code: def dice(intersect_area, pred_area, label_area):
      """
      Calculate DICE.
  
      Args:
          intersect_area (Tensor): The intersection area of prediction and ground truth on all classes.
          pred_area (Tensor): The prediction area on all classes.
          label_area (Tensor): The ground truth area on all classes.
  
      Returns:
          np.ndarray: DICE on all classes.
          float: mean DICE of all classes.
      """
      intersect_area = intersect_area.numpy()
      pred_area = pred_area.numpy()
      label_area = label_area.numpy()
      union = pred_area + label_area
      class_dice = []
      for i in range(len(intersect_area)):
          if union[i] == 0:
              dice = 0
          else:
              dice = (2 * intersect_area[i]) / union[i]
          class_dice.append(float(dice))
      mdice = np.mean(class_dice)
      return np.array(class_dice), mdice
  
file_path: PaddleSeg/paddleseg/utils/metrics.py
imports: import numpy
  import paddle
  import paddle.nn.functional
  import sklearn.metrics
label_desc: Use image node_id 4 for calling a global function with example usage: dice(intersect_area, pred_area, label_area) and returns: np,  mdice