code: def reduce(
      cls, comm, in_array, out_array, root=0, op="sum", stream=None
  ):
      arrays = cls._get_internal_arrays(in_array)
      # All the matrices must share the same size
      shape_and_sizes = cls._get_shape_and_sizes(arrays, in_array.shape)
      shape_and_sizes = cls._exchange_shape_and_sizes(
          comm, root, shape_and_sizes, "gather", stream
      )
      if comm.rank == root:
          if _get_sparse_type(in_array) != _get_sparse_type(out_array):
              raise ValueError(
                  "in_array and out_array must be the same format"
              )
          result = in_array
          partial = _make_sparse_empty(
              in_array.dtype, _get_sparse_type(in_array)
          )
          # each device will send and array with a different size
          for peer, ss in enumerate(shape_and_sizes):
              shape = tuple(ss[0:2])
              sizes = ss[2:]
              arrays = [
                  cupy.empty(s, dtype=a.dtype)
                  for s, a in zip(sizes, arrays)
              ]
              if peer != root:
                  nccl.groupStart()
                  for a in arrays:
                      cls._recv(comm, a, peer, a.dtype, a.size, stream)
                  nccl.groupEnd()
                  cls._assign_arrays(partial, arrays, shape)
                  if op == "sum":
                      result = result + partial
                  elif op == "prod":
                      result = result * partial
                  else:
                      raise ValueError(
                          "Sparse matrix only supports sum/prod reduction"
                      )
          # TODO, check output types
          # If out_array is coo we need to convert result to coo before
          # reasiging
          cls._assign_arrays(
              out_array, cls._get_internal_arrays(result), result.shape
          )
      else:
          nccl.groupStart()
          for a in arrays:
              cls._send(comm, a, root, a.dtype, a.size, stream)
          nccl.groupEnd()
  
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 6 for calling the _SparseNCCLCommunicator obj's underlying member method code with example usage: obj.reduce(cls, comm, in_array, out_array, root, op, stream) without return types