code: def statespace_comparison(austourists):
      ets_model = ETSModel(
          austourists,
          seasonal_periods=4,
          error="add",
          trend="add",
          seasonal="add",
          damped_trend=True,
      )
      ets_results = ets_model.fit(disp=False)
  
      statespace_model = statespace.ExponentialSmoothing(
          austourists,
          trend=True,
          damped_trend=True,
          seasonal=4,
          initialization_method="known",
          initial_level=ets_results.initial_level,
          initial_trend=ets_results.initial_trend,
          # See GH 7893
          initial_seasonal=ets_results.initial_seasonal[::-1],
      )
      with statespace_model.fix_params(
          {
              "smoothing_level": ets_results.smoothing_level,
              "smoothing_trend": ets_results.smoothing_trend,
              "smoothing_seasonal": ets_results.smoothing_seasonal,
              "damping_trend": ets_results.damping_trend,
          }
      ):
          statespace_results = statespace_model.fit()
      ets_results.test_serial_correlation("ljungbox")
      statespace_results.test_serial_correlation("ljungbox")
      return ets_results, statespace_results
  
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 33 for calling a global function with example usage: statespace_comparison(austourists) and returns: ets_results,  statespace_results