class_docstr: This is the quantized version of :class:`~torch.nn.Softmax`.
  
  Args:
      dim: A dimension along which Softmax will be computed (so every slice along dim will sum to 1).
      scale: quantization scale of the output tensor
      zero_point: quantization zero point of the output tensor
class_docstr_tok: ["This","is","the","quantized","version","of",":","class",":","`","~torch.nn.Softmax","`",".","Args",":","dim",":","A","dimension","along","which","Softmax","will","be","computed","(","so","every","slice","along","dim","will","sum","to","1",")",".","scale",":","quantization","scale","of","the","output","tensor","zero_point",":","quantization","zero","point","of","the","output","tensor"]
code: def forward(self, input):
      dim = self.dim
      if dim is None:
          stacklevel = 3
          # Note: adding the mypy ignore on _get_softmax_dim seems less bad
          # than making `_get_softmax_dim` an official API.
          dim = torch.nn.functional._get_softmax_dim(  # type: ignore[attr-defined]
              "softmax", input.dim(), stacklevel
          )
      return torch.ops.quantized.softmax(
          input, dim, self.scale, self.zero_point
      )
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 2 for calling the Softmax obj's underlying member method code with example usage: obj.forward(input) and returns: torch