torch.nn.functional.softmax
torch.nn.functional.softmax(input, dim=None, _stacklevel=3, dtype=None)
Applies a softmax function.
Softmax is defined as:
\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
It is applied to all slices along dim, and will re-scale them so
   that the elements lie in the range [0, 1] and sum to 1.
See "Softmax" for more details.
Parameters:
      * input (Tensor) -- input
  * **dim** (*int*) -- A dimension along which softmax will be
    computed.

  * **dtype** ("torch.dtype", optional) -- the desired data type
    of returned tensor. If specified, the input tensor is casted
    to "dtype" before the operation is performed. This is useful
    for preventing data type overflows. Default: None.

Return type:
      Tensor
Note:
 This function doesn't work directly with NLLLoss, which expects
 the Log to be computed between the Softmax and itself. Use
