code: def forward(
      self,
      state: torch.Tensor,
      src_seq: torch.Tensor,
      tgt_out_seq: torch.Tensor,
      src_src_mask: torch.Tensor,
      tgt_out_idx: torch.Tensor,
  ) -> torch.Tensor:
      return self.model(
          rlt.PreprocessedRankingInput(
              state=rlt.FeatureData(float_features=state),
              src_seq=rlt.FeatureData(float_features=src_seq),
              tgt_out_seq=rlt.FeatureData(float_features=tgt_out_seq),
              src_src_mask=src_src_mask,
              tgt_out_idx=tgt_out_idx,
          )
      ).predicted_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 3 for calling the Seq2SlateRewardNetJITWrapper obj's underlying member method code with example usage: obj.forward(state, src_seq, tgt_out_seq, src_src_mask, tgt_out_idx) and returns: self