class_docstr: Class to contain model results
  
  Parameters
  ----------
  model : class instance
      Class instance for model used for distributed data,
      this particular instance uses fake data and is really
      only to allow use of methods like predict.
  params : ndarray
      Parameter estimates from the fit model.
class_docstr_tok: ["Class","to","contain","model","results","Parameters","--","--","--","--","--","model",":","class","instance","Class","instance","for","model","used","for","distributed","data",",","this","particular","instance","uses","fake","data","and","is","really","only","to","allow","use","of","methods","like","predict",".","params",":","ndarray","Parameter","estimates","from","the","fit","model","."]
code: def __init__(self, model, params):
      super().__init__(model, 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 1 to create a new DistributedResults object from inherited base classes: LikelihoodModelResults with example: obj = DistributedResults(model, params)