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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 cross_entropy_loss(inputs, labels, rescale_loss=1):
 """ cross entropy loss with a mask """

    criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss)
    loss = criterion(inputs, labels)
    mask = S.var('mask')
    loss = loss * S.reshape(mask, shape=(-1,))
    return S.make_loss(loss.mean())