code: def _join_debiased(results_l, threshold=0):
      """joins the results from each run of _est_regularized_debiased
      and returns the debiased estimate of the coefficients
  
      Parameters
      ----------
      results_l : list
          A list of tuples each one containing the params, grad,
          nodewise_row and nodewise_weight values for each partition.
      threshold : scalar
          The threshold at which the coefficients will be cut.
      """
  
      p = len(results_l[0][0])
      partitions = len(results_l)
  
      params_mn = np.zeros(p)
      grad_mn = np.zeros(p)
  
      nodewise_row_l = []
      nodewise_weight_l = []
  
      for r in results_l:
          params_mn += r[0]
          grad_mn += r[1]
  
          nodewise_row_l.extend(r[2])
          nodewise_weight_l.extend(r[3])
  
      nodewise_row_l = np.array(nodewise_row_l)
      nodewise_weight_l = np.array(nodewise_weight_l)
  
      params_mn /= partitions
      grad_mn *= -1.0 / partitions
  
      approx_inv_cov = _calc_approx_inv_cov(
          nodewise_row_l, nodewise_weight_l
      )
  
      debiased_params = params_mn + approx_inv_cov.dot(grad_mn)
  
      debiased_params[np.abs(debiased_params) < threshold] = 0
  
      return debiased_params
  
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 7 for calling a global function with example usage: _join_debiased(results_l, threshold) and returns: debiased_params