code: def sparse_send_recv(dtype, use_mpi=False):
      def run_send_recv(rank, dtype, use_mpi=False):
          dev = cuda.Device(rank)
          dev.use()
          comm = NCCLBackend(N_WORKERS, rank, use_mpi=use_mpi)
          in_array = _make_sparse(dtype)
          out_array = _make_sparse_empty(dtype)
          warnings.filterwarnings(
              "ignore", ".*transferring sparse.*", UserWarning
          )
          if rank == 0:
              comm.send_recv(in_array, out_array, 1)
          else:
              comm.send_recv(in_array, out_array, 0)
              testing.assert_allclose(
                  out_array.todense(), in_array.todense()
              )
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_send_recv(MPI.COMM_WORLD.Get_rank(), dtype, True)
      else:
          _launch_workers(run_send_recv, (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 17 for calling a global function with example usage: sparse_send_recv(dtype, use_mpi) without return types