code: def validate(val_loader, model, criterion):
      # switch to evaluate mode
      model.eval()
      with torch.no_grad():
          tracker = LossTracker(len(val_loader), f"val", args.printfreq)
          for i, (images, target) in enumerate(val_loader):
              images, target = cuda_transfer(images, target)
              output = model(images)
              loss = criterion(output, target)
              tracker.update(loss, output, target)
              tracker.display(i)
      return tracker.losses.avg, tracker.top1.avg
  
imports: import argparse
  import os
  import random
  import time
  import warnings
  import json
  import collections
  import numpy
  import math
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  from torch.utils.data import Subset
  import models
  from models.vit import Block
  from utils import get_dataset, get_model, get_optimizer, get_scheduler
  from utils import LossTracker, run_cmd
  import deepspeed
  from deepspeed.runtime.data_pipeline.data_routing.helper import convert_to_random_ltd, save_without_random_ltd
label_desc: Use image node_id 3 for calling a global function with example usage: validate(val_loader, model, criterion) and returns: tracker,  tracker