code: def train(epoch):
      model.train()
      # Bluefog: set epoch to sampler for shuffling.
      train_sampler.set_epoch(epoch)
      for batch_idx, (data, target) in enumerate(train_loader):
          if not args.disable_dynamic_topology:
              dynamic_topology_update(epoch, batch_idx)
          if args.cuda:
              data, target = data.cuda(), target.cuda()
  
          if args.dist_optimizer == "neighbor_allreduce" and (
              batch_idx % 100 == 99
          ):
              optimizer.communication_type = (
                  bf.CommunicationType.allreduce
              )
  
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              # Bluefog: use train_sampler to determine the number of examples in
              # this worker's partition.
              print(
                  "[{}] Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                      bf.rank(),
                      epoch,
                      batch_idx * len(data),
                      len(train_sampler),
                      100.0 * batch_idx / len(train_loader),
                      loss.item(),
                  )
              )
  
          if args.dist_optimizer == "neighbor_allreduce" and (
              batch_idx % 100 == 99
          ):
              optimizer.communication_type = (
                  bf.CommunicationType.neighbor_allreduce
              )
  
imports: from __future__ import print_function
  from bluefog.common import topology_util
  import bluefog.torch
  import argparse
  import os
  import sys
  import warnings
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data.distributed
  from torchvision import datasets, transforms
label_desc: Use image node_id 2 for calling a global function with example usage: train(epoch) without return types