code: def test_transform_dataframe_when_transformer_is_StandardScaler():
      """
      Test the output of transform_dataframe when transformer is StandardScaler.
      """
      df_input = pd.DataFrame(
          {
              "A": [
                  1.16,
                  -0.28,
                  0.07,
                  2.4,
                  0.25,
                  -0.56,
                  -1.42,
                  1.26,
                  1.78,
                  -1.49,
              ],
              "B": [
                  7.5,
                  24.4,
                  60.3,
                  57.3,
                  50.7,
                  41.4,
                  87.2,
                  47.4,
                  2.7,
                  59.6,
              ],
          }
      )
      expected = pd.DataFrame(
          {
              "A": [
                  0.67596768,
                  -0.47871021,
                  -0.19805933,
                  1.67027365,
                  -0.0537246,
                  -0.70323091,
                  -1.39283021,
                  0.75615365,
                  1.17312067,
                  -1.44896038,
              ],
              "B": [
                  -1.47939551,
                  -0.79158852,
                  0.6694926,
                  0.54739669,
                  0.27878567,
                  -0.09971166,
                  1.76428598,
                  0.14448017,
                  -1.67474897,
                  0.64100356,
              ],
          }
      )
      transformer = StandardScaler()
      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 4 for calling a global function with example usage: test_transform_dataframe_when_transformer_is_StandardScaler() without return types