code: def test_pred_errors_against_y_test(fh):
      """Check prediction performance on airline dataset.
  
      Y_test must lie in the prediction interval with coverage=0.9.
  
      Arguments
      ---------
      fh: ForecastingHorizon, fh at which to test prediction
  
      Raises
      ------
      AssertionError - if point forecasts do not lie within the prediction intervals
      """
      y = load_airline()
      y_train, y_test = temporal_train_test_split(y)
  
      f = ThetaForecaster()
      f.fit(y_train, fh=fh)
  
      intervals = f.predict_interval(fh=fh, coverage=0.9)
  
      y_test = y_test.iloc[check_fh(fh) - 1]
  
      # Performance should be good enough that all point forecasts lie within the
      # prediction intervals.
      assert np.all(y_test > intervals[(y.name, 0.9, "lower")].values)
      assert np.all(y_test < intervals[(y.name, 0.9, "upper")].values)
  
file_path: sktime/sktime/forecasting/tests/test_theta.py
imports: import numpy
  import pandas
  import pytest
  from sktime.datasets import load_airline
  from sktime.forecasting.tests._config import TEST_OOS_FHS
  from sktime.forecasting.theta import ThetaForecaster, ThetaModularForecaster
  from sktime.split import temporal_train_test_split
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  from sktime.utils.validation.forecasting import check_fh
label_desc: Use image node_id 2 for calling a global function with example usage: test_pred_errors_against_y_test(fh) without return types