code: def decode(
      self,
      memory,
      state,
      tgt_src_mask,
      tgt_in_seq,
      tgt_tgt_mask,
      tgt_seq_len,
  ):
      # memory is the output of the encoder, the attention of each input symbol
      # memory shape: batch_size, src_seq_len, dim_model
      # tgt_src_mask shape: batch_size, tgt_seq_len, src_seq_len
      # tgt_seq shape: batch_size, tgt_seq_len, dim_candidate
      # tgt_tgt_mask shape: batch_size, tgt_seq_len, tgt_seq_len
      batch_size = tgt_in_seq.shape[0]
  
      # candidate_embed shape: batch_size, seq_len, dim_model/2
      candidate_embed = self.candidate_embedder(tgt_in_seq)
      # state_embed: batch_size, dim_model/2
      state_embed = self.state_embedder(state)
      # state_embed: batch_size, seq_len, dim_model/2
      state_embed = state_embed.repeat(1, tgt_seq_len).reshape(
          batch_size, tgt_seq_len, -1
      )
  
      # tgt_embed: batch_size, seq_len, dim_model
      tgt_embed = self.positional_encoding_decoder(
          torch.cat((state_embed, candidate_embed), dim=2)
      )
  
      # output of decoder will be later transformed into probabilities over symbols.
      # shape: batch_size, seq_len, dim_model
      return self.decoder(tgt_embed, memory, tgt_src_mask, tgt_tgt_mask)
  
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 3 for calling the Seq2SlateTransformerRewardNet obj's underlying member method code with example usage: obj.decode(memory, state, tgt_src_mask, tgt_in_seq, tgt_tgt_mask, tgt_seq_len) and returns: self