code: def __init__(self, obs_space, hidden_dim, num_layers=1):
      super().__init__()
      self.action_dim = obs_space["action_history"].nvec[0]
      self.step_dim = (
          obs_space["action_history"].shape[0] + 2
      )  # pad the start and end token
      self.hidden_dim = hidden_dim
      # first token is [SOS]
      self.embedding = nn.Embedding(self.action_dim + 1, hidden_dim)
      # self.rnn = nn.Linear(hidden_dim + self.step_dim, hidden_dim)
      self.rnn = nn.LSTM(
          hidden_dim + self.step_dim,
          hidden_dim,
          num_layers,
          batch_first=True,
      )
      self.fc = nn.Sequential(
          nn.Linear(hidden_dim, hidden_dim),
          nn.ReLU(),
      )
  
imports: from __future__ import annotations
  from copy import deepcopy
  from typing import Tuple, Callable
  import gym
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from typing_extensions import TypedDict
  from gym import spaces
  from tianshou.data import ReplayBuffer, Batch, to_torch, to_numpy
  from tianshou.policy import BasePolicy, PPOPolicy
  from nni.mutable import Categorical, CategoricalMultiple, Mutable, MutableAnnotation, Sample
label_desc: Use image node_id 1 to create a new Preprocessor object from inherited base classes: nn with example: obj = Preprocessor(obs_space, hidden_dim, num_layers)