code: def get_learning_curves(self, lc_extractor=extract_HBS_learning_curves, config_ids=None):
  		
  
  		config_ids = self.data.keys() if config_ids is None else config_ids
  		
  		lc_dict = {}
  		
  		for id in config_ids:
  			runs = self.get_runs_by_id(id)
  			lc_dict[id] = lc_extractor(runs)
  			
  		return(lc_dict)
docs: extracts all learning curves from all run configurations
  
  		Parameters
  		----------
  			lc_extractor: callable
  				a function to return a list of learning_curves.
  				defaults to hpbanster.HB_result.extract_HP_learning_curves
  			config_ids: list of valid config ids
  				if only a subset of the config ids is wanted
  
  		Returns
  		-------
  			dict
  				a dictionary with the config_ids as keys and the
  				learning curves as values