code: def get_encoding(encoding_name):
      with fetch(
          f"https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/{encoding_name}.tiktoken"
      ).open() as f:
          ranks = {
              base64.b64decode(token): int(rank)
              for token, rank in (line.split() for line in f if line)
          }
      n_vocab = len(ranks)
      specials = [
          "<|endoftext|>",
          "<|startoftranscript|>",
          *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
          "<|translate|>",
          "<|transcribe|>",
          "<|startoflm|>",
          "<|startofprev|>",
          "<|nospeech|>",
          "<|notimestamps|>",
          *[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
      ]
      special_tokens = dict(zip(specials, itertools.count(n_vocab)))
      n_vocab += len(specials)
      import tiktoken
  
      return tiktoken.Encoding(
          name=encoding_name,
          explicit_n_vocab=n_vocab,
          pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
          mergeable_ranks=ranks,
          special_tokens=special_tokens,
      )
  
imports: import sys
  import pathlib
  import base64
  import multiprocessing
  import numpy
  from typing import Optional, Union, Literal, List
  from tinygrad.jit import TinyJit
  from tinygrad.nn.state import torch_load, load_state_dict
  from tinygrad.helpers import getenv, DEBUG, CI, fetch
  import tinygrad.nn
  from tinygrad.shape.symbolic import Variable
  from tinygrad.tensor import Tensor
  import itertools
  import librosa
label_desc: Use image node_id 2 for calling a global function with example usage: get_encoding(encoding_name) and returns: tiktoken