code: def _convert_seq2slate_to_reward_model_format(
      self, input: rlt.PreprocessedRankingInput
  ):
      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
      src_seq_len = input.src_seq.float_features.shape[1]
      assert self.max_tgt_seq_len == tgt_seq_len
      assert self.max_src_seq_len == src_seq_len
  
      # unselected_idx stores indices of items that are not included in the slate
      unselected_idx = torch.ones(
          batch_size, src_seq_len, device=device
      )
      unselected_idx[
          torch.arange(batch_size, device=device).repeat_interleave(
              torch.tensor(tgt_seq_len, device=device)
          ),
          # pyre-fixme[16]: Optional type has no attribute `flatten`.
          input.tgt_out_idx.flatten() - 2,
      ] = 0
      # shape: batch_size, (src_seq_len - tgt_seq_len)
      unselected_idx = torch.nonzero(unselected_idx, as_tuple=True)[
          1
      ].reshape(batch_size, src_seq_len - tgt_seq_len)
      # shape: batch_size, (src_seq_len - tgt_seq_len), candidate_dim
      unselected_candidate_features = gather(
          input.src_seq.float_features, unselected_idx
      )
      # shape: batch_size, src_seq_len + 1, candidate_dim
      tgt_in_seq = torch.cat(
          (
              input.tgt_out_seq.float_features,
              unselected_candidate_features,
              self.end_of_seq_vec.repeat(batch_size, 1, 1),
          ),
          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,
      )
  
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 2 for calling the Seq2SlateGRURewardNet obj's underlying member method code with example usage: obj._convert_seq2slate_to_reward_model_format(input) and returns: rlt