code: def sparse_scatter(dtype, use_mpi=False):
      def run_scatter(rank, root, dtype, use_mpi=False):
          dev = cuda.Device(rank)
          dev.use()
          comm = NCCLBackend(N_WORKERS, rank, use_mpi=use_mpi)
          in_arrays = [_make_sparse(dtype), 2 * _make_sparse(dtype)]
          out_array = _make_sparse_empty(dtype)
          warnings.filterwarnings(
              "ignore", ".*transferring sparse.*", UserWarning
          )
          comm.scatter(in_arrays, out_array, root)
          testing.assert_allclose(
              out_array.todense(), in_arrays[rank].todense()
          )
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_scatter(MPI.COMM_WORLD.Get_rank(), 0, dtype, True)
          run_scatter(MPI.COMM_WORLD.Get_rank(), 1, dtype, True)
      else:
          _launch_workers(run_scatter, (0, dtype))
          _launch_workers(run_scatter, (1, 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 21 for calling a global function with example usage: sparse_scatter(dtype, use_mpi) without return types