code: def test_fit_predict(n_jobs):
      AirPassengersPanel = get_data()
      models = [
          AutoARIMA(),
          AutoETS(),
          AutoCES(),
          AutoTheta(),
          MSTL(season_length=12),
          GARCH(),
          ARCH(),
          HistoricAverage(),
          Naive(),
          RandomWalkWithDrift(),
          SeasonalNaive(season_length=12),
          WindowAverage(window_size=12),
          SeasonalWindowAverage(window_size=12, season_length=12),
          ADIDA(),
          CrostonClassic(),
          CrostonOptimized(),
          IMAPA(),
          TSB(0.5, 0.5),
      ]
  
      sf = StatsForecast(models=models, freq="D", n_jobs=n_jobs)
      df_fcst = sf.forecast(
          df=AirPassengersPanel[["unique_id", "ds", "y"]], h=7
      )
      sf.fit(df=AirPassengersPanel[["unique_id", "ds", "y"]])
      df_predict = sf.predict(h=7)
      pd.testing.assert_frame_equal(df_fcst, df_predict)
  
imports: import pandas
  import pytest
  from statsforecast import StatsForecast
  from statsforecast.utils import AirPassengersDF
  from statsforecast.models import AutoARIMA, AutoETS, AutoCES, AutoTheta, MSTL, GARCH, ARCH, HistoricAverage, Naive, RandomWalkWithDrift, SeasonalNaive, WindowAverage, SeasonalWindowAverage, ADIDA, CrostonClassic, CrostonOptimized, CrostonSBA, IMAPA, TSB
label_desc: Use image node_id 2 for calling a global function with example usage: test_fit_predict(n_jobs) without return types