code: def test_dispersion(self):
      res_drop = self.model_drop.fit()
      res_test = diac.test_poisson_dispersion(res_drop)
      res_test_ = np.column_stack((res_test.statistic, res_test.pvalue))
      assert_allclose(
          res_test_, self.res_disptest, rtol=1e-6, atol=1e-14
      )
      # constant only dispersion
      ex = np.ones((res_drop.model.endog.shape[0], 1))
      # ex = np.column_stack((np.ones(res_drop.model.endog.shape[0]),
      #                      res_drop.predict()))  # or **2
      # dispersion_poisson_generic might not be correct
      # or not clear what the alternative hypothesis is
      # choosing different `ex` implies different alternative hypotheses
      res_test = diac._test_poisson_dispersion_generic(res_drop, ex)
      assert_allclose(
          res_test, self.res_disptest_g, rtol=1e-6, atol=1e-14
      )
  
imports: import numpy
  from numpy.testing import assert_allclose
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.genmod import families
  from statsmodels.discrete.discrete_model import Poisson
  import statsmodels.stats._diagnostic_other
  import statsmodels.discrete._diagnostics_count
  from statsmodels.base._parameter_inference import score_test
label_desc: Use image node_id 2 for calling the TestScoreTest obj's underlying member method code with example usage: obj.test_dispersion() without return types