from torch.nn import Parameter

Parameter is a class in PyTorch that is used to define trainable parameters for a neural network. It is a subclass of Tensor, which means that it behaves just like a tensor but with the added functionality of being trainable. When a tensor is marked as a parameter, PyTorch knows that it needs to compute gradients for it during backpropagation. This allows the optimizer to update the parameter values during training, which in turn allows the network to learn and improve its performance.

To create a Parameter object, we typically initialize it with a tensor and set its "requires_grad" attribute to True. For example:

import torch.nn as nn

# Define a linear layer with trainable weights and biases
class LinearLayer(nn.Module):
    def __init__(self, in_features, out_features):
        super().__init__()
        self.weights = Parameter(torch.randn(out_features, in_features))
        self.biases = Parameter(torch.randn(out_features))
    
    def forward(self, x):
        # Compute the linear transformation
        out = x.matmul(self.weights.t()) + self.biases
        return out

In this example, we define a simple linear layer with trainable weights and biases. The weights and biases are initialized as random tensors and then wrapped in Parameter objects. During training, the optimizer will update these parameters based on the gradients computed during backpropagation.