code: def test_simulate_keywords(austourists_model_fit):
      """
      check whether all keywords are accepted and work without throwing errors.
      """
      fit = austourists_model_fit
  
      # test anchor
      assert_almost_equal(
          fit.simulate(4, anchor=-1, random_state=0).values,
          fit.simulate(4, anchor="2015-12-31", random_state=0).values,
      )
      assert_almost_equal(
          fit.simulate(4, anchor="end", random_state=0).values,
          fit.simulate(4, anchor="2015-12-31", random_state=0).values,
      )
  
      # test different random error options
      fit.simulate(4, repetitions=10)
      fit.simulate(4, repetitions=10, random_errors=scipy.stats.norm)
      fit.simulate(4, repetitions=10, random_errors=scipy.stats.norm())
      fit.simulate(
          4, repetitions=10, random_errors=np.random.randn(4, 10)
      )
      fit.simulate(4, repetitions=10, random_errors="bootstrap")
  
      # test seeding
      res = fit.simulate(4, repetitions=10, random_state=10).values
      res2 = fit.simulate(
          4, repetitions=10, random_state=np.random.RandomState(10)
      ).values
      assert np.all(res == res2)
  
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 27 for calling a global function with example usage: test_simulate_keywords(austourists_model_fit) without return types