code: def __init__(
      self,
      state_dim: int,
      candidate_dim: int,
      dim_model: int,
      num_stacked_layers: int,
      max_src_seq_len: int,
      max_tgt_seq_len: int,
  ):
      super().__init__()
      self.state_dim = state_dim
      self.candidate_dim = candidate_dim
      self.dim_model = dim_model
      self.num_stacked_layers = num_stacked_layers
  
      self.candidate_embedder = Embedder(candidate_dim, dim_model // 2)
      self.state_embedder = Embedder(state_dim, dim_model // 2)
      self.max_src_seq_len = max_src_seq_len
      self.max_tgt_seq_len = max_tgt_seq_len
  
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 Seq2SlateRewardNetBase object from inherited base classes: ModelBase with example: obj = Seq2SlateRewardNetBase(state_dim, candidate_dim, dim_model, num_stacked_layers, max_src_seq_len, max_tgt_seq_len)