code: def test_hier_neighbor_allreduce(hier_setup, dtype, dim):
      rank, size, local_rank, local_size = hier_setup
      # This particular value is chosen such that the local allreduce will result in the machine rank.
      # For example, there are 8 nodes with 4 nodes per machine.
      # The allreduce result from node 0 to 3 will be 0, and the one from node 4 to 7 will 1.
      tensor = torch.FloatTensor(*([23] * dim)).fill_(
          (rank - (local_size - 1) / 2.0) / local_size
      )
      name = "hier_neighbor_allreduce_tensor_{}_{}".format(dim, dtype)
      tensor = cast_and_place(tensor, dtype)
  
      reduced_tensor = bf.hierarchical_neighbor_allreduce(
          tensor, name=name
      )
      neighbor_ranks = bf.in_neighbor_machine_ranks()
      expected_value = (
          (rank - local_rank) / local_size + sum(neighbor_ranks)
      ) / (len(neighbor_ranks) + 1)
      assert (
          list(reduced_tensor.shape) == [23] * dim
      ), "bf.hierarchical_neighbor_allreduce (hier_NA) produces incorrect reduced shape"
      assert (
          reduced_tensor.data - expected_value
      ).abs().max() < EPSILON, "bf.hierarchical_neighbor_allreduce (hier_NA) produces incorrect reduced tensor"
  
imports: import itertools
  import os
  import numpy
  import pytest
  import torch
  import networkx
  import bluefog.torch
  from common import mpi_env_rank_and_size
label_desc: Use image node_id 9 for calling a global function with example usage: test_hier_neighbor_allreduce(hier_setup, dtype, dim) without return types