code: def input_prototype(self):
      return rlt.PreprocessedRankingInput.from_tensors(
          state=torch.randn(1, self.state_dim),
          src_seq=torch.randn(
              1, self.max_src_seq_len, self.candidate_dim
          ),
          tgt_in_seq=torch.randn(
              1, self.max_tgt_seq_len, self.candidate_dim
          ),
          tgt_out_seq=torch.randn(
              1, self.max_tgt_seq_len, self.candidate_dim
          ),
          src_src_mask=torch.ones(
              1, self.max_src_seq_len, self.max_src_seq_len
          ),
          tgt_tgt_mask=torch.ones(
              1, self.max_tgt_seq_len, self.max_tgt_seq_len
          ),
          tgt_out_idx=torch.arange(self.max_tgt_seq_len).reshape(1, -1)
          + 2,
      )
  
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 Seq2SlateRewardNetBase obj's underlying member method code with example usage: obj.input_prototype() and returns: rlt