I am trying to write a custom autograd.Function that solves logistic regression using LBFGS. While `solve_logistic_regression(Xv,yv,.1)`

works flawlessly, `lr(Xv,yv,.1)`

throws the following runtime error:

`RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn`

I’m not sure what is going on here.

The code:

```
import numpy as np
from torch.autograd import Function, Variable
import torch
N, n = 100, 2
X = np.random.randn(N, n)
y = np.random.randint(0,2,size=N)
Xv = Variable(torch.Tensor(X), requires_grad=False)
yv = Variable(torch.Tensor(y), requires_grad=False)
def solve_logistic_regression(X, y, lamb):
N, n = X.shape
theta = Variable(torch.ones(n), requires_grad=True)
optimizer = torch.optim.LBFGS([theta], lr=.8)
def closure():
optimizer.zero_grad()
pi = 1./(1.+torch.exp(-X.mm(theta.unsqueeze(-1))))
loss = 1./N*torch.nn.BCELoss()(pi.squeeze(), y) + lamb/2*torch.norm(theta[:-1])**2
print (loss.item())
loss.backward()
return loss
optimizer.step(closure)
return theta
class LogisticRegression(Function):
@staticmethod
def forward(ctx, X, y, lamb):
theta = solve_logistic_regression(X, y, lamb)
return theta
@staticmethod
def backward(ctx, grad_output):
return None, None, None
lr = LogisticRegression.apply
```