class_docstr: Abstract base class for transformation defences.
class_docstr_tok: ["Abstract","base","class","for","transformation","defences","."]
docstr: Return the state of the transformation object.
  
  :return: `True` if the transformation model has been fitted (if this applies).
docstr_tok: ["Return","the","state","of","the","transformation","object",".",":","return",":","`","True","`","if","the","transformation","model","has","been","fitted","(","if","this","applies",")","."]
code: def is_fitted(self) -> bool:
      """
      Return the state of the transformation object.
  
      :return: `True` if the transformation model has been fitted (if this applies).
      """
      return self._is_fitted
  
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 2 for calling the Transformer obj's underlying member method code with example usage: obj.is_fitted() and returns: self