tell me more about loss.backward

In PyTorch, `loss.backward()` is a method used to compute the gradients of the loss function with respect to the model parameters.

When we train a deep learning model, we aim to minimize the loss function, which is a measure of how well the model is performing on the task. The gradient of the loss function with respect to the model parameters tells us how much we need to adjust each parameter to decrease the loss function.

The `loss.backward()` method computes the gradients of the loss function with respect to all the parameters in the computational graph that led to the loss value. It works by using the chain rule of calculus to propagate the gradients backwards through the graph.

After computing the gradients, we typically use an optimization algorithm (such as stochastic gradient descent) to update the parameters in the direction of the negative gradient, which leads to a decrease in the loss function.