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","."]
docstr: Convert a batch of ids [batch_size, id] into text(ish).
docstr_tok: ["Convert","a","batch","of","ids","[","batch_size",",","id","]","into","text","(","ish",")","."]
code: def decode(self, token_ids):
      """
      Convert a batch of ids [batch_size, id] into text(ish).
      """
      text = "".join([self.decoder[token_id] for token_id in token_ids])
      text = bytearray([self.byte_decoder[c] for c in text]).decode(
          "utf-8", errors=self.errors
      )
      return text
  
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 8 for calling the GPT2Encoder obj's underlying member method code with example usage: obj.decode(token_ids) and returns: text