code: def test_wrap_one_hot_encoder_get_features_name_out(df_vartypes):
      ohe_wrap = SklearnTransformerWrapper(
          transformer=OneHotEncoder(sparse=False)
      )
      ohe_wrap.fit(df_vartypes)
  
      expected_features_all = [
          "Name_jack",
          "Name_krish",
          "Name_nick",
          "Name_tom",
          "City_Bristol",
          "City_Liverpool",
          "City_London",
          "City_Manchester",
          "Age_18",
          "Age_19",
          "Age_20",
          "Age_21",
          "Marks_0.6",
          "Marks_0.7",
          "Marks_0.8",
          "Marks_0.9",
          "dob_2020-02-24T00:00:00.000000000",
          "dob_2020-02-24T00:01:00.000000000",
          "dob_2020-02-24T00:02:00.000000000",
          "dob_2020-02-24T00:03:00.000000000",
      ]
  
      assert ohe_wrap.get_feature_names_out() == expected_features_all
  
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 16 for calling a global function with example usage: test_wrap_one_hot_encoder_get_features_name_out(df_vartypes) without return types