code: def __init__(self, max_size=None, vocab_file=None):
      from transformers import GPT2Tokenizer
  
      self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
      self.EOT = self.tokenizer.encoder["<|endoftext|>"]
      self.max_size = max_size
      self.vocab_file = vocab_file
  
      pad = 8
      vocab_size = len(self.tokenizer)
      padded_vocab_size = (vocab_size + pad - 1) // pad * pad
      for i in range(0, padded_vocab_size - vocab_size):
          token = f"madeupword{i:09d}"
          self.tokenizer.add_tokens([token])
  
imports: import contextlib
  import os
  from collections import Counter
  from collections import OrderedDict
  import torch
  from src.utils.distributed import sync_workers
label_desc: Use image node_id 1 to create a new OpenAIVocab object from inherited base classes: Vocab with example: obj = OpenAIVocab(max_size, vocab_file)