code: def _answers2df(answers):
      dfdata = []
      for a in answers:
          answer_text = a["answer"]
          snippet_html = (
              "<div>"
              + a["sentence_beginning"]
              + " <font color='red'>"
              + a["answer"]
              + "</font> "
              + a["sentence_end"]
              + "</div>"
          )
          confidence = a["confidence"]
          doc_key = a["reference"]
          dfdata.append(
              [answer_text, snippet_html, confidence, doc_key]
          )
      df = pd.DataFrame(
          dfdata,
          columns=[
              "Candidate Answer",
              "Context",
              "Confidence",
              "Document Reference",
          ],
      )
      if "\t" in answers[0]["reference"]:
          df["Document Reference"] = df["Document Reference"].apply(
              lambda x: '<a href="{}" target="_blank">{}</a>'.format(
                  x.split("\t")[1], x.split("\t")[0]
              )
          )
      return df
  
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 2 for calling a global function with example usage: _answers2df(answers) and returns: df