code: def __init__(
      self,
      partitions,
      model_class=None,
      init_kwds=None,
      estimation_method=None,
      estimation_kwds=None,
      join_method=None,
      join_kwds=None,
      results_class=None,
      results_kwds=None,
  ):
      self.partitions = partitions
  
      if model_class is None:
          self.model_class = OLS
      else:
          self.model_class = model_class
  
      if init_kwds is None:
          self.init_kwds = {}
      else:
          self.init_kwds = init_kwds
  
      if estimation_method is None:
          self.estimation_method = _est_regularized_debiased
      else:
          self.estimation_method = estimation_method
  
      if estimation_kwds is None:
          self.estimation_kwds = {}
      else:
          self.estimation_kwds = estimation_kwds
  
      if join_method is None:
          self.join_method = _join_debiased
      else:
          self.join_method = join_method
  
      if join_kwds is None:
          self.join_kwds = {}
      else:
          self.join_kwds = join_kwds
  
      if results_class is None:
          self.results_class = RegularizedResults
      else:
          self.results_class = results_class
  
      if results_kwds is None:
          self.results_kwds = {}
      else:
          self.results_kwds = results_kwds
  
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 1 to create a new DistributedModel object  with example: obj = DistributedModel(partitions, model_class, init_kwds, estimation_method, estimation_kwds, join_method, join_kwds, results_class, results_kwds)