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 from_float(mod):
      (
          scale,
          zero_point,
      ) = mod.activation_post_process.calculate_qparams()
      return Softmax(mod.dim, float(scale), int(zero_point))
  
imports: import torch
  from warnings import warn
label_desc: Use image node_id 4 for calling the Softmax obj's underlying member method code with example usage: obj.from_float(mod) and returns: Softmax