import torch.nn as nn

The torch.nn module in PyTorch provides a number of classes and functions that can be used to define and train neural networks. Some of the most commonly used classes in this module are:

- nn.Module: A base class for all neural network modules in PyTorch. This class defines the basic functionality of a neural network module and provides a number of methods that can be overridden in child classes to define custom behavior.

- nn.Linear: A class that implements a fully connected layer in a neural network. This layer takes an input tensor and applies a linear transformation to it to produce an output tensor. The weights and biases of this layer are learned during training.

- nn.Conv2d: A class that implements a convolutional layer in a neural network. This layer takes an input tensor and applies a set of learnable filters to it to produce an output tensor. This layer is commonly used in computer vision tasks to extract features from images.

- nn.ReLU: A class that implements the rectified linear activation function. This function is commonly used in neural networks to introduce non-linearity and improve model performance.

- nn.CrossEntropyLoss: A class that implements the cross-entropy loss function. This function is commonly used in classification tasks to measure the difference between predicted and actual class labels.

Overall, the torch.nn module provides a powerful set of tools for building and training neural networks in PyTorch.