code: def test_create_train_X_y_ValueError_when_fun_predictors_return_NaN():
      """
      Test ValueError is raised when `fun_predictors()` return NaN values.
      """
      series = pd.DataFrame(
          {"1": pd.Series(np.arange(5)), "2": pd.Series(np.arange(5))}
      )
  
      forecaster = ForecasterAutoregMultiSeriesCustom(
          regressor=LinearRegression(),
          fun_predictors=create_predictors_nan,
          name_predictors=["lag_1", "lag_2", "lag_3"],
          window_size=3,
      )
  
      err_msg = re.escape(
          "`fun_predictors()` is returning `NaN` values for series '1'."
      )
      with pytest.raises(ValueError, match=err_msg):
          forecaster.create_train_X_y(series=series)
  
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 11 for calling a global function with example usage: test_create_train_X_y_ValueError_when_fun_predictors_return_NaN() without return types