{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":91451,"databundleVersionId":11223220,"sourceType":"competition"}],"dockerImageVersionId":31011,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        os.path.join(dirname, filename)\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:16:17.240038Z","iopub.execute_input":"2025-07-02T12:16:17.240261Z","iopub.status.idle":"2025-07-02T12:16:52.246390Z","shell.execute_reply.started":"2025-07-02T12:16:17.240235Z","shell.execute_reply":"2025-07-02T12:16:52.245576Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### **Let's install some important dependencies, two Python packages for:**\n- #### loading and preprocessing of available datasets (wildlife-datasets)\n- #### tools / method for animal re-identification (wildlife-tools)","metadata":{}},{"cell_type":"code","source":"!pip install git+https://github.com/WildlifeDatasets/wildlife-datasets@develop\n!pip install git+https://github.com/WildlifeDatasets/wildlife-tools","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:16:52.247750Z","iopub.execute_input":"2025-07-02T12:16:52.248083Z","iopub.status.idle":"2025-07-02T12:18:50.278812Z","shell.execute_reply.started":"2025-07-02T12:16:52.248064Z","shell.execute_reply":"2025-07-02T12:18:50.277856Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport timm\nimport torchvision.transforms as T\nfrom wildlife_datasets.datasets import AnimalCLEF2025\nfrom wildlife_tools.features import DeepFeatures\nfrom wildlife_tools.similarity import CosineSimilarity","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:18:50.280017Z","iopub.execute_input":"2025-07-02T12:18:50.280356Z","iopub.status.idle":"2025-07-02T12:19:19.723084Z","shell.execute_reply.started":"2025-07-02T12:18:50.280322Z","shell.execute_reply":"2025-07-02T12:19:19.722326Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### We first need to specify the root, where the data are stored and then the image transformations.\n\n- #### The first transform only resizes the images and is used for visualization.\n- #### The second transform converts it to torch tensor and is used for operations on neural networks.","metadata":{}},{"cell_type":"code","source":"root = '/kaggle/input/animal-clef-2025'\ntransform_display = T.Compose([\n    T.Resize([384, 384]),\n    ])\ntransform = T.Compose([\n    *transform_display.transforms,\n    T.ToTensor(),\n    T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\n    ])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:19.723902Z","iopub.execute_input":"2025-07-02T12:19:19.724557Z","iopub.status.idle":"2025-07-02T12:19:19.729446Z","shell.execute_reply.started":"2025-07-02T12:19:19.724537Z","shell.execute_reply":"2025-07-02T12:19:19.728664Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 📊 Visualizing Data\n#### Since `AnimalCLEF2025` is the child class of datasets.WildlifeDataset from wildlife-datasets, it inherits all its methods and attributes. The following code specify which transform we want to use while loading images and that we want to load labels alongside images.","metadata":{}},{"cell_type":"code","source":"dataset = AnimalCLEF2025(root, transform=transform_display, load_label=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:19.731600Z","iopub.execute_input":"2025-07-02T12:19:19.731858Z","iopub.status.idle":"2025-07-02T12:19:28.157259Z","shell.execute_reply.started":"2025-07-02T12:19:19.731829Z","shell.execute_reply":"2025-07-02T12:19:28.156696Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Let's look at one example from the dataset\ndataset[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:28.157880Z","iopub.execute_input":"2025-07-02T12:19:28.158086Z","iopub.status.idle":"2025-07-02T12:19:28.212561Z","shell.execute_reply.started":"2025-07-02T12:19:28.158069Z","shell.execute_reply":"2025-07-02T12:19:28.211938Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport random\n\n# Choose 6 random indices\nindices = random.sample(range(len(dataset)), 6)\n\n# Set up the plot\nfig, axes = plt.subplots(2, 3, figsize=(12, 8))\n\nfor ax, idx in zip(axes.flatten(), indices):\n    img, label = dataset[idx]\n    ax.imshow(img)\n    ax.set_title(label)\n    ax.axis('off')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:28.213456Z","iopub.execute_input":"2025-07-02T12:19:28.213771Z","execution_failed":"2025-07-02T12:46:52.492Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 📌 Plotting a sample grid of the data. We can see that all animals in the testing sets are turtles. The column dataset states that all the photos come from the SeaTurtleID2022 dataset.","metadata":{}},{"cell_type":"code","source":"dataset.plot_grid()\ndataset.metadata","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:29.233797Z","iopub.execute_input":"2025-07-02T12:19:29.234008Z","execution_failed":"2025-07-02T12:46:52.493Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset.metadata[['species']].value_counts()","metadata":{"trusted":true,"execution":{"iopub.execute_input":"2025-07-02T12:19:30.378403Z","iopub.status.idle":"2025-07-02T12:19:30.390031Z","shell.execute_reply.started":"2025-07-02T12:19:30.378385Z","shell.execute_reply":"2025-07-02T12:19:30.389230Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"idx = dataset.metadata['identity'].str.startswith('SeaTurtleID2022')\nidx[idx.isnull()] = False\ndataset.plot_grid(idx=idx);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:30.390963Z","iopub.execute_input":"2025-07-02T12:19:30.391826Z","iopub.status.idle":"2025-07-02T12:19:31.047169Z","shell.execute_reply.started":"2025-07-02T12:19:30.391800Z","shell.execute_reply":"2025-07-02T12:19:31.046261Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Loading the dataset\ndataset = AnimalCLEF2025(root, transform=transform, load_label=True)\ndataset_database = dataset.get_subset(dataset.metadata['split'] == 'database')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:31.048220Z","iopub.execute_input":"2025-07-02T12:19:31.048512Z","iopub.status.idle":"2025-07-02T12:19:38.777138Z","shell.execute_reply.started":"2025-07-02T12:19:31.048489Z","shell.execute_reply":"2025-07-02T12:19:38.776528Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset_database[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:38.777934Z","iopub.execute_input":"2025-07-02T12:19:38.778202Z","iopub.status.idle":"2025-07-02T12:19:38.845159Z","shell.execute_reply.started":"2025-07-02T12:19:38.778177Z","shell.execute_reply":"2025-07-02T12:19:38.844415Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"new_database = dataset_database","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:38.845992Z","iopub.execute_input":"2025-07-02T12:19:38.846212Z","iopub.status.idle":"2025-07-02T12:19:38.849644Z","shell.execute_reply.started":"2025-07-02T12:19:38.846195Z","shell.execute_reply":"2025-07-02T12:19:38.849105Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"updated_database = []\n\nfor item in new_database:\n    new_item = list(item)\n    if item[1].startswith('Lynx'):\n        new_item[1] = 'Lynx'\n    elif item[1].startswith('Sea'):\n        new_item[1] = 'SeaTurtle'\n    elif item[1].startswith('Sal'):\n        new_item[1] = 'Salamander'\n    updated_database.append(tuple(new_item))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:19:38.852793Z","iopub.execute_input":"2025-07-02T12:19:38.853248Z","iopub.status.idle":"2025-07-02T12:24:08.533212Z","shell.execute_reply.started":"2025-07-02T12:19:38.853230Z","shell.execute_reply":"2025-07-02T12:24:08.532573Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(updated_database)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.534056Z","iopub.execute_input":"2025-07-02T12:24:08.534319Z","iopub.status.idle":"2025-07-02T12:24:08.539017Z","shell.execute_reply.started":"2025-07-02T12:24:08.534281Z","shell.execute_reply":"2025-07-02T12:24:08.538344Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"updated_database[13000]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.539662Z","iopub.execute_input":"2025-07-02T12:24:08.539838Z","iopub.status.idle":"2025-07-02T12:24:08.557666Z","shell.execute_reply.started":"2025-07-02T12:24:08.539823Z","shell.execute_reply":"2025-07-02T12:24:08.556961Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from torch.utils.data import Dataset","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.558280Z","iopub.execute_input":"2025-07-02T12:24:08.558511Z","iopub.status.idle":"2025-07-02T12:24:08.569404Z","shell.execute_reply.started":"2025-07-02T12:24:08.558495Z","shell.execute_reply":"2025-07-02T12:24:08.568562Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CustomDataset(Dataset):\n    def __init__(self, data):\n        self.data = data\n        # Extract unique labels and create a mapping to integers\n        self.label_to_index = {label: idx for idx, label in enumerate(sorted(set(label for _, label in data)))}\n    \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, idx):\n        tensor, label = self.data[idx]\n        # Convert label string to integer\n        label_idx = self.label_to_index[label]\n        return tensor, label_idx\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.570275Z","iopub.execute_input":"2025-07-02T12:24:08.570572Z","iopub.status.idle":"2025-07-02T12:24:08.586323Z","shell.execute_reply.started":"2025-07-02T12:24:08.570551Z","shell.execute_reply":"2025-07-02T12:24:08.585565Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Assuming updated_database is your list of (tensor, label) tuples\nmydataset = CustomDataset(updated_database)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.587132Z","iopub.execute_input":"2025-07-02T12:24:08.587403Z","iopub.status.idle":"2025-07-02T12:24:08.600395Z","shell.execute_reply.started":"2025-07-02T12:24:08.587382Z","shell.execute_reply":"2025-07-02T12:24:08.599730Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"mydataset[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.601068Z","iopub.execute_input":"2025-07-02T12:24:08.601412Z","iopub.status.idle":"2025-07-02T12:24:08.618034Z","shell.execute_reply.started":"2025-07-02T12:24:08.601387Z","shell.execute_reply":"2025-07-02T12:24:08.617478Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"len(mydataset)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.618659Z","iopub.execute_input":"2025-07-02T12:24:08.618822Z","iopub.status.idle":"2025-07-02T12:24:08.631694Z","shell.execute_reply.started":"2025-07-02T12:24:08.618809Z","shell.execute_reply":"2025-07-02T12:24:08.631128Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Let's seperate the cross validation set aside\n\nfrom torch.utils.data import random_split\n\ntrain_size = int(0.8 * len(mydataset))\n_size=len(mydataset) - train_size\ntrain_data, _data = random_split(mydataset, [train_size, _size])\n\ncv_size = int(0.5 * len(_data))  # 50% for CV\ntest_size = len(_data) - cv_size  # Remaining 50% for Test\n\ncv_data, test_data = random_split(_data, [cv_size, test_size])\n\nprint(f\"Train data size: {train_size}\\n\" + f\"CV data size: {cv_size}\\n\" + f\"Test data size: {test_size}\\n\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.632347Z","iopub.execute_input":"2025-07-02T12:24:08.632647Z","iopub.status.idle":"2025-07-02T12:24:08.651455Z","shell.execute_reply.started":"2025-07-02T12:24:08.632631Z","shell.execute_reply":"2025-07-02T12:24:08.650813Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Let's build ResNet50 from scratch in PyTorch 🚀🚀**","metadata":{}},{"cell_type":"markdown","source":"#### Let's import all the necessary packages ","metadata":{}},{"cell_type":"code","source":"import torch","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.652350Z","iopub.execute_input":"2025-07-02T12:24:08.652576Z","iopub.status.idle":"2025-07-02T12:24:08.665569Z","shell.execute_reply.started":"2025-07-02T12:24:08.652559Z","shell.execute_reply":"2025-07-02T12:24:08.664966Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from torch.nn import Sequential\nfrom torch.nn import ZeroPad2d\nfrom torch.nn import Flatten\nfrom torch.nn import Conv2d\nfrom torch.nn import AvgPool2d\nfrom torch.nn import MaxPool2d\nfrom torch.nn import AdaptiveMaxPool2d\nfrom torch.nn import BatchNorm2d\nimport torch.nn.functional as F","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.666286Z","iopub.execute_input":"2025-07-02T12:24:08.666587Z","iopub.status.idle":"2025-07-02T12:24:08.680697Z","shell.execute_reply.started":"2025-07-02T12:24:08.666563Z","shell.execute_reply":"2025-07-02T12:24:08.679900Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch.nn.init as init","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.681543Z","iopub.execute_input":"2025-07-02T12:24:08.681774Z","iopub.status.idle":"2025-07-02T12:24:08.695090Z","shell.execute_reply.started":"2025-07-02T12:24:08.681759Z","shell.execute_reply":"2025-07-02T12:24:08.694523Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"np.random.seed(1)\ntorch.manual_seed(0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.695946Z","iopub.execute_input":"2025-07-02T12:24:08.696256Z","iopub.status.idle":"2025-07-02T12:24:08.717240Z","shell.execute_reply.started":"2025-07-02T12:24:08.696237Z","shell.execute_reply":"2025-07-02T12:24:08.716539Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### In recent years, neural networks have become much deeper, with state-of-the-art networks evolving from having just a few layers (e.g., AlexNet) to over a hundred layers.