class NeuralNet(nn.Module): ''' A simple fully-connected deep neural network '''

The class `NeuralNet` is a simple fully-connected deep neural network implemented using the `nn.Module` class from the PyTorch library. This class serves as a base for creating custom neural network architectures in PyTorch. It allows you to define the structure and behavior of the neural network by specifying the layers and operations within the `forward` method.

To create a custom neural network using this class, you would typically define the layers and operations in the `__init__` method and implement the forward pass in the `forward` method.

Here's an example of how you can define a simple fully-connected deep neural network using the `NeuralNet` class:

import torch
import torch.nn as nn

class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

In this example, the neural network has two fully connected layers (`fc1` and `fc2`) with a ReLU activation function applied after the first layer. The `input_size` parameter specifies the size of the input features, `hidden_size` specifies the number of hidden units in the first fully connected layer, and `num_classes` specifies the number of output classes.

You can then create an instance of this neural network and use it for tasks such as classification or regression by passing input data through the forward method.