code: def backend_check_membership_accuracy_pred(
      attack, dataset, pred, attack_train_ratio, approx
  ):
      (x_train, y_train), (x_test, y_test) = dataset
      (pred_x, test_pred_x) = pred
      attack_train_size = int(len(x_train) * attack_train_ratio)
      attack_test_size = int(len(x_test) * attack_train_ratio)
  
      # train attack model using only attack_train_ratio of data
      attack.fit(
          None,
          y_train[:attack_train_size],
          None,
          y_test[:attack_test_size],
          pred_x[:attack_train_size],
          test_pred_x[:attack_test_size],
      )
  
      # infer attacked feature on remainder of data
      inferred_train = attack.infer(
          None,
          y_train[attack_train_size:],
          pred=pred_x[attack_train_size:],
      )
      inferred_test = attack.infer(
          None,
          y_test[attack_test_size:],
          pred=test_pred_x[attack_test_size:],
      )
  
      # check accuracy
      backend_check_accuracy(inferred_train, inferred_test, approx)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import pytest
  import numpy
  import keras
  from art.attacks.inference.membership_inference.black_box import MembershipInferenceBlackBox
  from art.estimators.classification.keras import KerasClassifier
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.estimators.regression import ScikitlearnRegressor, RegressorMixin
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ARTTestException
label_desc: Use image node_id 22 for calling a global function with example usage: backend_check_membership_accuracy_pred(attack, dataset, pred, attack_train_ratio, approx) without return types