PyTorch Autograd performs automatic differentiation on computations represented by a dynamically generated graph. After .backward() is called on a loss tensor, Autograd traverses the graph backward, computes gradients for participating model parameters, and stores them in their .grad attributes. An optimizer can then use these gradients to update the parameters.