class_docstr: Implement the pixel defence approach. Defense based on PixelCNN that projects samples back to the data manifold.
  
  | Paper link: https://arxiv.org/abs/1710.10766
  
  | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
      see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
      https://arxiv.org/abs/1902.06705
docstr: Create an instance of pixel defence.
  
  :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
         for features.
  :param eps: Defense parameter 0-255.
  :param pixel_cnn: Pre-trained PixelCNN model.
  :param verbose: Show progress bars.
code: def __init__(
      self,
      clip_values: "CLIP_VALUES_TYPE" = (0.0, 1.0),
      eps: int = 16,
      pixel_cnn: Optional["CLASSIFIER_NEURALNETWORK_TYPE"] = None,
      batch_size: int = 128,
      apply_fit: bool = False,
      apply_predict: bool = True,
      verbose: bool = False,
  ) -> None:
      """
      Create an instance of pixel defence.
  
      :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
             for features.
      :param eps: Defense parameter 0-255.
      :param pixel_cnn: Pre-trained PixelCNN model.
      :param verbose: Show progress bars.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
      self.clip_values = clip_values
      self.eps = eps
      self.batch_size = batch_size
      self.pixel_cnn = pixel_cnn
      self.verbose = verbose
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/pixel_defend.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
  from art.defences.preprocessor.preprocessor import Preprocessor
label_desc: Use image node_id 1 to create a new PixelDefend object from inherited base classes: Preprocessor with example: obj = PixelDefend(clip_values, eps, pixel_cnn, batch_size, apply_fit, apply_predict, verbose)