import torch
from torch.autograd import Variable
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F

v_in = Variable(torch.Tensor([0.1, 0.1]).view(2, 1), requires_grad=True)

def forward(v_in):
    f1 = lambda x: x * 2
    f2 = torch.nn.Linear(1, 1)
    grad_out = Variable(torch.ones(2, 1))
    gradient = torch.autograd.grad(outputs=f2(f1(v_in)), inputs=v_in,
                            grad_outputs=grad_out,
                            create_graph=True, retain_graph=True,
                            only_inputs=True)[0]
    out = gradient.sum()
    return out

# returns false
autograd.gradcheck(forward, [v_in])

forward(v_in).backward()
v_in.grad  # these are non-existent?