MarginRankingLoss
class torch.nn.MarginRankingLoss(margin=0.0, size_average=None, reduce=None, reduction='mean')
Creates a criterion that measures the loss given inputs x1, x2, two
   1D mini-batch or 0D Tensors, and a label 1D mini-batch or 0D
   Tensor y (containing 1 or -1).
If y = 1 then it assumed the first input should be ranked higher
   (have a larger value) than the second input, and vice-versa for y =
   -1.
The loss function for each pair of samples in the mini-batch is:
  \text{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \text{margin})

Parameters:
      * margin (float, optional) -- Has a default value of
        0.
  * **size_average** (*bool**, **optional*) -- Deprecated (see
    "reduction"). By default, the losses are averaged over each
    loss element in the batch. Note that for some losses, there
    are multiple elements per sample. If the field "size_average"
