code: def test_create_train_X_y_output_when_y_is_series_10_steps_1_and_exog_is_dataframe_of_float_int(
      dtype,
  ):
      """
      Test the output of create_train_X_y when y=pd.Series(np.arange(10)), steps=1
      and exog is a pandas DataFrame of floats or ints.
      """
      y = pd.Series(np.arange(10), name="y", dtype=float)
      exog = pd.DataFrame(
          {
              "exog_1": np.arange(100, 110, dtype=dtype),
              "exog_2": np.arange(1000, 1010, dtype=dtype),
          }
      )
  
      forecaster = ForecasterAutoregDirect(
          LinearRegression(), lags=5, steps=1
      )
      results = forecaster.create_train_X_y(y=y, exog=exog)
  
      expected = (
          pd.DataFrame(
              data=np.array(
                  [
                      [4.0, 3.0, 2.0, 1.0, 0.0, 105.0, 1005.0],
                      [5.0, 4.0, 3.0, 2.0, 1.0, 106.0, 1006.0],
                      [6.0, 5.0, 4.0, 3.0, 2.0, 107.0, 1007.0],
                      [7.0, 6.0, 5.0, 4.0, 3.0, 108.0, 1008.0],
                      [8.0, 7.0, 6.0, 5.0, 4.0, 109.0, 1009.0],
                  ],
                  dtype=float,
              ),
              index=pd.RangeIndex(start=5, stop=10, step=1),
              columns=[
                  "lag_1",
                  "lag_2",
                  "lag_3",
                  "lag_4",
                  "lag_5",
                  "exog_1_step_1",
                  "exog_2_step_1",
              ],
          ).astype({"exog_1_step_1": dtype, "exog_2_step_1": dtype}),
          {
              1: pd.Series(
                  data=np.array([5.0, 6.0, 7.0, 8.0, 9.0], dtype=float),
                  index=pd.RangeIndex(start=5, 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 11 for calling a global function with example usage: test_create_train_X_y_output_when_y_is_series_10_steps_1_and_exog_is_dataframe_of_float_int(dtype) without return types