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 with steps=1.
      """
      forecaster = ForecasterAutoregDirect(
          regressor=LinearRegression(),
          lags=5,
          steps=1,
          transformer_y=StandardScaler(),
      )
      results = forecaster.create_train_X_y(
          y=pd.Series(np.arange(10), name="y", 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=["lag_1", "lag_2", "lag_3", "lag_4", "lag_5"],
          ),
          {
              1: 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_step_1",
              )
          },
      )
  
      pd.testing.assert_frame_equal(results[0], expected[0])
      assert isinstance(results[1], dict)
      assert all(isinstance(x, pd.Series) for x in results[1].values())
      assert results[1].keys() == expected[1].keys()
      for key in expected[1]:
          pd.testing.assert_series_equal(
              results[1][key], expected[1][key]
          )
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect
  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 23 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