code: def test_get_features_names_out_regressor():
      """Check get_feature_names_out output for regressor."""
  
      X = [[1, 2], [3, 4], [5, 6]]
      y = [0, 1, 2]
  
      voting = VotingRegressor(
          estimators=[
              ("lr", LinearRegression()),
              ("tree", DecisionTreeRegressor(random_state=0)),
              ("ignore", "drop"),
          ]
      )
      voting.fit(X, y)
  
      names_out = voting.get_feature_names_out()
      expected_names = ["votingregressor_lr", "votingregressor_tree"]
      assert_array_equal(names_out, expected_names)
  
imports: import re
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.datasets import make_multilabel_classification
  from sklearn.dummy import DummyRegressor
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier, VotingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 22 for calling a global function with example usage: test_get_features_names_out_regressor() without return types