code: def test_get_feature_names_out_ohe(varlist, df_vartypes):
      transformer = SklearnTransformerWrapper(
          transformer=OneHotEncoder(sparse=False, dtype=np.int64),
          variables=varlist,
      )
  
      df_tr = transformer.fit_transform(df_vartypes)
  
      assert (
          df_tr.columns.to_list() == transformer.get_feature_names_out()
      )
  
      if varlist is not None:
          output_feat = [
              "Name_jack",
              "Name_krish",
              "Name_nick",
              "Name_tom",
              "City_Bristol",
              "City_Liverpool",
              "City_London",
              "City_Manchester",
          ]
  
          assert output_feat == transformer.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 22 for calling a global function with example usage: test_get_feature_names_out_ohe(varlist, df_vartypes) without return types