class_docstr: Class for black-box neural network classifiers.
docstr: Return the output of a specific layer for samples `x` where `layer` is the index of the layer between 0 and
  `nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results
  returned by calling `layer_names`.
  
  :param x: Samples
  :param layer: Index or name of the layer.
  :param batch_size: Batch size.
  :param framework: If true, return the intermediate tensor representation of the activation.
  :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
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code: def get_activations(
      self,
      x: np.ndarray,
      layer: Union[int, str],
      batch_size: int,
      framework: bool = False,
  ) -> np.ndarray:
      """
      Return the output of a specific layer for samples `x` where `layer` is the index of the layer between 0 and
      `nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results
      returned by calling `layer_names`.
  
      :param x: Samples
      :param layer: Index or name of the layer.
      :param batch_size: Batch size.
      :param framework: If true, return the intermediate tensor representation of the activation.
      :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
      """
      raise NotImplementedError
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from functools import total_ordering
  import logging
  from typing import Callable, List, Optional, Union, Tuple, TYPE_CHECKING
  import numpy
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin, Classifier
label_desc: Use image node_id 5 for calling the BlackBoxClassifierNeuralNetwork obj's underlying member method code with example usage: obj.get_activations(x, layer, batch_size, framework) without return types