docstr: Converts target tokens from the translation model dictionary
  to language model dictionary
docstr_tok: ["Converts","target","tokens","from","the","translation","model","dictionary","to","language","model","dictionary"]
code: def convert_hypos_to_tgt_tokens(self, hypos):
      """
      Converts target tokens from the translation model dictionary
      to language model dictionary
      """
      # TODO: (T41818693) Map translation model vs LM model differences
      # and come up with a solution
      max_tgt_len = max(len(hypo["tokens"]) for hypo in hypos)
      pad = self.task.dictionary.pad_index
      tgt_tokens = torch.full(
          (len(hypos), max_tgt_len), fill_value=pad, dtype=torch.long
      )
  
      for i, hypo in enumerate(hypos):
          tgt_string = self.forward_task.tgt_dict.string(hypo["tokens"])
          tgt_mapped = self.task.dictionary.encode_line(
              tgt_string, add_if_not_exist=False
          )
          tgt_tokens[i, : len(tgt_mapped)] = tgt_mapped
  
      return tgt_tokens
  
imports: import torch
  from pytorch_translate import utils
label_desc: Use image node_id 1 for calling the LMScorer obj's underlying member method code with example usage: obj.convert_hypos_to_tgt_tokens(hypos) and returns: tgt_tokens