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.
docstr: Fit the classifier using the generator that yields batches as specified. This function is not supported for
  ensembles.
  
  :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
                    training in Keras, it will.
  :param nb_epochs: Number of epochs to use for trainings.
  :param kwargs: Dictionary of framework-specific argument.
  :raises `NotImplementedException`: This method is not supported for ensembles.
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code: def fit_generator(
      self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs
  ) -> None:
      """
      Fit the classifier using the generator that yields batches as specified. This function is not supported for
      ensembles.
  
      :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
                        training in Keras, it will.
      :param nb_epochs: Number of epochs to use for trainings.
      :param kwargs: Dictionary of framework-specific argument.
      :raises `NotImplementedException`: This method is not supported for ensembles.
      """
      raise NotImplementedError
  
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 7 for calling the EnsembleClassifier obj's underlying member method code with example usage: obj.fit_generator(generator, nb_epochs) without return types