class_docstr: Configure RolloutWorker to error in specific condition is hard.
  
  So we take a short-cut, and simply forward ping() to env.sample().
class_docstr_tok: ["Configure","RolloutWorker","to","error","in","specific","condition","is","hard",".","So","we","take","a","short-cut",",","and","simply","forward","ping","(",")","to","env.sample","(",")","."]
code: def ping(self) -> str:
      # See if Env wants to throw error.
      _ = self.env.step(self.env.action_space_sample())
      # If there is no error raised from sample(), we simply reply pong.
      return super().ping()
  
imports: from collections import defaultdict
  import gymnasium
  import numpy
  import time
  import unittest
  import ray
  from ray.util.state import list_actors
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.dqn.dqn import DQNConfig
  from ray.rllib.algorithms.impala import ImpalaConfig
  from ray.rllib.algorithms.pg import PGConfig
  from ray.rllib.algorithms.pg.pg_tf_policy import PGTF2Policy
  from ray.rllib.algorithms.pg.pg_torch_policy import PGTorchPolicy
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.env.multi_agent_env import make_multi_agent
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.utils.test_utils import framework_iterator
  from ray.tune.registry import register_env
label_desc: Use image node_id 1 for calling the ForwardHealthCheckToEnvWorker obj's underlying member method code with example usage: obj.ping() and returns: super