code: def forward(self, input: rlt.PreprocessedRankingInput):
      input = self._convert_seq2slate_to_reward_model_format(input)
      state = input.state.float_features
      tgt_in_seq = input.tgt_in_seq.float_features
  
      # shape: batch_size, src_seq_len + 1, dim_modle
      tgt_in_embed = self.embed(state, tgt_in_seq)
  
      # output shape: batch_size, src_seq_len + 1, dim_model
      output, hn = self.gru(tgt_in_embed)
      # hn shape: batch_size, dim_model
      hn = hn[-1]  # top layer's hidden state
  
      # attention, using hidden as query, outputs as keys and values
      # shape: batch_size, src_seq_len + 1
      attn_weights = F.softmax(
          torch.bmm(
              output,
              hn.unsqueeze(2)
              / torch.sqrt(torch.tensor(self.candidate_dim).float()),
          ).squeeze(2),
          dim=1,
      )
      # shape: batch_size, dim_model
      context_vector = torch.bmm(
          attn_weights.unsqueeze(1), output
      ).squeeze(1)
  
      # reward prediction depends on hidden state of the last step + context vector
      # shape: batch_size, 2 * dim_model
      seq_embed = torch.cat((hn, context_vector), dim=1)
  
      # shape: batch_size, 1
      pred_reward = self.proj(seq_embed)
      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 4 for calling the Seq2SlateGRURewardNet obj's underlying member method code with example usage: obj.forward(input) and returns: rlt