code: def test_prediction_interval_na(load_pos_and_neg_data):
      """Tests predict model when interval is NA"""
  
      exp = TSForecastingExperiment()
  
      fh = 12
      fold = 2
      data = load_pos_and_neg_data
  
      exp.setup(
          data=data,
          fh=fh,
          fold=fold,
          fold_strategy="expanding",
          verbose=False,
          session_id=42,
      )
  
      # For models that do not produce a prediction interval --> returns NA values
      model = exp.create_model("lr_cds_dt")
      y_pred = exp.predict_model(model, return_pred_int=True)
      assert y_pred["lower"].isnull().all()
      assert y_pred["upper"].isnull().all()
  
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 5 for calling a global function with example usage: test_prediction_interval_na(load_pos_and_neg_data) without return types