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: Compute the loss of the neural network for samples `x`.
  
  :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
            nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
  :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
            of shape `(nb_samples,)`.
  :return: Loss values.
  :rtype: Format as expected by the `model`
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code: def compute_loss(
      self, x: np.ndarray, y: np.ndarray, **kwargs
  ) -> np.ndarray:
      """
      Compute the loss of the neural network for samples `x`.
  
      :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
                nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
      :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
                of shape `(nb_samples,)`.
      :return: Loss values.
      :rtype: Format as expected by the `model`
      """
      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 15 for calling the EnsembleClassifier obj's underlying member method code with example usage: obj.compute_loss(x, y) without return types