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 = []
      self.encoder["<BOS>"] = 0
      self.encoder["<PAD>"] = 1
      self.encoder["<EOS>"] = 2
      self.encoder["<UNK>"] = 3
      self.freqs[3] = 3
      self.start_token = 0  # bos from roberta
      self.delimiter_token = 2  # eos from roberta
      self.end_token = 2  # eos from roberta
      self.UNK_IDX = 3  # unk from roberta
      # If base model file doesn't contain a mask token, use <UNK> token idx
      self.mask_token = self.encoder.get("<mask>", 3)
  
imports: import os
  import finetune
  from finetune.encoding.input_encoder import BaseEncoder, EncodedOutput
  from finetune.base_models.gpt2.encoder import GPT2Encoder, bytes_to_unicode
  from finetune.base_models.gpt2 import encoder
  from transformers import RobertaTokenizerFast
label_desc: Use image node_id 3 for calling the RoBERTaEncoder obj's underlying member method code with example usage: obj._add_extra_toks() without return types