\n\n* #### The main benefit of a very deep network is that it can represent very complex functions. It can also learn features at many different levels of abstraction, from edges (at the shallower layers, closer to the input) to very complex features (at the deeper layers, closer to the output). \n\n* #### However, using a deeper network doesn't always help. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent prohibitively slow.\n\n* #### More specifically, during gradient descent, as you backpropagate from the final layer back to the first layer, you are multiplying by the weight matrix on each step, and thus the gradient can decrease exponentially quickly to zero (or, in rare cases, grow exponentially quickly and \"explode,\" from gaining very large values). \n\n* #### During training, you might therefore see the magnitude (or norm) of the gradient for the shallower layers decrease to zero very rapidly as training proceeds, as shown below:\n\n   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"}}},{"cell_type":"markdown","source":"<a name='3-1'></a>\n### The Identity Block\n\nThe identity block is the standard block used in ResNets, and corresponds to the case where the input activation (say $a^{[l]}$) has the same dimension as the output activation (say $a^{[l+2]}$). To flesh out the different steps of what happens in a ResNet's identity block, here is an alternative diagram showing the individual steps:\n\n![image.png](attachment:b7089448-8dbb-43b2-8ff0-fca0ca80202c.png)","metadata":{},"attachments":{"7670796d-a0a1-48f6-af90-bfc9ee714144.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"These are the individual steps:\n\nFirst component of main path: \n- The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (1,1). Its padding is \"valid\".\n- The first BatchNorm is normalizing the 'channels' axis.\n- Then apply the ReLU activation function. This has no hyperparameters. \n\nSecond component of main path:\n- The second CONV2D has $F_2$ filters of shape $(f,f)$ and a stride of (1,1). Its padding is \"same\".\n- The second BatchNorm is normalizing the 'channels' axis.\n- Then apply the ReLU activation function. This has no hyperparameters.\n\nThird component of main path:\n- The third CONV2D has $F_3$ filters of shape (1,1) and a stride of (1,1). Its padding is \"valid\".\n- The third BatchNorm is normalizing the 'channels' axis.\n- Note that there is **no** ReLU activation function in this component. \n\nFinal step: \n- The `X_shortcut` and the output from the 3rd layer `X` are added together.\n- Then apply the ReLU activation function. This has no hyperparameters. ","metadata":{}},{"cell_type":"code","source":"import torch.nn as nn","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.718023Z","iopub.execute_input":"2025-07-02T12:24:08.718265Z","iopub.status.idle":"2025-07-02T12:24:08.726007Z","shell.execute_reply.started":"2025-07-02T12:24:08.718249Z","shell.execute_reply":"2025-07-02T12:24:08.725237Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class IdentityBlock(nn.Module):\n    def __init__(self, in_channels, f, filters):\n        super().__init__()\n        F1, F2, F3 = filters\n\n        self.conv1 = nn.Conv2d(in_channels, F1, kernel_size=1, stride=1, padding=0, bias=False)\n        self.bn1 = nn.BatchNorm2d(F1)\n\n        self.conv2 = nn.Conv2d(F1, F2, kernel_size=f, stride=1, padding=f//2, bias=False)\n        self.bn2 = nn.BatchNorm2d(F2)\n\n        self.conv3 = nn.Conv2d(F2, F3, kernel_size=1, stride=1, padding=0, bias=False)\n        self.bn3 = nn.BatchNorm2d(F3)\n\n    def forward(self, X):\n        X_shortcut = X\n\n        X = self.conv1(X)\n        X = self.bn1(X)\n        X = F.relu(X, inplace=False)\n\n        X = self.conv2(X)\n        X = self.bn2(X)\n        X = F.relu(X, inplace=False)\n\n        X = self.conv3(X)\n        X = self.bn3(X)\n\n        X = X + X_shortcut\n        X = F.relu(X, inplace=False)\n\n        return X\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:24:08.726765Z","iopub.execute_input":"2025-07-02T12:24:08.727027Z","iopub.status.idle":"2025-07-02T12:24:08.743018Z","shell.execute_reply.started":"2025-07-02T12:24:08.727006Z","shell.execute_reply":"2025-07-02T12:24:08.742172Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def test_identity_block():\n    X = torch.randn(2, 64, 32, 32)\n    block = IdentityBlock(in_channels=64, f=3, filters=[64, 64, 64])\n    out = block(X)\n    assert out.shape == X.shape, f\"Expected output shape {X.shape}, but got {out.shape}\"\n    print(\"✅ Test passed: output shape matches input shape.\")\n\ntest_identity_block()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:27:39.561557Z","iopub.execute_input":"2025-07-02T12:27:39.561862Z","iopub.status.idle":"2025-07-02T12:27:39.577551Z","shell.execute_reply.started":"2025-07-02T12:27:39.561842Z","shell.execute_reply":"2025-07-02T12:27:39.576752Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a name='3-2'></a>\n### 3.2 - The Convolutional Block\n\nThe ResNet \"convolutional block\" is the second block type. You can use this type of block when the input and output dimensions don't match up. The difference with the identity block is that there is a CONV2D layer in the shortcut path: \n\n![image.png](attachment:d9850a5a-6567-4fa9-bd21-d6d7d5e70c48.png)\n\n* The CONV2D layer in the shortcut path is used to resize the input $x$ to a different dimension, so that the dimensions match up in the final addition needed to add the shortcut value back to the main path. (This plays a similar role as the matrix $W_s$ discussed in lecture.) \n* For example, to reduce the activation dimensions's height and width by a factor of 2, you can use a 1x1 convolution with a stride of 2. \n* The CONV2D layer on the shortcut path does not use any non-linear activation function. Its main role is to just apply a (learned) linear function that reduces the dimension of the input, so that the dimensions match up for the later addition step.\n\nThe details of the convolutional block are as follows. \n\nFirst component of main path:\n- The first CONV2D has $F_1$ filters of shape (1,1) and a stride of (s,s). Its padding is \"valid\".\n- The first BatchNorm is normalizing the 'channels' axis.\n- Then apply the ReLU activation function. This has no hyperparameters. \n\nSecond component of main path:\n- The second CONV2D has $F_2$ filters of shape (f,f) and a stride of (1,1). Its padding is \"same\".\n- The second BatchNorm is normalizing the 'channels' axis.\n- Then apply the ReLU activation function. This has no hyperparameters. \n\nThird component of main path:\n- The third CONV2D has $F_3$ filters of shape (1,1) and a stride of (1,1). Its padding is \"valid\".\n- The third BatchNorm is normalizing the 'channels' axis. Note that there is no ReLU activation function in this component. \n\nShortcut path:\n- The CONV2D has $F_3$ filters of shape (1,1) and a stride of (s,s). Its padding is \"valid\".\n- The BatchNorm is normalizing the 'channels' axis. \n\nFinal step: \n- The shortcut and the main path values are added together.\n- Then apply the ReLU activation function. This has no hyperparameters. 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"}}},{"cell_type":"code","source":"class ConvolutionalBlock(nn.Module):\n    def __init__(self, in_channels, f, filters, s):\n        super().__init__()\n        \n        F1, F2, F3 = filters\n\n        self.conv1 = nn.Conv2d(in_channels, F1, kernel_size=1, stride=s, padding=0, bias=False)\n        self.bn1 = nn.BatchNorm2d(F1)\n\n        self.conv2 = nn.Conv2d(F1, F2, kernel_size=f, stride=1, padding=f//2, bias=False)\n        self.bn2 = nn.BatchNorm2d(F2)\n\n        self.conv3 = nn.Conv2d(F2, F3, kernel_size=1, stride=1, padding=0, bias=False)\n        self.bn3 = nn.BatchNorm2d(F3)\n\n        self.shortcut_conv = nn.Conv2d(in_channels, F3, kernel_size=1, stride=s, padding=0, bias=False)\n        self.shortcut_bn = nn.BatchNorm2d(F3)\n\n    def forward(self, X):\n        X_shortcut = X\n\n        X = self.conv1(X)\n        X = self.bn1(X)\n        X = F.relu(X, inplace=False)\n\n        X = self.conv2(X)\n        X = self.bn2(X)\n        X = F.relu(X, inplace=False)\n\n        X = self.conv3(X)\n        X = self.bn3(X)\n\n        X_shortcut = self.shortcut_conv(X_shortcut)\n        X_shortcut = self.shortcut_bn(X_shortcut)\n\n        X = X + X_shortcut\n        X = F.relu(X, inplace=False)\n\n        return X\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:00.551593Z","iopub.execute_input":"2025-07-02T12:28:00.551874Z","iopub.status.idle":"2025-07-02T12:28:00.558901Z","shell.execute_reply.started":"2025-07-02T12:28:00.551855Z","shell.execute_reply":"2025-07-02T12:28:00.558128Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def test_conv_block():\n    X = torch.randn(2, 64, 32, 32)\n    block = ConvolutionalBlock(in_channels=64, f=3, filters=[64, 64, 64], s=2)\n    out = block(X)\n    assert out.shape == X.shape, f\"Expected output shape {X.shape}, but got {out.shape}\"\n    print(\"✅ Test passed: output shape matches input shape.\")\n\ntest_identity_block()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:18.161769Z","iopub.execute_input":"2025-07-02T12:28:18.162372Z","iopub.status.idle":"2025-07-02T12:28:18.174802Z","shell.execute_reply.started":"2025-07-02T12:28:18.162344Z","shell.execute_reply":"2025-07-02T12:28:18.173996Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a name='4'></a>  \n## 4 - Building Your First ResNet Model (50 layers)\n\nYou now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. \"ID BLOCK\" in the diagram stands for \"Identity block,\" and \"ID BLOCK x3\" means you should stack 3 identity blocks together.\n\n![resnet_kiank.png](attachment:6b3c08d3-1968-47c2-a5e1-171fc0726fd1.png)\n\nThe details of this ResNet-50 model are:\n- Zero-padding pads the input with a pad of (3,3)\n- Stage 1:\n    - The 2D Convolution has 64 filters of shape (7,7) and uses a stride of (2,2). \n    - BatchNorm is applied to the 'channels' axis of the input.\n    - MaxPooling uses a (3,3) window and a (2,2) stride.\n- Stage 2:\n    - The convolutional block uses three sets of filters of size [64,64,256], \"f\" is 3, and \"s\" is 1.\n    - The 2 identity blocks use three sets of filters of size [64,64,256], and \"f\" is 3.\n- Stage 3:\n    - The convolutional block uses three sets of filters of size [128,128,512], \"f\" is 3 and \"s\" is 2.\n    - The 3 identity blocks use three sets of filters of size [128,128,512] and \"f\" is 3.