requires_grad=True

In PyTorch, the attribute `requires_grad=True` is used to specify that a tensor requires gradients to be computed during backpropagation.

When a tensor is created with `requires_grad=True`, PyTorch automatically tracks all the operations that involve that tensor and builds a computational graph. This graph is then used during backpropagation to compute the gradients of the loss with respect to the tensor.

By default, the attribute `requires_grad` is set to False when a tensor is created. However, you can set it to True using the following syntax:

import torch

x = torch.ones(3, 3, requires_grad=True)

In this example, the tensor `x` is created with a shape of (3, 3) and all its values are set to 1. The attribute `requires_grad` is set to True, which means that gradients will be computed during backpropagation.