code: def test_get_feature_names_out_selectors(varlist, transformer):
      X = fetch_california_housing(as_frame=True).frame
      y = X["MedHouseVal"]
      X = X.drop(["MedHouseVal"], axis=1)
      tr_wrap = SklearnTransformerWrapper(
          transformer=transformer, variables=varlist
      )
      Xw = tr_wrap.fit_transform(X, y)
  
      assert Xw.columns.to_list() == tr_wrap.get_feature_names_out()
      assert Xw.columns.to_list() == tr_wrap.get_feature_names_out(
          ["MedInc", "HouseAge"]
      )
  
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 20 for calling a global function with example usage: test_get_feature_names_out_selectors(varlist, transformer) without return types