code: def __init__(self, encoder_path=ENCODER_PATH, vocab_path=VOCAB_PATH):
      self.tokenizer = RobertaTokenizerFast(
          merges_file=vocab_path, vocab_file=encoder_path
      )
      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
      self.mask_token = 50264
      self.mapping = {
          self.tokenizer.cls_token_id: 0,
          self.tokenizer.eos_token_id: 2,
          self.tokenizer.unk_token_id: 3,
          self.tokenizer.sep_token_id: 2,
      }
      self.initialized = True
  
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 RoBERTaEncoderV2 object from inherited base classes: BaseEncoder with example: obj = RoBERTaEncoderV2(encoder_path, vocab_path)