To automatically compute the gradients of a scalar loss with respect to all trainable parameters.

To build a static computational graph before executing the forward pass.

To zero out the gradients of the optimizer before a new backward pass is performed.

To enable hardware acceleration by moving tensors to the GPU or TPU.

To disable gradient tracking and reduce computational and memory overhead during inference or evaluation.