class_docstr: This class represent a Feature Adversaries evasion attack.
  
  | Paper link: https://arxiv.org/abs/1511.05122
docstr: Apply attack-specific checks.
code: def _check_params(self) -> None:
      """
      Apply attack-specific checks.
      """
      if self.delta is None:
          raise ValueError("The delta cannot be None.")
      if self.delta is not None and self.delta <= 0:
          raise ValueError(
              "The maximum deviation `delta` has to be positive."
          )
  
      if not isinstance(self.layer, int):
          raise ValueError(
              "The index of the representation layer `layer` has to be integer."
          )
  
      if self.batch_size <= 0:
          raise ValueError(
              "The batch size `batch_size` has to be positive."
          )
  
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 3 for calling the FeatureAdversariesNumpy obj's underlying member method code with example usage: obj._check_params() without return types