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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)