import torch.nn as nn

The torch.nn module in PyTorch provides various classes and functions for building neural networks.

Some of the commonly used classes in torch.nn are:

1. nn.Module: This is the base class for all neural network modules in PyTorch. It provides methods for defining and initializing the various layers of a neural network.

2. nn.Linear: This class implements a linear transformation of the input data. It is often used as the first layer of a neural network, and takes in the input size and output size as arguments.

3. nn.Conv2d: This class implements a 2D convolutional layer for processing image data. It takes in the input channels, output channels, kernel size, stride, and padding as arguments.

4. nn.RNN: This class implements a recurrent neural network (RNN) layer for processing sequential data. It takes in the input size, hidden size, and number of layers as arguments.

5. nn.Dropout: This class implements a regularization technique called dropout, which randomly drops out some units during training to prevent overfitting.

These are just a few examples of the classes available in torch.nn. The module also provides various activation functions, loss functions, and optimization algorithms for training neural networks.