code: def test_crossed_logit_map():
      y, exog_fe, exog_vc, ident = gen_crossed_logit(10, 10, 1, 2)
      exog_vc = sparse.csr_matrix(exog_vc)
  
      glmm = BinomialBayesMixedGLM(
          y, exog_fe, exog_vc, ident, vcp_p=0.5
      )
      rslt = glmm.fit_map()
  
      assert_allclose(
          glmm.logposterior_grad(rslt.params),
          np.zeros_like(rslt.params),
          atol=1e-4,
      )
  
      # Check dimensions and PSD status of cov_params
      cp = rslt.cov_params()
      p = len(rslt.params)
      assert_equal(cp.shape, np.r_[p, p])
      np.linalg.cholesky(cp)
  
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 8 for calling a global function with example usage: test_crossed_logit_map() without return types