code: def check_inference_w_connectors(
      policy, env_name, max_steps: int = 100
  ):
      """Checks whether the given policy can infer actions from an env with connectors.
  
      Args:
          policy: The policy to check.
          env_name: Name of the environment to check
          max_steps: The maximum number of steps to run the environment for.
  
      Raises:
          ValueError: If the policy cannot infer actions from the environment.
      """
      # Avoids circular import
      from ray.rllib.utils.policy import local_policy_inference
  
      env = gym.make(env_name)
  
      # Potentially wrap the env like we do in RolloutWorker
      if is_atari(env):
          env = wrap_deepmind(
              env,
              dim=policy.config["model"]["dim"],
              framestack=policy.config["model"].get("framestack"),
          )
  
      obs, info = env.reset()
      reward, terminated, truncated = 0.0, False, False
      ts = 0
      while not terminated and not truncated and ts < max_steps:
          action_out = local_policy_inference(
              policy,
              env_id=0,
              agent_id=0,
              obs=obs,
              reward=reward,
              terminated=terminated,
              truncated=truncated,
              info=info,
          )
          obs, reward, terminated, truncated, info = env.step(
              action_out[0][0]
          )
  
          ts += 1
  
imports: from collections import Counter
  import copy
  import gymnasium
  from gymnasium.spaces import Box, Discrete, MultiDiscrete, MultiBinary
  from gymnasium.spaces import Dict
  from gymnasium.spaces import Tuple
  import inspect
  import logging
  import numpy
  import os
  import pprint
  import random
  import re
  import time
  import tree
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Type, Union
  import yaml
  import ray
  from ray import air, tune
  from ray.rllib.env.wrappers.atari_wrappers import is_atari, wrap_deepmind
  from ray.rllib.utils.framework import try_import_jax, try_import_tf, try_import_torch
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.typing import ResultDict
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.tune import CLIReporter, run_experiments
label_desc: Use image node_id 4 for calling a global function with example usage: check_inference_w_connectors(policy, env_name, max_steps) without return types