class_docstr: This class loads in the Facebook bAbI dataset and vectorizes them into stories,
  questions, and answers as described in:
  "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks"
  http://arxiv.org/abs/1502.05698.
class_docstr_tok: ["This","class","loads","in","the","Facebook","bAbI","dataset","and","vectorizes","them","into","stories",",","questions",",","and","answers","as","described","in",":","''","Towards","AI-Complete","Question","Answering",":","A","Set","of","Prerequisite","Toy","Tasks","''","http",":","\/\/arxiv.org\/abs\/1502.05698","."]
docstr: Split a sentence into tokens including punctuation.
  
  Arguments:
      sentence (string) : String of sentence to tokenize.
  
  Returns:
      list : List of tokens.
docstr_tok: ["Split","a","sentence","into","tokens","including","punctuation",".","Arguments",":","sentence","(","string",")",":","String","of","sentence","to","tokenize",".","Returns",":","list",":","List","of","tokens","."]
code: def tokenize(sentence):
      """
      Split a sentence into tokens including punctuation.
  
      Arguments:
          sentence (string) : String of sentence to tokenize.
  
      Returns:
          list : List of tokens.
      """
      return [
          x.strip() for x in re.split("(\W+)?", sentence) if x.strip()
      ]
  
imports: from builtins import map
  import numpy
  import os
  import re
  import tarfile
  from neon import logger
  from neon.data.dataiterator import NervanaDataIterator
  from neon.data.datasets import Dataset
  from neon.data.text_preprocessing import pad_sentences
  from functools import reduce
label_desc: Use image node_id 4 for calling the BABI obj's underlying member method code with example usage: obj.tokenize(sentence) and returns: unknown