code: def _token_transform(
      self, tokens: List[str], tokens_b: Optional[List[str]] = None
  ) -> Tuple[torch.Tensor, int, int, List[str]]:
      tokens = [self._CLS_TOKEN] + tokens + [self._SEP_TOKEN]
      if tokens_b:
          tokens += tokens_b + [self._SEP_TOKEN]
  
      input_ids = self._convert_tokens_to_ids(tokens)
      token_len = len(input_ids)
      token_pad = self._max_seq_length - token_len
      # Zero-pad up to the sequence length.
      input_ids += [self._PAD_TOKEN_ID] * token_pad
      input_ids_tensor = torch.tensor(input_ids, dtype=torch.long)
      return input_ids_tensor, token_len, token_pad, tokens
  
imports: import copy
  import random
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from mmf.common.registry import registry
  from mmf.common.sample import Sample, SampleList
  from mmf.datasets.processors.processors import BaseProcessor
label_desc: Use image node_id 4 for calling the VinVLTextTokenizer obj's underlying member method code with example usage: obj._token_transform(tokens, tokens_b) and returns: input_ids_tensor,  token_len,  token_pad,  tokens