class_docstr: Base class for all poison filtering defences.
class_docstr_tok: ["Base","class","for","all","poison","filtering","defences","."]
docstr: Create an :class:`.ActivationDefence` object with the provided classifier.
  
  :param classifier: Model evaluated for poison.
  :param x_train: dataset used to train the classifier.
  :param y_train: labels used to train the classifier.
docstr_tok: ["Create","an",":","class",":","`",".ActivationDefence","`","object","with","the","provided","classifier",".",":","param","classifier",":","Model","evaluated","for","poison",".",":","param","x_train",":","dataset","used","to","train","the","classifier",".",":","param","y_train",":","labels","used","to","train","the","classifier","."]
code: def __init__(
      self,
      classifier: "CLASSIFIER_TYPE",
      x_train: np.ndarray,
      y_train: np.ndarray,
  ) -> None:
      """
      Create an :class:`.ActivationDefence` object with the provided classifier.
  
      :param classifier: Model evaluated for poison.
      :param x_train: dataset used to train the classifier.
      :param y_train: labels used to train the classifier.
      """
      self.classifier = classifier
      self.x_train = x_train
      self.y_train = y_train
  
file_path: adversarial-robustness-toolbox/art/defences/detector/poison/poison_filtering_defence.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  import sys
  from typing import Any, Dict, List, Tuple, TYPE_CHECKING
  import numpy
label_desc: Use image node_id 1 to create a new PoisonFilteringDefence object from inherited base classes: ABC with example: obj = PoisonFilteringDefence(classifier, x_train, y_train)