code: def test_create_train_X_y_output_when_series_10_and_transformer_series_is_StandardScaler():
      """
      Test the output of create_train_X_y when exog is None and transformer_series
      is StandardScaler.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10, dtype=float)),
              "l2": pd.Series(np.arange(10, dtype=float)),
          }
      )
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors_5,
          name_predictors=["lag_1", "lag_2", "lag_3", "lag_4", "lag_5"],
          window_size=5,
          transformer_series=StandardScaler(),
      )
  
      results = forecaster.create_train_X_y(series=series)
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                          1.0,
                          0.0,
                      ],
                      [
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          1.0,
                          0.0,
                      ],
                      [
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          1.0,
                          0.0,
                      ],
                      [
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          1.0,
                          0.0,
                      ],
                      [
                          1.21854359,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          1.0,
                          0.0,
                      ],
                      [
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                          0.0,
                          1.0,
                      ],
                      [
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          0.0,
                          1.0,
                      ],
                      [
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          0.0,
                          1.0,
                      ],
                      [
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          0.0,
                          1.0,
                      ],
                      [
                          1.21854359,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          0.0,
                          1.0,
                      ],
                  ]
              ),
              index=pd.RangeIndex(start=0, stop=10, step=1),
              columns=[
                  "lag_1",
                  "lag_2",
                  "lag_3",
                  "lag_4",
                  "lag_5",
                  "l1",
                  "l2",
              ],
          ),
          pd.Series(
              data=np.array(
                  [
                      0.17407766,
                      0.52223297,
                      0.87038828,
                      1.21854359,
                      1.5666989,
                      0.17407766,
                      0.52223297,
                      0.87038828,
                      1.21854359,
                      1.5666989,
                  ]
              ),
              index=pd.RangeIndex(start=0, stop=10, step=1),
              name="y",
              dtype=float,
          ),
          pd.RangeIndex(start=0, stop=len(series), step=1),
          pd.Index(np.array([5, 6, 7, 8, 9, 5, 6, 7, 8, 9])),
      )
  
      for i in range(len(expected)):
          if isinstance(expected[i], pd.DataFrame):
              pd.testing.assert_frame_equal(results[i], expected[i])
          elif isinstance(expected[i], pd.Series):
              pd.testing.assert_series_equal(results[i], expected[i])
          else:
              np.testing.assert_array_equal(results[i], expected[i])
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  from skforecast.exceptions import MissingValuesExogWarning
  from sklearn.linear_model import LinearRegression
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
label_desc: Use image node_id 27 for calling a global function with example usage: test_create_train_X_y_output_when_series_10_and_transformer_series_is_StandardScaler() without return types