code: def test_summary(austourists_model):
      # just try to run summary to see if it works
      fit = austourists_model.fit(disp=False)
      fit.summary()
  
      # now without estimated initial states
      austourists_model.set_initialization_method("heuristic")
      fit = austourists_model.fit(disp=False)
      fit.summary()
  
      # and with fixed params
      fit = austourists_model.fit_constrained({"smoothing_trend": 0.9})
      fit.summary()
  
imports: from statsmodels.compat.pandas import QUARTER_END
  from statsmodels.compat.platform import PLATFORM_LINUX32, PLATFORM_WIN
  from itertools import product
  import json
  import pathlib
  import numpy
  from numpy.testing import assert_allclose, assert_almost_equal
  import pandas
  import pytest
  import scipy.stats
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  import statsmodels.tsa.holtwinters
  import statsmodels.tsa.statespace.exponential_smoothing
label_desc: Use image node_id 29 for calling a global function with example usage: test_summary(austourists_model) without return types