def construct(self, *inputs):
    loss = self.network(*inputs)
    other = None
    if isinstance(loss, tuple):
        loss = loss[0]
        other = loss[1:]
    sens = F.fill(loss.dtype, loss.shape, self.sens)
    grads = self.grad(self.network, self.weights)(*inputs, sens)
    grads = self.grad_reducer(grads)
    loss = F.depend(loss, self.optimizer(grads))
    if other is None:
        return loss
    return loss, other