code: def validate(
      infer_fn, val_loader, log_fn, prof=-1, with_loss=True, topk=5
  ):
      top1 = log.AverageMeter()
      # switch to evaluate mode
  
      end = time.time()
  
      data_iter = enumerate(val_loader)
  
      for i, (input, target) in data_iter:
          bs = input.size(0)
          data_time = time.time() - end
  
          if with_loss:
              loss, output = infer_fn(input, target)
          else:
              output = infer_fn(input)
  
          with torch.no_grad():
              precs = utils.accuracy(
                  output.data, target, topk=(1, topk)
              )
  
              if torch.distributed.is_initialized():
                  if with_loss:
                      reduced_loss = utils.reduce_tensor(loss.detach())
                  precs = map(utils.reduce_tensor, precs)
              else:
                  if with_loss:
                      reduced_loss = loss.detach()
  
          precs = map(lambda t: t.item(), precs)
          infer_result = {
              f"top{k}": (p, bs) for k, p in zip((1, topk), precs)
          }
  
          if with_loss:
              infer_result["loss"] = (reduced_loss.item(), bs)
  
          torch.cuda.synchronize()
  
          it_time = time.time() - end
  
          top1.record(infer_result["top1"][0], bs)
  
          log_fn(
              compute_ips=utils.calc_ips(bs, it_time - data_time),
              total_ips=utils.calc_ips(bs, it_time),
              data_time=data_time,
              compute_time=it_time - data_time,
              **infer_result,
          )
  
          end = time.time()
          if (prof > 0) and (i + 1 >= prof):
              time.sleep(5)
              break
  
      return top1.get_val()
  
imports: import time
  from copy import deepcopy
  from functools import wraps
  from typing import Callable, Dict, Optional, Tuple
  import torch
  import torch.nn
  from torch.cuda.amp import autocast
  from torch.nn.parallel import DistributedDataParallel
  from .None import logger
  from .None import utils
  from .logger import TrainingMetrics, ValidationMetrics
  from .models.common import EMA
label_desc: Use image node_id 2 for calling a global function with example usage: validate(infer_fn, val_loader, log_fn, prof, with_loss, topk) and returns: top1