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 _decode_token(self, bpe_toks):
      decoded_bpe_toks = []
      for i, bpe in enumerate(bpe_toks):
          temp_toks = bpe
          st = None
          offset = 1
          while st is None:
              try:
                  st = bytes(
                      self.byte_decoder[c] for c in temp_toks
                  ).decode("utf-8")
              except UnicodeDecodeError:
                  if len(bpe_toks[i + offset]) == 0:
                      offset += 1
                  temp_toks += bpe_toks[i + offset][0]
                  bpe_toks[i + offset] = bpe_toks[i + offset][1:]
          decoded_bpe_toks.append(st)
      return decoded_bpe_toks
  
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 6 for calling the GPT2Encoder obj's underlying member method code with example usage: obj._decode_token(bpe_toks) and returns: decoded_bpe_toks