class_docstr: Implement the Cutout data augmentation defence approach.
  
  | Paper link: https://arxiv.org/abs/1708.04552
  
  | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
      see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
      https://arxiv.org/abs/1902.06705
class_docstr_tok: ["Implement","the","Cutout","data","augmentation","defence","approach",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1708.04552","|","Please","keep","in","mind","the","limitations","of","defences",".","For","more","information","on","the","limitations","of","this","defence",",","see","https",":","\/\/arxiv.org\/abs\/1803.09868",".","For","details","on","how","to","evaluate","classifier","security","in","general",",","see","https",":","\/\/arxiv.org\/abs\/1902.06705"]
docstr: Create an instance of a Cutout data augmentation object.
  
  :param length: Maximum length of the bounding box.
  :param channels_first: Set channels first or last.
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
  :param verbose: Show progress bars.
docstr_tok: ["Create","an","instance","of","a","Cutout","data","augmentation","object",".",":","param","length",":","Maximum","length","of","the","bounding","box",".",":","param","channels_first",":","Set","channels","first","or","last",".",":","param","apply_fit",":","True","if","applied","during","fitting\/training",".",":","param","apply_predict",":","True","if","applied","during","predicting",".",":","param","verbose",":","Show","progress","bars","."]
code: def __init__(
      self,
      length: int,
      channels_first: bool = False,
      apply_fit: bool = True,
      apply_predict: bool = False,
      verbose: bool = False,
  ) -> None:
      """
      Create an instance of a Cutout data augmentation object.
  
      :param length: Maximum length of the bounding box.
      :param channels_first: Set channels first or last.
      :param apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      :param verbose: Show progress bars.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
      self.length = length
      self.channels_first = channels_first
      self.verbose = verbose
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/cutout/cutout.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple
  import numpy
  from tqdm.auto import trange
  from art.defences.preprocessor.preprocessor import Preprocessor
label_desc: Use image node_id 1 to create a new Cutout object from inherited base classes: Preprocessor with example: obj = Cutout(length, channels_first, apply_fit, apply_predict, verbose)