class_docstr: Binary detector of adversarial samples coming from evasion attacks. The detector uses an architecture provided by
  the user and trains it on data labeled as clean (label 0) or adversarial (label 1).
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.
  :return: (report, is_adversarial):
          where report is a dictionary containing the detector model output predictions;
          where is_adversarial is a boolean list of per-sample prediction whether the sample is adversarial
          or not and has the same `batch_size` (first dimension) as `x`.
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.
      :return: (report, is_adversarial):
              where report is a dictionary containing the detector model output predictions;
              where is_adversarial is a boolean list of per-sample prediction whether the sample is adversarial
              or not and has the same `batch_size` (first dimension) as `x`.
      """
      predictions = self.detector.predict(x, batch_size=batch_size)
      is_adversarial = np.argmax(predictions, axis=1).astype(bool)
      report = {"predictions": predictions}
  
      return report, is_adversarial
  
file_path: adversarial-robustness-toolbox/art/defences/detector/evasion/binary_input_detector.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Tuple, TYPE_CHECKING
  import numpy
  from art.defences.detector.evasion.evasion_detector import EvasionDetector
label_desc: Use image node_id 3 for calling the BinaryInputDetector obj's underlying member method code with example usage: obj.detect(x, batch_size) and returns: report,  is_adversarial