Shape:
      * Input1: (N) or () where N is the batch size.
  * Input2: (N) or (), same shape as the Input1.

  * Target: (N) or (), same shape as the inputs.

  * Output: scalar. If "reduction" is "'none'" and Input size is
    not (), then (N).

Examples:
  >>> loss = nn.MarginRankingLoss()
  >>> input1 = torch.randn(3, requires_grad=True)
  >>> input2 = torch.randn(3, requires_grad=True)
  >>> target = torch.randn(3).sign()
  >>> output = loss(input1, input2, target)
  >>> output.backward()
