code: def __next__(self):
      try:
          batch = self.actual_iterator.__next__()
          first_elem = batch[0]
          is_valid_data_type = _is_valid_type(first_elem)
          is_paired_dataset = (
              isinstance(batch, list)
              and isinstance(first_elem, tuple)
              and len(first_elem) == 2
          )
          stop = False
      except StopIteration:
          stop = True
          is_valid_data_type = False
          is_paired_dataset = False
  
      is_new_epoch = self.actual_iterator.is_new_epoch
      ctrl_msg = _build_ctrl_msg(
          stop,
          is_valid_data_type,
          is_paired_dataset,
          is_new_epoch,
          self.actual_iterator.current_position,
      )
      self.communicator.bcast(ctrl_msg, root=self.rank_master)
  
      if stop:
          raise StopIteration
      elif not is_valid_data_type:
          raise TypeError(
              "Multi node iterator supports ndarray "
              "or tuple of scalars as the data type "
              "of the batch element only."
          )
  
      if is_paired_dataset:
          _xs, _ys = zip(*batch)
          xs = numpy.asarray(_xs, dtype=numpy.float32)
          ys = numpy.asarray(_ys, dtype=numpy.float32)
          self.communicator.bcast(xs, root=self.rank_master)
          self.communicator.bcast(ys, root=self.rank_master)
          return batch
      else:
          if isinstance(batch, list):
              batch = numpy.array(batch)
          batch = self.communicator.bcast(batch, root=self.rank_master)
          return batch.tolist()
  
imports: import chainer
  import numpy
label_desc: Use image node_id 2 for calling the _MultiNodeIteratorMaster obj's underlying member method code with example usage: obj.__next__() and returns: batch, batch