code: def __init__(
      self, state_dim, action_dim, num_hiddens, num_hidden_layers
  ) -> None:
      super().__init__()
  
      self.state_dim = state_dim
      self.action_dim = action_dim
      self.num_hiddens = num_hiddens
      self.num_hidden_layers = num_hidden_layers
      self.rnn = nn.LSTM(
          input_size=action_dim,
          hidden_size=num_hiddens,
          num_layers=num_hidden_layers,
      )
  
      self.lstm_linear = nn.Linear(num_hiddens, 1)
      self.map_linear = nn.Linear(state_dim, self.num_hiddens)
  
file_path: ReAgent/reagent/models/seq2reward_model.py
imports: from typing import Optional
  import torch
  import torch.nn
  from reagent.core import types
  from reagent.models.base import ModelBase
label_desc: Use image node_id 1 to create a new Seq2RewardNetwork object from inherited base classes: ModelBase with example: obj = Seq2RewardNetwork(state_dim, action_dim, num_hiddens, num_hidden_layers)