class_docstr: Implement the CutMix data augmentation defence approach.
  
  | Paper link: https://arxiv.org/abs/1905.04899
  
  | 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 CutMix data augmentation object.
  
  :param num_classes: The number of classes used for one-hot encoding.
  :param alpha: The hyperparameter for sampling the combination ratio.
  :param probability: The probability of applying CutMix per sample.
  :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,
      num_classes: int,
      alpha: float = 1.0,
      probability: float = 0.5,
      channels_first: bool = False,
      apply_fit: bool = True,
      apply_predict: bool = False,
      verbose: bool = False,
  ) -> None:
      """
      Create an instance of a CutMix data augmentation object.
  
      :param num_classes: The number of classes used for one-hot encoding.
      :param alpha: The hyperparameter for sampling the combination ratio.
      :param probability: The probability of applying CutMix per sample.
      :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.num_classes = num_classes
      self.alpha = alpha
      self.probability = probability
      self.channels_first = channels_first
      self.verbose = verbose
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/cutmix/cutmix.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 tqdm
  from art.defences.preprocessor.preprocessor import Preprocessor
  from art.utils import to_categorical
label_desc: Use image node_id 1 to create a new CutMix object from inherited base classes: Preprocessor with example: obj = CutMix(num_classes, alpha, probability, channels_first, apply_fit, apply_predict, verbose)