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`.
      """
      series = pd.DataFrame(
          {"1": pd.Series(np.arange(5)), "2": pd.Series(np.arange(5))}
      )
  
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors_3,
          name_predictors=["lag_1", "lag_2", "lag_3"],
          window_size=3,
      )
      train_X = forecaster.create_train_X_y(series=series)[0]
  
      assert train_X.columns.to_list() == [
          "lag_1",
          "lag_2",
          "lag_3",
          "1",
          "2",
      ]
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoregMultiSeriesCustom import ForecasterAutoregMultiSeriesCustom
  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 16 for calling a global function with example usage: test_create_train_X_y_column_names_match_name_predictors() without return types