code: def forward(
      self,
      src_tokens: List[str],
      dict_feat: Optional[
          Tuple[List[str], List[float], List[int]]
      ] = None,
      contextual_token_embedding: Optional[List[float]] = None,
  ) -> List[Tuple[List[str], float, List[float]]]:
      word_ids = self.source_vocab.lookup_indices_1d(src_tokens)
  
      # find, if there exists, the unk token in the source utterance.
      # If multiple we select the first unk token.
      single_unk_token: Optional[str] = get_single_unk_token(
          src_tokens, word_ids, self.copy_unk_token, self.unk_idx
      )
  
      (
          words,
          dict_tensors,
          contextual_embedding_tensor,
          src_lengths,
      ) = self.prepare_generator_inputs(
          word_ids, dict_feat, contextual_token_embedding
      )
      hypos_etc = self.sequence_generator(
          words, dict_tensors, contextual_embedding_tensor, src_lengths
      )
      hypos_list: List[Tuple[List[str], float, List[float]]] = []
  
      filter_token_list: List[int] = []
      if self.filter_eos_bos:
          filter_token_list = [
              self.target_vocab.bos_idx,
              self.target_vocab.eos_idx,
          ]
  
      for seq in hypos_etc:
          hyopthesis = seq[0]
          stringified = self.target_vocab.lookup_words_1d(
              hyopthesis,
              filter_token_list=filter_token_list,
              possible_unk_token=single_unk_token,
          )
          hypos_list.append((stringified, seq[1], seq[2]))
      return hypos_list
  
file_path: pytext/pytext/torchscript/seq2seq/export_model.py
imports: from typing import List, Optional, Tuple
  import torch
  from pytext.torchscript.vocab import ScriptVocabulary
label_desc: Use image node_id 3 for calling the Seq2SeqJIT obj's underlying member method code with example usage: obj.forward(src_tokens, dict_feat, contextual_token_embedding) and returns: hypos_list