code: def test_sklearn_ohe_object_one_feature(df_vartypes):
      variables_to_encode = ["Name"]
  
      transformer = SklearnTransformerWrapper(
          transformer=OneHotEncoder(sparse=False, dtype=np.int64),
          variables=variables_to_encode,
      )
  
      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],
          }
      )
  
      transformed_df = transformer.fit_transform(
          df_vartypes[variables_to_encode]
      )
  
      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 11 for calling a global function with example usage: test_sklearn_ohe_object_one_feature(df_vartypes) without return types