code: def __init__(self, dataset, batch_size, repeat=True):
      self.dataset = dataset
      self.batch_size = batch_size  # batch size
      # Number of completed sweeps over the dataset. In this case, it is
      # incremented if every word is visited at least once after the last
      # increment.
      self.epoch = 0
      # True if the epoch is incremented at the last iteration.
      self.is_new_epoch = False
      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)
      ]
      # NOTE: this is not a count of parameter updates. It is just a count of
      # calls of ``__next__``.
      self.iteration = 0
      # use -1 instead of None internally
      self._previous_epoch_detail = -1.0
  
imports: from __future__ import print_function
  import argparse
  import numpy
  import random
  import sys
  import chainer
  from chainer import configuration
  from chainer.dataset import convert
  import chainer.functions
  from chainer.functions.loss import softmax_cross_entropy
  import chainer.links
  from chainer import serializers
  from chainer import static_graph
  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)