\n- Stage 4:\n    - The convolutional block uses three sets of filters of size [256, 256, 1024], \"f\" is 3 and \"s\" is 2.\n    - The 5 identity blocks use three sets of filters of size [256, 256, 1024] and \"f\" is 3.\n- Stage 5:\n    - The convolutional block uses three sets of filters of size [512, 512, 2048], \"f\" is 3 and \"s\" is 2.\n    - The 2 identity blocks use three sets of filters of size [512, 512, 2048] and \"f\" is 3.\n- The 2D Average Pooling uses a window (pool_size) of shape (2,2).\n- The 'flatten' layer doesn't have any hyperparameters.\n- The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax 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9VaXfBPFwopEulG6F6UkopIkqVEqFA6SBLNf34z63338/5mv+eZOc2cPfu3P7NWmfM888wz+/P77P0+833m2bOgKBfHAgEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAqMmcMbXznMLCHRHXSMODgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBARYBAFxEgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCRCgEA3kUJxmBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECHRxAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAIgQIdBMpFIcJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQIBAFwcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCRCgEA3kUJxmBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECHRxAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAIgQIdBMpFIcJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQIBAFwcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCRCgEA3kUJxmBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECHRxAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAIgQIdBMpFIcJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQIBAFwcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCRCgEA3kUJxmBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAECHRxAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAIgQIdBMpFIcJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQIBAFwcgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCRCgEA3kUJxmBCAQH8Ejj/+eHfaaae5H/zgB/3tlD1BAAKdCFx//fXu6KOPdpdddpn7y1/+4jbffHO30047uQMPPNCtvPLKnfbFxhDom8B9993nTjnlFPeFL3zB3XjjjW6FFVZwm266qdtnn33cs571rL7fjv1BoDcCX/7yl518zjn11FPduuuu29t+2REEloXAG9/4Rnf11VfP+9KTTjrJPfShD533eZ6AAAQgAIH5CRDozs+GZyCQLYH999/f3XHHHdXJrDUI//3vf90Tn/hEd/vtt7urrrrKWvNoj1EC1vrkmWeeWQVjG2+8sdtrr73cokWL3M9//nP32c9+1m200Ubu/PPPdw960IOMVtNmsyw5eu+997qnPe1p7qKLLnL77ruv22677dy//vUv961vfcudc8457oADDnAf+9jHbBbSeKsseRoqlXxRttVWW7klS5a4G264wa2//vqhzXhsxASsObr22mu77bffvvrsHcIu46lswwIBCEAAAt0JEOh2Z8YrIGCewMte9rLqZOAb3/iGqbZed9117vDDD6+uWtliiy0IdE1V13ZjLPXJm266yT3qUY+qArMvfvGL7v73v/9s8STU3XHHHasrIOUqM5Z0CFhy9D3veY/7wAc+4H70ox+5JzzhCXOKcNRRR7m3vvWt7sILL3Q77LDDnOf4x/gJWPJU05YvrJ/+9Ke7a665pgpzCXQ1oTT+bcnRm2++ufpy9txzz3WLFy9OowAcJQQgAIGECBDoJlQsDhUCy4uApQ+TwuyQQw5xZ5xxhvvTn/40i5BAdxYFKwkQsNQn3/nOd1ZTLfz+9793D3nIQxr099tvP3fiiSe6O++8062yyiqN53lgnAQsObrZZpu5pzzlKe7Tn/50A/a///1vt+aaa7r3ve997m1ve1vjeR4YNwFLnmrSH/7wh93HP/5xd8QRR7jXvOY1XKGrASXyb0uOXnDBBe7JT35yNW0N0yokIiCHCQEIJEVAAl1XsEAAAuYJlFdsFHvvvXexySabFKuvvnpRzldZvO51ryv+8Ic/zLb9kksuKV7wghcUD3vYw4oyaKnWZZvJ5W9/+1vx3ve+t9h6662LhQsXFuutt17xpCc9qfjc5z43udmc9XJ+zOJNb3pTUV7NVKy11lrVe5dXyhbltAfFz372s+p97rnnnjmvkX9ceumlxate9aqivJqvep9nPOMZRXmy0thu2gNlcFRI22b+9/znP78oA91pL+N5CAxKINc+ucceexTlT9jnZVv+lL0oP0kVv/3tb+fdhieWD4EcHZW/Rbvsskvx9a9/PQi5nFu3WGONNYoPfehDwed5cPkTyNFTTbmci7xYddVVix/+8IdF+QV2NYaWV+jqzfh3JAK5OnrcccdVn98jYedtIQABCJgncPpXzy0WSCuTiqE5WAhAoBMBuTJ1m222qeZSe9GLXuS23HLLam7Ar3zlK+4f//iHu/jii10Z4roy3HXymNwE5u67767muCxPXF0Z6lbvJ9uW4a2Tm8XITQ7kKte77rqrurnYscceW/1E9dBDD51zbGWI6p797Ge7//znP+7Nb36ze+xjH1tdKSvvc+2117qDDz7Yvf71r6/mJ5y8Gk+OQa4w2XXXXd1znvMct84661THecwxx1Q/KfzSl77kVlxxxTnv1fYf8n7lSQ9TLrQFxna9E8i5T37ve99zZfAw78/V3/KWtzgZT7hCt3ftOu0wZ0eXBuonP/lJ9Xfwl7/8pdt2222XtinPLQcCeOqcXDX+uMc9zj3zmc905RcNTj4fydzkTLmwHARs8RY5OyrT0/z617923/nOd6rzDZnjWebL3WCDDVqQYxMIQAACEJhGgCt0zWf2NBACRVH+xLlYaaWVivJD5RwcZaBalHeTL8q7ys95/KUvfWkhV9Hp5e1vf3t15e4tt9yinyrkNXK1rl7KILl45CMfWfz1r3+d81Q511tRBjdF+cGuupKkvOHM7PNy1bAcV/mT1tnHZlZ+97vfFeXPXYvyp7AzD3X+r1x1zBW6nbHxgh4J0CfDMOUXAPILgac+9anhDXh0uRHA0bmo5W9U+UVkUd6sr3jta19byJW6LPEJ4GlRlKFZUX5RX5RfxFcF4Qrd+F5OHkHOjpbz5hbyv912261YsGBB9Xm/DCeKBz7wgcWRRx5ZlF9GTKJiHQIQgAAEOhKQK3SZcqEjNDaHQGoE9tlnnyokCZ2AfvSjH62C1ck2zRfoPu95z6vC4cltZ9bLG6hVH9T+/Oc/zzxUlHcErx4rv5mffWxyRU4+ZGoH+XA3Gei+/OUvr6Z0CB2vvL68YU2x4YYbFhIKL8tCoLss1HhNnwTok02aMnaUN0Qryiv1iyuvvLK5AY8sVwI4WuM+9dRTi/XXX78ob95X/a0qf3GyzH97lmsBM3mz3D0tf/FQjZm/+tWvZitOoDuLYhQrOTu60UYbVePm/vvvX5Q3mSxuu+224oorrqgu2JALN8r5dRlPR2EpBwEBCKRKgEA31cpx3BDoQOD000+vPlC94x3vKO64446pr5wv0J3vheV0ClXQK8GszFc7s8hcuzJf79IWubJEB7qLFi0qyjuMz/uy888/v3qNzM27LAuB7rJQ4zV9EqBPzqUpc3Cvu+661VX+5U/a5z7Jv6IQwNEa+9VXX13NES9zO0uYKyFEOdVCceutt0apC286l0DOnko4Jr9o+OAHPzgHCoHuHBzR/5GzowcddFBx8sknB2swcyHIpz71qeDzPAgBCEAAAtMJMIdumSSxQMA6AZlf7ZBDDqnuflzelKyag7a8wVg1t23orrNLu8NueSVtNf/shRde6MrpD6p5d+W/9957bzWPm9y5fuONN66Q7rvvvq68asTJXIPzLZ/85CfdG97whtk5dGWe3vKnWG7nnXeed46tMpR2Z599djWnrswb13VhDt2uxNi+bwL0yZqozNv96le/2smdsF/ykpe4o446ypU/ae8bN/tbBgI4GoZWfqHo9txzz2p+9zPPPDO8EY8uNwI5e1p++V7dk0CcXGGFFWaZM4fuLIpRrOTs6LQClNMrVe7K3PosEIAABCDQnYDMoctN0bpz4xUQSJJAedd4d+6551Y3MfvpT3/qyqs73HOf+1x3/PHHVzcdm2nUfIHuN7/5zepGZeV8uNVNYXbaaafqZmpbbbVVdaO08ufSbjLQLadOcOVPq9yll146s+vGf8sr89wrX/nK2UBXbpQmgfDee+/tyrl3G9tPPiA3eNt0000nH2q1TqDbChMbLQcCOffJ0047ze23337VzRWPPvpoJ+MHy/gI5OzofNUofz7sPvOZz7jbb7+9usHffNvx+PIjkJun5VWfTj5jnXPOOW6TTTaZA1o+58mNa+WL9wc/+MHVl2SrrbbanG34x/InkJujbQgfdthhrryC15W/uGuzOdtAAAIQgIAiwE3Rpl/FzBYQMElA5p+VG7zIz/V22GGHOW0MTblQ3i25WGONNYpXvOIVRXkV7Zzt5R/liUM1DcLklAtywwOZI3dpy7ve9a45Uy7I9A33u9/9ivLK3aW97H96jikX/id8vHggAjn1yWOPPbb66Xp5R/aivLp/IKLstm8COTgqP48+4ogjlopOpggpP08XN91001K348k4BHLwVOYkFQfb/O+rX/1qnELwrvMSyMFRucnpjTfeOC8DeeLwww8P3lB5qS/iSQhAAAIQmCXAHLqzKFiBgF0CBx54YCEnqaFF5q6SO89OnpiGAl25MYycOEiwG1okgJXnJwPd8ueo1WO/+MUvQi+pHttuu+2qbSZvirbFFlsU5VXC875GbrJ2wAEHVDdXmHejpTxBoLsUODy1XAjk3CfPO++8YsUVVyxk/jyW8RLI1dHyyttipZVWKu655555iyM35pQvOCWUYYlLIFdP5X4It9xyS/B/J5xwQvW56rLLLqueL3/yH7dImb97ro7KTZflZpLlLxnmNWCPPfYodtlll3mf5wkIQAACEFg6AQLdpfPhWQiYICA3ctlyyy2DV8Kdcsop1Qf/chqF2bZKoLv77rvP/ltWZsLZ66+/fs7j8o8777yzukmMDnTlalu5w628/3333dd43cyNIuR1k4HuJz7xieoqXbkTrl7k6mC56nf77bfXT7X+N4Fua1RsOBCBXPukjAMyFpU/Bx6ILLvti0Cujl511VXV38RjjjkmiFJuxrneeusVL3zhC4PP8+DyJZCrp0ujzE3RlkZn+T+Xq6PyhYJ8vtc37ZupwEUXXVRdUCK/2GGBAAQgAIFlI0Cgu2zceBUEkiJw8cUXV1fE7bXXXsW3v/3t4q677qoC1vImBMX6669fLF68eE575OqjRz/60YUEsjNB7M0331yUNysr5Nv0mat5JYQt52orHv/4xxe77rpr9cGtnM+tCnhndig/9ZOrncq5eovyBmnV/mRf5ZyZ1RVO5Vy51esmA125mmSbbbYp1l577UJOTJYsWVId81lnnVV9k1/OBVfta+Y9uv6XQLcrMbbvm0CuffKaa66p+vtHPvKRQk7mlvY/pmLo27pu+8vVUaEkvwCRK8ve/e53FxLgyiJfXMrfM/mScp111pn6U+LqRfzf4ARy9nQ+uAS685GJ83jOjpY3PS7KG/YV8rlbfp1z9913V5/pP//5zxcLFy4syhsgz55nxKkO7woBCEAgbQIEumnXj6OHQGsCErQ+/OEPr8IUOVFdeeWVi1VWWaXYZ599Cvnp3uRyySWXFKuuumqx+uqrF2uuuebsU+WdlIvNNtus2od8EJOpGmQOXrnSVj6kPeIRj6ieO/jgg2dfIyvyuvKmHdVzD3jAA6rXbb755kV5Z/tCft4q76UXCZ333Xff6hjlfeSY5Zv+bbfdtihv6KY37/RvAt1OuNh4IAI59snvf//7VT+Wvjztf3//+98HIs9u2xLI0VFhI39/ZB7dtdZaq/JUpleQv0MSTMjVdvNNPdSWK9v1SyBXT+ejSKA7H5l4j+fqqHwx+/73v78ob85XjaVy3iFjqdwr46CDDppzAUi86vDOEIAABNIlIIHuAjn88sSKBQIQME6gvNrWlVfIuT/+8Y9u3XXXdZtuuqkrT1iDrf7nP//pyqvn3AYbbFDdhX5mI9nHb37zG1dO0eAe85jHuHL6g5mnXHllrSunZHAbbrihK6/KnX18ZkVeI6+V1y1atKh6+NBDD3XlVU/Vcc1sN/nf8sOgu/LKK92tt97qyjC5Op7yw+DkJqxDIFkC9MlkS5fNgefsaPnLEXfddde58lcprvzi0W299dau/FIym9qn1NCcPU2pTjkfa86Olr/4c+U9Nlx5kzRXXiziyqmXGEtz7gy0HQIQ6I3AGV87zxHo9oaTHUEAApMEJLwtb5bmDjvsMFdeyTv5VLVe3lDGlVfquvInV+7EE09sPM8DEIBAvwTok/3yZG/9E8DR/pmyx/4J4Gn/TNljvwRwtF+e7A0CEIDAGAkQ6I6xKhwTBIwQuO2221w51YLbbbfdXDlflit/YjXbMglzy+ke3Mknn+wuv/zy6tv62SdZgQAEBiFAnxwEKzvtkQCO9giTXQ1GAE8HQ8uOeyKAoz2BZDcQgAAERkyAQHfExeHQIGCBwHe/+1334he/2JXzZ7ndd9/dlTdWq6ZdKOfBraZtKOfQrZ630FbaAIEUCNAnU6hS3seIo3nXP5XW42kqlcr3OHE039rTcghAIA8CBLp51JlWQiAqgSVLlrgTTjjBXXHFFdUcu+VdwqsrchcvXlzNixv14HhzCGRIgD6ZYdETazKOJlawTA8XTzMtfELNxtGEisWhQgACEOhIgEC3IzA2hwAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAQiwCBbizyvC8EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgY4ECHQ7AmNzCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKxCBDoxiLP+0IAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ6EiAQLcjMDaHAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIBCLAIFuLPK8LwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBjgQIdDsCY3MIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAArEIEOjGIs/7QgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDoSIBAtyMwNocABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgEIsAgW4s8rwvBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGOBAh0OwJjcwhAAAIQgAAEIAABCEAAAhCAAAQgMCYCCxYsGNPhcCwQGC2BoihGe2xdDoxAtwsttoUABCAAAQhAAAIjIJDzSZuVD+Ej0GjwQ8jZ08HhjvQN6J8jLQyHlQUBxtwsykwjeyBg5W8VgW4PMrALCEAAAhCwQYAPwjbq2LYVKX+Yw9W2VbaxXaqu4qkN/7q0IkVXc/Y0xXp18TG3bXN2Obda097/jYCVsY9A93/zgFdDAAIQgIAhAnwQNlTMFk1J+cMcrrYosKFNUnUVTw1J2LIpKbqKpy2La2SzFB1tix6X25Jiu9wJWBkHTAS6OQ9cVkSUASXnOuY4oFpyN8f6WW0z45DVyobblfI4hKvhmlp9NFVX8dSqkfO3K0VX8XT+elp8JkVH29ZBu3zWm65t+1Iz2z37/zYKtuX3f/hj8HHrD26y8YZzmnjBJx4959+5/GPnN/xmTlOtjAMEunPKmt4/rIgo5PUfoPSqwRFDAAJCIOVxiXEoL4ctubrX6XeZLd4ZL3qA2ba1bViqruox1bKnUsuQqze/4mVty5zkdos+d+qc407RVe3pnAbxD3MEUnS0bRG0ywS6nhyBbs2CQLfmYGUcIND1fTzJNSsiCnz9ByjJgnDQEICAqUDXcvgQCh5EX8vhg4XgYWaI0X8zcXWGjI3/WnE1J0/FvNC4anlMlTZbcDUnT0OOSh1zWiydP+u6aZcJdD0hAt2aBYFuzcHKOECg6/t4kmtWRBT4+g9QkgXhoCEAAQLdRByY76TOcvhgIXiY0Uv/zSTQnSFj479WXM3JUzEvNK5aHlOlzRZczcnTkKNSR8ueWnBUatRm0S4T6HpqBLo1CwLdmoOVHM1koGu5s+o5UKyIKN1K/wF66/cX173N4P9/9GnnBVu15Kitgo9beHCtgy630Aza0IJAyuOSHocIyVoUPKFNLJ3U4SoBRApdLydPpR6hsMxyUCZttjCu5uRpyFGpo2VPLTgqNWqzaJcJdD01yxmRb2VzTedHBLo1o5TPVyerTKA7SSOBdd0hrYgo6PUfIALdBITscIg60L3z8p07vDq9TVff6oI5B235A5W++UDK45Iehwh052ic/D8sndThKgFECh0yJ0+lHqGwzHJQJm22MK7m5GnIUamjZU8tOCo1arNoly2ff8zHQ5+XzGxHoFuTINCtOaR8vjrjtPyXQHeSRgLrBLoJFKnFIXKFrnMEui1ESWQT/cEp5T+Q+oMwgW4iErY8TEsndbhKANFS+6ib5eSpgA6FZZaDMmmzhXE1J09DjkodLXtqwVGpUZtFu0yg66kR6NYsCHRrDimfr3qrCXQnWSSxTqCbRJmmHiSBLoHuVEkS2oBAN6FiTRwqJ3WO+Z4nfBjzKq6m66oOFyx/SSZ9KOSq5aBM2mwhLMvJ05CjUkfLnlpwVGrUZtEuE+h6agS6NQsC3ZoDga7vG9HX9MBlubMS6EbXrZcDINAl0O1FpJHshEB3JIXoeBic1KUbkkmp9Wcfy0EZrqbrak6eSr8MuWo5KJM2WwjLcvI05KjU0bKnFhyVGrVZtMsEup6a5YzIt7K5pvMjAt2aEYFu05Voj+iBy3Jn1R3Siogij64jc+hG61KDvDFz6F47CNcx7JRAdwxV6H4MnNSlG5JJtfXfTALd7n1gzK+wEkDk5Kn4FBpXLQdl0mYLrubkachRqaNlTy04KjVqs2iXCXQ9NcsZkW9lc03nRwS6NSMrORpz6DadH/UjukNaEVGg6z9ABLqjVrHzwRHoEuh2libCC/Q4REgWoQgDvqWlkzpcJYAYsKv0tuucPBVoobDMclAmbbYwrubkachRqaNlTy04KjVqs2iXCXQ9NQLdmgWBbs3BSo5GoOv7eBJrBLpJlGnqQTLlAlMuTJUkoQ24QjehYk0cKid1XKE7ocOoV3E1XVd1uGD5SzLpRCFXLQdl0mYLYVlOnoYclTpa9tSCo1KjNot2mUDXUyPQrVkQ6NYcCHR934i+pgcuy52VQDe6br0cAIEugW4vIo1kJwS6IylEx8PgpC7dkExKrT/7WA7KcDVdV3PyVPplyFXLQZm02UJYlpOnIUeljpY9teCo1KjNol0m0PXULGdEvpXNNZ0fEejWjAh0m65Ee0QPXJY7q+6QVkQUeXQdmXIhWpca5I2ZcoEpFwYRq+ed6nGIkKxnwJF3Z+mkDlcJICJ3p1Zvn5OnAiQUllkOyqTNFsbVnDwNOSp1tOypBUelRm0W7TKBrqdmOSPyrWyu6fyIQLdmZCVHY8qFpvOjfkR3SCsiCnT9B4hAd9Qqdj44Al0C3c7SRHiBHocIdCMUYcC3tHRSh6sEEAN2ld52nZOnAi0UllkOyqTNFsbVnDwNOSp1tOypBUelRm0W7TKBrqdGoFuzINCtOVjJ0Qh0fR9PYo1AN4kyTT1IplxgyoWpkiS0AVMuJFSsiUPlpC7dn7FLGfVJG18+TMhtYNVKAJGTp6JdaFy1HJRJmy24mpOnIUeljpY9teCo1KjNol0m0PXUCHRrFgS6NQcCXd83oq/pgctyZyXQja5bLwdAoEug24tII9kJge5ICtHxMDipI9DtqEy0zXE1XVf1Z3TLXzxIBwm5ajkokzZbCMty8jTkqNTRsqcWHJUatVm0ywS6nprljMi3srmm8yMC3ZoRgW7TlWiP6IHLcmfVHdKKiCKPriNTLkTrUoO8MVMuMOXCIGL1vFM9DlkOHzipSzckE+1xlQCi5+FvkN3l5KkADI2rloMyabOFsCwnT0OOSh0te2rBUalRm0W7TKDrqVnOiHwrm2s6PyLQrRlZydGYcqHp/Kgf0R3SiogCXf8BItAdtYqdD45Al0C3szQRXqDHIQLdCEUY8C0tndThKgHEgF2lt13n5KlAC4VlloMyabOFcTUnT0OOSh0te2rBUalRm0W7TKDrqRHo1iwIdGsOVnI0Al3fx5NYI9BNokxTD5IpF5hyYaokCW3AlAsJFWviUDmp4wrdCR1GvYqr6bqqwwXLX5JJJwq5ajkokzZbCMty8jTkqNTRsqcWHJUatVm0ywS6nhqBbs2CQLfmQKDr+0b0NT1wWe6sBLrRdevlAAh0CXR7EWkkOyHQHUkhOh4GJ3XphmRSav3Zx3JQhqvpupqTp9IvQ65aDsqkzRbCspw8DTkqdbTsqQVHpUZtFu0yga6nZjkj8q1srun8iEC3ZkSg23Ql2iN64LLcWXWHtCKiyKPryJQL0brUIG/MlAtMuTCIWD3vVI9DhGQ9A468O0sndbhKABG5O7V6+5w8FSChsMxyUCZttjCu5uRpyFGpo2VPLTgqNWqzaJcJdD01yxmRb2VzTedHBLo1Iys5GlMuNJ0f9SO6Q1oRUaDrP0AEuqNWsfPBEegS6HaWJsIL9DhEoBuhCAO+paWTOlwlgBiwq/S265w8FWihsMxyUCZttjCu5uRpyFGpo2VPLTgqNWqzaJcJdD01At2aBYFuzcFKjkag6/t4EmsEukmUaepBMuUCUy5MlSShDZhyIaFiTRwqJ3Xp/oxdyqhP2vjyYUJuA6tWAoicPBXtQuOq5aBM2mzB1Zw8DTkqdbTsqQVHpUZtFu0yga6nRqBbsyDQrTkQ6Pq+EX1ND1yWOyuBbnTdejkAAl0C3V5EGslOCHRHUoiOh8FJHYFuR2WibY6r6bqqP6Nb/uJBOkjIVctBmbTZQliWk6chR6WOlj214KjUqM2iXSbQ9dQsZ0S+lc01nR8R6NaMCHSbrkR7RA9cljur7pBWRBR5dB2ZciFalxrkjZlygSkXBhGr553qcchy+MBJXbohmWiPqwQQPQ9/g+wuJ08FYGhctRyUSZsthGU5eRpyVOpo2VMLjkqN2izaZQJdT81yRuRb2VzT+RGBbs3ISo7GlAtN50f9iO6QVkQU6PoPEIHuqFXsfHAEugS6naWJ8AI9DhHoRijCgG9p6aQOVwkgBuwqve06J08FWigssxyUSZstjKs5eRpyVOpo2VMLjkqN2izaZQJdT41At2ZBoFtzsJKjEej6Pp7EGoFuEmWaepBMucCUC1MlSWgDplxIqFgTh8pJHVfoTugw6lVcTddVHS5Y/pJMOlHIVctBmbTZQliWk6chR6WOlj214KjUqM2iXSbQ9dQIdGsWBLo1BwJd3zeir+mBy3JnJdCNrlsvB0CgS6Dbi0gj2QmB7kgK0fEwOKlLNySTUuvPPpaDMlxN19WcPJV+GXLVclAmbbYQluXkachRqaNlTy04KjVqs2iXCXQ9NcsZkW9lc03nRwS6NSMC3aYr0R7RA5flzqo7pBURRR5dR6ZciNalBnljplxgyoVBxOp5p3ocIiTrGXDk3Vk6qcNVAojI3anV2+fkqQAJhWWWgzJps4VxNSdPQ45KHS17asFRqVGbRbtMoOupWc6IfCubazo/ItCtGVnJ0Zhyoen8qB/RHdKKiAJd/wEi0B21ip0PjkCXQLezNBFeoMchAt0IRRjwLS2d1OEqAcSAXaW3XefkqUALhWWWgzJps4VxNSdPQ45KHS17asFRqVGbRbtMoOupEejWLAh0aw5WcjQCXd/Hk1gj0E2iTFMPkikXmHJhqiQJbcCUCwkVa+JQOalL92fsUkZ90saXDxNyG1i1EkDk5KloFxpXLQdl0mYLrubkachRqaNlTy04KjVqs2iXCXQ9NQLdmgWBbs2BQNf3jehreuCy3FkJdKPr1ssBEOgS6PYi0kh2QqA7kkJ0PAxO6gh0OyoTbXNcTddV/Rnd8hcP0kFCrloOyqTNFsKynDwNOSp1tOypBUelRm0W7TKBrqdmOSPyrWyu6fyIQLdmRKDbdCXaI3rgstxZdYe0IqLIo+vIlAvRutQgb8yUC0y5MIhYPe9Uj0OWwwdO6tINyUR7XCWA6Hn4G2R3OXkqAEPjquWgTNpsISzLydOQo1JHy55acFRq1GbRLhPoemqWMyLfyuaazo8IdGtGVnI0plxoOj/qR3SHtCKiQNd/gAh0R61i54Mj0CXQ7SxNhBfocYhAN0IRBnxLSyd1uEoAMWBX6W3XOXkq0EJhmeWgTNpsYVzNydOQo1JHy55acFRq1GbRLhPoemoEujULAt2ag5UcjUDX9/Ek1gh0kyjT1INkygWmXJgqSUIbMOVCQsWaOFRO6rhCd0KHUa/iarqu6nDB8pdk0olCrloOyqTNFsKynDwNOSp1tOypBUelRm0W7TKBrqdGoFuzINCtORDo+r4RfU0PXJY7K4FudN16OQACXQLdXkQayU4IdEdSiI6HwUlduiGZlFp/9rEclOFquq7m5Kn0y5CrloMyabOFsCwnT0OOSh0te2rBUalRm0W7TKDrqVnOiHwrm2s6PyLQrRkR6DZdifaIHrgsd1bdIa2IKPLoOjLlQrQuNcgbM+UCUy4MIlbPO9XjECFZz4Aj787SSR2uEkBE7k6t3j4nTwVIKCyzHJRJmy2Mqzl5GnJU6mjZUwuOSo3aLNplAl1PzXJG5FvZXNP5EYFuzchKjsaUC03nR/2I7pBWRBTo+g8Qge6oVex8cAS6BLqdpYnwAj0OEehGKMKAb2nppA5XCSAG7Cq97TonTwVaKCyzHJRJmy2Mqzl5GnJU6mjZUwuOSo3aLNplAl1PjUC3ZkGgW3OwkqMR6Po+nsQagW4SZZp6kEy5wJQLUyVJaAOmXEioWBOHyklduj9jlzLqkza+fJiQ28CqlQAiJ09Fu9C4ajkokzZbcDUnT0OOSh0te2rBUalRm0W7TKDrqRHo1iwIdGsOBLq+b0Rf0wOX5c5KoBtdt14OgECXQLcXkUayEwLdkRSi42FwUkeg21GZaJvjarqu6s/olr94kA4SctVyUCZtthCW5eRpyFGpo2VPLTgqNWqzaJcJdD01yxmRb2VzTedHBLo1IwLdpivRHtEDl+XOqjukFRFFHl1HplyI1qUGeWOmXGDKhUHE6nmnehyyHD5wUpduSCba4yoBRM/D3yC7y8lTARgaVy0HZdJmC2FZTp6GHJU6WvbUgqNSozaLdplA11OznBH5VjbXdH5EoFszspKjMeVC0/lRP6I7pBURBbr+A0SgO2oVOx8cgS6BbmdpIrxAj0MEuhGKMOBbWjqpw1UCiAG7Sm+7zslTgRYKyywHZdJmC+NqTp6GHJU6WvbUgqNSozaLdplA11Mj0K1ZEOjWHKzkaAS6vo8nsUagm0SZph4kUy4w5cJUSRLagCkXEirWxKFyUscVuhM6jHoVV9N1VYcLlr8kk04UctVyUCZtthCW5eRpyFGpo2VPLTgqNWqzaJcJdD01At2aBYFuzYFA1/eN6Gt64LLcWQl0o+vWywEQ6BLo9iLSSHZCoDuSQnQ8DE7q0g3JpNT6s4/loAxX03U1J0+lX4ZctRyUSZsthGU5eRpyVOpo2VMLjkqN2izaZQJdT81yRuRb2VzT+RGBbs2IQLfpSrRH9MBlubPqDmlFRJFH15EpF6J1qUHemCkXmHJhELF63qkehwjJegYceXeWTupwlQAicndq9fY5eSpAQmGZ5aBM2mxhXM3J05CjUkfLnlpwVGrUZtEuE+h6apYzIt/K5prOjwh0a0ZWcjSmXGg6P+pHdIe0IqJA13+ACHRHrWLngyPQJdDtLE2EF+hxiEA3QhEGfEtLJ3W4SgAxYFfpbdc5eSrQQmGZ5aBM2mxhXM3J05CjUkfLnlpwVGrUZtEuE+h6agS6NQsC3ZqDlRyNQNf38STWCHSTKNPUg2TKBaZcmCpJQhsw5UJCxZo4VE7q0v0Zu5RRn7Tx5cOE3AZWrQQQOXkq2oXGVctBmbTZgqs5eRpyVOpo2VMLjkqN2izaZQJdT41At2ZBoFtzIND1fSP6mh64LHdWAt3ouvVyAAS6BLq9iDSSnRDojqQQHQ+DkzoC3Y7KRNscV9N1VX9Gt/zFg3SQkKuWgzJps4WwLCdPQ45KHS17asFRqVGbRbtMoOupWc6IfCubazo/ItCtGRHoNl2J9ogeuCx3Vt0hrYgo8ug6MuVCtC41yBsz5QJTLgwiVs871eOQ5fCBk7p0QzLRHlcJIHoe/gbZXU6eCsDQuGo5KJM2WwjLcvI05KjU0bKnFhyVGrVZtMsEup6a5YzIt7K5pvMjAt2akZUcjSkXms6P+hHdIa2IKND1HyAC3VGr2PngCHQJdDtLE+EFehwi0I1QhAHf0tJJHa4SQAzYVXrbdU6eCrRQWGY5KJM2WxhXc/I05KjU0bKnFhyVGrVZtMsEup4agW7NgkC35mAlRyPQ9X08iTUC3STKNPUgmXKBKRemSpLQBky5kFCxJg6Vkzqu0J3QYdSruJquqzpcsPwlmXSikKuWgzJps4WwLCdPQ45KHS17asFRqVGbRbtMoOupEejWLAh0aw4Eur5vRF/TA5flzkqgG123Xg6AQJdAtxeRRrITAt2RFKLjYXBSl25IJqXWn30sB2W4mq6rOXkq/TLkquWgTNpsISzLydOQo1JHy55acFRq1GbRLhPoemqWMyLfyuaazo8IdGtGBLpNV6I9ogcuy51Vd0grIoo8uo5MuRCtSw3yxky5wJQLg4jV8071OERI1jPgyLuzdFKHqwQQkbtTq7fPyVMBEgrLLAdl0mYL42pOnoYclTpa9tSCo1KjNot2mUDXU7OcEflWNtd0fkSgWzOykqMx5ULT+VE/ojukFREFuv4DRKA7ahU7HxyBLoFuZ2kivECPQwS6EYow4FtaOqnDVQKIAbtKb7vOyVOBFgrLLAdl0mYL42pOnoYclTpa9tSCo1KjNot2mUDXUyPQrVkQ6NYcrORoBLq+jyexRqCbRJmmHiRTLjDlwlRJEtqAKRcSKtbEoXJSl+7P2KWM+qSNLx8m5DawaiWAyMlT0S40rloOyqTNFlzNydOQo1JHy55acFRq1GbRLhPoemoEujULAt2aA4Gu7xvR1/TAZbmzEuhG162XAyDQJdDtRaSR7IRAdySF6HgYnNQR6HZUJtrmuJquq/ozuuUvHqSDhFy1HJRJmy2EZTl5GnJU6mjZUwuOSo3aLNplAl1PzXJG5FvZXNP5EYFuzYhAt+lKtEf0wGW5s+oOaUVEkUfXkSkXonWpQd6YKReYcmEQsXreqR6HLIcPnNSlG5KJ9rhKANHz8DfI7nLyVACGxlXLQZm02UJYlpOnIUeljpY9teCo1KjNol0m0PXULGdEvpXNNZ0fEejWjKzkaEy50HR+1I/oDmlFRIGu/wAR6I5axc4HR6BLoNtZmggv0OMQgW6EIgz4lpZO6nCVAGLArtLbrnPyVKCFwjLLQZm02cK4mpOnIUeljpY9teCo1KjNol0m0PXUCHRrFgS6NQcrORqBru/jSawR6CZRpqkHyZQLTLkwVZKENmDKhYSKNXGonNRxhe6EDqNexdV0XdXhguUvyaQThVy1HJRJmy2EZTl5GnJU6mjZUwuOSo3aLNplAl1PjUC3ZkGgW3Mg0PV9I/qaHrgsd1YC3ei69XIABLoEur2INJKdEOiOpBAdD4OTunRDMim1/uxjOSjD1XRdzclT6ZchVy0HZdJmC2FZTp6GHJU6WvbUgqNSozaLdplA11OznBH5VjbXdH5EoFszItBtuhLtET1wWe6sukNaEVHk0XVkyoVoXWqQN2bKBaZcGESsnneqxyFCsp4BR96dpZM6XCWAiNydWr19Tp4KkFBYZjkokzZbGFdz8jTkqNTRsqcWHJUatVm0ywS6nprljMi3srmm8yMC3ZqRlRyNKReazo/6Ed0hrYgo0PUfIALdUavY+eAIdAl0O0sT4QV6HCLQjVCEAd/S0kkdrhJADNhVett1Tp4KtFBYZjkokzZbGFdz8jTkqNTRsqcWHJUatVm0ywS6nhqBbs2CQLfmYCVHI9D1fTyJNQLdJMo09SCZcoEpF6ZKktAGTLmQULEmDpWTunR/xi5l1CdtfPkwIbeBVSsBRE6einahcdVyUCZttuBqTp6GHJU6WvbUgqNSozaLdplA11Mj0K1ZEOjWHAh0fd+IvqYHLsudlUA3um69HACBLoFuLyKNZCcEuiMpRMfD4KSOQLejMtE2x9V0XdWf0S1/8SAdJOSq5aBM2mwhLMvJ05CjUkfLnlpwVGrUZtEuE+h6apYzIt/K5prOjwh0a0YEuk1Xoj2iBy7LnVV3SCsiijy6jky5EK1LDfLGTLnAlAuDiNXzTvU4ZDl84KQu3ZBMtMdVAoieh79BdpeTpwIwNK5aDsqkzRbCspw8DTkqdbTsqQVHpUZtFu0yga6nZjkj8q1srun8iEC3ZmQlR2PKhabzo35Ed0grIgp0/QeIQHfUKnY+OAJdAt3O0kR4gR6HCHQjFGHAt7R0UoerBBADdpXedp2TpwItFJZZDsqkzRbG1Zw8DTkqdbTsqQVHpUZtFu0yga6nRqBbsyDQrTlYydEIdH0fT2KNQDeJMk09SKZcYMqFqZIktAFTLiRUrIlD5aSOK3QndBj1Kq6m66oOFyx/SSadKOSq5aBM2mwhLMvJ05CjUkfLnlpwVGrUZtEuE+h6agS6NQsC3ZoDga7vG9HX9MBlubMS6EbXrZcDINAl0O1FpJHshEB3JIXoeBic1KUbkkmp9Wcfy0EZrqbrak6eSr8MuWo5KJM2WwjLcvI05KjU0bKnFhyVGrVZtMsEup6a5YzIt7K5pvMjAt2aEYFu05Voj+iBy3Jn1R3Siogij64jUy5E61KDvDFTLjDlwiBi9bxTPQ4RkvUMOPLuLJ3U4SoBROTu1Ortc/JUgITCMstBmbTZwriak6chR6WOlj214KjUqM2iXSbQ9dQsZ0S+lc01nR8R6NaMrORoTLnQdH7Uj+gOaUVEga7/ABHojlrFzgdHoEug21maCC/Q4xCBboQiDPiWlk7qcJUAYsCu0tuuc/JUoIXCMstBmbTZwriak6chR6WOlj214KjUqM2iXSbQ9dQIdGsWBLo1Bys5GoGu7+NJrBHoJlGmqQfJlAtMuTBVkoQ2YMqFhIo1caic1KX7M3Ypoz5p48uHCbkNrFoJIHLyVLQLjauWgzJpswVXc/I05KjU0bKnFhyVGrVZtMsEup4agW7NgkC35kCg6/tG9DU9cFnurAS60XXr5QAIdAl0exFpJDsh0B1JIToeBid1BLodlYm2Oa6m66r+jG75iwfpICFXLQdl0mYLYVlOnoYclTpa9tSCo1KjNot2mUDXU7OcEflWNtd0fkSgWzMi0G26Eu0RPXBZ7qy6Q1oRUeTRdWTKhWhdapA3ZsoFplwYRKyed6rHIcvhAyd16YZkoj2uEkD0PPwNsrucPBWAoXHVclAmbbYQluXkachRqaNlTy04KjVqs2iXCXQ9NcsZkW9lc03nRwS6NSMrORpTLjSdH/UjukNaEVGg6z9ABLqjVrHzwRHoEuh2libCC/Q4RKAboQgDvqWlkzpcJYAYsKv0tuucPBVoobDMclAmbbYwrubkachRqaNlTy04KjVqs2iXCXQ9NQLdmgWBbs3BSo5GoOv7eBJrBLpJlGnqQTLlAlMuTJUkoQ2YciGhYk0cKid1XKE7ocOoV3E1XVd1uGD5SzLpRCFXLQdl0mYLYVlOnoYclTpa9tSCo1KjNot2mUDXUyPQrVkQ6NYcCHR934i+pgcuy52VQDe6br0cAIEugW4vIo1kJwS6IylEx8PgpC7dkExKrT/7WA7KcDVdV3PyVPplyFXLQZm02UJYlpOnIUeljpY9teCo1KjNol0m0PXULGdEvpXNNZ0fEejWjAh0m65Ee0QPXJY7q+6QVkQUeXQdmXIhWpca5I2ZcoEpFwYRq+ed6nGIkKxnwJF3Z+mkDlcJICJ3p1Zvn5OnAiQUllkOyqTNFsbVnDwNOSp1tOypBUelRm0W7TKBrqdmOSPyrWyu6fyIQLdmZCVHY8qFpvOjfkR3SCsiCnT9B4hAd9Qqdj44Al0C3c7SRHiBHocIdCMUYcC3tHRSh6sEEAN2ld52nZOnAi0UllkOyqTNFsbVnDwNOSp1tOypBUelRm0W7TKBrqdGoFuzINCtOVjJ0Qh0fR9PYo1AN4kyTT1IplxgyoWpkiS0AVMuJFSsiUPlpC7dn7FLGfVJG18+TMhtYNVKAJGTp6JdaFy1HJRJmy24mpOnIUeljpY9teCo1KjNol0m0PXUCHRrFgS6NQcCXd83oq/pgctyZyXQja5bLwdAoEug24tII9kJge5ICtHxMDipI9DtqEy0zXE1XVf1Z3TLXzxIBwm5ajkokzZbCMty8jTkqNTRsqcWHJUatVm0ywS6nprljMi3srmm8yMC3ZoRgW7TlWiP6IHLcmfVHdKKiCKPriNTLkTrUoO8MVMuMOXCIGL1vFM9DlkOHzipSzckE+1xlQCi5+FvkN3l5KkADI2rloMyabOFsCwnT0OOSh0te2rBUalRm0W7TKDrqVnOiHwrm2s6PyLQrRlZydGYcqHp/Kgf0R3SiogCXf8BItAdtYqdD45Al0C3szQRXqDHIQLdCEUY8C0tndThKgHEgF2lt13n5KlAC4VlloMyabOFcTUnT0OOSh0te2rBUalRm0W7TKDrqRHo1iwIdGsOVnI0Al3fx5NYI9BNokxTD5IpF5hyYaokCW3AlAsJFWviUDmp4wrdCR1GvYqr6bqqwwXLX5JJJwq5ajkokzZbCMty8jTkqNTRsqcWHJUatVm0ywS6nhqBbs2CQLfmQKDr+0b0NT1wWe6sBLrRdevlAAh0CXR7EWkkOyHQHUkhOh4GJ3XphmRSav3Zx3JQhqvpupqTp9IvQ65aDsqkzRbCspw8DTkqdbTsqQVHpUZtFu0yga6nZjkj8q1srun8iEC3ZmQ20D3uuOPc3nvv7VZbbbWmDSN9RA9cljur7pBWRBS1dB2ZcmGkHW4ZD4spF5hyYRnVWa4v0+MQIdlyxT/4m1k6qcNVAojBO0wPb5CTp4IrFJZZDsqkzRbG1Zw8DTkqdbTsqQVHpUZtFu0yga6nZjkj8q1srun8iEC3ZmQlR2tMubB48WJ3yimnuHXWWadpwwCP/PjHP3a33Xab23PPPZd573rgstxZdYe0IqIUX9eRQHeZu8QoX0igS6A7SjHVQelxiEBXAUr8n5ZO6nCVACKF7piTp1KPUFhmOSiTNlsYV3PyNOSo1NGypxYclRq1WbTLBLqemuWMyLeyuabzIwLdmpGVHK0R6DYVGPaRk046yd1www3uyCOPXOY30gOX5c6qO6QVEaX4uo4EusvcJUb5QgJdAt1RiqkOSo9DBLoKUOL/tHRSh6sEECl0x5w8lXqEwjLLQZm02cK4mpOnIUeljpY9teCo1KjNol0m0PXULGdEvpXNNZ0fEejWjIbO0c466yy39tprux133LFZlGV85Oyzz3YLFy50O+200+weJND9fwAAAP//G+gotQAAQABJREFU7J0FfBRHG8Yf4kIMTbDgGtzd3d2lUCjuWrSUlpYCRYq0BYpToNDiTnGKu1uQ4BBCEkhIAt+8c99e7jZ7yV24S+42M/zI7s7Ozu6+8+zc7n/ffSfVJ5bw/zR79mz06dMHTk5OWLJkCerXr4+1a9fiyJEjyJYtG0aPHo0MGTLw0tu3b4e/vz/u3buHFStWwNXVFTVr1kTnzp2l6vDrr7+iffv28PT01OatWbMGlStXRmBgIKZOnYrQ0FC+nzFjxmjLmDKTKlUqveJ37gbqLatpIVfO7Hqno9N0evm2uCBvx6H769viaRh1zDNr7FAsF/JzEcV8NWR6DbmodxphF6voLattIXWRQ3qntHngPb1lNS00mZND73RsuV+S90Nt1r7TOzc1Laxr66Z4Os+7dFTMV0NmhuWr9E5DaFXPHFa7ILQK2KpWU1KfSheQklbV3KfSOauhX01JOlXSKLWjmnWqBo1SGxmT5FpW8/OHIXvIn0ukcmpmRNI5Kk3l/OjQ/EJKxVSfV6XvFb1ztPR91bfffossWbLgiy++0Nvv5yx899138PX1RY8ePbTVrPtnB1Kxk9EC3SJFiuDYsWNInTo16tSpg8jISIwYMQKFCxfG5s2bMWfOHFy9ehWOjo4YPnw4zp8/j2LFinEI/O7dO4wbNw65cuXCzJkz+U4qVarEgXDmzJm1O23Tpg2vk2DwvHnz8Pz5c/Tt25fvQ1vIhBl5x6Xmi1V+Qeo0nQkWs86i8nYUQNc62ymxRyWArgC6idVOUm4n74cE0E1K61t+X2p6qBNaFQDC8lfM5+8hJemUrKUEy9QMyuic1dCvpiSdKmmU2lHNOlWDRqmNjElyLQugG2s1NTOi2LOMOyfnRwLoamxkDEcjx9adO3ciOjoa1atX53BWusamTZuGkSNH6hl84cKF6NixIw4fPowffvgBHh4eaNiwIWedVFf58uX5uo0bNyJNmjR8XbNmzbR1xFcnOdhSne7u7ny7fv368e0SBLodOnRAt27dtDspVaoUFixYgNKlS3Oge/HiRezevVu7PiIiAgEBATh06BAyZcqE+IAu1fHHH3/g0aNHGD9+vLYOU2cko0rbqflilV+QxghRsou1T+XtKICutbeYaccngK4AuqYpJnlKy/shAXSTpx0stVc1PdQJrQoAYanrxJz1piSdkt2UYJmaQRmdsxr61ZSkUyWNUjuqWadq0Ci1kTFJrmUBdGOtpmZGFHuWcefk/EgAXY2NEuJoBGsfPnyIr7/+Gs7OziBYS46oK1eu5BXkyJGDRyrQtXiFChWwYcMGfPz4EVOmTEHGjBl5tIJ8+fKBuOqrV69QpUoVdOrUCS9evMCwYcPQuHFjzlWpnoTq/P7775E2bVpeV/78+fmuEwS6s2bNQsGCBbXHSXCX3IarVq3Kd5w9e3b0799fu55mKOQCkel69eoJoKtnmc9fkF+QCQnx8/eYdDXIf4AE0E062yfFngTQFUA3KXT2ufuQ90MC6H6uRa1rezU91AmtCgBhXVeX8tGkJJ2SBZRgmZpBGZ2zGvrVlKRTJY1SO6pZp2rQKLWRMUmuZQF0Y60mgK7GFgLoauwQH0ejkLIUepYiE9jZ2WlFRGFjf/75Z5CTa3zw1c/PD/KQCwR0Kazt0qVLtfUR4C1RogSuXbsGNze3BOtMVMgFComQJ08e7U7lQJdCNHTp0kW7nmZ69eqFunXromXLlopAl/IpFq/w0NUzm1ELAugaZSarLyRi6AIihq7Vy9ToA5THqorvB9LoSpOpoPxGWADdZGoIC+1WTQ91QqsCQFjoMjFrtSlJp2Q4JVimZlBG56yGfjUl6VRJo9SOatapGjRKbWRMkmtZAN1Yqwmgq7GFALoaO8T3vPrnn3/yMLQUblY3kdetl5cXBgwYkCB8VQK6rVq1QosWLXSr5GOQESQmrpoQJLYI0KWjmT59ut5BEbn+5ZdfULRoUdSoUQPz58+H5BJMBYlC02BpAujqmc2oBQF0jTKT1RcSQFcAXasXqQkHKICuCcayoqLioc52B5oiGckf2sTLByu6uMxwKGoBEClJp9TsSv2qmkEZnbMatJqSdKqkUWpHNetUDRqlNjImybUsgG6s1QTQ1dhCAF2NHeIDur///jsPp0AhDnQTRS+gccMoDIMSfC1btiz++ecfGPLQ7d69O2rVqqVbJRo1aoSxY8fy+LoJ1WkRoLts2TIe2FcCttu2bcM333yDkydP8gOlwc5y586NoUOH8uW9e/eidu3afD0B3eXLl3NjTZw4Ue/ETFmQd1xqvlgF0DVFGdZbVgBdAXStV52mH5kAuqbbzBq2EA91Auhagw6NOQahVdvVqvweXc0vHkjLSlpVMyijc1YDLEtJOlXSKLWjmnWqBo1SGxmT5FoWQDfWampmRLFnGXdOzo8E0NXYKD6gS+OBkdfs33//rWfQL7/8kkciaN26NfLmzYuzZ88iderUvAzV5+vri/PnzxsEuhSqQeKitBFtQ/UcPXoUGTJkSLBOiwDdnDlzYvPmzXwEt7dv3yIkJATkokyxdSndv3+fx9Kl4L10kA4ODggPD8ekSZO4h+6tW7c4la5Zsyb35OUbmfhH3nGp+WKVX5DxCdFEMyZ7cXk7ihi6yd4kZj0AEUNXxNA1q6AsVJm8H1IzfBAPdbYLyUj+QqsCQFioGzRrtSlJp2Q4pX5VzaCMzlkNsCwl6VRJo9SOatapGjRKbWRMkmtZAN1Yq6mZEcWeZdw5OT8SQFdjo/g4WnR0NI+TS1EFyOuW0qVLl9CmTRsOcV1dXdGkSRM+uBnlUaLYuDTW2OPHjznQJe9eGhStR48efD3F0D1x4gQOHjyILFmy8DzyBN66dSs2bdrElxOq84cffuCDovXs2ZOXpz9xBkXTrjFiZvjw4dw1mGLi0ohvNJobUWl5ovzAwEAeBFg6eN0yr1+/RmhoKPz9/XWzjZ6Xd1xqvljlF2R8QjTagFZSUN6OAuhaScOY6TAE0BVA10xSsmg18n5IAF2LmjvJK1fTQ53QqgAQSX4BJWKHKUmnZB4lWKZmUEbnrIZ+NSXpVEmj1I5q1qkaNEptZEySa1kA3VirqZkRxZ5l3Dk5PxJAV2OjhDja9evXQeA0TZo0fGC0oKAg/PbbbyhWrBiv4MKFCxzqkoets7Mz56AXL17kcJZCLhC4HTRoEBo2bAjyrCWg26BBAyxatIiXffbsGa93zZo13PGVKk2ozsOHD6N///68TikchNmArsYsyfNX3nGp+WKVX5AJCTF5WiRxe5W3owC6ibOjtW4lgK4AutaqTd3jkvdDAujqWifx8yEfPmDjvUCkc3FBY/9suM2