Return the output of the wrapped PyTorch model for the given input,
        along with a callback to handle the backward pass.

def begin_update(self, x_data, drop=0.0):
        """Return the output of the wrapped PyTorch model for the given input,
        along with a callback to handle the backward pass.
        """
        x_var = torch.autograd.Variable(xp2torch(x_data), requires_grad=True)
        # Make prediction

        y_var = self._model(x_var)

        def backward_pytorch(dy_data, sgd=None):
            dy_var = xp2torch(dy_data)
            torch.autograd.backward((y_var,), grad_tensors=(dy_var,))
            if sgd is not None:
                if self._optimizer is None:
                    self._optimizer = self._create_optimizer(sgd)
                self._optimizer.step()
                self._optimizer.zero_grad()
            return torch2xp(x_var.grad)

        return torch2xp(y_var), backward_pytorch