code: def _mock_multi_agent_records(self):
      # Create some simple observations, actions, rewards, infos and
      # extra model outputs.
      observations = [
          {"agent_1": 0, "agent_2": 0, "agent_3": 0},
          # Here agent 2 is stepping, but does not receive a next
          # observation.
          {"agent_1": 1, "agent_3": 1, "agent_4": 1},
          # Here agents 1 and 3 have stepped, but received no next
          # observation. their actions should go into the buffers.
          {"agent_2": 2, "agent_4": 2},
      ]
      actions = [
          # Here agent_2 has to buffer.
          {"agent_1": 0, "agent_2": 0, "agent_3": 0},
          {"agent_1": 1, "agent_3": 1, "agent_4": 1},
      ]
      rewards = [
          # Here agent 4 has to buffer the reward as does not have
          # actions nor observation.
          {"agent_1": 0.5, "agent_3": 0.5, "agent_4": 0.5},
          # Agent 4 should now release the buffer with reward 1.0
          # and add the next reward to it, as it stepped and received
          # a next observation.
          {"agent_2": 1.0, "agent_4": 1.0},
      ]
      infos = [
          {"agent_1": {}, "agent_2": {}, "agent_3": {}},
          {"agent_1": {}, "agent_3": {}, "agent_4": {}},
          {"agent_2": {}, "agent_4": {}},
      ]
      # Let no agent terminate or being truncated.
      terminateds = {
          "__all__": False,
          "agent_1": False,
          "agent_3": False,
          "agent_4": False,
      }
      truncateds = {
          "__all__": False,
          "agent_1": False,
          "agent_3": False,
          "agent_4": False,
      }
  
      # Return all observations.
      return (
          observations,
          actions,
          rewards,
          terminateds,
          truncateds,
          infos,
      )
  
imports: import gymnasium
  import numpy
  import unittest
  from typing import List, Optional, Tuple
  import ray
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.env.multi_agent_episode import MultiAgentEpisode
  from ray.rllib.utils.typing import MultiAgentDict
label_desc: Use image node_id 13 for calling the TestMultiAgentEpisode obj's underlying member method code with example usage: obj._mock_multi_agent_records() and returns: observations,  actions,  rewards,  terminateds,  truncateds,  infos