code: def test_hier_neighbor_allreduce_dynamic_move_dst_weight_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
  
      expected_value = (machine_rank + 1) % machine_size
      src_machine_weights = {(machine_rank + 1) % machine_size: 0.5}
      dst_machine_weights = {(machine_rank - 1) % machine_size: 2.0}
  
      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],
              self_weight=0.0,
              src_machine_weights=src_machine_weights,
              dst_machine_weights=dst_machine_weights,
              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"
  
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 14 for calling a global function with example usage: test_hier_neighbor_allreduce_dynamic_move_dst_weight_fusion(hier_setup, dtype, dim) without return types