code: def __init__(self, comm, n_in=3, n_units=3, n_out=2, backend="auto"):
      super(ModelDistributedBN, self).__init__()
      with self.init_scope():
          self.l1 = chainer.links.Linear(n_in, n_units, nobias=True)
          self.bn1 = MultiNodeBatchNormalization(
              n_units, comm, communication_backend=backend
          )
          self.l2 = chainer.links.Linear(n_in, n_units, nobias=True)
          self.bn2 = MultiNodeBatchNormalization(
              n_units, comm, communication_backend=backend
          )
          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 ModelDistributedBN object from inherited base classes: chainer with example: obj = ModelDistributedBN(comm, n_in, n_units, n_out, backend)