code: def check_cop_rvs(cop, rvs=None, nobs=2000, k=10, use_pdf=True):
      if rvs is None:
          rvs = cop.rvs(nobs)
      else:
          nobs = rvs.shape[0]
      freq = frequencies_fromdata(rvs, k, use_ranks=True)
      pdfg = approx_copula_pdf(
          cop, k_bins=k, force_uniform=True, use_pdf=use_pdf
      )
      count_pdf = pdfg * nobs
  
      freq = freq.ravel()
      count_pdf = count_pdf.ravel()
      mask = count_pdf < 2
      if mask.sum() > 5:
          cp = count_pdf[mask]
          cp = np.concatenate([cp, [nobs - cp.sum()]])
          fr = freq[mask]
          cp = np.concatenate([fr, [nobs - fr.sum()]])
      else:
          fr = freq.ravel()
          cp = count_pdf.ravel()
  
      chi2_test = stats.chisquare(freq.ravel(), count_pdf.ravel())
      return chi2_test, rvs
  
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 1 for calling a global function with example usage: check_cop_rvs(cop, rvs, nobs, k, use_pdf) and returns: chi2_test,  rvs