class_docstr: Construct a "fast" CLIP tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
  Byte-Pair-Encoding.
  
  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`, *optional*):
          Path to the vocabulary file.
      merges_file (`str`, *optional*):
          Path to the merges file.
      tokenizer_file (`str`, *optional*):
          The path to a tokenizer file to use instead of the vocab file.
      unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          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.
      bos_token (`str`, *optional*, defaults to `"<|startoftext|>"`):
          The beginning of sequence token.
      eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The end of sequence token.
      pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The token used for padding, for example when batching sequences of different lengths.
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: transformers/src/transformers/models/clip/tokenization_clip_fast.py
imports: from typing import List, Optional, Tuple
  from tokenizers import pre_tokenizers
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
label_desc: Use image node_id 5 for calling the CLIPTokenizerFast obj's underlying member method code with example usage: obj.save_vocabulary(save_directory, filename_prefix) and returns: tuple