code: def train_remove_backdoor(
      classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
      x_train: np.ndarray,
      y_train: np.ndarray,
      x_test: np.ndarray,
      y_test: np.ndarray,
      tolerable_backdoor: float,
      max_epochs: int,
      batch_epochs: int,
  ) -> tuple:
      """
      Trains the provider classifier until the tolerance or number of maximum epochs are reached.
  
      :param classifier: Classifier to be used for predictions.
      :param x_train: Training set.
      :param y_train: Labels used for training.
      :param x_test: Samples in test set.
      :param y_test: Labels in test set.
      :param tolerable_backdoor: Parameter that determines how many misclassifications are acceptable.
      :param max_epochs: maximum number of epochs to be run.
      :param batch_epochs: groups of epochs that will be run together before checking for termination.
      :return: (improve_factor, classifier).
      """
      # Measure poison success in current model:
      initial_missed = measure_misclassification(
          classifier, x_test, y_test
      )
  
      curr_epochs = 0
      curr_missed = 1.0
      while (
          curr_epochs < max_epochs and curr_missed > tolerable_backdoor
      ):
          classifier.fit(x_train, y_train, nb_epochs=batch_epochs)
          curr_epochs += batch_epochs
          curr_missed = measure_misclassification(
              classifier, x_test, y_test
          )
          logger.info("Current epoch: %s", curr_epochs)
          logger.info("Misclassifications: %s", curr_missed)
  
      improve_factor = initial_missed - curr_missed
      return improve_factor, 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 2 for calling a global function with example usage: train_remove_backdoor(classifier, x_train, y_train, x_test, y_test, tolerable_backdoor, max_epochs, batch_epochs) and returns: improve_factor,  classifier