Let us define the following real valued functions f and g as follows:

f(x) = y (for instance f(x) = x^2)

g(y) = dy/dx

The objective is to compute dg/dy.

The g(y) is computed using torch.autograd.grad. If I then want to compute dg/dy using torch.autograd.grad again, it fails with the message “RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior.”

It seems that y is not part of the computation graph created after calling the first torch.autograd.grad, but I do not understand why. Here is a minimal example to reproduce the problem:

```
import torch
import torch.autograd as ag
# Input x
x = torch.empty((1, ), dtype=torch.float32).uniform_(-1., 1.)
x.requires_grad = True
# f(x) = y
y = x ** 2.
# g(y) = dy/dx
A = ag.grad(y, x, torch.ones((1, ), dtype=torch.float32), create_graph=True)[0]
# dg / dy - fails
B = ag.grad(A, y, torch.ones((1, ), dtype=torch.float32), create_graph=True)[0]
```

I assumed that the the computation graph would look as follows:

```
________________
/ f g \| dg/dy
x ------> y ------> A ----------> B
\_______________________/|
```

But it seems that the branch connecting **y** to **A** does not exist (not created by the first call to torch.grad.autograd).

Would anyone have an idea what is going on here and how I could get such a derivative? Thank you!