"size_average". When "reduce" is "False", returns a loss per
        batch element instead and ignores "size_average". Default:
        "True"
  * **reduction** (*str**, **optional*) -- Specifies the reduction
    to apply to the output: "'none'" | "'mean'" | "'sum'".
    "'none'": no reduction will be applied, "'mean'": the sum of
    the output will be divided by the number of elements in the
    output, "'sum'": the output will be summed. Note:
    "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: (N, C) or (C), where N is the batch size and C is the
        number of classes.
  * Target: (N) or (), where each value is 0 \leq
    \text{targets}[i] \leq C-1.

  * Output: scalar. If "reduction" is "'none'", then same shape as
    the target.

Examples:
  >>> loss = nn.MultiMarginLoss()
