code: def test_create_train_X_y_output_when_y_is_series_10_and_exog_is_series_of_float_int(
      dtype,
  ):
      """
      Test the output of create_train_X_y when y=pd.Series(np.arange(10)) and
      exog is a pandas series of floats or ints.
      """
      y = pd.Series(np.arange(10), dtype=float)
      exog = pd.Series(np.arange(100, 110), name="exog", dtype=dtype)
      forecaster = ForecasterAutoregCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          window_size=5,
      )
      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],
                      [5.0, 4.0, 3.0, 2.0, 1.0, 106.0],
                      [6.0, 5.0, 4.0, 3.0, 2.0, 107.0],
                      [7.0, 6.0, 5.0, 4.0, 3.0, 108.0],
                      [8.0, 7.0, 6.0, 5.0, 4.0, 109.0],
                  ]
              ),
              index=pd.RangeIndex(start=5, stop=10, step=1),
              columns=[
                  "custom_predictor_0",
                  "custom_predictor_1",
                  "custom_predictor_2",
                  "custom_predictor_3",
                  "custom_predictor_4",
                  "exog",
              ],
          ).astype({"exog": dtype}),
          pd.Series(
              data=np.array([5.0, 6.0, 7.0, 8.0, 9.0]),
              index=pd.RangeIndex(start=5, stop=10, step=1),
              name="y",
              dtype=float,
          ),
      )
  
      pd.testing.assert_frame_equal(results[0], expected[0])
      pd.testing.assert_series_equal(results[1], expected[1])
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
  from skforecast.preprocessing import TimeSeriesDifferentiator
  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
  from .fixtures_ForecasterAutoregCustom import data
label_desc: Use image node_id 13 for calling a global function with example usage: test_create_train_X_y_output_when_y_is_series_10_and_exog_is_series_of_float_int(dtype) without return types