negative distances required for the loss to be 0. Larger
        margins penalize cases where the negative examples are not
        distant enough from the anchors, relative to the positives.
        Default: 1.
  * **swap** (*bool**, **optional*) -- Whether to use the distance
    swap described in the paper *Learning shallow convolutional
    feature descriptors with triplet losses* by V. Balntas, E.
    Riba et al. If True, and if the positive example is closer to
    the negative example than the anchor is, swaps the positive
    example and the anchor in the loss computation. Default:
    "False".

  * **reduction** (*str**, **optional*) -- Specifies the
    (optional) 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.
    Default: "'mean'"

Shape: