code: def test_scale_vb():
      y, exog_fe, exog_vc, ident = gen_simple_logit(10, 10, 0)
      exog_fe -= exog_fe.mean(0)
      exog_fe /= exog_fe.std(0)
      exog_vc = sparse.csr_matrix(exog_vc)
  
      rslts = []
      for scale_fe in False, True:
          glmm = BinomialBayesMixedGLM(
              y, exog_fe, exog_vc, ident, vcp_p=0.5, fe_p=0.5
          )
          rslt = glmm.fit_vb(scale_fe=scale_fe)
          rslts.append(rslt)
  
      assert_allclose(rslts[0].params, rslts[1].params, rtol=1e-4)
  
imports: import numpy
  from statsmodels.genmod.bayes_mixed_glm import BinomialBayesMixedGLM, PoissonBayesMixedGLM
  import pandas
  from scipy import sparse
  from numpy.testing import assert_allclose, assert_equal
  from scipy.optimize import approx_fprime
label_desc: Use image node_id 18 for calling a global function with example usage: test_scale_vb() without return types