class_docstr: Implement the Cutout data augmentation defence approach in TensorFlow v2.
  
  | 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
code: def _check_params(self) -> None:
      if self.length <= 0:
          raise ValueError("Bounding box length must be positive.")
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/cutout/cutout_tensorflow.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2
label_desc: Use image node_id 3 for calling the CutoutTensorFlowV2 obj's underlying member method code with example usage: obj._check_params() without return types