class_docstr: This class represent a Feature Adversaries evasion attack.
  
  | Paper link: https://arxiv.org/abs/1511.05122
docstr: Create a :class:`.FeatureAdversaries` instance.
  
  :param classifier: A trained classifier.
  :param delta: The maximum deviation between source and guide images.
  :param layer: Index of the representation layer.
  :param batch_size: Batch size.
code: def __init__(
      self,
      classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
      delta: Optional[float] = None,
      layer: Optional[int] = None,
      batch_size: int = 32,
  ):
      """
      Create a :class:`.FeatureAdversaries` instance.
  
      :param classifier: A trained classifier.
      :param delta: The maximum deviation between source and guide images.
      :param layer: Index of the representation layer.
      :param batch_size: Batch size.
      """
      super().__init__(estimator=classifier)
  
      self.delta = delta
      self.layer = layer
      self.batch_size = batch_size
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/attacks/evasion/feature_adversaries/feature_adversaries_numpy.py
imports: import logging
  from typing import TYPE_CHECKING, Optional
  import numpy
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
label_desc: Use image node_id 1 to create a new FeatureAdversariesNumpy object from inherited base classes: EvasionAttack with example: obj = FeatureAdversariesNumpy(classifier, delta, layer, batch_size)