code: def convert_tiktoken_bpe_to_hf(tiktoken_url: str):
      bpe_ranks = load_tiktoken_bpe(tiktoken_url)
      byte_encoder = bytes_to_unicode()
  
      def token_bytes_to_string(b):
          return "".join(
              [byte_encoder[ord(char)] for char in b.decode("latin-1")]
          )
  
      merges = []
      vocab = {}
      for token, rank in bpe_ranks.items():
          vocab[token_bytes_to_string(token)] = rank
          if len(token) == 1:
              continue
          merged = tuple(_bpe(bpe_ranks, token, max_rank=rank))
          if len(merged) == 2:  # account for empty token
              merges.append(
                  " ".join(map(token_bytes_to_string, merged))
              )
      return vocab, merges
  
imports: import argparse
  import io
  import json
  import os
  import tempfile
  import urllib
  import warnings
  from typing import Any, Optional, Tuple
  import torch
  from huggingface_hub.utils import insecure_hashlib
  from torch import nn
  from tqdm import tqdm
  from transformers import GenerationConfig, WhisperConfig, WhisperFeatureExtractor, WhisperForConditionalGeneration, WhisperProcessor, WhisperTokenizer, WhisperTokenizerFast
  from transformers.models.whisper.tokenization_whisper import LANGUAGES, bytes_to_unicode
  from transformers.utils.import_utils import _is_package_available
label_desc: Use image node_id 8 for calling a global function with example usage: convert_tiktoken_bpe_to_hf(tiktoken_url) and returns: vocab,  merges, str