code: def evaluate(model, data_loader, device, num_classes):
      model.eval()
      confmat = utils.ConfusionMatrix(num_classes)
      dice = utils.DiceCoefficient(
          num_classes=num_classes, ignore_index=255
      )
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Test:"
      with torch.no_grad():
          for image, target in metric_logger.log_every(
              data_loader, 100, header
          ):
              image, target = image.to(device), target.to(device)
              output = model(image)
              output = output["out"]
  
              confmat.update(
                  target.flatten(), output.argmax(1).flatten()
              )
              dice.update(output, target)
  
          confmat.reduce_from_all_processes()
          dice.reduce_from_all_processes()
  
      return confmat, dice.value.item()
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/unet/train_utils/train_and_eval.py
imports: import torch
  from torch import nn
  import train_utils.distributed_utils
  from .dice_coefficient_loss import dice_loss, build_target
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, data_loader, device, num_classes) and returns: confmat,  dice