class_docstr: Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
  Wang et al. (2019).
  
  | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
docstr: Return the output of the second to last layer for input `x`.
  
  :param x: Input for computing the activations.
  :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
docstr_tok: ["Return","the","output","of","the","second","to","last","layer","for","input","`","x","`",".",":","param","x",":","Input","for","computing","the","activations",".",":","return",":","The","output","of","`","layer","`",",","where","the","first","dimension","is","the","batch","size","corresponding","to","`","x","`","."]
code: def _get_penultimate_layer_activations(
      self, x: np.ndarray
  ) -> np.ndarray:
      """
      Return the output of the second to last layer for input `x`.
  
      :param x: Input for computing the activations.
      :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
      """
      if self.layer_names is not None:
          penultimate_layer = len(self.layer_names) - 2
      else:  # pragma: no cover
          raise ValueError("No layer names found.")
      return self.get_activations(
          x,
          penultimate_layer,
          batch_size=self.batch_size,
          framework=False,
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.poison_mitigation.neural_cleanse.neural_cleanse import NeuralCleanseMixin
  from art.estimators.classification.keras import KerasClassifier, KERAS_MODEL_TYPE
label_desc: Use image node_id 7 for calling the KerasNeuralCleanse obj's underlying member method code with example usage: obj._get_penultimate_layer_activations(x) and returns: self