code: def test_create_train_X_y_output_when_lags_3_steps_1_and_exog_is_None_and_transformer_exog_is_not_None():
      """
      Test output of create_train_X_y when regressor is LinearRegression, lags is 3
      and steps is 1 and transformer_exog is not None.
      """
      y = pd.Series(np.arange(10), name="y", dtype=float)
      exog = None
  
      forecaster = ForecasterAutoregDirect(
          regressor=LinearRegression(),
          lags=3,
          steps=1,
          transformer_exog=StandardScaler(),
      )
      results = forecaster.create_train_X_y(y=y, exog=exog)
  
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [2.0, 1.0, 0.0],
                      [3.0, 2.0, 1.0],
                      [4.0, 3.0, 2.0],
                      [5.0, 4.0, 3.0],
                      [6.0, 5.0, 4.0],
                      [7.0, 6.0, 5.0],
                      [8.0, 7.0, 6.0],
                  ],
                  dtype=float,
              ),
              index=pd.RangeIndex(start=3, stop=10, step=1),
              columns=["lag_1", "lag_2", "lag_3"],
          ),
          {
              1: pd.Series(
                  data=np.array(
                      [3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype=float
                  ),
                  index=pd.RangeIndex(start=3, 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 24 for calling a global function with example usage: test_create_train_X_y_output_when_lags_3_steps_1_and_exog_is_None_and_transformer_exog_is_not_None() without return types