class_docstr: Abstract base class for transformation defences.
class_docstr_tok: ["Abstract","base","class","for","transformation","defences","."]
docstr: Perform the transformation defence and return a robuster classifier.
  
  :param x: Dataset for training the transformed classifier.
  :param transformed_classifier: A classifier to be transformed for increased robustness.
  :return: The transformed classifier.
docstr_tok: ["Perform","the","transformation","defence","and","return","a","robuster","classifier",".",":","param","x",":","Dataset","for","training","the","transformed","classifier",".",":","param","transformed_classifier",":","A","classifier","to","be","transformed","for","increased","robustness",".",":","return",":","The","transformed","classifier","."]
code: def __call__(
      self, x: np.ndarray, transformed_classifier: "CLASSIFIER_TYPE"
  ) -> "CLASSIFIER_TYPE":
      """
      Perform the transformation defence and return a robuster classifier.
  
      :param x: Dataset for training the transformed classifier.
      :param transformed_classifier: A classifier to be transformed for increased robustness.
      :return: The transformed classifier.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/defences/transformer/transformer.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  from typing import List, Optional, TYPE_CHECKING
  import numpy
label_desc: Use image node_id 4 for calling the Transformer obj's underlying member method code with example usage: obj.__call__(x, transformed_classifier) without return types