"size_average" and "reduce" are in the process of being
        deprecated, and in the meantime, specifying either of those
        two args will override "reduction". Default: "'mean'"
Shape:
      * Input: (C) or (N, C) where N is the batch size and C is
        the number of classes.
  * Target: (C) or (N, C), label targets padded by -1 ensuring
    same shape as the input.

  * Output: scalar. If "reduction" is "'none'", then (N).

Examples:
  >>> loss = nn.MultiLabelMarginLoss()
  >>> x = torch.FloatTensor([[0.1, 0.2, 0.4, 0.8]])
  >>> # for target y, only consider labels 3 and 0, not after label -1
  >>> y = torch.LongTensor([[3, 0, -1, 1]])
  >>> # 0.25 * ((1-(0.1-0.2)) + (1-(0.1-0.4)) + (1-(0.8-0.2)) + (1-(0.8-0.4)))
  >>> loss(x, y)
  tensor(0.85...)
