code: def test_smoke(data):
      stlf = STLForecast(data, ARIMA, model_kwargs={"order": (2, 0, 0)})
      res = stlf.fit(fit_kwargs={})
      res.forecast(37)
      assert isinstance(res.summary().as_text(), str)
      assert isinstance(res.stl, STL)
      assert isinstance(res.result, DecomposeResult)
      assert isinstance(res.model, ARIMA)
      assert hasattr(res.model_result, "forecast")
  
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 2 for calling a global function with example usage: test_smoke(data) without return types