code: def _exchange_shape_and_sizes(
      cls, comm, peer, sizes_shape, method, stream
  ):
      if comm._use_mpi:
          # Sends the metadata for the arrays using MPI
          if method == "send":
              sizes_shape = numpy.array(sizes_shape, dtype="q")
              comm._mpi_comm.Send(sizes_shape, dest=peer, tag=1)
              return None
          elif method == "recv":
              # Shape is a tuple of two elements, and a single scalar per
              # each array (5)
              sizes_shape = numpy.empty(5, dtype="q")
              comm._mpi_comm.Recv(sizes_shape, source=peer, tag=1)
              return sizes_shape
          elif method == "bcast":
              if comm.rank == peer:
                  sizes_shape = numpy.array(sizes_shape, dtype="q")
              else:
                  sizes_shape = numpy.empty(5, dtype="q")
              comm._mpi_comm.Bcast(sizes_shape, root=peer)
              return sizes_shape
          elif method == "gather":
              sizes_shape = numpy.array(sizes_shape, dtype="q")
              recv_buf = numpy.empty([comm._n_devices, 5], dtype="q")
              comm._mpi_comm.Gather(sizes_shape, recv_buf, peer)
              return recv_buf
          elif method == "alltoall":
              sizes_shape = numpy.array(sizes_shape, dtype="q")
              recv_buf = numpy.empty([comm._n_devices, 5], dtype="q")
              comm._mpi_comm.Alltoall(sizes_shape, recv_buf)
              return recv_buf
          else:
              raise RuntimeError("Unsupported method")
      else:
          warnings.warn(
              "Using NCCL for transferring sparse arrays metadata. This"
              " will cause device synchronization and a huge performance"
              " degradation. Please install MPI and `mpi4py` in order to"
              " avoid this issue."
          )
          if method == "send":
              sizes_shape = cupy.array(sizes_shape, dtype="q")
              cls._send(
                  comm, sizes_shape, peer, sizes_shape.dtype, 5, stream
              )
              return None
          elif method == "recv":
              # Shape is a tuple of two elements, and a single scalar per
              # each array (5)
              sizes_shape = cupy.empty(5, dtype="q")
              cls._recv(
                  comm, sizes_shape, peer, sizes_shape.dtype, 5, stream
              )
              return cupy.asnumpy(sizes_shape)
          elif method == "bcast":
              if comm.rank == peer:
                  sizes_shape = cupy.array(sizes_shape, dtype="q")
              else:
                  sizes_shape = cupy.empty(5, dtype="q")
              _DenseNCCLCommunicator.broadcast(
                  comm, sizes_shape, root=peer, stream=stream
              )
              return cupy.asnumpy(sizes_shape)
          elif method == "gather":
              sizes_shape = cupy.array(sizes_shape, dtype="q")
              recv_buf = cupy.empty((comm._n_devices, 5), dtype="q")
              _DenseNCCLCommunicator.gather(
                  comm, sizes_shape, recv_buf, root=peer, stream=stream
              )
              return cupy.asnumpy(recv_buf)
          elif method == "alltoall":
              sizes_shape = cupy.array(sizes_shape, dtype="q")
              recv_buf = cupy.empty((comm._n_devices, 5), dtype="q")
              _DenseNCCLCommunicator.all_to_all(
                  comm, sizes_shape, recv_buf, stream=stream
              )
              return cupy.asnumpy(recv_buf)
          else:
              raise RuntimeError("Unsupported method")
  
imports: import numpy
  import warnings
  import cupy
  from cupy.cuda import nccl
  from cupyx.distributed import _store
  from cupyx.distributed._comm import _Backend
  from cupyx.scipy import sparse
label_desc: Use image node_id 3 for calling the _SparseNCCLCommunicator obj's underlying member method code with example usage: obj._exchange_shape_and_sizes(cls, comm, peer, sizes_shape, method, stream) and returns: None, None, sizes_shape, cupy, sizes_shape, cupy, recv_buf, cupy, recv_buf, cupy