code: def sparse_reduce_scatter(dtype, use_mpi=False):
      def run_reduce_scatter(rank, root, dtype, use_mpi=False):
          dev = cuda.Device(rank)
          dev.use()
          comm = NCCLBackend(N_WORKERS, rank, use_mpi=use_mpi)
          in_arrays = [
              (rank + 1) * _make_sparse(dtype),
              (rank + 2) * _make_sparse(dtype),
          ]
          out_array = _make_sparse_empty(dtype)
          warnings.filterwarnings(
              "ignore", ".*transferring sparse.*", UserWarning
          )
          comm.reduce_scatter(in_arrays, out_array, 2)
          target = (rank + 1) * _make_sparse(dtype) + (
              rank + 2
          ) * _make_sparse(dtype)
          testing.assert_allclose(out_array.todense(), target.todense())
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_reduce_scatter(MPI.COMM_WORLD.Get_rank(), 0, dtype, True)
          run_reduce_scatter(MPI.COMM_WORLD.Get_rank(), 1, dtype, True)
      else:
          _launch_workers(run_reduce_scatter, (0, dtype))
          _launch_workers(run_reduce_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 25 for calling a global function with example usage: sparse_reduce_scatter(dtype, use_mpi) without return types