code: def run(self):
      self.device.use()
  
      self.setup()
  
      while True:
          job, data = self.pipe.recv()
          if job == "finalize":
              self.device.device.synchronize()
              break
          if job == "update":
              # For reducing memory
              self.model.cleargrads()
  
              batch = self.converter(self.iterator.next(), self.device)
              with self.reporter.scope({}):  # pass dummy observation
                  loss = _calc_loss(self.model, batch)
  
              self.model.cleargrads()
              loss.backward()
              del loss
  
              gg = gather_grads(self.model)
              nccl_data_type = _get_nccl_data_type(gg.dtype)
              null_stream = cuda.Stream.null
              self.comm.reduce(
                  gg.data.ptr,
                  gg.data.ptr,
                  gg.size,
                  nccl_data_type,
                  nccl.NCCL_SUM,
                  0,
                  null_stream.ptr,
              )
              del gg
              self.model.cleargrads()
              gp = gather_params(self.model)
              nccl_data_type = _get_nccl_data_type(gp.dtype)
              self.comm.bcast(
                  gp.data.ptr,
                  gp.size,
                  nccl_data_type,
                  0,
                  null_stream.ptr,
              )
              scatter_params(self.model, gp)
              del gp
  
imports: import multiprocessing
  import warnings
  import numpy
  import six
  import chainer
  from chainer.backends import cuda
  from chainer.dataset import convert
  from chainer import reporter
  from chainer.training.updaters import standard_updater
label_desc: Use image node_id 3 for calling the _Worker obj's underlying member method code with example usage: obj.run() without return types