code: def test_create_train_X_y_output_when_different_lags_steps_2_and_exog_is_None(
      level, expected_y_values
  ):
      """
      Test output of create_train_X_y when regressor is LinearRegression,
      different lags and steps is 2.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10)),
              "l2": pd.Series(np.arange(100, 110)),
          }
      )
      exog = None
  
      forecaster = ForecasterAutoregMultiVariate(
          LinearRegression(),
          level=level,
          lags={"l1": 3, "l2": [1, 5]},
          steps=2,
          transformer_series=None,
      )
      results = forecaster.create_train_X_y(series=series, exog=exog)
  
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [4.0, 3.0, 2.0, 104.0, 100.0],
                      [5.0, 4.0, 3.0, 105.0, 101.0],
                      [6.0, 5.0, 4.0, 106.0, 102.0],
                      [7.0, 6.0, 5.0, 107.0, 103.0],
                  ],
                  dtype=float,
              ),
              index=pd.RangeIndex(start=6, stop=10, step=1),
              columns=[
                  "l1_lag_1",
                  "l1_lag_2",
                  "l1_lag_3",
                  "l2_lag_1",
                  "l2_lag_5",
              ],
          ),
          {
              1: pd.Series(
                  data=np.array(expected_y_values[0], dtype=float),
                  index=pd.RangeIndex(start=5, stop=9, step=1),
                  name=f"{level}_step_1",
              ),
              2: pd.Series(
                  data=np.array(expected_y_values[1], dtype=float),
                  index=pd.RangeIndex(start=6, stop=10, step=1),
                  name=f"{level}_step_2",
              ),
          },
      )
  
      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 12 for calling a global function with example usage: test_create_train_X_y_output_when_different_lags_steps_2_and_exog_is_None(level, expected_y_values) without return types