class_docstr: The config generator determines how new configurations are sampled. This can take very different levels of
  complexity, from random sampling to the construction of complex empirical prediction models for promising
  configurations.
class_docstr_tok: ["The","config","generator","determines","how","new","configurations","are","sampled",".","This","can","take","very","different","levels","of","complexity",",","from","random","sampling","to","the","construction","of","complex","empirical","prediction","models","for","promising","configurations","."]
docstr: registers finished runs
  
  Every time a run has finished, this function should be called
  to register it with the result logger. If overwritten, make
  sure to call this method from the base class to ensure proper
  logging.
  
  
  Parameters
  ----------
  job: instance of hpbandster.distributed.dispatcher.Job
          contains all necessary information about the job
  update_model: boolean
          determines whether a model inside the config_generator should be updated
docstr_tok: ["registers","finished","runs","Every","time","a","run","has","finished",",","this","function","should","be","called","to","register","it","with","the","result","logger",".","If","overwritten",",","make","sure","to","call","this","method","from","the","base","class","to","ensure","proper","logging",".","Parameters","--","--","--","--","--","job",":","instance","of","hpbandster.distributed.dispatcher.Job","contains","all","necessary","information","about","the","job","update_model",":","boolean","determines","whether","a","model","inside","the","config_generator","should","be","updated"]
code: def new_result(self, job, update_model=True):
      """
      registers finished runs
  
      Every time a run has finished, this function should be called
      to register it with the result logger. If overwritten, make
      sure to call this method from the base class to ensure proper
      logging.
  
  
      Parameters
      ----------
      job: instance of hpbandster.distributed.dispatcher.Job
              contains all necessary information about the job
      update_model: boolean
              determines whether a model inside the config_generator should be updated
      """
      if not job.exception is None:
          self.logger.warning(
              "job {} failed with exception\n{}".format(
                  job.id, job.exception
              )
          )
  
imports: import logging
  import traceback
label_desc: Use image node_id 3 for calling the base_config_generator obj's underlying member method code with example usage: obj.new_result(job, update_model) without return types