code: def test_create_train_X_y_output_when_series_and_exog_no_pandas_index():
      """
      Test the output of create_train_X_y when series and exog have no pandas index
      that doesn't start at 0.
      """
      series = pd.DataFrame(
          {
              "l1": pd.Series(np.arange(10, dtype=float)),
              "l2": pd.Series(np.arange(10, dtype=float)),
          }
      )
      series.index = np.arange(6, 16)
      exog = pd.Series(
          np.arange(100, 110),
          index=np.arange(6, 16),
          name="exog",
          dtype=float,
      )
  
      forecaster = ForecasterAutoregMultiSeries(
          LinearRegression(), lags=5, 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, 1.0, 0.0, 105.0, 1.0, 0.0],
                      [5.0, 4.0, 3.0, 2.0, 1.0, 106.0, 1.0, 0.0],
                      [6.0, 5.0, 4.0, 3.0, 2.0, 107.0, 1.0, 0.0],
                      [7.0, 6.0, 5.0, 4.0, 3.0, 108.0, 1.0, 0.0],
                      [8.0, 7.0, 6.0, 5.0, 4.0, 109.0, 1.0, 0.0],
                      [4.0, 3.0, 2.0, 1.0, 0.0, 105.0, 0.0, 1.0],
                      [5.0, 4.0, 3.0, 2.0, 1.0, 106.0, 0.0, 1.0],
                      [6.0, 5.0, 4.0, 3.0, 2.0, 107.0, 0.0, 1.0],
                      [7.0, 6.0, 5.0, 4.0, 3.0, 108.0, 0.0, 1.0],
                      [8.0, 7.0, 6.0, 5.0, 4.0, 109.0, 0.0, 1.0],
                  ]
              ),
              index=pd.RangeIndex(start=0, stop=10, step=1),
              columns=[
                  "lag_1",
                  "lag_2",
                  "lag_3",
                  "lag_4",
                  "lag_5",
                  "exog",
                  "l1",
                  "l2",
              ],
          ),
          pd.Series(
              data=np.array([5, 6, 7, 8, 9, 5, 6, 7, 8, 9]),
              index=pd.RangeIndex(start=0, stop=10, step=1),
              name="y",
              dtype=float,
          ),
          pd.RangeIndex(start=0, stop=len(series), step=1),
          pd.Index(np.array([5, 6, 7, 8, 9, 5, 6, 7, 8, 9])),
      )
  
      for i in range(len(expected)):
          if isinstance(expected[i], pd.DataFrame):
              pd.testing.assert_frame_equal(results[i], expected[i])
          elif isinstance(expected[i], pd.Series):
              pd.testing.assert_series_equal(results[i], expected[i])
          else:
              np.testing.assert_array_equal(results[i], expected[i])
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeries import ForecasterAutoregMultiSeries
  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_series_and_exog_no_pandas_index() without return types