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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 get_learning_curves(self, lc_extractor=extract_HBS_learning_curves, config_ids=None):
 		""" 		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 		"""


		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)