class_docstr: Construct a "fast" T5 tokenizer (backed by HuggingFace's *tokenizers* library). Based on
  [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
  
  This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
  refer to this superclass for more information regarding those methods.
  
  Args:
      vocab_file (`str`):
          [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
          contains the vocabulary necessary to instantiate a tokenizer.
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sequence token.
  
          <Tip>
  
          When building a sequence using special tokens, this is not the token that is used for the end of sequence.
          The token used is the `sep_token`.
  
          </Tip>
  
      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.
      extra_ids (`int`, *optional*, defaults to 100):
          Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are accessible as
          "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be retrieved by
          calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids method
      additional_special_tokens (`List[str]`, *optional*):
          Additional special tokens used by the tokenizer.
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code: def get_sentinel_tokens(self):
      return list(
          set(
              filter(
                  lambda x: bool(re.search(r"<extra_id_\d+>", x))
                  is not None,
                  self.additional_special_tokens,
              )
          )
      )
  
file_path: transformers/src/transformers/models/t5/tokenization_t5_fast.py
imports: import os
  import re
  import warnings
  from shutil import copyfile
  from typing import List, Optional, Tuple
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
label_desc: Use image node_id 7 for calling the T5TokenizerFast obj's underlying member method code with example usage: obj.get_sentinel_tokens() and returns: list