code: def validate(epoch):
      model.eval()
      val_loss = Metric("val_loss")
      val_accuracy = Metric("val_accuracy")
  
      with tqdm(
          total=len(val_loader),
          desc="Validate Epoch  #{}".format(epoch + 1),
          disable=not verbose,
      ) as t:
          with torch.no_grad():
              for data, target in val_loader:
                  if args.cuda:
                      data, target = data.cuda(), target.cuda()
                  output = model(data)
  
                  val_loss.update(F.cross_entropy(output, target))
                  val_accuracy.update(accuracy(output, target))
                  t.set_postfix(
                      {
                          "loss": val_loss.avg.item(),
                          "accuracy": 100.0 * val_accuracy.avg.item(),
                      }
                  )
                  t.update(1)
  
      if log_writer:
          log_writer.add_scalar("val/loss", val_loss.avg, epoch)
          log_writer.add_scalar("val/accuracy", val_accuracy.avg, epoch)
  
imports: from __future__ import print_function
  import torch
  import argparse
  import torch.backends.cudnn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data.distributed
  from torchvision import datasets, transforms, models
  import byteps.torch
  import tensorboardX
  import os
  import math
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: validate(epoch) without return types