code: def sparse_all_to_all(dtype, use_mpi=False):
      def run_all_to_all(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 = []
          warnings.filterwarnings(
              "ignore", ".*transferring sparse.*", UserWarning
          )
          comm.all_to_all(in_arrays, out_array)
          testing.assert_allclose(
              out_array[0].todense(),
              (rank + 1) * in_arrays[0].todense(),
          )
          testing.assert_allclose(
              out_array[1].todense(),
              (rank + 1) * in_arrays[0].todense(),
          )
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_all_to_all(MPI.COMM_WORLD.Get_rank(), 0, dtype, True)
          run_all_to_all(MPI.COMM_WORLD.Get_rank(), 1, dtype, True)
      else:
          _launch_workers(run_all_to_all, (0, dtype))
          _launch_workers(run_all_to_all, (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 24 for calling a global function with example usage: sparse_all_to_all(dtype, use_mpi) without return types