class_docstr: A general QA container to take Q&A dataset, which has already been
  vectorized and create a data iterator to feed data to training.
class_docstr_tok: ["A","general","QA","container","to","take","Q","&","A","dataset",",","which","has","already","been","vectorized","and","create","a","data","iterator","to","feed","data","to","training","."]
docstr: For resetting the starting index of this dataset back to zero.
  Relevant for when one wants to call repeated evaluations on the dataset
  but don't want to wrap around for the last uneven minibatch
  Not necessary when ndata is divisible by batch size
docstr_tok: ["For","resetting","the","starting","index","of","this","dataset","back","to","zero",".","Relevant","for","when","one","wants","to","call","repeated","evaluations","on","the","dataset","but","do","n't","want","to","wrap","around","for","the","last","uneven","minibatch","Not","necessary","when","ndata","is","divisible","by","batch","size"]
code: def reset(self):
      """
      For resetting the starting index of this dataset back to zero.
      Relevant for when one wants to call repeated evaluations on the dataset
      but don't want to wrap around for the last uneven minibatch
      Not necessary when ndata is divisible by batch size
      """
      self.batch_index = 0
  
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 3 for calling the QA obj's underlying member method code with example usage: obj.reset() without return types