Hello folks,

I have a model which I’m refactoring to support torchscript for use in a c++ library. An issue I have run into is *function definitions aren’t supported* when using a closure for the LBFGS optimiser (which according to the docs is necessary). So I’m wondering if it’s possible to use the LBFGS optimiser in torchscript?

```
import torch
class Test(torch.nn.Module):
def __init__(self):
super(Test, self).__init__()
def forward(self,
input_: torch.Tensor,
target: torch.Tensor) -> torch.Tensor:
optim = torch.optim.LBFGS(tensor)
max_iter = 50
n_iter = 0
def closure():
optim.zero_grad()
loss = input_ - target
loss.backward()
n_iter += 1
return loss
while n_iter <= max_iter:
optim.step(closure)
return tensor
test = torch.jit.script(Test())
test.save('test.pt')
```

The truncated output is:

```
UnsupportedNodeError: function definitions aren't supported:
File "/tmp/ipykernel_4162/3211224755.py", line 15
n_iter = 0
def closure():
~~~ <--- HERE
optim.zero_grad()
loss = input_ - target
'Test.forward' is being compiled since it was called from 'Test.forward'
File "/tmp/ipykernel_4162/3447201789.py", line 20
target: torch.Tensor) -> torch.Tensor:
optim = torch.optim.LBFGS(tensor)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
max_iter = 50
n_iter = 0
```