiDz55JNpur8fvzNaRUix6L4XHEz9LPo7uojI2Jj7DrRp0yYo27kT1ZEvvfRSml67di2UNdJz2PpajYaOAasvaKS8sedta8P7PX/6p3+apq+44goo6+3t3W99LXZcdf7Vr341lB1//PGQ122baBwbjatVbGhtkFW7PPzww/utrx3/6cKO6YUXXgj50047LU3btkwlvqfrvcNe06oxtBpq69at03LNibB9eMopp0D+D/7gD9L02WefDWU29vW5JlJW3njjjWn65ZdfhrLPfe5zkL/jjjvSdLmM92Jn2vvbRNgY0GvuihUroExruURE9uxx9/8OJCb5y3FCCCGEEEIIIYQQQgghsw7eHCeEEEIIIYQQQgghhBAy6+DNcUIIIYQQQgghhBBCCCGzjkk7x31Bp1MozhcWheigap6Hpw2UpzbOoevMKxv/dJ/za3l70LNcHzIe5kHnZhoZR//aYMk4kpz6TIYLWL9iiMfqtrW0o6MnZ9y5QVV5on3jZkuwrUnovDeh6YdiE7421+nykfHUjo6iSzk3OJSma3XsI994dj3f1SEyrnjJoC+urQNdy21z3DgXfPRi1Xzsl7py/0ZYJInxZ8d114ejQ0NQNtCDdYyVRzwcHRAsxPN64vo4qWK/9D+B7qita5xPbnEXdngmb5zeiWtbInieyDNtUx7pSgk7YmjU+Kgryhdv+ixOTNwpl3yS4HmqMY7jWOzivV7FYzPmvJlQOb0jjJ04i96mOHYxWrJzyHjTysorPmqc4/UxrEMyrryKOaxveZfp313uYQL18UEok6px6NfV2mL+LpgYZ12svPJjgy147Dh6vLw5Ll4yOeP1C3Bex6Frq5eY5yQY1VxUVf2d4JpVqmD960Mm9vtdvOdGsCxr5n256s49VsJKVOvjkI+Vn6+0AD3t4yvwfWD+InedvHGBV42b31f5qmd9d9inddUt+Rgd9IFRqkXqtTXjHfM9405U88gzTvrxKub7Sm6OPbfZ+MgfRZdmdszVYbSIYzHTOVB/4MG47War75YcGI3cqeTQcrQ5LG19Z0K8zMQ+nC1zaib2vcZ6xLVn3Jb19fVBfssW90ymjRs3Qpn132q/tnVKNxp/64RtasLPhJdeemmatl5g61ZeunTpfssa+bInQvucx8fx86z20oqg09t6rLWXXQS97QczR2xbajX8TKt98U8//XTDY3ft2pWmrdO9kTvetrVRDEylrfZYe941a9ak6XPOOQfK/vM//xPyUxlzHYcnnXTSfstsnabiGLdljeaCdVPffvvt+z3vdKLraNv9qle9CvK6/GDGeLqYKHbscxMOBXZM7br527/925B/3etel6bb29sbnmsq/abnrnbDi4h88YtfhLyeUzfffDOU2fVvpmH7xD574pprrknT73//+6HMjs0zzzyTpn/4wx9OuS785TghhBBCCCGEEEIIIYSQWQdvjhNCCCGEEEIIIYQQQgiZdfDmOCGEEEIIIYQQQgghhJBZx6Sd44mHftxa1r20ZR66rLqXoycmbnX+sLqRwlZHMV/a6jxqpR0jUDbeh67fSPnXgoExKGs1buV8zR1bacK2eMb/nS06702xFduWaUf/cHXQ1TcK8LytbehIalKO2yCPXutcNw5FrdXVqWL/huEZX6/vXhskeGzOeNB95RuueFhWb0LH+NyV6Kia2+k8u4Gp07hx9JZrblzjOnrRmjx8bU05hkd60Ik0Ysd8xHnGk8iUGed4TvmIs8afHe3pgfzuzcen6cpadHY31czYKN96YFzrken/uObisG8MHXD1YfTdhRUVwxG2JTRecT9xba8br3xixtyru7gMqsahb7RdQRypY02cGee40nJLztS3aq5THnJtTyrYFhkx8T3k+tQo9CUq45jHFXfdeoxl+Tr2d6JiIDJzSATr60futfUanif2jZ885+ofZPC8no+x72fcdXwr4y/ja0t1d2xQxvpVhoyvfszEixJz12o4br2juC5piXcuss9fMPGt5nJtZz+U9dXwvJmkK03PW2Bcfm3Gy63c914drxn7Zr1W8R0LnscT7O+acp3HkfG/+6bPVDqpm7Eo4fvL5l3uXNseHoWy4kY8Vq/JJTHPSTiGmC5/ayMv8ETXOBJ+3GPZyUsImd3MRE/7wTDTPeOasvm8qz3jAwP4WcJ6xXfv3p2mR0fxM8rixYshr73M1k1tXbk6b/uyWMTvkCtXrkzT1s9rHen6vNalbfONPhNYf7buQ+s937lzp+yPlhb8rr9gwYL91tcylTljj7Vjddddd6XpHTt2QJn1MI+M4D2TRtfR+Yl8yNM15+158uo+yDve8Q4o0+0W2duhrrH17+joSNPWOZ7JmO8TB+hTn6gOum12XhyMQ38q6Pg48cQToWzVqlWQt/0y07Bjo59LMJ39p68Thnjv56yzzoL8RRddBHntvZ7KnJpK/e157Dp1ww03pOmHHnoIyu677z7I18137SONjcG3ve1tkP/zP//zNF0o4H06i55zlcrUn/XFX44TQgghhBBCCCGEEEIImXXw5jghhBBCCCGEEEIIIYSQWcektSqB+Sl/2Ox+/j73lLlQ1rVwDr4257ZElSI8z+ggboEa3uV+/j42iNtoxkvDkNfGglqA560PYtOiIeePqI3jz/Grefwpf77d5fOtWNa9GHUo9UF3rmajUVl2Cm6l8WN3ruEqHltpw+tsGVNb14yGJJfDfLbD1SEziJ6MIMbz1tVL4wxun2tehm1bsg71OJl2d7wXGzVDbLQPVbddIzY7N3Jmu0niuTqPDA9BWa0XFQVxVW+lQfVB1uxMyWpVhoeV8Mx2xUqf28pWLXVAmd+EJ856Si0h2A/GLiOqG2RkGOtbwKaKn6hzGVWKn9ip6vowMsojP98K+VxOxYc5NsihOscv6rjDa8ZGuRHrcjP+cQnbOjqitq4Nm22Qo7hlMim7eZ8fxe0wWavRCNx1Qh/HIjJ5L3F1iIwuxDf9nfWV0sRs9UmacY7FOaUwMUFoliVJfLWVqm70MiYfZNyx8bjR1Ixj/NZKJl5Ul5ZHzZrbj3OqJePGtWmhiQfj3RlUuzajcRyLTK/RoTS59Xo8g2tJkDFrWpMrzwjWT2LTTxm3tdhLzPoW4LF6jYgFY8ksH+KpGBg1cdbTj+8Zu59258puRL2MN4IuoJI6VblmJv0MYyrbSg/X9vSpXEcfa9syXfU9GrUCR2OdD4RDpYA4mlQMZGZztMfS0aZZOZr726pTxseddnJ4GL8PP/LII5DXio2mJvxcd/LJJ0N+2bJladqqBBqNry2zKoHmZve5bsWKFVDW2dkJea04sfoQ3W57Xque6e3thbzup7Ex/Gxp6/uKV7wiTdu2LV26tOFrG6Fj0Majrb/W4YiI3H///WnaKld0P4iItLe37/c6Nt+o/hMpIaYLfV6r/bCKBa3WsW2xepk5c+bsMy1y6NrWqL9tTDZSxBwMjebf+eefD2Xz58/f73kO9DO3yKHrT6tL0n16qNb4bBa//65btw7yWt8jMjVFT6M1wTKV82q1y8c+9jEoe+KJJyC/Z49T+2pNzZGitRXvXV1//fWQ1/E8UT9orZF9r5kM/OU4IYQQQgghhBBCCCGEkFkHb44TQgghhBBCCCGEEEIImXXw5jghhBBCCCGEEEIIIYSQWcfkneNZ9FG3rXJem4Vru6Gs0IYumDjjLlOJ0GszPoYeodKwc4JZz1itivlQ+WbroXVQGR+1565THbO+XlPfgjtvwceyhce1Qb6oql9YDEUydw36c8aVp3vbdmx3zw6sb2mT88VljJ83UzXu6nbn4UmUc0xkb993XbmUs4vQQ7fuTPTyLF+Gnt2g4Oofmz7zxfh/YufkrRuVUd441APl+k3qGJKh9TCrv+cEZmxyxoMeqDrVE/RaV33smFA5yb0A4ywJsZ9icXMhTNAdFntY35rK5kegSDzjnQ9853QvBNbjhfWNlE89aUHvXLgYHWtzu119i83omQ86zBKQV+XGPVgNTJ/Frk/rNfTDlUZxjMeGXD/5/SYojZ+8UHDHBq3GY+3jOrS75pxg4zUsk6pxhSvndGjk8IFxjicZd97WOfjsgOaiGbescplnjYvfkKj49a1KOzQTpeCuW68Zv3cOj63UcU3za+46uQr2YWDW4EKbiucC9qFXNC75QXedaNyc1/ju6sPu2OHdOBZNWeOD1n74IsZd3XRpRvnik73+vms98+oaMZ43CHFN8NX7SWSOHd+B/ett3ZamwzJ6LDNVPG9NOfSlYhYBcsg4GA/hTHSvT4WZ7gEmxwaHyutPCBEZGsJnlOjvxFu2bIGy9evX7/dY6xc+77zzIN/S0pKmrb95Ku8l9lh9Lus9t47bvr6+NP3Tn/4UygYHByGvXcDWvW77RXu4zz77bCizHvTVq1enaevztnndVtuWRv5p65t+6aWXIP8P//APkNfjav3k2i8sIrJw4cI0bZ3djVzbE7njp+I8ngqNzmXjcCrMmzcvTReL5tlvU3DFTwU75toPf99990GZjYHpet+0Y37qqaem6UsuuaThsdrpbmk0rw8Vtk8qFXxmlH6mwnSi25rL4f2Sk046CfLWSa6ZyMWuy+36MZXnPDQam9NOOw3KXv3qV0P+e9/7Xpo+VB78idD11+uvCK5n9tiJqNfdfYJGsb0/+MtxQgghhBBCCCGEEEIIIbMO3hwnhBBCCCGEEEIIIYQQMuvgzXFCCCGEEEIIIYQQQgghs45JO8fjeehtWvAK54KZNx/d2pkcemHKyvHk1dHDUzf+7KiknLZVdAr5cQnyifJnB3n0Y3udKPSN8u683jg224+MA7vi3DtZQedQcS5ep3Why2dQ2S1RAV873OP8cXt2DkLZ4GP9kK8NqGv66HcKzXmz85Uf+fS5UFYwaqi4yf1H2xL0Cx93AvrLmtqwnyLPvdYzf1epGn9yPVZu8wTHOPQwXw/cuTKJ8SkZ51Ped96pyMP6xz46qUTVwaqK/GYcx475yvedxxg1TZNYNT0QdE5ljDuqOXaxtMC44ze34Wsru13/2vNGAeaTFucLy62aB2Urz1wA+SXHOVdeSzd6/3ItOK+TwHWUbzRocYL1T5QPvhqhf3q0bMZ40I2N349l1r0dFl1b8214Tb+OXqyepi6XjtH9XAjw2ChyY16qoKewZtqazHHHLjyhA8rybQ2cjMZnZ9TxUlVLbmyei2BX4zCjOqaGAZzJYvxao1bFc2tnoRNjp5jHOtZCFwNjNWxbSxXHtZB3lawXcf7VynhsbUg1Pm888+14nbjFxUc1MG7KAvZTRq8fgfW4Ydu0m9/3sZeyCdbB992cynq4lgTxAObV8wyyGePiD0wMBO68yQxTQc8mN/V0tXUmepVn0zjOJo62cZ3prv6jrT/J7GZgAD93PPHEE2n63nvvhbJNmzZBXntW58zB5xAtX74c8mE46VsBDZnKnLaeXe0UfvLJJ6FMt1tEpLe3N01bD7f1AGuP7cUXXwxly5Ytg7z2oluvsnVV6/rbOlhH+tiY+26ydetWKLvtttsg/53vfGe/r7VYD/qSJUv2W9+JvOJHAu0Ffvzxx6HMOqYbYeNu8+bNaVq77EVEOjrw+9yBYuO3VML7U9///vfT9KOPPgplUWTvKhwYdgznzsX7PxdddFGabjfPo7PObv2MAvt8AP1MAhGMu4nWjgN9z7V99OKLL0Jer43T+VlCn8vWwT4DwpbrmJjoeSw6P1H9p9KHOiasM/3SSy+FvH4POVLOcU1/P94HteuofpbARH2i+9S+j06GI786EkIIIYQQQgghhBBCCCGHGd4cJ4QQQgghhBBCCCGEEDLrmPReqrbVXZBftMopODJNRoVhtpVnfKU0MZKKusknWp1iVA2e2TLvhS4fNuM2kNBu28+4OnpGZxB4eGzkufP6Hm7FLxRRQxG3u20Llcw4lI2X8af8QwNue9TIllE8dmcv5IMR1/aqj/qC0TxupWpqdtuE2s9Gxcbxi/C19RZ33kwLtrvdtM3LQFb8WCkVzHaSPrOlaHzUbWnIRGYsPBzHMOu2cxRCLGtuxRCtjLpK1WLcMhKbrSk1NXb1Am4Lajse++m41S5+cgXs31piNDwqXbdOELPVKpdxbV9glDxLTsQ59dKo2+aU7MHYSZpwq2DLyvlp+oSz5kPZ8Sei5qiz0/VZ2IyDGoRG36KUFZ7R4SQxvrasjq0Zr0dkFBv+uOunqGQ0GSbOck2un5pacUyLRmFR7XbHtrQY1c923AIlZXehcaNsikI8b3612562cnU3lDXlcBxzyj+TxNgRdbPW+Gq9S2xZbFQpvlqzjJInzuKWw8jsMKqpsGxvxjWgKcQ6jvW6fCnC/h4YwxPXE9eHQ2XchpUM43kzql+627F/syaWJHbzJKzjebzY1MFzMZvxsL42ZjOeq+NeeiSjnEo8V8dsiNecMx/rO7ravbjeimtLfQeet7bHnTfxUOdzNDETdSKHCyogjg4OVZ9NtEX2UF2HEHJwHK65eyj4yU9+st+y+++/H/J267hWI8yfj98RrGJBqxEOZg2yfau1A1ZBYHUhWvPw8ssvQ9lLL70E+dFR9/3Z6i2K9nusas/KlSuhzOoiMurLyFS27VtFxX333Qf5b3/722n66aefhrIdO/ADo9WJ6OtkzJelNWvWQF5rNazuYia8t9ix0jFwyy23QJlWrog0nrf2vDp+vve970HZ+973Pshbfc5k0RoSEZF77rkH8p/+9KfTtNVD2PpOBa3NsPoTqw06/fTT07Qdf6sq0toSq6JZtWoV5H/91389Tbe24v2GfB6/b+r6TkU1Ytezz372s5A/VBoQXQc7F60SaXgYv89ZpZPGtl3nJ1IeNepDi66/PU+hgPcYpjI2hwp9ne3bt0PZV7/6Vch/6EMfStPd3XhfxtLT05Omn3322SnXi78cJ4QQQgghhBBCCCGEEDLr4M1xQgghhBBCCCGEEEIIIbMO3hwnhBBCCCGEEEIIIYQQMuuYtHN88YnG2dzu3DU5486VDDpjE+VXCgQ9UoEY51CoXK6hcVVH6PPJZVUdOtAz1tyM+aKnnGp5dOsUjCdInVbyefz7QRAar3XVeZHGy+gnqlTQQzY44q47Ooh9FJfwtdm66zPtgRYRGTeeXRl0bStXl0JRbi46qdqLyg1u+8H4zKrG8R4kzhlX84w/rmocyOPOJWa13GXMSqD+RpMrYH27Ctj2SP89Zxc6x8tldIDVQ/faruPQuXf6OYshv3iJi5fEx37JR1iHinKZR8ZXXzBjFWVdvmMudsSJp6L/qZ53PryxHf143jZs64pVzrd0/Ap0fjV34rjlC25cIzPjk8T+h/N4WeNU3TioqsolP2Kk45XEDLrqh0wLusKKRYy7XLubj4Wi8Ze1mPMqf3b7fOzPeCc6DZOy8h+a+vnmGQWFec473zkH15JsxtQh0N4urK94GO3gD0yw3ZHp8VD5sqsBjlO2iMc2NeF1anV3fCaPsWOmvVR9t06ND2E/jIzieWPlARw1651n/GylnOvDTIBjE2SwPZESgNfr2L+e7W7db9ZZZ+ZjJnbjkfjmWRO+faaFKw99jOemTqzEotM73XlGsaz0MsZ3YaNbc+tbO+RIc6DuyaPZ3UrIdDJdc2EmeGCPFI3afjBry2zu08PB0d6/uv4z/T3sqaeegnxvr3s21e7du6GskcfYOoStZ1n3g+2TRmudPdZ6gLWXec+ePVC2c+dOyD/33HNpesuWLVBm/eQ19X3D1s+27bzzzkvTCxYsgDLr8LaO3kbo69bM959HH30U8j/96U/TdH8/frebyK2tr5PLme+BK1ZAXvufp/IeZWOnkQN5Kn1kr2k93XfddVeafvLJJxueq5Gj2Y659ts/8MADUHbOOedA/vjjj0/T1sVv+6Vcdt+H7HlvvPFGyOv4tr79qWD7u7nZfa963eteB2Xvete7IK8d9DZG7dry0EMPpenNmzdDmXVVf/e7303Tl112GZRdeeWVkNcxa/vX1kk7vP/iL/4CyqzH/2D6dLLY+tlnQNi267baZx/YuatjtpFjXGT63nOtI/1g3PeHArvO33rrrZDXcXn11VdDWWdnJ+T1e5E972TgL8cJIYQQQgghhBBCCCGEzDp4c5wQQgghhBBCCCGEEELIrIM3xwkhhBBCCCGEEEIIIYTMOibtHJ+3BF3QmbxyYmfxHnsuQT9OJXHeVy9AT1BTHn3fTUXnzBoqou+nEGC+2OmOnb8IfTPt89D9m1Xq3EzG+nvx2Pwcl8/NQU9QzStBfrzqHD7VAP1alSr6zCpDrl+iMePKxaZJDH2IhV4VHWXRsKvjSL851njDs55zR8XGj103XqM4weuI5/IZQcdXc9a45nTTzZj7xtseKYdwsQn7rLUV69Q0z722bxdeszKKzuNMqxvnhevQNbf65C7IF3NuzI0CTkaM2qqmXFcZ02c1M6Ni5T3PtGKczTkOjz21w82xeBTdxJmscUx3un5qazZ+uDyOTTXj6uAbx3gNDxUvdrGUxNi/1QTjZdeoS79k/N7VPnxtk/JyL+42HvE2zAc5F6NRDtudbcYKt6rYKnai4yteYPzOytWfiU2sZ4zjSzmni8ZnF/gmfkX3izmPmX8Z9cyFxMj4PeO5zqjzRhnse+2RFxEpduA6WoNxNoNcwdcWim7MK6O4vlXK6Cir1JS3vYxjnhdsT6GonOPm2Q2VuqlTzZ2rHprzVDA+vNCdK8piWzJiPItqzfJCXMvjBN/TPLXeJcY1VzQLdNjq6h80YTzE5rkJrfPcWPjzzEJ/GDjaHbEHymxt97HOTBzXmVino5ljuT/57IaZw0wfi02bNkFeO4+tq9pSUc+AsQ7vUgk/52lXte0D66XV5fY8u3btgvzDDz+cpq0j3XrQH3zwwTRtfeTW/auxYzhv3jzIv/rVr07TWfN8sUbrzERrUCMPt+0z7fqdyDHeCFsn6zXWHuaJxlHnrSve9rd2s1tPu83r807kYv/Lv/zL/da3pQWfc6dd221tbQ3rMDg4mKbtHHrPe94D+bPPPjtN/8qv/AqU2Trdc889afrOO++EMhuzB+rEDkP8jt7Vhfcq3vve96bpd77znVC2cOFCyOv6W9/70qX4fLpvfetbaVo/20Bk79jR7ufHH38cyv73//7fkJ8zZ06atm54O256nRodHYUyu9YcjrXajuHGjRshr/tMBGPLOsYbPUvAzuvp+vxj5/WLL77YsE5HGhtn9vkMd999d5r+8Y9/DGU29levXp2m9XowWfjLcUIIIYQQQgghhBBCCCGzDt4cJ4QQQgghhBBCCCGEEDLrmLRWpakFt+/4odsOEZgdAPUAtw3pHQI5oy9o78At83O1CiHCn/xnjKKgaaHbvr5wNW6zae0sQD6vq4Q7DaSYx2Oz7a5tddwhL6NV1AyMFdy2oajUDGX1YdwiUOtz20Ky47jdqG627VeVciHr4TD5id3m5s6bVHHrTL2OaolIDUbi4xaXmtlhsZfqRSksfAwH6ZiD41jqVh1utlYFWay/1j4U5hiNyhwcgCalMCiuwvN4dRzYYrvbltU1F+Oj0Izbearqsv0VrMOWIbxOT9Xliwle87gm3La3uMUd64UmJluwX+YV1RaeOh4beDg4YejqWAvNNjejNZKK2mpnlCB1u9Un0WVYv8Eh3GL07NNui+fGx3CLZNCLxxbzSt2xGsd0ZRfWP5d11/UzZhuWUZFkcm5NqGdx3akbFY2oeeQnZiug6YfAc+PqezZezbLpufoGgmqf2Ohb6p6rb2gUIBJgnURpPrIe9kMpj68NClinSB0e17H+QdbUSW3NzGTKUFbIm7FRSqo4g/1dKOL63LXAbQeMzbbTmtkiF5ZceRwa5Y3RMCWRmlNG5WK7NFEB7RvtVRCZNVfpq2pGKRUERtOlVGKBickkj9vpsu0q7orYbrI3x7JSYTYxlXFstL2T8XBoOZittRybY5/ZNMbTtWZNF1YloJUcE11fKwG2bdsGZT/96U8h/9rXvjZNa32FyN59onUtWq8gIvJv//ZvkH/yySfTtN3abrUqOj+RQkHXydb3yiuvhPyaNWvStNU6HIzOQNfJalWsLkIfezBxYzUJP/vZzyB/6aWXpmmr57Bt04qep556Csqs/kRfd8WKFVB2wgknQF7H3bPPPgtl//iP/wj5np6eNL1kyRIo+43f+A3IX3jhhWl6wQLUpFplyHe+8500fccdd0DZE088Afnbb799v8dazYPuMz0PRPZWcEzlM42On/b2dii75pprIK/VHXPnzt3veWwdmprwe7fuTxGRr371q2natruRwmloaGi/ZSIiAwMD+6yPyN79oDU89tgjoQCx19RtERH50Y9+BPnTTjstTb/xjW+EMqt0svOz0XV1P03UL3qs9uzZA2Vac2WPnYnYtun5Z9dCra4SQTWQXS8mA385TgghhBBCCCGEEEIIIWTWwZvjhBBCCCGEEEIIIYQQQmYdvDlOCCGEEEIIIYQQQgghZNYxaed4MYcOoqzywFaty9U4sX3l+i0Yf2xbm3EOHefu1xfmtkBZNov+7EK3c/i0zcNjW3Lo/Coo92xgnMyh8TQFOVffkRp6bMaNV6paaXVle/A8Q7vRaVvrVa7cCjrVfOPO1b0UeHjegu3vrHJ2G6e0PW8orl9qxisVmHxtLw+ze20SYOi0tGB/d893bS9HxoM1jsdqRW9zB5b5rXidqu/qmK+P4XmN37mlyY1NwUMPcNXEaJ/yiD+2Geu74wn0mY30OjdekKDD7vnF7ZBfdZJrzyuW4DU7jLcdhjmXN2UYL0HdjU3NxGQkOG41rWZK8FjPjKMur8boo+rZiP3d/9D2NF1+thfKsmMY+2V1na1D6EmrZdEHdcqJbl7njPO6lsf6Bsr9nDfzpGLiOQpcnWLfuraMA05cvEQB9lndzLGsjjsPj409Ox9VneoYO5GfMXndHqxvjLo7ifPW06363z5MwMc+jNWcMmEnXoA+R1GO9448rrmtBax/03znuPMD9IPFMcZSFLsLJ2Yca2YNy8Su3Boia2Y+ihorz8P6ebYTVezHMdYhymI8x6FrW8HHTotzWKtMwfVTrX7s+FutL/BAnYAz0Wl7JPyGM4VGfsPJvu5grjmdTNc4zsQYPRimq18mcoiSiZmudXQ64TjODKwT1rqAG6HjyPq9v/a1r0E+n3efYU488UQos27c0dHRNP2Tn/wEyr773e9Cfvfu3Wl6bMx8XzNxrp3Ntsy6lDs63L2At7/97VB2/fXXQ147nK1z/GBoNEdGRkYgP5Vxs+i+sJ7rn//855C/+eab0/Sb3vQmKGttbYX8L37xizT9//7f/4My6yrWDngbD4VCYb956wW2/b9u3bo0/aEPfQjKzj33XMjncvgdXmPj5V3velea7urqgrIPf/jDkNeu4onGaSprs45ZGyu2D/XYXHbZZVB27bXXQr67u3uf19hXXmPrsGjRIsjrefTpT38ayuw6pPthoj5r5LVu9N43E94HLbYten0TQfe99eC3tOD3Zd1vU3n2QSMPtwiu9Xo9EBHZsGED5K0nf6aj227rbuNQ90PNPPdwMvCX44QQQgghhBBCCCGEEEJmHbw5TgghhBBCCCGEEEIIIWTWwZvjhBBCCCGEEEIIIYQQQmYdk3aOJ1l0RXme8wIHCXqlEnvP3XO+l5zxfQceumByS1WVQnRkVarov63mXJ38JqxfrhmbllGuc+v2TUyd4sQ5fEb7jGe5gn7ZHXtcP/Sux34YG8C2RXuch8wz7rBshL4cT3uBjS/bN37hVuX7btlLEWzGLXHtCSLsI+vs9gLMZ5X3eNyETi6L/p/mblceihmbKrY1E7rrZM04+jn0K1VVP3k4NFIxbuW4pPq4aNzVVYzRDf2uDi/8CD1S4S9ehnwy1q9y2EcjL6Lf7Ik9zuuVvXAelL16Cb5W+55DH+MjNh7meqDc8SWMu9ow9u9YxeUj428uGp+2r9zmI3U8z85t2OGjm5yrzR9GN382xrHIV1z9SxvRn76zBcd8ySLnJJ8/H2M/yJhnFHjuteaSUjX1j5T7vuJjWWiWrKrv5m5oPNb5CC+UKI+/1aQlvnW6u7UFjWQiWeMyDzwXA3FgXPx2HbV/5lRu8FIZzxua2K/Hrn3FNuzvJtMxXtH1m19AaX4+h/mw2fVhYhxqsekWXznSA+uhswer+sahcfkZD5l+vkRSx3kikfG6qfUvW8NjSz4eq5fOOMA+y9r3F7VWRsbLfixxtHlqZ6LTcKZxNIzp4RjHo92tzVgnmqMtfsnBYd3PGzduhPxPf/rTNL1gAT4DqK2tDfLa/ax93iJ7O4+1I9t6XxutSdbJvHDhQsh/7GMfS9NvfvObocy6tbXnejrjXjtubds2bdoE+UP1jIfBwUHIP/DAA2n6ueeegzLrJtZO5GHz/a2R6972oX2tHjs7jitXroS89n+/9rWvhbJsNgv5qYydduiff/75ULZ06VLIP/vss2naeoyn8r5vfeqZjPusb33ptm2LFy9O02effTaU2fnYyGXeCHtssYjf1371V381Tf/jP/4jlL38Mt4D0fFh57xtm56P1k9vPdG9ve7ZZdavPxP82La+1iv+yCOPpOmzzjoLyuxaqWPLxo5dr3V+aGgIyh577DHI33LLLWn6Zz/7GZQ1ehbC0f4Z0dZf95l93sVk4C/HCSGEEEIIIYQQQgghhMw6eHOcEEIIIYQQQgghhBBCyKxj0loV8XA7QU0pAAKjzYh93JIT1t12jrFRVCr01HHrRF/eeUGqQ7g9o/bcKORLiduiMbQCj123Cps2t9NtyQhC/Pm9MSzIuNIO9IzgVpTne3C7w7anXVvjF7F+3gj+lL8wMuDqMI7tFqNJyClVQyZnypqwbc2dTWm6qxu9KpmMUaco/0KQxb+NxFYXYZQsNaVVMcYVqZs6NrW61+aMZsC6JwJR+RCPDQLcrlYUrXUwCogR3Cbkq2PrHm7J2VPG8z7/ohvX2jMvYB12o2YF9QzYlszwAORrVRcTL8xvgrJVc3D738KsO1fNM4oYE6R+4tpewrCTvh6cY/Goi7WsOU/FxEde6S1GjVJjVy++dnB00GXq2J9i5pQo/UW9iutDbQc2YGTIbaH0zLY838N5Xq25c5VKOKdGzRwbqLrYGquhHimXx7Z2d7r2dLVjY4pme1qkxsozf2+sm3UzW9Fb4rDP4gDb6oFOCbejZXyjsTHXkYKrU6aKsR/VMZ/LuLxfwH4JjMKpqUPVwSiPogDHVRlvJDDelyTA+iZq/fCNiiYwwZR4eosnrlFW0xUoBUsUWRWN2UKpzuWbeZGtt+Cxag2rm7UwY+oUJy4OE8E+OlY5mC16h2rL/9G+bZCQmY6eY1R3HBi636ZzzeJ4kF8yd+5cyL/xjW9M08uXL4cyq1/QeoNLLrkEyvbs2QP5z3/+82naahKsokDrGNauXQtlf/iHfwh5fd2J9Bs6fzBzwM5FrZbo6+uDss2bN0P+UCkhrP5EK05GR/F7VaM62LZN17rT1ITfed/ylrdAXqsntIZEpPE4ToQ+V0dHB5S95z3vgbxW9Fg9TiOsKsXOKa1DsToRG7MrVqxI06effjqU2T480Hi2x1qVR3d3d5o+55xzoGznzp2Q13N3zpw5UPa6170O8pdffnmaXrVqFZRZZdPPf/7zNP2nf/qnUPbMM89AXsf+4fpsb69jtSq/+MUv0rRWHInsrVVpbnb36uzauH79esj/6Ec/StP3338/lG3duhXy/f1O+zsT1TSHCx2jdp2cDPzlOCGEEEIIIYQQQgghhJBZB2+OE0IIIYQQQgghhBBCCJl18OY4IYQQQgghhBBCCCGEkFnHpJ3jcYJ+MC9x3uVor1vs6OWpKEfzwDB6uLcOoM9s28YRl9nSC2VtGwchH0XOmbR9UyfWb/x4yBfOcM6ntk50LyWmF0rK1bV7NzqoNj9lPLvPuTqGe9A7FlXRPx1Frg9D4xgPPPRt5ZTzNp9HX1XBOMfbu1x503x0WSXGK+55zimcRNYjj231Bf1E9didy68bl7npxEzWtS9EtbbUTXzEnnttxviFfeMrz6hjkyyep2o8wWHdnatqXL89Q/jakU09aTo7MAhlUQXdbUnNeaYiDx30vhnHcMDVobR5IZT1DKNva36T69/QOKT9AOtbU2NXKWEsVYawrf6gcnOZPqpkjdetRXu5sQ4jvei5jsfdXE6qOK+rCcZOrJ4PEAfoagur6EivKj9U3SxRZeP8Gul319m10zy/oB/n3zaljh838zrThGOx5CSXP/l044vrsB58d92K8fR7th+Ua9vGehKb+af96kap1mzc8dUCjmOswrISm/pn8GTFvL6ucQ/msT3FeS4fNptnCRjteb3s2p7EeJ4WM09KOfV+YjznkRivXlxOk0ENF5fQnLfuq9fGxulunOOBWluqzeheD6s4zyV2rw3NMzY887yAQK2byTH89+jp8v7Z8xyoJ/Roc4zTCXz0MF0xSv6HQzVXj/ZxsfU/2tY0cnD4vnlWS4Pxb7QmWW/41VdfDXntOc7n8bOPrYPGepevueYayG/bti1Nf/WrX4Uy67/Vjubf+73fg7KLL754v9c9GDd1I2x/Wkf62Jj7znPPPfdAmXWQT2Xe2vqH6rlL1hNtPd2lkvsuZfvX1v9Qoeu/dOlSKHvta18LeRs/h6IO9hrary8i8sUvfjFNP/fcc1Bm+6zRnDrttNMgr735elxERHp78d6Wdo5bR3qj+Xcw2DjT8/4Nb3gDlD3yyCOQ131q57z1ys+bNy9Nh6F9rhbWQa8Btj/f/va3Q/7JJ59M01NxxR8ME60JGzduTNO33HILlA0ODkJeu9rt8wHuvPNOyG/fvn2/57HzXOcP1bMEjjYOpN3H7jd1QgghhBBCCCGEEEIIIWQ/8OY4IYQQQgghhBBCCCGEkFkHb44TQgghhBBCCCGEEEIImXVM2jleNv7hinLn5ox03JqtRsacI3ZnGUt3vjwI+dJDztnj9aGHJzR1yCbuuvWxcSjbE6CLa3DJkjTd3o4OqsR4vGo1l+/dY3w+67G+2X4nMg4qQ1AmdXTRas945BmnbYjupWze1T