code: def test_transform_series_when_transformer_is_StandardScaler_and_inverse_transform_is_True():
      """
      Test the output of transform_series when transformer is StandardScaler and
      inverse_transform is True.
      """
      input_1 = pd.Series(
          [
              1.16,
              -0.28,
              0.07,
              2.4,
              0.25,
              -0.56,
              -1.42,
              1.26,
              1.78,
              -1.49,
          ],
          name="y",
      )
      transformer = StandardScaler()
      transformer.fit(input_1.to_numpy().reshape(-1, 1))
      input_2 = transformer.transform(input_1.to_numpy().reshape(-1, 1))
      input_2 = pd.Series(input_2.flatten(), name="y")
      expected = input_1
      results = transform_series(
          series=input_2,
          transformer=transformer,
          fit=False,
          inverse_transform=True,
      )
  
      pd.testing.assert_series_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 4 for calling a global function with example usage: test_transform_series_when_transformer_is_StandardScaler_and_inverse_transform_is_True() without return types