code: def loglike(self, params):
      params = np.atleast_1d(params)
      cd = self.copula_distribution
      # ll = cd.logpdf(self.endog, args=(params[:2], params[2:]))
      cop_args = params[: self.k_copparams]
      if cop_args.size == 0:
          cop_args = ()
      if len(params) > self.k_copparams:
          marg_args = np.split(params[self.k_copparams :], 2)
      else:
          marg_args = None
  
      ll = cd.logpdf(
          self.endog, cop_args=cop_args, marg_args=marg_args
      ).sum()
      return ll
  
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 3 for calling the CopulaModel obj's underlying member method code with example usage: obj.loglike(params) and returns: ll