code: def test_create_train_X_y_output_when_y_is_series_10_and_transformer_y_is_StandardScaler():
      """
      Test the output of create_train_X_y when exog is None and transformer_y
      is StandardScaler.
      """
      forecaster = ForecasterAutoregCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=5,
          transformer_y=StandardScaler(),
      )
      results = forecaster.create_train_X_y(
          y=pd.Series(np.arange(10), dtype=float)
      )
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                      ],
                      [
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                      ],
                      [
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                      ],
                      [
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                      ],
                      [
                          1.21854359,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                      ],
                  ]
              ),
              index=pd.RangeIndex(start=5, stop=10, step=1),
              columns=[
                  "custom_predictor_0",
                  "custom_predictor_1",
                  "custom_predictor_2",
                  "custom_predictor_3",
                  "custom_predictor_4",
              ],
          ),
          pd.Series(
              data=np.array(
                  [
                      0.17407766,
                      0.52223297,
                      0.87038828,
                      1.21854359,
                      1.5666989,
                  ]
              ),
              index=pd.RangeIndex(start=5, stop=10, step=1),
              name="y",
              dtype=float,
          ),
      )
  
      pd.testing.assert_frame_equal(results[0], expected[0])
      pd.testing.assert_series_equal(results[1], expected[1])
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
  from skforecast.preprocessing import TimeSeriesDifferentiator
  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
  from .fixtures_ForecasterAutoregCustom import data
label_desc: Use image node_id 20 for calling a global function with example usage: test_create_train_X_y_output_when_y_is_series_10_and_transformer_y_is_StandardScaler() without return types