code: def test_confint_proportion(method, case):
      count, nobs = case
      idx = res_binom_methods.index(probci_methods[method])
      res_low = res_binom[case].ci_low[idx]
      res_upp = res_binom[case].ci_upp[idx]
      if np.isnan(res_low) or np.isnan(res_upp):
          pytest.skip("Skipping due to NaN value")
      if (count == 0 or count == nobs) and method == "jeffreys":
          # maybe a bug or different corner case definition
          pytest.skip("Skipping nobs 0 or count and jeffreys")
      if method == "jeffreys" and nobs == 30:
          # something is strange in extreme case e.g 0/30 or 1/30
          pytest.skip(
              "Skipping nobs is 30 and jeffreys due to extreme case problem"
          )
      ci = proportion_confint(count, nobs, alpha=0.05, method=method)
      # we impose that confint is in [0, 1]
      res_low = max(res_low, 0)
      res_upp = min(res_upp, 1)
      assert_almost_equal(
          ci, [res_low, res_upp], decimal=6, err_msg=repr(case) + method
      )
  
imports: import warnings
  import numpy
  from numpy.testing import assert_allclose, assert_almost_equal, assert_array_less, assert_equal, assert_raises
  import pandas
  import pytest
  import statsmodels.stats.proportion
  from statsmodels.stats.proportion import confint_proportions_2indep, multinomial_proportions_confint, power_proportions_2indep, proportion_confint, samplesize_proportions_2indep_onetail, score_test_proportions_2indep
  from statsmodels.tools.sm_exceptions import HypothesisTestWarning
  from statsmodels.tools.testing import Holder
  from statsmodels.stats.tests.results.results_proportion import res_binom, res_binom_methods
label_desc: Use image node_id 1 for calling a global function with example usage: test_confint_proportion(method, case) without return types