code: def _join_naive(params_l, threshold=0):
      """joins the results from each run of _est_<type>_naive
      and returns the mean estimate of the coefficients
  
      Parameters
      ----------
      params_l : list
          A list of arrays of coefficients.
      threshold : scalar
          The threshold at which the coefficients will be cut.
      """
  
      p = len(params_l[0])
      partitions = len(params_l)
  
      params_mn = np.zeros(p)
      for params in params_l:
          params_mn += params
      params_mn /= partitions
  
      params_mn[np.abs(params_mn) < threshold] = 0
  
      return params_mn
  
imports: from statsmodels.base.elastic_net import RegularizedResults
  from statsmodels.stats.regularized_covariance import _calc_nodewise_row, _calc_nodewise_weight, _calc_approx_inv_cov
  from statsmodels.base.model import LikelihoodModelResults
  from statsmodels.regression.linear_model import OLS
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _join_naive(params_l, threshold) and returns: params_mn