code: def test_means(self):
      # library onewaystats, BF test for equality of means
      # st = bf.test(y ~ g, df3)
      statistic = 7.10900606421182
      parameter = [2, 31.4207256105052]
      p_value = 0.00283841965791224
      # method = 'Brown-Forsythe Test'
      res = anova_oneway(self.data, use_var="bf")
  
      # R bf.test uses original BF df_num
      assert_allclose(res.pvalue2, p_value, rtol=1e-13)
      assert_allclose(res.statistic, statistic, rtol=1e-13)
      assert_allclose([res.df_num2, res.df_denom], parameter)
  
imports: import numpy
  from numpy.testing import assert_allclose, assert_equal
  import pytest
  from statsmodels.regression.linear_model import OLS
  import statsmodels.stats.power
  import statsmodels.stats.oneway
  from statsmodels.stats.oneway import confint_effectsize_oneway, confint_noncentrality, effectsize_oneway, anova_oneway, anova_generic, equivalence_oneway, equivalence_oneway_generic, power_equivalence_oneway, _power_equivalence_oneway_emp, f2_to_wellek, fstat_to_wellek, wellek_to_f2
  from statsmodels.stats.robust_compare import scale_transform
  from statsmodels.stats.contrast import wald_test_noncent_generic, wald_test_noncent, _offset_constraint
label_desc: Use image node_id 2 for calling the TestOnewayScale obj's underlying member method code with example usage: obj.test_means() without return types