code: def __init__(self, n_in=3, n_units=3, n_out=2):
      super(ModelNormalBN, self).__init__()
      with self.init_scope():
          self.l1 = chainer.links.Linear(n_in, n_units, nobias=True)
          self.bn1 = chainer.links.BatchNormalization(n_units)
          self.l2 = chainer.links.Linear(n_in, n_units, nobias=True)
          self.bn2 = chainer.links.BatchNormalization(n_units)
          self.l3 = chainer.links.Linear(n_in, n_out)
      self.train = True
  
imports: import chainer
  from chainer.backends.cuda import cupy
  import chainer.testing
  import chainer.utils
  import mpi4py.MPI
  import numpy
  import pytest
  from chainermn.communicators.naive_communicator import NaiveCommunicator
  from chainermn.communicators.pure_nccl_communicator import PureNcclCommunicator
  from chainermn.links import MultiNodeBatchNormalization
  from chainermn import nccl
label_desc: Use image node_id 1 to create a new ModelNormalBN object from inherited base classes: chainer with example: obj = ModelNormalBN(n_in, n_units, n_out)