code: def test_wrap_polynomial_features_get_features_name_out():
      X = fetch_california_housing(as_frame=True).frame
  
      varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
      tr_wrap = SklearnTransformerWrapper(
          transformer=PolynomialFeatures(), variables=varlist
      )
  
      tr_wrap.fit(X)
      expected_features_all = [
          "Population",
          "AveOccup",
          "Latitude",
          "Longitude",
          "MedHouseVal",
          "1",
          "MedInc",
          "HouseAge",
          "AveRooms",
          "AveBedrms",
          "MedInc^2",
          "MedInc HouseAge",
          "MedInc AveRooms",
          "MedInc AveBedrms",
          "HouseAge^2",
          "HouseAge AveRooms",
          "HouseAge AveBedrms",
          "AveRooms^2",
          "AveRooms AveBedrms",
          "AveBedrms^2",
      ]
      expected_features_varlist = [
          "1",
          "MedInc",
          "HouseAge",
          "AveRooms",
          "AveBedrms",
          "MedInc^2",
          "MedInc HouseAge",
          "MedInc AveRooms",
          "MedInc AveBedrms",
          "HouseAge^2",
          "HouseAge AveRooms",
          "HouseAge AveBedrms",
          "AveRooms^2",
          "AveRooms AveBedrms",
          "AveBedrms^2",
      ]
  
      assert tr_wrap.get_feature_names_out() == expected_features_all
      assert (
          tr_wrap.get_feature_names_out(varlist)
          == expected_features_varlist
      )
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.base import clone
  from sklearn.datasets import fetch_california_housing
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier, VotingClassifier
  from sklearn.feature_selection import RFE, SelectFromModel, SelectKBest, VarianceThreshold, f_regression
  from sklearn.impute import MissingIndicator, SimpleImputer
  from sklearn.linear_model import Lasso
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import Binarizer, FunctionTransformer, KBinsDiscretizer, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PolynomialFeatures, PowerTransformer, StandardScaler
  from feature_engine.wrappers import SklearnTransformerWrapper
label_desc: Use image node_id 7 for calling a global function with example usage: test_wrap_polynomial_features_get_features_name_out() without return types