code: def test_cov_params():
      mod, res = get_dummy_mod()
  
      # Smoke test for each of the covariance types
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          res = mod.fit(res.params, disp=-1, cov_type="none")
          assert_equal(
              res.cov_kwds["description"],
              "Covariance matrix not calculated.",
          )
  
          res = mod.fit(res.params, disp=-1, cov_type="approx")
          assert_equal(res.cov_type, "approx")
          assert_equal(
              res.cov_kwds["description"],
              "Covariance matrix calculated using numerical (complex-step) "
              "differentiation.",
          )
  
          res = mod.fit(res.params, disp=-1, cov_type="oim")
          assert_equal(res.cov_type, "oim")
          assert_equal(
              res.cov_kwds["description"],
              "Covariance matrix calculated using the observed information "
              "matrix (complex-step) described in Harvey (1989).",
          )
  
          res = mod.fit(res.params, disp=-1, cov_type="opg")
          assert_equal(res.cov_type, "opg")
          assert_equal(
              res.cov_kwds["description"],
              "Covariance matrix calculated using the outer product of "
              "gradients (complex-step).",
          )
  
          res = mod.fit(res.params, disp=-1, cov_type="robust")
          assert_equal(res.cov_type, "robust")
          assert_equal(
              res.cov_kwds["description"],
              "Quasi-maximum likelihood covariance matrix used for robustness "
              "to some misspecifications; calculated using the observed "
              "information matrix (complex-step) described in Harvey (1989).",
          )
  
          res = mod.fit(res.params, disp=-1, cov_type="robust_oim")
          assert_equal(res.cov_type, "robust_oim")
          assert_equal(
              res.cov_kwds["description"],
              "Quasi-maximum likelihood covariance matrix used for robustness "
              "to some misspecifications; calculated using the observed "
              "information matrix (complex-step) described in Harvey (1989).",
          )
  
          res = mod.fit(res.params, disp=-1, cov_type="robust_approx")
          assert_equal(res.cov_type, "robust_approx")
          assert_equal(
              res.cov_kwds["description"],
              "Quasi-maximum likelihood covariance matrix used for robustness "
              "to some misspecifications; calculated using numerical "
              "(complex-step) differentiation.",
          )
  
          with pytest.raises(NotImplementedError):
              mod.fit(res.params, disp=-1, cov_type="invalid_cov_type")
  
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 9 for calling a global function with example usage: test_cov_params() without return types