code: def __init__(self, dataset, batch_size, repeat=True):
      super(ParallelSequentialIterator, self).__init__()
      self.dataset = dataset
      self.batch_size = batch_size  # batch size
      self.repeat = repeat
      length = len(dataset)
      # Offsets maintain the position of each sequence in the mini-batch.
      self.offsets = [
          i * length // batch_size for i in range(batch_size)
      ]
      self.reset()
  
imports: from __future__ import division
  import argparse
  import sys
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
label_desc: Use image node_id 1 to create a new ParallelSequentialIterator object from inherited base classes: chainer.dataset with example: obj = ParallelSequentialIterator(dataset, batch_size, repeat)