FIrst order gradients are used for outer loop optimization

[sebamenabar]

Hi, there was a bug on the original code of OML where they did not create the computation graph for the inner loop updates. Looking the ANML code it seems to have the same issue, specifically here it is currently

grad = torch.autograd.grad(loss, fast_weights, allow_unused=False)

but to correctly backpropagate through the inner optimization it should be

grad = torch.autograd.grad(loss, fast_weights, allow_unused=False, create_graph=True)

I was wondering which version was used for the results on the paper, OML's author said fixing this bug improved performance and reduced training time.
Thanks

p.d.: congratz on the work, is really cool