class_docstr: Abstract base class for postprocessing defences. Postprocessing defences are not included in the loss function
  evaluation for loss gradients or the calculation of class gradients.
docstr: Property of the defence indicating if it should be applied at test time.
  
  :return: `True` if the defence should be applied at prediction time, `False` otherwise.
docstr_tok: ["Property","of","the","defence","indicating","if","it","should","be","applied","at","test","time",".",":","return",":","`","True","`","if","the","defence","should","be","applied","at","prediction","time",",","`","False","`","otherwise","."]
code: def apply_predict(self) -> bool:
      """
      Property of the defence indicating if it should be applied at test time.
  
      :return: `True` if the defence should be applied at prediction time, `False` otherwise.
      """
      return self._apply_predict
  
file_path: adversarial-robustness-toolbox/art/defences/postprocessor/postprocessor.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from typing import List
  import abc
  import numpy
label_desc: Use image node_id 4 for calling the Postprocessor obj's underlying member method code with example usage: obj.apply_predict() and returns: self