representing the minimum difference between the positive and
   negative distances that is required for the loss to be 0.  The
   input tensors have N elements each and can be of any shape that the
   distance function can handle.
If "reduction" is not "'none'" (default "'mean'"), then:
  \ell(x, y) = \begin{cases}     \operatorname{mean}(L), &
  \text{if reduction} = \text{`mean';}\\
  \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
  \end{cases}

See also "TripletMarginLoss", which computes the triplet loss for
   input tensors using the l_p distance as the distance function.
Parameters:
      * distance_function (Callable, optional) -- A
        nonnegative, real-valued function that quantifies the
        closeness of two tensors. If not specified,
        nn.PairwiseDistance will be used.  Default: "None"
  * **margin** (*float**, **optional*) -- A nonnegative margin
    representing the minimum difference between the positive and
