```
for j in range(n_rnn_batches):
print x.size()
h_t = Variable(torch.zeros(x.size(0), 20))
c_t = Variable(torch.zeros(x.size(0), 20))
h_t2 = Variable(torch.zeros(x.size(0), 20))
c_t2 = Variable(torch.zeros(x.size(0), 20))
for s in range(n_steps / n_bptt_steps):
h_t, c_t, h_t2, c_t2 = Variable(h_t.data), Variable(c_t.data), Variable(h_t2.data), Variable(c_t2.data)
optimizer.zero_grad()
for s2 in range(n_bptt_steps):
data, target = next(iter((train_loader)))
data, target = Variable(data), Variable(target)
output = optimizee(data, x)
loss = F.nll_loss(output, target)
grads = autograd.grad(loss, x, retain_graph=True)[0].view(x.size(0),-1)
grads.volatile = False
grads = grads.detach()
out, h_t, c_t, h_t2, c_t2 = rnn(grads, h_t, c_t, h_t2, c_t2)
x = x - out
print x.volatile
loss.backward()
optimizer.step()
```

I’m trying to implement Learning to Learn by Gradient Descent By Gradient Descent. But I’m getting the `Trying to backward through the graph a second time`

error in the `loss.backward()`

step, even though I have set `retain_graph`

to True. Can someone please help?