9TwLa0tTZhfn57mm4xPvLQt35vdR3jQ64bd1G9iq/dPepeWynhsW15jIG5rcr5lTEObOvRC1zeM+7yuo+uLlHu+9CEb8XHfFxzdawNG3fYAI5VOLbHXVPQXZ2YfgqUGzqMTdsCPFbqbpyDEbxmecQ407tVHBpXfDFC53Gk6pBJjAPbPB8gqbo6mSEWzziytQq/buZbfdiMhTqvxDj/7CpQU95lCdGL32TmXxi6/q/VsH/Hy/jaXXvc2rLj2UEo27EV14/+Xe55BonxyidhM9a3x7nyijn05uVPxTjzm1RbI+OfjowPUTm9Aw/bHcVmXui5a54HIBk8r18060nNxUtQMuudZ9zmLSqdw/5tbsY65rSaPW9dj1j/SouKuwrOKanhmuaF7jqecXhbD3pdOcitjq+W4HljFYdxYM4bWw+Zytt3xpx5pkJNzWvrjvfMddT6liSHx413ODhc/jp9nYl8orPVqUfI0cbhmKvHmhv+aK8/mRrWKa3H3/pubazr177+9a+Hsquuugry2p9sv5818t7bstZWfAbMm970pjRtPbrj4/id4TWveU2avuCCC6Asm8XPpfq6h2tOWLev9kb/6Ec/grKK/byrsPW1HvHu7m7In3feeWn6zDPPhLLOTnzW2mc/+9k0rf3HIofOOW7bUyi4Zxyde+65UHbccccd8HUaxV2j9xIbz21t+Hyp173udWl6w4YNUGbHsZHLfNmyZZA/5ZRT0vT69ev3Wz8RnH+6/+w195WfLvR5m5vx+7D2p4uIrFmzJk2/9a1vhbL58+dDXnvGJ2qLHivbnzq2RdBBbh3uh8uv3+g5BDaWvva1r0Fet71u7hUODeG9Il1u1yEyMXSOE0IIIYQQQgghhBBCCCGTgDfHCSGEEEIIIYQQQgghhMw6Jq1VGR3B7QPN7W5LQGhUEvUa5kdGXX7DAG6P6n92G1Zo14DLlMegbDTBe/l5pSbJxKhQCMb68LzRvDSdxLhtxf7kXusMPMHtDoGpU6bsVBOZBI+N7Z7/2PVZ2cPtOn4Wt89lC66fCkUcpvwcVCgU57mtbNksbvURMdv41TaQqlFADJfKkN86hOP43LNKq7ITt4J1LkHVyyvWufov7DRKAtNPoecUELFvtgwZVUOghsrYfCSJhiE/NOKuO9qLWpK4FxUhTeL6v6+IY+GPGq1K3VUijs0UMloYX28FMzt9xocxlobKbmtVaxEPDn2jOKkrZYxRd2RijI84dMcmphIhNlUyenra7Tt10w9aJ2H0Pb5RhNSVQiQxF801Ycx2NLt+CCp4nsFejJ09W1zM7tmAW5FGtuBWq/JYf5rOVDAeaoLx0PeSG8eXOrC+nV1zIB8e744tmJgMzNqSU1soI6NG8cwWrVhcv2SMAqSewXW0mMfyWqzaVzVqKHOujIpZP2/it83EQNHVKQnN3DTvAxmlOcoaTVCtZHREnqtTJsF+iTPmtSqdNWuJZ3RVvo5RozRJArPuK12LZ+Vg5tisysbmfcmv47FVX83VBOOOkMPBVLYVHgl1w9Gmw6He4ujkWNOskGMbqympVt3nB7u93m7x7+rqStO/+Zu/ud8ykanNg0bHaoWCCCoWOjo6Gh7b0tKy37Kp1GG6sGtFqYTfEV566aU0/eKLL0KZHQuti7DajHXr1kH+ox/9KOS1VkX30b4466yz0vSv/uqvQtnLL78M+el6z7VjdcIJJ6TpN77xjVA2Uf01jdbqqdTdalWsDkUrh/71X/8VyhrNMT0XRUQ2bdoE+blz56bpxx9/HMrKZbzXYufj4cD2Ya3mvh/t2rULypYuRbWojkmr9rGaoKkokHS5jSsd2yIil19+eZq+7bbboMyOzeFCx4fuTxGR/v5+yOv+t2NhNStH2+fjmQa1KoQQQgghhBBCCCGEEELIJODNcUIIIYQQQgghhBBCCCGzDt4cJ4QQQgghhBBCCCGEEDLrmLRzfHAn+raaQ+f08eagP9szbtfeivM27VyPnmXZil7guDziMsbZY1SuEisPbDZjPEc5dPKK8kwlsfEexXidnPLjts9B33BQwNdmPV0p/FtDbHzZfuC6OzCO5mymCPlQecmy7diWYAEeW+hw16nn0HWWBDjE1cT1w84+HNPecfRrbd2Edex9cKur0x4cx13z5kP+hXBZmm45Hfuwy/ShrxzDe5mizJ9vQuX3TYyffHTE1Pd5F5fjPXjm8hC+Np8of30retGiUfTXxzXXp1GMfegnxtGcuLZXhtEz1rNpAPJ9rS4OC1msQ70ZPXWB8rRnTfzGgYkBpWHOGt9aps3EVqurfz7Bed1i3M915XILPIzJmom7Qt6Vt3Sin37xcmxbZ6erU6WC4zTei1750h6XHxzEtWR8BB1f1Yrrfz/CsfAE8/UhF0u9z2L8bljUDPk53a7+RhEpvlkDqmqZqpllKPSwz/QylVgVf2A8ennrg1fjGmP96755RoTyumeaMB7CPMZAJevqWDT1DQOcC6I88/WcaUBgnvtQcnOshku5eKF9rSozMZo3ba0GylNoPPiJh2OeVYtNxUxjz4xjEutKYLtrHtYhUs8EqEczy+c8FXcnvXcHz+FyHB/MWOnX0sm8b+iunjpcPwiZGgsXLoR8seg+Rw8M4PeH5mb8XPq6170uTZ9xxhlQZj3MB7p+2dfZ8+q89U339PRAfnjYfZavVPBzneVQvUc18gCPj49D3nqkNXqcRETa29vT9G/8xm9A2dVXXw35lStXQj6bNfcyGrBixYo0fdNNN0HZ9ddfD3nrvZ4s1im9ZMkSyH/qU59K06effjqUWd/6gXrEp+Kutue1/bl27do0fcopp0DZI488Anntkbbx8MQTT0B+8+bNadrGeiOnvo2H6YrvibzWO3bsSNPr16+HMvvsg+XLl6fpTMY8o+0QrS3WFf++970vTd9xxx1QZttmnwFwqGgUw9Zf3+hYflY68vCX44QQQgghhBBCCCGEEEJmHbw5TgghhBBCCCGEEEIIIWTWwZvjhBBCCCGEEEIIIYQQQmYdk3eOv4DO5tZx55z2BZ1D1pHd1+/8P/569AD7o+htqkbO6eTHeM3A+pw9l6/FeM2KOTYR5/AJEuu8xr8RhMpXPnc+usNa5rVBvt476tLj6H4OjDw3l3fd3ZxHf1Ixjx5mXznLwjY8NtuKdRLld44j40lLsA4v9bnypx9BV27VOLG9HegVjzY7b1bN+LbCcXztpp+7Oi1cOA/KWpZge/Kq/pkAx81PMERraswjYyiv92Pbyy+7Og3vwfpGdeNojlw/BS0deGwbxmys3OxJDc8bx+jKi2oulpLd6Mve8/wQ5Le0uLjs6EA/nx1yPeWyLTjG1Qr6tTI5V54r4FwN243gWddBcJxa5mGdBnJOZh7UsO/DPFZ4znGuT5ef1A5l3SfjnMq1uPlY7sH+HOnBto0OqrgbMc7rCsZvUFPPSTDjZN3gkcqP9e+Bst3ruyA/fLqL784OM+cjdJ9J3fVpZNadJDQeW1GxHpr62tcaD6CoeR/gsiS+cf5X1bMbkgDrEFtHtlpf4hDnkD3US9xYBQH2Q9Uo1bQ63PMw7iQ2636o5pSH8VAT04exWhuNwy7ycc3y1bMyfEE3okRmrVHdn/hmjcJXSqQ86KXSzPp79ETu5KPNfXegDstDxaFyUR+uttGtPTnoaSeETDevfOUrIb948eI0bb3c1ml7zjnnpOmmJvx+ORWm8l5j3b7aT20dzZbnn38+TT/66KNQdvHFF0M+n1ffPexnX8NUPhPocustrtlnoKnyjg78zqjd3yIi11xzTZq+4ooroMy64q2PWtd/ovcW7dO+5JJLoGzp0qWQf+mll9L0VJzM1tl9+eWXQ17HrI07O1aNHO82b13Wmql8brXn6e7uTtPvfe97oWzDhg2Q37VrV5q28816xfUzAexctWP85JNPpumHH34YyvSct/Vv1Cci2A82frUTXUTkS1/6Uprevn07lL3+9a+HvHboT6UOB/PZyF5Hx/OyZcug7Nlnn4W8bvvh+uxs66vXLJG9n0ugGRwchPyRqP9sZ2Z9UyeEEEIIIYQQQgghhBBCDgO8OU4IIYQQQgghhBBCCCFk1jFprUr/Ltxi1Bq7n/b7zXiavDlrPOC2U4UDg1AWeWaLgNJmeB5uW/GMDkVvk88kWD8vxvNWyy4/brbIFyLc6pP13Tb4OV3YmFVrccv/9qQzTZd3mS0juJNGijmlSinitv2Ch1uVsjmlvwjw2NFxPDY/5I6N2rHdWwexX574mVOEjP4EtwxlR1Ef4mE3STSiNBVG3xJGqCiovuS25ezehn22cG4n5LM5pTMwXRjb+FAxEHs4bt5edVL5EsZStYSN85RGw2/D7S9RO+pEosERd2zZ6C0i3J6WxK7/4xHUqPRvQ13HthalolmCx7Z3o3qkmHEx0dqEOpGM6bMkcsdmQ4wlH5sq9bzrp7YQB2PpulbIj25y29Hi7aiMyXXgdrrlr1qYpo87BbcRFrowX1Xb1UoDGFej/UbP0ecmWWy0RhmjNKlrDU+C4xQL5rOxO1dcwTpUenFshvrdlsrxJdjuJMYYzSi1SxjkzLEYo2HWDY7v4bjZrVX1GrY1qrr2jJZxDeuvYsyOj7tjQ1OH1grGQHuL65d6wShNYjw2hMlstr6WcKwiNY+iEOuXEaxTVr0v+D6uhXFg/96rNDC+fT8pmLx7n0o8rJ/dORipcyXmb8yheZ8qq27qHRByjHO0q1TIwTGbVTQzTWs0EdThkJnM2rVrIf+qV70qTff19UGZ3ca/YMGC/ZZZpmse2Ndq9cjcuXOhbMuWLZAfGnKfq++44w4oW7RoEeSXL1+epq26I5NBdWSj9tgyrReZaB3X+hCrC7Hj9rrXvS5NW52Cfe10qSdaWvB765VXXgn5v/qrv0rTU9Gq2P5dvXo15HX7GiliLBNpVTQTxfNUVDS5nPsedsYZZ0DZqlWrIL9nj/vOXq2aGyQGXb7X9zWj7NFKlq9+9atQZpUmra3ue3gjTY2tg1WN/N//+38hr7VG8+ahCvfEE0+EvFbrTGUsDgZ7Hq0pede73gVln/vc5yCv10qruLGxfzCfW/TcsBojW0etvbKqom9+85uQ//rXv56me3t7oWw663+sciAxyF+OE0IIIYQQQgghhBBCCJl18OY4IYQQQgghhBBCCCGEkFkHb44TQgghhBBCCCGEEEIImXVM2jk+OoIemwHlm61vRIdPdx7vuTfXnb81Y5ywGeOC8cR5hALjMvI8dOtEvq4+upcKHr52VLm3BwfQ9et3YNu0xnaO8YP5p2Jb2xY6X/Lw7m4oi8bRcVsou+v6FXROlcexHxJ1mUoJHV/1Pei6Gsy4/h0wap1fbEUvXc9DL6Tp7LbNeN4a9q+XmP6P3HUqxqWcmP7Ojo6m6fG+USgbT9Ax3a58ykGM7qXEuooD59cuCo5bcxf2U3WxcwhH42aM+0wcxu61XhGvuSPEcR0tOjdefRhd25k6XiebuHPFVYyd8gg63oe3u1gb3I4+5Mpq9OoVCm6ehC04jZty2LZ6DWNYEwfGwxy78+bMeZafiN7zbHlJmh7ZMIh16MA5tvg014cdC3CcfA/rV+tzcVYeRZ96aXQM8vGgG4vAuMS0711EJKNcXDXfxLpZCnOJq2NgHNLBOF6nd4+Ls+xObHfOzIt2tW62N2Gs5I0/TtT8881zEmIz/2pj2NbdKr+5B/3ZfXvwusGgqn+M9e9qw7GpLnDnam3D2MnkTD/l3VwOccilNIYe91qoHHb22RJ2DfDV8y5M/0bm2CSoqDKsb96o2XxxdfAi+3wLPFY/08I+FsEo3WVUrQk927EOM41jyVc3Fc/cVNp9uNzEx9JYkCOLjdnpii16uo8sM3HdItOHdf0uW7YsTc+ZMwfKdu/eDXnt5LVeWptv5A0+mLjR/unTTjsNyqxzfFR9Z9T+YxGRm2++GfKveMUr0vTFF18MZdaX3NbmvrdYR7N1/WqiyHxezOMDmk466aQ0vXjxYiibP3/+fq8znY7xRlg3+Fvf+lbI//3f/32artXwO0IjrGv7vvvug/wFF1yQpq132dZJ97GNQRuj9rUa26dTQb+2vb0dylasWAH5hx56aL/nabQWT+Sv123duXMnlN12222Qf81rXpOmFy5cCGX9/XhP4bnnnkvTn//856Hs5ZdfhrzuX+3DFhFZuXIl5LVLfqL4PVTP9dBz6k1vehOU2XHbsME9X+/ee++Fsh/96EeQHxtz9xhsDNr627jT/v2//du/hTL9jAIRdN1brPv+/PPPT9Of+MQnoGzzZnMfT/nsJ4o7XT7Re8TRhm7rgawP/OU4IYQQQgghhBBCCCGEkFkHb44TQgghhBBCCCGEEEIImXXw5jghhBBCCCGEEEIIIYSQWcekneNV41YeDpxLLBpFN00ui6dtyzrfS1er8ezk0KVcUXLXcoJuq2odXb/a+xqHxmUUYR12bHeO27KH/plKBV1WHa3uXNkC1re9G73LzZ3Oe1Revn9vuIiIOD2yjGxFl/LgHjy4NOD6tFxC99n4GPb36E732rE+PO/u9eh1q+9w+aA0AGVxgm0NjXIoUQ4iq9eqmD+z1JVffXAPniisYP+HsRur0LigI/P3G+3+9X0ct6ZufG39pLw61tRhu/ErRa5OpU7TmD48tidoT9NDIfrUo2gE8oEol3mC58kad1uk/NpjQ9jBNRtLOgx9E3c+uvFi5VquxnjeusnHiWtPNkAfX3sbetDDVznneLQO3WeZDDqlm5uUyzyP8VyvY16r/moYzhKV0K3tq9dmEzxPbPolUHFWF/OsAw9jX/ehb9azKEQ/345BV8mdj2N9q2YdKna69W7tMuzfE+ebZxRkXN46/atlrMPu0jjkn+137et9EY8tbMaYLZT0MyFwbkZzWiBfris3eAnrFLZDVvKqv5MY2zYygHVKWlx9WwOcJ8UsvkfklBsvMvVNzBwLlbc93MuricdWPVcnX6zL3Lgp1Xk94zKvRTinRkbcefu245pLpsZEDr0DZSb4eOkYJ4eLmRDvM4GZ3g/TuSYcKvfrbOFIrM/d3fi8I+3wto5mO6ba52zrbn3a+lz2PBNdp9GxLS3u8+Ov/uqv7vd1IiI/+9nP0vSePXug7LHHHtvvsbfccguUaS+7iMi5556bprWvWWRvp7vuX+sEtvmurq403dHRAWXWcasdzYcSPTZ2LKwHvanJfa4eGcHvrY1i3TrHf/CDH0D+z/7sz9L09ddfD2VLliyBvPa42z5r5GS2ZVP5XNiozHrNbXzocbT90Og6dixs/fV5bVu++MUvQv4//uM/9vk6EZHe3l7Ia395qYRfpq1TWvvLL730UijT3n4RHKuJ3ksO9L1mojHVY2WfM3DJJZdA/qKLLkrTv/3bvw1l//Iv/wL5P/7jP07T1uFu67B8+XLI/9M//VOats9YmMoaYJ+F8LrXvS5Nf/zjH4cy66Tv63PPGbTjZp36OoaffPJJKLNr8ETxfqSxY0PnOCGEEEIIIYQQQgghhBAyRXhznBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6Ji3BGR0Yg3y+6hzkYQEdx6MFdBMX57p78As6m7GsjB6bmnIxDRnPa892dOXWe10dYkGH00gPOni957e79EgnlFX70WnbP9/Vf94i7KKuVmxboeDK80UoEs943SpK4j0+hH6ceMD44pR3d9xDz3Kpgr7e+pgTUo+X0ZEUb90B+WDU9VndeIBzMfZZPcbrRsoBFRtvex6rJLWKG7uRPhzj0bJx9MYuPjKCzi/r0/aUw7mQGNd2DuOlutilwwCPLXahxLtade0ZMj4w30d/dq3JlZeUf1xEpOIZn3bi+tCzzmOrdUtcJyZVvKZv+ixTV+fKmGua0w6qtu3GRwdIUrV+Z1eprnY8tmA8WB1qKset1lePa0LgKd9lYNoSY1sjNa7VmvEdGgl5TnVikMH6JQnO1ajm4rBgfGZBFo9NCq1pOm7Btoy34JpQ3TmYpuvDO6GsUjUe9DZ33sH+xVCWO70d8qsXu36wLv7xcZxTI0PYT6Pr3VzOPo8euuwQRkhBrbPZPJ4nGMZ5kmRdPQYjnFNNCb42qbm2j/XiNcd7jXO8Qz3nYQmOY8s8XBMCcQtt1qxRsVkrRTnIA+OZj40Kz4vdPEnM5DSPX5BExbN5NIbUzZwa2OHiO96G72Hk4GjkxjwavLpHm2f8aHO+H239Sw4v0xUf0xm/hyNmpzKPp1Kfo2HNPZqwjljtKrYOW9v35bL73GF9wzav3c/Wh2z9t1NxjuvzHn/88VB29dVXQ177nb/zne9A2f333w/5YfUMNOtZtq7cTZs2pelnn30Wyj74wQ9CftWqVWna9oP1UU/ls4fOH6n3pIK9L1Ms7ufIxlhX9egofqa988470/QjjzwCZSeffDLkTzrppDS9Zs0aKDvrrLMgrx3vdixs3B0o9jwT5TV2zLXT/eyzz4ayq666CvJjY+7+mnU/W6f7L37xizRtPdD1uvk+FNtvLg67fmjn+Lp16xoe2+gZBZapxPtUjtV1mGj8dR1tn/zmb/4m5F9++eU0/Y//+I9Qptezfb1Wx7P1XE/lvdEeq9dg7U8X2Xud0p7x4447Dsqsm12P6+bNm6Hsfe97H+R/+tOfpulGcXW4mGhu6v5vbsb7zpM6/4FVixBCCCGEEEIIIYQQQgg5euHNcUIIIYQQQgghhBBCCCGzjklrVeKK2Tvuu+329QDvsdeGUSUwrpQA+Qz+vH3BKsiKP99tlxkO8Njqj/og3zPoth6MV1H7Uu9BnUi27rYalCu49STZjduLxna4bQq18TlQFixvg3yHMrT4RdwKUcfdDlLz3XUT32wfSbDPylW37a00ji6MyiiOhVdxCoVkZBDKMqMjWAelMPEjs7XSaFbiBNUHng4XU//IiAeykaujH2F/hyVzXnVZz+gMfFOHjCqvWz2HUb0Ule6natQMxSbURZTGXB1jD9tSq+G4hp1ue1p2Bw5yrYxbEEVtE6ondhsk1ikfZlTaaB2MriX2XL/4Ec6/4RGs7/M7XHnPy7idsqmM/dCmlCHRCmzLoi6sb67J9Zlv+iwOccx939U/8bDPohrOvyBw20HN0iJ5M0/Ec/Wt51uhKMjgFqis2oJmVTle+1zIl+d0p+lazsS60RpV1+9K0/HwEJRlIrNuhi52+kbxvC8vxPqumuv6sBbjsWNlo1rqx9hq1gqqYdQl+RgCUve0ogfr4JfNVt0h157QMxqbrFG99LvYGtiEZYN9Zp7nXf2DcYyPpizGVphRsZ8zqpTQxKFyp3gRrg++WbOS2LWnFuzlPIKcF7u2RXWjtBnCtu7e4dbvaMD4p44hpmtr/nRxqBQghBwuZqL2g+zN0b7WTFecHe39YDnSWia7VVz3py2zmgxdd61YERHp68Pv0vpcnZ2oHZ0zB78D6y3+jfQhIri1vaWlBcqsZkWXL16MysENGzZAXqsnrErCaj50PwwODkLZ0BB+Xtf1t1qERpoEGyfTqS06VHNous5r6z8+7r5vbNy4Ecp2794Nea1qOOWUU6DMamBe85rXHFQ994euv42l/n7U1Op5YuPBKpCuu+66NP27v/u7DY/Vmoqenh4oe/e73w35Bx98ME1XKkZ7eYBaEhGR0047LU1rNce+jm1EozpERjVs9Rw6Jm18NloLpxLL9jxW0fOGN7whTd91111QptU+IiKXXHIJ5LV2ZSp1mmjcdLnVXL3iFa+AfHe3u3dh11wbsxqrNfqrv/oryF9wwQVpemQE7yseLnSf2nHTGiOL1eFMBv5ynBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6eHOcEEIIIYQQQgghhBBCyKxj0s5xMe7nSOWjOrpf6hF6hHylp6nVjPt5AXpicq3OMxUYT0xHHv1PfYFyeBu3b2DcUbHy2I72op+8NmAc3gPK6xajN2g8wC5ryrrzFrL4twY/wXy55nw/w0Y9O1ZDL3ClpDy1g9juZBjrH9acT057lUVEwhj7QbtzrSfaMx7xwHiOs6o95cT4lMxrfeXs9ZpMWYjeqbxoFzSWVYxHPPZd/Pgets2ofyVQbc01Gy++ZzzSketvP0an00gW2zqqfOA14wIPjcc4o/7+VPKwDkGIsdRRyKk0zhMTSlLWzuwqzqFdPdgve15wXr3oxZ1QNmKc0hU1ViM1dA0W12J+btYdG1k/X2LGTeUD67q364XyiCcF43s3Hrqgyc2FXA7HLWfqlI3dQpRrwfMWF6BLbHfR+cuHt+P8q+9EJ1x9qDdNx1X0HYYeOuHCxNU/3rYNysoD+ACGSuTak/PQGVmpY1srxmUdqnH1BfvMD4yXu67i0CjdQzHOdOW394z/vWZc5sM7XBwObMV+GRg0dciotppxnDMP21bsdOMaZXEO+TGOeaTWEz/OmGOx/pG4+npm7dNu+/852PVvZN57dpuxGHzZxUAyiONIDh9Hwod7pJ21sx07xjN9PGZ6/Y4GZtqzD2Yzx5qDfCYxkQdYO1nHxvA7Y29vL+RffPHFND1//nwos65n7SRv5LAVaexIt+7cuXPdc3+sI/b666+H/Ec/+tE0bb3hjbCu56VLl0Jee65D8/2sUX9P5ByfCodqjtg6WTf7obqOxrrvdQzkcvisIescnyjWDqQ+IujBtn2yfv16yOuxsY7/Cy+8EPI33nhjmrbe/kZ1tMe+//3vh/zDDz+cpksl86VrCtg4a+SRnkp82zLtcbf1rdXMM+XUmmXXADv/9HWmMmcmek6CdlfbmLMObxujU5n3+thGfSYiUlX39ay33a5pzc3uWY2NnpNgsf174oknQl4/I+Kpp56CMuuOP1To+re24jPmli1bBnnd9gNZU/nLcUIIIYQQQgghhBBCCCGzDt4cJ4QQQgghhBBCCCGEEDLr4M1xQgghhBBCCCGEEEIIIbOOSTvHs8bRHKh8Node3YxxxkpZeW+G0GsU5dHblKsrR1kBfTmZdnQQZZVvNhOgu8g3LqOK8nIPD+PfBJoEPViFMXfeWoCumrFu9GK1LJjnXtdsnFjGIzQ27jxCg8Poqe3rQ29auW/QnaYffXH+ONY3EtfWUHAsssYj5Km8H+Pwx4Keo8D+6UR5xoO9HD7oHKor/3qmFcctX8A+jJW7Og7Qp7SXK8h3+Viwv5ME25P4ri+8BNtWG8f+76+6+veYcdu8G2NpdLfzklWq6NCyfagUzZLJmthpQ1f4/AVuLrTNxXmRz5t+UFXqH8Rr9mxDb1qysS9Nl3ZhmV/C14bqOlEWx3SsCwNivMm5rQqBcW1ljDM9ceeKxXgoE/O8AOWSbyoa53UH1tcru3wYY39mCxh3UnTrUjAPXWFRB/b3zg3KE72jD8rqQ/h8gKjiBsOLjFPNxKjnuzrkBce01TcPIlCxHgS4pmYjPDZbxzp5oevvfBO+Ns7gPClVlGs7i/MvbsPXBgUXA2EG15rxIXxt7zY3zkO78dhKCeOwmnGxNLgbx6I0jOOaKL9Z1cd48M000a7+xDwfwLMO8kTFh4f1q+8Vs+5Co2a96Ntl1sItg2k6LuNafixxoA7Aox16jGcuHJv/4Ui4nw+X752OcTJdzLR4sT5t7SOeyPPaaF7Y12q38ksvvQRl2gUuInLaaaelaesFto7bRp8JbF77p63b99xzz4X8vHnue7f1qdu2aSfv61//eihbsmQJ5HV7DtRxPVUOl2N8eHgY8tpjfLji3jqazzzzzDT9wQ9+EMpOOeUUyGsXtPUjN3qvsWU2PrQHe9OmTVC2ZcsWyGsndkdHB5S9733vg7x28080xro8Y7476/kmgq5l6wmfyjha3/cLL7yQpreZ52FZv3qj61TNc+903O3Zs6dhnfR8nMhzPtEzF/aHPY+Nhx07dqRp2xbb34ODg5CvVNQ9JzPmjZzpdiysm72nxz3nzLr529raIH+grm17rH3mwvLly9P0008/PenzTie6bfZ5ERdddBHkN27cmKb7+/G5cZOBvxwnhBBCCCGEEEIIIYQQMuvgzXFCCCGEEEIIIYQQQgghs45Ja1XmtOG2iuYut8Wl3Sgggiajltijtu/EZqsE7nSXjPpp/7jglobEaB38DredICgP4omqRj1SctvTyr5REJi9+Endba0qmm0UMozb7XMlt70rrmPbKkZ9UBpx+SGjSqntGcDr9LqtKOEYbvvwanidqlKplAOjD4lwiPN6S0li9DdG8+CJ3ZLh8llTFvu4Faiqtnp0z8Mtck3NqEmoe26rVWzUEp7ZAhNEbltFNcTgSTxUN8SxK69U8DzrB/G1Lwy5OuzeiXWoPoIx4O9y+bCGsZQkGHeJ+vtTrhnHovP4TsgvWOP6rGkJbj/LZ3D7YkWpfwZ3YHxUtuCWnHqPq291EOM3axQ9SbPTW/iDWFYaMBqNYRcDntnqkzW6i9BXW4iMqig0i0BGqWk6OvE8rcdhW8ebXT95EdahKYvrktfm+jDsboeywRr2WdTntn+VhoymxqwtceTyWrchsvccStS2oNy8OVC2cIHRDyk1kZdgu/MhnrepgGtjvdXFR87D7aFRbPJ1N67KbiIiIoVOjNlAlVcx9GXgZRyrPbvdGpeYdTQ2a2MSu7W+XsP+Fc8os1T85MUomowqqqa8KoEYZYxv3nyU0itJzN+NzTo0rrKjY3hsrQ/nSWtZvf9lZ49u5Fhlpm29J46ZMDZT2co6E+p7uDhcqqXZ1KdTgfqZo4+dO3dCvll9Prdb/rWWRAS331s9S7mMn7HGx92HObul/+WXX4b8ypUr07Tdej+RSmCy2PNqlYQIalU2b94MZVZLsWzZsjT9qle9CsqsomAqSoIDnTMT6WUmyk+2TlGEn2+ff/55yFsdzaHA9q/V2vyv//W/0rSOK5G9Y0DH0lTGxo6TjnURke3bt6fpW2+9Fcrs/NPXPfXUU6Fs7dq1+63vVLBts7Gv1UpaASIytZi08fHss8+m6d/7vd+DshtuuAHyuq12HbJ1evHFF9O01YecfvrpkF+8eHGaPph50AjbR3Ye3H777Wm6r88oVev4Hfe//uu/IK/Xpe7ubiizmiatYLF6ll27dkFer8mrV6+GMjvHDtXnLB0vR0qbqfvwxBNPhDLbL1rDZFVhk4G/HCeEEEIIIYQQQgghhBAy6+DNcUIIIYQQQgghhBBCCCGzDt4cJ4QQQgghhBBCCCGEEDLrmLRzfOFJHZDvWOb8yIUudOXWynjaiq+cvRG6zgqo5ZE459y6gzvRa1MexnxOuaqrEfrWIkE3kF9zfp/IR0dPzTNebk/5fSPjLTa+Iu1Qj2vocCpXjAd4yNXf70PvVW4Y61tVUtugir5hO2rZrPuPWox1CEydCr7yHhmnV1nQiVSLcay09zg0fVbPoay4eeH8NH38UnRmZfPY/+K7c8U+tjU03vl64vrJN20V89qacgjvHkSP3i+24zj2veicfLVn0ZmV2bQV61BSLuU6+oX9BMcxUC72oIiu9eIJCyDfflJ7ms5341hkQlPfPnedod3DUFbrR7+gjLlxTCrYn1JFB1ht3LUtHDcu5TK6tyMV37EJUTH9EGXcGAc+xk6UxRdnm1ydmkOMlaSA/vqWBa5PPeO9z5sYTZSfOs7jejE+YuqrQisynrTQtC1W8yYSrINn2uoX3Zo1d4XxKM7B12aUQ9IoukVMPGSKuE7lml2fZovYv4H1w+fd+t2K3StBAeukn6Ow5QXrGLdOeheXgXHzZ8xYFQLXnqZ2jP2mNozDULU9CYxjLzEOTJWuGw++9bgrxbsk5v2kJti2mmrPUD+uQ7UhXDeb1HpXMv7AY4kj5aE7HNDJ+z/MxDGeCWMzE/uFHJ1MlxucjvGJmen9cO+990J+61b3XWTTpk1Qdtxxx0H+pJNOStM9PT1Q1t/fD3ntk9VecxF0t4qgdzc2H/xtf+r8wayR9jqFgvscnc/j/Qfr9j333HPT9MKFC6FsIme6plHbJuqHA/VPHwzWHf+Vr3wF8tafPF1o5/urX/1qKPvTP/1TyGsfvHXFW/TYTGXe2nZa//7999+fph966CEoGxjAZ8G1tLgvSBdccAGU2XlyoPFuX2fjefny5Wn66aefhjIbh42wfTg66r7f//jHP4Yyex09b+x5tEvbHrto0SIomzt3LuRf+cpXpunpnDO6jtaR/txzz0H+5z//eZoeMc/Ksm3TfnLLKaec0vC1L7zwQpq2a7mNgbPPPjtNW9e9nTcHGnd2HO36sWHDhjQ9lTg7GGxbiuo+h/bTi4isWrUK8voZHL29vVO+Nn85TgghhBBCCCGEEEIIIWTWwZvjhBBCCCGEEEIIIYQQQmYdvDlOCCGEEEIIIYQQQgghZNYxaef4vDNRRjt3sct7WfSz+iU87ajS4ZaLeD8+24lOmb6a8zb1PIcuZX/nGOSzJed2zUToywmMP9tTbvC6oC/Hw0PFS1ydIh99ZnHBOJCzzsEb19D9UyvjiWOl3c1WjLPbDEU9cHXMmPrlCuiEy85tU2V4bGUbeqJzI64SfoRjkRF05w5HODZV1cXlEC/UNBf92WtOc26pxcfhsdkcXscLXB/WErymLybvq0rExjtmFGq1cXedjf045ts3mth6/CmX3mb8RCV0VPnKYZbEti1Yp1Bc/PgBeqE75qCjbG6HG+is8UjFxo88UnL+qqEBdBzX+tEV5Zdd/2bMmItn6qu87YGZJ1LCtlYrrh8ygvMiH5r5p5zjEmFZNTZBq+IhzBt3X4DzsR67+PACnBdeBj3XiXrugBfh+GdMnHXMdf0/nMG5mSnjsZ7n2l73sH9jM08y8+el6UUntENZtsWKxV1962YuxjXr1saxyTa5OrU14bFhO+abm12dM1kcmyQ2HvF+lx/rx7Gpj2H9Y93fZqp6Jr6L7a6f2pfhe01mrnlGQVat5WZtlNiuF65OkdUUmv8oqTU48YyD3sy/sZK7zkiPeU4Cvk2Jn1fPxhhDh93h4GBcn43cjnTakiMBY2vq0Il+aDlc/ctxnF1Y9+8DDzyQpq0PeenSpZDXzlvr79XuZBGRVvUslNWrV0OZdZlrx+1U4tGu2wfq9xYRKZfxO4+maJ6/097ePskaIhN5xLW72Hqtbdu0/9aOhXVKTwVbp5p6PtLDDz8MZTaWpssbbNva3e0eJPf+978fymyMah/1VGJpomMb+eDtvPnZz36Wpvv6+hqeV88T7f6eTJ0my0Sx39XVlaan08ut+8n22YE4m3+JrmPNPL9Le7dFsK1T+Zw30bH6mQrWI/5v//ZvkB8cHEzTth/sGNv2PP7442n67rvvhrLhYbznoJ8DYdeAOXPmQH7t2rVp2j5jYbriTveRiMhLL70E+V27dk3LdaaCbVuj9yk9L0TwPcK+300G/nKcEEIIIYQQQgghhBBCyKyDN8cJIYQQQgghhBBCCCGEzDomrVVpXd4K+eIc9zP1fIJb5L06bnHIKU3CqNm2Xwpwe1TvVrcVv7JzEMqSEePNUJqHRIyaITFb8XWRb1USqIVJAqe78Iqoasi3YT+EBfez/9hsjcgY7Uc2cNuwmgtmO1SzqYNSskShUSZ0mG1MJ7rtBHMXt0GZbETFxuiLu915R1CZUKjg30rKRqng+267WqEFt3ac+MrFkH/FGa5Oze1GsRHgVhVfKQt8z5SZHSOB2j5TswoLozgpD7v+7tuBbcm+sAfytd3OhRCXcKtMJbL1dXEYGg2Ml9gtLy4uvWbc7lfswunn59y5rOYjNtqSpsC1tWjaHdexvrFSqRTFzM0cHptXOo4wh+MWVrAOWu3hGfVIzriKaurvcFGC4xZFOK9B52L1Mll8bU613Rcc46pZ3aK8qm8Fj834ePD8NW59G3i5A8rKL5n6Vva/PTHpwPVj2clOq7JsGZaFBWxbVQ3rYM2UVXHdzPuYz7W4cc6YuZppw3Pl826sYrNNr2Ziq6b6v1pB3ZBEOG9C5VIxQy6JWSs7Ol1szV+IBxdbMGYziXqt0QT5HtbBU3PXM7Ef+XbbrIuJ2Mw/u222b9hdt7IbiqRowmFcncqvWXXOzOZgtk2Tox9qHAg5NjgS6/NMXD+Opvcpu7V9fNypAu2Wfrvt/dlnn03TCxag9vKss86C/CWXXJKm16xZA2V2i7/Wlmgthsje432g42/HqFQy32NHnS7UqiXsZ7WxMffdzp7HKlga1ddqB/S2fatMsP2Sy7nPt4VCYb9lIlPTrNgY2L3bfRj9xCc+AWVWJzFd8yBjvqPpWDrzzDMbHqvHbjrXikbntf2t55Q91o6jVvRYvcV09ac9j41nPR9t/Ww8HKo6TYVGShOtiRIRefe7352mbf/aed5IKWN1KHreW5XLU089BXkdA21teD9t2bJlkF+1ahXkdXxs2LABynbs2AF5vS7ZuGuk1rHtni5tplVV/dVf/RXk9Zo7XUomi22LXQtPPvnkNH388cdDWXOzuZ+i5kbWqL8nA385TgghhBBCCCGEEEIIIWTWwZvjhBBCCCGEEEIIIYQQQmYdvDlOCCGEEEIIIYQQQgghZNYxaed4Uyu6X3JKrJr46OKSunEDxc7rFUfo/NqzHR0zO9c7r011BB04yThep1pW5zJe6MQzzh7ll02MD3k0QO9YIXT5TFsTlDV1oK+qoLzMScZcM49t62xS3qBOrEO9jHWoKHd1ZWQIykby2NagxfmRuxbMhbIT5mOdysodXxkegLLRPuzfwhhep63g+qJrUTuUrVCOcRGR1sXOLRZkzNgItj1QbnYvtE5e495WTqI4Nn1oXjpYcrE0tBXjrrAHHXFV9dqaWMexObHyNAViHGoBerKKeedBapuDsWMd2bsGXHvCLPqcm7LYh4XQXbe7E2On1oGvDUZcPhKsX8E44AIV72Er+raCEPNh5DxOgXGixx560urKDT1WwnldN470ovLxJ2LO4+N1qspJlbXudRM7SaA83EbTVRR87dxlrk6VsxdC2e4C+qvKAy7OEvP8he5l+IyCNa90c7WtA89TFVxreodcHbbvwRgcGTDO8XEzV5tcA+eGGPudGbxuLuuODQPjrDOexYJa07LteJ4gj23Pjju3Yt3D8/imD9vnuWPndGNZ3tQ3G7sYLifGe26eZxAl7rqRWfeDCGMp0ccmxrVu1oDKkKtTNGDmWw1j1lNO+kJx/8/CILOPRr7Ao8mNeyiZif0wXX7UiVyTZOroPj1U/TkTXdrk2EN7XkXQP2wd2NZNrP3D1nO9ZMkSyGsHufbmiuzta9Ue2Kn4b6cyZ6zTtq+vD/K9vb1p2nrE7ZzX7vWNGzdCmfUaawe59d3aOunrWNfztm3bIK/HpqsLvyvbfFMTfg/Q/WbHfPv27ZD/wAc+kKafeeYZKLOvPVAmcni/9rWvTdPWV3+41k19HRuj1nuuvdHPPfdcw2M7OzvTdH9/P5RVq/g9QM+bRn5sEYwlO4+1E10EY806x2c6dm7a5yTcddddafqKK66AMhtLdmw0dt3UnvF///d/hzLr2j7hhBPS9Nlnnw1ll112GeQXL8Zn7VXUs8zmzZsHZf/6r/8K+U2bNu2r6nudR0Tk6aefTtN2Hh/MZxy9ptk++8lPfgL56Vo/poKN77Vr16Zp2792LW9tdfde7PvfZOAvxwkhhBBCCCGEEEIIIYTMOnhznBBCCCGEEEIIIYQQQsisgzfHCSGEEEIIIYQQQgghhMw6Ji8sCnyTVa5t41muRMbPKs5rs7WGXqYXt45BfmiH8zglg1iWGKl0HCv/mnHCVhNTX+WRjoyjxzMuMcm4bmnqQp9zUze2NVNU1wmNH9k4sZuUYziuoMetlkVvV0/e+XIGnzMe4D6sb+/zzrm2PYdeoJWrsP4LT3X+5KYQnT1Sxf4eq+C5csoT3Gx81E2t5u8seeURt46yyHg2Pd0e9EjVxYyNcjp7xgscx8Z1r9xdhX70SsU1vE5OOcESH/vM961rzuU93/qb8bXNc9w4tjSjS274RYyP8cDVsWbc2plmyEpccDHaUcBrzluM4ziSuPJozLQ7wOtkVTznjXswyZnYj7T/EOdUzcS+lJRDaxx971YAHiqHXSbBsnodXVyR8opHIdYhNPFbVfM+DvC8nlFBtzW5fsqsxc5v78JxTMbduGVDvGbHfJwnXYudczzJY7wODWLbnn/WOcAGf4E+sLgHPXRl6/Re7Oq8YDXWd7EZ84Wh64tW89yEjI9vEc0Fd+yclcb/vhO9XkPKjVfyjFd+LsZh8xIXW0nBxE6Ex9ZEP3cA61BPzDxXxwYRtsWLzfMM1PuWZzz4sfGI5yJXxxbzTIW6eVfVOvjmRTgWRzOzyY88FVfmVPql0bH0Gs9cDib2D8e4Mnb+h+l0uh9on86UWKGL/ejD+oe199WOoe177ZO1btyrrroK8nPnumdVWc9rI1/yRON9oPFgHd6/+MUvID8w4J6XZV3P1o2rPdKPPvoolNl+0d5a6zS2/aCd5NZPbj3tL774Ypq+7777oGxwcBDy1o+r888//zyU3X333ZDXDnLbh4cKGy+6L6YzPg40luzYWMe7dlu3teH3taEhfN5bR0dHmrZjPDIyAnkdP7aP7NzVc3V4GL8fW6/8jh070vSR8EAfDLbd1vf90EMPpelTTz0Vyuw4NjrPk08+Cfnvfve7aXrr1q1QtmzZMshffvnlado6xrXHWqRxTN54442Qt/7yT3ziE2n65ZdfhjLbT0888USatl5820/6GQATzRl9HduHM8Fnbz3iK1euTNP22Qz2mRDa227fRycDfzlOCCGEEEIIIYQQQgghZNbBm+OEEEIIIYQQQgghhBBCZh2T/t28/XG+pzQldcGf41erqAfoqTglwIYduA2k5wXctpIddltTMmOoh6hEuH0qid25ggh/Nu+ZXYOJ2l7gmT8JhFn8j0Kb+yl/20L86X6hFbfxe2pnje/jto9Cs93S6c5bzGI/lFrw2AFnl5GR0QEoS0axX2TcbbPpreB5KtVuyMdr56TpE5fgtrG2uS2Qn2P+dhLq9tkdLiH+h9YZiFE+JL7tF/faKDbHGj2OOlRCwUEOjNYmn3PlXWaLXM14SnylUima7Tu1xCgWEnedILRaEjxv2Obip9yDbanvwNhvanLXzSSo0YhC7LOS7odO3N4VtJjtJotdHTsT7CMThuIHbknwi7i9zw/tKuD6Jani9hfxcEtfRtx1sxnUwIhRAXlqO4/nYduKZrtRTWt3rL7HvDaouWOtBqYUYHz0V1x79oyaODMqj/ZOtxWvYwEe29WJ+azaIjku2O7hXTivB3++J02PPodrQFgpQX7E2IfG1blGBudA2dBajMPScpdfNQ/r1FzEfsnkXH7BCjy2IB2Q7+12/TRWwveIoAPHqrfZnTc21p1lBTy2ruZ5YNaSwGytUkuA1ARjv2be1UK13sUmfkNzbFRVMVrFMa7H+LaaV/HdtdjMk6OIo12jcrRt+Z9o2/yxzNEea4040LbNpvE/VByqPjxU8Xo0rAEzsU7HKo362pY1N7vvItdffz2U6e3pIqiAaKRRmUp9poqOdatK+f73vw/5Usl9/rVqCbu9Xmsp7r33Xig777zzID9njvusbNtm+0Xn7RZ/+9pFixal6aeffhrKtEpCRGTz5s2Q1woZ3W6RvfvpQPQBB4tdo3bt2pWmrdrFqkg0ts+mosRqFId23Ky2Zs2aNWnaKldGR/F7uO5ve007FuPjTn1plSB2nMbUva5NmzZB2fe+9z3Ia8XQ4VLnTBdWVaT7XkTkTW96U5peunQplNk+1GNjtSR33HEH5HWf2fE///zzIf+GN7whTbcbtayNpUZxVyzifY6zzjoL8p/97GfT9E033QRldo3o6enZ77Ef//jHIX/mmWemaa1YEWmsprFtveiiiyCv1yWrYDlUn3/s2q7H3JbZuaDn45i5lzwZ+MtxQgghhBBCCCGEEEIIIbMO3hwnhBBCCCGEEEIIIYQQMuvgzXFCCCGEEEIIIYQQQgghs45JO8e1X1hEJFH+VqNklrpxjm/e4w7Y8Qw6s+Id6NLNjzmnjGc84nEdnU6+8lMbbbjUAuMGipULOkDvVaGIrtymRa1pumUJOmIzRaxTlFPX8bF+WSPmriqXcibbCmVxHX05pVHXh/Uy+nJqVexDT/V3sBHrMFTDY59Xlyk0d0LZicZVnS1gW5NEC9aNrNq4lzLKQRQYv3Ps43nrShBf38sxbjxv2pm9ly8bj803ObfUgqVYNr4VvVPVceeNTiKUHlesLyzj4jmTxfgImtHbHkSu3OsZgbJSzbjySi5Gm4yaOMxg/xYSN5BeBWNnaAC9Xl5Geafm4pi3NaM7qpB1MRrksN05E/uhcup7IcZo3SwtORU7mYzx5JlxrEWu/hnjqAv28tC5OlrbXhDiwhSq9WRPCev7DC5DsnO9e+3gRowHzzjHF3S5cTs5xDjrxqVFJKvWzTL2Ua0fOyLY7a4bK3+diMh4jGNeruOaG42719bMa8tjGIc7xxek6WLQDmXHd5t5reKjuQPXMH8dZCXocnUa2I3XHBrGOu3ZOJimq/14zSazXjer+djimRg1CjjPUw59H+PB8/f/nmbxBedUQa25FeO49BMci+6lro7dy/Z7CUIacqDOzaMR3Z5j2T8+FSbqh2MtBg6UY9npPl0O8oNpq63DbFqXZhK2b63j9sorr0zTl19+OZRZ97N26R6pMdOu8P7+fijTzmAR9MtOFI/aN7t161YoGxwchLxu+0QObJ23LuKWFvweuHz58jStncYiIuvXr4f8E088AfmhIfdsKuvZPVzvjbqt1lucz+OX1Q0bNuwzLSKybBl+ANauduujbuR8n8hH3mgcwxC/d2k3vy0bGcHvLb29vWnattvOKd1Ptg6VCn5H6OvrS9M//vGPoeyee+6B/JYtW9K0dZfPxM9Kul+sd/tLX/oS5LVn3MaDjf22NnfPxvqmOzvxPkejz5NnnHEG5HVM2lifaE1oVKbjTERk7dq1afo973kPlH3qU5+CvH7uwM9//nMo+8QnPgH5z33uc2l63Tr8Ut7o2Qi27GMf+xjkn3zyyTT9s5/9DMqmy31vx8bOkx/+8Idp+pRTToEyOx8fe+yxNG3X/cnAX44TQgghhBBCCCGEEEIImXXw5jghhBBCCCGEEEIIIYSQWQdvjhNCCCGEEEIIIYQQQgiZdUzaOW5UupKIdvjgacoV9Mbsetl5r0vrd0JZbhh9vvWqctdYl6v1+ygfUCaLDumODPqfKoGrk29c4M0t6KppO869tnk+XjOfNR5j5d4ez6B3J+NhP2SUEztM0J+UKxovVsFdp2baHUXoia6Lu05YQp9zsg3rUFfVf7YTncEL2iArTRl8bU35kjPm7yqJ4HUj37028XxTZsYVKog+58i4les199rxsvF4Ge+bn3eOp/nrjL/MQ/9Te5dLD/aa2DE6pUTFpTeMseRH6MkKq65O2cicqGIcx8oPn+nAPiqG2C+FVnfdWox1yIzia2sV56saKWHsJF3YD8Fcd52OLjxvaPzZXqvyymfRDVU3U7Uauj716sbjFWM/1NRDDIIEj40jbFtQc/2dD7EOiYmzocRV6vEdGCsvPjSIddjo3HKJ8syJiHgBzpttXXPSdDHA/mztRvdZd86tlXXTbs/UP1Ju+7qH60WphuMoVVxHQ+WzDyI8b3WjeYaB7+JysIhxNlBEd2KbXnMzWP/mAr4PlFrda8e3Y33jrVin7E5Xp7gN50kPTkfxMy4Q23LGwZgxsRWo8hjnph/jmhWrdTYy61sYYFvnzFexNYJ9FlfxtZ3L3DzJzMX6zXRmosNwstB3S2YTeq4ey7F/qNak6fJ5H04Oh+/7YPr7aOzT/TGR5/hw18H6cFeuXAn5D33oQ2m6tdU8H8Z8Vzoc4zIVN/hdd90FZTt34n2DqfS9dhX3ms/y1ik9lfNOxTecy7nPiNa7/eu//uuQ/853vgN57UU/VDFn48H6s7XPd9GiRVC2ePFiyHd1uS/T1p+u3ckiIvPnz0/T7e3tUGa97boO1kdt698IOzYBfKfJ7LdMBJ3k9pqFAt6D0ueyx9rnA+i8dsyLiOzevRvy5bL7LjUTP5/b/l2yZEma/sIXvgBlK1asgHwjr3yjsVi4cCGUnXfeeZD/7//+7zTd0YE3MrS7fCKmsk5OVH9dj7PPPhvKTj/9dMg/+OCDaVqvkyLooBcRueOOO9L0qlWroMx6xXV/2xjt7u6G/Ec+8pE0/c53vhPKhs193OmKS+syf/jhh9O0fT6Azet1/0Cc6PzlOCGEEEIIIYQQQgghhJBZB2+OE0IIIYQQQgghhBBCCJl1TFqrkjG30TNKk+DXcPtArYxb5ms73DaAYHAUyuK62dquvB9xYjQZ5mf/VaUayGVxm8r8btwqkZnrmhpFZstTiFtpuo93W9AKc1G5EmfwtXHo6lvwsDurvlV5qO33xRKUNbXia3Pd6rpGVxCPWC3FeJpOjEKhPma2Am5zfdbz39hHm5egHmBOs1ELqJ1WvofbFKwsIFEKi5rZTmJ3XPix+49ahJqE4XHUGewZdFuKBsbxWIlxW1Ne3Lh2t6HuYvGZWKc5q50aY3wPbrUbKaGeY2SXi9nhJ7G/MyUcV2Ufkig2KhoTz7XYnSvO4/aXjInDqOjamoxhH+WrmM8V3MCVauNQNr4Hx7FYUDqO49qhLGw2o1xUqiKjEIqMZqeSdR3hC46bb9QponUXfrnhsYmotlqVi/nb35CKpT1PGb3MYy9D3hvYoyqPa1bGx7GRcXeuntYlUDS4ag7kW5pcP8VZ0w+BiQ814coJxmBSwzjLVbE9ekkOzZzyR3Bsqtvd+jza3gNlA3NwThXzrv8LObM+GL1WPnCVqBqVjgxj3Pll9drYzKHduM0wt8j1f9CF8yLB6kocqrgziqZ4r5h1/eQHZu0uYL64xI3NvAxeNDQ6H2WBkbKYthEyDcwEfcHh2uJ7MG2biduQycxkJsypg+For/+h4mjqh0YqF6tmeMMb3gB5rSw4EhoVEaxvZD6Hlsx3pRdffDFN33rrrVCmVRIHUwerUfnFL34B+QsvvPCAr9MI3f9aDyIism7dOsifdtppkN+1a1eatn04FeyYaw3ImjVroOzyyy+H/BlnnJGmrarB6kT0ddavXw9lzz77LOS///3vp+nmZvyO/prXvAbyJ598cpq2agyrVGik55gKlQp+v9eKG62PEdlbydJIq2LrpJUyVrtTr+P3+VhpXWfi5xmtEBJBHceJJ54IZVY1MpWx0n1qY8deZ/ny5WnariV2HWrEwbyn2mO1umjBggVQ9p73vAfyem3ctGkTlFnNitbwTKV/bZmdU7pP/3/23jvO0qyu8z9PuqlyVVfnNN0z9OQBhjCwgARFHPQFikRZEROKEVx3kdX9KZhRAUFdcVcWFEVJApIchgHDwAjMSJgcuqdzqOquXDc84ffHvvY538+np291dVd3V/f9vP86p88TzvmecO99us774bF+rshJrX3smH8+8dnPfhbKeNxZhQwrm04H/eW4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Bx6OC6EEEIIIYQQQgghhBCi5zht53iTHKz1ivENZ+ifCXLMD3W8W2c2Qj/VdIL+nygzfvKMHLzk3Q2M6TojB07fFnRSbbjOuHca5MYlV/jI47xzvNHA6xb0/wmpcfoUIXl0C/ZM+fq3SeIeD2B+/Tpfp8n16ChrTpN/2jiTigzrEDiKr8mGu3dD2eG96EdeuGIt5MeNGzyLuW/I32vaGpDr12XoQTpmPGp7DqHj6+Ae9CnNHD1RpqemcEz2zaNPeyA1rrkr0Ul12TUDkB8c9+2pNLAtMXnxFxeNP44cxwV7r+d824sUY7ZADvLUeLqrGzBGa29Az3VqQrr/frxpSuOu3vD5RorOuuYixtvNe7/21Dw6DQdGcJ70Ox+noqg4BD1pbTMe4gjjW+TkxbLzvENzPsQ6BLFvTxFi2/jdAgvT/r7JQfKIL+DYCVrGP03vVAhCbFs4P+XTk4NQlrbQZxaabNBBR5Zr0rw2cyqndwm0C1prAoy/XbXCgt6bkPE7DPy4a+5FJ+PRsRnI14f9utqok2MvwrgkxgnXybGtRYr1zdt+nicxjo96FcdHUjP9EZD7jOKSmTEa0DrfJDd4HPo4JI76Jsf6Vmq+Pyob8bpJTF7I0MyTJq2FYkkuJkfsauF8+YYvhPNSLuWludRitBrdqkKcb6xDll3PnLdu6/O1/rMfecF8rz506BCU3XvvvZB/17veVabZTc3u2TOFHdKf+MQnIP/DP/zDZZodtuzZPdOYsn96YAB/i7Jv+POf/3yZ7nTO/Psju86/+7u/u0z/5m/+JpRt3bq167mWk377m9/zPB4mJiYgf9ttt5XpuTn8TbZ3795T1uGGG26AMnYrnyk8zqan8X1H+/bte8z6PNa5Fh4rPAasf3rXrl1QNjo6eso68T1Xw+ckx8U61NljfTbYmHI8eS2044PHSruNz3fs+OX48rkr5SBf6j0Ez3rWs8r0wYMHu9bJXmupdXM59bVxWqn1eLnY9YRd65y368kCPd85HfSX40IIIYQQQgghhBBCCCF6Dj0cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ7jtAVAHVIZFUarmuXoranV8Jn7hhGfXxxCJ28zQ3dNJzOOWPIWuxzdQJXYO8H6R9CPPHoZ5sd2+qbmo+gSi6rYuNq49xU1QnTNhi2qr0kXAfvJCePpiQKsQ6WG192203vIZq7eBGX7j5CH2/p7O+i7DcmJFAbeTxS3yNE8ifl2h1zQxledkPu5iMhtbsZERm7tdgf79ZHDPv/gF49C2dwjxyHvFow3egrHQ0j1bRkP9tQJ9Jk9mq+H/FU3+GP7ySPeJl9ype77ar5OfuF5ckMv+LjkjuuH/ZhZN/Ew+Zw3YqUW2r6+xW70osUB+ZU6xpEeYF/092HbAvP/Za0T6J+ujWAdsoapA42HbAHdeFFi+4rGDr1bIImMKzEl/3tMjjXn498JyH3XwnEWZH4MDA1gWYs6Pbf9lvNMJl+cWf4GqzgPkgDHQ2bWk3ABx2+yiP1Ws+518orl/P+adJ/AeNzzHK+bp7g2urbvj/AEHfvoPsh3tvj75kPDUJb2Y0wj4w7PHLrXM4dtj5yvf1THNXdwBH1s1bp5z0OE1ykCmo+FeS9FgeMuonmShP4+kcM6FDQGMhtf0p7H9JmRQx3ZzS8uNazHbzU4IC91bIzZoaj4XxpoTp093eaJWJ2wT9a6Xtn7yl7VZtN/F7VO47PFjiN2YLNT+n//7/9dpj/84Q9D2eTk5CnPPRu3djesT9g55x544AHI33nnnWX6hS984TmpA8MeZnZO276zfepc97WQr/vEJz4R8r/9279dpnfu3AllPO7serHUZ6y979q1+N6yyy+//JT1tT5v5072SFtnPTuZVwq+J+et15gdxxwHm+9W5hzGe8OGDVB2zTXXQN46p9npfiHg8cBrDbv6zwf8bgE77xcX8bcor5t2XV2q387mc9Sey/NtaGgI8s997nPL9Gc+8xko4zWhmzO9W3uWmtcnTvj3/fG4uxDfyZbynltHOvf56aC/HBdCCCGEEEIIIYQQQgjRc+jhuBBCCCGEEEIIIYQQQoie47S1KmkT/2y+2fR/gl9p4p/jN2iLwJrL/W2Ko/1QFpKO4egJvyVjsXkCygK67sDocJlee/0aKBu8Grdy1Nf5+yT9+H8CGVkG+owWJkyxbTlWwRXmT/vjEP/MP6L/e0gjszUpx3bXQ9RbrFs7UqZnr0etwMzBKcg3O2bb/hRt1yHlTcVs+e8jxUZtEbeyJWwoMM3rOGxrXOB9QtP2ToBBm2zjNpD7/sNvT2p/E7UqweQM5LO22fKS01bBkLUJpo6TuA1k8du4pa+5dbxMr9mCY2egwHMHhn17JiPSiZB+oWPlOhmpLxzmk8Rfiwwb7vAhjOHigj83jrAvkn48tpL7jowSLIsiHHed0PdjaxbbPU/KjWqfHy+VCAdLVGDbYnOfsInjLshoGRr1a0SUsz4Es4UZhzmNwYUUx1lqtg2lg6TNGMA55qb9opBlOC+qBY6PKPFtS2oY3yLEODhzbkRqkQppo/orZs2qYozSWRrrtKspLHydE5qrEc9VoxOp0ZrV18T7BHMm3m08NipoTTBjbeMavOfUWlx0g7Yfa0mNOjmmeJs5xZu5UlJbVYxKJQ9wPIeszDLrRUY6n4C2cAVmTEQJxig/aaed0ctQH692LoTOQFv+Vy9SWoheQevQ2aMYnh2sxrBbyTm2rAix2pJaDb/fsvqA9REWXvPtdvWjR/H32vvf/37IW60KH8vqFKsDOFefM3xdVmO8+93vLtPPec5zoKyvrw/yVhexkuO8vx+fkZxpLLiPX/CCF0B+8+bNZZr7n9vTrX3dyqpVeq6xbh3kR0dHT3nu4cOHIX///feXaauZWG6dlnNevY66zfFx/5yA28bKHqueWEpZYbUaw8PDUHbzzTdD/itf+UqZZiXIavh+xrqLmRn/DOdc1Y9jz3qnY8eOlWnuC1Zu2PXtQv3mYRXN+vVeA8xrua2vc8499NBDZXrv3r1QxmPLzvulFDL2Whzv1Yit/5noh/SX40IIIYQQQgghhBBCCCF6Dj0cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ7jtJ3jbXJZzxnfbEju2Zz0Ln1rvUOr9gR06/SNoj9ncJ/3ZE3Pob83XIv5dZd7L/eWK9HT1b8ZfVBxnz+X9N6uUkU3V2CcvCm5deKAfb0+LhVy7rZDPLcw/xdRRBikiPzfQ/2+Tlsvx+t0nrkZ8nHF+4mm7kZPV35iFutQ+OumIXqNkgDdRVWHLqa04gMXB9jWkNreMf71grzts3N47MLDx8v04BR6otsdHKJzC3M+w96jARwDmekb0n27Bt0nb/vxEZBLbIC80WvW+vT0Nhw7h6bw3GDKn9tXwT4eraPDLmoY9/MiefEfmIZ8YhzN7ZTc9h3y7Vd9/KMKli2QW7nZ8jGtZtj/WUHjuebbzua4kDzMrma93Dj2qwXVPzbzhF3PJHTOjU+7Sr7pKo2PWTOWZlF36Fwb+zg357ZJOxZRHCLjna/0o6OuUsOTq4HP5+Srr9Qwn1TMtcg9GYYYh9Th2piYYvbKV+ndDdannVNfJCGuzzbChcMgRvQehcisfxvH0DEebcA6tIw6LycXZbuNYyDt+BhW6X0ArsD5mGfG+5fg+hbTeIkzH7ScfPBZisdmhS9PyPnvKA5F3jHp5bvPVgtLeRPFyShmFxbF///C7ZYLWpwuS42VbnNK4+7saDTwe5N1vUb0Pc56dZ1z7sMf/nCZZt/0ZZddBnnrT+Y+arXwu7F1h3/2s5+Fsve+972nPLbZxN9czIVYm9mP/LWvfa1Mf/CDH4SyH/3RHz0ndeB2T09Pn7J8qRjZvhscHISyG2+8EfLWSb6Uc/xM4THKvuTrrruuTN96661Qxk76f/u3fyvTL3vZy6CM22rjtJy28LHsf7/88svLNDvHu91nqXXQOqZ5zu/YsQPy7EFfbbAve2hoqEyv5PrfzSnN7184ftw/Y+KxPjuLz8jsmL1Qn1dcRxtTXrP4vQkHDhwo0/fddx+UXXPNNZC377RYqq3n6h0L5wo7Pjhmp4P+clwIIYQQQgghhBBCCCFEz6GH40IIIYQQQgghhBBCCCF6Dj0cF0IIIYQQQgghhBBCCNFznLZzfGqCvK/W9Zqhf6YSoCsqafhn8PWt6HBKNuB1B3cZJ3abPLWj5ORd4/1V9VH0MCX96IMKY+OgyvH/BDrkk20ZD3OcY1sKcgHFNp+hT6so0NUWGwdOJ8J7RgXF0By7Zgh9VZXr1kK+ZhxUe8mDdfR+9NB1Zn2/xcMY35Ht6Ot1A+hxCo2jPCnY/Uwe48K0L8PrnJjDGDanvYtuMOO+IWdo6vsjcewiJg9zx9+3Qv6ygDzMoYlbWKE4xHjdkXW+bdtuxDHZIf/w8W/5OvST97xOYzSq+muxc6q/jfdJjb88IJdf1Ma25n3mWnTdfBEr1Wp6p3sao3d5rA+vW438uIsKdL7VyPFUGI9XngxAWYc814X1cod4nWqL3G0VH5cswetEIfZjlvn6t8mj2J7H/ELqfevsn65EeN141PdjY9sIlCV1PLaIjc+MvOG1QVw/aibeNbrnosM40BLswsDHpaB3CxQUl9zMk/kW+suqxRjkB42nOyP9YRHQGmDqVIvIaZhQhe18xHC79gIe22z6fIXGWZKjDz41czemePPngH1tQk5lWboA+XbsY5YU+O4AJjPzKDsD99lq5WLwzglhWY4XVAhxMpon54+NGzeesoydx+xz/o//+I8yvWfPHigbHx+H/NatW8v0li1boGz37t2Q/+IXv1imv/zlL0OZdYw7hy5g/k0T03t0bJ7HGHvPrUN2Jcfj3Jz//fOrv/qrUMb+6Ze//OVlmtvCbbVwfdtt/ML7/ve/H/JLudot9vON3dVjY2OnPPZ8UaHf1uvXrz/lsRMTE5C39f3oRz8KZeyDHx31b8Fi7/ly3kPCc8r6s7mP+T7d4HNtHfg6PLbOxJ98PuG29fX530fnyjnOc+Suu+6C/OTkZJlmZzu77bvFl+t/vuaQrSO3les/NTVVpr/61a9C2Yte9CLI83y0cNu2bdt2yvMuxXf76C/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc4ba3K7B7c+hNX/Pb7NGWdBf6Z/8Cw34o/tgW3qTRwp49rbDJb1GnHQl7Bf0iM8qRSwa0oWYL5TsfnWzO4bWJhDvPzLb81f6AflSDjw6QHCM1WMEfeDNqa3wr9tYIcj41OUkv4OjVIARJQHa64wm9hGAjXQNn6zXhua9bfd3QtbhPbct0Q5Ou0bS8P/RhoF6yFwW0Uual/WqDqYHIex0d7xl93ronjLGzjFpI+s5tjcBC3x8w1SbMy769VIb1FnmGdOqbvAkd6HEcajZq/7tgGjFGc4HbF/YN+fCw+gmNpbhrrUDMhTDo4fnOskqvWzdStYB0caTSswqJFW57Sk1QYvjwijUp9I07Wyhq/bS8ewrHjanjd0GxPq9BYJ9uFy0IflyjDtmQR61pM0EJczgLantZn2hNWSS9Du4ByU4eghuPMjaEWpu9qv4WvvhHrW4lpXsd+LCWkjKmvxfrX1xvVz0M4BqOI9DLUr7XCH8/Kppw0R84oWwq6bifB+RfUfTkNMxeHpHoxqqUsw3md53OQh88Q0qrks7OYX/T9keX0EUZxqMRGBUW6ryLF+LfMx2FKzpiiwJjFVhMTcTxpu2XH16E5d/pbZMWlx/nSekh5s3wuxa2h/w+Nh3PLpTx2VhIbF43JpXnyk58M+V27dpVpqwpwzrm1a1G3ec8995Tpe++9F8o+8YlPQN5uzbcqlMfCakD42Ix+VyWJ/+6zc+dOKHvBC14A+e/6ru865T3/8i//EvKf+tSnyvTCAirvzmbu2XOPHz8OZW94wxsgb3UXN998M5SxusFelzUIt956K+Tf8573QH6p/jgVrFVhRcj5WKOWWhft+LBKG+dO7td9+/aV6T/90z/tep/Xvva1ZXqIfpvaey5VX1ac2JhyW7qpdJaz1vH4YK2RjdNq/Jzh8WrjvZJaEnufY8dQH/y1r30N8tPT02Wa43vw4EHI2z6/UJ9RvI4eOnSoTLNiittjtSs8PrqNl6X6Zt26dWWa17cTJ06c8rqrgTOZJ/rLcSGEEEIIIYQQQgghhBA9hx6OCyGEEEIIIYQQQgghhOg59HBcCCGEEEIIIYQQQgghRM9x2s7xY/vQ/xQNG39Vh3zTs3hs0vLu3GAtOpwqUR/mrQs4Rj9rO0RnVlh4104W43Vb5K2dOu79Ocd3Y/2OHpiH/MKkb09lDP1UO67HkG3b4uswkJBLuUBPcGLzETqFihDzsfGV5+S9qgTk7x3wMdy0E31PazZiHawqfKCBZdU6uqujmBzORkGUO3aMQ9YVuW9PkyTCrTlsa33e93O0OIN1IlVQbcjXaWTzIJR1DpLPrOOvW6tiP1apr6qBOTfF8REEeG5uvNxxDb3FQ+PoeQtN0I4neN1j96HTLp/1Y6nZwXsmHXJKp9751HI4PuKQHFQLxp9dIWd3THPVaMWHN6KTfmAturarfX4+Rg2cxxG9AyA0452GunMxDp7QOMmLAMdziwTluYlvLcC21QKM2YjxZQ9vQGfWzDr00lWNo350DNs2tBnjMLbDz5vhBtZ3oIIx7Kv48dsk7zl7+6sbfN/EQziuxiqYbztsT7/zdYoL8oaTczxKfFurQ9TWbbgmDK7zdU7IK5+Sczzo+L7JsWtclNLEtm1Pyam2iOtz0Dbev3QYyuIcx34e+Hhn9A4IV1ClUuPSpPckuATXQusXzBOMfRjQuxrmvBs0beP6Js6O5bjkVrvjdrXXr9ew/bEa3Z5iZbnU59+l3r5Lke/93u+F/OMe97gyzT5k9u5aR6x11jp3ss/Z5tl32w0eU+xzftrTnlam/+AP/gDKrrzySsizE9vyHd/xHZB/97vfXaZ/7dd+Dcqsc3clYQf5j/3Yj5XpK664AsquueaaU17nwQcfhDz74BcXFyG/nM8eOybGx/H9V93iu9Q9znTtWOq61vdsXfbOnTwOrWP68OHDUPZnf/ZnkLfj/XWvex2Ubd26FfLLcZBbcvpRw65t+xthqTlly9mB/Xd/93eQX23Oca7D1NQU5P/pn/6pTF977bVQxp72bvHmGFrP9Xvf+14oe+SRRyA/P+9/Q3K/HThwAPJ2LRwcxGdM5wquE6/Pn/nMZ8r0LL+Di861eZ5TZ/MdwL5jgdeS1f7OlTNpt/5yXAghhBBCCCGEEEIIIUTPoYfjQgghhBBCCCGEEEIIIXoOPRwXQgghhBBCCCGEEEII0XOctnO8eXQK8vNNf2pCPucow2fuqRFSt8jz2h+gRygwQuIsRKdaRLLiMPL3aRZYh5kJdO3s+5Z3+Bx9BJ2280dOQD4+5o9tVjBEB+bRtRNHw2V6ywb0AA8k5LQ1XuucFFRZgPcpwJ+MDrIa/Z9GVPH5+jA6klr95M+u+zhVI3SO5+R0CgL2ZPl+TENsW5ZW6EhTnmM/JuzmMsVBgPGNHDqEw9DX/0SKfR4U2K+1Pl+nsIrOofpa9AIPDPk6RSH1BfmTMuMUDqKQjsW29Zu+apNvP0jRVTyx38ds5jC2JUox3nMLvk4Z+ZTG+vDYpOPHQMRxGMO2Vkd8HUc2ooe7b5zGfr+PQ1TFtjlyiUWZ8bfS4I8zGkt27NMYDKkvitRfN6D1grrRDdX8ta7YjMcWN41APmiuKdNrB3GcDQ1hnYYaPj9Sx3bX+3CcFaZOXL+Q1pqBtf6+Q/TugOZ+cqRjsSs6fv3LU6yDC7E9tVHvGR963DCUrXvcGB47ZsZ+jeZFgjG168kcrfsp+b8j816Cglya8D4A51we+WuF5JV3lK/lfh5lEbY7pc+TPPdzN6d7ugTnTWzWXNaZhTSe51vmPhWc8+eDbv63i8FDu1L+Or7OxdB2sTTnox9Xo1OxV8fv2fRFL8Wsl9p6qbJr1y7Ib9q0qUzzuLeOcefQrczH3nnnnZC3HmN22DJ2XFUq+MWT3eBve9vbyjQ7xuOT3ml16vFareJ32J/6qZ8q05/+9Keh7J//+Z8hvxyHejc4htYN/q1vfQvKvv3tb0O+2/srlsp3g53N9br/fslubY7DUv18LmAfvB2H7FrvVj/2e7M3+q//+q/LtPVNO+fcf/tv/w3y1s3O47mbV7ybt98556anp095Ha6TdaR/9KMfhbJ//dd/hXyrhc8GVhvsuX7/+99fpl/0ohdB2Y4dOyDfzdM+OTkJ+d/+7d8u0x/60IegzMbeOYw/j0Geu/bdAuvXr3fnCjvPuU/vueceyN92222nPLbb+jEyMnLax3IZzzEb/3P1XoeV5Gy//+gvx4UQQgghhBBCCCGEEEL0HHo4LoQQQgghhBBCCCGEEKLnOG2tysxR3AaSzHt9R4I2EZdXUb9QnfO3yY7RFv9h/NP3dXW/lSKKaYsL1bZwfpt8u41/5n9sP27ROfqtGX/sCVR1DOe4DSQI/baF+Xm8zsxuzB9c77cxDQxgBetoOHEVo4tIA1IQhBiHwCpYHOlDyGARBL7+QYL/35FkFDSjUslZQ0E6nILVArmpE1VikVQZxlDgWk3sx4EabteoDPo61o/iLYMOxmVh3t8nfXQW79nG+gcVPw7HxjGGG69CvcHAsD+3CDnAGIfQqFPSDo67LKdtK0bdUK1hjNJhnDgNMyzDbALKqqSAaJvmFDG2u28U29aoDfjr1khT08A6VAf8hSv9VFan7cyxn8sdcoQkBc6T1ISFVTlBged2zDyJC1Ir0U6Zlpm7WYaxT2I8d6Dux+GmtRjPAdq2mbf8jWoOb9qo0Byr+njXarj2BaRWyju+7XGHt/BhHPqGfF9surwfyg48gotLPoUxXTDbyGIKWjKCc2H0Oq+Q2fp41Kis2YRjIG74tocVvG5ECpyWaXrWwjhEOda3GvvySgP7bXCEPk/6fHlCW3Mj0msVgdElOaTIsP6t0I+lkNQzYYL5auTHS5XW2LxF23hNnzu6zoVmNSoJzpeywt5HCgKxHLqNl5UcvxqXS7MaY9RNo3A+7rlauRjquJoYGkJ9nv1+ybFkDYFVFDz3uc+Fsle96lWQ/7M/+7MyzXqIbjqyNWvWQNnrX/96yO/cubNML0ejwvCxVh/yJ3/yJ1D29Kc/HfJTU1OnfZ8z5WzUKMshIuXg4CD+DrjmmmvK9BOf+EQo4xha1cS5Us6dpCSlMfqNb3zjlGXLgc+1Wo1bb70VytauXQv5H/iBHyjTrNFgRcjRo/4Bxb333gtlX//61yH/8MMPl+ndu3dDGWspTpw4ccqyTgd/K60GpVs3uH42DqxVeclLXgL5zZs3l2lWP1m1iHPOTUz4ZyRLrVk2z2OFlTxW0fKmN70Jynj+LWeecJ2sHoXHx5vf/GbIW+3OUjoku+7bGDl3srooMb9HuS2zs/h87WMf+9gpy1b7mDyT9Ux/OS6EEEIIIYQQQgghhBCi59DDcSGEEEIIIYQQQgghhBA9hx6OCyGEEEIIIYQQQgghhOg5Tts5vngcneNzC5NlOkzQnZuRizY8bpzjHfQ9VWqjkO/v9y6x/gr5fUi1Y+8SttA5lM5MQb497V07wTw6r+M6Om2tbraSYruLWXJb3efdVlMbMJyDDfQYx8ZTW3FYhyY5eEPjcM5D/D+MvMC2OuvZZV12js5gqwbKyKWc03+VpEWH8v6ABVQXuck5jMvMtG9fQM6sCrmgh0d9/PMGOr7aM8chny36+xSLGMNWQQ3ITL8ObIWi6lZ0KycVP+7Cgvzp7GPLvCsqo7a5lLztFdMe8rKHlRbkG8ZJ3rexj8rQIx7UfQzDKk6MeADHc9Tv8xU6tqji3K0Gxr1ODumIHYfGvR6xLy4gp7QZaxmN55SXocLXsR2yOwzrb/XOEa07Ma1LFVP/4X4cOwMxObNSf2xO94wr5I02L12IaDi0mlintOX7LSJvfy3BOtWM433DlTiP544NQ/7EN2cg32+GT2MNxnvdFbjmrrvar8lj63GcVaq09phi0sy7oCCvl5k3FXbxR3jdaNDHtEbX7SPveW3E5+MavTchwvUjtO9uCGhu0jsWQmcc1LQ2FtRXQeHX8qJgxzi2Ncp8nfKY3rlxEbGUV04+2Usf7uPV7hq8EGgeCCFWAn6HjfXJ8joT0vdq65Ot0jt1fvZnfxby//Iv/1Km2Z3Mjl7r3d26FX9XXXnllaesw0pi27plyxYos95t55z78pe/XKaX8vWuBrhf+/r8l/nHP/7xUPaa17wG8rZ8fHwcyngMnA843ux/P3jwYJleye8SdswePnwYyv7mb/7mlHUYGBiAssnJScjfddddZZp9znNzc6esw1I+ddv2S+07lXWmW/+4c8694x3vgHy3+ckxPNOY8XW6ubWtj965k9cWXt/s3OW2sBf9W9/6Vpn+r//1v0IZr8HWfb/U+wHS1D9HePTRR6Hs/vvvhzyvnRb7meCcc+9973vL9FKO99XA2c4p/eW4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Bx6OC6EEEIIIYQQQgghhBCi5zht53hBbuWm8w6csI2eV9fEbGCUPtPkRTu+Dn2ym4xfdriBTuaAnLGdincHVciPXI8x3xd7/08nY/8Mun4rzp/bTNg1i8dmh73rd/4gHru4GZ3Hw8ZrXYQYz4LdyqGPaeDQA5wXeN0w9G1jX3YYYn1BLE4e4LQgYTmdenze1+nAYTz32EPoZp/a79sXcd+MoEcvafj2dAbIR01u807b+5UiqmAU4HDuaxjH9FYs6x9gYfKpp0LBHmkz9h37yR3m08i412N0UIVVjHd/YNozjH64Rh/GzFW96yqmIjdATv3E16ka41gKydOdG7F/QQ7pwpGv3HjFw4LcWzyU8i7XjXH96BgXdEKe9izFOiSp6Zx+vGdRJfdr068tAc0/7ovYyPt5WkQxXjdK/fjN57Ati3N4bHvWr2lxjdadGh5rutiNjOK8uOLJ6MaboD6v5CNleng9rrGD64Yg3xjy9a/UaP1IMN5tM15oyXJZHpwyn9A6GvVje2pmXFaprLoR17vErBFhTO+aCPHYNLLzGj9PThqH5p0QBX00JhH5Pc3ak3awz3P6jAiMzz4IVp+bbaWwXjd5ly8eVqMvsBsaW+JiYqnxupz5txrG/mqoQ68Qx/g9xMZ+Of3A12Ef9etf//rHTDvn3OIi/giz43V6ehrK2LtsfcNch5WCvb/PeMYzIP/v//7vZfpicI6zZ/67vuu7yvSv//qvQ9mOHTsgb33wvK5w2+2xK4m9b6uF37k/+9nPQn5mxj8/OVffQ9r0fIod5J/+9KfLdIeec3HetofjeTGMrQsN9zGPjwsBO8itF/1lL3sZlP33//7fIf+CF7wA8nZN5rXwox/9KOTf+c53lmn21/O4W87csPOa4/ulL30J8ps3by7TX/nKV6DsC1/4AuSPHDlSppdy6F8K6C/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc47X1OlQj/rD/M/Z/9B/wn9qTjCBJ/m870DJTN78c/+5+9wl9rdASrF5EmITaKkCTGLUJ9G2chP3adr+/U/bgNq7NImhKzPSZM0RETs7bE6BfcAja8TdvtF2Lf1prDeCa0xT9zvo6BIyWBw2OD3F83CFDHkQZYB6s4YbtMlmG8F5q4tePoQR+LR++g7XTfPg751jGz/SjE+lbGRiAfjfn0QAO3lDWG0JXRNOqRrIVKhf4qnju23d9n/dVr8J4NjGlqYpjTtGi3cQwUkc+3I9xKlYZ43U7LjNFFDHi1jfHN+/04LOI+LIuwTmHk+zFI8P+4ogqOgTjx1w1IaxQ4nAux81soi5C2jQV4bBH6OhWknYiKmI41miDafRampEAyapqUdl01yeAUGlVNQkqbMEKFjI1TRiqXWkLaDBumAPsiJH1IYbw7LbqO452vZm3JaM53aPxWKsNleiDHsVLbim0dXoPzJEj9GOiLaa1JSKdU83nqRkfLqitMvwa0FrZyGvumP7IKjrtwhHRVZhwWAzjOkiEaW0anFfC8oLXGWngS+pjKSfcUGCUL63xC/n9ko0dJQx53eKj1HoU8oMVJ8Hbxi037sdpRPIVYPUhTIs41PMaqVfyNcNNNN5XpgQFU9nXTqtit9s6dvG1/586dp7xnyL9FVmgeXHbZZZC32hXWFawGOA4bNmyA/Jve9KYyvWvXLihjpUw3+HN/OfHupszj69oYHz+OzwVYLcHKk3MB6074nraO3Jal8uLip5vq5ZFHHoGyn/mZn4F8o4HK0nrdP3OYncVnkM0mPkey4/BsxhWvH/39/lmAXX+dc25sbAzyVo9y9913Q9mxY8cgn6b+2dvFNg/OpL76y3EhhBBCCCGEEEIIIYQQPYcejgshhBBCCCGEEEIIIYToOfRwXAghhBBCCCGEEEIIIUTPcdrO8YEYfbhWQZ6yc6ogqbDJk4bbzU6hu7o1571k82306Cb0LD8OzX1r6A0fWovndq716TxE99nkf2ClUiNFrlYwRLnD+zijNc77yPdNmptWxTt76hkem6fknzYhzMn3HoSUN8ey89rl7IL25xbkmy4c1qFFru3pgxNleu5+dMfP7JvC+84ZdzWPj2m8TzLhg1ivYp1GBochH9eM33cRYzg6hm7oDU/yfqX1WwahbJD81FabP5eiY89l5BxPfMCLGOsQUD8GuXHHL6Ij3SU4T+I+P9byeTy008L6JsYjTepqVw1wjFbNvGnRf4elMbY1Ni7lgi6c0rgLY3+xKjma2addmPuSIstlGa4tmRFFd5oYsyDCuVpU/XiPApyrMY19642u0DxuR/QeAlPJiN6hkOc0ns07AYKA/N4xOqYD09SwijHrp/kXmPYUtA5VaS1sxFieWdc2xSHgBplTWUOYU18VpiPZnj2T4smTc74SsyfwnjGpH2Ozdg41MIaVPqyEdVdG5ByPAjy2sBObHf90rjPvDwjY005jH9bVnD4T6LpVM88bi7iWiKWxjsuLzXW3WriY4yYnsxBCrBy8po6M+PczWWetc85NTExA3npq2av753/+55DfunVrmb755puhjB3kZ+rAto5g55y79957IW+d06vxfSZcp6uvvhry27dvL9Mxfc/nc7vFsFtbl3Jr2xh2czQ759zRo0fL9F/8xV9A2Te/+U3IZ/y+uvPActomehseD/z+Bc6f6fxbDuwYHx4ehvwrX/nKMv0jP/IjULZx40bIWw86O9F///d/H/K81l/q6C/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQoufQw3EhhBBCCCGEEEIIIYQQPcdpO8eTCvvBvKO348jh1EF3TcfIt5MC/axxir6qLPUOqk6Ojth6C88NjV87qGJZfx39027Uu4qL7Vi0cBC9V4uHjCc6wXYX5FZOhkx6DIpcXwXjUAu9W7uI0DcdUldUOsapFuA9rffXOedy6zwuMJ6hQ9dvVvi2ZQ795CH5kosM/UntE748PY6upc48tjVoeWl2TI7mok2O9wXf9vlhDOLItlHIb97kpc0hOYSHN+J9xq8wLvMaxiHMMb/YNnErUPgdVjHegfVc0/8vtRPyI9dN34xg/eJoAPJp7uubz2OfxyHGLA5M30XkWXZE5I9NyF+fBuSdN/613LGvHi8b5H4MpDSviwDHd575OuY07gKaCx3jbW/SdVyNxlLDXysjF1c7xT4uOt5fHmXkGCdVmJ0KacQ+PhJmm7bxeHC0LlVMTIsaHptF2OeBjSmtASe5zfg9BHYU5AmV4X075tyABw+1NTXrxyy5zA/P4rEHH/DzaOEhfLeEW6Q1YMj387rLG1DWfw3Wv1bz9w3pHQt5RA7Gjq9Ti8adK/C6kXm3QEz9lhXoxXeZXy+CANsdk429U/F1imP5k8+G1egMFUKIS5WzWWP1voCzw/qQzyXWN8tOW66DHQ9t+i23e/duyP/BH/xBmd65cyeU7dq1C/LWQb7UuLH3ffTRR6HsjjvugPyF8FovB24re4EbjcYpj13O/FrOsSn9drK+4SNHjkAZ9/k//MM/lOmPfvSjUDY/j7+tV8P3t9VQh17lTN8zsFo5V3W07xrYsGEDlP3SL/0S5F/xileU6dFRfH7WLd6vec1rIM+O8be85S1lmt8zcDH0zXLRX44LIYQQQgghhBBCCCGE6Dn0cFwIIYQQQgghhBBCCCFEz3HaWpWohlvQ2y2j50hxW3nkUEUSGX1AfQTLxjfgVvf+hv/z/DjALVtFREoWZxQQtMXfxfjcv97oL9OD61ABsulKDMNkaNo6h1vKogTrO7zd33dsFGNUr6MewKow4gDv2aaeKDK/rSlJ6f8wCmxrGpttYznGrONYh2OvRVv2Mta1kOrF+fYlpM4JUta3+GsFBR4b0za3ttmSkbexj13/Fshuum6Nr0OM2z6SMbxuw1Q/COegbJ4MCzZuUUBxqJDOJzFaB9pOMpBhzNK66atoGMoC6pvmvL9Wh1QeFdrqGNZsGfUjKTY6ZlgGCc2TFPsmz31geKNMSMqY0PRxh48tcOwXVgvC+h5Hio3Aj6UsxPomjvUtPt6VDOOQtbGTM+NOaXdoDGbUWpNNSIVBXeFCMx9ZS5LVKN4VX19aAtxJphSj68iosEOVCGguW7tIGpFehtaPsPDnFnSfVoZj1Cqyjk3jsQ8+OAP54984WqYX905BmSMFS5x47Uo2tQbKNq1bC/mhAd+vIeupWDll+rUg/VBIMQvtPA9x/HbaGMPYKFjCAMtSHqNmGBan/YkrToez0axoy/+FQ7EXYvVwrrZF83U175fHSmlVuB94W/xtt91Wpg8dOtT13G5lrFn5xje+UaZ/5Ed+BMp+/dd/HfLPe97zyrTVCDh3subjwIEDZfrtb387lO3Zs6drHVc7tRoq/Gz9z1VbeJwtLqI29fbbby/T73vf+6Dsm9/8JuQPHjxYphcWFqDsfGmCxOokSfAZ2bp16yA/PDxcpo8fPw5lx44dg7xdEy62Ob4UvP5dfvnlZfrXfu3XoOyFL3wh5Pv6vFKaFVndPn/r9TrkX/3qV0P+T//0T8u0nePOrf74n0n99JfjQgghhBBCCCGEEEIIIXoOPRwXQgghhBBCCCGEEEII0XPo4bgQQgghhBBCCCGEEEKInuO0DajtEN2zoTmzHqPPpa9vEPL9G71Dq28rulzXbUfPzcha7ySqVdEZXE3QE5xXvT+nCNFtFeTkyzbZ4QEsq+1Cx1f/kPdTx3PoUAtreG593Du0KmuwbVFM3lpT/ZTcuKyNLjLjJ8+oLEAfWFZ4x20Rkh87xxhGznuaQvq/kRZZpisB+qGq9dpjpp1zLo7RD1V0/H1Sco7nDq+bFt4/HEUY79og1r+vzx9bGSR38iDGO2j69gQBOo7zBtY/caZtAY2zGOvQifyYraR4bBFj/BPjwc7IV5+2yddn6pt2sL5ZHdsaxUO+fgn530OMb2D6OSzQNdgOaXxEdtzhdTOSZAeFv0+YYxyCEPux4nx5lmP9soIE8Oa6sWNffUF5X6cOeaUyyjdNFQ9M49g/cgwnYN3061A/+g6Ha7gW1mu+rXVeUev0ngTjL09jjFEesvPflFOfslvbORwfuXGLnfw/oNSP5oBOB8sWOhjDI9N+XN53D875vXccgHzngcNluj2N63NKzvd66D1ptWGs8eL0AF439fkqrZvRSf5vH8OEtGMd8rrFzq8fUQevU2njOCzM+yNIi++ykOaCPbagdypcQqwGn+xqqIN4bNQ3Qghx+szN4buSBgb8d5+l1lPrd2b387//+79D/q1vfWuZnp+fh7Kz8clad/W3v/1tKPuFX/gFyD/72c8u009/+tOhjF3m1nP91a9+FcrY1zs6OnrK63RzsXfoNxj7ss+VZ9f61J072bferQ4r9RnLTvovfOELZfpf/uVfoIzd0JeyC1osDY/B/n7/vr/Xvva1UPbTP/3TkF+zxr9vanYW3yn3u7/7u5D/m7/5mzLN6+TFBq9ZV155JeTf8Y53lOmnPvWpUMaucBt/do53wz77cc65oaEhyF933XVl+siRI1DG8/xSmPf6y3EhhBBCCCGEEEIIIYQQPYcejgshhBBCCCGEEEIIIYToOfRwXAghhBBCCCGEEEIIIUTPcdrO8cUOOqhG6t4rPrgBXc+jW/ogP7jVHzu6AZ2rfWv6IV/t916voIremryCLiPrynUB+sBiR6JuI4aNKliHyhj6fCv93r1TpOTSSbAOncRXYr7AcKYL5DOb99dNm1iWFFj/4dj7zkgLfZJHKMq84yurYBlV1xXGnx1keGwaoVc3StC5VjHq36QfPUd5gh7mbMF6x/A63Dc1MwzrCV63OkSNN+7tyhAe6xLyfdsQRxjfnNzgLjVe7gj9fFFA3mjje2a3fZ6TR9z4h3NycQUtGi+pn2MZhsyF7K42fVMh6XHg0Ku3mJv6BjiPwxyvm1s3OHmjcvY5R37eRAXOIUdjtDC+vqDA6+SUdya+SRXd8BXywcepj+EizaF2E697bMrXcd+96O2fvh+9eRXj358awzVqcR1ed3TcVH0I45DwexNMlwf0X5NZhP+QmbEVBOyxpnch5Dj/OsZ1H/P/gRY4Xmw3L2BY3NHj2NaHHvQH7P3KQShbuOdRyGfTEz6d44Au6F0IVnXebI5CWauF/RpG1qlGfU7z0RnHdztkdyM53k3MFsllnjj8h8D49/n9ETH5962fPGBB+UWMHNJCiPPN2fgse3nNuhQ8oL3G1NQU5K0/m13U7AqfmPDfvz7ykY9A2Xvf+17IW881u7VXiizD70Xs1v7Qhz5Upm+55RYoGxzE95itX7++TLOf/KqrroL8zp07y7R1tjt3smf3oYceKtP/9E//BGVf+tKXIH/s2LEy3c0LvhQc74cffhjy09PTZdr6m51bnlN4OfBasX///jLNLmhu+4VYZ5azri/H0641c/lUq/hb9Pu///vL9G/8xm9AGc9r2xd2rXPOuT/8wz+EvH2Pwt/+7d9CGa81qxHrGd+1axeUWce4c8497WlPK9MVen7J43elvuPwfW6++eYyzWvh2ax/qxX95bgQQgghhBBCCCGEEEKInkMPx4UQQgghhBBCCCGEEEL0HKetVWnS1p/qeq9S2fmf1kLZ4Hr8s/76kFcjNAZQk1D0YxWS2N8njWnbO21XD41KpSBVQEZNS6wuokJbZUjHEJj7xinpCmibzdE5f9+9B7Fs4ghqSuYmjAphHssS3F3irtjm61Tvx5g1ElQ3DA/4tg4OkI+jivkk8tsf0pC2Z9D/lYQxxrA24I9PBlFpEkR4rSyYM9fF+obkk0hi3/ihOip5Bmh8FFWjVYlIaRKReqLqt6ClZGepnLR10ChNQuzznHapRKnRL6ClxDWbpC0xfVWN8UJ2rDvnXL3mx0/Sh2OpUsdzq1WjHonwWP4fr7bRRWQp6U5IlRI5f5/FKtYvSXAchkbLU0RYv5zULh17GwpokGMdrLYmr3JrcCwV5lp5B8uOz2Bcjjzk+2bhPlSCVPbjVsEkMMqYaXSNRG0co1nk18KiQYoNrLxbNHGqBjQoOS4mXeS8nvGVKYZBcYoS5/IMx+jcvO/nwyfw6H0P41bdPV85UqYX7t0NZcXxw5APO37bW+xwvWjRnGoHfj1c5O3B8zivW4W/FomVXMrT2qx3IemcAlKFBRWjQGLtS5XUW2YNC1NcY/MCz7ValYJ1XxcRl7KS4Fxtn72UYybE+WKl5udyttQLcaGx6g7nnBseHi7TR44cgbLbbrsN8lZT8sADD0AZqzEuhIaA79ls+u+AMzMzULZhwwbI//RP/3SZfvaznw1lQ0NDkLf6AtaQ8Hpg9QUvf/nLoezwYfx++5u/+Ztl+u///u+hjBU33eA6sErnxIkTZXrTpk2nfd3lwOtgTL/9rcKi0yFF4nn67mTrxHqc8fFxyNuxZLU0zjk3NzcHeTsOLzaNCseINUE2Znwst9WqMVj10y0uPKe2b98O+V/91V8t06xR6aYF4vrWavj84XWve12Z/tjHPgZly5l/54ql+ubKK68s0+9617ug7MYbb4S8VdWcK40KX4f7xuqpEnoGubhIPtZLAP3luBBCCCGEEEIIIYQQQoieQw/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc4bef4ADmmN1zr3UHrrxmGskqdHMh14/COyCNOHuY0NF6eAv237Iy1vuGowLKIHeRhYcqw2VnBPlnvz2kG6AafRFWb2/N1f99Dd6CTbObgFOTTeX9ykJOTeQBdTM1d3i02NoZu3EH2/WwZLtON7Rjfygj1RejjEkfkyg0wLlGM59aHfZ0b69BJVamhT6ll6hg4dHgnjtzbFX9uo5/+vybAfs1a/r4tuk4/+YYz6xtOsH4B+e5y45hOSFy8kOG5Mwv+ukcO4/iYOIgxHVzjY7plM7atj5zptX4f75M853WsQxj5+uYxuftIZZ2Fvr4R3TON8eCkbbzLMbussB+dGR4LHZyr022s08K8H8Nt8mcPUR+PRHZuoN87iXHsp6aKWYrjdW4S582x3d7d1z6MTrK4if3Wdj4uLRpXI8MYs0rbtyem/2+Mc1rvzLoUZRiHkOzgHROH3PF16H0BGfnkjBO7yNHVdnwWx+yjB/x9j+5Dd9iR+ychP/PwQ2U6O4HOy6yD5yaZaU+A8Q1Dmn9mwGcLeJ32FK1T5j55jn3O6/5i7o+lV0u4JMPPtCzx1wpCcriTLy4y72sIaNylEfn6zBjIs9P+yL0g9JKD93z4JZe6Ry/FWwhx/rjY/LniZO6++27IWy/wrbfeCmXs3d27d2+Zbrfx9yY7hVcb7Lx+xSteAfnv+Z7vKdN9ffgboZvHmOl2LDuCt23bBvl3vvOdpzz3/e9/P+SX43Q/fvw45L/+9a+X6auvvvq0r3M2cNttjJcT3+XA34UajQbkn/WsZ5XpN7/5zVB2xRVXQN7G+5//+Z+h7A1veAPkJyYmyrSdX6sVG//R0VEou+666yD/vd/7vWWa5wmPyX/9138t01/84hehzHrvnUPvPLvA+R0Aa9f6dxKezffdbm7zeh1/y1lHvnPL+yy0dVzKw93tujyHNm7cCPlf/uVfLtM33HADlHFMV8PvBPaMX+roL8eFEEIIIYQQQgghhBBC9Bx6OC6EEEIIIYQQQgghhBCi59DDcSGEEEIIIYQQQgghhBA9x2kLULc9Ht1G25/k830j5NkN0LXTrng/URqR04lc4S7352YOncGVGN2zkfNOqpCcu5HDOmSBdwPF7CMn93ZsRMbtNpbt24P5A3ccLdPz9+7G607PQL6e+XMLRy5wcjqFM9NlemGUXM8DQ5CtGAf24hD200A/eoKyivHqRliHOKV+xLu6oREf7/6N6LAbqJPPKvKu35Dc4JUQr9w/6s8dXI8u5XwW67h42NexMYjevGwQ6w99Th7xoiDHdOydVSk2zc1OY37vPn/uo3ehu/rEbsz3jfs4dJ6FXvkrtqNTrZL4uET9eM+igjHshL6SEfubyR0fGM94RLEPWW7u/HUr7HNuoat60bjYD81jfB89hEE8bGLWXMA1oDqMdbp8xNdp4zDWIR8i133VHxuS9zxcxDoUU74jowVcW1oLtLaYeR806P0AGfrAoqrvx+QkJxm5q82soiXLLZAHcs7K48ltFtH6kdCaWzXvWCjI/z55FOt06AEfl6mH8L0JCwePQj6f8eVpGydGmmMdcnhHBMYwC9ARVzHu+1oD14DBBq5hsQlxkNPYL8jNnvp4pzRPogrWKYj9fQty8xfUr4HpK/bB40hy8EkUuqZbTawGl935YjU6eG2dzmVf2GtfqDicr7aeLmfqoew1FKeLg9W4vomzg92/d9xxR5n+zGc+A2XT0/h9zDqFL7axYX3Czjn3kpe8BPLdHNgrtQbxddghbL3AP/IjPwJlH/zgByG/uIjv0bFw37Av+ZZbbinT7F6vVPC78pnCbeU6jY2NlWn2wbPPvtvnPOfttYaG8LnGj/3Yj0HeusLXrFlz2vXnsTM7iy+Os+7nmRl8ZrMa5g3H+/LLLy/Tv/M7vwNl1svunHMDAwNleqk+fu1rX1umH3jgASh717veBflPf/rTZZrd8Dt37oQ8z5szhetvxz7Pg25t5TJ2aVs3OMdzw4YNkD9yBN+7dd9995Vp62V3zrkXv/jFkH/a055WptkH320NW2p9W6n1j530d911V5nmtl2K6C/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc4ba3K5qeh52Fws99SFFRRB5CzjiEyf55PWznaDrf457A1H8tSUgdERlmQp3Rsjn/2n0ZeTVIltUsFq+8yex9SNUzvwy1P03sny3R7BrdOVVqkErD/FUHb/4MUr5se9zELFnHbR5XUEtlar+sISKGQ5Xhu7vy53PlBgPWNaTtMveFVCMObUEsxdgVuCyk6Rh+wiH3RGEUtxcgmn68M4ThLSauSHTNqmg2kjBnAvCv8dp9OB3UGeYc0JVUft4xUOhOHMab7bvfbF4/fPQllzeM4BlqH/Li7t4593lfH+G4dM/oTGpRRjHUInG9rx2G70xjPrUY+H9GOm7CD53ZyP94T2g1VNLFtxxf9db/1CG5rOvYV3LoWPOTjlNN4mB5D3czMlpEyPU/jbMtOjMP4Gh/fCmmCohjjHVV8vpVR4zo49qO2b2tRJ4VJTEqk2Ac1C/G6Aa01dg1ok87p0BS27cFHTbxbOFvzGqqWxtfguVcM+WtX2zQmpzCmzUNTZTo9cBzKguOYrzT9PAppne8EOMfaRpkVkubKrsfOOdeo+zGwYfsIlG3dhmOrbvo5TPE6HdLNZJmvU5SQNqqCapfAaGDsOumccxH9P3JhjTekjeK1vTDzMQvow+Y8INWBWG3wlt7zNUbPdJv0harv+WAlt453u9ZqjNm5Uv2cLxXNatj2L84dX/va1yA/NzdXpk+QijPPz/93i5XEzgPWF4yOjp7y2Au1rlidy9q1a6GMVRjLgXUGX/nKV8r0nj17oIwVFtWq/z68nLjw2OFzd+zY8Zj3cM65ZhO/99s1iZUbVlnhnHM33nhjmX7GM54BZS972csgb8cAq3SYbm1/+ctfDvn3ve99Zdpqi5xzLqXnSucDrvvw8DDk3/jGN5bp5z//+VBmVT98raW0KnbMXn311VD2W7/1W5C3Wpv7778fyliz02r535s8HpYzRrt9B1vqujbPGhWrN3HOud/7vd8r09dffz2U8bzmeXPgwIEybeetcyevYVZ5s9T3y7PRrJwu3BZWQX384x8v06xVuRS/h+gvx4UQQgghhBBCCCGEEEL0HHo4LoQQQgghhBBCCCGEEKLn0MNxIYQQQgghhBBCCCGEED3HaYux+jejnzVuGJcRqc5IDe4y4zwuMnLrkKomM67lqI2u2U4Lb9QJzbHkvMkLdHgXufd4sTcoDNDTZP/HoF6gz2fAoWsndt6vtFCQF5rqlOXGe0RO9CDDtrVz7/GqUIziPnKDN4yruor/35EEWKfctK5gBxU5msMM4181bV+7Djs5/E/oIRte473BwQTFbBN6xcPQx7+YRHdU1CKHs3FkxzmWUQjdnPGKTx5Cz1Rrgvqm34+XRg0vNHcMvWOTD82U6fQYOsfr5Kh3xm09+02M5/61OO6Gn+jj1IgxnnEL6x+aiWOd1845F2bsEvPlKTmZiwDrG5jrdshX38ywH/cf9WPryJcPQ1n7awch31jw9ykKvGdxHN2JJ454z/WDBzbhsZM4drJr/dgf2YzzerCB9xkc9u2Zj8gLHdA7AMy87yM/di2mOpg4ZRTfmPzTmZlDC22s7+5Hca7uuWW/v85xjH0zRsf/wR3o6Q5vGivT20fJDT6P47s249eT9iK9A2Ie7+tS39ac1pZKjvmOWWuiEOsbVQYgP2rWiy3XopttaCPFO/FxS+k9CYttev+FCX89wPEc5xj/LLZrC73XgddKc9+A/OQhfSBGkZ/3UXFh/JhC/D+Wck9eSpyrtl3sDvIL0ecXm498OZxNPC/2sdSNS6ktF4KjR49C3rpeL3bHeDcWFvD3+2p03Nqxzd7ts4HbduTIkTL9pje9Ccp+53d+B/Lbt28v0/ycg13L3WDX9uMe97gyPT4+DmU8Dq0j+6d+6qeg7IUvfCHkraudvdH1Oj5zWsozfir4PL6ujemP/diPQRl7/a0PnvtppcYk1/e6666D/Ite9KIyzY7x5cSo29rMY2dkBH9f2jjdcsstULZv3z7I2zVsaGgIys60T/lc9rLzWLdtveGGG6DsT//0TyG/a9euU9aPY8Zjf8uWLaes78TERNdzu93ndMvOBp7zX/3qVyH/wAMPlGl+L8KliP5yXAghhBBCCCGEEEIIIUTPoYfjQgghhBBCCCGEEEIIIXoOPRwXQgghhBBCCCGEEEII0XOctnO8r4J+58S4dPOCHKuOReLeixTk6BJjc43RcrugQJ9sQNW1OqAUtcAuykh8XvhzOxWsb5KSs7nwfrO4hv9/sHYDeqM3rvE+3MVprG9BnrSayRfUNkcxDAp/nyBBR1ZSR19v34hvfFwnHxGFoWL8uG3+v5GC+jjA+meR79fBBrqugsupTutN+Sy2bdFhZ7Uf8ffNUurjCN1RhY0FOdIXO3ifPYf96Dp8+3EoCw7hOFys+mPH12MME/K45+1FX582+dRDrG8UeY9TPo3+srmHMGbTO3zMWhnGt9PCOlWN77m/gTGLhzC+YezjG5JXqqA+7pj3A8BkdM7Ntechf+DB6TLdvgfdiOnkFOQXzIsImuRozjstyFcX/H1mFjH2BxfQRx2ZuRrEGM91I7gOja73cZkdwXk8N4/OwI7x1UcV7It2h94tMOvzETbFReyuzvx1pxaw7IBx2Tvn3HET02JhDsoqKTnIj6yD/MH+K8v0+JPR91115GJPfJ2yKn0k0Hhuhb6tYYTxrdBiExmve6UPnXWN0WHIb7vKl2/YifGu9NH7JGwdyH2Wd7AfE+MnL0I8NiNHehAO+mNzXJ9TR2PUvF8ic+ylw36NjWc+CnDcCXGhWY7DcLW5qpnV4Di+2OorVhd2/Gh89Dbs2r6UXa923O/evRvK9u/fD/n+fv+7m73L52vO2L648847oazdbvPhZ4y91m233QZlP/7jPw755z3veWX6xhtvhLJnPetZkLfubXZMcwzXrfO/Ly677DIo4/yLX/ziMv2qV73qlPd8rPucbtnZEEX4O+Wmm24q02984xuh7Itf/CLkrTf60UcfhbLjx/EZw5nOVfZccz/asX+uYsTX5fGxfv36Mv2UpzwFytitfeDAgTK9Y8cOKOO+WE577Ll83Yceegjy1knOY5LHr63DUvXhvrJrmO0n504eH/Y9BUt9ZzwfnnF+v8Vb3vIWyM/M+OcTq+GdD8vhTOqrvxwXQgghhBBCCCGEEEII0XPo4bgQQgghhBBCCCGEEEKInuO0tSq8hd4FXi0RhLhVvEPb1+0u86II6VjSUCx63UUe4Pb/Kik27Pb6k7YdBNi03KgcOqQViDPSAxi9SEpKk/HteJ8tT/ZxONTGdi82pyEfG3VKq0PbmBxun4P609aNTg3rMDTsj23UcJtKweeGvu1xhtv/i4AUJhFtrbHVa9B9XAPylZq/djaA8c6OYJ0WWj6GVCVXzXFsxTWru8BjJ+Yw/ge+5rexdL6JW30S0i80m37r2rFZjMPQFtwe0x/4+i/QtqCQK1Xz14ocxrM4gWqXY4e99qPvKCpMXAvH6ELs29oZHoSyPtJbVM08ykOsQxCRxqbq79MOsd8WKL5zD/htNvmRKSjr0LZCqxGKcmxLrUBlRRL6OCzM4nbKaVJWJEYJkvRj7IefiONseK3PH96I43XuOMY7avu4pBnGIZ9BxUk2bdQdC7he5KQJapm15sTxRSibfRDHaDYz6evQwnuGpGEKJ7GtJ+4f8+mtOB4G+nAMZKM+FsUcjvVsFudJO/X1r9HY74SkhurzmpvR8TVQNrIFVS/brvZ9PrQO29Ko0voc+/rH9BFWJVVKO/HXymheBBmuuZXc93NE27DyHI9NzTyKaO3mDVx5aOpIn2FCiJM5022bF2q750rVt1c1GkvFwebPVR+vxr5YjXXqxmqv38VGTlq+i207+3KwbZucnISyP/mTP4H8W9/61jI9Pj4OZWejalgOVpvx0Y9+FMpS0leeDTYuCwv4m/Eb3/gG5B988MEy/fjHPx7KNm7cCPmrrrqqTLM2g7E6FHuec85t27YN8j/wAz9QphsN/J3FGopu8FhfKd0Unzsw4H+n/PAP/zCU2bY451yS+O/ve/fuhbI3v/nNkL/jjjvK9HLGA/fFFVdccco6nC84ZnY8bN++HcquvPJKyFtdB49fbstyNDs2z2PQalScc+7aa68t009/+tOhbCml0HKw45uvOzs7C/m5Of+bfu3atVBWqayMfpPnEH+e2L759V//dSj71re+BXmrd7qUP4f+H/rLcSGEEEIIIYQQQgghhBA9hx6OCyGEEEIIIYQQQgghhOg59HBcCCGEEEIIIYQQQgghRM9x2s7xgEyquXG5kprY5QH6cqzBJyvIu9xBD0/eNn5k8lq32UGVmmuhatal5ORtmkMD0uVUc3T/VoNama45dCINjuD/J2x5kved7Z/FC++fOIF1yLwDuyD3D+moXTX2Htu4jscObCSH8HrfjTHFLK+S/914gXNqW8jOYKpTaHzJAfmUauRmDzLvKp4jz3VK7up0xo+XlLzAtQh9vmHNjI8I+2J2P8Z//h4f/3ge+zil8IeLpo77sX5RFeuwZtDXcXoRnWqtJo1RMxcqNZpuDczPHPHu7ayFk8p6zp1zbiH0dUqncQ6F5DUejP14Dmvklcfqu8hZRzMGaXqe6jvtr9vpYIziHOtfN97ugrzhYcFuex/DmOZmaxHn1OEjvg71h3AMrt+K/uyxcV8+vB2K3MxRjEvS8X0R0TsUOm2s0+Ki92fXFjCgQQfbtmhuM38I+y09gJ7FuOmdZFELXW05eRVjGi8Le/xac+yhISi7/jqMU7HZx9A18dg23taFoXHSd9ArH1SqkB9aM1ymR7YMQNnADozLyDbfV5VB7Lc4xuvGZj3JQizLHbrZC+NIz2n6BRHGLMx8f+QZzRN6L0WW+DFchBQH+vzLzedUSu5ycf5gl2AvePPOlvMVI/XF/+VcOaYvtviudtf2aonn+XCxL6cOYmW5EH3aze3rnHO1Wu0x086dXN9Wq/WY6cc61uatz9s552677TbIf+ITnyjT7Ilmr/VKjU/29U5NTZXpu+66q+ux54o2vdvJxu3gwYNdj7VxWmqc2XNHRkag7MlPfjLkrcN7qbFkWaoOy5kL3dZFzts4jI2NQdmaNWtOeeymTZug7L3vfS/kn/WsZ5Xpw4cPn061nXMnu6rZFX8h4H6zcejvx99rl19+OeRvv/32Mr1/P75DzLrLnXOuWsXfcxbuNzvW+b0D7PBev359mR4cxHe0nSt4HZqexncQ7tmzp0zv2LEDytjFbvNLrWc2TvxcxjrGnXPu7W9/e5n+8Ic/DGXz8/gettXyned8ob8cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Byn7Rzv5ORRzfypIfnI0YbrXBF4701C3prmQhPyndjfp8jw2f18ih6yqWnv3mnOkceYPMFB3R87RK7qVr1NeXNsiO6fvoBctKPeMbzzcehemrp/FPITxuFTSUnUHmJ83ZD3GDcetwGKNj15M+QH1vtj2dMehKd2foUBu9mwTkFBbTWe46BAf1wQ47Ft41xL58g1N499lRgtUpazfB3bU2n4Os7neM8TE+hXmjnh/cOVWZQnF20aH7mPRZX6otPGOA1v830eVLC+x1Hp5OLEl/dtpLatx2wx6+dCPoMuvw79N1Yr823NKtiWah+euzDqvV79Mca+OEmNZ+Y1KaZa7Jhu+jpUaQ1wNP8qhY9pTm0pyOGdWi8+1a/dRA9WHnoH+YnDOB5mjqH/e2yTL798E7rN2pcPQ3624+sfNrHCKTnT51Mfl3iRPNZNPDY3c6p1COdFSGM0tt5F1n2lWKeY5m4x66+dzmJZpQ/jNLLRj4+gReszvcuhNuTnY7ONa3e9ECO9xQAAooNJREFUgnN1bIOPcWUDxntgM+XHvKewmmDb2PmeJ/7coIMfYWlB76kwA2gwIU97TmOgbfojxTHpAvqoLHw+YP87/Z9zbtY0Cqe4gMjXe+GQ/12cKRo7F5ZeXbMuJbgPI/Mdhp3ST3rSkyD/kz/5k2Wa/cIRfReamZkp05/61Keg7Mtf/jLk77zzzjLdze/tnHMf+tCHyvSLX/xiKBsdxd/dtq3LGbu8riws4Pfz973vfWX6+PHjUHah3tVh48YeZvZpW7c1O95Tej4xMTFRpoeG8L1E7OW2Y+Bs1goeA+xPtrDf2daBr9Pt84PHbzdnOpexg/z6668v08eOHYMyjre9VjfvNtf3QmHry450dpCfOOF/o3/2s5+FMh5L69atK9PcF9yPzab//cl9wWN982b/zMw68R/r3JWC+4nfAfD1r3+9THOf85pm61yp0LNYiot9P8Du3buh7N3vfjfkP/7xj5fp2Vl6/+N5em/CakV/OS6EEEIIIYQQQgghhBCi59DDcSGEEEIIIYQQQgghhBA9x2lrVUKHW+gD57dOFKQZcAVt8TeP4HPawkB2C1cYpcLMFFZv4ijl7/HbB7KjWL80pvoO+voOj2AdhjaiLmJm2N+nM4jHru3Httaq/tjNO+tQNv0k3J62sOi3ZWUTuI2itg4VEOsv81vDrrwKt0eN78LrVhu+/kkFt3JkEalRTDonAU41xG1LRYZxAeUGKU3IbuCClu/0fIHGB23ZshaTOo2PGikgoqq/7lQLj91/GJUbCyf8lr7ONG6JC0gRUg18f7TreM/FNtZ/8xbfV6yoiGqkjDEamMHLcXzMkDYomvXxr5N1Z46UQgst355qjG0pZnHbUNHx980c1Zc1NpnvjJS2BSW0XMSBHz9t3qbpSNFjxmGY47hrOVLe5H7uFk28ThzivJ6PvEJmsTkHZe1F6vPQb+FqjOB827ET23og9/ftHCX9CW3hi023hgUpQDoY79Q4hBZJuRKkeJ/YGe0DqX6igrb7Ubxz01UDwzSn+nEcNvqMpoS0UXVa/9ptf+7ULK4XkcNjB83u1nAY+7xv3TDkK6ZOYURbDkkNZW8TpjNQlAWk+LK6J1KDYbSdc0b/FSW8juLYL+x4puqFpO2KzDyPT5KO9QbnaivoxbbF/2Kr76XGalDpiJVFmpX/y1Jry3LionXq0oZVCNu2bSvTf/AHfwBlz3nOcyBvtQnLGXM33ngjlM3N4ff1W265pUz/8R//MZR9+9vfhvy+ffvK9Ec+8hEo+8Ef/EHIW00Mf3fnvFUJLC6iWo+VEG9729vKdKt10rfJC47VKzh3ch1tnpUlrIl55JFHyjTHjNUYZ6qxYVjtMjk5WaYffPBBKKvVUCU6PDx8yuuuXbsW8vW6/+2xlFalG3zsUnqUU7HUOm3H6FLHnqt1vNt1WfthlTJ2HDmHuh7nnHv1q19dplnvxNx9991l+ktf+hKUzc/jsyA7Rrl+K4ntD1bn7N27F/J2DB84cADK7rrrLsh/53d+Z5nesWMHlHFbra6K18b77rsP8lYVdSlrVM5kHugvx4UQQgghhBBCCCGEEEL0HHo4LoQQQgghhBBCCCGEEKLn0MNxIYQQQgghhBBCCCGEED3HaTvH2U9tn6oHIfmnyTlupeNFgN6rgpzY+2a89+aR+9CntHA/Osqyh6bKdEI+rZCMskXFn9vuw7ZMb0YH0cCQ99NEV0GRG72SHN4176saHcKya56wHvK5CVprAd3JG9avgfzajd4bPTbGjmPstijxbqPoJP87eY2Nk5d9ValjLw/mQ+OnbrFot0C/WaXjy5Mm+ajpvoFRZFdp6NSGqUp1355Z8ohPPXoM8tmM91lVWnjPnHxQVv/dbmFbslk8t13z42X9DVi98W3kfq5471je1w9lrT3kdDe+/SIgGX8Hr1vr+GNrMfriag7PtUOAndIJ9XGe+7ZWHI6d4X6cN0PjPr/wEPrVsiZ6sJpmDQjIOd4OycNs3Fch/f8d16nlvBcwr2LbKnU610ybSg3L1mzEc13s+ypbj/3GzvQw8eMhpncSVGsY36nIzL8M+zQrsE6pOTUiz19BrviclvLAePSS9X1Uhucmxj85UEUf+eAoXred+vo35tDJ6Do0z+247MMx2UdrZd1MsSLF+GYRrTUdf60sp7UkwDjVCvs5QGuhw7g4E+OT/IE0RDPTjxHds6BzA3ssj/VLlPPl/e12n7PxRa5U/eXuXV3Yfl1JR/Nqp5e83Gfqlb/U5+ql3j5xatgTfdlll0H+fe97X5lmNzj7yc/0c5XrMDg4CPnv+Z7vOeWxP/dzPwf5gwcPlunf/d3fhTJ2+/7sz/5smWY3NbfNurbf9a53Qdn73/9+yE9NTZXp1bKe2nocPXoUyj7/+c9D/tnPfvZjnucc+pydQz/y9ddfD2XW2e3cyq0z7By3vuq3v/3tUHbo0KFTXmfjxo2Q//Ef/3HIP/WpTy3T7KPmuHT7bGFH/ZEjR05ZJ8Zeq9nEZ0MPPfQQ5J/5zGeW6STB31GrYY3nuFjP9Te+8Q0ou+OOOyD/4Q9/uEzb9yA81nXtPLdz0bmT1xa7niwVs3M1fvm9Cffee2+ZZuc/x+UTn/hEmea1keNi3+XA7x3g+6yWdWs1or8cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Byn7Rw/2Ubtn6u3HTp8wgBdOx3jwc5jdBPPtPHYe+7xvpzsKyegbGD/DOSb1ttNLteA1K6BcU5ni+jZyRYxDPmY9/LMD6ODau4KrH8t8p6phMI5tBb9Ztc8fV2ZTsnTPhTgfeqmOCdHcBySFyv0XqGCfNPsbA4K4wLP0T+UU9By8sPbPs8Cvg+NkNjHuFpDF1d/H/kvY9/YOMb6NsawTi3jip4/gG6u8Ag66aPUxIXcSnXHMfTtaZMrKp7BOOQz/lp915C/ahNeNzPx7izg+GhU0Ae1YJTIaYH35JnaZ/qqMUZjZxjrVDXFIV2nE1MfV3zbG+R+3jCIbrnLrhst0zOHyf/+AMnjF/1YqzlsdzXE62aRmWPkts9p3MU139b+NezLpkXAFBc4NV0S47GjkT8gGyM3dYFBTE17ogjbHZGsOmqb9wNUcS0pYnZX+3GW8rsEyB0fxDgGRsbMuxBG8D7c1sicWiEfW0He9sy4+gf6yTHewb5JjRc9alBcsLou7fi2hm28bichj3vgx1JKE6MIaa4a4T6/JiGkmOaRb2uHDo5pHa2afExrXyvDxoWZv096CTveVpu/rpsvcilWg7vxUma1jZXHotsYuBjqLzSPxcXNSr0rIIrwO9/LX/5yyN9wg3950tk4xpcDu3Ot7/maa66Bsip9V7ZucHZrv/e974X85z73uTK9adMmKJufx3cj7dmzp0wfPnwYytjfm+f0nXwVYMcHe7jf+c53Qt56jJdqm/V2W+e1cyf340qNl4zeC2b73PrHnXNu3759kLduZe7HL3zhC5C/9tpry/Tw8DCUdfM7c4xOnMDnVdY5vpyx0qL3533gAx+A/POf//wyvWPHDihjn3bY5T1KKwW3jeeUdY7bPnysc20M+d0BZ/P+EOv/Ppfz1l6b55SNg3POLSz4ZyY81hn20HfDxknfU88c/eW4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9BynrVUpaJu51WoEBW5TiAPSlhi1RDvEsuMnsAqd//AKjv5j01CWhHifBaOWqAa8hRrr37JbHNAm4sKYDu74+xQLeHCY433Cwjor8DKVOm6rWJM0yjRrEmqOt1X47TGsW3ABbYGyW2dy3gZE+pDAb89IybGRFLj1rk3xtjaGKCc1Q4ExrNRMfrQBZSGpD5zRdyQJaWv6MN8xKpjjE6hrmUezh8tMX3UKjG8lwrZXTONaNHiqWH3XqPlj4wT9HHkd86HZ1tIXolqiOoQxnKr4vslpnixSvwZVX8eR0X685yjqRRKjsUkiHDsFqXSsaqKIsf/rA3jdHdf4sZW18Lp3O2xr8+EJf50m9kWNNEGZ2QLaomkxE+CWrfqIV55s3ILXWTOGY6du5hFrgMIY+y0e8PMvcrzlEMdOZPo4CkhD4rAfa87HtDGCjQv6sP6dKV//hFQ/UR37PFm/BvJbrhsp0+vWYhziCm0rq/j28LqZ0HavwqhTgpgULFU8NjcfLwVti83o8yQwOpo0wetErk55Pz5S3ppLYzaLfL5Kfc5rVsc0nnYhOxdhHQITlyLF9Sxpk6bLrKtBTrqhixht2bv0Walt/XwtjZ3H5nxpQFZ7X0iHIsTKMTQ0BPmXvOQlkGdtyYXAql9YA8N5q83g9WtiYgLyk5OTZfqee+6BMlYs2GstpTpY7bDWwWo+nEPFBetD+vvx98XmzZvL9M6dO6FspTQ83I9cf9uvrNJhlYftV1Z5PPTQQ5C3/bzUZ2G3z03O2zG7nO9RPO54zL7rXe8q07/3e78HZdwX5wPup1tvvRXydtyl6fn5/WOVJc6hLon7YiW/a9hY3HbbbVDGmqPlxGI1fke71NFfjgshhBBCCCGEEEIIIYToOfRwXAghhBBCCCGEEEIIIUTPoYfjQgghhBBCCCGEEEIIIXqO0xYUBeTILoy4u5KjIyksyDFtPLxZE/2scwfQ6Z0c99dqhOTADtBx2069ZyqK0XsUkV+20/F1SOj/BEJy/8TGv9XXh2VYA+fizN8nCElmHuF9YnNfthxlMTmFjNObXeuZw3xovMYFecQLEqwHub9zSt5fV6CnKXAk3k39tRNySqfs8634OiWDOB4CquOQiX8ak1uO4jI748fP5BTWt1iYwvoWvu0ZhTcrcBymga9j0IdtWXcF+obHLvN+vrBCnrEA4xKYsZRXsG2NEbxPOuJH19QB9KlnJ7D+ruLvc3AN1q/j0J9drXiXdUyudRpaLsu7eLnJVT20xtd3+5PHoCxBfba72zS98zC2LclpDTBVDBdxbcka2LbBrQNless2jENtCGdrGBlfXEjXpX6zLvaAvP2VDsYhD3yF84id/xjvmllHR9bgdYfHBiCfG6d+0Y9jZ3zHKOQ33TAO+V1P8NfqH2HHGmRdmPk6R+z75jWi6mMaZhizNrny8tS42OmdCgWtLR1z35QGT4PeuWDfqxBW8LoDAY6PBfNujDylsU91smt51KHPD4qLMx7xhNRxrYze1WDfsRDo/6PFxcvF7oJeqfqvpIv9QnMpteVS42Kfb2JlOdN3Baxbtw7y69evX7E6rRS2Pa0W/j5jh/CpznusvIUd45cyS8XF+pF5nanV8Hu09YzzWGIf/JnWkfuG+/wLX/hCmZ6dnYWybu5466d37uT62raze73b+svX4bg885nPLNP79++HMvZ0W7ifFhfx9/KnPvWpMn3zzTdD2Xd8x3dAvq/Pv5NrOW1bCuvLnpqagrK//Mu/hDzH/3zAPu/bb7+9TB8+fBjKLrvsslNeh2PGfcP3sV7xd7zjHVDWbDaduHjQL3UhhBBCCCGEEEIIIYQQPYcejgshhBBCCCGEEEIIIYToOfRwXAghhBBCCCGEEEIIIUTPcdrOcSYwpwYFeXnIRx0U3lO7OIse4Pl9x7FCLeNXCru7cmPjuO2w7zZAN1DD+MrDGJ22YdSPx44Y5/hG8mlRxArrtGU/L3l128ZftFiQz5n8vVGwaMrIIx5QnSLrdKL/7yjIkm4k02lGDio6Ns65X43vjn3kAbnNjZ09ruKxGdU/6nj/VifEvllYxDhNHPVxau5HJ1llET11LvfetDaNSUfjo6j6GG+4Ah3pOx5fhXx1zMclZsc0Sbw7hb9vRj71yhDFpeHvuzA/CWXRHHrHGua6M+Q4nk6ovkM+poM8nqn+We7jEFKMQvK6xVXfNwOjw1C24/HYj2nLH3t/gm3rHEChemymcjFAXu6t2Dfbr/fuxHVb0Nmd9GPbIjO2igjblp/0TgVTnpNAvcJj3cYU5yrdxlUqvk6jG/ugbMuuEcjX5n0gkkGsw/YnD0J+07W4ho1u8teOYvJn01yAtYd1jeT8D0M/rys5OfPJzR4VxpnO/sOU4m9PrfA9sR/TzPjgU1z3gwTrVC/8XEhpPYtorQw6duDhdaspOgIzEweOGc+bPPTxLchtL4S4+DlTFzDT7dxL2T99KbdtKXq57eLcYb833XTTTVDWaDT48JILNR6tv/eOO+6Asvn5eciv1HsRuK3d1vHluM0vBmz9OQ4jI/hbxLqsBwfxt0c3h/dyYA83u6G//OUvn/LYbn3BZXNzc5CvVv1vhKXq3s1PXq/jO69+8zd/s0z/27/9G5Q98sgjkO/mwuf6Hzx4sEy//vWvh7I//dM/hfzznve8Mm3b6dzJ9e9GRs/XrA/+Pe95D5Q9+OCDkL8Q84Rd4Pfdd1+Z/uAHPwhlb3zjGyFfMe9oO+m3J113YmIC8r/4i79Ypu+9914o66X3Haw2zmQM6i/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc4ba1KmOLWmTj128qDgreR4xaMjtmivtCkrQUzqMKIjN6gydoPOjVJfJ06bSwMCqxvkXjFRlLDZlcHUScyuH2oTPdtqEFZFKE6wG7CyR22O3OoXwg7vo6tJm5xyZoYh8A0tqhifOt13BJXM02NqN1R1oR8p+Pb3qItIi1SpQwmGNMw7qIHCEj9Epp8jnGpkDYhs8qbNulxTtDYOuTbk0xi22oBxrSd+K1XUUFKEBr69RF/7oYbcNvY0Gbs87pRY7gAx2gnoLHvmuZQvE6lhnWyu9Nq9P9WGfXNvOnH4iiOndZdRyG/d52/1roxrEMfrQBBYK5FO8yykNYAM+/7YqxvYxD7YtcNfk5Vh/DY43twPOfTfmxFQ1Dk+negPuSyLeO+bBTvGZANxSqFQnJhpGwjMvGPeTslbb3LzbUiOjYgPUdoxt26EWx38aR1kF8/5O8TD2JHrduJW/j612Jc6rE/Pk9xnGW0JuSpX6ciClqniuoXu9hkEc1V0qrEVpdUsPYqobw5j7VRNBdCs9anNK8Dmic2H0W0RtFaGVT8+MlC1BjFBelyTDojd05U4NqYWQWZI83VRcTFto1YugJxvsbAxTY3usExO1dtu5jn59nE6GJut7h4sONs8+bNUMYqjAsBawas7uIDH/gAlC0u4vexM51vSYLfv9atw+/cmzZtKtNTU1NQdujQIchb1ctSygQbb1bacF+w9qPT8d/Pz9VazPqInTt3Qv7aa68t03GM34XPZj2zcbOqDuec+9CHPgT5I0eOPOZ5S8ExYzUG5y3d2sZl3I9r164t0+9+97uh7Ad/8Achz33eDas4sYoV55z7mZ/5Gcj/5E/+ZJl+6UtfCmVbtmw55T04vjwXPvKRj5Tpd77znVDWbOLvywsB941tz7/8y79A2eMe9zjIX3755WV6eHgYyqyexTnn/viP/xjyt99+e5lm9Y+4uNBfjgshhBBCCCGEEEIIIYToOfRwXAghhBBCCCGEEEIIIUTPoYfjQgghhBBCCCGEEEIIIXqO03aO5+SMjZz3NOXkG2bfd2I8zLUMPTx18pO3rNe4hc7uxQ66oQrjsg5C8r6S27Vu6thANbGrj5KH2XjG+/vQDxZHWCfrrQ0z8lORh3tuwZ87tQfbMnmEvbU+Hw+hU3rNVnJVG490HuI9F9rYF8fmfcwOHcV7Vip47GWD2Dcjg/6+bVQeu4AE1dYdH8ZU3wzrGJr/oynIvZ7NoIcsXPB1LnJyTCfUno45N8Z4J+QHqw57t3LfCDrH+6s4luLQNz51OJ4DchOHucmTo7laxzr0D/h+XKhRgBsYw5nM+N/b9H9cU1inifu9G+/INQNQtrmO/RabOVUjl3JKLuhOYF3b5Nuv4XUHx318r+jH+s5fhmLxfNG3LYjxnpU+PHdowM/VCjnyXchue1/HMMc+ZQNjYv4lo/WsQ2O9cP6+Ebnacr6PObVK823tNvSGZ+Om/gnek9elGnkAO23vfWu3eY2ltdzEqepwvsUkY89yH++A5lASs8Del+cOPXRFgeM7DPzaSMuzq5Az3ZmxXyW/d4dc5oF5J0Ac4nXSHGPaif38C+jdAVnOXkJ/3YzGaJLxsb48DfT/0Rc7K+n97CX/8KXk5b4QcPzO19jppTF6pihGYrVRrfpvUtaj69zJfuQLMX6tO9k59Cc/8MADXY/tBrelXvffNb/zO78Tyn7lV34F8jt27DjlPb/+9a9D/v3vf3+Z/uY3vwll4+PjkH/a055Wpl/4whd2Pfazn/0s5N/2treVaevddm7lPlMrFXwG8oIXvADya9asKdPnauzMzMxA/otf/CLkWy18NnC6cIwmJiYgb53uZwPHwfrtbf87d/J8tONnOT51Pnb//v2Qf8tb3lKm/+qv/grKnvrUp0LevpfA+vSdc+5LX/oS5B988MEyfTbvA+gGe/D5fQE2z+OXvf4W9rvzGtDf73+Hs2ud/fTT09OQt55xfd+9uNEvdSGEEEIIIYQQQgghhBA9hx6OCyGEEEIIIYQQQgghhOg59HBcCCGEEEIIIYQQQgghRM9x2s7xLD21rzWPyDlOBl97qnWVO+dcUkGPkNX5Fh28bpqSfzr3rqic/LFZBc214bB3EFUGa1DWN4J1qDWMAztBD3AYoGPIGbd2h5xTrMqdnfEOosn70MF74iB6rzrzvrzaoOtOY9via328WxW86THUa7n9+/x1WwcxntUY7zO/Hf3UV+7w9R+mthZ1vK/Vf7OSrEPu39z5g9MODsl0ge5jwlSQzzknH1Qc+nOLGjqoQvKIJ8b3XRnE8VurYryz2Ljochqj5DaPC1+HNMN7ZuTEbgwbT/sA3jNp4XWHjVt+agavkzfRqdV51LvcJg/3Qdm6IZwLYeHrmPVhX+Ts0DJhiCP0nIcOHWBZxce/LyInYA3nQmrqEAc01gsaZ/Z9ByF578mtXRj/e5sl4+RTz8x6khbYthlS3012zLk056vkmB4wcUpijGc1wZiFw2b+0fsLSFfvCnLPFS1/nxaNs5zGaFT4OnbovQkB9bn1k9ciGh8OAxOYeRLQeChC9uf6BsX0sRSmGP/U1CmjPs9Drr8JFHnxcxofsfk8CWmNyshlHuXmuhmu3ewVtzWqnOQjv3hgp2IvOfUuRFvlNl8+F6qd9r69NC+EEKsT68w+ceIElC3Ha7xS8LrYbOL3/o997GNlenJysuu53WAn9vXXX1+mf//3fx/K2P3MnmMLe7if9axnlelDhw5B2fDwMOQHBvx3+VoNf3MxV1xxBeSvueaaMv2yl70MytjTfaZYL7tzzq1duxby1u+8kp+x1uF89OhRKNuzZw/kz/RzdSnnuPVnb9++veu5y2m7PZb7/Id+6Icgf88995Rp661eLlxf61O37XTOuYcffhjyduzzdXi9OFfrh52769atg7KXvvSlkH/e855Xpi+77DIoYw/63r17y/RXv/pVKPvIRz4CeTuXZ2dnoWypOOi73+rkTNYs/eW4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9BynrVVpt0kJYbaSBydtbcetBba4UcHn8ZVhvE9R8dsqFmk7Pe10d0nd16lo4nXDCtYp7Pfl1WFUNQyOY9uqw/7YvMpqBtx+lBu3REr/19DKsf7ZrNcOtCZx28fCFG63n5vwW87iCMsWHaoxWsYYMhvhVrWHH0DVwfz+42V6aB6vO+SQA/NYx0rfaJm+qopb14YiVAtEiVE1hHhsk3Y4ZCZueRvjHeSkPjCDIKS25tk85AujNOmEqMcJ0PLgGmt8TEf68NiCZkkY+JiSzcdFvA0r9DcKM6ovbfVoDPk4FX04lhYOki7C+XE4T6qfjOIQz/h+7hzYBGXpJoqv0V8EMW4Fy6iPA+fHN+grnHNhgJM1jP2xnRiDH5NZKTT9lmR4nZj0ItZqwyqMlPoGuibE6xR0bNNceC/NkzsfwPyRff7CocN4Vuu41gwYpdM60jldM4J1Gh8wWhIKUkq7t1oB9k0R+jqGpD+JEwq4uRbrs1oRtqdhlCxRiG0rWjhmczvvacJVHJ7rAj83sgTL7NZA55wLzNjKHK6xgTu1tqQgjQr/z3AQ+UAkjj7DWBUW+PsUpFEhs5XrBP5anVDb7s4lF5s+xG7DPJd1P1/3EWI1c762PWuOiZVgpVRm+/btg7xVrpxLbH1T0l4eOHAA8v/wD/9wymO7tZtj1N+Pv99e97rXlWnWZrBGpdu8ZV1LX5//zbhz586u17H3We7a8MxnPrNMP+1pT4Oyz3/+85BfTr/aejQaqB1lpcXZ1N/STftx++23Q9nCAmpfV0qrMj+Pv4+PHTt21vdYijjG32A333wz5N/61reW6bPRqnSD28