code: def naive_policy(env):
      actor = ActorNetwork(
          env.observation_space["cur_step"].n, env.action_space.n
      )
      critic = CriticNetwork(env.observation_space["cur_step"].n)
      policy = A2CPolicy(
          actor,
          critic,
          torch.optim.Adam(
              list(actor.parameters()) + list(critic.parameters()),
              lr=1.0,
          ),
          torch.distributions.Categorical,
          discount_factor=1.0,
      )
      return policy
  
imports: from __future__ import annotations
  import pytest
  import nni
  from nni.mutable import *
  from nni.nas.evaluator import FunctionalEvaluator
  from nni.nas.execution import SequentialExecutionEngine
  from nni.nas.space import *
  from nni.nas.strategy import GridSearch, Random, RegularizedEvolution, PolicyBasedRL
  import torch
  from torch import nn
  from tianshou.policy import A2CPolicy
  from tianshou.data import to_torch
label_desc: Use image node_id 9 for calling a global function with example usage: naive_policy(env) and returns: policy