code: def test_with_defences(self):
      (x_train, y_train), (x_test, y_test) = self.mnist
  
      # Get the trained Keras model
      model = self.classifier_k._model
      fs = FeatureSqueezing(bit_depth=1, clip_values=(0, 1))
      classifier = KerasClassifier(
          model=model, clip_values=(0, 1), preprocessing_defences=fs
      )
  
      # Create the classifier
      classifier = QueryEfficientGradientEstimationClassifier(
          classifier, 20, 1 / 64.0, round_samples=1 / 255.0
      )
  
      attack = FastGradientMethod(classifier, eps=1)
      x_train_adv = attack.generate(x_train)
      x_test_adv = attack.generate(x_test)
  
      self.assertFalse((x_train == x_train_adv).all())
      self.assertFalse((x_test == x_test_adv).all())
  
      train_y_pred = get_labels_np_array(
          classifier.predict(x_train_adv)
      )
      test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
  
      self.assertFalse((y_train == train_y_pred).all())
      self.assertFalse((y_test == test_y_pred).all())
  
imports: import logging
  import unittest
  import keras.backend
  import numpy
  from art.attacks.evasion.fast_gradient import FastGradientMethod
  from art.estimators.classification.keras import KerasClassifier
  from art.defences.preprocessor import FeatureSqueezing
  from art.utils import load_dataset, get_labels_np_array
  from art.estimators.classification.query_efficient_bb import QueryEfficientGradientEstimationClassifier
  from tests.utils import master_seed, get_image_classifier_kr, get_tabular_classifier_kr
label_desc: Use image node_id 5 for calling the TestClassifierAttack obj's underlying member method code with example usage: obj.test_with_defences() without return types