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: Fit the parameters of the postprocessor if it has any.
  
  :param preds: Training set to fit the postprocessor.
  :param kwargs: Other parameters.
docstr_tok: ["Fit","the","parameters","of","the","postprocessor","if","it","has","any",".",":","param","preds",":","Training","set","to","fit","the","postprocessor",".",":","param","kwargs",":","Other","parameters","."]
code: def fit(self, preds: np.ndarray, **kwargs) -> None:
      """
      Fit the parameters of the postprocessor if it has any.
  
      :param preds: Training set to fit the postprocessor.
      :param kwargs: Other parameters.
      """
      pass
  
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 6 for calling the Postprocessor obj's underlying member method code with example usage: obj.fit(preds) without return types