code: def test_vectorize_round_robin(self):
      env = MultiAgentEnvWrapper(
          lambda v: RoundRobinMultiAgent(2), [], 2
      )
      obs, rew, terminateds, truncateds, _, _ = env.poll()
      self.assertEqual(obs, {0: {0: 0}, 1: {0: 0}})
      self.assertEqual(rew, {0: {}, 1: {}})
      self.assertEqual(
          truncateds, {0: {"__all__": False}, 1: {"__all__": False}}
      )
      env.send_actions({0: {0: 0}, 1: {0: 0}})
      obs, rew, terminateds, truncateds, _, _ = env.poll()
      self.assertEqual(obs, {0: {1: 0}, 1: {1: 0}})
      self.assertEqual(
          truncateds,
          {
              0: {"__all__": False, 1: False},
              1: {"__all__": False, 1: False},
          },
      )
      env.send_actions({0: {1: 0}, 1: {1: 0}})
      obs, rew, terminateds, truncateds, _, _ = env.poll()
      self.assertEqual(obs, {0: {0: 0}, 1: {0: 0}})
      self.assertEqual(
          truncateds,
          {
              0: {"__all__": False, 0: False},
              1: {"__all__": False, 0: False},
          },
      )
  
imports: import gymnasium
  import numpy
  import random
  import tree
  import unittest
  import ray
  from ray.tune.registry import register_env
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.dqn.dqn_tf_policy import DQNTFPolicy
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.env.multi_agent_env import make_multi_agent, MultiAgentEnvWrapper
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.rollout_worker import get_global_worker, RolloutWorker
  from ray.rllib.evaluation.tests.test_rollout_worker import MockPolicy
  from ray.rllib.examples.policy.random_policy import RandomPolicy
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole, BasicMultiAgent, EarlyDoneMultiAgent, FlexAgentsMultiAgent, RoundRobinMultiAgent, SometimesZeroAgentsMultiAgent
  from ray.rllib.policy.policy import PolicySpec
  from ray.rllib.policy.sample_batch import convert_ma_batch_to_sample_batch
  from ray.rllib.tests.test_nested_observation_spaces import NestedMultiAgentEnv
  from ray.rllib.utils.numpy import one_hot
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 7 for calling the TestMultiAgentEnv obj's underlying member method code with example usage: obj.test_vectorize_round_robin() without return types