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Let's get our hands dirty and build a **Siamese network** using PyTorch. We'll start by defining the network architecture, then move on to training and testing it. This example will focus on image similarity, where the goal is to determine if two input images are the same. First, we need to import the necessary libraries. We'll need `torch` for the core PyTorch functionality, `torch.nn` for the neural network modules, `torch.optim` for the optimization algorithms, and `torchvision` for the image datasets and transformations. Here's how you can import them: `import torch; import torch.nn as nn; import torch.optim as optim; from torchvision import datasets, transforms`. Now, let's define our Siamese network class. This class will inherit from `nn.Module`, the base class for all neural network modules in PyTorch. Our network will consist of two identical subnetworks, which we'll call
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