class_docstr: A modified wrapper for a public python BPE tokenizer. The modifications allow encoding directly into the formats
  required for finetune. Particularly with respect to formatting with multiple inputs.
class_docstr_tok: ["A","modified","wrapper","for","a","public","python","BPE","tokenizer",".","The","modifications","allow","encoding","directly","into","the","formats","required","for","finetune",".","Particularly","with","respect","to","formatting","with","multiple","inputs","."]
code: def _add_extra_toks(self):
      self.special_tokens = ["_delimiter_", "_classify_"]
      for token in self.special_tokens:
          self.encoder[token] = len(self.encoder)
      self.start_token = self.encoder["<|endoftext|>"]
      self.delimiter_token = self.encoder["_delimiter_"]
      self.end_token = self.encoder["_classify_"]
  
file_path: finetune/finetune/base_models/gpt2/encoder.py
imports: import os
  import json
  import traceback
  import regex
  from functools import lru_cache
  import numpy
  import finetune
  from finetune.encoding.input_encoder import BaseEncoder, EncodedOutput, get_pairs
label_desc: Use image node_id 3 for calling the GPT2Encoder obj's underlying member method code with example usage: obj._add_extra_toks() without return types