code: def test_onehot_transformer():
      """Tests that categorical features can be correctly one-hot encoded."""
      X, y = fetch_german()
      ohe = make_column_transformer(
          (OneHotEncoder(), X.dtypes == "category"),
          remainder="passthrough",
          verbose_feature_names_out=False,
      )
      dum = pd.get_dummies(X)
      assert ohe.fit_transform(X).shape[1] == dum.shape[1] == 63
      assert dum.columns.symmetric_difference(
          ohe.get_feature_names_out()
      ).empty
  
imports: from functools import partial
  import numpy
  from numpy.testing import assert_array_equal
  import pandas
  from pandas.api.types import is_numeric_dtype
  from pandas.testing import assert_frame_equal
  import pytest
  from sklearn.compose import make_column_transformer
  from sklearn.preprocessing import OneHotEncoder, minmax_scale
  from aif360.datasets import AdultDataset, GermanDataset, CompasDataset, LawSchoolGPADataset, MEPSDataset19, MEPSDataset20, MEPSDataset21
  from aif360.sklearn.datasets import standardize_dataset, NumericConversionWarning, fetch_adult, fetch_bank, fetch_german, fetch_compas, fetch_lawschool_gpa, fetch_meps
label_desc: Use image node_id 22 for calling a global function with example usage: test_onehot_transformer() without return types