code: def setup_class(cls):
      # here we do not need to check convergence from default start_params
      start_params = [
          13.1996,
          0.8582,
          -2.8005,
          -1.5031,
          2.3849,
          -8.5552,
          -2.88,
          1.14,
      ]
      mod = NegativeBinomial(endog, exog)
      res = mod.fit(
          start_params=start_params, method="nm", maxiter=2000
      )
      marge = res.get_margeff(dummy=True)
      cls.res = res
      cls.margeff = marge
  
      cls.res1_slice = cls.res1_slice = [0, 1, 2, 3, 5, 6]
      cls.res1 = res_stata.results_negbin_margins_dummy
      cls.rtol_fac = 5e1
  
imports: import numpy
  from numpy.testing import assert_allclose
  from statsmodels.datasets.cpunish import load
  from statsmodels.discrete.discrete_model import NegativeBinomial, NegativeBinomialP, Poisson
  import statsmodels.discrete.tests.results.results_count_margins
  from statsmodels.tools.tools import add_constant
label_desc: Use image node_id 1 for calling the TestNegBinMarginDummy obj's underlying member method code with example usage: obj.setup_class(cls) without return types