code: def test_black_box_no_encoder_after_feature_slice(
      art_warning, get_iris_dataset, model_type
  ):
      try:
          orig_attack_feature = 1  # petal length
          new_attack_feature = slice(1, 4)  # petal length
  
          # need to transform attacked feature into categorical
          def transform_attacked_feature(x):
              x[x > 0.5] = 2.0
              x[(x > 0.2) & (x <= 0.5)] = 1.0
              x[x <= 0.2] = 0.0
  
          def transform_other_feature(x):
              x[x > 0.3] = 2.0
              x[(x > 0.2) & (x <= 0.3)] = 1.0
              x[x <= 0.2] = 0.0
              x[x == 2.0] = "A"
              x[x == 1.0] = "B"
              x[x == 0.0] = "C"
  
          (x_train_iris, y_train_iris), (
              x_test_iris,
              y_test_iris,
          ) = get_iris_dataset
  
          # training data without attacked feature
          x_train_for_attack = np.delete(
              x_train_iris, orig_attack_feature, 1
          )
          # transform attacked feature
          x_train_feature = x_train_iris[:, orig_attack_feature].copy()
          transform_attacked_feature(x_train_feature)
          x_train_feature = check_and_transform_label_format(
              x_train_feature, nb_classes=3, return_one_hot=True
          )
          # training data with attacked feature (after transformation)
          x_train = np.concatenate(
              (
                  x_train_for_attack[:, :orig_attack_feature],
                  x_train_feature,
              ),
              axis=1,
          )
          x_train = np.concatenate(
              (x_train, x_train_for_attack[:, orig_attack_feature:]),
              axis=1,
          )
  
          # transform other feature
          other_feature = (
              5  # was 3 before 1-hot encoding of attacked feature
          )
          x_without_feature = np.delete(x_train, other_feature, 1)
          x_other_feature = (
              x_train[:, other_feature]
              .copy()
              .reshape(-1, 1)
              .astype(object)
          )
          transform_other_feature(x_other_feature)
          # training data with other feature (after transformation)
          x_train = np.concatenate(
              (x_without_feature[:, :other_feature], x_other_feature),
              axis=1,
          )
          x_train = np.concatenate(
              (x_train, x_without_feature[:, other_feature:]), axis=1
          )
  
          new_other_feature = other_feature - 3
          x_train_for_attack_without_feature = np.delete(
              x_train_for_attack, new_other_feature, 1
          )
          x_train_for_attack = np.concatenate(
              (
                  x_train_for_attack_without_feature[
                      :, :new_other_feature
                  ],
                  x_other_feature,
              ),
              axis=1,
          )
          x_train_for_attack = np.concatenate(
              (
                  x_train_for_attack,
                  x_train_for_attack_without_feature[
                      :, new_other_feature:
                  ],
              ),
              axis=1,
          )
  
          # test data without attacked feature
          x_test_for_attack = np.delete(
              x_test_iris, orig_attack_feature, 1
          )
          # only attacked feature
          x_test_feature = x_test_iris[:, orig_attack_feature].copy()
          transform_attacked_feature(x_test_feature)
          x_test_feature = check_and_transform_label_format(
              x_test_feature, nb_classes=3, return_one_hot=True
          )
  
          # transform other feature
          x_test_without_feature = np.delete(
              x_test_for_attack, new_other_feature, 1
          )
          x_test_other_feature = (
              x_test_for_attack[:, new_other_feature]
              .copy()
              .reshape(-1, 1)
              .astype(object)
          )
          transform_other_feature(x_test_other_feature)
          # training data with other feature (after transformation)
          x_test_for_attack = np.concatenate(
              (
                  x_test_without_feature[:, :new_other_feature],
                  x_test_other_feature,
              ),
              axis=1,
          )
          x_test_for_attack = np.concatenate(
              (
                  x_test_for_attack,
                  x_test_without_feature[:, new_other_feature:],
              ),
              axis=1,
          )
  
          # transform features for full test data
          x_test_without_feature = np.delete(
              x_test_iris, orig_attack_feature, 1
          )
          # test data with attacked feature (after transformation)
          x_test_with_feature = np.concatenate(
              (
                  x_test_without_feature[:, :orig_attack_feature],
                  x_test_feature,
              ),
              axis=1,
          )
          x_test_with_feature = np.concatenate(
              (
                  x_test_with_feature,
                  x_test_without_feature[:, orig_attack_feature:],
              ),
              axis=1,
          )
          x_test_without_feature = np.delete(
              x_test_with_feature, other_feature, 1
          )
          # test data with other feature (after transformation)
          x_test_for_pred = np.concatenate(
              (
                  x_test_without_feature[:, :other_feature],
                  x_test_other_feature,
              ),
              axis=1,
          )
          x_test_for_pred = np.concatenate(
              (
                  x_test_for_pred,
                  x_test_without_feature[:, other_feature:],
              ),
              axis=1,
          )
  
          categorical_transformer = OrdinalEncoder()
          encoder_for_pipeline = ColumnTransformer(
              transformers=[
                  ("cat", categorical_transformer, [other_feature]),
              ],
              remainder="passthrough",
          )
          model = DecisionTreeClassifier()
          pipeline = Pipeline(
              [("encoder", encoder_for_pipeline), ("model", model)]
          )
          pipeline.fit(x_train, np.argmax(y_train_iris, axis=1))
          classifier = ScikitlearnClassifier(
              pipeline, preprocessing=None
          )
  
          attack = AttributeInferenceBlackBox(
              classifier,
              attack_feature=new_attack_feature,
              attack_model_type=model_type,
              non_numerical_features=[other_feature],
          )
          # train attack model
          attack.fit(x_train, y_train_iris)
          # infer attacked feature
          x_train_predictions = np.array(
              [np.argmax(arr) for arr in classifier.predict(x_train)]
          ).reshape(-1, 1)
          x_test_predictions = np.array(
              [
                  np.argmax(arr)
                  for arr in classifier.predict(x_test_for_pred)
              ]
          ).reshape(-1, 1)
          inferred_train = attack.infer(
              x_train_for_attack, y_train_iris, pred=x_train_predictions
          )
          inferred_test = attack.infer(
              x_test_for_attack, y_test_iris, pred=x_test_predictions
          )
          # check accuracy
          train_acc = np.sum(inferred_train == x_train_feature) / len(
              inferred_train
          )
          test_acc = np.sum(inferred_test == x_test_feature) / len(
              inferred_test
          )
  
          assert 0.0 <= train_acc
          assert 0.0 <= test_acc
          print(model_type, train_acc, 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
  import torch.nn
  import torch.optim
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.preprocessing import StandardScaler, OrdinalEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.pipeline import Pipeline
  from art.attacks.inference.attribute_inference.black_box import AttributeInferenceBlackBox
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification import ClassifierMixin
  from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier, ScikitlearnClassifier
  from art.estimators.regression import ScikitlearnRegressor, RegressorMixin
  from art.utils import check_and_transform_label_format
  from tests.attacks.utils import backend_test_classifier_type_check_fail
  from tests.utils import ARTTestException
label_desc: Use image node_id 15 for calling a global function with example usage: test_black_box_no_encoder_after_feature_slice(art_warning, get_iris_dataset, model_type) without return types