code: def test_transform_dataframe_when_transformer_is_OneHotEncoder():
      """
      Test the output of transform_dataframe when transformer is OneHotEncoder.
      """
      df_input = pd.DataFrame(
          {
              "col_1": ["a", "a", "a", "a", "b", "b", "b", "b"],
              "col_2": ["a", "a", "a", "a", "b", "b", "b", "b"],
          }
      )
      expected = pd.DataFrame(
          {
              "col_1_a": [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
              "col_1_b": [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
              "col_2_a": [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
              "col_2_b": [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
          }
      )
      transformer = OneHotEncoder()
      results = transform_dataframe(
          df=df_input,
          transformer=transformer,
          fit=True,
          inverse_transform=False,
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
file_path: skforecast/skforecast/utils/tests/test_transform_dataframe.py
imports: import re
  import pytest
  import re
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
  from skforecast.utils import transform_dataframe
label_desc: Use image node_id 5 for calling a global function with example usage: test_transform_dataframe_when_transformer_is_OneHotEncoder() without return types