backward propagation with loss

def backward(self, loss):
        """backward propagation with loss"""
        with mx.autograd.record():
            if isinstance(loss, (tuple, list)):
                ls = [l * self._scaler.loss_scale for l in loss]
            else:
                ls = loss * self._scaler.loss_scale
        mx.autograd.backward(ls)