class_docstr: Method from Chen et al., 2018 performing poisoning detection based on activations clustering.
  
  | Paper link: https://arxiv.org/abs/1811.03728
  
  | Please keep in mind the limitations of defences. For more information on the limitations of this
      defence, see https://arxiv.org/abs/1905.13409 . For details on how to evaluate classifier security
      in general, see https://arxiv.org/abs/1902.06705
docstr: Pickles the self.classifier and stores it using the provided file_name in folder `art.config.ART_DATA_PATH`.
  
  :param classifier: Classifier to be pickled.
  :param file_name: Name of the file where the classifier will be pickled.
docstr_tok: ["Pickles","the","self.classifier","and","stores","it","using","the","provided","file_name","in","folder","`","art.config.ART_DATA_PATH","`",".",":","param","classifier",":","Classifier","to","be","pickled",".",":","param","file_name",":","Name","of","the","file","where","the","classifier","will","be","pickled","."]
code: def _pickle_classifier(
      classifier: "CLASSIFIER_NEURALNETWORK_TYPE", file_name: str
  ) -> None:
      """
      Pickles the self.classifier and stores it using the provided file_name in folder `art.config.ART_DATA_PATH`.
  
      :param classifier: Classifier to be pickled.
      :param file_name: Name of the file where the classifier will be pickled.
      """
      full_path = os.path.join(config.ART_DATA_PATH, file_name)
      folder = os.path.split(full_path)[0]
      if not os.path.exists(folder):
          os.makedirs(folder)
  
      with open(full_path, "wb") as f_classifier:
          pickle.dump(classifier, f_classifier)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import pickle
  import time
  from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  from sklearn.cluster import KMeans, MiniBatchKMeans
  from art import config
  from art.data_generators import DataGenerator
  from art.defences.detector.poison.clustering_analyzer import ClusteringAnalyzer
  from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
  from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
  from art.utils import segment_by_class
  from art.visualization import create_sprite, save_image, plot_3d
label_desc: Use image node_id 9 for calling the ActivationDefence obj's underlying member method code with example usage: obj._pickle_classifier(classifier, file_name) without return types