code: def test_create_train_X_y_output_when_transformer_series_and_transformer_exog_steps_2(
      transformer_series,
  ):
      """
      Test the output of create_train_X_y when using transformer_series and
      transformer_exog with steps=2.
      """
      series = pd.DataFrame(
          {
              "l1": np.arange(10, dtype=float),
              "l2": np.arange(10, dtype=float),
          },
          index=pd.date_range("1990-01-01", periods=10, freq="D"),
      )
      exog = pd.DataFrame(
          {
              "col_1": [
                  7.5,
                  24.4,
                  60.3,
                  57.3,
                  50.7,
                  41.4,
                  24.4,
                  87.2,
                  47.4,
                  23.8,
              ],
              "col_2": [
                  "a",
                  "b",
                  "a",
                  "b",
                  "a",
                  "b",
                  "a",
                  "b",
                  "a",
                  "b",
              ],
          },
          index=pd.date_range("1990-01-01", periods=10, freq="D"),
      )
  
      transformer_exog = ColumnTransformer(
          [
              ("scale", StandardScaler(), ["col_1"]),
              ("onehot", OneHotEncoder(), ["col_2"]),
          ],
          remainder="passthrough",
          verbose_feature_names_out=False,
      )
  
      forecaster = ForecasterAutoregMultiVariate(
          regressor=LinearRegression(),
          lags=5,
          level="l1",
          steps=2,
          transformer_series=transformer_series,
          transformer_exog=transformer_exog,
      )
      results = forecaster.create_train_X_y(series=series, exog=exog)
  
      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,
                          -0.04719331,
                          0.0,
                          1.0,
                          -0.81862236,
                          1.0,
                          0.0,
                      ],
                      [
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          -1.21854359,
                          -0.81862236,
                          1.0,
                          0.0,
                          2.03112731,
                          0.0,
                          1.0,
                      ],
                      [
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          -0.87038828,
                          2.03112731,
                          0.0,
                          1.0,
                          0.22507577,
                          1.0,
                          0.0,
                      ],
                      [
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          0.87038828,
                          0.52223297,
                          0.17407766,
                          -0.17407766,
                          -0.52223297,
                          0.22507577,
                          1.0,
                          0.0,
                          -0.84584926,
                          0.0,
                          1.0,
                      ],
                  ]
              ),
              index=pd.date_range("1990-01-07", periods=4, freq="D"),
              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",
                  "col_1_step_1",
                  "col_2_a_step_1",
                  "col_2_b_step_1",
                  "col_1_step_2",
                  "col_2_a_step_2",
                  "col_2_b_step_2",
              ],
          ),
          {
              1: pd.Series(
                  data=np.array(
                      [0.17407766, 0.52223297, 0.87038828, 1.21854359]
                  ),
                  index=pd.date_range(
                      "1990-01-06", periods=4, freq="D"
                  ),
                  name="l1_step_1",
              ),
              2: pd.Series(
                  data=np.array(
                      [0.52223297, 0.87038828, 1.21854359, 1.5666989]
                  ),
                  index=pd.date_range(
                      "1990-01-07", periods=4, freq="D"
                  ),
                  name="l1_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 30 for calling a global function with example usage: test_create_train_X_y_output_when_transformer_series_and_transformer_exog_steps_2(transformer_series) without return types