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 serialize(self, serializer):
      # It is important to serialize the state to be recovered on resume.
      self.iteration = serializer("iteration", self.iteration)
      self.epoch = serializer("epoch", self.epoch)
      try:
          self._previous_epoch_detail = serializer(
              "previous_epoch_detail", self._previous_epoch_detail
          )
      except KeyError:
          # guess previous_epoch_detail for older version
          self._previous_epoch_detail = self.epoch + (
              self.current_position - 1
          ) / len(self.batch_indices)
          if self.epoch_detail > 0:
              self._previous_epoch_detail = max(
                  self._previous_epoch_detail, 0.0
              )
          else:
              self._previous_epoch_detail = -1.0
  
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 6 for calling the ParallelSentenceIterator obj's underlying member method code with example usage: obj.serialize(serializer) without return types