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
class_docstr_tok: ["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"]
code: def _check_params(self) -> None:
      if not isinstance(self.classifier, KerasClassifier):
          raise NotImplementedError(
              "Only Keras classifiers are supported for this defence."
          )
  
file_path: adversarial-robustness-toolbox/art/defences/transformer/poisoning/neural_cleanse.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, TYPE_CHECKING, Union
  import numpy
  from art.defences.transformer.transformer import Transformer
  from art.estimators.poison_mitigation.neural_cleanse import KerasNeuralCleanse
  from art.estimators.classification.keras import KerasClassifier
label_desc: Use image node_id 4 for calling the NeuralCleanse obj's underlying member method code with example usage: obj._check_params() without return types