code: def backend_targeted_tabular(attack, fix_get_iris):
      (x_train_iris, _), (x_test_iris, y_test_iris) = fix_get_iris
  
      targets = random_targets(y_test_iris, nb_classes=3)
      x_test_adv = attack.generate(
          x_test_iris, **{"y": targets}, x_init=x_train_iris
      )
  
      check_adverse_example_x(x_test_adv, x_test_iris)
  
      y_pred_adv = np.argmax(
          attack.estimator.predict(x_test_adv), axis=1
      )
      target = np.argmax(targets, axis=1)
      assert (target == y_pred_adv).any()
  
      accuracy = np.sum(y_pred_adv == target) / y_test_iris.shape[0]
      logger.info(
          "Success rate of targeted boundary on Iris: %.2f%%",
          (accuracy * 100),
      )
  
file_path: adversarial-robustness-toolbox/tests/attacks/utils.py
imports: import pytest
  import numpy
  import logging
  import keras.backend
  from art.utils import random_targets, get_labels_np_array
  from art.exceptions import EstimatorError
  from tests.utils import check_adverse_example_x, check_adverse_predicted_sample_y
label_desc: Use image node_id 7 for calling a global function with example usage: backend_targeted_tabular(attack, fix_get_iris) without return types