code: def ask(self, question, batch_size=8, **kwargs):
      # locate candidate document contexts
      doc_results = kwargs.get("doc_results", [])
      if not doc_results:
          return []
  
      # extract paragraphs as contexts
      contexts, refs = self._split_contexts(doc_results)
      contexts = [c.replace("\n", " ") for c in contexts]
  
      # batchify contexts
      context_batches = self._batchify(contexts, batch_size=batch_size)
  
      # locate candidate answers
      answers = []
      mb = master_bar(range(1))
      answer_batches = []
      for i in mb:
          idx = 0
          for batch_id, contexts in enumerate(
              progress_bar(context_batches, parent=mb)
          ):
              answer_batch = self.predict_squad(contexts, question)
              answer_batches.extend(answer_batch)
              for i, answer in enumerate(answer_batch):
                  idx += 1
                  if not answer["answer"]:
                      answer["answer"] = None
                  answer["confidence"] = (
                      answer["confidence"]
                      if isinstance(
                          answer["confidence"],
                          (int, float, np.float32, np.float16),
                      )
                      else answer["confidence"].numpy()
                  )
                  answer["reference"] = refs[idx - 1]
                  if answer["answer"] is not None:
                      formatted_answer = self._span_to_answer(
                          question,
                          contexts[i],
                          answer["start"],
                          answer["end"],
                      )["answer"].strip()
                      if formatted_answer:
                          answer["answer"] = formatted_answer
                  answer["answer"] = self._clean_answer(
                      answer["answer"]
                  )
                  answers.append(answer)
              mb.child.comment = f"extracting information"
      return answers
  
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 3 for calling the _QAExtractor obj's underlying member method code with example usage: obj.ask(question, batch_size) and returns: answers, list