class_docstr: Abstract base class for QA Datasets with common utility methods 
class_docstr_tok: ["Abstract","base","class","for","QA","Datasets","with","common","utility","methods"]
docstr: Split on whitespace so that different tokens
  may be attributed to their original position.
  ex: context_text = "hi yo"
      char_to_word_offset = [0, 0, 0, 1, 1]
      doc_tokens = ["hi", "yo"]
docstr_tok: ["Split","on","whitespace","so","that","different","tokens","may","be","attributed","to","their","original","position",".","ex",":","context_text","=","``","hi","yo","''","char_to_word_offset","=","[","0",",","0",",","0",",","1",",","1","]","doc_tokens","=","[","``","hi","''",",","``","yo","''","]"]
code: def split_into_words(context_text):
      """
      Split on whitespace so that different tokens
      may be attributed to their original position.
      ex: context_text = "hi yo"
          char_to_word_offset = [0, 0, 0, 1, 1]
          doc_tokens = ["hi", "yo"]
      """
  
      doc_tokens = []
      char_to_word_offset = []
      prev_is_whitespace = True
      for c in context_text:
          if is_whitespace(c):
              prev_is_whitespace = True
          else:
              if prev_is_whitespace:
                  doc_tokens.append(c)
              else:
                  doc_tokens[-1] += c
              prev_is_whitespace = False
          char_to_word_offset.append(len(doc_tokens) - 1)
  
      return doc_tokens, char_to_word_offset
  
imports: import collections
  import pickle
  from functools import lru_cache
  from typing import List
  import psutil
  import torch
  from nemo.collections.nlp.data.data_utils import is_whitespace
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import EVALUATION_MODE, INFERENCE_MODE, TRAINING_MODE
  from nemo.core.classes import Dataset
  from nemo.utils import logging
label_desc: Use image node_id 12 for calling the QADataset obj's underlying member method code with example usage: obj.split_into_words(context_text) and returns: doc_tokens,  char_to_word_offset