code: def test_wrap_selectors(transformer):
      # load data
      X = fetch_california_housing(as_frame=True).frame
      y = X["MedHouseVal"]
      X = X.drop(["MedHouseVal"], axis=1)
  
      # prepare selectors
      sel = clone(transformer)
      sel_wrap = SklearnTransformerWrapper(transformer=transformer)
  
      # Test:
      # When passing variable list
      varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
      sel_wrap.set_params(variables=varlist)
  
      Xt = pd.DataFrame(
          sel.fit_transform(X[varlist], y),
          columns=X[varlist].columns[(sel.get_support())],
      )
      Xw = sel_wrap.fit_transform(X, y)
  
      selected = X[varlist].columns[(sel.get_support())]
      remaining = [f for f in X.columns if f not in varlist]
  
      pd.testing.assert_frame_equal(Xt, Xw[selected])
      pd.testing.assert_frame_equal(X[remaining], Xw[remaining])
      assert Xw.shape[1] == len(remaining) + len(selected)
  
      # when variable list is None
      sel_wrap.set_params(variables=None)
  
      Xt = pd.DataFrame(
          sel.fit_transform(X, y),
          columns=X.columns[(sel.get_support())],
      )
      Xw = sel_wrap.fit_transform(X, y)
  
      pd.testing.assert_frame_equal(Xt, Xw)
  
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 4 for calling a global function with example usage: test_wrap_selectors(transformer) without return types