code: def test_sample_from_early_done_env(self):
      ev = RolloutWorker(
          env_creator=lambda _: EarlyDoneMultiAgent(),
          default_policy_class=MockPolicy,
          config=AlgorithmConfig()
          .rollouts(
              rollout_fragment_length=1,
              num_rollout_workers=0,
              batch_mode="complete_episodes",
          )
          .multi_agent(
              policies={"p0", "p1"},
              policy_mapping_fn=lambda agent_id, episode, worker, **kwargs: (
                  "p{}".format(agent_id % 2)
              ),
          ),
      )
      # This used to raise an Error due to the EarlyDoneMultiAgent
      # terminating at e.g. agent0 w/o publishing the observation for
      # agent1 anymore. This limitation is fixed and an env may
      # terminate at any time (as well as return rewards for any agent
      # at any time, even when that agent doesn't have an obs returned
      # in the same call to `step()`).
      ma_batch = ev.sample()
      # Make sure that agents took the correct (alternating timesteps)
      # path. Except for the last timestep, where both agents got
      # terminated.
      ag0_ts = ma_batch.policy_batches["p0"]["t"]
      ag1_ts = ma_batch.policy_batches["p1"]["t"]
      self.assertTrue(np.all(np.abs(ag0_ts[:-1] - ag1_ts[:-1]) == 1.0))
      self.assertTrue(ag0_ts[-1] == ag1_ts[-1])
  
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 11 for calling the TestMultiAgentEnv obj's underlying member method code with example usage: obj.test_sample_from_early_done_env() without return types