code: def test_wrap_transformers(transformer):
      # load data
      X = fetch_california_housing(as_frame=True).frame
  
      # prepare selectors
      tr = clone(transformer)
      tr_wrap = SklearnTransformerWrapper(transformer=transformer)
  
      # Test:
      # When passing variable list
      varlist = ["MedInc", "HouseAge", "AveRooms", "AveBedrms"]
      tr_wrap.set_params(variables=varlist)
  
      Xt = pd.DataFrame(
          tr.fit_transform(X[varlist]), columns=X[varlist].columns
      )
      Xw = tr_wrap.fit_transform(X)
  
      remaining = [f for f in X.columns if f not in varlist]
  
      assert Xt.shape[1] == 4
      assert Xw.shape[1] == 9
      pd.testing.assert_frame_equal(Xt, Xw[varlist])
      pd.testing.assert_frame_equal(X[remaining], Xw[remaining])
  
      # when variable list is None
      tr_wrap.set_params(variables=None)
  
      Xt = pd.DataFrame(tr.fit_transform(X), columns=X.columns)
      Xw = tr_wrap.fit_transform(X)
  
      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 5 for calling a global function with example usage: test_wrap_transformers(transformer) without return types