Softmin
class torch.nn.Softmin(dim=None)
Applies the Softmin function to an n-dimensional input Tensor
   rescaling them so that the elements of the n-dimensional output
   Tensor lie in the range [0, 1] and sum to 1.
Softmin is defined as:
  \text{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}

Shape:
      * Input: (*) where *** means, any number of additional
        dimensions
  * Output: (*), same shape as the input

Parameters:
      dim (int) -- A dimension along which Softmin will be
      computed (so every slice along dim will sum to 1).
Returns:
      a Tensor of the same dimension and shape as the input, with
      values in the range [0, 1]
Return type:
      None
Examples:
  >>> m = nn.Softmin(dim=1)
  >>> input = torch.randn(2, 3)
  >>> output = m(input)
