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 _fit_classifier(
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int,
      nb_epochs: int,
      **kwargs,
  ) -> None:
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Union, Tuple, List
  import numpy
  from art.estimators.certification.abstain import AbstainPredictorMixin
  from art.utils import to_categorical
label_desc: Use image node_id 3 for calling the NeuralCleanseMixin obj's underlying member method code with example usage: obj._fit_classifier(x, y, batch_size, nb_epochs) without return types