class_docstr: Abstract base class for transformation defences.
class_docstr_tok: ["Abstract","base","class","for","transformation","defences","."]
docstr: Fit the parameters of the transformer if it has any.
  
  :param x: Training set to fit the transformer.
  :param y: Labels for the training set.
  :param kwargs: Other parameters.
docstr_tok: ["Fit","the","parameters","of","the","transformer","if","it","has","any",".",":","param","x",":","Training","set","to","fit","the","transformer",".",":","param","y",":","Labels","for","the","training","set",".",":","param","kwargs",":","Other","parameters","."]
code: def fit(
      self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs
  ) -> None:
      """
      Fit the parameters of the transformer if it has any.
  
      :param x: Training set to fit the transformer.
      :param y: Labels for the training set.
      :param kwargs: Other parameters.
      """
      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 5 for calling the Transformer obj's underlying member method code with example usage: obj.fit(x, y) without return types