code: def numberize(
      self,
      text_row: Optional[List[str]],
      token_row: Optional[List[List[str]]],
  ) -> Tuple[List[int], List[int], int, List[int]]:
      token_ids: List[int] = []
      segment_labels: List[int] = []
      seq_len: int = 0
      positions: List[int] = []
      per_sentence_tokens: List[
          List[Tuple[str, int, int]]
      ] = self.tokenize(text_row, token_row)
  
      for idx, per_sentence_token in enumerate(per_sentence_tokens):
          lookup_ids: List[int] = self._lookup_tokens(
              per_sentence_token
          )
          lookup_ids = self._wrap_numberized_tokens(lookup_ids, idx)
  
          token_ids.extend(lookup_ids)
          segment_labels.extend([idx] * len(lookup_ids))
      seq_len = len(token_ids)
      positions = [i for i in range(seq_len)]
  
      return token_ids, segment_labels, seq_len, positions
  
file_path: pytext/pytext/torchscript/tensorizer/bert.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 pytorch.text.fb.utils.transform_utils import pad_2d
  from .tensorizer import ScriptTensorizer, VocabLookup
label_desc: Use image node_id 5 for calling the ScriptBERTTensorizerBase obj's underlying member method code with example usage: obj.numberize(text_row, token_row) and returns: token_ids,  segment_labels,  seq_len,  positions