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: Create one-hot representation of an answer.
  
  Arguments:
      answer (string) : The word answer.
  
  Returns:
      list : One-hot representation of answer.
docstr_tok: ["Create","one-hot","representation","of","an","answer",".","Arguments",":","answer","(","string",")",":","The","word","answer",".","Returns",":","list",":","One-hot","representation","of","answer","."]
code: def one_hot_vector(self, answer):
      """
      Create one-hot representation of an answer.
  
      Arguments:
          answer (string) : The word answer.
  
      Returns:
          list : One-hot representation of answer.
      """
      vector = np.zeros(self.vocab_size)
      vector[self.word_to_index[answer]] = 1
      return vector
  
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 8 for calling the BABI obj's underlying member method code with example usage: obj.one_hot_vector(answer) and returns: vector