code: def _lookup_tokens(
      self,
      tokens: List[Tuple[str, int, int]],
      max_seq_len: Optional[int] = None,
  ) -> Tuple[List[int], List[int], List[int]]:
      if max_seq_len is None:
          max_seq_len = self.max_seq_len
      max_seq_len -= 1  # because _wrap_numberized_tokens adds a token
  
      return self.vocab_lookup(
          tokens,
          bos_idx=None,
          eos_idx=self.vocab.eos_idx,
          use_eos_token_for_bos=False,
          max_seq_len=max_seq_len,
      )
  
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from fairseq.data.dictionary import Dictionary
  from fairseq.data.legacy.masked_lm_dictionary import BertDictionary
  from pytext import resources
  from pytext.common.constants import Token
  from pytext.config.component import ComponentType, create_component
  from pytext.data.tensorizers import Tensorizer, TensorizerScriptImpl
  from pytext.data.tokenizers import Tokenizer, WordPieceTokenizer
  from pytext.data.utils import BOS, EOS, MASK, PAD, UNK, Vocabulary
  from pytext.torchscript.tensorizer.tensorizer import VocabLookup
  from pytext.torchscript.utils import pad_2d_mask, ScriptBatchInput
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils.file_io import PathManager
  from pytext.utils.lazy import lazy_property
  from pytorch.text.fb.utils.transform_utils import pad_2d
label_desc: Use image node_id 1 for calling the BERTTensorizerScriptImpl obj's underlying member method code with example usage: obj._lookup_tokens(tokens, max_seq_len) and returns: self