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 training time.
  
  :return: `True` if the defence should be applied when fitting a model, `False` otherwise.
docstr_tok: ["Property","of","the","defence","indicating","if","it","should","be","applied","at","training","time",".",":","return",":","`","True","`","if","the","defence","should","be","applied","when","fitting","a","model",",","`","False","`","otherwise","."]
code: def apply_fit(self) -> bool:
      """
      Property of the defence indicating if it should be applied at training time.
  
      :return: `True` if the defence should be applied when fitting a model, `False` otherwise.
      """
      return self._apply_fit
  
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 3 for calling the Postprocessor obj's underlying member method code with example usage: obj.apply_fit() and returns: self