torch.sparse.softmax
torch.sparse.softmax(input, dim, *, dtype=None) -> Tensor
Applies a softmax function.
Softmax is defined as:
\text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)}
where i, j run over sparse tensor indices and unspecified entries
   are ignores. This is equivalent to defining unspecified entries as
   negative infinity so that exp(x_k) = 0 when the entry with index k
   has not specified.
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 casted
    to "dtype" before the operation is performed. This is useful
    for preventing data type overflows. Default: None
