class_docstr: This class represent a Feature Adversaries evasion attack in TensorFlow v2.
  
  | Paper link: https://arxiv.org/abs/1511.05122
docstr: Apply attack-specific checks.
code: def _check_params(self) -> None:
      """
      Apply attack-specific checks.
      """
      if not isinstance(self.delta, float):
          raise ValueError("The value of delta must be of type float.")
      if self.delta <= 0:
          raise ValueError(
              "The maximum deviation value delta has to be positive."
          )
  
      if not isinstance(self.lambda_, float):
          raise ValueError(
              "The value of lambda_ must be of type float."
          )
      if self.lambda_ < 0.0:
          raise ValueError(
              "The regularization parameter `lambda_` has to be non-negative."
          )
  
      if not isinstance(self.layer[0], (int, str)):
          raise ValueError(
              "The value of the representation layer must be integer or string."
          )
  
      if not isinstance(self.max_iter, int):
          raise ValueError("The value of max_iter must be of type int.")
      if self.max_iter <= 0:
          raise ValueError(
              "The maximum number of iterations has to be a positive."
          )
  
      if self.batch_size <= 0:
          raise ValueError("The batch size has to be positive.")
  
      if self.optimizer is None and self.step_size is None:
          raise ValueError(
              "The step size cannot be None if optimizer is None."
          )
      if self.step_size is not None and not isinstance(
          self.step_size, (int, float)
      ):
          raise ValueError(
              "The value of step_size must be of type int or float."
          )
      if self.step_size is not None and self.step_size <= 0:
          raise ValueError("The step size has to be a positive.")
  
file_path: adversarial-robustness-toolbox/art/attacks/evasion/feature_adversaries/feature_adversaries_tensorflow.py
imports: import logging
  from typing import TYPE_CHECKING, Optional, Tuple, Union
  import numpy
  from tqdm.auto import trange
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
label_desc: Use image node_id 4 for calling the FeatureAdversariesTensorFlowV2 obj's underlying member method code with example usage: obj._check_params() without return types