class_docstr: Implementation of a postprocessor based on rounding classifier output.
docstr: Create a Rounded postprocessor.
  
  :param decimals: Number of decimal places after the decimal point.
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
docstr_tok: ["Create","a","Rounded","postprocessor",".",":","param","decimals",":","Number","of","decimal","places","after","the","decimal","point",".",":","param","apply_fit",":","True","if","applied","during","fitting\/training",".",":","param","apply_predict",":","True","if","applied","during","predicting","."]
code: def __init__(
      self,
      decimals: int = 3,
      apply_fit: bool = False,
      apply_predict: bool = True,
  ) -> None:
      """
      Create a Rounded postprocessor.
  
      :param decimals: Number of decimal places after the decimal point.
      :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.decimals = decimals
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/defences/postprocessor/rounded.py
imports: import logging
  import numpy
  from art.defences.postprocessor.postprocessor import Postprocessor
label_desc: Use image node_id 1 to create a new Rounded object from inherited base classes: Postprocessor with example: obj = Rounded(decimals, apply_fit, apply_predict)