class_docstr: Class allowing to aggregate multiple classifiers as an ensemble. The individual classifiers are expected to be
  trained when the ensemble is created and no training procedures are provided through this class.
code: def __repr__(self):
      repr_ = (
          f"{self.__module__ + '.' + self.__class__.__name__}(classifiers={self.classifiers}, "
          f"classifier_weights={self.classifier_weights!r}, channels_first={self.channels_first}, "
          f"clip_values={self.clip_values!r}, preprocessing_defences={self.preprocessing_defences}, "
          f"postprocessing_defences={self.postprocessing_defences}, preprocessing={self.preprocessing})"
      )
  
      return repr_
  
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
  from art.estimators.estimator import NeuralNetworkMixin
label_desc: Use image node_id 13 for calling the EnsembleClassifier obj's underlying member method code with example usage: obj.__repr__() and returns: repr_