torch.special.softmax(input, dim, *, dtype=None) -> Tensor
Computes the 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.
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 cast to
    "dtype" before the operation is performed. This is useful for
    preventing data type overflows. Default: None.

Examples::
      >>> t = torch.ones(2, 2)
      >>> torch.special.softmax(t, 0)
      tensor([[0.5000, 0.5000],
              [0.5000, 0.5000]])
torch.special.spherical_bessel_j0(input, *, out=None) -> Tensor
Spherical Bessel function of the first kind of order 0.
Parameters: