code: def test_sklearn_ohe_all_features(df_vartypes):
      transformer = SklearnTransformerWrapper(
          transformer=OneHotEncoder(sparse=False, dtype=np.int64)
      )
  
      ref = pd.DataFrame(
          {
              "Name_jack": [0, 0, 0, 1],
              "Name_krish": [0, 0, 1, 0],
              "Name_nick": [0, 1, 0, 0],
              "Name_tom": [1, 0, 0, 0],
              "City_Bristol": [0, 0, 0, 1],
              "City_Liverpool": [0, 0, 1, 0],
              "City_London": [1, 0, 0, 0],
              "City_Manchester": [0, 1, 0, 0],
              "Age_18": [0, 0, 0, 1],
              "Age_19": [0, 0, 1, 0],
              "Age_20": [1, 0, 0, 0],
              "Age_21": [0, 1, 0, 0],
              "Marks_0.6": [0, 0, 0, 1],
              "Marks_0.7": [0, 0, 1, 0],
              "Marks_0.8": [0, 1, 0, 0],
              "Marks_0.9": [1, 0, 0, 0],
              "dob_2020-02-24T00:00:00.000000000": [1, 0, 0, 0],
              "dob_2020-02-24T00:01:00.000000000": [0, 1, 0, 0],
              "dob_2020-02-24T00:02:00.000000000": [0, 0, 1, 0],
              "dob_2020-02-24T00:03:00.000000000": [0, 0, 0, 1],
          }
      )
  
      transformed_df = transformer.fit_transform(df_vartypes)
  
      pd.testing.assert_frame_equal(ref, transformed_df)
  
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 14 for calling a global function with example usage: test_sklearn_ohe_all_features(df_vartypes) without return types