code: def make_agent_from_model(
      env: Gym,
      model: ModelManager__Union,
      publisher: ModelPublisher__Union,
      module_name: str,
  ):
      publisher_manager = publisher.value
      assert isinstance(
          publisher_manager, FileSystemPublisher
      ), f"publishing manager is type {type(publisher_manager)}, not FileSystemPublisher"
      module_names = model.value.serving_module_names()
      assert (
          module_name in module_names
      ), f"{module_name} not in {module_names}"
      torchscript_path = publisher_manager.get_latest_published_model(
          model.value, module_name
      )
      jit_model = torch.jit.load(torchscript_path)
      policy = create_predictor_policy_from_model(jit_model)
      agent = Agent.create_for_env_with_serving_policy(env, policy)
      return agent
  
imports: import json
  import logging
  from typing import Optional
  import gym
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from reagent.data.spark_utils import call_spark_class, get_spark_session
  from reagent.gym.agents.agent import Agent
  from reagent.gym.envs import Gym
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.gym.policies.random_policies import make_random_policy_for_env
  from reagent.gym.runners.gymrunner import evaluate_for_n_episodes
  from reagent.gym.utils import fill_replay_buffer
  from reagent.model_managers.union import ModelManager__Union
  from reagent.publishers.union import FileSystemPublisher, ModelPublisher__Union
  from reagent.replay_memory.circular_replay_buffer import ReplayBuffer
  from reagent.replay_memory.utils import replay_buffer_to_pre_timeline_df
  from .types import TableSpec
label_desc: Use image node_id 6 for calling a global function with example usage: make_agent_from_model(env, model, publisher, module_name) and returns: agent