class_docstr: This class implements a Classifier extension that wraps a classifier and a detector.
  More details in https://arxiv.org/abs/1705.07263
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docstr: Initialization for the DetectorClassifier.
  
  :param classifier: A trained classifier.
  :param detector: A trained detector applied for the binary classification.
  :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier. Not applicable
         in this classifier.
  :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
  :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
         used for data preprocessing. The first value will be subtracted from the input. The input will then
         be divided by the second one. Not applicable in this classifier.
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code: def __init__(
      self,
      classifier: ClassifierNeuralNetwork,
      detector: ClassifierNeuralNetwork,
      preprocessing_defences: Union[
          "Preprocessor", List["Preprocessor"], None
      ] = None,
      postprocessing_defences: Union[
          "Postprocessor", List["Postprocessor"], None
      ] = None,
      preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
  ) -> None:
      """
      Initialization for the DetectorClassifier.
  
      :param classifier: A trained classifier.
      :param detector: A trained detector applied for the binary classification.
      :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier. Not applicable
             in this classifier.
      :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
      :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
             used for data preprocessing. The first value will be subtracted from the input. The input will then
             be divided by the second one. Not applicable in this classifier.
      """
      if preprocessing_defences is not None:
          raise NotImplementedError(
              "Preprocessing is not applicable in this classifier."
          )
  
      super().__init__(
          model=None,
          clip_values=classifier.clip_values,
          preprocessing=preprocessing,
          channels_first=classifier.channels_first,
          preprocessing_defences=preprocessing_defences,
          postprocessing_defences=postprocessing_defences,
      )
  
      self.classifier = classifier
      self.detector = detector
      self.nb_classes = classifier.nb_classes + 1
      self._input_shape = classifier.input_shape
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.classification.classifier import ClassifierNeuralNetwork
label_desc: Use image node_id 1 to create a new DetectorClassifier object from inherited base classes: ClassifierNeuralNetwork with example: obj = DetectorClassifier(classifier, detector, preprocessing_defences, postprocessing_defences, preprocessing)