code: def all_to_all(dtype, use_mpi=False):
      if dtype in "hH":
          return  # nccl does not support int16
  
      def run_all_to_all(rank, dtype, use_mpi=False):
          dev = cuda.Device(rank)
          dev.use()
          comm = NCCLBackend(N_WORKERS, rank, use_mpi=use_mpi)
          in_array = cupy.arange(N_WORKERS * 10, dtype="f").reshape(
              N_WORKERS, 10
          )
          out_array = cupy.zeros((N_WORKERS, 10), dtype="f")
          comm.all_to_all(in_array, out_array)
          expected = (10 * rank) + cupy.broadcast_to(
              cupy.arange(10, dtype="f"), (N_WORKERS, 10)
          )
          testing.assert_allclose(out_array, expected)
  
      if use_mpi:
          from mpi4py import MPI
  
          # This process was run with mpiexec
          run_all_to_all(MPI.COMM_WORLD.Get_rank(), dtype, True)
      else:
          _launch_workers(run_all_to_all, (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 11 for calling a global function with example usage: all_to_all(dtype, use_mpi) without return types