code: def test_forecaster_with_initial_level():
      """Check prediction performance on airline dataset.
  
      Performance on this dataset should be reasonably good.
  
      Raises
      ------
      AssertionError - if point forecasts do not lie close to the test data
      """
      y = np.log1p(load_airline())
      y_train, y_test = temporal_train_test_split(y)
      fh = np.arange(len(y_test)) + 1
  
      f = ThetaForecaster(initial_level=0.1, sp=12)
      f.fit(y_train)
      y_pred = f.predict(fh=fh)
  
      np.testing.assert_allclose(y_pred, y_test, rtol=0.05)
  
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 3 for calling a global function with example usage: test_forecaster_with_initial_level() without return types