code: def reduce(dtype, use_mpi=False):
      if dtype in "hH":
          return  # nccl does not support int16
  
      def run_reduce(rank, root, dtype, use_mpi=False):
          dev = cuda.Device(rank)
          dev.use()
          comm = NCCLBackend(N_WORKERS, rank, use_mpi=use_mpi)
          in_array = cupy.arange(2 * 3 * 4, dtype="f").reshape(2, 3, 4)
          out_array = cupy.zeros((2, 3, 4), dtype="f")
          comm.reduce(in_array, out_array, root)
          if rank == root:
              testing.assert_allclose(out_array, 2 * in_array)
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_reduce(MPI.COMM_WORLD.Get_rank(), 0, dtype, True)
          run_reduce(MPI.COMM_WORLD.Get_rank(), 1, dtype, True)
      else:
          _launch_workers(run_reduce, (0, dtype))
          _launch_workers(run_reduce, (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 3 for calling a global function with example usage: reduce(dtype, use_mpi) without return types