class_docstr: Constructs a Speech2Text2Tokenizer.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
  the superclass for more information regarding such methods.
  
  Args:
      vocab_file (`str`):
          File containing the vocabulary.
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sentence token.
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sentence token.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
  
      **kwargs
          Additional keyword arguments passed along to [`PreTrainedTokenizer`]
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code: def vocab_size(self) -> int:
      return len(self.decoder)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/speech_to_text_2/tokenization_speech_to_text_2.py
imports: import json
  import os
  from typing import Dict, List, Optional, Tuple
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 2 for calling the Speech2Text2Tokenizer obj's underlying member method code with example usage: obj.vocab_size() and returns: len