class_docstr: Callable class used by Ray Serve to handle async requests.
  
  All the necessary serving logic is implemented in here:
  - Creation and restoring of the (already trained) RLlib Algorithm.
  - Calls to algo.compute_action upon receiving an action request
    (with a current observation).
class_docstr_tok: ["Callable","class","used","by","Ray","Serve","to","handle","async","requests",".","All","the","necessary","serving","logic","is","implemented","in","here",":","-","Creation","and","restoring","of","the","(","already","trained",")","RLlib","Algorithm",".","-","Calls","to","algo.compute_action","upon","receiving","an","action","request","(","with","a","current","observation",")","."]
code: def __init__(self, checkpoint_path):
      # Create the Algorithm from the given checkpoint.
      self.algo = Algorithm.from_checkpoint(checkpoint_path)
  
file_path: ray/rllib/examples/inference_and_serving/serve_and_rllib.py
imports: import argparse
  import gymnasium
  import requests
  from starlette.requests import Request
  import ray
  import ray.rllib.algorithms.algorithm
  import ray.rllib.algorithms.algorithm_config
  from ray.rllib.algorithms.dqn import DQNConfig
  from ray.rllib.env.wrappers.atari_wrappers import FrameStack, WarpFrame
  from ray import serve
label_desc: Use image node_id 1 to create a new ServeRLlibPolicy object  with example: obj = ServeRLlibPolicy(checkpoint_path)