code: def __init__(self, out_features=1, return_state=False):
      super().__init__()
      self.fc = nn.Linear(32, out_features)
      self.return_state = return_state
  
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 to create a new PolicyNet object from inherited base classes: nn with example: obj = PolicyNet(out_features, return_state)