code: def create_for_env_with_serving_policy(
      cls,
      env: EnvWrapper,
      serving_policy: Policy,
      *,
      obs_preprocessor=None,
      action_extractor=None,
      **kwargs,
  ) -> "Agent":
      # device shouldn't be provided as serving is CPU only
      if obs_preprocessor is None:
          obs_preprocessor = env.get_serving_obs_preprocessor()
  
      if action_extractor is None:
          action_extractor = env.get_serving_action_extractor()
  
      return cls(
          serving_policy,
          obs_preprocessor=obs_preprocessor,
          action_extractor=action_extractor,
          **kwargs,
      )
  
file_path: ReAgent/reagent/gym/agents/agent.py
imports: from typing import Any, Dict, Optional, Tuple, Union
  import numpy
  import torch
  from reagent.gym.envs.env_wrapper import EnvWrapper
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.random_policies import make_random_policy_for_env
  from reagent.gym.types import PostEpisode, PostStep, Trajectory, Transition
label_desc: Use image node_id 3 for calling the Agent obj's underlying member method code with example usage: obj.create_for_env_with_serving_policy(cls, env, serving_policy) and returns: cls