True
```
When does autograd start tracking history now?
requires_grad, the central flag for autograd, is now an attribute on Tensors. The same rules previously used for Variables applies to Tensors; autograd starts tracking history when any input Tensor of an operation has requires_grad=True. For example,
```python



x = torch.ones(1)  # create a tensor with requires_grad=False (default)
x.requires_grad
False
y = torch.ones(1)  # another tensor with requires_grad=False
z = x + y
both inputs have requires_grad=False. so does the output
z.requires_grad
False
then autograd won't track this computation. let's verify!
z.backward()
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
now create a tensor with requires_grad=True
w = torch.ones(1, requires_grad=True)


