class_docstr: Construct a "fast" Reformer 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.
      additional_special_tokens (`List[str]`, *optional*):
          Additional special tokens used by the tokenizer.
code: def save_vocabulary(
      self, save_directory: str, filename_prefix: Optional[str] = None
  ) -> Tuple[str]:
      if not self.can_save_slow_tokenizer:
          raise ValueError(
              "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
              "tokenizer."
          )
  
      if not os.path.isdir(save_directory):
          logger.error(
              f"Vocabulary path ({save_directory}) should be a directory"
          )
          return
      out_vocab_file = os.path.join(
          save_directory,
          (filename_prefix + "-" if filename_prefix else "")
          + VOCAB_FILES_NAMES["vocab_file"],
      )
  
      if os.path.abspath(self.vocab_file) != os.path.abspath(
          out_vocab_file
      ):
          copyfile(self.vocab_file, out_vocab_file)
  
      return (out_vocab_file,)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/reformer/tokenization_reformer_fast.py
imports: import os
  from shutil import copyfile
  from typing import Optional, Tuple
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
label_desc: Use image node_id 2 for calling the ReformerTokenizerFast obj's underlying member method code with example usage: obj.save_vocabulary(save_directory, filename_prefix) and returns: out_vocab_file