code: def test_larger_p():
      # tests when p > N / m for the debiased and naive case
  
      np.random.seed(435265)
      N = 40
      p = 40
      m = 5
  
      beta = np.random.normal(size=p)
      beta = beta * np.random.randint(0, 2, p)
      X = np.random.normal(size=(N, p))
      y = X.dot(beta) + np.random.normal(size=N)
  
      db_mod = DistributedModel(m)
      fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
      assert_equal(np.sum(np.isnan(fitOLSdb.params)), 0)
  
      nv_mod = DistributedModel(
          m,
          estimation_method=_est_regularized_naive,
          join_method=_join_naive,
      )
      fitOLSnv = nv_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
      assert_equal(np.sum(np.isnan(fitOLSnv.params)), 0)
  
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 12 for calling a global function with example usage: test_larger_p() without return types