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: Return the list of classifier weights to assign to their prediction when aggregating results.
  
  :return: The list of classifier weights to assign to their prediction when aggregating results.
docstr_tok: ["Return","the","list","of","classifier","weights","to","assign","to","their","prediction","when","aggregating","results",".",":","return",":","The","list","of","classifier","weights","to","assign","to","their","prediction","when","aggregating","results","."]
code: def classifier_weights(self) -> np.ndarray:
      """
      Return the list of classifier weights to assign to their prediction when aggregating results.
  
      :return: The list of classifier weights to assign to their prediction when aggregating results.
      """
      return self._classifier_weights
  
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 4 for calling the EnsembleClassifier obj's underlying member method code with example usage: obj.classifier_weights() and returns: self