class_docstr: Base class for QA
class_docstr_tok: ["Base","class","for","QA"]
docstr: ```
  splitup contexts into a manageable size
  Args:
    doc_results(list):  list of dicts with keys: rawtext and reference
  ```
docstr_tok: ["``","`","splitup","contexts","into","a","manageable","size","Args",":","doc_results","(","list",")",":","list","of","dicts","with","keys",":","rawtext","and","reference","``","`"]
code: def _split_contexts(self, doc_results):
      """
      ```
      splitup contexts into a manageable size
      Args:
        doc_results(list):  list of dicts with keys: rawtext and reference
      ```
      """
      # extract paragraphs as contexts
      contexts = []
      refs = []
      for doc_result in doc_results:
          rawtext = doc_result.get("rawtext", "")
          reference = doc_result.get("reference", "")
          if len(self.tokenizer.tokenize(rawtext)) < self.maxlen:
              contexts.append(rawtext)
              refs.append(reference)
          else:
              paragraphs = TU.paragraph_tokenize(
                  rawtext, join_sentences=True
              )
              contexts.extend(paragraphs)
              refs.extend([reference] * len(paragraphs))
      return (contexts, refs)
  
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 9 for calling the ExtractiveQABase obj's underlying member method code with example usage: obj._split_contexts(doc_results) and returns: contexts,  refs