code: def test_create_train_X_y_column_names_match_name_predictors():
      """
      Check column names in X_train match the ones in argument `name_predictors`.
      """
      forecaster = ForecasterAutoregCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors,
          name_predictors=["lag_1", "lag_2", "lag_3", "lag_4", "lag_5"],
          window_size=5,
      )
  
      X_train = forecaster.create_train_X_y(
          y=pd.Series(
              np.arange(10),
              index=pd.date_range(
                  start="2022-01-01", periods=10, freq="1D"
              ),
          )
      )[0]
  
      assert X_train.columns.to_list() == [
          "lag_1",
          "lag_2",
          "lag_3",
          "lag_4",
          "lag_5",
      ]
  
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 10 for calling a global function with example usage: test_create_train_X_y_column_names_match_name_predictors() without return types