code: def test_multi_agent_env(self):
      ev = RolloutWorker(
          env_creator=lambda _: EpisodeEnv(NUM_STEPS, NUM_AGENTS),
          default_policy_class=EchoPolicy,
          # Episode only works with env runner v1.
          config=AlgorithmConfig()
          .rollouts(enable_connectors=False)
          .rollouts(num_rollout_workers=0)
          .callbacks(LastInfoCallback)
          .multi_agent(
              policies={
                  str(agent_id) for agent_id in range(NUM_AGENTS)
              },
              policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: (
                  str(agent_id)
              ),
          ),
      )
      ev.sample()
  
imports: import ray
  import unittest
  from typing import Dict, List, Optional, Union, Tuple
  import numpy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.examples.env.mock_env import MockEnv3
  from ray.rllib.policy import Policy
  from ray.rllib.utils import override
  from ray.rllib.utils.typing import TensorStructType, TensorType
label_desc: Use image node_id 4 for calling the TestEpisodeLastValues obj's underlying member method code with example usage: obj.test_multi_agent_env() without return types