code: def test2m(self):
      cop = self.copula
      args = self.cop_args
  
      cev = CopulaDistribution(
          cop, [stats.norm, stats.norm], cop_args=None
      )
      k_marg = 2
      mod = CopulaModel(
          cev, data_ev + [0.5, -0.1], k_params=self.k_copparams + k_marg
      )
  
      # TODO: patching for now
      mod.k_copparams = self.k_copparams
      mod.df_resid = len(mod.endog) - mod.nparams
      mod.df_model = mod.nparams - 0
      res = mod.fit(
          start_params=list(args) + [0.5, -0.1], method="bfgs"
      )
      # the following fails in TestEVAsymLogistic with nan loglike
      # res = mod.fit(method="newton", start_params=res.params)
  
      assert mod.nparams == self.k_copparams + k_marg
      assert res.nobs == len(mod.endog)
      assert_allclose(res.params[[-2, -1]], [0.5, -0.1], atol=0.2)
      res.summary()
      assert res.mle_retvals["converged"]
      assert not np.isnan(res.bse).any()
  
imports: import numpy
  from scipy import stats
  from numpy.testing import assert_allclose
  from statsmodels.base.model import GenericLikelihoodModel
  from statsmodels.distributions.copula.api import ClaytonCopula, GaussianCopula, FrankCopula, GumbelCopula, IndependenceCopula, CopulaDistribution
  from statsmodels.distributions.copula import depfunc_ev
  from statsmodels.distributions.copula.extreme_value import ExtremeValueCopula
label_desc: Use image node_id 2 for calling the CheckEVfit1 obj's underlying member method code with example usage: obj.test2m() without return types