code: def test_no_var_pred(sunspots, not_implemented):
      class DummyPred:
          def __init__(self, predicted_mean, row_labels):
              self.predicted_mean = predicted_mean
              self.row_labels = row_labels
  
              def f():
                  raise NotImplementedError
  
              if not_implemented:
                  self.forecast = property(f)
  
      class DummyRes:
          def __init__(self, res):
              self._res = res
  
          def forecast(self, *args, **kwargs):
              return self._res.forecast(*args, **kwargs)
  
          def get_prediction(self, *args, **kwargs):
              pred = self._res.get_prediction(*args, **kwargs)
  
              return DummyPred(pred.predicted_mean, pred.row_labels)
  
      class DummyMod:
          def __init__(self, y):
              self._mod = ARIMA(y)
  
          def fit(self, *args, **kwargs):
              res = self._mod.fit(*args, **kwargs)
              return DummyRes(res)
  
      stl_mod = STLForecast(sunspots, model=DummyMod, period=11)
      stl_res = stl_mod.fit()
      with pytest.warns(UserWarning, match="The variance of"):
          pred = stl_res.get_prediction()
      assert np.all(np.isnan(pred.var_pred_mean))
  
imports: from statsmodels.compat.pandas import MONTH_END
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  import pytest
  import statsmodels.datasets
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tsa.base.prediction import PredictionResults
  from statsmodels.tsa.deterministic import Fourier
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  from statsmodels.tsa.forecasting.stl import STLForecast
  from statsmodels.tsa.seasonal import STL, DecomposeResult
  from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing
label_desc: Use image node_id 8 for calling a global function with example usage: test_no_var_pred(sunspots, not_implemented) and returns: self, DummyPred, DummyRes