code: def test_get_feature_names_out_polynomialfeatures(varlist):
      X = fetch_california_housing(as_frame=True).frame
      tr_wrap = SklearnTransformerWrapper(
          transformer=PolynomialFeatures(), variables=varlist
      )
      Xw = tr_wrap.fit_transform(X)
      assert Xw.columns.tolist() == tr_wrap.get_feature_names_out()
  
      if varlist is not None:
          output_feat = [
              "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 output_feat == tr_wrap.get_feature_names_out(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 21 for calling a global function with example usage: test_get_feature_names_out_polynomialfeatures(varlist) without return types