code: def test_random_effects_getters():
      # Simulation-based test to make sure that the BLUPs and actual
      # random effects line up.
  
      np.random.seed(34234)
      ng = 500  # number of groups
      m = 10  # group size
  
      y, x, z, v0, v1, g, b, c0, c1 = [], [], [], [], [], [], [], [], []
  
      for i in range(ng):
          # Fixed effects
          xx = np.random.normal(size=(m, 2))
          yy = xx[:, 0] + 0.5 * np.random.normal(size=m)
  
          # Random effects (re_formula)
          zz = np.random.normal(size=(m, 2))
          bb = np.random.normal(size=2)
          bb[0] *= 3
          bb[1] *= 1
          yy += np.dot(zz, bb).flat
          b.append(bb)
  
          # First variance component
          vv0 = np.kron(np.r_[0, 1], np.ones(m // 2)).astype(int)
          cc0 = np.random.normal(size=2)
          yy += cc0[vv0]
          v0.append(vv0)
          c0.append(cc0)
  
          # Second variance component
          vv1 = np.kron(np.ones(m // 2), np.r_[0, 1]).astype(int)
          cc1 = np.random.normal(size=2)
          yy += cc1[vv1]
          v1.append(vv1)
          c1.append(cc1)
  
          y.append(yy)
          x.append(xx)
          z.append(zz)
          g.append(["g%d" % i] * m)
  
      y = np.concatenate(y)
      x = np.concatenate(x)
      z = np.concatenate(z)
      v0 = np.concatenate(v0)
      v1 = np.concatenate(v1)
      g = np.concatenate(g)
      df = pd.DataFrame(
          {
              "y": y,
              "x0": x[:, 0],
              "x1": x[:, 1],
              "z0": z[:, 0],
              "z1": z[:, 1],
              "v0": v0,
              "v1": v1,
              "g": g,
          }
      )
  
      b = np.asarray(b)
      c0 = np.asarray(c0)
      c1 = np.asarray(c1)
      cc = np.concatenate((c0, c1), axis=1)
  
      model = MixedLM.from_formula(
          "y ~ x0 + x1",
          re_formula="~0 + z0 + z1",
          vc_formula={"v0": "~0+C(v0)", "v1": "0+C(v1)"},
          groups="g",
          data=df,
      )
      result = model.fit()
  
      ref = result.random_effects
      b0 = [ref["g%d" % k][0:2] for k in range(ng)]
      b0 = np.asarray(b0)
      assert np.corrcoef(b0[:, 0], b[:, 0])[0, 1] > 0.8
      assert np.corrcoef(b0[:, 1], b[:, 1])[0, 1] > 0.8
  
      cf0 = [ref["g%d" % k][2:6] for k in range(ng)]
      cf0 = np.asarray(cf0)
      for k in range(4):
          assert np.corrcoef(cf0[:, k], cc[:, k])[0, 1] > 0.8
  
      # Smoke test for predictive covariances
      refc = result.random_effects_cov
      for g in refc.keys():
          p = ref[g].size
          assert refc[g].shape == (p, p)
  
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 a global function with example usage: test_random_effects_getters() without return types