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: Save a model to file in the format specific to the backend framework. This function is not supported for
  ensembles.
  
  :param filename: Name of the file where to store the model.
  :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
               the default data location of the library `ART_DATA_PATH`.
  :raises `NotImplementedException`: This method is not supported for ensembles.
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code: def save(self, filename: str, path: Optional[str] = None) -> None:
      """
      Save a model to file in the format specific to the backend framework. This function is not supported for
      ensembles.
  
      :param filename: Name of the file where to store the model.
      :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
                   the default data location of the library `ART_DATA_PATH`.
      :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 14 for calling the EnsembleClassifier obj's underlying member method code with example usage: obj.save(filename, path) without return types