code: def test(args, model, device, test_loader, epoch):
      model.eval()
      test_loss = 0
      correct = 0
      with torch.no_grad():
          for data, target in test_loader:
              data, target = data.to(device), target.to(device)
              output = model(data)
              # sum up batch loss
              test_loss += F.nll_loss(
                  output, target, reduction="sum"
              ).item()
              # get the index of the max log-probability
              pred = output.max(1, keepdim=True)[1]
              correct += pred.eq(target.view_as(pred)).sum().item()
  
      test_loss /= len(test_loader.dataset)
      logging.info(
          "{{metricName: accuracy, metricValue: {:.4f}}};{{metricName: loss, metricValue: {:.4f}}}\n".format(
              float(correct) / (len(test_loader.dataset) / WORLD_SIZE),
              test_loss,
          )
      )
  
imports: from __future__ import print_function
  import argparse
  import logging
  import os
  import time
  from torchvision import datasets, transforms
  from torch.utils.data.distributed import DistributedSampler
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torch.optim
label_desc: Use image node_id 2 for calling a global function with example usage: test(args, model, device, test_loader, epoch) without return types