code: def evaluate(model, data_loader, device):
      model.eval()
      mae_metric = utils.MeanAbsoluteError()
      f1_metric = utils.F1Score()
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Test:"
      with torch.no_grad():
          for images, targets in metric_logger.log_every(
              data_loader, 100, header
          ):
              images, targets = images.to(device), targets.to(device)
              output = model(images)
  
              # post norm
              # ma = torch.max(output)
              # mi = torch.min(output)
              # output = (output - mi) / (ma - mi)
  
              mae_metric.update(output, targets)
              f1_metric.update(output, targets)
  
          mae_metric.gather_from_all_processes()
          f1_metric.reduce_from_all_processes()
  
      return mae_metric, f1_metric
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/u2net/train_utils/train_and_eval.py
imports: import math
  import torch
  from torch.nn import functional
  import train_utils.distributed_utils
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, data_loader, device) and returns: mae_metric,  f1_metric