## In the valide function, upload training metrics acc1 and acc5.

def validate(val_loader, model, criterion, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

  ## Call Tikit commands to upload metrics. 
  
        client.push_training_metrics(int(time.time()), {"acc1": float(format(top1.avg, '.3f')), "acc5": float(format(top5.avg, '.3f'))}, epoch=epoch)
        # TODO: this should also be done with the ProgressMeter
        print('TIACC - * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} Epoch={epoch}'
              .format(top1=top1, top5=top5, epoch=epoch))

    return top1.avg