Hello,

I would like to change the value of a variable while we compute the gradient between two iterations. As an example, consider the following code:

```
optimizer = torch.optim.Adam([{'params':param1, 'lr':1e-2}])
for ite in range(ite_max):
optimizer.zero_grad()
loss = compute_loss(param1)
loss.backward()
optimizer.step()
# Here, we store the new value of the param1
store_value = param1.detach().clone()
# Now, I want to change the value of param1 to compute a new grad
# where the new_value change at each ite
param1 = torch.tensor([new_value], requires_grad = True)
```

My question is about my last line because I guess it is wrong but I donâ€™t really know how to do it.