class_docstr: Class for black-box regressors.
docstr: Returns a classifier representation of the regressor. Maps real values to classes based on the provided
  thresholds.
  
  :param thresholds: The cutoff values for mapping real values to classes.
  :return: BlackBoxClassifier instance
docstr_tok: ["Returns","a","classifier","representation","of","the","regressor",".","Maps","real","values","to","classes","based","on","the","provided","thresholds",".",":","param","thresholds",":","The","cutoff","values","for","mapping","real","values","to","classes",".",":","return",":","BlackBoxClassifier","instance"]
code: def get_classifier(
      self, thresholds: List[float]
  ) -> BlackBoxClassifier:
      """
      Returns a classifier representation of the regressor. Maps real values to classes based on the provided
      thresholds.
  
      :param thresholds: The cutoff values for mapping real values to classes.
      :return: BlackBoxClassifier instance
      """
  
      def predict_class(x):
          predictions = self.predict(x)
          binned = np.digitize(predictions, thresholds)
          return np.eye(len(thresholds) + 1)[binned]
  
      classifier = BlackBoxClassifier(
          predict_class,
          self.input_shape,
          len(thresholds) + 1,
          clip_values=self.clip_values,
          preprocessing_defences=self.preprocessing_defences,
          postprocessing_defences=self.postprocessing_defences,
          preprocessing=self.preprocessing,
          fuzzy_float_compare=self._fuzzy_float_compare,
      )
      return classifier
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from functools import total_ordering
  import logging
  from typing import Callable, List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.estimator import BaseEstimator
  from art.estimators.regression.regressor import RegressorMixin, Regressor
  from art.estimators.classification import BlackBoxClassifier
label_desc: Use image node_id 4 for calling the BlackBoxRegressor obj's underlying member method code with example usage: obj.get_classifier(thresholds) and returns: classifier, np