


x.grad
tensor([0.1051, 1.7676])
y.grad  # None




```
Using torch.autograd.grad
An alternative to backward() is to use torch.autograd.grad(). The main difference to backward() is that grad() returns a tuple of tensors with the gradients of the outputs w.r.t. the inputs kwargs instead of storing them in the .grad field of the tensors. As you can see, the grad() code shown below is very similar to backward.
```python
def grad(
    outputs: _TensorOrTensors,
    inputs: _TensorOrTensors,
    grad_outputs: Optional[_TensorOrTensors] = None,
    retain_graph: Optional[bool] = None,
    create_graph: bool = False,
    only_inputs: bool = True,
    allow_unused: bool = False,
   is_grads_batched: bool = False
) -> Tuple[torch.Tensor, ...]:
outputs = (outputs,) if isinstance(outputs, torch.Tensor) else tuple(outputs)
