code: def test_sparse(self):
      cur_dir = os.path.dirname(os.path.abspath(__file__))
      rdir = os.path.join(cur_dir, "results")
      fname = os.path.join(rdir, "pastes.csv")
  
      # Dense
      data = pd.read_csv(fname)
      vcf = {"cask": "0 + cask"}
      model = MixedLM.from_formula(
          "strength ~ 1",
          groups="batch",
          re_formula="1",
          vc_formula=vcf,
          data=data,
      )
      result = model.fit()
  
      # Sparse
      model2 = MixedLM.from_formula(
          "strength ~ 1",
          groups="batch",
          re_formula="1",
          vc_formula=vcf,
          use_sparse=True,
          data=data,
      )
      result2 = model2.fit()
  
      assert_allclose(result.params, result2.params)
      assert_allclose(result.bse, result2.bse)
  
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 8 for calling the TestMixedLM obj's underlying member method code with example usage: obj.test_sparse() without return types