code: def test_true_label_baseline_regression(
      art_warning, get_diabetes_dataset, model_type
  ):
      try:
          attack_feature = 1  # sex
  
          (x_train, y_train), (x_test, y_test) = get_diabetes_dataset
          # training data without attacked feature
          x_train_for_attack = np.delete(x_train, attack_feature, 1)
          # only attacked feature
          x_train_feature = (
              x_train[:, attack_feature].copy().reshape(-1, 1)
          )
  
          # test data without attacked feature
          x_test_for_attack = np.delete(x_test, attack_feature, 1)
          # only attacked feature
          x_test_feature = (
              x_test[:, attack_feature].copy().reshape(-1, 1)
          )
  
          baseline_attack = AttributeInferenceBaselineTrueLabel(
              attack_feature=attack_feature,
              attack_model_type=model_type,
              is_regression=True,
          )
          # train attack model
          baseline_attack.fit(x_train, y_train)
          # infer attacked feature
          baseline_inferred_train = baseline_attack.infer(
              x_train_for_attack, y=y_train
          )
          baseline_inferred_test = baseline_attack.infer(
              x_test_for_attack, y=y_test
          )
          # check accuracy
          baseline_train_acc = np.sum(
              baseline_inferred_train == x_train_feature.reshape(1, -1)
          ) / len(baseline_inferred_train)
          baseline_test_acc = np.sum(
              baseline_inferred_test == x_test_feature.reshape(1, -1)
          ) / len(baseline_inferred_test)
  
          expected_train_acc = {
              "nn": 0.45,
              "rf": 0.99,
              "gb": 0.97,
              "lr": 0.68,
              "dt": 0.99,
              "knn": 0.69,
              "svm": 0.54,
          }
          expected_test_acc = {
              "nn": 0.45,
              "rf": 0.6,
              "gb": 0.65,
              "lr": 0.68,
              "dt": 0.54,
              "knn": 0.45,
              "svm": 0.47,
          }
  
          assert expected_train_acc[model_type] <= baseline_train_acc
          assert expected_test_acc[model_type] <= baseline_test_acc
  
      except ARTTestException as e:
          art_warning(e)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import pytest
  import numpy
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import OrdinalEncoder
  from art.attacks.inference.attribute_inference.true_label_baseline import AttributeInferenceBaselineTrueLabel
  from art.utils import check_and_transform_label_format
  from tests.utils import ARTTestException
label_desc: Use image node_id 6 for calling a global function with example usage: test_true_label_baseline_regression(art_warning, get_diabetes_dataset, model_type) without return types