I was wondering why the first parameter of torch.autograd.functional.jacobian is not the output?

I have to define a dummy function that does nothing but return the output

```
import torch
x1=torch.tensor([1.0, 1.0], requires_grad=True)
x2=torch.tensor([2.0, 2.0], requires_grad=True)
y1=(x1+2*x2).sum()
y2=(3*x1+4*x2).sum()
def dummy_func(*args):
#It is impossible to write the computation process/graph here
#because it has already been done in some other functions/sections
#y1=(x1+2*x2).sum()
#y2=(3*x1+4*x2).sum()
return (y1, y2)
gg=torch.autograd.functional.jacobian(dummy_func, (x1, x2), vectorize=True)
g=torch.autograd.grad(y1, [x1, x2])
# fail because "Trying to backward through the graph a second time,
# but the saved intermediate results have already been freed."
```

The problem of this hack is that torch.autograd.functional.jacobian will not retain the graph from (x1, x2) to (y1, y2)