I’m trying to get a hessian vector product, but the following codes from DARTS model don’t work as expected while `self.named_parameters()`

is iterating over pairs `n,p.`

the error is: `RuntimeError: One of the differentiated Tensors does not require grad`

How can I fix this error? Thank you in advance.

```
def _hessian_vector_product(self, vector, input, target, r=1e-2):
R = r / _concat(vector).norm()
for p, v in zip(self.model.parameters(), vector):
p.data.add_(R, v)
loss = self.model._loss(input, target)
grads_p = torch.autograd.grad(loss, self.model.arch_parameters())
for p, v in zip(self.model.parameters(), vector):
p.data.sub_(2*R, v)
loss = self.model._loss(input, target)
grads_n = torch.autograd.grad(loss, self.model.arch_parameters())
for p, v in zip(self.model.parameters(), vector):
p.data.add_(R, v)
return [(x-y).div_(2*R) for x, y in zip(grads_p, grads_n)]
```