code: def test_hier_neighbor_allreduce_fusion(hier_setup, dtype, dim):
      rank, size, local_rank, local_size = hier_setup
      machine_rank = (rank - local_rank) // local_size
      machine_size = size // local_size
  
      neighbor_ranks = bf.in_neighbor_machine_ranks()
      expected_value = (machine_rank + sum(neighbor_ranks)) / (
          len(neighbor_ranks) + 1
      )
  
      K = 50  # number of tensors send in short time
      tensor_list, handles, names = [], [], []
      for i in range(K):
          tensor = torch.FloatTensor(*([23] * dim)).fill_(
              i + (rank - (local_size - 1) / 2.0) / local_size
          )
          tensor = cast_and_place(tensor, dtype)
          tensor_list.append(tensor)
          names.append("index{}_{}_{}".format(i, dtype, dim))
  
      for i in range(K):
          handle = bf.hierarchical_neighbor_allreduce_nonblocking(
              tensor_list[i], name=names[i]
          )
          handles.append(handle)
  
      outputs = []
      for i in range(K):
          output = bf.synchronize(handles[i])
          outputs.append(output)
  
      for i in range(K):
          assert (
              list(outputs[i].shape) == [23] * dim
          ), f"{names[i]} (hierarchical neighbor allreduce fusion) produces incorrect reduced shape"
          assert (
              outputs[i] - expected_value - i
          ).abs().max() < EPSILON, (
              f"{names[i]} (hierarchical neighbor allreduce fusion) produces incorrect reduced tensor"
              f" when K = {i}"
          )
  
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 12 for calling a global function with example usage: test_hier_neighbor_allreduce_fusion(hier_setup, dtype, dim) without return types