class_docstr: Base class for QA
class_docstr_tok: ["Base","class","for","QA"]
docstr: Reconstruct text of *either* question or answer
docstr_tok: ["Reconstruct","text","of","*","either","*","question","or","answer"]
code: def _reconstruct_text(self, tokens, start=0, stop=-1):
      """
      Reconstruct text of *either* question or answer
      """
      tokens = tokens[start:stop]
      # if '[SEP]' in tokens:
      # sepind = tokens.index('[SEP]')
      # tokens = tokens[sepind+1:]
      txt = " ".join(tokens)
      txt = txt.replace(
          "[SEP]", ""
      )  # added for batch_encode_plus - removes [SEP] before [PAD]
      txt = txt.replace(
          "[PAD]", ""
      )  # added for batch_encode_plus - removes [PAD]
      txt = txt.replace(" ##", "")
      txt = txt.replace("##", "")
      txt = txt.strip()
      txt = " ".join(txt.split())
      txt = txt.replace(" .", ".")
      txt = txt.replace("( ", "(")
      txt = txt.replace(" )", ")")
      txt = txt.replace(" - ", "-")
      txt_list = txt.split(" , ")
      txt = ""
      length = len(txt_list)
      if length == 1:
          return txt_list[0]
      new_list = []
      for i, t in enumerate(txt_list):
          if i < length - 1:
              if t[-1].isdigit() and txt_list[i + 1][0].isdigit():
                  new_list += [t, ","]
              else:
                  new_list += [t, ", "]
          else:
              new_list += [t]
      return "".join(new_list)
  
imports: from transformers import AutoModelForQuestionAnswering, AutoTokenizer, TFAutoModelForQuestionAnswering
  from whoosh import index, qparser
  from whoosh.fields import *
  from whoosh.qparser import QueryParser
  from ...None import utils
  from ...imports import *
  from ...torch_base import TorchBase
  from ..None import preprocessor
  from ..None import textutils
  from itertools import chain, zip_longest
label_desc: Use image node_id 5 for calling the ExtractiveQABase obj's underlying member method code with example usage: obj._reconstruct_text(tokens, start, stop) and returns: str, txt_list