code: def forward(self, input: rlt.PreprocessedRankingInput):
      input = self._convert_seq2slate_to_reward_model_format(input)
      state, src_seq, tgt_in_seq, src_src_mask, tgt_tgt_mask = (
          input.state.float_features,
          input.src_seq.float_features,
          input.tgt_in_seq.float_features,
          input.src_src_mask,
          input.tgt_tgt_mask,
      )
      # encoder_output shape: batch_size, src_seq_len, dim_model
      encoder_output = self.encode(state, src_seq, src_src_mask)
  
      batch_size, tgt_seq_len, _ = tgt_in_seq.shape
      # tgt_src_mask shape: batch_size, tgt_seq_len, src_seq_len
      tgt_src_mask = torch.ones(
          batch_size,
          tgt_seq_len,
          self.max_src_seq_len,
          device=src_src_mask.device,
      )
  
      # decoder_output shape: batch_size, tgt_seq_len, dim_model
      decoder_output = self.decode(
          memory=encoder_output,
          state=state,
          tgt_src_mask=tgt_src_mask,
          tgt_in_seq=tgt_in_seq,
          tgt_tgt_mask=tgt_tgt_mask,
          tgt_seq_len=tgt_seq_len,
      )
  
      # use the decoder's last step embedding to predict the slate reward
      pred_reward = self.proj(decoder_output[:, -1, :])
      return rlt.RewardNetworkOutput(predicted_reward=pred_reward)
  
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 5 for calling the Seq2SlateTransformerRewardNet obj's underlying member method code with example usage: obj.forward(input) and returns: rlt