code: def process_question(
      question,
      include_np=False,
      and_np=False,
      remove_english_stopwords=False,
  ):
      result = None
      np_list = []
      if include_np:
          try:
              # np_list = ['"%s"' % (np) for np in TU.extract_noun_phrases(question) if len(np.split()) > 1]
              raw_np_list = [
                  np
                  for np in TU.extract_noun_phrases(question)
                  if len(np.split()) > 1
              ]
              np_list = []
              for np in raw_np_list:
                  N = 2
                  sentence = np.split()
                  np_list.extend(
                      [
                          '"%s"' % (" ".join(sentence[i : i + N]))
                          for i in range(len(sentence) - N + 1)
                      ]
                  )
              np_list = list(set(np_list))
          except:
              import warnings
  
              warnings.warn(
                  "TextBlob is not currently installed, so falling back to include_np=False with no extra question processing. "
                  + "To install: pip install textblob"
              )
      result = TU.tokenize(question, join_tokens=False)
      if remove_english_stopwords:
          from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
  
          result = [
              term
              for term in result
              if term.lower().strip()
              not in list(ENGLISH_STOP_WORDS) + ["?"]
          ]
      if np_list and and_np:
          return f'( {" ".join(result)} ) AND ({" ".join(np_list)})'
      else:
          return " ".join(result + np_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 4 for calling a global function with example usage: process_question(question, include_np, and_np, remove_english_stopwords) and returns: str, str, str, str, str, str