code: def test_non_zero_params():
      # tests that the thresholding does not cause any issues
  
      np.random.seed(435265)
      N = 200
      p = 10
      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, join_kwds={"threshold": 0.13})
      fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
      ols_mod = OLS(y, X)
      fitOLS = ols_mod.fit_regularized(alpha=0.1)
  
      nz_params_db = 1 * (fitOLSdb.params != 0)
      nz_params_ols = 1 * (fitOLS.params != 0)
  
      assert_allclose(nz_params_db, nz_params_ols)
  
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 13 for calling a global function with example usage: test_non_zero_params() without return types