class_docstr: Construct a "fast" GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with
  the following peculiarities:
  
  - lower case all inputs
  - uses BERT's BasicTokenizer for pre-BPE tokenization
  
  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`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges file.
      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.
code: def save_vocabulary(
      self, save_directory: str, filename_prefix: Optional[str] = None
  ) -> Tuple[str]:
      files = self._tokenizer.model.save(
          save_directory, name=filename_prefix
      )
      return tuple(files)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/openai/tokenization_openai_fast.py
imports: from typing import Optional, Tuple
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_openai import OpenAIGPTTokenizer
label_desc: Use image node_id 3 for calling the OpenAIGPTTokenizerFast obj's underlying member method code with example usage: obj.save_vocabulary(save_directory, filename_prefix) and returns: tuple