code: def test_transform_dataframe_when_transformer_is_ColumnTransformer():
      """
      Test the output of transform_dataframe when transformer is ColumnTransformer.
      """
      df_input = pd.DataFrame(
          {
              "col_1": [7.5, 24.4, 60.3, 57.3, 50.7, 41.4, 87.2, 47.4],
              "col_2": ["a", "a", "a", "a", "b", "b", "b", "b"],
          }
      )
      expected = pd.DataFrame(
          {
              "col_1": [
                  -1.76425513,
                  -1.00989936,
                  0.59254869,
                  0.45863938,
                  0.1640389,
                  -0.25107995,
                  1.79326881,
                  0.01673866,
              ],
              "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 = ColumnTransformer(
          [
              ("scale", StandardScaler(), ["col_1"]),
              ("onehot", OneHotEncoder(), ["col_2"]),
          ],
          remainder="passthrough",
          verbose_feature_names_out=False,
      )
      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 6 for calling a global function with example usage: test_transform_dataframe_when_transformer_is_ColumnTransformer() without return types