code: def recurrent_generator(self, advantages, num_mini_batch):
      num_processes = self.rewards.size(1)
  
      assert num_processes >= num_mini_batch, (
          f"PPO requires the number processes ({num_processes}) "
          f"to be greater than or equal to the number of PPO mini batches ({num_mini_batch})."
      )
  
      num_envs_per_batch = num_processes // num_mini_batch
      perm = torch.randperm(num_processes)
  
      for start_ind in range(0, num_processes, num_envs_per_batch):
          observations_batch = []
          states_batch = []
          actions_batch = []
          return_batch = []
          masks_batch = []
          old_action_log_probs_batch = []
          adv_targ = []
  
          for offset in range(num_envs_per_batch):
              ind = perm[start_ind + offset]
              observations_batch.append(self.observations[:-1, ind])
              states_batch.append(self.states[0:1, ind])
              actions_batch.append(self.actions[:, ind])
              return_batch.append(self.returns[:-1, ind])
              masks_batch.append(self.masks[:-1, ind])
              old_action_log_probs_batch.append(
                  self.action_log_probs[:, ind]
              )
              adv_targ.append(advantages[:, ind])
  
          observations_batch = torch.cat(observations_batch, 0)
          states_batch = torch.cat(states_batch, 0)
          actions_batch = torch.cat(actions_batch, 0)
          return_batch = torch.cat(return_batch, 0)
          masks_batch = torch.cat(masks_batch, 0)
          old_action_log_probs_batch = torch.cat(
              old_action_log_probs_batch, 0
          )
          adv_targ = torch.cat(adv_targ, 0)
  
          yield observations_batch, states_batch, actions_batch, return_batch, masks_batch, old_action_log_probs_batch, adv_targ
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/ppo/rollout_storage.py
imports: import torch
  from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
label_desc: Use image node_id 7 for calling the RolloutStorage obj's underlying member method code with example usage: obj.recurrent_generator(advantages, num_mini_batch) without return types