code: def test_create_train_X_y_output_when_lags_5_steps_1_and_transformer_series_StandardScaler(
      transformer_series,
  ):
      """
      Test the output of create_train_X_y when exog is None and transformer_series
      is StandardScaler with steps=1.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10), dtype=float),
              "l2": pd.Series(np.arange(50, 60), dtype=float),
          }
      )
  
      forecaster = ForecasterAutoregMultiVariate(
          regressor=LinearRegression(),
          lags=5,
          level="l1",
          steps=1,
          transformer_series=transformer_series,
      )
      results = forecaster.create_train_X_y(series=series)
  
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                      ],
                      [
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                      ],
                      [
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                      ],
                      [
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                      ],
                      [
                          1.21854359,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          1.21854359,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                      ],
                  ]
              ),
              index=pd.RangeIndex(start=5, stop=10, step=1),
              columns=[
                  "l1_lag_1",
                  "l1_lag_2",
                  "l1_lag_3",
                  "l1_lag_4",
                  "l1_lag_5",
                  "l2_lag_1",
                  "l2_lag_2",
                  "l2_lag_3",
                  "l2_lag_4",
                  "l2_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="l1_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.ForecasterAutoregMultiVariate import ForecasterAutoregMultiVariate
  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 28 for calling a global function with example usage: test_create_train_X_y_output_when_lags_5_steps_1_and_transformer_series_StandardScaler(transformer_series) without return types