Net(nn.Module):

Net is a class in PyTorch that inherits from the nn.Module class. It is used to define a neural network model in PyTorch.

When defining a neural network using Net, we override the constructor and define the layers of the network as class attributes. We also define the forward method that specifies how the input is passed through the layers to produce the output.

Here is an example of a simple neural network defined using Net:

import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

This neural network has two fully connected layers (fc1 and fc2) with a ReLU activation function applied in between them. The input to the network is a 28x28 image (784 pixels), which is flattened and passed through the network to produce a 10-dimensional output (one for each class in the MNIST dataset).