code: def test_gev_genextreme(case):
      gev = stats.genextreme(0)
      # check ev copulas, cdf and transform against R `evt` package
      ev_tr, v1, v2, args, res0, res1, res2 = case
      y = [v1, v2]
      u = gev.cdf(y)
      res = copula_bv_ev(u, ev_tr, args=args)
      assert_allclose(res, res1, rtol=1e-13)
  
      ev = ExtremeValueCopula(ev_tr)
      # evaluated at using u = y
      cdf1 = ev.cdf(y, args)
      assert_allclose(cdf1, res0, rtol=1e-13)
  
      # evaluated at transformed u = F(y)
      cdf1 = ev.cdf(u, args)
      assert_allclose(cdf1, res1, rtol=1e-13)
  
      cev = CopulaDistribution(ev, [gev, gev], cop_args=args)
      cdfd = cev.cdf(np.array(y), cop_args=args)
      assert_allclose(cdfd, res1, rtol=1e-13)
      pdfd = cev.pdf(np.array(y), cop_args=args)
      assert_allclose(pdfd, res2, rtol=1e-13)
  
imports: import warnings
  from statsmodels.compat.pytest import pytest_warns
  from statsmodels.compat.scipy import SP_LT_15
  import numpy
  from numpy.testing import assert_allclose, assert_array_almost_equal
  import pytest
  from scipy import stats
  from statsmodels.distributions.copula.archimedean import ArchimedeanCopula, ClaytonCopula, FrankCopula, GumbelCopula, _debyem1_expansion
  from statsmodels.distributions.copula.copulas import CopulaDistribution
  import statsmodels.distributions.copula.depfunc_ev
  from statsmodels.distributions.copula.elliptical import GaussianCopula, StudentTCopula
  from statsmodels.distributions.copula.extreme_value import ExtremeValueCopula, copula_bv_ev
  from statsmodels.distributions.copula.other_copulas import IndependenceCopula
  import statsmodels.distributions.copula.transforms
  from statsmodels.distributions.tools import approx_copula_pdf, frequencies_fromdata
  from statsmodels.tools.numdiff import approx_fprime_cs, approx_hess
label_desc: Use image node_id 8 for calling a global function with example usage: test_gev_genextreme(case) without return types