code: def test_create_train_X_y_output_when_lags_3_steps_1_and_exog_is_None(
      level, expected_y_values
  ):
      """
      Test output of create_train_X_y when regressor is LinearRegression,
      lags is 3 and steps is 1.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10), dtype=float),
              "l2": pd.Series(np.arange(100, 110), dtype=float),
          }
      )
      exog = None
  
      forecaster = ForecasterAutoregMultiVariate(
          LinearRegression(), level=level, lags=3, steps=1
      )
      results = forecaster.create_train_X_y(series=series, exog=exog)
  
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                          -0.87038828,
                          -1.21854359,
                          -1.5666989,
                      ],
                      [
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                      ],
                      [
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                      ],
                      [
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                      ],
                      [
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                      ],
                      [
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                      ],
                      [
                          1.21854359,
                          0.87038828,
                          0.52223297,
                          1.21854359,
                          0.87038828,
                          0.52223297,
                      ],
                  ],
                  dtype=float,
              ),
              index=pd.RangeIndex(start=3, stop=10, step=1),
              columns=[
                  "l1_lag_1",
                  "l1_lag_2",
                  "l1_lag_3",
                  "l2_lag_1",
                  "l2_lag_2",
                  "l2_lag_3",
              ],
          ),
          {
              1: pd.Series(
                  data=np.array(expected_y_values, dtype=float),
                  index=pd.RangeIndex(start=3, stop=10, step=1),
                  name=f"{level}_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 10 for calling a global function with example usage: test_create_train_X_y_output_when_lags_3_steps_1_and_exog_is_None(level, expected_y_values) without return types