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: Fit the detector using clean and adversarial samples.
  
  :param x: Training set to fit the detector.
  :param y: Labels for the training set.
  :param batch_size: Size of batches.
  :param nb_epochs: Number of epochs to use for training.
  :param kwargs: Other parameters.
code: def fit(
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int = 128,
      nb_epochs: int = 20,
      **kwargs,
  ) -> None:
      """
      Fit the detector using clean and adversarial samples.
  
      :param x: Training set to fit the detector.
      :param y: Labels for the training set.
      :param batch_size: Size of batches.
      :param nb_epochs: Number of epochs to use for training.
      :param kwargs: Other parameters.
      """
      self.detector.fit(
          x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs
      )
  
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 2 for calling the BinaryInputDetector obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs) without return types