code: def test_fit_misc():
      true = results_sarimax.wpi1_stationary
      endog = np.diff(true["data"])[1:]
  
      mod = sarimax.SARIMAX(endog, order=(1, 0, 1), trend="c")
  
      # Test optim_hessian={'opg','oim','approx'}
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          res1 = mod.fit(
              method="ncg",
              disp=0,
              optim_hessian="opg",
              optim_complex_step=False,
          )
          res2 = mod.fit(
              method="ncg",
              disp=0,
              optim_hessian="oim",
              optim_complex_step=False,
          )
      # Check that the Hessians broadly result in the same optimum
      assert_allclose(res1.llf, res2.llf, rtol=1e-2)
  
      # Test return_params=True
      mod, _ = get_dummy_mod(fit=False)
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          res_params = mod.fit(disp=-1, return_params=True)
  
      # 5 digits necessary to accommodate 32-bit numpy/scipy with OpenBLAS 0.2.18
      assert_almost_equal(res_params, [0, 0], 5)
  
imports: from statsmodels.compat.pandas import MONTH_END
  import os
  import re
  import warnings
  import numpy
  from numpy.testing import assert_, assert_allclose, assert_almost_equal, assert_equal, assert_raises
  import pandas
  import pytest
  from statsmodels.datasets import nile
  from statsmodels.tsa.statespace import kalman_filter, kalman_smoother, sarimax, varmax
  from statsmodels.tsa.statespace.mlemodel import MLEModel, MLEResultsWrapper
  from statsmodels.tsa.statespace.tests.results import results_sarimax, results_var_misc
label_desc: Use image node_id 5 for calling a global function with example usage: test_fit_misc() without return types