code: def test_tau(self):
      copula = FrankCopula(k_dim=2)
  
      theta = [2, 1, 1e-2, 1e-4, 1e-5, 1e-6]
      # > tau(frankCopula(param = 2, dim = 2))
      tau_r = [
          0.2138945692196201,
          0.110018536448993,
          0.001111110000028503,
          1.111110992013664e-05,
          1.111104651951855e-06,
          1.108825244955369e-07,
      ]
  
      tau_cop = [copula.tau(th) for th in theta]
      assert_allclose(tau_cop[:-1], tau_r[:-1], rtol=1e-5)
      # relative precision at very small tau is not very high
  
      # check debye function
      taud = 1 + 4 * _debyem1_expansion(theta) / theta
      assert_allclose(taud, tau_cop, rtol=1e-5)
  
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 2 for calling the TestFrank obj's underlying member method code with example usage: obj.test_tau() without return types