+6Dn85Km2QrtUqeDHRnctx77k8XRy1Oabe0ZND3VCq5YBEEKr5r3qUpJOyXJKsMxSoExo1XxaTUk6VdIoWdISOhUaNZ9Gja1JrmUBdGMtp2ZGFHuWcefk/EgAXY2NjOVoT548QUxMjNarVtfClH/37l14enpyb1zddTRP0JacWwnwEtClwdTKly8PgsMu7JmIohjIkzF1UplMmTLxTT8L6I4ZM4a7IDdr1kx+HEm6LO+41Hyxyi9IY4WYpA2SyJ3J21EA3UQa0ko3E0BXAF0rlabeYcn7IQF09cyT6IVbIW9RcdMWFE2bBnsa1se6O3fR/+jxOPVlcXfHoqqVUCJdujjrzJEhgK45rJj0dSQlgBBaNW/7pqQ+lSynpFVLgDLal9AqWcE8KSXpVEmjZEVL6FRo1Dz6NKUWuZatGeh+/BSDoOC7iIyOgJ+XP9ydPU05VYNl5WN7SAXVzIikc1SayvmRALoaKyU1R9MFukrtlNi8zwK6id2pubeTd1xqvljlF2RSC9Hcbadbn7wdBdDVtY7tzwugK4CuLahY3g+ZAnQ/xUQj/MV9uPr4wd7ZLd7TjXz7EqGPb+LTxxh4ZM4HF68M8Za3xEpreKhrlTMHeuTLi/fsTfORp08x69IVeDo64kKr5nBlMffNnQTQNbdFk6Y+oVXbjff8OX1q0qjLvHtR0qolQBkdtSFYJvpV09s0JelUSaNkMUvoVGjUdC1+7hZyLZsT6PZeXh3P3j6CXSo7LOl+DF6uGu/ChQcmYNflNciaJjfmdNihdwozdg3GkVvbUCp7dYxt9BtfFxn9HosPTcH+axvwISZSW750jhroXnkcMnvn0OYlZkYAXX2ryfmRALoa+yQ1R+vWrRv69Omjjcmr30qJXxJAN/G2S5Yt5RdkUgvRkict/wESQNeS1k76ugXQFUA36VVn+h7l/ZAxQDci5DnOLhmCx2e242NUJBusyg6pM+VBweYj4V+5vd5BvHvxAGcWD8KTc7v08tMXrIxSvX6Bh18ebf6GTmnYZzrR8CteF5VGrNfmB989h73jqsLJzQtNFz3E+WUjcWvXQqT2zYX6M89py9HMyQVf4f7hNfAtXAOVx/yjt84aHur6FyqICSWLa4+r2a49OPbsOeZWLI+2uXJq8801I4CuxpLi5YNhRRkCEEKrhm0W35rE9Knx1Wft65T6VUuAMrKD0Kr51JBYndpaX0oWU9Io5VtCp0KjZNmkTXItmwvoXn9yFiPXt9SeTK+qk9CoaFe+fOnRcYzd2IHPL/7iKNJ7aD4FJw/cTr+VRFhkCIbW+RnV8jdj3rjvMfTPpnj4+hYc7B1RwK8knB1ccfXxabz7EAof9wyY0XYT0qWOOy6TducJzAigq28gOT8SQFdjH7VwNAF09fVu9UvyC1ItQiTDy3+ABNC1ejmadIAC6Aqga5JgkqmwvB9KCOhGhYdgx7DiiAh+qgW54U/v4mNMFD+DgLYTULDFaD4fHRGGXcNLcy9eeydXpMtbDqns7fHyxn+gdc5e6VF/xjk4eaTh5dd38AQ9LFKqOnYLMhapyecJ6O4ZUxGO7l5ovuQJ2/449k/QrGsw+xIHu7zgp0/Y9FV2RIa8QOneC5CjuubGm69jf6zxoW7S6bOYf/UaBgYUwrgSmkEHpOM1xzSlA13x8iFhFRkLIIRWE7YllTC1TzWuVustpdSvWgKUkQWEVs2nA1N1aqt9KVlMSaOUbwmdCo2SZZM2ybVsLqD764GJ2HZxOTxcvBEa8Qb5fIvjpzYb+ckRuO22uBzevHuJvjW+Q70ADdy99uQ0Rq1vDSd7Z6zoeQauTu5Y9d9MrD05l9Xjg8nNliNXhgBex90XV1nZVhz4NizSBV9V+ybRhhNAV990cn5kTUD3xZsoONilgo+n+b/K07cCUKXvFb0stXA0AXT1mtXyC9HR0bh//z4PhOzrmxHZs+eAk5OT0TuWX5BqESIZQP4DJICu0bKwiYIC6AqgawtClfdDCQHdM4sG4s6eRTxkQtVx2+CVrRAonMLVjT/g1o75IHDb5Ld7cHT1xNk/huL2zoWashN2wCtLAW6SsGd3ceDbBiDv3WyV2qLcgD94vi7Q9cpaEHWmnUAqO3vIgS4YuN3Sj4UteBWE4l/MQJ56ffj2r++cwd6vK8POwYkdQyCc3L31msAaH+p+uXwVk8+ew6hiRTCsSGG94zXHQkoGuuLlg3EKMhZACK0aZ09T+1TjarXeUkr9qiVAGVlAaNV8OjBFp7bcl5LFlDRK+ZbQqdAoWTZpk1zL5gC6MSw8WNfFpfH2fTAG1f4Js/eM4Cf1W9cD8GWxbylJwLdsztra0Aorjk/H+lPzUD5XPYxpuIDdrn5E24WFERH1Dt0qjkaLkl/xbaU/l4NO4M7zy/ByS4tq+RI/RpMAupJFNVM5P7IU0O3x/R0EPo0NoaF7FG4udtgyLT/P+vjxE9bseYU1e1/ibXgMz0vr5YAqxTzRv6UvHB1S6W5qtnkBdM1mSvNXJO+4lGLo/vbrQkyfPt3gztOk8cF/J05hyZLF+GHqVIPl2rRtgylTvsehQ4fwZY/uccrRCHd58uTGwEGD2Qh2FfTWr1u3ltX9PUJCQrT5bmxU7yFDh6Fr126wZ55aCSX5BSmAbkIWs871M2voxxeSjjLk5yLSbJxpjZ9v48Kj93r5jvapkDuDM1oU88LgmhlYTCO91QYXQt7H4PDtcLg4pkKt/B4Gy8lXFJ1yHS/DorFzQC4UzuwqXx3vsgC6xgPdXsuq4UVoELdng8Kd0LPqRK1tf9o5AMdu7+TLAZnL4tvmK7XraOZR8B0MWFWP5+XJWATTWm/QW7/s6I/459wi/gLl53Zb4Z82L1+/8/JqfjNG/ekPrdYjb8aietvFtyC/cbLlfkn+exIf0CXv2Q2d03Jv3OJdf0KeBv20ZqLQCxTu4GP0B+RrPBhp85TBpp7ZOOwt3m068tTvqy1LM4EHVvDydo7OaLn8JQe3EtD19i+MN/cvoeSXc5Cr9pdxgS7b/vzyUbi5bS78itXRhla48td3uLL+O2QqUR+VRunrgPZpjQ91A9lAaX+yAdP+rlMLFdlLT3OnlAx0xcsH49RkLIAQWjXOnqb0qbo12uKn7HT8Sv2qJUAZ7UtolaxgnmSKTm25LyVrKWmU8i2hU6FRsmzSJrmWzQF0Twfux+TNPeDp6oPlX57C8LXNcfv5JXQsNxRtywzgJ3gl6CTGbGjLni3dsbrXOR5OYfCahiDP25H1f0GlPA3xnMXf/XJpZV5+VvttyJm+oEWMI38ukXaixIikdWqeyvmRpYBu58m3cZ8BXQfGJ+i/bnJ1tsOmH/PxrMl/PMLeUxoelt7bER+iPyIkTAN2A3K6Ydag7HBijMLcSQBdc1vUjPXJOy6li3XRot8x6+eZcfb6/r0GkKVNmxYnT50BlZv6/Xews7ODs7NznPKtWrXGpG8m4+CBA+jevRtf7+3jw6cf2aAub9++5fO0/YaNf6NIEQ0UWbVqJSaMH8fX+ftnR/78+XHnzm3cvn2b5w1iAJggcEJJfkHaMjiRn6u8HYWHrr6Fqs68hfMP38PNyY6DWOaUBwKz7CUXTxMb+mJoLeMGVqJ6qD4/L0dcn6TxEtTfm/JSrvFXOdA9ODQPimUVQFfZSprc1EUO6a025YaKbnbopicV+5eD3ezMar9VWxd90hQc/hyf2L/CWcrjuxartetoZtXxGVh76hdtnu7bc8qkt+L9VtZmwPgxH6BgQpMlLGZVGL5aVpXp6TUaFmWfOVX9Rru9MTPyGydb7pfk/VB8QDfsyW1sH6x5CUOxa2lwM0PpQ+hr/PNlFr667owzWu9cqfy7lw+xtZ9meylsggR0q4zZhKPT28LBxR0N5lxB2NM7eiEXqI5Xt05i37hq3CO42ZIg2Du6YO/YKnh9+zTK9l8cJ5YvbWNtD3WBoWGouXU7Ithv6Z32beBixEtOOg9TUkoFuuLlg/EqMQZACK0ab09T+lSq1ZY/ZafjV+pXLQHKaF9Cq2QF8yRjdWrrfSlZS0mjlG8JnQqNkmWTNsm1bMrzh6Ejnb5zEA7d3Iw6hdqif80f8Nfp+Vh+7Cdk8cmF+Z338s3I+/aLJRXwOvwZprRYxdfRM4uLoxsLt3Cax8nVjbVLIRi8XDUhxhb8Ow4n7+3T7t6ThWOY3WG7dtnUGflzibS9EiOS1ql5KudHlga6/Vv5ok0NzYB5cruevhaGoXPv8+xh7f3QtHIaRMd8wuELoZiy9BGioj/hmx5ZUL2kl3zTz14WQPezTWi5CuQdl7EX68YNGzBixDB+YN9++x06dOyoBboEYv/+Z5PBg5aArqurKy5fuaYt9/DBA9StWxuRkZG8Pqr38ePHqFunNt69C0eXLl0xfsJEDoxpo1mzfsbcObPhwEbz3rJ1G/LmNQwEqLz8grRlcELno5vk7SiArq51WAzN/wPduW2zoEs5zQ/gB9YB9l71EBvOvUHJbG7YPyS3dqPwyI+48SwCEaxjLJbFlYNgWhnNCPDJwHeoP/cOMno44NzY/CymkZ3Wuzc0IgZXnkTA29Ue+X1dtPXRjC7Q9WNv1C4HvUf2tE7IlT7uyw+9DdmC8NA13kNXArqZfXLiyZv7WPPVBR53SnqzTTdQ5ImrBHR7MTD7NOSBNsaV7ttzqU1O3duPb7f04It003Xu/mFsOLOQD0Awr9Nevi+prDFT+Y2TLfdL8n4oPqD79OJeHPquCTcRxbKlmLaGUnDgBewZVZ6vpoHMnD30b3TIo/evTpqXg9VYOIYMhapCArr1fz6PwEOrce3vacjbaCD8K7aNA3Qp7MLW/vlBYJgAsE/O4tjcKzsPt9D09/twcI3riW8ND3X5vL1QJE0avP3wAUfZYGhhUVHoVSA/ppQuaciUn5WfUoGuePlgvGwMAQihVeNtqFvSlD7V1j9lp/NW6lctAcpoX0KrZAXzJGN1aut9KVlLSaOUbwmdCo2SZZM2ybX8uUCXHEE6/16Kx7btU30KimStgKDXdzBla09+Yj+326KNg/v7wW+w5cJSNC/RkwPduftGo3LexhhRbw4v+/D1be5UQgvzOu1G1jSagYAphMPxO7uYq8pHvP8QzkBvWg6B+UaJ+CN/LpGqUGJEjRs10DrYSeWkqbt7apw+cxbDhw3F5s2bMXTYMPTurQlrJpUxNL148QKePX2GfPnzIVs2f17s+vXrIFaUI2dO5M4d+8xuqA5z5cv5UXIC3XG/PcSh829RNLcb5g7NoXeKa/awkHWB71EklxtaGwDCehuYuCCArokGS8ri8o5L6WKVH8+lSxfRtk1rDl7btm2H76f+wItIHrqJBbpUSdcunXHkyGG0adMWU3/4EatXrcL48WORPn16HDx0RM/zN4Z5ItWuVZPF1Q3EkCFD0X/AQPmh6i3LL0hbBid6J8YW5O0ogK6+hZSALpWYufc5vtn2FHULemJdz+x8o40M8A5Y+4iNKvqRL1Ow8Yq53bG8qz8O3AxD12WaN2N8Jfuzb3BulPJ3w5Jjr/D1P0/wPkqzXSYGbXtWTKv1/JWA7sDq6bHg0EtEMaBMqWv5NJjTRuN9yDMU/gigazrQrVGgJfZf28DDKhTNWhEHbvyDmbuGoGaBVth37a84QFcagdbNKTU6lR+O3w5OAkHhBZ1j33pLTfPD9r4sdMMOdjOVmwPgqJgPPOYVxb4yNclvnGy5X5L3Q/EBXQqDsHtkWW6uhsxz1j2j/o2Jrh0/hAXjnx6ZeVbd6adBMXF1k56H7pzLSJ0xpx7QdU2TiXsDk6dvmb6/4b853bSDokn1XFj5NW5smcXDOaTJWQIn5n2JLGWaosKwNVIRvak1PNRJB2TPQn0U9PFGw2zZMKRIAPNNt0xKqUBXvHwwXk+GAIRUg9CqZAnjpqb0qbb+KTtZRKlftQQoo30JrZIVzJOM1amt96VkLSWNUr4ldCo0SpZN2iTX8ucC3X+v/42fdw81eBLNGLztXulrvl4aBI1ALTmfHL+zE183/BXlctXh62M+RqPV/AKgKXn6ksevbtpxaSUW/Ds+SYFuXRbii76YJuc6R0dH3cMBhcakL7iHDhmMTZv+wbDhI9C3b2x4Nb3CsoWBA/pj27atjAFNRLcvvuBrv5k0EcuXL+N1UF1JleT8yNJAt1SB1CiYXf9L3gblvZEpnRO6TrmNe48j0bNJBnSulz6pTMD3I4BukprbtJ3JO66EgO7r16/RpHFDPHnyBMWKFcOaP9dpByaTgK6HhwdKlSod50B++HEa0qVLpw254OLigl27NZ8afGQBwy9fvoxBAwfg48ePWLjwN9SuUweTJ3+DZUv/YJ679TB/wcI4dY4ZPQoUX7dx4yaYNVvzBitOof9nyC9IWwYn8nOUt6MAuvoWkoBuCeaJm4N5xVJ69jYKp+6/4/BjZffsqF3Ag3vg5plwlX3yEoPOZdPwGLu/Mvj6OCQKkxv7oUGAJ34/8gq/Hn6J1CyezYg6GdGulDcevI5C7dm3eb1tS/mwwPcx2Hn1LTn+YXv/XKiYy13roevMgpW3LuGNh2xkyoMMEFPaweLqVsjpzueV/gigazrQpZFi5+8fi/ZlB7H/g/lNzp6ra9mN01gOa+UeugsPTMD2iytQNV9T9Kg8jg1gUJYPQDCz3Wamg8J6zfIq7Cn6rqzF34TTigq562N0g/l6ZYxdSKlANzoyHBu7aG5G5GENYqIicGx6O8SwGLoBbcYjXb7y2NTLH5EhLyCPt0t2vndgOU4t6M1DJbRYwWLoprLTA7oemfIi8OBKnJzfi4Pj8GdsoDXmEUyewVKSBkFL7ZsLBHQfHFuP8oNXIGv5llIRvWlSPtTp7TgZF1Iq0BUvH5JRdInctVq0Kr+3M/SSTA2fslNTK/WrlgBliZSVRTZTg1aN1amt96WGNEr5atapGjRq7MUr1/LnAt1Jm7ri7P1DyOCZBRk9s2oP4827FyCP27SpfbHki6P8vpXCwfVgYRdesmcMJ3tnHkeXQis42mueW2njOXtHYu/V9fB2S8ccVlZpx/IIenMPYze05yEbktJDVwK6Y8eNZ+E0NV8vak/y/zOGgG5QUBACA+8hU6bMyJEj1qkjIiKCOeoNwu5duzBq1Gh06dqNO64RE/pzzWr07NkLgwYPAX3pTYkc/CgU55PHT7hHr6+v3//3DO58+CY4GE7OTvDxScP51cuXLxAQUDiOM5x2I9mMnB9ZGujKds8Xfx6UHSXzuaPO4GuI+PARozplQsMKmi8TlcpbIk8AXUtY1Ux1yjuu+IBuDBvEpnOnjjhx4gQHs5s3b0VGX1/tkUhAV5shmyEP2yxZsmiBrmy1dpE8fsnzl1Lv3r2wZ/dudOzYCZO/naItI83MmP4T5s+fh5IlS2Hd+r+kbMWp/IIUQFfRTFafmZhB0SSgq3Ry5Em7mgHd4iyu7VMGeRcffQ0fN3v0rZqOh1xovygQ+2+Eaj1plWLodl/+gIduaMdg7q8dNT/Yo/5+zMMzdGMhHsgLV/LQHVMvI0bXzcgPJWAy+3wk+AMWdMiKDqUNd8wC6JoOdGnAs+k7ByJ7ugLcS3fQ6gZs5E9n9ka7HX5hnzHpAl162911cRk+Au2YhgvZiLJ1Mfqv1rj6+DSaFf8S3SuPjSMdKSYWrZAGLIhTyIiMlAp0yTRHZ7RD0MnN3KOWQiW4pcuKqPdv+WBkNEgZDXJGIQ8cXT1xfsVo3Nw6B85e6VF13DZ4Zwvg1qVPOA9MacjDJWSv1hll+vzK83VDLhDQpbcrPC7unTN8vRzoUua2AQUR/jyQx9IlKEz7tnd24+Xlf5TAA5URD3VyS1nnsvzexxAoo6MXLx+ssw3jOyq1AAhjdaqGT9mpPZX6VTX3qXTOatCqsTq19b7UkEYpX806VYNGqY2MSXItfw7QffPuJSgO7sdPMaAxN0plr649hAevbqL/qrp8mcK3FcmiGQx+8eEp2HRuMc+vnr85htSZqd2GZt6+D+ZhF0Lev4K9nQPy+5VAZNR7Psiag70jA8EuHABT3N3EJvlziVSPEiNKDNANDw/nXrt79+6RqgaNqTSUDXRPvKdZ0yagr8GlVKZMGebt644DB/6VspArVy7s3rMPDx7cx8ABA/TK16hRE3Pm/sKB7/79+9Dzyx4oUbIkfLx9sG+fxpGQ+NXEiZO4w6C2UgMzcn5kaaBbvaQnSuVPrXc05QM8kM7LAe0m3MLjlx/QtwVzKKuVTq+MpRcE0LW0hT+jfnnHpXSxStV/++1kLP1jCXerX7lqNUqXLiOt4lMJ6ObOkwczZuh3QFQgX7783B1fiqFrzwZtqV69Bt82NCwUF86fB72VKViwINauW88vXmmfderWxYIFmgd1vsH//0geuk2aNMXPs2brroozL78gBdAFG2H+E94+fY/U6dibQGf7ODbTzXj/5gNePwzHJxZHNk02d7j5OOuuTrL5zwG6Y+tnRNOi3vxY37yLwbTdz7D3eih702mPu98WZD+OqfjgaStPvsahW2G4yzpNbWgEBmbnsBi8SkC3yoxbuPDoPWa2yoweLMyCUpKA7jbmsVuJeexSoli8x+6G4+fWmdG9gvJ2VE4A3cQBXYpFdfHhcSzpfhQdfyuBJsW+4DGn5EBXGoGWbo5mtN0EZ0dX7Lq0Gv+cW4Q07hnxR/dj/O05tQWluy+uYOifTflNGi37emXDLx13wclBP24yrUsoyW+cbLlfkv+exAfJyC40QNkuFnYhJvIdG7QsNfeMfX3vHKLfh3KzFe82nYdAoAUqs3NEaZB3LQ1aljZPGaSys8fLWyf4OhevDKjH4uU6uWuu7zhAl9Xx6uYJ7BuvuaFWAroXV4/H9U0z+L6zVmiF8oOW83mlP0rggcqJhzola1lfnqlaFS8frK8N4zsitQAIY3Wqhk/ZqT2V+lU196l0zmrQqrE6pfO15b6Ujl9Jo5SvZp2qQaPURsYkuZY/B+jSMwjFxU3t7MVj2tIzhm76ank1PtZHbRY6YQALoUDp5tPzGL6uOZ+XQ2Ceyf7Q4M6z947gnr9SXmbvHOhd/Vseg/fGk3NJFkNXArqVKlVGUfbltm5q3ao1srJQYHIP3bVr/8TXY0Yja9asaMMc+O4HBuKvv9ZzvnTp8lVs27qFj8tEMXNr1qyFxk2a8HWLFy/CubNnUa58eRD3ofCcrVo2x3nGjwoXLoIqVati65YtPBSnxIUkoEvHVbBQIVSsUJHt6y8EB79G5syZcejwUd1DVpyX8yNLA934BkUb/st9nLwahirFPDGlV6zHNx344q3PcenOO5RmIRs61jE/7BVAV1Ee5s3cyUIXZPLzQ0ChAtpBw4zZg7zjMgR0//57Iw9qTXVO+mYyOnfuEqd6CegmNoburVs3Ua+uJk7M6jVrUbZsWaxZvRrjxn2tCdXAPHwpTIOUdGPo0ludfv0HSKsUp/IL0pbBifwE5e2YUMiFd8GR2D/nKu4ce44YFvOVtvfJ6oayHXOjQO1MetW/ffYe+2Zdwb0TL/TysxRNg9rDAuCTJTZUwJx6uzgkzlE2PZpOKakt/+xmCNb0Ow7n1I7o83dNHJh3Def/uQ/vzG7otrSKthzN7Jp2Edf2PIZ/yXRo/kMpvXW08DlAV3dQNKpLgrM0f3REXgZ0geo/32af0n9Ei+LeqJYnNc48fIdlx1+jazxAt9yPN3HtaQRms1i43Zg3rlKSgO7BoXlQjHkDUxJAV8lSQOoih/RWmHJDJQ2KRh66t55dZCPJTkPXiqOw7OiPGMXCIoRHvo3jofsT8+Q9fHOL3j51F3TfntOb9uFrm/O34YWzlGNw9yqvs1WpPuhSYaTuZkbNp2SgSwYKfXILJ37pgde3Y70JHN08UbjtJOSu11vPhu9fBeHMksF4fHqbXn7GgOoo2XMOKFyClJSALq37b+4XeHBkbZyQC7ROd/C1isPXInPpxpStmMRDHTk9f1K0jS1kyn8zxcsHW2g1449RLQDCWJ2q4VN2al2lflXNoIzOWQ1aNVandL62/CKXjl9Jo5SvZp3amkZ37t7HmIivyUyE2lGuZVOeP2j7pEzvPoSCPH0plIOPewaz7Vr+XCJVrMSIJKArldGdrli5ChUYQJUD3R07tkMDa2uCmNGFCxfQonlTvuneff/y8AvGxNAlkNuyRTPuAHj8vxNInTo1rrDwnU2aNOLhQE+cPI3Tp09xD117ewecOn0GXl5eOHv2DFq3asn3d+36TW3oUN1j152X86PkBLq7T77BlKVBTKfA919lQ8UiHjy847FLoRj/+0NEM0e9id2zoGYpwwNN656bKfO2BHRN4aLr/tmBVOyBxiqeaNas/Qv3Ah8wd3Jv9vahrNGdmLzjUrpY6eJo3bolj0PSslUrTJs2XbH9PxfoUqUlihdFSEgIH2iNwi48e/YM1FmEhoZyN/yJk74BefZSmjljOubN+4VfiFu37eDu93yFgT/yC9JKms7A0ZqWLW/H+IBuZFgUln5xGOGvIrUg983jd/gYrZFyxe55UbaTBoxEsViwy3oc5l685MGbqZA37OxTIehyMPs0OgZu3k4cyLp4agKhz6q9kwNdOvqWP5XmUJbmCeiu6n2MA91+m2vhMdv+z4H/0Sp0X1GVg12+wA5hYct9eMe8geuMKIyA+ll4tu4fcwHdGOZpPGLjYxZi4RX7PCUVAr8rxOcnbHmCanlTY1OfnHy3NWfdxmkWa1cOdDN6OODmZM3gTB2XBGLrpbfoVMYH89pr3pi1/PUeTgS+w4/NM6EjyxdAV7cV4583F9Clt+Ffb2jHb3TojfYf3Y/jzP2DekBXdwRaipXrygZFkxLdKNFnTbUKtsHAWj/ybPoUij6Jok+bfum4Gyfu7sEfR77nnz7Nar+VxbPKJ21u1FR+42TL/ZK8H0oIkukaiAYsC3l4hYFWb3hkysO9cHXX685/CH+D0Mc32dcCMaxsXjh7pNVdnSTz4qEuZQFdEpV4+ZAkl5ZZdmJrAMLQSRvbp6rhU3aygVK/qmZQRuesBq0aq1M6X0q22pfSsStplPLVrFNb02himQi1o1zL1gx06XgtkeTPJdI+lBiRBHQbNmyEChUrSkX5lL7CzpgxYxygSw55a1gs3AP//ouLFy/g1atX2u327N2PnDlzsjAKCQ+KJjkbOjk5IUOGWKD96NEjXt+Gjf/g9etXHOhSjF6CxZRofdUqlfj8pctXOBDmCwb+yPlRcgJdoo79ZtzD5btsDCAGdfNlc0Uo++I46MUHfvQlWJzdmQP8mXMnW2nmZEtA15Q+wCqBrtR2xoJdeceldLFKYQ2obophYk9ujDrJ09OTu6xLQJfqpNEO5Yni59LFJIVcoGDWl69c0ytWpXJFUJBsimtCQbAp/fnnGoz9egyfJ/f8fPnz4w4bUfHevXs8b+iw4ejXrz+fj++P/IK0RnBiyhsF3XOVt2N8QHfvz1dwccsDHjKhFYOu6XJ6gMIp/LfyNs5tvM9DL/TeUANObg7ci5c8aSm8QusZZZA2uwZ2EQD+a9hJkPdu/pqZ0GBsUX44ukA3XQ4PdP69IvssOlUcoMvivuP3dv8i9EUEqrO4lcWb+/Ptn94Iweo+x2DvYIev2DG4eOiPmEmFPgfoerjYw9VR08nRwGUR/4fYrdggZYs7Z8M+Fn6hBQOxBGsnNvLD2QfvsIgBX0oSrL35PBKlp97gHWn/qul5rN2rzDuXAC51rm1Kxg6K5sLO48Y3BdiIo/YC6HIrGvfHXECXYku1W1iEjwib3iMTFrOBB3ZfWasHdKURaJ0dXPknSi6OsTFTN579DUuPTIWbkwdfR/Gw+q2sDYLALUv25p6/UTEsltGKWmyQvYc8ltWPrdYzHej3kfGdtfzGyZr6JVP7I3k/ZArQjc9G1rhOPNRZF9A11SPnc7QqXj5Y4xWpf0zWCiAs2afa+qfs1IJK/aqaQRmdszVq1ZI6pXOWkq31pXTcShqlfDXr1Bo1SjY3lCSYI603lolQefm9gQC6khUBJUYkAV1TBkWbOGE8Vq5cAT/2ZXmzZs15CIUve3RHVFQUEgN006VLh1YsvIM8tWvfAfT1N8XQzc/Y0bbtO3kRWwa6dAI0KNq8Dc+w5WgwPjLnNErEH+qX80a/lr7wYGEkLZFsEehKdoivD1AEut//GDd2rFRZckxz5siOVi00sUeU9i/vuJQuVl2gq1QHAd1z5y/yeCdTv/9OqQjP4wOisbAJ8QHddm3b4NSpk6hWrToWL/lDW9fGDRvw3fdTQCMVSsnd3R3DGMzt3KWrUWEmbAHoSj9C8QlPOn/dqbwdDQFdipk7pz4Li8BAZrV+BVCiZXZtNRR6YdePF9nI8p9Qqk0O+BX0xoIW+zjsrd6fQdcWGugqbXBlZxAPj2DvaIeBO+pwcCsB3fS5PPDiTihqDSmEIo2zxQW6rJKD86/hzF+ByFEmvTa0wvFlt3F82S3kLJ8Bzb4rKe1Kb/o5QFe3IheHVPBjA6IRzB1WKwMDvXb8s4W2iwJ5XF3y4A3I5IJ6hTwxfc9z5M3gjGMj83Jv3sbz7/IYu1TfvsG5UcrfDbP3v8C3259qY+6mT+2AFV/4o3xOTUiKpPbQrdXlPE6zTzB0kz3zRPbP7IIa5X0weXAOuLkYBx437X2JriOuwdnZDs/+038DK9X/8SOQpuRhvrhiegE0rhkbv+f7Bfcx7bcHKJTHHUfXlZA2MTg1F9AtmpV96vNnEx4eoXLexhhRb04coCuNQFsxTwOMqj9P75gevwlE7+XVed6Yhguw58p6ULxdiqu7sMt+SPD3yK1tmLZD81KpD4thVb9wJ7164luwZqBran8k74cE0NW0/LKbt1i8Zc1NF+XYM+Cf09MDZdKnYwNYaG68Tj5/gSvs960C82bI5234U6nTL15ix8OHeBAWjozspWiFjBnQIJt+HC3axyM26MSuh4/wkE1zs9/o8qxcLjaV0prbdxDBvCS65csLzSsugOq+9Po1HO3s0C53LjjQHaJOsuaHOqFVnYaSzZoCIIRWZcYz86IldWrrn7KTqZW0agiUCa2aWZw61VlSpzq7sclZJY3SiSjpNKVp1NoYiFxgxETatWkhz9Zblt/HCqAbax4lRpQYoFumdEnulfvH0uWoUqUKD4vQto0GyMqBri4o/mbSRCxfvgy9e/fBiJGjtKEa7Ng96959++Hvnx1hYWGgkA7Ujk2bNsPhw4dsBujGWtq4uQ9Rn3DvSQQbFO8Tsvs6w9Nd8zxh3Naml0os0LWmfoH6ADkXVQS6ppvHPFtIP75SbcYCQXnHpXSxSnUm95Rc9B89eshd5X19/diF66/oCWzoOG0J6ErnkNh2NAR0gx+F448uh3j1FLuWBjczlCLeRmF+s718ddcllbXeuVL50OfvmZftAb4ohU2QgG4LFvt284SzbHR6B3RfWRVvgsL1Qi7QRk+uvsGa/se5R3DfTbXg4GSH1X2P4en1ENQfUzROLF++I/YnMUBX2tbY6fPQaOaF+RHZ0jgZ3CQs8iPCImOQjoFbh/9/2vCOvTW7zrx13di55M3oAnN98ZCYQdGqdTiH05dDObR1YZ7JlMLCo0E/AJQaVE2LdXML8fmE/vyz5yU6DbsKFwZ0X57SfKYi34aArmcxjbZWzyyIJjqjb06ZF4gfftUA3RMblEG9bn2fA3R167GFeVsAupIdE+qP5L8nAuhqLJdl1Rp8iGEXiCwVSZMGm+rVhjv7omUCi+218Op1/FSuDLrmzSMrqVmccfESprGXp3QFE2qVEHEj/2xYXLWyFsz+E3gfg4/9h3fR0dp6nNgNL9XdnoFaSgXW/YVXEZF43LkDB7fHnz1HW/apWwwDz1RXvaxZtNtKM7YAdKVjFVqVLKEMyWitEoAQWo21myXmTL1XN7VPteVP2cneSrBMSadUVmiVrGCZZGmdWuaok6ZWJY3SnpV0KjSaNG0i34tcv97sJXmlCuWMCkcp73MF0I21rhIjSgzQ7dK5E44ePcK9c0uVKs28dZfzmLq0p1279iB3njwYNXIEHyiNBjKjcZtoALRp037ErwsX8BCbHTp0Yl9ydwU5Ap45c5p74FZlzoD/7t+PmzdvoEaNmvh90WJIg6LZgodurKWtcy6xQDc5zkbeB8T3TGCVQDe+A1YyqLzjUrpYlbazxTxTgK41vU0gWyu9UdBtA3k7GgK690+/xIaRp/imFMuWBikzlJ7feouVXx3lq2kgM1cvfbhJHr2z6+7i61vPLIOsxdJCArpfLKuCq7uDcGLVHZRsnYOFZfCLA3R52IX2B0BgmABwxrxeLH7ufhY7MxV6b6zJQz4oHVtSAF2l/SZn3ucA3SlDc2JwNw2coWDpw6fewaJ1j/np3P23HDKk1bRrBAPUV26FI4TB7OIFPeDjFRs2RQBdy7W+MUDXVvojeT8kgK5GN/RQR9B2dQ2Nt/ebDx/w7dlzuBr8hsPTxgzIJgR0T714gcY798CbxQpbULkiKvpmxM03IejNRui9yeLOTytbhnnb5sHTd+9RYdMWRLIXoN8yL4hKrNzuh0H4+dJlhEVF4UzLZsjCvm7RBbpXXgejBRtYlTx2DcFcOhNjgK41alXJI0do1TCAEFq1XH9vqGZD93iJ1aktfspOtlGCZUqgjMqmhH7VGvtTuXcTtUVidUrb2lpS0iidg5JOU4JGrbH9JJhjCsiVzkOuZQF0JcuYL+TCsWNHMWL4cDx9+gSOjo7o0eNL7Nu3j4dHmPztFD5m0vHjx9D7q17c47ZMmTJY8+c6PuBZ165dEBz8mkPd3Xv28Tq+ZmE56etvKVWpWhU//TQDFIpBAF3JKp8/tUWgawwXtSqga2q8I6lZ5R2XALqSZZJnKv0ISXs3RohUVt6OhoAuhUFY0fMIr77Hqqrw8ouNFyrtU5pGhDIP3aZ7+WKXxZVAMXF1k56HLvPC9c7kpgd0U6dzwZIuBxEREoW6o4tg+5Tz2kHRpHoOLbyO0+vu8XAOGfN5YefUi8hTOSMaf2P4s3wBdJmn7cUqkgkNTiUPXV2gS4VPnH+LmiwcA6Xru8siC/tM4+iZEPQYcx2PnkbyfPoziEHgKUNyMm0BAuhqzWL2GWOArtl3amSFpvZH8n5IAF2NoemhztvJGZdbx37q982Zc5h35Sp+ZwMzNM3unyDQ7XfkGNbfvYd+hQpiYsni2hb8624g+h45ivxsQNRDTRrih/MXMPPiZe5hu7x6VW25pTdu8VANBH3rs1j0EtA92LghmjOYG8pg76Kqlfg67UayGWOArmyTJFuUazWhBzmhVcMAQmjVcrKV6zShe7yUpFOyuhIsUwJlVFb0q2QFyyShU8N2VdIolVbSqdCoYTtack1imQgdk7zPtSTQffchDI+C7+A9m2byzoF0Hn7sSyv9UFe6dgqNCOblo2OikcUnJx/wWXe9ueblzyVSveZkRDReCHnSUpgEFxcXaRd6U/oy+82bN/xrbC8vTSiyj+xzUMqzYw+n3j4+2vLPnz9HEBvsLBv7ejttWvMOlCx3CLTUoGjak7HSGVsCuqb0AVYFdBPb9vKOy5wXa2KPyVLbyS9Iaxp8SDpn6SYqoZt8qbw0lbejIaAbFRGDuQ12883kYQ2iWbiALSxMAnneVvgiDzIF+GAhi6H7jg2YJo+3SxVc2fmIxdC9xEMlDKAYuqxz1fXQ9cnqjqu7grCTxeUlcBzy5F0coCsNguad2Q0EdG/sf4JGE4ohbzU/6dTiTAXQNQ3oDv8yK75opbHnm7fRmDw3ELsOv0ZhNhLm8fUluUdu0Uan8DI4CvWqpEH+nG5Y8c8zvHoThe+HsZFGu2YRQDeOCs2XIb9xsqZ+ydT+SN4PyYHu3X1/4EN4bBx0sqKjqye8/AOQLm85rVHf3L+Epxf2wNWHhdap3F6brzTz7sUD3N67CKFBN1jfFQGPTHmRrUJrpM1TJk7xjzFReHh0PYLvnWcDCUTDJ3tRZCxcA27pYmPQBp3awkfgzlCQhaTJXUpbx6P//kbYczbwoR0b4LBmd2zsllG7TnfG0EMdlanKBoCgFBwZiQuvXqMAg7DGhlyos20nzrORgDfUqYnKvr68HvrzKiICBddtYLG97XC/Qzv0YnHqt9x/gCnMO7dXgfzacvIZCehSHN5n79+jb8ECmFTK8Is02t4WgG5CIFeyQ0JalcqpYWoqgKBzFlq1TMubu0+1zFEmX61KWlXqU+kICZZRElrlZjDrH6FTw+ZU0iiVVtKp0KhhO1rrGvm9gbFAd+T6Vrj17ILeaaV29kKuDAHoVH4YcmcorF0XHvkWS49Oxe7La9nHqlLwLCBrmjzoX/N7FPCLvfekjV6FPcXCAxNw4u4ebR00Q+WoPG0npS6LSiM04g0G1pqG6vmbS9kmTeXPJdLGamZE0jkqTeX8SABdjZWs6XlVqd2MzRNA11hLWUk5+QVpjUI05Y2CrlnlP0CGgC5tQ7Ftbx95xj1qKVSCRwZXfHgXjeNLb/FBymiQMwqx4OTmgIMLruPM+ntw83ZCy+llkD6nxkuXYvH+NfwUD5dQqF5m1B1ZhB+OHOjS7xSPi3sjhK+nEA8U6kE3Le54gMHe9zyWLnmD0r4dnDUxX3XLSfMC6JoGdCW76U4D8rpj+6IiSMMGhaPwC4On3EaR/Klx7P8Dli3d+BT9J91E3hxuOLuplAC6usYz87z8xsma+iVT+yN5PyQHutsHFQYN3KOUMpdpgopD2QM66wTu7l2C07/3R9rcpVHzu4NKxXkewdcTc7sjOjJcr0wqNuhYoVZfoyD7L6XwF/dx9Kc2IFismxzd2IBhg1fCt6imXzo6vS2o3iIdpyB/k6G8qHQ8VG+p3vORo1oXRU8yKmzooY4GWfT3SM3rC4+K5hCVlldWr8YHQUso5EKpjZvYQGhhOPv/kAm8IvaHHgXy/rkeISyMw+12bdBx/784wQZYW1urBqpnMvxiTAK65OVAA7bRZ/b7GjXgg7VJdcun1gx0za1V+bnb8rKpAEJo1XKtLXQav22VtKrUp1ItBMuEVuO3Z2LXCp0atpySRqm0kk6FRg3b0VrXyO9jjQW6w9c2w00GdJ0cXODs4Mr6piiQBy4lVyd3zO24Cxk8MnOAO25jB1x69B/zMrVHPt/i8HDxYuOvnMXb98Gwt3PArPZb4Z82H9/2Q3QEBq6uDxqk2cneGfn8SrAX+E649uQ08+wNZ9t645dOu+Hjlp6X7/hbCQZ0gzG49nTUKNCS55n6R/5cIm0vgK7GEgLoauxgTc+rkkYTMxVANzFWS8ZtbAHoJtY88h+g+IDum6B3WP7lEQZBYph3nD18mWfss5tvOdSl/VfvX5CHQKB5KrOs+xHuXUuDlvkV9GYeaqnw+Mobvs7NxxndllaGi4cmFm8coMvqkAY/o/qUgO7h32/g1Jq7tBr5qvuh4fhifN7QHwF0TQO6+XO5IVdWVzBmg2t33+Hew/fshiEV5k/Oi45NMmLUtDuYtzIIqdnomGm9NO34gXlpP3nxgTdB8NnK2PbvqwQHRaP6PYpqBkVbxQZFa6ozKBp5BU/7/YHWK9hQ20r5YlA0yRK2NZX3Q4aAbqFWY5HaLxc+fYxB6ONbuPb3NH6itaYeQZqcJYwCuhEhz7FzaHF8CAtG1vItkb/pMPYiyA2Bh9do66s+cRfSF6jE6z74XWM8u7gP7hlzoHCbCTzv3r/L8ezyv7Czd0TjBbfh7JUecqD74Nh6nJjzBb8JL/0Vg7nVu/JtTX2ok3/GToObTWeDnNEgZbPZYB0JAd02bMCyA4+fYBYr2+H/A5vRgZCnb+1tO5COfbJ2tU1LHn6BwjAMLRKA0cWK8mOlPy+YJ29QeDh8Xd3g6+aqDbnwS8Xy+I8B4JW32EsdNkjb9gZ1QQOoKSVrBrpKxxtfXkJaTcne5EKr8SknadclpNOkPRrL702pX1UCZXQkBMuEVi3fJsbsIT6dqqkvdXD1MPllrtCoMQqynjJyLZsKdDuUG4J2ZQbyE7r38hpGrG3OBsWNRLeKo9Gi5FfYcWklFvw7Hg7svnNikz9QNGtFXpa8aidv7o4bT89xb94Zbf9h/g12+OPI9/j77O/wdPXB5GYrkTN9QV7+eWgQxm3siKch91E+Vz2MabiA5wugy81g1j9yfiSArsa8AuiaVWafV5m841Lz2xf5BakWIZIC5O0YH9Cl8uRhu+P7C3h6XeM5S3nO7g6o2CMvijXzp0VtCn0Rgf2zr+DOsefaPJrJViItag0JAIVLkJIS0KV127+7gOv7HisCXd3B15pMLoHclZQ/ZZb2IYCuaUBXHkO307Cr3OO2cmlv7FhcRAt0KQRDnUppJDNrp1/38ceOg68TBLq0QeGGpzgw/mFELvTvnFlbR6+xN7B6yzMOeQn2JpQE0E3IQta5Xt4PGQK6Nb7drxdiYe/YKnh9+zTKD1mJrOVaGAV0L64ah+ubZ8InZ3HUnqoZvFGyypFprfD4zHakL1gZBHWfXdqPg1Ma8dV1pp2At3/sp297xlRE8N1zKNblR+RtOEAP6HplKciXKTxD6V7zkKNGN2kXn/1Qt+DqNUw8fVYb6zYhoCsB4Ew7n+sAADyFSURBVDpZMmNljWra45Bi5tZl+StY/uLrNzDm5GkUTuODHfUZnLW35168bRkQPsiAMHnhBrB1kofu484d2MNGDKpv2Y57oaH4ioVpoMHUlFJKArop2ZtcDiCEVpWuhqTJS6hPTZqjSLq9fC7QFVpNurbS3VN8OlVTX0rnrKRRyld68aD00kFolKxlvUmu5c8BunSWY7k37nE0LNIFX1X7Bl9vaIfLQSdQL6AD+tb4Ts8Q5OFLnr6UFnb5l8XVzY7uf1TAy9AnWiCsu8Hhm1vw086B3NN3fd+r3HNXAF1dC5lnXs6PBNDV2FUtHE146LL2bN26JS6wQVh0E8WwKxQQgKFDh6FwYc2n+NL6Jk0a4fq163xxH/s0NCsbnEU3tWefjJ45c1Y3Cz4+3sibNx8mTpyE3Hny6K0zZUF+QapFiGQD+Q9QQkBXslvE2yi8vBfKQSvFvCUvXEOJBkkLfhjOvOo+gcq6ejkZKmrR/MQC3WLfXcfD11EoksUV+wbnZj+AmsN8HhqNApOu8YWHUwvBLR4bWPTE4qnca8hFvbWfMyjaglVBGPHjHWTK4Iybe8uykAtPWMiFW8iQln3Cs6sMnNn5BwZF4NDJN/BM7YBmtdMZFXKBDrDLiGvYuOsFalbwwd/zC4Mc/ULDYlC+9Rle56SBOUAxfRNKlga61jQQgfzTJlvul+T9kCGgG9BmPI91S27jIUHXcW3DD7B3ckXjhXdAIRCkEAfxhVw4/ENzPDm3CyW+mInc9XrrSer2rl9xdskQOHmkQbNFjzj4JQDs7JkOTX+7z8M6SBuQlzD9p9i49F/y0M1Uoj7z3j2AmA/vUfLLOchV+0tpEz419aGOBrpo4p+Nb0sDkB1+8hTh0dHcO5e8dCWgW5D93mVLrQnNIO1wQeWKfJiMypu34mFYOGpkzsTi6GbE9TchWHfnLoe2Bxo3QC5PTw5nK2zaysMzkCdu8+zZsftREO68fcsHX6NB2CjpAl0HFnbh7MtXaLRjF6JZmxAwJnAsTykR6KZEb3KhVbnyk285oT41+Y7MMntW6leVQBntnWCZ0Kpl2sHUWuPTqQR01dCXkl2UNEr5SjoVGiXL2FaSa/lzgO6ztw/Rf2VdREa/R5cKI9GqVB90/r0UQt6/wqj681AxT4M4xmmzoBAiot5hXKPfUSRrBdAypZntNuvF4aU8qofqo0QhHfzTsi8vRcgFbg9z/pHzIwF0Nda15edVXX0IoMus0aJ5U1y4cIGPUOjCBleJZg+qYSzOHyV3d3fs2LkbmTNrHgxv3bqJenXr8HX0Z+iw4ejXr792mWbatG7FgO5puLK6nNknpDHsgTeUeQ1RorwZM39G3br1+LKpf+QXpFqESHaQ/wAZC3RNtaE1lE8s0C387XU8eK0JIzC/fVZ0LOPDT4eAbp4JV/n8kx8DVA90V256ht7jb8DHywEPD1dAcAjzPmxxGk9ZiIVqZb15LN2/drzA4+eRGNPbH2P7+muBLhnJyfH/JJxbTPNnxte58UVLP6zb/hzdR2te2OTK5oo82V1x4VoYD9/g6myHPcuLoVgBfVilU4121hSga+sDEaREoKttaJ0Zetgr1HoszzEG6EoPiZVG/oVMJfVvikOf3MKOwZpwA82WPMaFlV/j3v6lyFKuOSoMWaWz17izEtDVXVOg+UgUbjdJN8vkh7oPMR+121NIg0zubuiaNw/6FdJ4rEtAV1tIZ+ZG29bwcXbCzZAQDDr2H868eKldS/B3ZvmyqOIXO1DaIxZaYcDR4zj69Jm2XCUGgOdWrIDMbL+U5ECX8mZcvIQfWSiINM7OONC4IQ/NQPlSSolANyV6kwutSopP/qn83k73JZnaPmUnayvBMiVQRmUJlgmtkiWSP8WnU+m3Wg19KVlaSaOUr6RToVGyjG0luZZNBboUGsHTJQ2i2ZddFA6BEsW8ndV+G3y9sqHlPE1s3Kkt16JQ5riD9/ZaVpVt94B78xbJUgH9VtbmdSzpfhzpUsfe51EmDajWbG5u5hvxEROb/oGS/tUE0OXWMu8fOT8SQFdjX7VwNAF0WXtKQHfw4CEYMHAQb+Fr166hZYtmiGQjeY8aNRq9vtJ4T03/aRoWLJgPbzay95s3b7i37a5de/SuOgnojh49Bj17fcXXvWIje48dOwZ7du+Gr68fDh46DAc2gIupSX5BqkWIZAf5D5AAunHVoQt0fT0dcfbrfHBnkNEQ0KXBNq4/i2SjlkYio6cDCmd2RWpWXkovw6LxnsWaTcdCVTizgeRO33+HUv5ueB0ezR4yPiENy7dn7PP8o/fIlc6ZLWsGenscEoV7Lz+w+lzYj77hwd+k/dDUnB66B5nnbcMvLzLNAP+uKo5SAR44eyUU/dggaJduaAaXonVftsmEqcNzwoWd8z97XvKQC7rHpDs/e3we9GitGYBp1eZnGP3THQ6KpTLZMrlg8dR8KF/cS8qKd2oK0LX1gQhSItCleLepfXOxu9FPePfyIa5tmo5PMdEo228R/Kt0MMpDd+/XlfH6zhmUG/AHslVqq6en0KAb2MHi66ayd0DLFa9wfvko3N65EP6V26Ns/8V6ZeULukA3Y5GaPO4uxTGrOm4rMgRU0xY35aFOu5GZZige7oPQMGRkHriZ2YtTdrkqpuDIDyyMwlv4ubnx/4qFTMhMiUA3pXmTmyAHo4oKrRplJoOF5Pd2ukBXAmVKG9vaIJPSOSj1q0qgTCpvzqnQauKtaYxO1dCXkoWUNEr5SaFToVGytGWTXMumAl3do6MB0rKny48OZYeghH8VvqrLotJ48+4lRtb/BZXyNNQtzue1HrqNF7EvSsuj7YIADm4T8tD9hXnoZhMeunHsaY4MOT8SQFdjVbVwNAF0WXsqAV1q5o4d2+O/48fRuXMXTPpmMntu/4Sq7FPPoKAgTP3hR4wbOxYx7AF+y9btKFhQ46FE2ykBXcq/euUKGjOPIUqLFi9B9eo1+Lwpf+QXpFqESDaQ/wAJoBtXGRLQDWBw8fLjCAyvnQHjG/gqAl3y5O267AHOPninrcjbzR4LO2RF/UKePK/jkkBsvfQW3zT2w6+HX7IRSKPw/KfCaLrgLo7fDcfA6umx9PhrvI2I4eEdfmyeGXdeRmLhIY13XXoWzmB9rxwozgYsSyglBugmVKd8PbtEeViEl8FRyJ/DDR6pjYPN8nqk5SAGwh8wO+dldaX1dpSyjZomBuja6kAEKRHoyj11Tv/WD+RtlqVsM1QYutoooHtyXk8EHlqFPA36oXjXn/R0df/QapyY9yU8MudD/ZnncGvHfJxbOhze2QJQ56eTemXvHViON/cvwbdwTfiVqKcNuZCrTk+U7D4LB79vwqGui48v6k47ycM2UAXJ+VCndwJJuJASga6SedXsTa50vraYpxatyu/tlICuWj5lJ50p9atJAcqSU+Nq0KoxOlWysa31pYY0Svlq1qkaNKqkP6U8uZZNBbqtS/dD61J9edUujpovonT3M3Zje1x69B/qBrRHvxrf667CzafnMXxdc573a5cD8PP2R48/KuJF6GN0rTgKLUvqhxc7dHMzpu8cxGPo/tX3Gh9oTYRc0DOpWRbk/EgAXY1Z1cLRBNBl7akEdB8+fIj69erg/fv3GDFyFHr37oNTp06iXds2cGOeQqfYQDBf9uiO48ePoWfPXhg95mvtBWcI6Eaz0AuFAwriw4cPGDduAr7o3l27jbEz8gtSLUKk85f/AAmgG1cVEtBd3s0fg9c/wrvIjzjNvHSdHezihFyo8fNtnGEwN4OHAzqU9sG/N8NwgXnaOjGX29Nj8rE4RU6QgK6LQyr2aQ2QNY0jTo7OhybzNUDXngXprV+Ieb8+fM9hL3m90vZNinhh+5W3CGf7r5XfAxu+yhH3YGU5SQF0ZbtM1sXPBbp08LYyEEFKB7rkmbtzWElQmAQOUXvMNgroPvrvbxz7uSPsnd1Qc/J+eGfXxGuPCHmOfyfW5vXlazIERTt+h7Ant/k+PsZEoUy/35G9Skeu7zeBF7FvQg3ERL7jHrgZC9fQAt0iHacgf5OheB/8BLuGl8KHsGD4FauDyqP/pg5XETxQpeKhjpvW6v/IfzN1QRkdvOT9KLzJrb4pFQ9QLQAiPp1KGpW/ILPFQSalRhRAlz5cYW/XbSwZo1M19KXULEoapXzx209WsP0k17KpQFfXuUTJGnuurMXcfaNhb+eA8Y0Xaz13QyOC8c2mL0ADo+XzLY5pbTbwGOErjk/H+lPz4OHig8nNliNXhgBeLcXnHf93Jx6eoXLexhhRbw7PF0BXyeqflyfnRwLoauxpi79VSkoQQJdZRQK6Pj5pkIaNnB0VFY0HD+5zezk5OXEP3Ny5czMI+zXWrF6Nhg0bYc7cX7BixXJMmjiBh1A4fOQoGzxJ8ym7IaBLFdasUR2BgfcwZMhQ9B8wkO/DlD/yC1ItQiQbyH+ABNCNqwwJ6G7ukxO3X0Ri6F9BaF7MG9NaZNIDuucYgG3wyx1ewbVJBZDJS+NdWmn6LVwKeo++VdNharNMWqCbhw0utmtgLqRlIRYo1Zt7h3vojqqbEV/Xy4iHwR8QMFkTV5bgLUHc1aeC0Wf1Q2RnYPjCuPx8u/j+CKB7z6B5pJALujdRtjQQQUoEuhRuwYENgkbxv969eoSo8BDWh9mh2sSdSF+gkhbo2jk4wcU7o17b2zu6oP4szUCcR39qg6DTW/mAar5Fa8HB2R1PL+1DZMgLuGfMgXo/neLAlyqgsAs3t83lMDZDoao8xMOLa0d43X7F66LyqI18nRRyQQK6VODRyU04NqM9L1us8w/I22igeKhj1rDl31D5b6YhoCuHZcKbnF8GVv8nJQFdtXzKTqJSgmVqBmV0zmrQanz9qaEXD7bYlxrSKOWrWadq0Ci1kTFJrmVzA106BgKxFx4e5fe9eTIWYeH3fHDj6TmERrzhXrazWbzdrGny8MONivmAQasb4FEwGzSYxeLN61uMT68/OccGTwvnoHd+573wck3Dy0tAl4Cx/FxypCuAGW038XLx/ZE/l0hl79wNlGZT1FTOjwTQ1TS/LT8D6ApYAF1mDQno6hrGhQ1mlj9/fgwaPBRVqlRhkDcK5cqymDEsbi5549aqVRtPnz5Bp44d+GarV/+JsuXK8XlDQJfCMxQqWIDXRSEb2rTRj5mou39D8/ILUi1CpPOVd9oC6MZVgS7QrZTbHQRorz6JwArmsdt5qeYlBA2KtupkMIZvCEJ+XxecGJVXW9GELU8we/8LVM2bGgSFJQ/dfgzwfs8Ar5QkoDu3bRZ0KUeB8T8h7bBLfPXlCfmR1ccJO5mHbttFgcjEQhFcm1hA2tTgVADdhIGurQ5EIL9xsuV+Sd4PGYJkukJ3dPOEZ5YCKNB0ODKV0oTVkQZF0y0nzRPQbbnyNV+Mev8WF1ePx909ixlcjB10zLdILZTsOQfuGbJLm/H1N7bMwpV1UxATFaHJZ562Oap2RrEuP8LRXRPfWQnoUuFTC/vg3r/LQJCZIN/eMZW0devOiIc6XWtY77yxWtUFusKb3HrbU35kagEQ8elUAmXyc6dlW/yUnY5bAF3bfFFmjE7V0Jca0ijli99+soLtJ7mWLQF0330IxbKjP2LnpdXcsUGymn/afOhfcyr30JXyaPo6/Bl+O/gNjt3eoZvNB1WjsA1ZfNiYFP9PEtCVlnWnOdMX5IOz6eYpzcufS6QyAuhqLCGArsYOtvy8KmmapgLoMiNIQLdv337o01cTM8bNzV3XTti7Zw+++qqnXp7uQrt27fHd91N5liGge+bMaR5flwpt2rwFAQGFdaswal4AXaPMZPWFZtbQ/0GTDjjkZ80n19KyfKoLdAnKHmRhFJqweLfkJRv46gMvTkD3z9PBGLI+CDnTOeHc2Fjv2dF/P8YCFv+2Rj4P/N07hxboTmnqhwHV0mt3Fx/QvTKhAPvhdcQOBnTbMaCbmQHdqwLoam0nzSQm5IK0LU1taSAC+Y2TLf9Aym+E5UBXt43MOR8dGY7Qx7c4qPX0ywsnD42ngtI+CMq9ZeEdosLfwDNTvnjLKm0v5SmBB1onHuokC1n3NCGtSrBMeJNbdzsaOrqUBHTV8ik7taVSv6rmPpXOWQ1aja8/VVNfakijlK9mnapBo9RGxiS5lo0FusbULS8TEfUOQcF38e5DGDL75GCDZ2eUF9FbDo98y8tHf4xm5XNqvXL1CplhQf5cIlUpgK7GEgLoauxgy8+rkqZpKoAuM4IEdAcPHoIBAwfp2kc7P6B/P2zfvg1+fn7wz55dmx/8Ohg3blyHl5cXTpw8DUdHR8VB0ahM3z59eLiFfPnyY/uOndo6TJkRQNcUa1lvWXMBXTrDDosDse3yW+3JEtClAdNqz77N886wGLu50ztzL9sK027iBhvoa1CN9JjMBkKTPHQ/B+gS3CXIm1ASHroJe+ja6kAE8hsnW/6BlN8IJxXQTej6scR6JfBA+0mOh7qImBhcfPUaLg72KODtDcf/hzAy93mr6aEuIa1KEELXhsKbXNcaiZt/FRGBa29CkN/bC+nY11yWSmrRanw6lTSq6/lI9rTVT9np2JX61eToU0PYeB3BkZoX/XRclNwdHZDeAppVg1aN0anGipq/ttqXpslZQlGjdFbJoVPx26+rKvPMy7VsSaBrniM2fy3y5xJpDwLoaiwhgK7GDrb8vCppmqYC6DIjJAR0w8LCUKZ0SURGRmL27Llo1Lix1oaPHz9G5UoV+PLChb+hdp06WqBLXr5ubq48xEJISAgv4+3jg1Wr1vBwDtpKTJgRQNcEY1lxUXMC3XsvP6DMDzfwIUYzCAUBXTcnOzScdxdHbofx+ebFvHDoVjiPhevK1p1jkNePxdUVQNdyIkmMh65uDF2lI7PWgQjkN062/AMpvxEWQFdJiebNO/z0KTruOwB6sKOUy9MTy6tXRR4vT/PuiNWmBvAgGSW5tJqSvck3Bd5H/6PHEcm0asfCnZTLkB4LK1eCL7vXM3dSi1bj06kS0LXVsCBS+1sL0P3u3HnMvnRFOiw+bZrdH79XUQ61o1fQxAU1aDU+nZpoDqOLJ0dfSgenpFHKT2qgK377yermT3ItC6Aba2MBdDW2EEBXYwdbfl6NVbUAutwWCQHdDX/9hZEjh4Pi6p46fYZBWv1wDE2aNMKVy5fRoEFDzP1lnhbo6ho6S5YsKF26DL7s2SvRMJfqE0BX16q2O29OoEtWmMhi485isXEpSUD3ZVg0erNBy/ZcC+X59Ie8aRd3zoZyOTQaNgfQpXi6FFc3oSQ8dBP20E0I6JKNrXEgAgF0E1K/da63loe6Ehv+wRvmTTa8aGHmUcZenDIIUck3IzbWqWV2w6kBPEhGkT+0iZcPkmUsM/3w8SMKrP0L3s5O+L5Maex9FIRlN2+hW748mFa2jNl3qhatxqdTCeiqISyIJAClfjWpQRkdS+/DR3Hs2TMMKxIb3i2Hhweq+PlKh2q2qRq0Gp9OzWYoK6lISaN0aEmtU/HbbxlByLUsgG6snQXQ1dhCAF2NHQTQjb02kn1O3nGp+WIVQDfZ5WaWA0gs0E3Mzl+FR+MmC7NAHrn+aZzY4HOJqeXztxFA1zxA1xoHIhBA9/Ovj+SoIake6lbcuo0Jp86gVPr0WF+7BhZcvYZp5y+ieY7sGFm0CIpt+Bst2fw89rULfWeQe806Pr3bvo3ZzaIG8CAZRX7vI4CuZJnETxPS6piTp1ArS2Z0zJ0LJ5+/QKOdu9E2V07MrVg+8Ts1sKVatBqfTiWgq2sCW/6Unc5DqV+1BCiLT6szy5dF/R27eHiFpdWqIObTJ4uFsaFzVoNW49MpnaOakpJG6fzMrdP4NCp++y2nKLmWBdCNtbWaGVHsWcadk/MjAXQ1NhJAN65Wki1H3nGp+WKVX5BqESKJR96OQ/fXTzZNWXrHSQl0LX0uxtYvgK5hoGusDXXLWdNABALo6raM7cwn1UMdQdqmO/fgv+fPuSfjlHPn4GxnjyNNG8HH2VnPYFSmCStbLZMf1tWqobfOHAtqAA+SHeS/mQLoSpZJ/NRYrS69cQurbt9msXTf4K/aNVnohQyJ36mBLdWi1eTQaXJ9yk5NqdSvmhuU0X4S0mqhdRvgbG+Plyzms6uDA1rnzIHBhQtZJO6zGrSaHDqldkyOpKRROg5z6zQhjeqeu/jt17XG583LtSyAbqw91cyIYs8y7pycHwmgq7GRWjiaiKEbV/NWnSO/INUiRDK6/AdIAF2rlqLJByeA7j2TbWYrGwigaystpX+cSfVQR3u9FxqKqpu3aePkUozcelmz6B3Q2Zev8MWBQ3j+/j2WsvV1mSekuZMawINkE/lvpgC6kmU+b2qMVvOx0AsUHsSHhV9YxrQqgK5hm6cknZIVlPpVc4MyydqGtEoxnrOt+hNeTk7oH1CQhQd5zF+oNciWFeSxa+6khn41JelUSaOkCUvo1JBGdTUofvt1rfH583ItC6Aba1MBdDW2EEBXYwe1cDQBdGOvcZuYE0DXJpopwYMUHrpA2EXzP1QkaPgkLGDKoGhJeFgW2ZUAuhYxq8UrTcqHOjqZAWwwqbV37vLBzo42jR1clNYtv3kb9Dk7eZQtqFzRIjCX9qMG8EDnQUn+0CaArsYu5vgbn1ap/o/sE/aVt+5g+H8nUJ15k68V3uQGzZ6SdEpGUOpXLQHKJIMb0ippVAq1EBYVhbx/rocL89QVoWwky+lPU5JOlTRK1rCUTg1plPYpfvvJCuZNci0LoBtrXwF0NbYQQFdjBwF0Y6+NZJ+Td1xqvlgF0E12uZnlAATQFUDXLEKykkoE0LWShjDxMJLyoe46+zS91tYdiGIDS9FnmLMrlEN7FoeUEn2+PvLESRRNmwaLqlSGv0dqnm+JPwLoWsKqlq/TGrRKMXOHHP8PPfPn5wOh0SBpuVi8ZzsWmP5+h7ZmN4JatCq/R1fziwcSgZJWLQXKDPWrD8PCseHePZRMnw6VfX1BHrt5GNB1dbDHjbathVYVLJCSdKqkUTKJJXRqSKO0P/HbT1Ywf5JrWQDdWBurmRHFnmXcOTk/EkBXYyMBdONqJdly5B2Xmi9W+QWpFiGSeOTtKEIuJNslZZEdi5ALIuSCRYRl5krl/ZCa4UNSPdRFM0+xett24uLr15jPBj4bevwEnOztcKhJI2R0deWeY6HMg6xpdn94OjpqW3RauTKwN/MojmqBZGQkoVXzA4j4tJrawRH51q7nn7EPLRKAWyFvsezmLdTKnAmra1bX6tZcM2rRakrSKbW9Ur9qCVAWn1bTubig1MZ/eP85rkRx/Pv4CdaxryM6sJdos9jLNHMnNWg1JelUSaOkCXPrND6Nit9+c1+FsfXJtSyAbqxt1MyIYs8y7pycHwmgq7GRWjiaCLkQV/NWnSO/INUiRDK6/AdIAF2rlqLJByeArgC6JosmGTaQ90MC6H5+I/x04RJ+unARTfyzYVHVynye8mowEDamWFHU3rZDcSePOrWHk52d4rrEZqoBPEjnLrRqfgARn1b/ZND278D7GHzsP7yPjubNUCxtWiypVhlZ3N2lZjHbVC1aTUk6pcZXgmXmBmW0n4S0uuX+A/x4/iJuhoRQce6pu6hqpTgDUfKVn/lHDVpNSTpV0ihJwNw6jU+j4rf/My+6eDaXa1kA3VhjCaCrsYUAuho7qIWjCaAbe43bxJwAujbRTAkepAi5IEIuJCgSGyogQi7YUGPpHGpSPdTp7DLZZ9UAHiQjyh/axMsHyTKWnVJc0ttv38KdxSPNzEBuKgvtTi1aTUk6JSko9avmBmWmSO7u21AeasHPzc2UzUwqqwatpiSdKmmUGjw5dWqS4BJRWA0aNfa05VoWQDfWcgLoamwhgK7GDgLoxl4byT4n77jUfLEKoJvscjPLAQigK4CuWYRkJZUIoGslDWHiYYiHOsCWb+bk9z4C6Jp4AVh5cbUAiJSkU5KUUr+qZlBG56wGraYknSpplNpRzTpVg0apjYxJci0LoBtrNTUzotizjDsn50cC6GpsZMvPALqtLDx0da1hA/PyC1ItQiTTy3+ARMgFGxCkCYcoQi6IkAsmyCXZisr7IQHJkq0pLLJjNT3UCa0KAGGRi8TMlaYknZLplGCZmkEZnbMa+tWUpFMljVI7qlmnatAotZExSa5lAXRjrSaArsYWAuhq7KAWjiaAbuw1bhNzAujaRDMleJDCQ1d46CYoEhsqIDx0baixdA5VPNQJD10dOVj1rNCq7WpVDhfU/JKMLiIlraoZlNE5qwGWpSSdKmmU2lHNOlWDRqmNjElyLQugG2s1AXQ1thBAV2MHAXRjr41kn5N3XGq+WAXQTXa5meUABNAVQNcsQrKSSgTQtZKGMPEwxEOd7UIyamr5vY+aQZnQqu1qNSXplK5LJa2qGZTROasBlqUknSpplNpRzTpVg0apjYxJci0LoBtrNTUzotizjDsn50cC6GpsJIBuXK0kW46841LzxSq/INUiRBKPvB1FyIVku6QssmMRckGEXLCIsMxcqbwfEpDMzAZO5urU9FAntCoARDJfTkbtPiXplAyiBMvUDMronNXQr6YknSpplNpRzTpVg0apjYxJci0LoBtrNTUzotizjDsn50cC6GpspBaOJkIuxNW8VefIL0i1CJGMLv8BEkDXqqVo8sEJoCuArsmiSYYN5P2QALrJ0AgW3KWaHuqEVgWAsOClYraqU5JOyWhKsEzNoIzOWQ39akrSqZJGqR3VrFM1aJTayJgk17IAurFWE0BXYwsBdDV2UAtHE0A39hq3iTkBdG2imRI8SBFyQYRcSFAkNlRAhFywocbSOVTxUGe7n7FTM8of2sTLBx1xq2BWLQAiJemUZKfUr6oZlNE5q0GrKUmnShqldlSzTtWgUWojY5JcywLoxlpNAF2NLQTQ1dhBAN3YayPZ5+Qdl5ovVgF0k11uZjkAAXQF0DWLkKykEgF0raQhTDwM8VAngK6Jkkm24kKrtqtV+T26ml880AWipFU1gzI6ZzXAspSkUyWNUjuqWadq0Ci1kTFJrmUBdGOtpmZGFHuWcefk/EgAXY2NBNCNq5Vky5F3XGq+WOUXpFqESOKRt+P/2jmjI8lxIwo6IFvOBLkon2SCPFGEDJBeIDbQV8SqwVtggXqV/BGv1U0Cr7Iww+xZsOXCsZbacmO2XGDLhS1gLb5oXIec5QMPdXklmbCHVQTE4uVvy+UqcaoAR+uqsyjTnB1kWSVOR4yqjs6cOjCqGs0ckWWEbk/N2RH1WT7Poj9C6LaMXDwaWy48mb/6ldiQLiAq9PgDCKF7NYqvB4fQRei+hubAB+I6hNA9UISNt3R6qINVBMTGVll26UqcKrSRLHMWZZqzw7paidMRo6qjM6cOjKpGM0dkGaHbU0PotiwQui0HF4+G0O09nuIMoZuiTF8HyZYLbLnwFZJEb2DLhUTF+hgqD3X8he4HDlefwmpeVqNccP6STE00YtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p4bQbVkgdFsOCN3eG8fP4sLl3KwI3eO4LRkAQhehuwSkSy6C0L2kEC+HwUNdXkmmUsfffZxFGazmZbUSp+rLEavOokxzdpBllTgdMao6OnPqwKhqNHNElhG6PTVnR9Rn+TyL/gih2zJC6D5ZOfZKXLicmzU2pAuIgifWkS0XjrXUlhuz5QJbLmwBa/FF4zqEJFsc8OHLOT3UwSoC4nA7Td2+EqcKZCTLnEWZ5uywrlbidMSo6ujMqQOjqtHMEVlG6PbUnB1Rn+XzLPojhG7LyMWjseXCk/mrX4kN6QKiQo8/gBC6V6P4enAIXYTua2gOfCCuQwjdA0XYeEunhzpYRUBsbJVll67EqUIbyTJnUaY5O6yrlTgdMao6OnPqwKhqNHNElhG6PTWEbssCodtycPFoCN3e4ynOELopyvR1kGy5wJYLXyFJ9Aa2XEhUrI+h8lCX95+xq4zxoY0vHz7gNjh1ERCVOBV2o3XVWZRpzg6sVuJ0xKjq6MypA6Oq0cwRWUbo9tQQui0LhG7LAaHbe+P4WVy4nJsVoXsctyUDQOgidJeAdMlFELqXFOLlMHioQ+i+RObY22E1L6vxd3TnLx7UICNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqzo6oz/J5Fv0RQrdlhNB9snLslbhwOTdrbEgXEAVPrCNbLhxrqS03ZssFtlzYAtbii8Z1yFk+8FCXV5IJe1hFQCxe/rZcrhKnCnC0rjqLMs3ZQZZV4nTEqOrozKkDo6rRzBFZRuj21JwdUZ/l8yz6I4Ruy8jFo7HlwpP5q1+JDekCokKPP4AQulej+HpwCF2E7mtoDnwgrkMI3QNF2HhLp4c6WEVAbGyVZZeuxKlCG8kyZ1GmOTusq5U4HTGqOjpz6sCoajRzRJYRuj01hG7LAqHbcnDxaAjd3uMpzhC6Kcr0dZBsucCWC18hSfQGtlxIVKyPofJQx1/ofuBw9Sms5mU1ygXnL8nURCNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqCN2WBUK35YDQ7b1x/CwuXM7NitA9jtuSASB0EbpLQLrkIgjdSwrxchg81OWVZCp1/N3HWZTBal5WK3Gqvhyx6izKNGcHWVaJ0xGjqqMzpw6MqkYzR2QZodtTc3ZEfZbPs+iPELotI4Tuk5Vjr8SFy7lZY0O6gCh4Yh3ZcuFYS225MVsusOXCFrAWXzSuQ0iyxQEfvpzTQx2sIiAOt9PU7StxqkBGssxZlGnODutqJU5HjKqOzpw6MKoazRyRZYRuT83ZEfVZPs+iP0LotoxcPBpbLjyZv/qV2JAuICr0+AMIoXs1iq8Hh9BF6L6G5sAH4jqE0D1QhI23dHqog1UExMZWWXbpSpwqtJEscxZlmrPDulqJ0xGjqqMzpw6MqkYzR2QZodtTQ+i2LBC6LQcXj4bQ7T2e4gyhm6JMXwfJlgtsufAVkkRvYMuFRMX6GCoPdXn/GbvKGB/a+PLhA26DUxcBUYlTYTdaV51FmebswGolTkeMqo7OnDowqhrNHJFlhG5PDaHbskDothwQur03jp/Fhcu5WRG6x3FbMgCELkJ3CUiXXAShe0khXg6DhzqE7ktkjr0dVvOyGn9Hd/7iQQ0yYtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p+bsiPosn2fRHyF0W0YI3Scrx16JC5dzs8aGdAFR8MQ6suXCsZbacmO2XGDLhS1gLb5oXIec5QMPdXklmbCHVQTE4uVvy+UqcaoAR+uqsyjTnB1kWSVOR4yqjs6cOjCqGs0ckWWEbk/N2RH1WT7Poj9C6LaMXDwaWy48mb/6ldiQLiAq9PgDCKF7NYqvB4fQRei+hubAB+I6hNA9UISNt3R6qINVBMTGVll26UqcKrSRLHMWZZqzw7paidMRo6qjM6cOjKpGM0dkGaHbU0PotiwQui0HF4+G0O09nuIMoZuiTF8HyZYLbLnwFZJEb2DLhUTF+hgqD3X8he4HDlefwmpeVqNccP6STE00YtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p4bQbVkgdFsOCN3eG8fP4sLl3KwI3eO4LRkAQhehuwSkSy6C0L2kEC+HwUNdXkmmUsfffZxFGazmZbUSp+rLEavOokxzdpBllTgdMao6OnPqwKhqNHNElhG6PTVnR9Rn+TyL/gih2zJC6D5ZOfZKXLicmzU2pAuIgifWkS0XjrXUlhuz5QJbLmwBa/FF4zqEJFsc8OHLOT3UwSoC4nA7Td2+EqcKZCTLnEWZ5uywrlbidMSo6ujMqQOjqtHMEVlG6PbUnB1Rn+XzLPojhG7LyMWjseXCk/mrX4kN6QKiQo8/gBC6V6P4enAIXYTua2gOfCCuQwjdA0XYeEunhzpYRUBsbJVll67EqUIbyTJnUaY5O6yrlTgdMao6OnPqwKhqNHNElhG6PTWEbssCodtycPFoCN3e4ynOELopyvR1kGy5wJYLXyFJ9Aa2XEhUrI+h8lCX95+xq4zxoY0vHz7gNjh1ERCVOBV2o3XVWZRpzg6sVuJ0xKjq6MypA6Oq0cwRWUbo9tQQui0LhG7LAaHbe+P4WVy4nJsVoXsctyUDQOgidJeAdMlFELqXFOLlMHioQ+i+RObY22E1L6vxd3TnLx7UICNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqzo6oz/J5Fv0RQrdlhNB9snLslbhwOTdrbEgXEAVPrCNbLhxrqS03ZssFtlzYAtbii8Z1yFk+8FCXV5IJe1hFQCxe/rZcrhKnCnC0rjqLMs3ZQZZV4nTEqOrozKkDo6rRzBFZRuj21JwdUZ/l8yz6I4Ruy8jFo7HlwpP5q1+JDekCokKPP4AQulej+HpwCF2E7mtoDnwgrkMI3QNF2HhLp4c6WEVAbGyVZZeuxKlCG8kyZ1GmOTusq5U4HTGqOjpz6sCoajRzRJYRuj01hG7LAqHbcnDxaAjd3uMpzhC6Kcr0dZBsucCWC18hSfQGtlxIVKyPofJQx1/ofuBw9Sms5mU1ygXnL8nURCNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqCN2WBUK35YDQ7b1x/CwuXM7NitA9jtuSASB0EbpLQLrkIgjdSwrxchg81OWVZCp1/N3HWZTBal5WK3Gqvhyx6izKNGcHWVaJ0xGjqqMzpw6MqkYzR2QZodtTc3ZEfZbPs+iPELotI4Tuk5Vjr8SFy7lZY0O6gCh4Yh3ZcuFYS225MVsusOXCFrAWXzSuQ0iyxQEfvpzTQx2sIiAOt9PU7StxqkBGssxZlGnODutqJU5HjKqOzpw6MKoazRyRZYRuT83ZEfVZPs+iP0LotoxcPBpbLjyZv/qV2JAuICr0+AMIoXs1iq8Hh9BF6L6G5sAH4jqE0D1QhI23dHqog1UExMZWWXbpSpwqtJEscxZlmrPDulqJ0xGjqqMzpw6MqkYzR2QZodtTQ+i2LBC6LQcXj4bQ7T2e4gyhm6JMXwfJlgtsufAVkkRvYMuFRMX6GCoPdXn/GbvKGB/a+PLhA26DUxcBUYlTYTdaV51FmebswGolTkeMqo7OnDowqhrNHJFlhG5PDaHbskDothwQur03jp/Fhcu5WRG6x3FbMgCELkJ3CUiXXAShe0khXg6DhzqE7ktkjr0dVvOyGn9Hd/7iQQ0yYtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p+bsiPosn2fRHyF0W0YI3Scrx16JC5dzs8aGdAFR8MQ6suXCsZbacmO2XGDLhS1gLb5oXIec5QMPdXklmbCHVQTE4uVvy+UqcaoAR+uqsyjTnB1kWSVOR4yqjs6cOjCqGs0ckWWEbk/N2RH1WT7Poj9C6LaMXDwaWy48mb/6ldiQLiAq9PgDCKF7NYqvB4fQRei+hubAB+I6hNA9UISNt3R6qINVBMTGVll26UqcKrSRLHMWZZqzw7paidMRo6qjM6cOjKpGM0dkGaHbU0PotiwQui0HF4+G0O09nuIMoZuiTF8HyZYLbLnwFZJEb2DLhUTF+hgqD3X8he4HDlefwmpeVqNccP6STE00YtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p4bQbVkgdFsOCN3eG8fP4sLl3KwI3eO4LRkAQhehuwSkSy6C0L2kEC+HwUNdXkmmUsfffZxFGazmZbUSp+rLEavOokxzdpBllTgdMao6OnPqwKhqNHNElhG6PTVnR9Rn+TyL/gih2zJC6D5ZOfZKXLicmzU2pAuIgifWkS0XjrXUlhuz5QJbLmwBa/FF4zqEJFsc8OHLOT3UwSoC4nA7Td2+EqcKZCTLnEWZ5uywrlbidMSo6ujMqQOjqtHMEVlG6PbUnB1Rn+XzLPojhG7LyMWjseXCk/mrX4kN6QKiQo8/gBC6V6P4enAIXYTua2gOfCCuQwjdA0XYeEunhzpYRUBsbJVll67EqUIbyTJnUaY5O6yrlTgdMao6OnPqwKhqNHNElhG6PTWEbssCodtycPFoCN3e4ynOELopyvR1kGy5wJYLXyFJ9Aa2XEhUrI+h8lCX95+xq4zxoY0vHz7gNjh1ERCVOBV2o3XVWZRpzg6sVuJ0xKjq6MypA6Oq0cwRWUbo9tQQui0LhG7LAaHbe+P4WVy4nJsVoXsctyUDQOgidJeAdMlFELqXFOLlMHioQ+i+RObY22E1L6vxd3TnLx7UICNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqzo6oz/J5Fv0RQrdlhNB9snLslbhwOTdrbEgXEAVPrCNbLhxrqS03ZssFtlzYAtbii8Z1yFk+8FCXV5IJe1hFQCxe/rZcrhKnCnC0rjqLMs3ZQZZV4nTEqOrozKkDo6rRzBFZRuj21JwdUZ/l8yz6I4Ruy8jFo7HlwpP5q1+JDekCokKPP4AQulej+HpwCF2E7mtoDnwgrkMI3QNF2HhLp4c6WEVAbGyVZZeuxKlCG8kyZ1GmOTusq5U4HTGqOjpz6sCoajRzRJYRuj01hG7LAqHbcnDxaAjd3uMpzhC6Kcr0dZBsucCWC18hSfQGtlxIVKyPofJQx1/ofuBw9Sms5mU1ygXnL8nURCNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqCN2WBUK35YDQ7b1x/CwuXM7NitA9jtuSASB0EbpLQLrkIgjdSwrxchg81OWVZCp1/N3HWZTBal5WK3Gqvhyx6izKNGcHWVaJ0xGjqqMzpw6MqkYzR2QZodtTc3ZEfZbPs+iPELotI4Tuk5Vjr8SFy7lZY0O6gCh4Yh3ZcuFYS225MVsusOXCFrAWXzSuQ0iyxQEfvpzTQx2sIiAOt9PU7StxqkBGssxZlGnODutqJU5HjKqOzpw6MKoazRyRZYRuT83ZEfVZPs+iP0LotoxcPBpbLjyZv/qV2JAuICr0+AMIoXs1iq8Hh9BF6L6G5sAH4jqE0D1QhI23dHqog1UExMZWWXbpSpwqtJEscxZlmrPDulqJ0xGjqqMzpw6MqkYzR2QZodtTQ+i2LBC6LQcXj4bQ7T2e4gyhm6JMXwfJlgtsufAVkkRvYMuFRMX6GCoPdXn/GbvKGB/a+PLhA26DUxcBUYlTYTdaV51FmebswGolTkeMqo7OnDowqhrNHJFlhG5PDaHbskDothwQur03jp/Fhcu5WRG6x3FbMgCELkJ3CUiXXAShe0khXg6DhzqE7ktkjr0dVvOyGn9Hd/7iQQ0yYtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p+bsiPosn2fRHyF0W0YI3Scrx16JC5dzs8aGdAFR8MQ6suXCsZbacmO2XGDLhS1gLb5oXIec5QMPdXklmbCHVQTE4uVvy+UqcaoAR+uqsyjTnB1kWSVOR4yqjs6cOjCqGs0ckWWEbk/N2RH1WT7Poj9C6LaMXDwaWy48mb/6ldiQLiAq9PgDCKF7NYqvB4fQRei+hubAB+I6hNA9UISNt3R6qINVBMTGVll26UqcKrSRLHMWZZqzw7paidMRo6qjM6cOjKpGM0dkGaHbU0PotiwQui0HF4+G0O09nuIMoZuiTF8HyZYLbLnwFZJEb2DLhUTF+hgqD3X8he4HDlefwmpeVqNccP6STE00YtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p4bQbVkgdFsOCN3eG8fP4sLl3KwI3eO4LRkAQhehuwSkSy6C0L2kEC+HwUNdXkmmUsfffZxFGazmZbUSp+rLEavOokxzdpBllTgdMao6OnPqwKhqNHNElhG6PTVnR9Rn+TyL/gih2zJC6D5ZOfZKXLicmzU2pAuIgifWkS0XjrXUlhuz5QJbLmwBa/FF4zqEJFsc8OHLOT3UwSoC4nA7Td2+EqcKZCTLnEWZ5uywrlbidMSo6ujMqQOjqtHMEVlG6PbUnB1Rn+XzLPojhG7LyMWjseXCk/mrX4kN6QKiQo8/gBC6V6P4enAIXYTua2gOfCCuQwjdA0XYeEunhzpYRUBsbJVll67EqUIbyTJnUaY5O6yrlTgdMao6OnPqwKhqNHNElhG6PTWEbssCodtycPFoCN3e4ynOELopyvR1kGy5wJYLXyFJ9Aa2XEhUrI+h8lCX95+xq4zxoY0vHz7gNjh1ERCVOBV2o3XVWZRpzg6sVuJ0xKjq6MypA6Oq0cwRWUbo9tQQui0LhG7LAaHbe+P4WVy4nJsVoXsctyUDQOgidJeAdMlFELqXFOLlMHioQ+i+RObY22E1L6vxd3TnLx7UICNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqzo6oz/J5Fv0RQrdlhNB9snLslbhwOTdrbEgXEAVPrCNbLhxrqS03ZssFtlzYAtbii8Z1yFk+8FCXV5IJe1hFQCxe/rZcrhKnCnC0rjqLMs3ZQZZV4nTEqOrozKkDo6rRzBFZRuj21JwdUZ/l8yz6I4Ruy8jFo7HlwpP5q1+JDekCokKPP4AQulej+HpwCF2E7mtoDnwgrkMI3QNF2HhLp4c6WEVAbGyVZZeuxKlCG8kyZ1GmOTusq5U4HTGqOjpz6sCoajRzRJYRuj01hG7LAqHbcnDxaAjd3uMpzhC6Kcr0dZBsucCWC18hSfQGtlxIVKyPofJQx1/ofuBw9Sms5mU1ygXnL8nURCNWnUWZ5uwgyypxOmJUdXTm1IFR1WjmiCwjdHtqCN2WBUK35YDQ7b1x/CwuXM7NitA9jtuSASB0EbpLQLrkIgjdSwrxchg81OWVZCp1/N3HWZTBal5WK3Gqvhyx6izKNGcHWVaJ0xGjqqMzpw6MqkYzR2QZodtTc3ZEfZbPs+iPELotI4Tuk5Vjr8SFy7lZY0O6gCh4Yh3ZcuFYS225MVsusOXCFrAWXzSuQ0iyxQEfvpzTQx2sIiAOt9PU7StxqkBGssxZlGnODutqJU5HjKqOzpw6MKoazRyRZYRuT83ZEfVZPs+iP0LotoxcPBpbLjyZv/qV2JAuICr0+AMIoXs1iq8Hh9BF6L6G5sAH4jqE0D1QhI23dHqog1UExMZWWXbpSpwqtJEscxZlmrPDulqJ0xGjqqMzpw6MqkYzR2QZodtTQ+i2LBC6LQcXj4bQ7T2e4gyhm6JMXwfJlgtsufAVkkRvYMuFRMX6GCoPdXn/GbvKGB/a+PLhA26DUxcBUYlTYTdaV51FmebswGolTkeMqo7OnDowqhrNHJFlhG5PDaHbskDothwQur03jp/Fhcu5WRG6x3FbMgCELkJ3CUiXXAShe0khXg6DhzqE7ktkjr0dVvOyGn9Hd/7iQQ0yYtVZlGnODrKsEqcjRlVHZ04dGFWNZo7IMkK3p+bsiPosn2fRHyF0W0YI3Scrx16JC5dzs8aGdAFR8MQ6suXCsZbacmO2XGDLhS1gLb5oXIec5QMPdXklmbCHVQTE4uVvy+UqcaoAR+uqsyjTnB1kWSVOR4yqjs6cOjCqGs0ckWWE7kxqtd6D0G31dvFobLmQrH8RuskK9pPh8he6/IXuT9BI+TJ/oZuybEPxoJnwUJejnvGhjS8fctRtdpQuAiJyOjt/3pc3gYwPyZHTiutpXuLejzwjo7OzjCzPfo73kUC1BFzWAYRuMnIRuskK9pPhInQRuj9BI+XLzkI3ZUF+cdAI3V8M8Dd9nIe23xT0JbfJ+uABp5cA9BuHkZFVOP2NgFxwq4yMzsYGy7NJ8b7qCbisAwjdZCQjdJMV7CfDRegidH+CRsqXEbopy8ag/5dA5l/meGirhXBWVuG0FqeabUZW4bQWpxkZna0QLM8mxfuqJ+CyDiB0k5FcSegmK82S4f77H38suc6NF2EPXfbQvZHLOCZ+EY6JeP935l/mYNWbzTi7rKzCaayk/39nZBVO/bn8nGFGRj/H///OYfmZjnO9n7N9vgITz0z0igsXlkJ3XDLPV11AVHVYbDwZ/dms/vOvv//s/7J4/W9//NNiHn9lEpnXJdahv1LxvJ+B1by1qzbyzKxWqxXzzZdA5Z/9rC35eGXEJEACJPAjAYTujySS/q/TD+HKv0wlxY9hk8AwAad1aThBsxcrr72wagYz0yEBEiABEiABEiABEiCBIgkgdJMX2ulhtLJUSI4hwyeBPyXgtC79aWL8BwmQAAmQAAmQAAmQAAmQAAmQAAlckABC94Ii/MoQECe/kt75zyKxz9eAEaxPgHVpfaZckQRIgARIgARIgARIgARIgARIgAR+JGAhdH9Mhv8lARK4N4Hq8hrJeS+bjIwESIAESIAESIAESIAESIAESIAEMiWA0M1ULcZKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRQOgGEbunyM3kSIAESIAESIAESIAESIAESIAESIAESIAESIIFMCSB0M1WLsZIACZAACZAACZAACZAACZAACZAACZAACZAACZROAKFbuvxMngRIgARIgARIgARIgARIgARIgARIgARIgARIIFMCCN1M1WKsJEACJEACJEACJEACJEACJEACJEACJEACJEACpRNA6JYuP5MnARIgARIgARIgARIgARIgARIgARIgARIgARLIlABCN1O1GCsJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEDpBBC6pcvP5EmABEiABEiABEiABEiABEiABEiABEiABEiABDIlIKH7X/dLJLPg9VRGAAAAAElFTkSuQmCC"}}},{"cell_type":"code","source":"class ResNet50(nn.Module):\n    \n    def __init__(self, in_channels, classes, in_features):\n        \n        super().__init__()\n        \n        self.in_channels = in_channels\n        # Define weight parameters and make initialisations for stage 1\n        self.zero_pad1 = nn.ZeroPad2d(3)\n        self.W1 = nn.Parameter(torch.randn(64, in_channels, 7, 7))\n        self.bn1 = nn.BatchNorm2d(64)\n        self.max_pool1 = nn.MaxPool2d(3, stride=2)\n        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc = nn.Linear(in_features, classes)\n        nn.init.xavier_uniform_(self.fc.weight)\n        nn.init.zeros_(self.fc.bias)\n\n        # stage - 2\n        self.cnv_b2 = ConvolutionalBlock(in_channels=64, f=3, filters=[64, 64, 256], s=1)\n        self.idn_b2 = IdentityBlock(in_channels=256, f=3, filters=[64, 64, 256])\n        self.idn_b22 = IdentityBlock(in_channels=256, f=3, filters=[64, 64, 256])\n\n        # stage - 3\n        self.cnv_b3 = ConvolutionalBlock(in_channels=256, f=3, filters=[128, 128, 512], s=2)\n        self.idn_b31 = IdentityBlock(in_channels=512, f=3, filters=[128, 128, 512])\n        self.idn_b32 = IdentityBlock(in_channels=512, f=3, filters=[128, 128, 512])\n        self.idn_b33 = IdentityBlock(in_channels=512, f=3, filters=[128, 128, 512])\n\n        # stage - 4\n        self.cnv_b4 = ConvolutionalBlock(in_channels=512, f=3, filters=[256, 256, 1024], s=2)\n        self.idn_b41 = IdentityBlock(in_channels=1024, f=3, filters=[256, 256, 1024])\n        self.idn_b42 = IdentityBlock(in_channels=1024, f=3, filters=[256, 256, 1024])\n        self.idn_b43 = IdentityBlock(in_channels=1024, f=3, filters=[256, 256, 1024])\n        self.idn_b44 = IdentityBlock(in_channels=1024, f=3, filters=[256, 256, 1024])\n        self.idn_b45 = IdentityBlock(in_channels=1024, f=3, filters=[256, 256, 1024])\n\n        # stage - 5\n        self.cnv_b5 = ConvolutionalBlock(in_channels=1024, f=3, filters=[512, 512, 2048], s=2)\n        self.idn_b51 = IdentityBlock(in_channels=2048, f=3, filters=[512, 512, 2048])\n        self.idn_b52 = IdentityBlock(in_channels=2048, f=3, filters=[512, 512, 2048])\n\n    def forward(self, X):\n\n        # stage - 1\n        X = self.zero_pad1(X)\n        X = F.conv2d(X, self.W1, bias=None, stride=(2, 2), padding=0)\n        X = self.bn1(X)\n        X = F.relu(X, inplace=False)\n        X = self.max_pool1(X)\n\n        # stage - 2\n        X = self.cnv_b2(X)\n        X = self.idn_b2(X)\n        X = self.idn_b22(X)\n\n        # stage - 3\n        X = self.cnv_b3(X)\n        X = self.idn_b31(X)\n        X = self.idn_b32(X)\n        X = self.idn_b33(X)\n\n        # stage - 4\n        X = self.cnv_b4(X)\n        X = self.idn_b41(X)\n        X = self.idn_b42(X)\n        X = self.idn_b43(X)\n        X = self.idn_b44(X)\n        X = self.idn_b45(X)\n\n        # stage - 5\n        X = self.cnv_b5(X)\n        X = self.idn_b51(X)\n        X = self.idn_b52(X)\n\n        # AvgPool\n        X = self.avg_pool(X)\n\n        # output layer\n        X = torch.flatten(X, start_dim=1)\n        X = self.fc(X)\n        \n        return X","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:28.461166Z","iopub.execute_input":"2025-07-02T12:28:28.461764Z","iopub.status.idle":"2025-07-02T12:28:28.474699Z","shell.execute_reply.started":"2025-07-02T12:28:28.461743Z","shell.execute_reply":"2025-07-02T12:28:28.473947Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Using device:\", device)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:29.455810Z","iopub.execute_input":"2025-07-02T12:28:29.456507Z","iopub.status.idle":"2025-07-02T12:28:29.460855Z","shell.execute_reply.started":"2025-07-02T12:28:29.456482Z","shell.execute_reply":"2025-07-02T12:28:29.460234Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def test_resnet50():\n\n    # Instantiate model\n    model = ResNet50(in_channels=3, classes=10, in_features=2048).to(device)\n    \n    # Input image tensor: batch size = 2, channels = 3 , height = 224, width = 224\n    X = torch.randn(2, 3, 224, 224).to(device)\n\n    # Forward pass\n    out = model(X)\n\n    # Expected output shape: [batch_size, num_classes]\n    expected_shape = (2, 10)\n\n    # Assertion\n    assert out.shape == expected_shape, f\"Expected output shape {expected_shape}, but got {out.shape}\"\n\n    print(\"✅ ResNet50 test passed: output shape is correct.\")\n\n# Run test\ntest_resnet50()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:33.776570Z","iopub.execute_input":"2025-07-02T12:28:33.777202Z","iopub.status.idle":"2025-07-02T12:28:35.438072Z","shell.execute_reply.started":"2025-07-02T12:28:33.777183Z","shell.execute_reply":"2025-07-02T12:28:35.437204Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Let's check the model summary \n!pip install torchsummary -q\nfrom torchsummary import summary","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:38.316388Z","iopub.execute_input":"2025-07-02T12:28:38.316930Z","iopub.status.idle":"2025-07-02T12:28:42.095473Z","shell.execute_reply.started":"2025-07-02T12:28:38.316910Z","shell.execute_reply":"2025-07-02T12:28:42.094672Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"example = ResNet50(in_channels=3, classes=3, in_features=2048).to(device)\nsummary(example, tuple(mydataset[0][0].shape)) # The shape of our input example is (3, 384, 384)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:28:51.197126Z","iopub.execute_input":"2025-07-02T12:28:51.197537Z","iopub.status.idle":"2025-07-02T12:28:51.496266Z","shell.execute_reply.started":"2025-07-02T12:28:51.197497Z","shell.execute_reply":"2025-07-02T12:28:51.495501Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Let's train the ResNet50 on our dataset** 🫏","metadata":{"execution":{"iopub.status.busy":"2025-07-02T12:36:30.416259Z","iopub.execute_input":"2025-07-02T12:36:30.416612Z","iopub.status.idle":"2025-07-02T12:36:30.420177Z","shell.execute_reply.started":"2025-07-02T12:36:30.416591Z","shell.execute_reply":"2025-07-02T12:36:30.419405Z"}}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:39:52.635355Z","iopub.execute_input":"2025-07-02T12:39:52.635962Z","iopub.status.idle":"2025-07-02T12:39:52.639845Z","shell.execute_reply.started":"2025-07-02T12:39:52.635938Z","shell.execute_reply":"2025-07-02T12:39:52.639128Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Device config\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Using device: {device}\")\n\n# Instantiate your model\nmodel = ResNet50(in_channels=3, classes=3, in_features=2048).to(device)\n\n# Loss and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=1e-4)\n\n# Number of epochs\nnum_epochs = 1\n\n# Sample DataLoaders (replace these with your actual loaders)\ntrain_loader = DataLoader(train_data, batch_size=32, shuffle=True)\nval_loader = DataLoader(cv_data, batch_size=32, shuffle=False)\n\n# Training loop\nfor epoch in range(num_epochs):\n    model.train()\n    running_loss = 0.0\n    correct, total = 0, 0\n    \n    loop = tqdm(train_loader, leave=True)\n    for images, labels in loop:\n        images, labels = images.to(device), labels.to(device)\n        \n        # Forward pass\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n        \n        # Backward and optimize\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        \n        # Stats\n        running_loss += loss.item() * images.size(0)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n        \n        # TQDM live metrics\n        loop.set_description(f\"Epoch [{epoch+1}/{num_epochs}]\")\n        loop.set_postfix(loss=loss.item(), acc=100. * correct / total)\n    \n    # Epoch summary\n    epoch_loss = running_loss / total\n    epoch_acc = 100. * correct / total\n    print(f\"\\nEpoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.2f}%\")\n\n    # Optionally add validation loop here too 👇\n    model.eval()\n    val_correct, val_total = 0, 0\n    with torch.no_grad():\n        for images, labels in val_loader:\n            images, labels = images.to(device), labels.to(device)\n            outputs = model(images)\n            _, predicted = torch.max(outputs.data, 1)\n            val_total += labels.size(0)\n            val_correct += (predicted == labels).sum().item()\n    \n    val_acc = 100. * val_correct / val_total\n    print(f\"Validation Accuracy: {val_acc:.2f}%\\n\")\n\nprint(\"✅ Training complete.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-02T12:43:07.286155Z","iopub.execute_input":"2025-07-02T12:43:07.286860Z","execution_failed":"2025-07-02T12:46:52.493Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}