code: def numberize(
      self,
      text_row: Optional[List[str]],
      token_row: Optional[List[List[str]]],
  ) -> Tuple[List[int], List[int], List[int], List[int]]:
      token_ids: List[int] = []
      start_indices: List[int] = []
      end_indices: List[int] = []
      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, start_ids, end_ids = self._lookup_tokens(
              per_sentence_token
          )
          lookup_ids = self._wrap_numberized_tokens(lookup_ids, idx)
          token_ids.extend(lookup_ids)
          start_indices.extend(start_ids)
          end_indices.extend(end_ids)
  
      seq_len = len(token_ids)
      positions = list(range(seq_len))
  
      return token_ids, start_indices, end_indices, positions
  
file_path: pytext/pytext/torchscript/tensorizer/roberta.py
imports: from typing import List, Optional, Tuple
  import torch
  from pytext.torchscript.utils import pad_2d_mask
  from .bert import ScriptBERTTensorizerBase
label_desc: Use image node_id 2 for calling the ScriptRoBERTaTensorizerWithIndices obj's underlying member method code with example usage: obj.numberize(text_row, token_row) and returns: token_ids,  start_indices,  end_indices,  positions