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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def extract_HBS_learning_curves(runs):
 	""" 	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. 		 	"""

	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,])