code: def test_transform_series_when_transformer_is_OneHotEncoder():
      """
      Test the output of transform_series when transformer is OneHotEncoder.
      """
      input_series = pd.Series(["A"] * 5 + ["B"] * 5, name="exog")
      transformer = OneHotEncoder(sparse=False)
      transformer.fit(input_series.to_frame())
      results = transform_series(
          series=input_series,
          transformer=transformer,
          fit=False,
          inverse_transform=False,
      )
  
      expected = pd.DataFrame(
          data=np.array(
              [
                  [1.0, 0.0],
                  [1.0, 0.0],
                  [1.0, 0.0],
                  [1.0, 0.0],
                  [1.0, 0.0],
                  [0.0, 1.0],
                  [0.0, 1.0],
                  [0.0, 1.0],
                  [0.0, 1.0],
                  [0.0, 1.0],
              ]
          ),
          columns=["exog_A", "exog_B"],
      )
  
      pd.testing.assert_frame_equal(results, expected)
  
file_path: skforecast/skforecast/utils/tests/test_transform_series.py
imports: import re
  import pytest
  import numpy
  import pandas
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
  from skforecast.utils import transform_series
label_desc: Use image node_id 5 for calling a global function with example usage: test_transform_series_when_transformer_is_OneHotEncoder() without return types