code: 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,
      ]
  
imports: import copy
  import os
  import json
  from hpbandster.core.base_iteration import Datum
label_desc: Use image node_id 1 for calling a global function with example usage: extract_HBS_learning_curves(runs) and returns: list