code: def evaluation(model, dataloader, isCUDA):
      mseloss = nn.MSELoss()
      model.eval()
      total_loss = 0
      with torch.no_grad():
          for data, target in dataloader:
              if isCUDA:
                  data, target = data.cuda(), target.cuda()
              y = model(data)
              loss = mseloss(y, target)
              total_loss += loss * len(target)
          total_loss /= len(dataloader.dataset)
      avg_total_loss = bf.allreduce(total_loss)
      return avg_total_loss.item()
  
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 7 for calling a global function with example usage: evaluation(model, dataloader, isCUDA) and returns: avg_total_loss