code: def test_est_unregularized_naive():
      # tests that the shape of all the intermediate steps
      # remains correct for unregularized naive estimation,
      # does this for OLS and GLM
  
      np.random.seed(435265)
      X = np.random.normal(size=(50, 3))
      y = np.random.randint(0, 2, size=50)
      beta = np.random.normal(size=3)
      mod = OLS(y, X)
      res = _est_unregularized_naive(mod, 0, 2, fit_kwds={"alpha": 0.5})
  
      assert_equal(res.shape, beta.shape)
  
      mod = GLM(y, X, family=Binomial())
      res = _est_unregularized_naive(mod, 0, 2, fit_kwds={"alpha": 0.5})
  
      assert_equal(res.shape, beta.shape)
  
imports: import numpy
  from numpy.testing import assert_equal, assert_, assert_allclose
  from statsmodels.regression.linear_model import OLS
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.genmod.families import Binomial
  from statsmodels.base.distributed_estimation import _calc_grad, _calc_wdesign_mat, _est_regularized_debiased, _join_debiased, _est_regularized_naive, _est_unregularized_naive, _join_naive, DistributedModel
label_desc: Use image node_id 6 for calling a global function with example usage: test_est_unregularized_naive() without return types