code: def setup_class(cls):
      cls.results = results_meta.eff_prop1
      ss = """\
              study,nei,nci,e1i,c1i,e2i,c2i,e3i,c3i,e4i,c4i
              1,19,22,16.0,20.0,11,12,4.0,8.0,4,3
              2,34,35,22.0,22.0,18,12,15.0,8.0,15,6
              3,72,68,44.0,40.0,21,15,10.0,3.0,3,0
              4,22,20,19.0,12.0,14,5,5.0,4.0,2,3
              5,70,32,62.0,27.0,42,13,26.0,6.0,15,5
              6,183,94,130.0,65.0,80,33,47.0,14.0,30,11
              7,26,50,24.0,30.0,13,18,5.0,10.0,3,9
              8,61,55,51.0,44.0,37,30,19.0,19.0,11,15
              9,36,25,30.0,17.0,23,12,13.0,4.0,10,4
              10,45,35,43.0,35.0,19,14,8.0,4.0,6,0
              11,246,208,169.0,139.0,106,76,67.0,42.0,51,35
              12,386,141,279.0,97.0,170,46,97.0,21.0,73,8
              13,59,32,56.0,30.0,34,17,21.0,9.0,20,7
              14,45,15,42.0,10.0,18,3,9.0,1.0,9,1
              15,14,18,14.0,18.0,13,14,12.0,13.0,9,12
              16,26,19,21.0,15.0,12,10,6.0,4.0,5,1
              17,74,75,,,42,40,,,23,30"""
      df3 = pd.read_csv(io.StringIO(ss))
      df_12y = df3[["e2i", "nei", "c2i", "nci"]]
      # TODO: currently 1 is reference, switch labels
      # cls.count2, cls.nobs2, cls.count1, cls.nobs1 = df_12y.values.T
      cls.count1, cls.nobs1, cls.count2, cls.nobs2 = df_12y.values.T
  
imports: import io
  import numpy
  import pandas
  import pytest
  from numpy.testing import assert_equal, assert_allclose
  from statsmodels.regression.linear_model import WLS
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.stats.meta_analysis import effectsize_smd, effectsize_2proportions, combine_effects, _fit_tau_iterative, _fit_tau_mm, _fit_tau_iter_mm
  from .results import results_meta
label_desc: Use image node_id 1 for calling the TestEffectsizeBinom obj's underlying member method code with example usage: obj.setup_class(cls) without return types