code: def new_result(self, job):
          
          super().new_result(job)
  
          conf = ConfigSpace.Configuration(self.config_space, job.kwargs['config']).get_array()
  
          epochs = len(job.result["info"]["learning_curve"])
          budget = int(job.kwargs["budget"])
  
          t_idx = np.linspace(budget / epochs, budget, epochs) / self.max_budget
          x_new = np.repeat(conf[None, :], t_idx.shape[0], axis=0)
  
          x_new = np.concatenate((x_new, t_idx[:, None]), axis=1)
  
          # Smooth learning curve
          lc = smoothing(job.result["info"]["learning_curve"])
  
          # Flip learning curves since LC-Net wants increasing curves
          lc_new = [1 - y for y in lc]
  
          if self.train is None:
              self.train = x_new
              self.train_targets = lc_new
          else:
              self.train = np.append(self.train, x_new, axis=0)
              self.train_targets = np.append(self.train_targets, lc_new, axis=0)
  
          if self.counter >= self.n_points:
  
              self.lock.acquire()
              y_min = np.min(self.train_targets)
              y_max = np.max(self.train_targets)
  
              train_targets = (self.train_targets - y_min) / (y_max - y_min)
  
              self.model.train(self.train, train_targets)
              self.is_trained = True
              self.counter = 0
              self.lock.release()
  
          else:
              self.counter += epochs
docs: function to register finished runs
  
              Every time a run has finished, this function should be called
              to register it with the result logger. If overwritten, make
              sure to call this method from the base class to ensure proper
              logging.
  
  
              Parameters:
              -----------
              job_id: dict
                  a dictionary containing all the info about the run
              job_result: dict
                  contains all the results of the job, i.e. it's a dict with
                  the keys 'loss' and 'info'