code: def standard_train(model, optimizer, dataloader, isCUDA):
      mseloss = nn.MSELoss()
      model.train()
      for data, target in dataloader:
          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 3 for calling a global function with example usage: standard_train(model, optimizer, dataloader, isCUDA) without return types