code: def extract_HBS_learning_curves(runs):
  	
  	sr = sorted(runs, key=lambda r: r.budget)
  	lc = list(filter(lambda t: not t[1] is None, [(r.budget, r.loss) for r in sr]))
  	return([lc,])
docs: function to get the hyperband learning curves
  
  	This is an example function showing the interface to use the
  	HB_result.get_learning_curves method.
  
  	Parameters
  	----------
  
  	runs: list of HB_result.run objects
  		the performed runs for an unspecified config
  
  	Returns
  	-------
  
  	list of learning curves: list of lists of tuples
  		An individual learning curve is a list of (t, x_t) tuples.
  		This function must return a list of these. One could think
  		of cases where one could extract multiple learning curves
  		from these runs, e.g. if each run is an independent training
  		run of a neural network on the data.