code: def _ppo_policy():
      actor = PolicyNet(2, True)
      critic = PolicyNet()
      policy = PPOPolicy(
          actor,
          critic,
          torch.optim.Adam(
              tuple(actor.parameters()) + tuple(critic.parameters())
          ),
          torch.distributions.Categorical,
          action_space=NoopActionInterpreter().action_space,
      )
      return policy
  
imports: import os
  import random
  import sys
  from pathlib import Path
  import pytest
  import torch
  import torch.nn
  from gym import spaces
  from tianshou.policy import PPOPolicy
  from qlib.config import C
  from qlib.log import set_log_with_config
  from qlib.rl.interpreter import StateInterpreter, ActionInterpreter
  from qlib.rl.simulator import Simulator
  from qlib.rl.reward import Reward
  from qlib.rl.trainer import Trainer, TrainingVessel, EarlyStopping, Checkpoint
label_desc: Use image node_id 1 for calling a global function with example usage: _ppo_policy() and returns: policy