code: def test_save_load_raises(load_pos_and_neg_data):
      """Tests the save_model and load_model that raises an exception. i.e. when
      only model is saved (without pipeline) and after loading, setup is not run.
      """
  
      fh = np.arange(1, 13)
      fold = 2
      data = load_pos_and_neg_data
  
      exp = TSForecastingExperiment()
      exp.setup(
          data=data,
          fh=fh,
          fold=fold,
          fold_strategy="sliding",
          verbose=False,
          session_id=42,
      )
  
      model = exp.create_model("ets")
      exp.save_model(
          model, "model_unit_test_oop_raises", model_only=True
      )
  
      # Mimic loading in another session
      exp_loaded = TSForecastingExperiment()
      loaded_model = exp_loaded.load_model("model_unit_test_oop_raises")
  
      # Setup not run and only passing a estimator without pipeline ----
      with pytest.raises(ValueError) as errmsg:
          _ = exp_loaded.predict_model(loaded_model)
      exceptionmsg = errmsg.value.args[0]
      assert (
          "Setup has not been run and you have provided a estimator without the pipeline"
          in exceptionmsg
      )
  
file_path: pycaret/tests/test_time_series_base.py
imports: import numpy
  import pandas
  import pytest
  from pandas.testing import assert_frame_equal
  from sktime.forecasting.compose import ForecastingPipeline
  from time_series_test_utils import _return_compare_model_args, _return_model_parameters
  from pycaret.time_series import TSForecastingExperiment
label_desc: Use image node_id 9 for calling a global function with example usage: test_save_load_raises(load_pos_and_neg_data) without return types