code: def __init__(
      self,
      state_dim: int,
      candidate_dim: int,
      num_stacked_layers: int,
      dim_model: int,
      max_src_seq_len: int,
      max_tgt_seq_len: int,
  ):
      super().__init__(
          state_dim,
          candidate_dim,
          dim_model,
          num_stacked_layers,
          max_src_seq_len,
          max_tgt_seq_len,
      )
      self.gru = nn.GRU(
          input_size=dim_model,
          hidden_size=dim_model,
          num_layers=num_stacked_layers,
          batch_first=True,
      )
      self.end_of_seq_vec = nn.Parameter(
          torch.zeros(candidate_dim), requires_grad=True
      )
      self.proj = nn.Linear(2 * dim_model, 1)
      self._init_params()
  
imports: import copy
  import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import types
  from reagent.core.torch_utils import gather
  from reagent.model_utils.seq2slate_utils import subsequent_mask
  from reagent.models.base import ModelBase
  from reagent.models.seq2slate import Decoder, DecoderLayer, Embedder, Encoder, EncoderLayer, MultiHeadedAttention, PositionalEncoding, PositionwiseFeedForward
label_desc: Use image node_id 1 to create a new Seq2SlateGRURewardNet object from inherited base classes: Seq2SlateRewardNetBase with example: obj = Seq2SlateGRURewardNet(state_dim, candidate_dim, num_stacked_layers, dim_model, max_src_seq_len, max_tgt_seq_len)