code: def __init__(
      self,
      vocab_file,
      num_image_tokens=20000,
      max_blank_length=80,
      byte_fallback=True,
  ):
      assert vocab_file is not None
      self.vocab_file = vocab_file
      self.num_image_tokens = num_image_tokens
      self.special_tokens = [
          "[MASK]",
          "[gMASK]",
          "[sMASK]",
          "<unused_0>",
          "<sop>",
          "<eop>",
          "<ENC>",
          "<dBLOCK>",
      ]
      self.max_blank_length = max_blank_length
      self.byte_fallback = byte_fallback
      self.text_tokenizer = TextTokenizer(vocab_file)
  
imports: import os
  from typing import Dict, List, Optional, Union
  import numpy
  import sentencepiece
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import BatchEncoding, EncodedInput
  from transformers.utils import PaddingStrategy, logging
label_desc: Use image node_id 1 to create a new SPTokenizer object  with example: obj = SPTokenizer(vocab_file, num_image_tokens, max_blank_length, byte_fallback)