code: def test_evaluation_wo_evaluation_worker_set(self):
      # Use a custom callback that asserts that we are running the
      # configured exact number of episodes per evaluation.
      config = (
          ppo.PPOConfig()
          .environment(env="CartPole-v1")
          .callbacks(callbacks_class=AssertEvalCallback)
      )
  
      for _ in framework_iterator(frameworks=("tf", "torch")):
          # Setup algorithm w/o evaluation worker set and still call
          # evaluate() -> Expect error.
          algo_wo_env_on_local_worker = config.build()
          self.assertRaisesRegex(
              ValueError,
              "Cannot evaluate w/o an evaluation worker set",
              algo_wo_env_on_local_worker.evaluate,
          )
          algo_wo_env_on_local_worker.stop()
  
          # Try again using `create_env_on_driver=True`.
          # This force-adds the env on the local-worker, so this Algorithm
          # can `evaluate` even though it doesn't have an evaluation-worker
          # set.
          config.create_env_on_local_worker = True
          algo_w_env_on_local_worker = config.build()
          results = algo_w_env_on_local_worker.evaluate()
          assert "evaluation" in results
          assert "episode_reward_mean" in results["evaluation"]
          algo_w_env_on_local_worker.stop()
          config.create_env_on_local_worker = False
  
imports: import gymnasium
  import numpy
  import os
  from pathlib import Path
  from random import choice
  import time
  import unittest
  import ray
  import ray.rllib.algorithms.dqn
  from ray.rllib.algorithms.bc import BCConfig
  import ray.rllib.algorithms.ppo
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
  from ray.rllib.examples.parallel_evaluation_and_training import AssertEvalCallback
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
  from ray.rllib.utils.test_utils import check, framework_iterator
label_desc: Use image node_id 6 for calling the TestAlgorithm obj's underlying member method code with example usage: obj.test_evaluation_wo_evaluation_worker_set() without return types