class_docstr: Dataset iterator to create a batch of sentences.
  
  This iterator returns a pair of sentences, where one token is shifted
  between the sentences like '<sos> w1 w2 w3' and 'w1 w2 w3 <eos>'
  Sentence batches are made in order of longer sentences, and then
  randomly shuffled.
class_docstr_tok: ["Dataset","iterator","to","create","a","batch","of","sentences",".","This","iterator","returns","a","pair","of","sentences",",","where","one","token","is","shifted","between","the","sentences","like","'","<","sos",">","w1","w2","w3","'","and","'w1","w2","w3","<","eos",">","'","Sentence","batches","are","made","in","order","of","longer","sentences",",","and","then","randomly","shuffled","."]
code: def previous_epoch_detail(self):
      if self._previous_epoch_detail < 0:
          return None
      return self._previous_epoch_detail
  
imports: import logging
  import os
  import random
  import chainer
  import h5py
  import numpy
  from chainer.training import extension
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling the ParallelSentenceIterator obj's underlying member method code with example usage: obj.previous_epoch_detail() and returns: self, None