class_docstr: Implementation of a postprocessor based on adding the Reverse Sigmoid perturbation to classifier output.
docstr: Create a ReverseSigmoid postprocessor.
  
  :param beta: A positive magnitude parameter.
  :param gamma: A positive dataset and model specific convergence parameter.
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
docstr_tok: ["Create","a","ReverseSigmoid","postprocessor",".",":","param","beta",":","A","positive","magnitude","parameter",".",":","param","gamma",":","A","positive","dataset","and","model","specific","convergence","parameter",".",":","param","apply_fit",":","True","if","applied","during","fitting\/training",".",":","param","apply_predict",":","True","if","applied","during","predicting","."]
code: def __init__(
      self,
      beta: float = 1.0,
      gamma: float = 0.1,
      apply_fit: bool = False,
      apply_predict: bool = True,
  ) -> None:
      """
      Create a ReverseSigmoid postprocessor.
  
      :param beta: A positive magnitude parameter.
      :param gamma: A positive dataset and model specific convergence parameter.
      :param apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
      self.beta = beta
      self.gamma = gamma
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/defences/postprocessor/reverse_sigmoid.py
imports: import logging
  import numpy
  from art.defences.postprocessor.postprocessor import Postprocessor
label_desc: Use image node_id 1 to create a new ReverseSigmoid object from inherited base classes: Postprocessor with example: obj = ReverseSigmoid(beta, gamma, apply_fit, apply_predict)