loss。backward()

The `loss.backward()` function is used in PyTorch to perform backpropagation and calculate gradients of the loss function with respect to the model parameters. It is usually called after the forward pass of the model, where the input is passed through the network and the output is obtained.

After calculating the loss using a loss function such as mean squared error, cross-entropy loss, etc., the `loss.backward()` function is called to compute the gradients of the loss function with respect to the model parameters. These gradients are then used to update the model parameters using an optimizer such as stochastic gradient descent (SGD), Adam, etc.

In summary, `loss.backward()` is a method in PyTorch that is used to compute gradients of the loss function with respect to the model parameters and update the parameters of the model using an optimizer.