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: Unpickles classifier using the filename provided. Function assumes that the pickle is in
  `art.config.ART_DATA_PATH`.
  
  :param file_name: Path of the pickled classifier relative to `ART_DATA_PATH`.
  :return: The loaded classifier.
docstr_tok: ["Unpickles","classifier","using","the","filename","provided",".","Function","assumes","that","the","pickle","is","in","`","art.config.ART_DATA_PATH","`",".",":","param","file_name",":","Path","of","the","pickled","classifier","relative","to","`","ART_DATA_PATH","`",".",":","return",":","The","loaded","classifier","."]
code: def _unpickle_classifier(
      file_name: str,
  ) -> "CLASSIFIER_NEURALNETWORK_TYPE":
      """
      Unpickles classifier using the filename provided. Function assumes that the pickle is in
      `art.config.ART_DATA_PATH`.
  
      :param file_name: Path of the pickled classifier relative to `ART_DATA_PATH`.
      :return: The loaded classifier.
      """
      full_path = os.path.join(config.ART_DATA_PATH, file_name)
      logger.info("Loading classifier from %s", full_path)
      with open(full_path, "rb") as f_classifier:
          loaded_classifier = pickle.load(f_classifier)
          return loaded_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 10 for calling the ActivationDefence obj's underlying member method code with example usage: obj._unpickle_classifier(file_name) and returns: loaded_classifier