code: def validate(
      epoch,
      model,
      loader,
      loss_fn,
      cfg,
      log_suffix="",
      logger=None,
      writer=None,
      local_rank=0,
  ):
      batch_time_m = AverageMeter()
      losses_m = AverageMeter()
      prec1_m = AverageMeter()
      prec5_m = AverageMeter()
  
      model.eval()
  
      end = time.time()
      last_idx = len(loader) - 1
      with torch.no_grad():
          for batch_idx, (input, target) in enumerate(loader):
              last_batch = batch_idx == last_idx
  
              output = model(input)
              if isinstance(output, (tuple, list)):
                  output = output[0]
  
              # augmentation reduction
              reduce_factor = cfg.TTA
              if reduce_factor > 1:
                  output = output.unfold(
                      0, reduce_factor, reduce_factor
                  ).mean(dim=2)
                  target = target[0 : target.size(0) : reduce_factor]
  
              loss = loss_fn(output, target)
              prec1, prec5 = accuracy(output, target, topk=(1, 5))
  
              if cfg.NUM_GPU > 1:
                  reduced_loss = reduce_tensor(loss.data, cfg.NUM_GPU)
                  prec1 = reduce_tensor(prec1, cfg.NUM_GPU)
                  prec5 = reduce_tensor(prec5, cfg.NUM_GPU)
              else:
                  reduced_loss = loss.data
  
              torch.cuda.synchronize()
  
              losses_m.update(reduced_loss.item(), input.size(0))
              prec1_m.update(prec1.item(), output.size(0))
              prec5_m.update(prec5.item(), output.size(0))
  
              batch_time_m.update(time.time() - end)
              end = time.time()
              if local_rank == 0 and (
                  last_batch or batch_idx % cfg.LOG_INTERVAL == 0
              ):
                  log_name = "Test" + log_suffix
                  logger.info(
                      "{0}: [{1:>4d}/{2}]  "
                      "Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  "
                      "Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  "
                      "Prec@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  "
                      "Prec@5: {top5.val:>7.4f} ({top5.avg:>7.4f})".format(
                          log_name,
                          batch_idx,
                          last_idx,
                          batch_time=batch_time_m,
                          loss=losses_m,
                          top1=prec1_m,
                          top5=prec5_m,
                      )
                  )
  
                  writer.add_scalar(
                      "Loss" + log_suffix + "/vaild",
                      prec1_m.avg,
                      epoch * len(loader) + batch_idx,
                  )
                  writer.add_scalar(
                      "Accuracy" + log_suffix + "/vaild",
                      prec1_m.avg,
                      epoch * len(loader) + batch_idx,
                  )
  
      metrics = OrderedDict(
          [
              ("loss", losses_m.avg),
              ("prec1", prec1_m.avg),
              ("prec5", prec5_m.avg),
          ]
      )
  
      return metrics
  
imports: import time
  import torch
  from collections import OrderedDict
  from lib.utils.util import AverageMeter, accuracy, reduce_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: validate(epoch, model, loader, loss_fn, cfg, log_suffix, logger, writer, local_rank) and returns: metrics