code: def evaluate(
      model,
      criterion,
      data_loader,
      device,
      print_freq=100,
      log_suffix="",
  ):
      model.eval()
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = f"Test: {log_suffix}"
  
      num_processed_samples = 0
      with torch.inference_mode():
          for image, target in metric_logger.log_every(
              data_loader, print_freq, header
          ):
              image = image.to(device, non_blocking=True)
              target = target.to(device, non_blocking=True)
              output = model(image)
              loss = criterion(output, target)
  
              acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
              # FIXME need to take into account that the datasets
              # could have been padded in distributed setup
              batch_size = image.shape[0]
              metric_logger.update(loss=loss.item())
              metric_logger.meters["acc1"].update(
                  acc1.item(), n=batch_size
              )
              metric_logger.meters["acc5"].update(
                  acc5.item(), n=batch_size
              )
              num_processed_samples += batch_size
      # gather the stats from all processes
  
      num_processed_samples = utils.reduce_across_processes(
          num_processed_samples
      )
      if (
          hasattr(data_loader.dataset, "__len__")
          and len(data_loader.dataset) != num_processed_samples
          and torch.distributed.get_rank() == 0
      ):
          # See FIXME above
          warnings.warn(
              f"It looks like the dataset has {len(data_loader.dataset)} samples, but {num_processed_samples} "
              "samples were used for the validation, which might bias the results. "
              "Try adjusting the batch size and / or the world size. "
              "Setting the world size to 1 is always a safe bet."
          )
  
      metric_logger.synchronize_between_processes()
  
      print(
          f"{header} Acc@1 {metric_logger.acc1.global_avg:.3f} Acc@5 {metric_logger.acc5.global_avg:.3f}"
      )
      return metric_logger.acc1.global_avg
  
imports: import datetime
  import os
  import time
  import warnings
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import torchvision.transforms
  import utils
  from sampler import RASampler
  from torch import nn
  from torch.utils.data.dataloader import default_collate
  from torchvision.transforms.functional import InterpolationMode
  from transforms import get_mixup_cutmix
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, criterion, data_loader, device, print_freq, log_suffix) and returns: metric_logger