code: def res_opg(self):
      res_ols = self.res_ols
      nobs = self.nobs
      moms = self.moms
      moms_obs = self.moms_obs
      covm = self.covm
      moms_deriv = self.moms_deriv
      weights = self.weights
      L = self.L
      x = self.exog_full
  
      res_ols2_hc0 = OLS(res_ols.model.endog, x).fit(cov_type="HC0")
  
      res_all = []
  
      # auxiliary regression
      ones = np.ones(nobs)
      stat = nobs * OLS(ones, moms_obs).fit().rsquared
      res_all.append(("ols R2", stat))
  
      tres = res_ols2_hc0.compare_lm_test(res_ols, demean=False)
      res_all.append(("comp_lm uc", tres))
  
      tres = CMTNewey(moms, covm, covm[:, :-2], weights, L).chisquare
      res_all.append(("Newey", tres))
  
      tres = CMTTauchen(
          moms[:-2], covm[:-2, :-2], moms[-2:], covm[-2:, :-2], covm
      ).chisquare
      res_all.append(("Tauchen", tres))
  
      tres = diao.lm_robust_subset(moms[-2:], 2, covm, covm)
      res_all.append(("score subset QMLE", tres))
  
      tres = diao.lm_robust(
          moms,
          np.eye(moms.shape[0])[-2:],
          np.linalg.inv(covm),
          covm,
          cov_params=None,
      )
      res_all.append(("scoreB QMLE", tres))
  
      tres = diao.lm_robust(
          moms,
          np.eye(moms.shape[0])[-2:],
          np.linalg.inv(covm),
          None,
          cov_params=np.linalg.inv(covm),
      )
      res_all.append(("scoreV QMLE", tres))
  
      return res_all
  
file_path: statsmodels/statsmodels/stats/tests/test_diagnostic_other.py
imports: import numpy
  from numpy.testing import assert_allclose
  from statsmodels.regression.linear_model import OLS
  from statsmodels.stats._diagnostic_other import CMTNewey, CMTTauchen
  import statsmodels.stats._diagnostic_other
label_desc: Use image node_id 4 for calling the TestCMTOLS obj's underlying member method code with example usage: obj.res_opg() and returns: res_all