code: def setUp(self):
      self.comm = create_communicator("naive")
  
      batchsize_per_process = 5
      batchsize = batchsize_per_process * self.comm.size
      if self.comm.rank == 0:
          labels = [
              np.random.choice(
                  np.arange(3, dtype=np.int32), size=(32, 48)
              )
              for _ in range(10)
          ]
      else:
          labels = None
      initial_count = self.comm.rank * batchsize_per_process
  
      labels = self.comm.bcast_obj(labels)
      self.labels = labels
  
      self.dataset = TupleDataset(
          np.random.uniform(size=(10, 3, 32, 48)), labels
      )
      self.initial_count = initial_count
      self.batchsize = batchsize
  
imports: from __future__ import division
  import numpy
  import unittest
  import chainer
  from chainer.datasets import TupleDataset
  from chainer.iterators import SerialIterator
  from chainer import testing
  from chainercv.extensions import SemanticSegmentationEvaluator
  from chainercv.utils.testing import attr
  from chainermn import create_communicator
label_desc: Use image node_id 1 for calling the TestSemanticSegmentationEvaluatorMPI obj's underlying member method code with example usage: obj.setUp() without return types