code: def forward(self, obs, state=None, **kwargs):
      res = self.fc(torch.randn(obs["acc"].shape[0], 32))
      if self.return_state:
          return nn.functional.softmax(res, dim=-1), state
      else:
          return res
  
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 2 for calling the PolicyNet obj's underlying member method code with example usage: obj.forward(obs, state) and returns: nn,  state, res