code: def do1(reml, irf, ds_ix):
      # No need to check independent random effects when there is
      # only one of them.
      if irf and ds_ix < 6:
          return
  
      irfs = "irf" if irf else "drf"
      meth = "reml" if reml else "ml"
  
      rslt = R_Results(meth, irfs, ds_ix)
  
      # Fit the model
      md = MixedLM(rslt.endog, rslt.exog_fe, rslt.groups, rslt.exog_re)
      if not irf:  # Free random effects covariance
          if np.any(np.diag(rslt.cov_re_r) < 1e-5):
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore")
                  mdf = md.fit(gtol=1e-7, reml=reml)
          else:
              mdf = md.fit(gtol=1e-7, reml=reml)
  
      else:  # Independent random effects
          k_fe = rslt.exog_fe.shape[1]
          k_re = rslt.exog_re.shape[1]
          free = MixedLMParams(k_fe, k_re, 0)
          free.fe_params = np.ones(k_fe)
          free.cov_re = np.eye(k_re)
          free.vcomp = np.array([])
          if np.any(np.diag(rslt.cov_re_r) < 1e-5):
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore")
                  mdf = md.fit(reml=reml, gtol=1e-7, free=free)
          else:
              mdf = md.fit(reml=reml, gtol=1e-7, free=free)
  
      assert_almost_equal(mdf.fe_params, rslt.coef, decimal=4)
      assert_almost_equal(mdf.cov_re, rslt.cov_re_r, decimal=4)
      assert_almost_equal(mdf.scale, rslt.scale_r, decimal=4)
  
      k_fe = md.k_fe
      assert_almost_equal(
          rslt.vcov_r, mdf.cov_params()[0:k_fe, 0:k_fe], decimal=3
      )
  
      assert_almost_equal(mdf.llf, rslt.loglike[0], decimal=2)
  
      # Not supported in R except for independent random effects
      if not irf:
          assert_almost_equal(
              mdf.random_effects[0], rslt.ranef_postmean, decimal=3
          )
          assert_almost_equal(
              mdf.random_effects_cov[0], rslt.ranef_condvar, decimal=3
          )
  
imports: from statsmodels.compat.platform import PLATFORM_OSX
  import os
  import csv
  import warnings
  import numpy
  import pandas
  from scipy import sparse
  import pytest
  from statsmodels.regression.mixed_linear_model import MixedLM, MixedLMParams, _smw_solver, _smw_logdet
  from numpy.testing import assert_almost_equal, assert_equal, assert_allclose, assert_
  from statsmodels.base import _penalties
  import statsmodels.tools.numdiff
  from .results import lme_r_results
label_desc: Use image node_id 2 for calling a global function with example usage: do1(reml, irf, ds_ix) without return types