Softmax
class torch.nn.Softmax(dim=None)
Applies the Softmax 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.
Softmax is defined as:
  \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}

When the input Tensor is a sparse tensor then the unspecified
   values are treated as "-inf".
Shape:
      * Input: (*) where *** means, any number of additional
        dimensions
  * Output: (*), same shape as the input

Returns:
      a Tensor of the same dimension and shape as the input with
      values in the range [0, 1]
Parameters:
      dim (int) -- A dimension along which Softmax will be
      computed (so every slice along dim will sum to 1).
Return type:
      None
Note:
 This module doesn't work directly with NLLLoss, which expects the
 Log to be computed between the Softmax and itself. Use
