code: def measure(self, dataset_timeout):
      # dataset_timeout: timeout in seconds or None
  
      status, prefetch_state, _ = self._comm.check()
      if status == _Communicator.STATUS_RESET:
          self.prefetch_state = prefetch_state
  
      (
          self.prefetch_state,
          indices,
      ) = _statemachine.iterator_statemachine(
          self.prefetch_state,
          self.batch_size,
          self.repeat,
          self.order_sampler,
          len(self.dataset),
      )
      if indices is None:  # stop iteration
          batch = None
      else:
          batch_ret = [None]
  
          def fetch_batch():
              batch_ret[0] = [self.dataset[idx] for idx in indices]
  
          if dataset_timeout is None:
              # Timeout is not set: fetch synchronously
              fetch_batch()
          else:
              # Timeout is set: fetch asynchronously and watch for timeout
              thr = threading.Thread(target=fetch_batch)
              thr.daemon = True
              thr.start()
              thr.join(dataset_timeout)
              if thr.is_alive():
                  _raise_timeout_warning()
              thr.join()
  
          batch = batch_ret[0]
          self.mem_size = max(map(_measure, batch))
          self._allocate_shared_memory()
  
      return batch, self.prefetch_state
  
imports: from __future__ import division
  import datetime
  import multiprocessing
  from multiprocessing import sharedctypes
  import signal
  import sys
  import threading
  import warnings
  import numpy
  import six
  from chainer.dataset import iterator
  from chainer.iterators import _statemachine
  from chainer.iterators.order_samplers import ShuffleOrderSampler
label_desc: Use image node_id 5 for calling the _PrefetchLoop obj's underlying member method code with example usage: obj.measure(dataset_timeout) and returns: batch,  self