Zj5XytCRaef3Z+/tEf/RGUPfe5z4W8HbNL6W+sWuk7v/M7oWz9+vWQf+c731mmp6amoEwald5BfzkuhBBCCCGEEEIIIYQQoufQw3EhhBBCCCGEEEIIIYQQPYcejgshhBBCCCGEEEIIIYToOU7bOZ4vogMs7fjn6jVyEafkuA2Me7bSQPfs+Cb0Gk/v8+7Z1nHy6FbR923VxZUhvO7QGnL9bvDlw2PoDltzJda3Ys6txNjunNzKmfGKR+SadQU6nNKmv1ariddtzZ6AfGfae62bMf4fxuxevM2skdy2CvSEzzw4hVWa8PfpFOgenq+jyzzM8FpTa33803E0lAcNcv0av29AXqmIvE2BET7H5E4O4lO7q/sq6FNvV/DcOPbtqaGGzg0MYp9v3OiPrVfJa03jOzfe64gcx2FOXi/rpA9ZUI7nVgd8ef84Xmf2IaxDZ8HHIZubgrIkJf97y8ybw9ini0cHIB/kfkw0A/L+1bEvKsa1zAtJiyZDYMZDTJ6uiM6OzHApAnJts/fcXCsjH3mH+sZO3ZjGYJ7juUeaPk7/fi+64g9/7hGs06MTvg7UtkoF59SJ/mF/nY2DUNa6cS3kn3qNb/twP41BfgEDuflz41Sn6eeCHNfG3Pi+HbniExoDUerP7ZCPPC/4XRO+DvxeirTDTm9fySilCmc8H02f45GuUvBc9ffNHb1ogI4NC+MyL/Bzif31zoytCn0muICzvv45vX9BiP+H/IVCrCwXak7J8S9WAh47Nr/U2LY+58997nNQ9ou/+IuQr9X89x12a3cbv1wHdvJaf/L+/fuh7B3veAfkH3rooTJ9pu5s55wbHR2F/FOf+tQyvZy2LXUf60teynm8nPuwh7lS8d9b3/CGN0DZl770JcifqTeaPdcHDx6EvO3XpdraDR4f1un80Y9+tGudVopWC58b2Bi+9KUvhbKE3890hnCM2HM9NOSfp8zOzkLZpfy9sFrF36J2XWI3uF2jnMOYLjUGbT/ysa9+9ashv3evf8D253/+51DWbOKzgEu5b3od/eW4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Bx6OC6EEEIIIYQQQgghhBCi5zht53hxAt066bD39qQxOnwKco5HxmNbNLBs+DJ0gK2b9I7edB79VIuL5DI3PvDBDeiGWns1esdGN3m3cmUA3UWVYbxuXPf/ZxCSO7lw5Bgyjq/ckSOdPMCtRR+nxdkFLJtCl1Fn0Z+bkWe5k85DPi+M1zjHYytHJvHYOeM2L9D31G7hubUQRd3R5Iyvbwud41mG/Vgk/loxxcHl+H8ymfH3pgk5jxMaSyY7OkDjYR36nfuGfZ/XR2pUhm0fXu+93EGMfVw4HFtxbjzGITnpya2cB77trBsuHP5DreLzozvRj9zeh3XIH/ZO7CpdN6Rpbb35EfVxfgS9bmnkx2WrgjGq0GrRrvo4peTJKwKsVGyc4wW5+DOWNKfG10nxbNN/54WZKWftWIT/YFTVriC3/XyKHrpHD/mDj/zbUSgr7kPneGfmeJmupuTJCxqQzc2LEpqHcQ4dyq6E/LH1l5Xp4RqNq5P6HOufOuumxGNbAXrnnfH4R330ToWMAm46IKvg2M/Y+2biH1Ad8gzXO+sDjwvyqVNfucLXNyaPeFbwR5q/Fr8fIHdtyvu2BQF7Fck1FxrvOTnHQ6pvYe4bF5fO/0ezu+98+Pfk7714WY4v90yvuxq4EPNCeBRvcanAjuzljG3rd969ezeU/f3f/z3kX/e615Xpbm5troP1mjvn3IkT+O6sz3/+82X6f/7P/wll99xzD+QXFvxvj7OZw918ziv5WdHtWufqPlu2bIEy6yN37mQn8umyuIi/Ce69917IWw8233M5beX72DHw8MMPQxn7yVeKNMXfAV/96lfL9MTEBJRt3rwZ8ivVrzyv7Rg9cOAAlF1Kn2ccv2uuuQby3/d931emuznGHyt/uvdlj/zgIL776/Wvf32Z/tjHPgZl7OLn9e9S6qte59L5pS6EEEIIIYQQQgghhBBCnCZ6OC6EEEIIIYQQQgghhBCi5zhtrcrcJG5BH1rjt6YUtTqUBVXaDhP6fFxDpUJjFLfobNjln9cXi3js3EHcMhT2e+3DuutQqTF0OSoLGg2/jSWp4P8JVBKsQ1619yV1B22biEK/9SMltYGj7fbRgtehhCm2LaTrprlva96cg7J6E+tUN+qOxRCv20dKk7apU5GTZqCN/RaSw6KYNVtIOtjWVkjqEaM7CMh3EYU87Eyc8DIurOK4Syo+3mvWrcVjhzCGSeSPjRqoCBkawj6vJbatGIe8wEqFRqlQ5HjdkOIdx6ZtEZalGW7JyUKfH96OsW9eh4qbxVnfd0mT1BK0da1S9/GvU+jDRWxrsGgUEB1sd5RhvjDdmgWkzilIA5L5G4c0HrIA6xAZrU0e0Rik/8+Ljf6iYK9KBxubWsUNDbRFisP+B6f8eQ/uhbJwDudj3PbbQYMO60KwjwPnxwtZSVx7N+pbFo77LZTZemxLWJAex2GfZ8Y/VMQYM1b/VExMiwopAZrYr0Fu5j1pmSLSaTmrEEkxLllB2z/NHEtSXDeDFOvfMmMiyLG+CWtgzGdPi+NA87qwGhjHKiieOGatyXD+FXRukvk6JOdmp+iqYKW0GatNk3GpwX1zIeK9HPWIxoMQohdhBYCFtRPd1lCrLHHOube97W2Qj2P//eYVr3jFKcucc67d9r/J7rvvPij7rd/6LcjfddddZXp6ehrKWG9xphoN/nzg+rIW5mKGx8PZfDba8cK6k1tvvRXyz33uc8v0k570JCjr68NnLzbe3KdWz+Kcc5/61KfK9MzMjDsf8DzZt29fmb7lllug7JWvfCXk63WjX11G7PmenK9W/W+pS/n7Ds/FG2+8EfJjY2OnPHel4rLUejE+Pl6meS18//vfD/nJSdIWm/GeZfg7cKkxIM4dZxLrS+dTQwghhBBCCCGEEEIIIYQ4TfRwXAghhBBCCCGEEEIIIUTPoYfjQgghhBBCCCGEEEIIIXqO03aOpyfmMT876NODg1AW9KMn2qhcXZST77uGLuXGJn/uFvLJtjbVIB8PGpfyZvS+VvrZh+udM1GMrrMwxPo647g92VRDXm7jVo6pvh1qa2jzEXp14wq1rWL85G10F1UdHtswPp1qQa7cRgOyC6aOrUWMA7vBc6p/3vLXDshbHEUYb6ONdhE7TimGHTNAQrpuGlBc+nyd+8bJdZbhuXMtH+M4RadaUJDLOvXntlvkta6SHNo46iNyYlfIvZ3YeJPnukreaOtdLup4z9HHYT9OTPhzqy30uLlZnAuDwz5OSQ37yTm8bpgaVzX51POIPGnG953nOJ477JQ1rrG0wPq5DPum46z7mfzTYUrH+uvGdGwU4LzOzWxuB7j0NdvYF9MHfEw708ehrK+J9S1SEwdyXofsrjYxcx2MZ04OxrTl88dPYDybx8mrGOE8qTf8tet1jMtiSutz3de5QvOY3x+RZ8aNR+M5p8US3qNAa0lBbv7QzPMOjaWQ+tXeJ6Cb0mseXBr7+6QBjjv+LLLvkwhzcjvSJ0Fq5m5OY5LXFvtOhU6w6HqBS9mbKFaeS3m8LMevLoQQ/49aDb/XWed0p4PfWbutK+y/PXToEOT/x//4H2X6L/7iL6Bsw4YNkD9y5EiZPnjwIJRNTU1B3nrFz9Qp/ljYNZU9xnyfiYmJMj06OgplfO5q/Byy7eF+WynY/37//fdD/v/7//6/Mv0rv/IrUHbTTTdB3o5Rji97548e9e9ZYvfz+WLOvEPqd3/3d6HsKU95CuSvvPLKMs317fY5z3OT/eoc/0sVjtE111wDeTt2ztdc5Ps0zDOz173udVB2+eWXQ/7ee++F/COPPFKmb7/9dig7fhyfI9g+13fC1Yf+clwIIYQQQgghhBBCCCFEz6GH40IIIYQQQgghhBBCCCF6Dj0cF0IIIYQQQgghhBBCCNFznL5zvIUO1mLe5/NmEw9u42UT44FNQ3TrJCG60GoNf93Kdnx2X91MfmTjs4rq6F8LI3b/em9XkNexvkVKWevWxvo1A4qD8csGBZYl5DwO+nwgqlWU41YivE+/kXYnCXrniph9w8a9XiGvbh+2NSr8tdjJvJiiq62IMC5947599QGMbxJj/YPAXyuL0OkUkG+4bfqxQ248F+LY6jeK+mqDvOctchOf8DGMW3jZjNzs8/M+FvUZimGNxkfFn5tH5Cam/27qgF8d+40dyKHxUVcCbMvwOF73sut9ICZDLAymsbGN2M+bah1jllXIW2ia06b+T07yiPv6F+xPJ9e2nWI5zbckxTUhM2OnSNjxT2PU/v9eyE5D8r8bH3iH1iHutyjzdezQkJwnN39sXffkm44c9nFi3esxjp1oANe3g5P+2IOHqBIzOHfbFSxfe5m/1g07sG8GIhwfnYofl1lA/uyI/fDm3Q3UFyHNazuCU1obCxrfeeTrkDqsA3v9zfBwYbSA183xPQSFmecVGqNch8LcNyLPeRbiubbtGfnrcxp3do3I9P/R4gKzGr2qvYSN/3Jck+q3i4cz7eNu1xG9R38/vpPLjqXZWXzX0HKc3jwmT5w4UabZC3333Xef9nWWynfDjnUe95yvmN+59Tr+xh0bG4P8/v37y/S2bdugLKJ3f9lnCmfzrgg+djnzmM+1buKvfe1rUNZq0Q/bM4TvubCA36vvueeeMv2//tf/gjLr7HYOvczr16+HMnZrP+1pTyvT//Zv/wZl8/P4njv25q8Udt7YseKcc7/xG78B+be//e1lemRkBMqqVXxfk41pu42/1x544AHI81y+VOE1ih36tnyp+bZSn418HbsmbNy4Ecpe/vKXd72WrbOdM84590u/9EuQt3OZx4cc5Bce/VIXQgghhBBCCCGEEEII0XPo4bgQQgghhBBCCCGEEEKInuP0tSpt3A7RahoFRIu2wZMmwWoH4oB0C7S1vWFUDe0+UjXQuYHZ2l6h7fRZhNsUMqNCKGjbe0rbKjLT1KyJ7T7eweu2jEahL8Fw1gvUoVRH/PajoTW4BWduBs+ttH1cKiEqCfIE4x3a6kd4nYJ0LS27xT/m7SQY7zVrUamw6Wqf7xvF/1eJY+zHjtkWErAmgbQUYepjmmW4lapRxbFUq/uY1gusX05jtJ349mQTeGyLNUGzvj3VOo2PPmxbnPj6VyOaF7TTpzDlYcrTDdtWjf1WtijCrYJsDFm7ybenQuqO9lE8OJq36h+8TkjaoMCM4SDBPmaFTMf0K183o/oGZo2g5cKRjcMVdgyT/iYhxUZkdD45jasgxHgHudEw0RrQX8G2bVrvz53uQ91JZ5a2+9mtgqTYKEjPkVcHy3QyOgplfRvWQr55t99yNrvvOJRx0DL6b87pg2vKdDVaB2VXrMf2pDW/NXMgwTg02hRvE+KMOz2iLbWm7XG2iGWky4nMWEoy2lZKMQyd1fmQkocVToWfR7zuBzSWitzHJaV5zGtWZBQtWDvn2nyu2Q4aOW2XuxhZKU2CEBZpMy5NVmqNOF9by8XqpNFonDLPSoKVYjl6luVglSXOnayhWLPGf2fdsGEDlK1bh99hn/nMZ5bpxUX8brl582bI7969u0xv2rQJyrZv3w55q2jh+i6HlZyXTaOt/fSnPw1lrClZKXgMWHXKP//zP0PZ/fffD/kXv/jFj5l2zrlR+s3zlKc8pUw///nPh7IPfvCDkLf9vJJj1K6xTVIE/9M//RPkX/Oa15TpH/3RH4WyZz3rWZC3dWRtysc//nHIs8roUoU/z2677TbIW/VIQr9FWYF0rrBzl+sQx6f9yNQ98YlPhPxHP/pRyP/Wb/1WmX7f+94HZTMzM5DXb47zj/5yXAghhBBCCCGEEEIIIUTPoYfjQgghhBBCCCGEEEIIIXoOPRwXQgghhBBCCCGEEEII0XOctkAnzNBH3el411VB/uakTe7ZhvFikTy5U5BHKPLHhuSETQr0a6WFr35OPllHjlij8HatAuuwMI91ODrr3VazUy0oO3AY77O44POjY+gY37ZmAPLjde9YG9mJbclyPLZZ8TGM2aWdkvd8zteh06G2zeJ9phf8uUUHy/pq6ICrbRiE/OBm73WPKjx08FqBqUZI/rV2jv3aMe3LaDg0htCnXYm9cy8mp3SR4X0aFV+nmRb240Ib6xvPeNfYPLUt6sO8URO7ooZjnVVzHVMcheSKL8jpbf+vKqCxT1M1qfpr5eNYlpGPupNYpxr9f1iO/uyo6sdHWEXveRFjPnfeQxeyc7yN/dYx47A1j3VoF7i2RA3jfye1XN6HbYsK6xxHCvq/v8C8s4D02K5OfvUrHufH+tw16Ds8Qpq/xQm//uUp14Kc44NDZXrD9Xjd8cvRLznxpUfKdHHsKJR1yEGW0/zL2r5fH6ljv53YhPN6YKPv8x278NjKINY/MB2dxxizkNb23KztBc35kPz1FXNuHvJagte1dcgKendAjOMjTP198wDXN+sCdw7fAJAH9L4Lh/nUDKCA65BjPrVti86Nz1MsTTdvn9y9Qlwa9NL7AWz7tIZdeqxdi++hqdX8b8xKhb5jd/A7ymoY+9bbzR7xl73sZafMsxu8rw/fKWZdwO02/h6enJyE/O23316mv/zlL0MZu6C3bt1apoeHh6GM420dyDz3zmYusk97ft5/l3/44Ye7HrtSdBs7HO8TJ05A/t577y3Tj3vc46Ds6quvhryN8ZOf/GQo+9znPnfK+3L9ztVYt7F3DsfPgQMHoGzXrl2Qt23bv38/lFkPvnPOLSz4942thnl7ruC2Pfroo5D/+te/Xqaf9KQnQdngIP5utWvLcubf2cR3OffhdxbYdyo459zv/u7vlumbbroJyl73utdB3jr/L+XxsZrQX44LIYQQQgghhBBCCCGE6Dn0cFwIIYQQQgghhBBCCCFEz6GH40IIIYQQQgghhBBCCCF6jtN2jrcK9G1lxiuekwKnTR7xOPN+sICdq+QVD4zbNSdfb5hjvmKczS2SHgfkcy6Mp3tiEsu+fc8C5A8+5J1lrYUmlC0cWIR8MOPPPbxmCMqmr0Cn8HVXeJ/v+Db0J20eQtfv1B4fs+axOYeF6DnqZMYpvYiOrNYiuraL+ZkynTm8Z1al4VDBmCYV7+wNQvT3spM+Nn5464Z3zrkWHdtsmTER47GVCnqY88CXz4foDE4CHKNV4yBvjLJnnrzcc358VBfQoVZk6LtzgY/bPI0zbmtoHPpxgX0Rka8qM578nPz6YUhuq5q/T53KMvJeRxV/36KJ7eYFIBjy464a8/zDc43O2XVI+d/uYFtTM8XSaSzLC+pHU/8gpxpWqa3Gp56QsD4PyEtn6hum5JWnvli3xb8D4Lrnb4WyvnF8t8C++/1cXjyM60NS4HXHrhgp09f8J1wfIrysOzLs/4EU9C5YxDUha2MMc7Pezd+J5y7cj2tPZYNvay3oh7LhJ+CNYzPWMqpURp8DoXm3Q4fWizzBuET2MyPD8ZHR+yRi41kMaQSnGbnNzRgo6Dq5owoXPmYVOjZz6BF3pg6RQwdjQGsAVkn/H30uOVMf31LnWbcgewblABRidXK+5upqWBP4nnKQX/zE9HvIerut83q1wJ7djRs3lunf/u3fhrIXvehFkLc+dW4bu7XtfThGPO6vvPLKMv0v//IvUPa3f/u3kN+2bVuZ/u7v/m4osz5y59CDzu0+G3geHz58uEzPzMx0PfZMWcqlXK367+/sTmYv/siI/42zd+9eKGN3vPWVT09PQxnfp2XeG8bjIU3x97L1k7MjPcv4/XSnhu/TbPrnQewN37dvH+RtzPh9AHxdruOZcjZr/oX4zOL3A/zcz/1cmX7LW94CZTfffDPk7TsAeA1gbNu4/7uN/aXWluXAa4Rd71784hdD2Yc+9CHIf/KTnyzTPNbFuUG/1IUQQgghhBBCCCGEEEL0HHo4LoQQQgghhBBCCCGEEKLnOH2tygL+Kb+1JmQBllVCvGwc+vKMyshK4fLcb0XJHCpNKgGe2zE7HHJSuTjaOjFjbCOP3I3ajIc/fwjyc3uOlumUtsPEc1OYb/s6tvajAuTIUdS1jDS9RqGflArrt+K2/Urdqw4ma+hbSINZyLtZr1gIYtq+QwqTxDhwEtq6FtVwW01lgPoxMfUocFsQKwpyq7xpY9nUCbzvYtPXsW8Uy+YreJ/pGX+tyQXs4z6q/3Dux11QQYVMvZ+2S7X8fWMyKAQJXnem46977DjGaIFUHmsavnxdA+9ZJLRV0GiEYtJxtENSpZgtOhHpcMIhrENUN8ficHBJMQD5tGriX6VtuhHOxywwF8sxaGGHtmgZzYdbxLa0aDtXbMZOEeHaUl+kzjF9k3G/0e65woz9nHZHxaSm6Tdx2LZjGMoGSYG05XK//W/mMOpOQor3miv8sRvWooZpfmEC6/Q4vz1x7qEjUBY0cXtlJ1+gcr/lLJjArWvhzBTk0+ZomV44OAZlxdW4DTKMfVBD8mnFpL1qRT6mRY66mTxGzUon8GMiJoVJhdaazPlzY1L98Cdaaq9LRq+sINVL4OuY5biWBwV1ZGy1Yjh20hjjkkJ9sd/E2XG+toLa+yy1Bfl0ryOEOL+slP5EyhJxPhgfH4f88PBwme7vRwXe7Cz+LrwQnzVWJeGcc6997WvL9Pd+7/dCWaOB37GsdmA5n7GsPuDr2pht2bIFyv793/8d8kePmt/+pC942cteBvl63f8OOBvFDfcT3/fWW28t01brsRRLrVHd9BGsNHnGM55Rpv/zf/7PXY+1dXz00Ueh7NAhfNbyta99rUyzlmT9+vWQ37Rp02PWxznnrr32Wsjb9nzsYx+Dsr/6q786ZX2XM2dYldJN7bLUdVfqc2g586bbPc+XfozzExP+N/D73vc+KGMVjVUgXXHFFVDG88T2zfHjx6HMaoA4PziIClJWo5zN9wB7bpLgb95f+IVfgPznPve5Mi2tyvI5k37SX44LIYQQQgghhBBCCCGE6Dn0cFwIIYQQQgghhBBCCCFEz6GH40IIIYQQQgghhBBCCCF6jtN2jqctdBCFxgscxehziaroxMmMeDcnf2y1g8/ns8B4l8njlQd4bG49oB10z84vYv7oAe8g2n/vFJQ1H92P1z16rExHdF2Xo8uo6Xx5nqKDav4AeoIP1Xy4129El++mNegfXrPOt63IKlDWWiAf9YR3WwVDw1gH0hPZfquSy7y+lVxtu9BHHfb7/ghS8gAH7Hz35emJFhQtPIT+4Vbh65/U0b00kWFbH77Hy+NP3I99EfVhX23a7OO9tUFO6ZB86wM+xg1USrsWue4fOTJVpu9+GNvSnML7jI/5ft1xHToCd27G6w5WrROb6ucwb53kITn/mzU8Nqj5Pm+0sX5hivGuJb48C8mhRnO3Y7ziOXaxc3M4V4umua7DmAVFQnnfFwW57IOMlqzM3ycnoXrE9TXXClKcU1mCYyc07zCoJRiH9SPoVRyr+ba1tuMcykP2Ifp53+i0oawZUp3MkpBWsH5pgecGDvPVzPj2HfZ5Qn74ou3jlOUYw4zmeWBjSEL1lH1yxkEekIM+chjTyKxxIenu+L0UQeHPLej/dwOSyVtnfSfCsrjAQRuZc3N6d0BB8y8w9c9DGr85jX0Tl7Q4c0edkLP7Yma1+Z3PZizJOX1poH4Uq5m5OXyHjfXusvP4QsAO3quuugry1k/NjvTl+Hu7lbHvm4+17urt27dD2cAA/sZ9+OGHy/Q///M/Q9nWrVshbx3q7OxezrrCLmV2x//jP/5jmV6qz+19OS6c7+vzv0XY2fzGN74R8s9+9rPL9OjoqOtGZt73Zj3hzjn3+c9/HvL/8R//Uaanp6eh7MYbb4S8jfczn/lMKLNtYWzdnXPupptugvwv/uIvlumZmTN/JxB/n8jovXeny3I84jzuKhX6DWnqwK7qM60fw/Vjf7adYzzf7PsAnEPn/9133w1lb3jDGyBv1xN+1wHHwbadHfnPec5zIG/fLcDjiteslYKvy3HhfhbnHv3luBBCCCGEEEIIIYQQQoieQw/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc4bZFNGJLL1Xh2wyo+Yw8juqxxEqEJCJ2wzjlntU0p+2QLctwaZWxrHv2x05Po8Tpxn/dZ5bvRKxXNLGC+7V3WQcbeZfJKGZ9vlqH31y3ifZrHBsv07KEpLJtDH9vgsPc2DYyjG2p2hvzeC16SHQ1ihPsH0P+UTXqf2dB6LFt3A7qYtm4bhnzd9GtMcegE2De5Kc7a7ILGvuqv+Ri3O9i2+x8+DvndX/BOqs4jGN8iQu/UwhbvRotvGIGyK9eRJ3/AVHgQ63C0hePj2w+dKNP7b5+EsvAY+rQX+3wdjh7YjGXPQHfb9bt8nQaq5Dwmd3VhncikTg0j8sEbf3aY4HXiCMesVSJ3MoxDq4k3mpvxY615nNzgszh3EzMgInKSJeSDb1T9/CvIi5/HNP9C68vG60y3sf4Hjfe8tYDzukoL09YhH6e+GOdffxXjmxh/fY1c8XkF2xobD3pAbUlz8hZOT/jr0rsP5mj+BeRXz5zPhwWOyYy91xXjKawVVITjoxP49sQFrfM5HhuaOmeOvXlY38KMjxbHkJx2VfM5UJD3PKN5YoXlrBjOKGaxyebk2y/ofRcuN450ihE1zYHqUY7bZbPaPONcn4vBW3wh6rhS/baS8V6NdRJCiMeCfdQTE/47YavFL/o5/7Bf2HqhnXNu7dq1ZfpsHOPd1m0+lu9TM78hNm/G32BPfvKTIW/jyw7sT37yk5C//PLLyzS71rs5sLkt7Bh/3/veB/k777yzTLM3ejmfQxyXwUH/POKHf/iHoey7v/u7IW/9zkt5l20dxsfHocyOB+ew7dy2q6++GvJPfepTH7M+S9WJXes/9EM/BHnruf7VX/1VKOM6nSuW02/Wr83x5flox/NSY2c5341sTEdG8NnKq171KsjffPPNZfqaa67pes9bb721TP/1X/81lN1xxx2Qn5qaKtNL+dNtfY8fx2dKY2NjkH/+859/yuuezbsFusFxOHIE31fYbtOzRXHO0V+OCyGEEEIIIYQQQgghhOg59HBcCCGEEEIIIYQQQgghRM9x2lqVvkHcXhCN+e3seT9tz0hQbxEa70MW4LF5gdtACufvE/BOiRT/obXo89NTuO1g+ijlH5zz9ziKmoxqB7ebhLnZip/jPfOCKmW2Q7DWw9Gxs7nXRfzHCbzn9D7MPzXx2/bX1vD/MDZsxq12M3W/PWphDq8Tn0Atxfy0v+74Buz+8cehVmVwhLYuGVVDUFDMQurXyLc96iONymUYl46p/9EOtu3YN49CfuG+vWW6OYtalaDA7VPR3FSZfrSxBcou24BbgUaHfJxmSGmy5xBurzt4j9+W03l0H5Qli03IN6d9HdrzJ6DswfbjID8+6uvYvxGKTtKqpM6Pb9Y4FBFt9cl9XFKHGop2zOoi36+LtGtz/jjOqdndvh8Xj+B1KhXaojXsKznYh2OybxiPrdZ9W9u1OpTxHOtkvj1HZjEQd+5GncihPf7Y4Bj2U9hoQP7oE73y5pod1DasvqtUfRzyCo7BkJdYO/Qz2v6JS6FLEn+tmOJZSbC+rQ72TWrikjssy6u45bO+xjdoaAwrESRYx47RYFVyLMsd6Z7sWKOiKKY45XZcYltD1rcYOVca4Vqe0+dLZFQ1BfVFSFqV1HzghKSJKljxlfs4hKSpSUnBYu05oZN+oZdZznbxlbxPr3K+lDz2Por92bNS/aa+EBcze/bsgbzVJiwu4vfb87XW2Tm1YcMGKHvuc58L+Yb5Xr3UXFypucrXsTEbHh6Gsu/4ju+AvNUofPrTn4Yy7ovf+q3fKtOveMUroMyqGZxDHQPrcD7wgQ9A/rd/+7chb/t5qT625azk4fyaNf73/k033QRlrIWxao+l+skeW6/j7zerouH78D2f+MQnQn5gYOAx73E6dbKweuQnf/Iny/Sf/MmfQNmBAwcgzzHshq0T15dVL7a8mxbIOecuu+yyMn399ddD2dzcHORvu+22Mn026wPXyeqJ3vrWt0LZi170IsizAqcbr3zlK8v0M57xDCh74QtfCPndu3eX6aX0N7actSqHDx8+5Xnna03tdPD35j/8wz9AfiltjFh59JfjQgghhBBCCCGEEEIIIXoOPRwXQgghhBBCCCGEEEII0XPo4bgQQgghhBBCCCGEEEKInuO0nePVrZivrfPupbAffU+dBD2wsfOOp4D8rBEpffLA+2Q7OXqE2uQcn1/wJ7en8Tn/wiweuzjjnT7pPDp4XU6OMhOW3JGDl3y4sXHcxuSadRl6hBbmveto8T68Z06C54X5nWX6pl0DULatjvcZ2OxjNtTCdvdvxLZ2Ov7YygB6r/ooX7Af3np2I/Lq0rGZcVTlA+hHjgbQoZVHvk5ze9CZNb9vFo+d9THMmvN43QDr3zRVmjyEjvHJbBzywwO+DtNz6KPeuxvz2UMTvj7zE1DWYfVV6s8NaTxM3o8urgMPe9H41rEKlPUF2I9F6OdfO6R+y3HMdjIfiHka623KV00MizY6DdsUl6bx5rdQ/+6SIbxupeLrlIyQG34El6Gg5vNRgG1ZpDgca/o6fPNBXHce/gL64PM93i0WzmPb8hjH6LeOeTde6ztQAB9fjn2zbtjHJXJYFtD/P7aM17pG3rn+BOOw6TLv6zu+cRTKKidoXrfISW/ceEEV21bfvg7ylz/Jz4XNG3Bu1shf3zJe/zQi/15Gi7kZS6S2d0WGccrsbegyKcWwsO8+yGns4KnkA8e1MXQ8Wf1YyzIcd2GAa01hqpSfVAc8Nw2sn9yJS4xuXsLleFbPxm+4GtzKF8Lv7dzqaLs4O87V2OmlsXIpt61XmZjA3xfWncue3fO1/lr/8Pbt26Fs27ZtkO82Ji/E+zfYgb11Kz7YeN7znlemT5zAd0TdeuutkL/nnnvK9B/90R9B2V/91V9BfuNG/xvi7rvvhjLOszf6TOPC57G32HrQx8fx9/DZYOPNbu3RUfwdYx3ktj7OodfauZO91ytRP+fQdf5rv/ZrUPbzP//zkGdfvKVbW6+66iooY1e49YrzuOP62jG7du1aKLvrrrsgb8fAcsYR37NaxXd9/fAP/3CZ/r7v+z4oY8e4vdZSn1HWB79p0yYo+8M//EPIv+Y1rynTHLNubeV189ChQ5C3LvOrr74aytg5z+3p1j6uk63Ho48+CmXsHF/KqS5WHv3luBBCCCGEEEIIIYQQQoieQw/HhRBCCCGEEEIIIYQQQvQcejguhBBCCCGEEEIIIYQQouc4bef4wOXooh00qqOgiq6laoHP3K1dtk2P48MMHT65cdyG5CePm3if9oJ3aLWbeJ1wEY8NTFOLFh5boIrLFUYMm+d8HWxAAK5tvFCQoqe2OuP92ZXFaSirzKC0+UTbX/fbIfqq+q9BX+/ggI9T0kDf7SApsjrW/1RDj1GS4MEhuXOTwMewTbL4kHzrFdN11YT86uRlyjNfj/kjGLN0El1Seepd0VGGjumAfM+F89dqkS97KkWHVrvq2zZzHNu2sB/9zsGUv27cIRd4jl5xl3rfc5hhHdKJScgffeRYmW5evx7K+isY3yz2MVykedJewH7db7Ttjx7DY6dmcS7UzTRfG2NZP123MJ0cD9B4GKZ3C6zxcyNE/buLyaFfmPUkJAF11sE5tnfKN+6BO49CWfOePZDPp72/sZLSukO26vAOP7b2z2Edqt+Jfr6nPcHXd2gAXeZZwP41c4+E1gtq64ZN3o+493p0yy0cRxe/y9iF5vOVbWNQ9rj/hF7IK67ZUKb7qN/imNxtZm3MAnJ4k/s+MOUF6cnDDNeawvgGI4cHJwHO6zw055JfPznpHRZ+PhbsPSffvr1rTv70IGSPuPGpU+PyENeAvJAvbjmcL3fq+WA5zmM5g8+eS2nsiJXnfDnIV+pdAsu5j7j0mJ+n30PGPXuh1jrrBX7mM58JZQMD+H6s5fiGzxXWVc0xYwe5dabffPPNUHb8+HHI33///WWavcXf+ta3IG/bzv5gdoGvVL8udZ0NG/z3fo7DSvUVe8LZR/2kJz2pTLOzm8eSvdZKjiXrOn/2s58NZWvWrIH8sWP+NzrXl+fCL//yL5fpJzzhCVBmPefOocu63cZnCgsL+JzDMj2Nz5H43I997GOnPHc578rZsmUL5H/gB36gTHdzjD9Wvhu2j62H3TnnnvGMZ0D+JS95SZl+73vfC2XdHN0833jufvKTnyzTPB7Yi1+p4G/Tbl78Tgd/Fx4+7N+B9lM/9VNQZseZc/peeyHQX44LIYQQQgghhBBCCCGE6Dn0cFwIIYQQQgghhBBCCCFEz6GH40IIIYQQQgghhBBCCCF6jtN2jlfHGpCv9w2W6YDcxBXyyxbG55uR+LUToEsnzbzPLHDoT8rIRRu0vVeow17gFPNDxo88i/pYFyySs9l4gzqolXJJjo4haxEqsjkoi3JyCue+Tgm5cuMpdMu5hx8uk8fGB6Ho+LatkB/sNy4u8nsnIXZxHpoYkjc8jbC+7P7NQlt2kjwe72Pd8Tk7p8ifbU6dm8Zjm03yXBs3F4uMowKdTjbeHRLLNyO8T5GYa4UUwxb1o/U7FzhA4oI96L5OkcP6hSk6yAPjFgtO8uBjnVLTtuNT6G/e9zB6r+9/0KcXHyBX9RSO2bmqd8/N0ZyPUb/mqnVfh42jWOH+jeR/N9rrKureT/J0BaZfW7S2tGk8HD7k77t4Pzrcgxl0BEYL3ut/0twkZ35hPPhz96D7bM8wHnv1Du8gH67hfEtYgm38a52Q3n1A82Swz8fw2mvRv9eXojf82APYjzXz7oHN12DH7dyF11rT7/s8qtNLCsixFkS+zjF9fPB60TYu/DTkeONtwsCsh0GLjqV1KTu19zyjsZQbj3tBN00c+2dN2yJqNx1r75KTa90V+LkVmc+/IJR/XAixfM6Vd/JCuYBXg0fT1uF8+Mf5nss9V/QW7M5dDc5xSzcf8kqyHD9yN/hY/u1hPcfbt2+HshtvvBHyH/jAB8p0q4XfWdlrbLlQ/Wa91s6hM5tdyucK66t3zrkrrrjilMey09tyrt4dwZ7z4eFhyM/N+d9ZV155JZS97W1vg/xVV/l3xXVrC8Me60YDf4fbtnN92Xu+bt26Ms3O/G5jlONpXfzOObdx48ZTHrtS8HXZ0/4TP/ETZfrv/u7voGx2Fp9zdJtzHIdHHnmkTH/961+Hsuuuuw7yPD7sesLzzV7XOefe+MY3luk77rija53E+Ud/OS6EEEIIIYQQQgghhBCi59DDcSGEEEIIIYQQQgghhBA9x2nvpWkkuNUjN1s/InrEntEWhtj5rWAJbTUogpSO9dtPihSPzR1uTWk7r4+okFIjJW1GY63fmjKyEbdnzIe4JWphwTcopm1jGTV2Pvfn5rRjvmCNRhHbQjy2hW2Np3zbgkMTUNac3QT5yhqjVYmx3ay8qRsNQacgZUlGW5VialDm4x8FtEWLtBRNq2CJsazq8Lot0885aQcyUr+EUEccD2mAeWsiiWlXU0wKmTj0aon+Osaw3sCTT5gxkJPuJM7RGVIxdQwdzqF6guNw46Av76d2sz4ibfv8kb0Ys4f+DfUi2cNeJ1I7NgVlIW0HjCK/7S0/iFu2pvuwbeGwj9nC4/qhLB/H+q8x2owkxK11CfVbahQ4eYrxzUiXtHDAz5NwagrKXIf0FmarEtmcTq6Dmcz5/AyU5Ydxy1Zr3mtLigyvU1BbU9OPrMpxtIaFZg0bGh+Dss03omppw3as42C/v+8Q6XEGMOuixIyfhD4SYtY0+bGf8tYvWhNiZ7RBrEbJUVUDHxk0hwLqmyL3dQwDUilRTAs77mg5O+lcsy5x1/CCnRv1T05jqUPqsCj0YzR32i63FGejIRDiYuZCjPVztUVdPDaKr7iYsWoX1g4cOXIE8v39/ncBKzVWA93moq27c87t3LnzlOeyBuFi+M5i1RNLfQac6ZrF57FexOZZEcJ6kXO1btq2T9FvSFaaWL3Iz//8z0PZrl27IG/btpy6s+qH893KeIy+6lWvKtNvfetboYyVTd2uu2YNqjm7zeVz1U+s/tm61euFBwfx97DV3zjXXZ9WJc/r+vXry3Sng78RmcVFVNhalcrDRo3snHO/8iu/Anm7drKW6WJYPy519JfjQgghhBBCCCGEEEIIIXoOPRwXQgghhBBCCCGEEEII0XPo4bgQQgghhBBCCCGEEEKInuO0neM5+Vld4B05lRwv08nQ6dMyWTTNOtdmt5Xzzp6CvOEuRA9PpeLz9QEsS6voRKr0eSdRTr7pIsF8e7+/bzXn/z9Ar26n48+d75CHKUOPUGg8ta7A61TIiV0tjKu604SyJEOXsrNx4h4ld1FmfLgFiZfzjNpWI983eNGxrCB/eWT6Nc6xH/MAK1mLfQwH+mk89NOIMS6svOCxQ36z0N8nqWHfjFLbEhP/oUF0UK3Zjj6ro/d4N1rRQreVy7Ejc5MPYnKOD6FjbWiDd5AHCbWNxMbzC74vju1F71V79zTkoyk/fvopRmGEfZEY/9Z0hn3afwzbmk37vunMofN6f7oB8iP9PoZ9/TinaPnAfiwwngG77hf82Kl2sL5sVAthctC7D8hfn5j3A4S07vSFOP9C+96EAPuY519h5k1G6+RsBwOxf87fZ89h8t5P0PrWwTF6hRnCaxJcu/sCPLdjHPoRO/R5vTNxoSUM2uacc7nJ8/sAKhH2TjX373bIApx/RU6eblOHPKS1nD4yAltfep8B93lo6huQGzwtMGb2XRkhzakqO9Kd78eM3rkhlsY6Ai92F5/8zmK1ozG6spzNmqXY9zbsaO7mCT5fWL82O8ff/OY3Q/4P//APy/SWLVugjB3Cq2Gs2zos5cvOL7LvclzfBx98sEzPz89DGfu/u8VlOfD43b9/f5nm8cHHsoP8TOH1uN32389vueWWrnXYsWNHmb7pppugbKXG83LO4zFZq+Hzkte+9rVl+h//8R+h7Ctf+Qrk7bzmGO3duxfy1sXNx56r7w98Xdv2vj58f1s3Tzs7xm+44QbIW5f8ddddB2UcX26bdZ0/8MADUMZ5O+4u9t81lyL6y3EhhBBCCCGEEEIIIYQQPYcejgshhBBCCCGEEEIIIYToOfRwXAghhBBCCCGEEEIIIUTPcdrO8SIlJ07b+6vyiBzCObq2q8Zx2wrR3ZqRQ9g6bnN+dp9gvm/QV589r3mGXqHc+Kc75DxuksM7WPCeqSJGx9Ac+W/TGe9z7rTJ15vhfQqoI7meY6xvrdEo04NjdSgjVbVzkfW043WLDH3DqfHjxlkDyvJsAfIuoz43zclScv2yZ8rUqRNghfOAnOlVf+74Gurj8X7IL+zx4ydYxOsk5CqOjVtqYC3WYXwU4xQbx/fwANZhy9U4Zg/cO1KmpxbQB59GWKdW5M8dWTcEZZufgp7o9dtN/WMcSwukXT4072N25BD64vI5rNNQ23i5Y5wn7OqKjDdtnm5aI7/zYsvP8/ZR9J4vfBPr/8ioH2tjw9gX62LuC1PfANeSqIrjbGyNb8/hBq0t0zQXIn9uRP49fr1BbPzwdYrR2i04b4YGfX0z8lqzbq1T+GMnm3jsHYew/ru/4cdS8O1JvM7kFOQzWv+C6/zY2jSK46yvhnGpZNYniF4/nquRWf/SgNcA8pPb5lEgcnr7RNOURzFeN6LXXTjjAycVv2MNZGLqmJEbvCCvuPX6t9gdH9P7I4woPwsxnlVa9zuFmfcB9qNYHuz4u9hcfavBq3opc6HGx5n268Uwfm0dNX5Pj4uhX8Xqp9HA75p2/i0s4O+18+XAtmPbOnadc+6zn/3sKc97+9vfDvl169ZB3rqAeZ1ZzrpzrhzHDz/8MOSbzeYpj70YmJiYKNMHDx6Esg0bNvDhZwSPyelpfB/W7bffXqatz9s556699tpTXvds4m1dz845t3v37jL9mc98Bsq4/k960pPK9Pj4+BnXYaVYyos/PDxcpn/v934Pyl784hdD/vjx42Wand1HjhyB/NTU1GPe47HqsFJwnx89evSUZeynr9f9M7RnPetZUMbvSbCecY4Dt43vmyT+N/wTnvAEKONrXYxrRi+hvxwXQgghhBBCCCGEEEII0XPo4bgQQgghhBBCCCGEEEKInuO0tSqLs7gFIDWP1WsJbjWokhLCVfy5BW3TL3J+Pu/34icFXqcdo4agbrQqSR9t5UhRx5GZ7THr6ridPnNjkJ/K/LHZBNY3b+Ie/+ig39oWdGahLMgxvGHut1xECaolaoOo3Kht9+Wbr1kPZSOjqCRwsa9TjWKW8v9/mDiEBcaz7bBtnYB0F+ZSBakPQodKkyD39Ugj0hcUqI+Ije5i/XpUyGy9BpUQe474WCweQEVBFGJcBse9/mTnlXid4WFUZVgtT1TFOGzegn1z7VP8dsB9tJNxAYeAS/p9W3fcgNfZ+STculbf6OvUoh03EzPYV7v3+pgu7MXtlYOLeOyA2epTq9JcHcBxaE06UdqhMmxsuOjzlQL7uH0ct1vOf9P31SHWktBYGhzwY4e1RjXSXezc6XUXR7ZjfA9NzEDepV79EhU0tgO8TzTsx874Vbh9bsf1uLY0qkYbRXPe8Xxc8AH+xhHsp3u+eBjy+R2PlOn6ieNQlnZQndOm+h9pbPHpp+LYHx3DOkZGBVOjcdchXUucmzqHpDBgDZNZe4oI53yU4XWj0I+1lDRdQUqfJ2Z95g+wLMI6xJk/Ig8w3o7aZpU3RUCaKIdjPzPjJ6I+DmgdDQLfnkj/H72inM0Wam1rXFlWo/LmXNWpV/UiJ+nzLvI4rHYdzqUWb7E8rrrqKshblcqBAweg7NixY5BPU/q+cw7g8dlqoX7ulltuKdM/8iM/AmX/5b/8F8hb3YFVrDh3sibhXM0Dq9FgZcx73vMeyFutysWIHUvctne84x2QZ71PN+yYYPXPpz/9acjfdtttZfob3/gGlK1duxby3/3d312mB+h3a7fxwGNyz549kH/3u99dplkvw+qU5zznOWXaKjQeqw4X4rsGHxsbZek111wDZa985Ssh/3/+z/8p0zy2OYaPPOJ/m7IeqZuKZDltyTJ8LnDixAnI/+Vf/mWZ5rlaq+Hv4Sc+8Yll+k1vehOUsb7HxozbwnSL99atW6GMx5Jdr1fDd2WB6Je6EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Bx6OC6EEEIIIYQQQgghhBCi5zht5/gR1OG64qh3EvVV0LszPIKuoOFh7xyqoSrKheQRz0Pv/ApC8seSA9t6bENy9oRt8q2ZKtUH8NjRreT/Nt6x9Bi2pTY7DfnJmvdGL84uQlmdvLqh8RFHDbxnfRv6tTY/xTuP1+8agbJaFV1XVouUO+yLkPOhr0MnY+c4uaICcv8Wvs8zVMK5kHzDcdPkc4qDQ39VYNxdawfRbfbkJ2FcRo3f98D9GMMKuYq37fSu5W2PH4ay/j5yTld8HYoA6zc0hi7rq5/m77N5A7qtmrPoGw77vZ96/SZ0Vcdr0HueG0/7zCTG8+AedN9P3Okd1NGBKShDO59zQdXHNKjj2CmqGLOWkZ0vpnjPoINzIQp9v4YV7OMK+Z2j4949d+xe9NCNj2KdasYvSEPHVei/89at9THd9ngcK80DKIDvHDQXy9EhHdG4W3udd9tffdMaKNu0DcddlPr65/Ruhg69Y+HRCb9GPPjv6MyPv3IvXvfAo6eqrgty6osAJ2TrmM8vzqLrrBNivCuBz7Pvm53pmSnn9TilPreRqEQYh5jW9qgwo7aFjS3IdV9Efn6GHWx3XrD3z8cpCzFmeYgzJczM50mAx8bkluuY6seO3ktxktvc15HfQ3E+OBufnRy3Z4fit/KcTUztuRfKG30pcbE5sVd7/YSwvPCFL4T84x//+DL9wAMPQNnf/d3fQf4rX/lKmT4f/vHHYnHRf9/98pe/DGXs/v2Jn/iJMv2iF70IyjZt2gR56/ZdyfeOWNfyZz7zGSi76667IM9O5IsNW38eO9/zPd8D+e/7vu8r00t5mOfn58v0HXfcAWW/8zu/A/lDhw6V6YcffhjK7rnnHsjbufCa17wGykZG8BmJdZ1/7Wtfg7KPfexjkD9y5EiZ5vHwhCc8AfKXXXZZmeY4dPss5LJu3wmWGs/LGe+2jn19+LzBzjfnnPvCF75Qpnfv3g1l7PT+7Gc/W6bZ2d3t/QBLxcw6//kdCh/84Ach/7nPfa5M8/q2ZcsWyL/iFa8o07t27YIyu5ZwHZe7tthz2dPPzvd778Xf+2J1ob8cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9BynLUCd2oue4IpxK3diLEt2oAe237jBC1Q0uyxB5xDYcAt8dl9xeN1OZHyyKcqJc/LqusB4a3P0Ew3V0CsUr/NO4WaVjsWmurZxLX9zajPWYT/6yRPjUxrdOAxllz0ZfclbrvUu8/4hbEtCMSuM+7kVkI+cfL25aftCgW3LKC61nPzZpj/iAuNdkJspTPyxUUp+rRCPtc1JQuzjreuHIT/4DH/dy65Ch3dIjvShNX6w9Q0PQlm1gl7xwpxrXfbOOVcLMYbBen/f2igO6Dxnl5hvzwD531NyIB+fML6t+3CgTXwL/dm1fd7rNkRu+0YFY9g0dUpb2McJuZVbxh3fN0Ce6BGMQ7zgY5ZRn3YC9I7lkV9qFh49AWWTG7D+fXXveO+naVylFate9ffdej2Nh8UNkJ/5ph8DnRbWd2QDnrvxRn/susuxj+sJxqG54PPteRyDqcP3EDyyx/dr+z/2Q1kweRTy8E6ADOdilGP9A3KFF8b1uNDC+Eb0f6Ix2MHJFR/Qgp37c/ndAXmKnZMFvs5xhPXNc3qfgXGQB7RmZXwfs9659jyUFeS+b5l5HRbkwnOndgRm9N/GIX0WRbl3Uxa07mQBrY0m3Y4vXf+w+L/Ia/x/6RaHpTzc5yOG5+oel7JjfCls25fjTr0YYnYx1FFcWnzXd30X5Hfu3Fmmn/a0p0HZlVdeCXnrFN67dy+U5fwSm3OEnTPsBWav8R/8wR+U6TvvvBPKfvM3fxPya9f638vsOD7d+jjnXLuNv3HsfX/t134Nyti7fLGvB7b+1hPunHO/9Eu/BPlHH/XvP3riE58IZbYvnHPuIx/5SJl+z3veA2WHD+PL67p52zsd/M3z8Y9/vEzfcsstULZmDb4Xyvar9Y87d/I4jCL/fZ3b9oIXvADy7O3uho1vN7c214mPTRL8EWzru5T//VTnOXeyl/t5z3temf7Lv/zLrvX94he/WKb5fQDW7+0curerVXqGR3189Kj/Dfznf/7nUPaJT3wC8nY+st/7uc99LuRvuummMt3Nib6ScLzZzW7d9xf7+wsuRfSX40IIIYQQQgghhBBCCCF6Dj0cF0IIIYQQQgghhBBCCNFznLZWZe4Abimqz/otA7UIL5M2cOtJPu7LW8MDUJZktN0+tlvbcctFTHqOyCggXIbbPgpShOSF35oSoLzFVSLc0pAbz0engtetxlj/XQP+WrUaXnfygSHI11p+e8/YFeuhbPN1I5DvG/XXqtawDiFpVWwIgww1Dm3aohMaVUPYxK1UC6QvqJFbIDdbeFJSKKSkSQiN+iCMsY8j7ivTjynpIWoVjOmI0az0rcG+iHPsx8BsR6pFpPko8NjUKCDIduLIIOMqRt2QkE6kyCkuRceksW3z83jsxAHfd8fvPY7XOYr9OlL1Mausw/nWmcM6zZ3wep+A6lcjb8mabb5t/TtIUxKhYqOzz7dndgqDtrjQhHyr5fs8mcF5PfcoKmPmBv2xjTpulwqovu3IX2toHdYvegZu92pvMdvrFnAM1odxfIyanYK1mDQ1i5hvH/Ntnz9B21UL3NLX2e/X0WIKy1qsMTJrWEZjuyiwj6sFaVeavh6tGVpjmxjTzKwvcUTKJtrulcV+fASkEMppXgdGP1MlVRGfmxqVSpjgGI1oG6TrmPUixm2xKW3bS5yvf0zrWUoxtEtl2KFJT1qYyCipYlow2nkdjzV9V0svLuXGcjQJvYrisnwutZhd7FvsLzQ8HlYqnpfaOBO9xbp16yBv9QGsOrDqAOece+lLX1qm3/Wud0FZs4nfz88HPKdbLfy9aZUbn/zkJ6GMFSxWs/LkJz8ZylhnYGHFxre//W3I/9zP/VyZPnDgAJSdjfrgbBRj5wOuw759+yD/pje9qUwPDODvbo6LHVuszViOzofrZDUa3I88nu19WEvC+csvv7xMv+QlL4EyVhXZsbUcbRjDcbBtm5mZgbKhIXyONDjolZ/cluV83rHi5NnPfnaZ/vu//3som5qagrydj3/9138NZYuL+Kzi6U9/+inr+81vfhPyH/3oR8v0Qw89BGXT06gptnF4znOeA2Xf//3fD/mNGzeWaV4fVvI7gr0W3+fxj3885LutU+LCo78cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Byn7RzvHEHva9A0zuMqOntaM+gVcgveSRWSlzYnB3J/6o9tUu06AXmiW/6+C030UwUd8t8670GqkD+2Rsqh0Nx3jhzYQYTX7asPl+ltu9ANNTaC7qiKcXr3jeGxfWPY2MRogdMY61B16NuK2sYFXmA/xfT/H4vGSb7YPAFlzRT9YEMD6M7NnXU6U7xzdP8axbQj1bYLyT8cmSoHDuvQofjbbH+Bzqwwwb7JAu90ih3GpeOw0wPjnY/JRdwJ2MPs/dQhKdQ6bTy2Yxq/2MY+XjiOxy4c8d60+Unyp5OjLBrxbWfXc2sanV/OnNvqYFlfHWM4vmu0TFe3kGPvONZ/fsD0FWnzKjV0eM/PmvFBc6iYR1/c4qRPL0zhdV2MYzKo+utWUhxX9QJd7PGAr1NUwfGQDNE8r/hjU3JVz89hY6eO+fqnx7At1k/vnHNV4wKvUcya1MeFCWpE4zWi9yZgVJwLjee6g0p311nE9rTNAhhn9D4Del9AGPr2sY8850GQ+7HVpjlViXGeB7kpJx+5C+kdEWY+tmroxQ9orYwKv+amOTousxT7qm3W0ZjWqCRAn719Z0GL2h3Rex8K8/4FWp6FW9q5ea68wfIRC3Hu4Hm9nPm2HAf5Ss3j1eD+FcLS14ffYa2nlp21PH6/93u/t0y/5z3vgTL2fZ/p2Oc6dMuzf5qxHubZWfzS+vWvfx3yP/uzP1umX/SiF0EZO8jrdf/t+B//8R+h7MMf/jDkJyYmTru+lzLsxLb5EydO8OHA+VhH2Ze9Zs0ayPf3+98FGzZsgLL16/F9b9bVz2OH77Mc57iFY8L5+Xn//jd2bY+NjUH+sssuK9PWu831W6qOXAf7fgP7bgPnTvZ9t9v+d/fk5CSU3XLLLZB/5JFHyjT71O+9917IHzlypEzzGlWp4DMF6/C271dwDj3yzjkXx/7Zxfn63s/34X6Uc3x1o78cF0IIIYQQQgghhBBCCNFz6OG4EEIIIYQQQgghhBBCiJ5DD8eFEEIIIYQQQgghhBBC9Byn7RxvTqFTuuaM8zhCx2rQRl9YkY/7sgI9O2FBbivjcg1TFLTONdH/NH3CnztzHP1Eg+TDTYznuNNAf2zSh/9HUITGwUt+8qxAD1lupNN9VfTU9o2jLy5P/H3jfvQ9JQ3y1lZ8TNl8ttjGuGSma1LybrPWqHDes5uQh3ssQqdTHJDTzvmYtkPyoGfs9Pa1bmd43ZDO7TPXLVIcknnE7m0TJxofroLHVpzxEecYxYL89YHxSufkOXfkYe4Y1/3kPMb7xDSOlyML/twpGqP1BZxT4Zz3ji2QDyxrJ3Ssv0+NnOhBhHFp9Bn3+iL5mxvobE5tWCbJTX2C5qpxnScD5MwfxDkWV/14TpsYh4R81Hnq8/kitiVdQE90s+UrnE5jnzYn8NjCLEsZje3Q4bntxKxDAY6dhTmsU/PYXJkOprBtaT+uWevNWjM3PoDXPYH+uKJl3uvgaKzkOKeCEGMYG+d7JcRzU8dzytyTxj51jSuMGzzmMUqqwaDw8W8X6LCLEqy/y/x12WUe5bSIRcbFHtB60SE3u5nnBfVjuoB91W768mwE65dXMC6h8VHmOb3nIScvvqlSWuA9e4Wz8VCeD9+wEOLCsZLrw9m4YIVYTfBvpW7wuE8S/13feredO9np3W0ecB2s5/i7vuu7oOzqq6+GvPXsfuYzn4GyBx98EPJ79+4t0yn9tms28bv8fffdV6bZ0Wx9086hH9mmHyvPru2V4lJaZ85XW3jcWXf4D/7gD0LZq1/9ashbV//AAP7OWlzE51W1mv+tyo5xnje2Tjzfun3ucBm3zd6H/elcXzt3rUvbuZPrb8t5bLPT+9ChQ2Xarh2PVV+bHxkZgbJdu3ZBfseOHWX6zjvvhDL2+ts6sZP7hhtugPzP/MzPlGnrH3fu5Hc12DgsZ01dLnZucLzZt34prQmXIvrLcSGEEEIIIYQQQgghhBA9hx6OCyGEEEIIIYQQQgghhOg5TlurUktx+0NY8dtAwiod24fbIapGUZA4Uh2QHsBuRMg7+Oz+xCRuA5nY67deLRyj7eqkGQgTv4Uhq2EdOmtQZ9AY8tuy6rV5KMsKUnmYtgWkYKnUSckS+hgGVdp6X8ctGE2T7aS0/aJYgGxkNAOOtCoupr6I/HaZaoHtjmJUYWQJdmyY+mslGWlKTtKW+HMDDLdrzWO/Vqo+hlEVY5bGeHJsticFpHLJaSy1a748iPGeQYZxSo0/IiWNQ9DCrXcHj/ixds9eHB+H96ASYvKI76tg8hiUNUilMzjq70v2Iddq4XXzpolTH26BimLst0rDT/NahcZSC2N4/AHTjzEpKjq4XNi+qI1iheNhzPf3me1dKbalkWDfNIZ9vj6E98ywqa4zbTQl+7Ets0dxTSia5roV1HxUq1iHeMD3eYt2y3VInVJM+/tGpFwJHMZhfL3vm+ZVuH3u6FHcdrXYtIopHIOOtulF/Rin8cv9GrZ1E2mkIpyrkfPzJo1wfOQhrftGq9JypBsivU9i+jUMsX4hjaXAro0n+VkwmwXmuin1Wwf7Jqv4+wQhzvlaHdeL1EybIMJteRWH5xb/f3vnGmPXdZf9tS/nOveL7XHsJk5cp7k0oYmSqrQltLwoRFSBqiBUpdwK9CKBKPChCkIqlSLxAfGFbyChUigiAqGilhZUmhLa0qZ52yZpkyZOnIvt2M54xnM/c6778n5A717/53F9ZiYztmdynt+ntbz2WXut/7rMnD1ev200XV0qyyo4BypGX9Wq0TiKbbEdpYIQ/dCx18vL1YivxlTsJbby84yP8S8sLBRpVij0uw/fc2xsDPIPPvhgkf7whz8MZVa54hwqDPjaH/3oR5D/+Mc/XqSfe+45KGP9idWupKTh42v7rXntB7sXqztxzrk/+IM/KNK//uu/DmXj4+OQt2uBx5hVHv3m/kZ6EctW5hnfx2pADh06BGXNJj7vsSwuLkKeVSSW9XV8VsE6os997nNFem5urm97rS7pAx/4AJS9733vg7zVHB0+fBjKrMrFOeeWlpaK9L59+6Dsl37plyB/2223Fel6Hb/P8zjZ/YL7wjHbjnbFjjPvuS+99BLk+8077UtXH/3PcSGEEEIIIYQQQgghhBADhx6OCyGEEEIIIYQQQgghhBg49HBcCCGEEEIIIYQQQgghxMCxaed4GKOfNTefrNSwmtL4BOTTsv9sFqAXLWadtvHNrqyhh+f8LLrEVs56h9LyGfT7zOV4n1Js3LNldAwtH8D2T1/r8/sOUD0VCplxz5aoMzG5i0Jz2yjawJWaG48auZ+DMrq4rNY4CNCRVXLkFzbjmOQ4pim1P6T7hIFxILcwLinFO499PiKHd95A91XT/I0mJGVWQIr30LjDU3Iepw4vjhJ/n5grpnxm6srJ3bfaxHF84fRKkX7x/56GssbJFch35rz3r9xew/aW0TvfPeA9Xl2HZWkH+zps3M8uxmvHhsib5vycyMmLn1C9rfN+jaUJxTPC+VId9jGMyXteifGz4T5/bVQmx3iV/OT7fXkwgXMwR72gS1f9um+fxbnemsN8mvp6yxPkpi6RZ95421Pysgc09yNT3uvRuw7avN/5/DUzeM9zb8Z9c97Mw4DWV2UCA3HgyDDkD91yTZEe28dzCevqlFu+fWUc41JKazf3Mcza5O4rcd7ft0T7fLlH7u3I9yfPcY/t0N9wU/Peiiiinz0ZxiU3PvA0Rxd/Qu99qAR+TmQZye3ZU2eaVOcfoyHGMMp9vaVwMJzYV8uZZ+8r/7hg5HK8cmj9bR/FcLBhb6116/Je1mq1IP+d73ynSPM86pe3/mPnnPvYxz4G+Y9+9KNFmh3j/eqNY/w96a677oL8Zz7zmSL9wAMPQNkLL7wAedt3joP2+B9Pv7HZDTFk1/Pb3vY2yP/Gb/xGkWbHeD/XNsPzsN8euxX/dL8YbhRP6zbnNcXec+sOn5/H95g9/fTTkH/kkUeK9NmzZ6HslVdegfzamn8+wZ5zbsORI0eK9P333w9lN9xwA+RtvPl9ANdffz3kZ2dni/SNN94IZXfccQfkbZx4nDje7bb/zsht4Plg8zyveK70c5uz237//v2Qn5jw3/dt+7gecXXQ/xwXQgghhBBCCCGEEEIIMXDo4bgQQgghhBBCCCGEEEKIgUMPx4UQQgghhBBCCCGEEEIMHJt2jrfITVwqe5druYrP2CsldNpaKXYpQScse6J7xhW0hGpqN3cenWqdU6tFunl2AcqSJnqFgtC72qIY/Um9C+T3Sb2LdnJoDIpqqKl1ruzvU6pg36IQ7xPl3gt8kf8rxzaUEh+HVob9Dir42bFopEjnbWxDL8CxyYwLOnPoU0oDnA7VlJzpmfG59vCzYYR9jc24JmXyT0/hfewcSBIa9JhcUs47qMvkJo4ohkHmrw3470A55qu5H8cm1bPWRO/f7Ck/19on0OOVzS7ibRre49VJsd40WMb2rvq+JyXywYfoS04rfsyTFOdoLyVnc+jHrURjmsS4VqvGx5878jeTOLpi1YTD6NcqjWF7q/v8Z2tVnCvWXe6cc9UJv8iyCrY3aGAM48TfN6C9JcImuci872CojtcODZPb3HjRwxbGk15Z4JKSX4+pw75l5K/Pjct6fBzX8d13T0H+wmHfpoTeOzA8g/GtTePG1Msni/TCLO4fSxnujWPX+f5NYrWuEuKYZ8Z9nuXkGCf/d2T2i4gccN2A3oVg5lpGLxrIHDm8TUzDgH4uke+7Z9ZyRHtAQPudjUqZ5r7LsN449+PB+2g3I6eh2afirOH2EnvZecvewb3cF/H6kH92Z9Ea+l84Djs5zxRj8f9hP679fZKdtmfOnIG8dY6zu5yxbl12Bn/wgx+E/MiI/+6xFSfzRr7eW265pUhbr7lzzv3xH/8x5Dfqj9jYM2/jz99TmCvxc5Tnw3333Qf5fvNuK3vmVj7bb5/nmPBatXmObz/veZridw/2Tzca/jvEa6+9BmVPPfUU5L/3ve8V6bm5OSiz7nJuI/eN21s276ZiXzavTVu+uIjPRzhvY8b9Zu+53f94THksTpw4UaRPnjwJZRzvgwcPFumZmRkoq1TwCzK77205x+yd73wn5D/96U8X6YceegjKeC+3bdTvtFcG/c9xIYQQQgghhBBCCCGEEAOHHo4LIYQQQgghhBBCCCGEGDg2rVUJMzyCUTNqj+oUHjUo1fCZexQZRQGdYMmoCUHPH+1YWUcdQHt2BfPnlop0c56OiLQwH+debxGSViXr4rGK9SGvJFg7VIey0XE6bm9O28cl0pBEeJQm6tmjM3hkpElH5FqpP5rSXsN+B3VUQIzVzHGjHAMc0XGexMoDIlIz0J9KQmqTPXaTpXhsJSyTViPybYzLrHqhGK77sek156EoJY9NZvQ4AesWEhyrKPfHeUoRXrsekxbGHGvK6YRZs4uBaS75sYnJ/ZOu41iFXd+GLCN9j6Ox6TZ9CSkf4oi0GRO+/ZX9WOv+cWxvu+3bm7dIlRLhfCmP+XwY4j2r+0nnM+XXUTyMa2p6DMei6qeoi4ZwLIxtyDnnXKnm52UW4jjlpAmKh33fhmewvXHUprzv+8ibSAEygWshrvj5XCnjMabeCCk2Rv21aYfULhTf3ISpXsX2VmZGIT9yk58vYUh6lgDbsLKM62/pBXOU7aUmlLkE27Sy4tsRDWN862h6cVnXt6NL07nepc3dKJzSkI94Yrzz0O8nocN4l0kDY9dNRNqXds7eKx8XsgK5gI8OmqGL6JheTqqULPKfzUjBEpBmJTNzNk9xnQghxG7mcmk+bL3bOSq8GzQk/dqwUd92StGyG+IgdhZWIdTM96HV1VUoe+SRRyD/yiuvFGlWFPBcqdf97+v3338/lLFmZSsqlX5wG6yG4Gd/9meh7E//9E8hL63Kxmykles3jhfpIK+AysGqfZxz7sCBA5cs385et53P2jjwmrpw4QLkX3755UvWMzyMX3rt+mNdEjM7O1ukFxZQJ7y0tAT5lRX/PKLZxO+BG93Hwn21feN9Z21tDfKtln+O99nPfhbKWANj23j69OlL3tM5nB+sMJmfx+dIn//854v0N7/5TShjNY0dGx4nq35yzrnf+73fg/yxY8eKNCtYqlV8bmd1VYcOHYKyX/3VX4W81c9Iq3Jl0P8cF0IIIYQQQgghhBBCCDFw6OG4EEIIIYQQQgghhBBCiIFDD8eFEEIIIYQQQgghhBBCDBybdo6P7Mfn6FNv9v6csRvGoWxoEn24pWHjyCmhczUiv2zDeLnnFtB7lc6R+3fFOMmb6BcOm+hjy4yfOrcCZOdc2kRHklsxXqk18se20WkbDdty7LcjJ6/RqbuUhL2dBNsQ1/zQTKfohkoi9CAFJobdDK8NAv77R2jKaPh72NckJSe2cXWVyPEUkZM3zky8yclbdThWrcA4hRs4bkkPfcPdnq8rG52EskqC7a1m5r7kOc/r5D03DmfuiyP/mut6313UQ/ddQGMeGAd8lJEDLienuzO+b/KRhzQWLvExLB/CcTt4E4qiq23vRW+Sx/+iBpe8JysJycM9Qo7Asr9v2MO5387QOR4kft7VA3KDh9iGzMQsyHH8M3JMh6N+Pxk6gvVUDmCbynb+kmO8OkTzt2zWDbnZahPYt6zp2xSUcW5X6b0D5X1+38yHsX2VkSHIh1Xfn4Ac/ytNbO9rZ7DvC8+Z9zGcxzblGY5jZvrXuB7j0hqhvWbNz5+uw728Qi6/kp37JfId0rsREuPpzvlvtgE51sx8Yfe6dYE751xg1lzu+J4Yw9TcNqb1l0fo6wvtnhbQ+wtch/K+4pLb3V7YnfIxCiG2zk75pzeqd7dxpdp3ueK7lXsKsVlefPFFyFvv8o9+9CMos15d59BXzg5pnpNTU/47A/u+2ZV7ubBtYtcv+6h3O/3WPLu+2ZfMn7Vjx+5nHtd+8F63235f28iRnqbpJct4fuxU3/rFrN3G71XPPfcc5P/5n/+5SPM4leg52P79/sVh7Fo/cuQI5IeG/PfEG2+8sW+9586dK9JPPPEElPFcsn3jOVqr8bucPD/4wQ8gzw5y60V/9dVXoYy95zZO7AL/q7/6q0vWy87uH/7wh5D/6le/esk28Dhab7t1wTvn3OgovheM90ab32hd2xi/4x3vgDLeg7/whS9csr27bR2/UdD/HBdCCCGEEEIIIYQQQggxcOjhuBBCCCGEEEIIIYQQQoiBQw/HhRBCCCGEEEIIIYQQQgwcm3aOH34runamrpsu0iPXoPu5NkI+aqPSTcrk4iJdznzTO4iWZsnnvIqunazj/UQVcjp12RttvLDc6ThA71Ep9i6moRK5nyN0OgWhL89y8l7R3x66xoHdIwd2WEPXb63m453F49heilmaeR9xkpNDOsX2Zrl39LY65Bhfx7722piPqr7NEzWst0Yu6Mz0NUjJoVbCayPT95TcVhE5x3PnncdBTE568nK3Uz9f6qzWzsijV/KxIBu5G4rxwzWjoepSWUg+9cg4szOH8yxKyfll/PAheZazkLzGkWnlGHqXa4cxhlPGQ9Zs4zxLyPnVafm4rLfIybyK3uXGuilbIdd93oD80D7fhsmj2JfREuZjM13aOY7GKsUsi/zF1Ukc05EA+xqZmMYxlVVwPoclf9/KMK7jMMWxiU29yX7a+wKckxXzjoJyHevNQ/YU+vIOvTtgdRX3xoVnMd7t836sqjHOyZTc/J1V3/7mHI5jOkOOd3PfmP2tvNfY9zGUcRzTGPN2uqfku3M093Ozt4QB/UwIyHVv4t+hdyzE9MOnalY+a9xyGsee9ebTWi3TuwUSUx6w41+IHWAr3sFBciBfDcf0TnIlxmo7Mdnrc2k3tP9y+XF3Q9/E9vjSl74Eeev3/dznPgdl1jHunHPdrv/uwXODHcI/8zM/U6RvuukmKGOf8+WaV7aNZ8+ehbKtuLWvFDYO7Be2Xmjn0CN99913Q9ltt90GeR6b+fn5Iv3www9DGfuT+405Yx3ezNX4OcnteeyxxyD/y7/8y0Wa48tz8krM0U4Hv4OdPn0a8k8++WSRXl5ehjJu38GDB4v0Bz7wASg7fPjwJa9l5/XNN98M+TvvvLNIf+Yzn4Gyf/qnf4K87c/ICL6Xj53Y+/btc5fi6aefhrx1h7da+M6zfvNsZWUF8t/97nchb+PN+wP71K1HnMt43tk2VSr4nOAXf/EXIX/ddddBvlz2z2I2mpPWOc5r/t5774X8V77ylSLN826v/U67V9D/HBdCCCGEEEIIIYQQQggxcOjhuBBCCCGEEEIIIYQQQoiBY9NalZlbZyA/ZhQGFTzJ7spDeNSjV/HHANKY1Rd4jP+1VZ8Pl1AVkPboGJA5Xh/T0YJOSEfoc6NfiEnrMIFHSMYO+nx1ElUBOSkgstT/fcGqRJxzrkR/esjM0fyANA4uxJilie9PGJKCoIdHMDKjUmnT8ZJGE49gLK34us7OY+zrs3jso9wl7Ufd1738Jrz20LXY/jHT95RmWUzqg7zi6wrGpqEsap7Hz9a9wqfkMIZphoqFXs/fOKX4lhz11YxdQgqL4Tre58B1Y0V6YQLr7S6QZsWMTcAOIVLR5KBgwTFOApx3aeDb1OtigLs0v4Nxf225h33pLuAaW/enj1yH5kfjNZxLq/Nmri1i7KukP0lmfJxSh3M0K+ERuc6IH4tTS3gM69ULkHVDqW/TdYexnjfvo6OOZX/fnKw6WRnbZM0/CRt4aBjjiulbSgqWhPJG15KUcA52aS9st/yNllexfbMnsRHNWVTeWJVRfRg7kKQ4PzpNH6fWIml2uqScMmMXt0g5RXPU1XwbcrqnY4WMUY+kEe533Qw/W7F6nIyOrtHfewPzIy4PaW2SHic15UGOx+lSUrLkZu8J6FheFuA6Sa2uxV19dNx+b3K59AuM5scbj6tx9HaQdS390DFosVm+853vQP5rX/takba6Auf6azJCUtWxjuHtb397kWalwuWC10Gz6X+H/fu//3soa7fx+8XVgPeket1/j2VVyu/+7u9C3pZPTU1BmVUxOHfxWNlx/ehHPwplDz30EOQ/+9nPFukeKTN3+77DaoxHH30U8mfOnCnSrLNg/YVVAW30s+T1/qzhceJ6rEplYWEByljDY9Upt956K5Rdf/31kLfzjpVHPMZ2nf/+7/8+lL3lLW+BvN1PWM9y++23Q35szDwDob6dOHEC8rOzs5dsXz94P2OdiJ0PG41hPy0Tj8X0tH8G9Wu/9mtQ9v73vx/ydiy4HVuZdzyXeGxKRo37Rv7daDeh/zkuhBBCCCGEEEIIIYQQYuDQw3EhhBBCCCGEEEIIIYQQA4cejgshhBBCCCGEEEIIIYQYODbtHK9OowO5Mj3sKwnRe5RUMR+F3qmVBeh9zdrYhM6aL4/X0ROU5/jZsvloRt6ggBRDtYp3HQ1fg97l6evRv3bwzd5dXJ5AH1haJa94yd8oDLDeTsJ/ezCfDbDeOCGndOT9awHVm3Sxc2vGGXyCvOEXTnch3znlpdLp3BKUpd1RyFfJiVwyLu5TZ3A+dHrYn1uO+f5EI+iZKpew/XHXX5vX0B3Wi/bjtSUz73KMb5KR3zkz9yXHcRaSr9yMR+TQb1cZwvZPXuu958EhdPGnr8zhfQIf/yin+UxrITce9JTL2NXV8eXJiziOjdvQ2x5MGrcV6aqSBq6bbH61SLfn0Fm3/NIq5BsXvA+81MZ51otxPmTGk1UeW8d6Rtcg/1Lm58DxH65ge09ifMeMf7p3y7VQVn3vBOTffNCvvxoFIrzIie3HKiqRU5rHMTZ1pTTPejgne4n/bLeD9SwmOO9ai75N689jWfsUxjBuoHO8ZP7uGfRw7kQB5gOTby/hmGfkLTRbuQtofwtCvDYyrvAIp7PjrTGMfBsCdoyTV7xk7pPSPppG6KWLUzv3yclJHvE09f64gPzkNDtcnPu536E2ZOQrj3LfpqthixtUR91e6/ducYLaduy1GIr/5UrNpct1H653r83D3bKWxd7m7NmzkO92/e/ZSZLw5ZeE5+Pw8DDkjx07dsnP7tTa4zawR/zrX/96kf7yl78MZezPvhJwv9nF/kd/9EdF+uMf/ziUsVec3dBbwX52Zga/b/7Zn/0Z5J966qki/eSTT0IZz5fdtkdxey5cwBdMPfbYY0X64MGDUMbOZlsXx57HdSueaHsf64F2zrn9+/fz5T+2PT+uTdZJz/56Xqu2DVvxWo+Pj0PZT//0T1+y3okJ/O7MbbBwvX/+538O+Q9+8INF+plnnoGyfi5whq/tN385LrZv3Bd2vP/2b/92kb7vvvugzPrIud4fd9/XSz+Hvtg6r2dc9D/HhRBCCCGEEEIIIYQQQgwcejguhBBCCCGEEEIIIYQQYuDQw3EhhBBCCCGEEEIIIYQQA8emnePtCJ3CEzX/XL1MTuy0hN5X61KOyboaZNiEYeP67Q2TU2gM79N13hW+itpwVyaV0eh+75M9dHQcyg7eMAn52vXevV2aRE9XEJO/KvTtTzJsb+KwEaXcu2nzjH3TGN/Q+MnbXYzZWgM/e/K4v+8rT6C/ef0UeqLLjYUiPR6ThydoQTYlz1GU+/inS8tQNl/Hutb3e+faRIX+BkOO3lLX++RKbgzKwqgO+dh4pzKaOxc5kE0MM+prmZzTnZJvQ5RjvZUcfXdj4/4+9SP7oGz1GWyv63gXdObIXZ3hfHbOj2tAfv2UfMlB6tdYsoj+6WwZ25smvk1RgGXllO5jPICtFfRYt5cWsbWNhv9cijELSe+cm3cJVJax3tZZXDenTi/7zz37KpRlZv4651wj9O8H6DRxTJ+dQrfY+IS/9kANr61E5DMzY5OQVywhr3VgOpu1cc1kPcyvNvwaazagyC00cWzWnvcxy87g/tBcQF9j0KS9p+zzWYxlacL7s29/1MCNNO7gfaoTvj+k7XdRhP9QMk7vdkB7QEbzOfLXlvldArSPtl39ktdGKcYpNz/i2Cuf5tjXit1zyVGWO/aVmzL6McXO9DTy+13ieM2LnURuYtEPOz8UeyHEbsY6xp3bmme8H6ur+L3wG9/4RpF+29veBmXsVt6K/9Z6gptN/L3/f/7nfyD/iU98okjPzeG7ha7UXm1/PtRq+F4t6yJ2Dts7Oorv6+rntd4OHHv2J3/6058u0g888ACUrazg+5t2O60WPo/44he/WKTvueceKGPHe7nsf+fm+cv5rbxjxZbzWLB7296H6+Vr3/WudxXpahW/l2zHa22v5X738+JzG/p523ltHjlyBPJ/8id/UqR/67d+C8p4T9jKOu83btzXAwcOFOmf//mfhzJeJ7fffnuRrtfxec5W/PUbYdu/kZM+TdNLXis2Rs5xIYQQQgghhBBCCCGEEGIT6OG4EEIIIYQQQgghhBBCiIFj01qVhI78J+aofoWPzAf83/7N8QGHxwXiMh5XPzjj85W34pGGcB82t7Hij06M9PC4WZlOrx+a8cePJg6g1qM8WYF8NO7bWCtjWUr/Oz91vjwgLUmYow6gbMpZQ9Gha62iZXkZbzp3HK89+/hSkV4/MQ9lvWVUbjij42jX8ShYlfQLMQ9r5GMcJXjcr3sG9QvrS36+TNYwhr0E279u6uLTD70eximNffujEs7JKCA1TeiPBoUZVhym2FdjgHDJRX8ywjbsq/n5ccNRPNbWuvVayK+bIzDpyhKUZW1SmqR+rKKctA7UptjofYYnsXCoSkcvQx+ngE5EBiVcC4FRFaVdnM+O22QUEW3SJVVTnA+5UeB0aEwXTmN7Gydm/ecaqAnqtlEJYu04wfwslM0/MQT5k0dvKtJTQzj+IYbB2SaGpMkIe6zv8fmkhRV1LuD+tr7g299apD21QTE76eOfLeOGlqziZ7ttHKss9deHbdrD1jHeSerromVykVImrPijpiH99MjJpdM1cyKiiZc4bH/H3KcU4tjkpGApmTUV5VhG8hOXhua+pP8KSCmUBGZvyXEcewHtQ2a/LpHyKKR8z3Snk+/MsehBZa9pUxgdidw98Fza62Oz19vfj60cfb8avJFjL64eOzWvrN7EuYsVG//wD/9QpPfv3w9l73vf+yA/PT19yfuw9mV21v9O/nd/93dQ9rd/+7eXvNZqBK4kcex/z/u5n/s5KHvwwQchb1UqrL64XPDeZ9vrnHM333xzkWZ1x9oafpeyc2I37l88B77//e8X6aeffhrK7r77bsjbvnHMWFmxU2PHCiTbfqt5cc65kZERyB8+fLhI85juFByHSgW/F9r7chv66UM2qve9731vkbbz0znnnnzySchvZd3b+7IGhufDhz70oSLN63rfPlTj2vZz33ZynfdT07D2SlqV7SGtihBCCCGEEEIIIYQQQgixCfRwXAghhBBCCCGEEEIIIcTAoYfjQgghhBBCCCGEEEIIIQaOTcuNlkhdPeW8Q6keoM8sI7+stQgF5Nauk+d6Ysp7xkcq6GlKZ/DapGEbhc6h0jB6j2qR/ztAGqLPJxvFNkUj/to8wHqCDrpys8h4u8hpm5FXt2d8RV3yGPdIidNq+WvnXkKXVePpJrZp1rvEKg10D3db6DEOjAs6T/FvI50KxrA6hI3KSqbNPWx/mTzGvUVf3hrDspz+JtNrG+cxeYDb5OV2I37KVkdw+obsvqr6+5Qj8osG5KhP/FzrBjjvohBjWPfaZXfsCLYhf/thyJ80867xPF7bm0MHeafjV0pCnuU8xLlVqXqf9ug4zXXy7Ue5j29K9TjywZfLfg4EIc6HPKxBHvxbGc71mLzR4CEj59vcwjLeZ3nBt5cc40GP5r7xzJcdXhudOg35c8e9W+zGg+hNnBrHNpXMfImpb1168cBK08+XlXPkTz+HbsfOvM93F3GtkorfJWavSXvkSKe9xZHv2xkvejqPm3evS/t12Y9rXMWyKjnqSyB5530f55bdIkoO28BrNTM++4DmfuB47foxzzLcSxJyg+fGSZ5EFOAMx8q2KUrx2oojz5t5z0ZK79HIMtyvQRGHRW8o+nndtuPJ242O4c2y1/2A3P69PBYbcTUc5G/keF4urtac3OtrWew92HHL7vDXC7t9T506VaQ/9alPQdmXvvQlyN91111FemgI3+vz4osvQv7rX/96kT5z5gyUtVr4++/V8Izz3mF965/85CehbGJiou9ndwO1mvldfgNv9GbLmKu1Dy4vLxfphx9+GMqmpqYgf+TIkUvWUyrh9xTbn+38bGk0GpBvt/1zA/aRs/fcrqPtxJc/288r388jvh24nuFh/162j3zkI1D2iU98AvJb2QNsDI8ePQplf/iHfwj5n/zJnyzS7OLnsejnU99J7Hhwv8+dOwf5q/UOhr2MHbvX44rX/xwXQgghhBBCCCGEEEIIMXDo4bgQQgghhBBCCCGEEEKIgUMPx4UQQgghhBBCCCGEEEIMHJt2js8toq/oaMe4jIbQ2RM78kQH3pfDPvIowufztar3E7kQvbpJDeuNprzrN8nYNUtO3o53/ebkOc/K6KAql3xdCXXFOmydcy4xLqA8IB95gNfavl7k3Sb/99KCv/HKs+jrTS+gA7tmHL2VMrl8I+xrZPy41TK6tUPy9bKnJze++DjAshJpsiLrEA7QBd3s4Wfbq77erIvXtlEx7aprpt4pcmaNoiO7XPae64B82Z0MP1sOfd9LNHd65CoOU1/vyNAYlO2/FvvWWvFjVWrhOK73sK/pov9sO8E25OTUDyIzVhHOsxb5p3tmHtp+OudcpY7Xhsa3H9FcKtMgR2adVEKMUTnGz1anfPt7I3ht+xy5Bzs+ZhnFKMopb9cR+aejBq6TzskLRXq1MQJlE1O4B5Qyn89yrDft0rsPFvw7DFqzeM/mLL7foDnv2x+v4rUXObHXzbU9bG91GF2P9TLuq+vGUZ410HcXkyO7POz3gaEpnB/RELapm/u6woT2tyqOeeJsDDFmMfm+I7O3pA73JXxrhXNR7j8b0LUB7UO5+Wyekm/fUd74+Wg7Zr26y8w7C4KEbkofzkMfszhEL+GgsBv9nJeLQXUTb9TvvTYHbHsv15gOktN9rzGo61jsHvg7mPUls4d2O/PVOpHn5+eh7JFHHoH8o48+WqTL9B2SPeLWedzr0Xeaq7C+eH+t1+uQt67im2++Gcpej7d2p+GY8RxYWPDva6pW8TtjPwc5l/G42vs2m/ilfCfnYT/s/Hnssceg7JZbboH8b/7mbxbpKzVu7BXn+W7pdPB7LPvKNwvHmvOJeYDF7blcceE1Zp3ex44dgzJ4F5nDuHBfuL3WM/+xj30Myu655x7IW6f7Ri7+nXp30ka/y9m6eD7wuxtsm9mRvp3fIfs56d9Iv//IOS6EEEIIIYQQQgghhBBCbAI9HBdCCCGEEEIIIYQQQggxcGxaq5J1SRniMshZUnrmnpuz7lbN4ZxzGf1398joUSI69R6W8Nq45+vqduhoTxePqaR1f7wkdHiUg2wMrmvamNHx/zDD9kehL8+wCS6P6JhN5I928DH9ZhOPMJx52R+zSM/iMaawgddWev4YRTSMx8TyFG+Ud317h0eGoSyluFRIjZHHvoNhja49jEc9agf8Z7NRvDZskGal4ce83cH2J01S6+T+PnEN21ebxumclfy1PT6ik5E6xagaSg4VLFYR45xzZaMpqYasBaI41H2+UsdjbmtVVGN0TPMTmpQ8Z3tG9bGW4UJpk9bIRpv1J9UqjeOwV31U6tTvGsaw2/V1lam9tSGsd2S/zy9WcaHEPVq7mb9Pj/aLEilBbGmPlE2jMY5jJTNHPknXkpB2p2zGtdfDedWlI1DtVd/+3grVuwRZl/pTjy5r4TglpMexES1VSIUyheskHsdy29fuPM6zfB3HcXjcj83oBLY3IY1UnPg9LcGQuXWahz2zISZdWhcJ5kdGfTqiNRXQ+gtD3/6U5kdO2qA4Nuuc4huRWinIsE1wT/oZ1zMKrYD0X1GK9aSJ0bXkFDQhdjFbORq6EfbavaAP2WvHSq+EBuaNzF6P2V5YU2JrsAKAFRY7Rb+5z7oIO8+4rJ9i40qtr36ahOFh/M577733Qv5XfuVXijQrV3ZKt8Bwvf3q4viurKxA/r//+7+LNKs6WJViFT233347lN15552QHxnxWsd/+Zd/gbIXXngB8lbfsZNjbutaX0dF6bPPPgt5q5cZG0P1ab/5sZU9lFUN/eLL7WXFyauvvlqk3/KWt/Rtr9VqcHy5Xqs54mu5vTtFvzHnNWVj5Fz/+PO1do6+5z3v6XsfG7Ot/E6b0fM+zvN6tPdh/QljP8s6qtHRUcjfeOONRXp5eRnKeE+74447ivTEBH6h57H52te+VqRPnDgBZf0UWRvtWfZajtmVwrbx9cx1/c9xIYQQQgghhBBCCCGEEAOHHo4LIYQQQgghhBBCCCGEGDj0cFwIIYQQQgghhBBCCCHEwLFp5/jwMHmuQ+M3JN9wTqqdMDVObHocn8TkJwrXfJr0alGKzqFu7v2+nSa6frMKuXKNuy0I0PUbh1hv1PWO7zxAd3JQovbm3iOdJZf2YzvnXBb6a/N1dLWdPYPtX3zWx6GyiteGXQxiJTX+W4pnpY4+oqju7xPWyJGVkP+9iuWJ0TgNzWBcDr4NHUljB4xfndzlGH3nSsbhvHgWS/M2+XzrPhatFNtX7ZF7OzNxC7HeqE2OpMTXFZDbPohwmeSRmc8BzrMqyeSrga+3TC7zIEAvljPu4oy9aLxUQ9+3KMO+5BleG2RmrAKMWbmK9xkb9TFsTpM7eRnb3yn5NvB7BqrjI5AfmvGfbWfoyQsq2KaeedlALcS+RbSmrJ/cBdTvMZyT+w76No1W2QdGjvfY50ttvHK1g59tGzVXukaO9ATj2zGzPyHndUxqrqji+1MZwvaN0dhEExjDyDi+u7SHRWvYoapxjqe0QZ8+iY0Khow3eBRd5ssXcI2lTV9Xh959METO/0NHfF0zUxiXHnnyM/NzYHGdf4ThZ6dHfRyqrPsmB3ke+fvwX417NO8C4xXPyTHe5vc8mL2H4yvEbkPuYiGuHFtxDvfjcjmdd3I/eL1tHNQ9iT27V8LbvZVYs0/2Ss1Bm+cYWT+2c85de+21RfpDH/oQlN1///2Qn5mZKdLslN5KXDgOW4kLx9R6pNkx/sgjj0D+4YcfLtLNJr6rrFrFd169613vKtK/8zu/A2XsILe89a1vhfwnP/lJyJ8+fbpI93PQbweuh53ec3NzRfrYsWNQ1m8ubQXuW4feRWV9/Dym58+fh/wXv/jFIn3TTTdBmZ2TzuF85zYsLeFLrmxchobw+9rk5CTk+70fYDtz38aF3fAcl37+d/Zn33PPPUWavdtboZ8vm+cVx3dxcRHy1m/P8eX9xN6H1+q73/1uyP/ET/xEkR4fH4eyQ4cOQd7uf3xP5sEHHyzS3/jGN6Ds3/7t3yA/OztbpK+//noo47h873vfK9Ivv/wylLEXfyt7wlbmoXW+12pbf9eXvqkLIYQQQgghhBBCCCGEGDj0cFwIIYQQQgghhBBCCCHEwKGH40IIIYQQQgghhBBCCCEGjk07x6emyOljnONJiM7VMGdXl3fr9EL262E+db6uHrmUe+voqmk2vMsoac1DWZSgdyy3rldyHMcl9DYFxgWUkfM4JGdzu+mvbXTItZSh5yZd921oLrag7NXjDcivvejdYmkL+z0asTPLuKIcjsUwaqZcEBvPdUb+5hjbG42io6w66dsxfcsYlI0ewXgHdd+mOEMXccB/kjGO3nAZ/epZjmPTNv0rLZErago/66r+RlGtv0MraPs2ZuRpCssY/zAzc4CujUOcH7WSX2KlOjrGyzUcnLjs+xpk2JcSBa1ufPAV8oaXI4x3aBz7MTnRgzL6qOvGPz15Dc6HmHVPTf8PaZnaN0Ke6ykfh7y1BmWTb8K4rC/5uVVZw3URJBiXoOdjFsW0D+3H+TtzbJ+vd4jaR31LjOe6F5Hzmqz5uXH+l9lBP4Q+xCz1fc1ofwtorjszrrUDOK8mjtC7EKaoP2avbPB7HUYxTlUTi/lVnOsrz6FHr5V5X3lYp/1uaRnb0PBxy1O8tlvHHz3pkr92+Tr8WZNW6edAy8+1ZA796bUQ50t4nV9j028ib3gV4505P1ZRSP5Ah3MgTsxnuW/kPc9T39cW7eVid3AlXK67lavh892O0/JKYdu01+bHdjzW/cZiN8RhN86VnWQ7ftfLwUb32O3zZS9j35XlHHqMd5J+rl92evfz2LJP1np1t7MH8T2tV/euu+6Csg9/+MOQv/POO4s0+5vr9J3Memo3Wof9HM1Jgt8RbBw4Rnwt+4ets5e9wP/6r/8K+RdffLFIs4+6UsHvCNZBfcMNN0AZe9ttXI4ePQpl09PTkD9z5swl27AdbIzbbfy9//nnn4f8t771rSLNjnR2r/Mas/AcsOPYauEznGeeeQbydpzZR85j/p//+Z9Fmvv2zne+E/J2DvN+wE5v2zf2yL/jHe+AvI3vRjHqtzZ4ftv58Nd//ddQxnPdxpfXPMf7+PHjRfrVV1+FMnZM27nO+xm318b/hRdegDL2cNv15hx60d/znvdA2f79+y+ZL5fx+z2vR7t2+drtvBthdNS/l+2+++6DMrtvOofzY6N7/Nd//VeR/tSnPgVl586dg7zdIzb6XoDvjuz/7gA75vv27XNbRf9zXAghhBBCCCGEEEIIIcTAoYfjQgghhBBCCCGEEEIIIQaOTWtVxsZIF2H0AXlC/8U+wmMKXfPf/jt0aUxH0nsdf3yns4RHLuYW1iGft3x5CU+puLEaXhuW/DGhnDQwaUD58NLHB1bW8RjLyrI/BrK2go1YO4Of7b3mdRLri8tQtngWVRO9c6u+7aTqGBqhY24VfwQmiEiZUMPPuqr/LB95ikv4t5LKPlQJVI/4oyrjpFGpD+Fnu5mvu+dwPuQRHo+JRvyRo2AKp2RnBftTsVNiibQwHcyXK75NMcUli0mVYeZEKSLdQobxhuN0VI8jBcTwkK93itZQb98E5MPUt7e2jvO3HGAbZvb7o2zjE3iEqFrGa6PQ9IfiUHLY3nzMl4+8CbUv1TE8lpd0fXvzGO9Zq2Fc8qof10p3Espu7tAxp8yvm6Xnz0JZZRnXWNkcN6ocGIWyyZvwyOTYtf7aEuuG6M+EsWlDHuJ8jfHEmRuumzU1ivM3o7GIzTxrhVjWTugY5LBfF+PX4k1HDtF9RkkVZeZldRL3liQhVVTi47J+HPfc1jnUlPRW/ZGzPJzDMjo6WDGnAysc4Br2Z3nN33f5ZZwfwRD2tWz2luEOtjencW2Y20zsw3tmZdIPmZ9TPVrWTYpZx+41AdbTS7GvTaOXWTpJR4v/jxNXiUE98v9G11LsJa7UWGjMdw/bGYvduGftxjbtFFe7b3wc/Pz580W60Wjw5a8bq81g/Qa3wR5ttxoE55ybm8PfCa32gfUFm22PcxdrEt797ncX6YceegjKbrzxxku2l+tlXu/a5HnST9WwsrICZcvLy5BnVcPjjz9epP/jP/4DylhRYDUVG6lp7PxhZQXrLqyKgtvLY3651oytl59dcJu++c1vFul77rkHysbGUAlr49JPseIcjiv3+6mnnoK8VanwOuEYzc97LfBXv/pVKLPKFeew/aurq1DGa9fO97vvvhvKDhw4APnrrrvuku1jFYmNEytiuE0PP/xwkT5x4gSU9dsTuA2sm/nud79bpK0exDnn7r33XsjfcsstRXoj5ZxVqfzjP/4jlP3gBz+A/Nmz+HzCqjwee+wxKLvtttsg//73v79IszrH1uMcjmM/rdVWsbHgennf7zdWPAesyojnmZ3r3Aben1nZdPPNNxdpVv+sreFzDqufGR4mLfgm0P8cF0IIIYQQQgghhBBCCDFw6OG4EEIIIYQQQgghhBBCiIFDD8eFEEIIIYQQQgghhBBCDBybdo7HFXLcxt7nEofof8oz9B7FxkcTk7ammaLDe23Z+3/On0LH0NIiOtYqxj07RL7e+ji2IS/5ess5unHDEjqIunV/bYn8sWur6OZ67SX/2eVT6JXqniBf76J3ja22sOxCE9sUG+d0VMK+LEboUl5PfRumc3QP52XMR6aveQXLylN4n/p1OOZD13lvT2mc/q4S4ji6wPcnD2jQKziu+QHjTL+A/qdgHmNaNn6iIXKk12L00kXG0x1H5ESvYT7IjXfMoT8pIUdVkvs2xTn2rVpBZ1Iw4WOaHMIxzkLsa9VokaZXcYxD8lPvP+i9Y5Mz6FOq4UedVUll5C7vxOQcr/kxj6neaJzGuOv7FjmsNy3j/I4i47bK0Cl9bYm2oVFf1zPTGM/GcfS8hT3f/toNWO/hW9HbVZ/0dQU0TnFAe5jzY5yHNAfr5JWf9u3t5hizVoPH2M+taBzrrdB7E+oTfl2MvQmdanZeOedcaQjrisw4Vkfw2naX9rRF/9lGD9u72sRxDNf82q07jFmQksPbTBf788I557IcxzxKzHsH6L0OIb1jIRjxnw3JR14q0fsCUu8hS1K8NswwDp3Mr+XZNsbz3BzW21wxe0IP94f2Ku5vnVlfb+8suhzdR5y4Qlxtf+yVRI7pnWWjeG5lbu3lsdnJOOzkfa/EPV5v3/byeL8R2Mv7PjtXFxYWijT7WdnD3K/f7Je1efYWs9vVlvM92Q1uvct8T/Yw23XCa6ZM32NnZmaK9FZ8shyTnfL/c1869P6dxcXFIn369Gkou3DhAuTZXf3oo48Wafb1shsc3odFfeU2WmfzX/7lX0LZL/zCL0Dexvhv/uZvoIwd6jwnrgQch1deeaVI/8Vf/AWUPfDAA5B/+9vfXqQnJ/E7JPfF+rT//d//Hcqef/55yFvf/kaeazs2HE9eN9atzPWy79uum29/+9t92/BTP/VTRdrGxDnnxsfH3aWwc9u5i13sn//854u0jcmPa0O/Mp6/r776apHmsWA3+K233lqkeT9jB72N08mTJ6GMHeM8P9bNczt+JwR7/UdG/Hv77rzzTteP3fD7j40/rzfew7785S8Xad4L+T7WX8/7/A033AB5u3atf9y5i+Nt38/x5JNPuq2i/zkuhBBCCCGEEEIIIYQQYuDQw3EhhBBCCCGEEEIIIYQQA4cejgshhBBCCCGEEEIIIYQYODbtHC9F6IkJA+PvJUczW4QC40xKyKvbIv/t8rz3tc6dRn9Sdx5dTJm5Uc+hR8itoZ+obrzBkyG1t45tsIrvNCff9Ao6k9qvePfO6nHyjs2hi6m95vO9DtbbS9chn/Z8HJrk503TZcyXvCdrPZ2AsmoNHT4jkY9TeQTLhmfQ7Tt+GOXV8ZhvRxCgR6iZYUyj2DibM3TjuTL2pzZsPOI18hENsbPZt6l6Dbntx/E2UeTLg5za57Dvdgak5M9OyJkONdG6qNQwLvm4r2u4S3OUXNu1Ce/rC5sYs3KAcRia9p8d3Y/VDtGYVyKfD2k+dwPyAJryhB2GZWxDxXSnnJHruYQxdMbpnoXYhukInYHlqr9PdRJjtPgm9IO5NR/vAzPoGL9mBj3dJeukL7EjHauNemaUAxy3Upnm0rhxYIc4V6JhbH/cMX5ycmAH5K+vD/m4RGPU3iGMd2UE54t9j0IvRA9kN8cxP9/0dV2Ywz0sWUJ/dm3d73d5GeMSRtjGXuT7k6S4LiL6u2xgXGPxKLZh/CjOl9K0z9eGqA00jtVhOzbsGMf4r3W8E272HPrhXvkWreszvo1hD9ubNfBnRLrm61rtLjkhxBsLeaX/FxuH7bied2M8d2ObdjuK2fZgt6v14y4t4e8SvN76rT/2ldfr/nsVO7z75dtt/P2wn2eX3bOM/f2M3bPWz+ucc0ND/nf9M2fOQNnEBH4HtnOQ6+UY2fJ+TnTnnOv1/O+3HAceG+u/ta5k5y52VbMf1zqdeT70g3/fZT+yHY/jx4/3/WxiHtw899xzUMYe6X4/A3ZqP+D2cb3W/Wz9484594UvfAHyr732WpE+evRo33qtD/wrX/kKlNn3ATDb6fd2HO52bHhtPvvss5C37wvgdwfwONo1xvOZ55L1oNs141z/uGzkabd1sfOa/d7Ly8tFmueOdV4759zs7GyRZv87t7/fHsFrle9j5+WpU6egbN++fZC3+zPHZTtzy7af+9Zvb2f3OjveH3/88SJt16JzF8fB5nnvvv322yF/zTXXXLJe+zPBOdyzEn7wvAn0P8eFEEIIIYQQQgghhBBCDBx6OC6EEEIIIYQQQgghhBBi4Ni0VmW9if+Fvdzw/5U/iPHYRy9fg3zg/BGulHQRFxbwWMLcy/4+66/MQ1n7PB5xaAb+GH8c4zGm5iIeCxmJzLGsKqoZhkkPUDInMlpdPAr2GqldXjvvj06szOKxlWQN872mj0vGyooUj2B0U38MICb9AtkAQFHQzFBtMFbBY2S9CZ+vz+AxldIBHJu0hscqel1/vItPKeQl1pb4o3flBOdOu4bXtp0PeGmMdCLXYj4Y8seEkklS3tARl0bDt78Xk64lpyN+RluS0zGV1PFxRTN2Ad6zm1CbzLVRGf8WVR/GORCacU1YxxFjG6Jhn0+reBymmZP6x2gfAppnnTa2odHwbVxvkwqjhPWOVv1cGqmi4mYopTiYrqf0J7k4o2uNEmRqAi8evxmP3biuj0MckyKGlDEtcyyo3MN+dyu4XzRTX2/GyqAeHVfNfPzTMsUsJCVSzeeznOa2Iy2TUbQEEa7FnNQoaQ/XfWy0MK0uzo9GE/szN+/rWjyHR1Qrq7gH262+S2qlxOEccEYTE6SkJgox/kMj/kjU5LWoc5o6in2rDZn70F4Y0Dovm30pyVDTlTSxveuZ78/CcTyW1/guHs3Ml8xaoOkQJdi3tpk/a21sgxgsNjoqulP1Xin63bdf36RbeOOjMd55Ltf+IXYnd9xxB+StHoBhtYBVKvDxf9aUVCr+d6Hp6Wkos0f6nUPlAs/HAwcOXLJ9rBlgHYc9Xl8i7eHk5CTkrcLi+9//PpSxPsKqUqampvq2wcaFNTCsA7DqBu7b2ho+A7Ht5XHi9rLSwqoGeL2zPsKW87UcU3sfVndYlYtzGItqFZ+XsArB6g643n6KEJ6jrL+w/eG+8LV2ztq57dzFypuXXnrpkmWMHWeOr1UecZu4bxyHftcyW9nzrT6JlUKctxoN1mTwmNv48p7EeTs/WG/Ba6zf/GUVVL++cXvtPsXrmLHaKJ5XvN76aVW4vaz9sJ/99re/DWV830OHDl2yjNeC1aNwGe93FtYCsVbF6qt4v3vqqacuWS+vP96D7dixwov3xieeeKJI8z7JP6defvnlIs192Qz6n+NCCCGEEEIIIYQQQgghBg49HBdCCCGEEEIIIYQQQggxcOjhuBBCCCGEEEIIIYQQQoiBI8glrRNCCCGEEEIIIYQQQggxYOh/jgshhBBCCCGEEEIIIYQYOPRwXAghhBBCCCGEEEIIIcTAoYfjQgghhBBCCCGEEEIIIQYOPRwXQgghhBBCCCGEEEIIMXDo4bgQQgghhBBCCCGEEEKIgUMPx4UQQgghhBBCCCGEEEIMHHo4LoQQQgghhBBCCCGEEGLg0MNxIYQQQgghhBBCCCGEEAOHHo4LIYQQQgghhBBCCCGEGDj+H3rGaAAfQ92iAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}