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 __init__(
      self,
      encoder_path=gpt2_encoder.ENCODER_PATH,
      vocab_path=gpt2_encoder.VOCAB_PATH,
  ):
      self.freqs = {}
      index = 0
      with open(DICT_PATH, "r", encoding="utf-8") as freq_dict:
          lines = freq_dict.readlines()
          for line in lines:
              idx = line.rfind(" ")
              if idx == -1:
                  raise ValueError(
                      "Incorrect dictionary format, expected '<token> <cnt>'"
                  )
              if "madeupword" in line[:idx]:
                  index += 1
                  continue
              token_idx = int(line[:idx])
              self.freqs[token_idx + self.offset] = (
                  index + self.offset
              )  # add 4 for the special tokens at beginning
              index += 1
      super().__init__(encoder_path=encoder_path, vocab_path=vocab_path)
  
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 1 to create a new RoBERTaEncoder object from inherited base classes: GPT2Encoder with example: obj = RoBERTaEncoder(encoder_path, vocab_path)