code: def _lookup_tokens(
      self,
      tokens: List[Tuple[str, int, int]],
      languages: List[Tuple[str, int, int]],
      max_seq_len: int,
  ) -> Tuple[List[int], List[int]]:
      token_ids: List[int] = self.token_vocab_lookup(
          tokens,
          bos_idx=self.token_vocab.eos_idx,
          eos_idx=self.token_vocab.eos_idx,
          use_eos_token_for_bos=True,
          max_seq_len=max_seq_len,
      )[0]
      language_special_idx: int = self.language_vocab.idx.get(
          languages[0][0], self.language_vocab.unk_idx
      )
      language_ids = self.language_vocab_lookup(
          languages,
          bos_idx=language_special_idx,
          eos_idx=language_special_idx,
          use_eos_token_for_bos=True,
          max_seq_len=max_seq_len,
      )[0]
      return token_ids, language_ids
  
file_path: pytext/pytext/torchscript/tensorizer/xlm.py
imports: from typing import List, Optional, Tuple
  import torch
  from pytext.torchscript.utils import pad_2d_mask
  from pytext.torchscript.vocab import ScriptVocabulary
  from .tensorizer import ScriptTensorizer, VocabLookup
label_desc: Use image node_id 3 for calling the ScriptXLMTensorizer obj's underlying member method code with example usage: obj._lookup_tokens(tokens, languages, max_seq_len) and returns: token_ids,  language_ids