I am interested in combined derivatives using Pytorch:

In the implemented code below, I have tried, but the code compute two partial derivative (e.g. it computed firstly d’f/d’x and secondly d’f/d’y). Is it possible modify the code in some way that we can compute this derivative with respect two parameters?

```
import torch
def function(x,y):
f = x**3+y**3
return f
a = torch.tensor([4., 5., 6.], requires_grad=True)
b = torch.tensor([1., 2., 6.], requires_grad=True)
derivative = torch.autograd.functional.jacobian(function, (a,b))
print(derivative)
```