class_docstr: Abstract base class for all evasion detectors.
docstr: Perform detection of adversarial data and return prediction as tuple.
  
  :param x: Data sample on which to perform detection.
  :param batch_size: Size of batches.
  :param kwargs: Defence-specific parameters used by child classes.
  :return: (report, is_adversarial):
          where report is a dictionary containing information specific to the detection defence;
          where is_adversarial is a boolean list of per-sample prediction whether the sample is adversarial
code: def detect(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> Tuple[dict, np.ndarray]:
      """
      Perform detection of adversarial data and return prediction as tuple.
  
      :param x: Data sample on which to perform detection.
      :param batch_size: Size of batches.
      :param kwargs: Defence-specific parameters used by child classes.
      :return: (report, is_adversarial):
              where report is a dictionary containing information specific to the detection defence;
              where is_adversarial is a boolean list of per-sample prediction whether the sample is adversarial
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/defences/detector/evasion/evasion_detector.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  from typing import Any, Dict, List, Tuple
  import numpy
label_desc: Use image node_id 3 for calling the EvasionDetector obj's underlying member method code with example usage: obj.detect(x, batch_size) without return types