docstr: In the reward model, the transformer decoder should see the full
  sequences; while in seq2slate, the decoder only sees the sequence
  before the last item.
code: def _convert_seq2slate_to_reward_model_format(
      self, input: rlt.PreprocessedRankingInput
  ):
      """
      In the reward model, the transformer decoder should see the full
      sequences; while in seq2slate, the decoder only sees the sequence
      before the last item.
      """
      device = next(self.parameters()).device
      # pyre-fixme[16]: Optional type has no attribute `float_features`.
      (
          batch_size,
          tgt_seq_len,
          candidate_dim,
      ) = input.tgt_out_seq.float_features.shape
      assert self.max_tgt_seq_len == tgt_seq_len
  
      tgt_tgt_mask = subsequent_mask(tgt_seq_len + 1, device)
      # shape: batch_size, tgt_seq_len + 1, candidate_dim
      tgt_in_seq = torch.cat(
          (
              self.decoder_start_vec.repeat(batch_size, 1, 1),
              input.tgt_out_seq.float_features,
          ),
          dim=1,
      )
  
      return rlt.PreprocessedRankingInput.from_tensors(
          state=input.state.float_features,
          src_seq=input.src_seq.float_features,
          src_src_mask=input.src_src_mask,
          tgt_in_seq=tgt_in_seq,
          tgt_tgt_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 4 for calling the Seq2SlateTransformerRewardNet obj's underlying member method code with example usage: obj._convert_seq2slate_to_reward_model_format(input) and returns: rlt