code: def dynamic_neighbor_allreduce_train(
      model, optimizer, dataloader, isCUDA, dynamic_topo_gen
  ):
      mseloss = nn.MSELoss()
      model.train()
      for data, target in dataloader:
          send_neighbors, recv_neighbors = next(dynamic_topo_gen)
          optimizer.dst_weights = send_neighbors
          optimizer.src_weights = {
              r: 1 / (len(recv_neighbors) + 1) for r in recv_neighbors
          }
          optimizer.self_weight = 1 / (len(recv_neighbors) + 1)
  
          if isCUDA:
              data, target = data.cuda(), target.cuda()
          y = model(data)
          loss = mseloss(y, target)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import pytest
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import Dataset, DataLoader
  import bluefog.torch
  from bluefog.common import topology_util
label_desc: Use image node_id 5 for calling a global function with example usage: dynamic_neighbor_allreduce_train(model, optimizer, dataloader, isCUDA, dynamic_topo_gen) without return types