code: def get_learner_stats(grad_info):
      
  
      if LEARNER_STATS_KEY in grad_info:
          return grad_info[LEARNER_STATS_KEY]
  
      multiagent_stats = {}
      for k, v in grad_info.items():
          if type(v) is dict:
              if LEARNER_STATS_KEY in v:
                  multiagent_stats[k] = v[LEARNER_STATS_KEY]
  
      return multiagent_stats
docs: Return optimization stats reported from the policy graph.
  
      Example:
          >>> grad_info = evaluator.learn_on_batch(samples)
          >>> print(get_stats(grad_info))
          {"vf_loss": ..., "policy_loss": ...}