code: def reduce_scatter(dtype, use_mpi=False):
      if dtype in "hH":
          return  # nccl does not support int16
  
      def run_reduce_scatter(rank, dtype, use_mpi=False):
          dev = cuda.Device(rank)
          dev.use()
          comm = NCCLBackend(N_WORKERS, rank, use_mpi=use_mpi)
          in_array = 1 + cupy.arange(N_WORKERS * 10, dtype="f").reshape(
              N_WORKERS, 10
          )
          out_array = cupy.zeros((10,), dtype="f")
  
          comm.reduce_scatter(in_array, out_array, 10)
          testing.assert_allclose(out_array, 2 * in_array[rank])
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_reduce_scatter(MPI.COMM_WORLD.Get_rank(), dtype, True)
      else:
          _launch_workers(run_reduce_scatter, (dtype,))
  
imports: import sys
  import time
  import warnings
  import cupy
  from cupy import cuda
  from cupy.cuda import nccl
  from cupy import testing
  from cupyx.distributed import init_process_group
  from cupyx.distributed._nccl_comm import NCCLBackend
  from cupyx.distributed._store import ExceptionAwareProcess
  from cupyx.scipy import sparse
label_desc: Use image node_id 5 for calling a global function with example usage: reduce_scatter(dtype, use_mpi) without return types