code: def store_episode(self, episode_batch, update_stats=True):
          
  
          self.buffer.store_episode(episode_batch)
  
          if update_stats:
              # add transitions to normalizer
              episode_batch['o_2'] = episode_batch['o'][:, 1:, :]
              episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :]
              num_normalizing_transitions = transitions_in_episode_batch(episode_batch)
              transitions = self.sample_transitions(episode_batch, num_normalizing_transitions)
  
              o, g, ag = transitions['o'], transitions['g'], transitions['ag']
              transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g)
              # No need to preprocess the o_2 and g_2 since this is only used for stats
  
              self.o_stats.update(transitions['o'])
              self.g_stats.update(transitions['g'])
  
              self.o_stats.recompute_stats()
              self.g_stats.recompute_stats()
docs: episode_batch: array of batch_size x (T or T+1) x dim_key
                         'o' is of size T+1, others are of size T