code: def keypoint_auc(pred, gt, mask, normalize, num_step=20):
      """Calculate the pose accuracy of PCK for each individual keypoint and the
      averaged accuracy across all keypoints for coordinates.
  
      Note:
          - batch_size: N
          - num_keypoints: K
  
      Args:
          pred (np.ndarray[N, K, 2]): Predicted keypoint location.
          gt (np.ndarray[N, K, 2]): Groundtruth keypoint location.
          mask (np.ndarray[N, K]): Visibility of the target. False for invisible
              joints, and True for visible. Invisible joints will be ignored for
              accuracy calculation.
          normalize (float): Normalization factor.
  
      Returns:
          float: Area under curve.
      """
      nor = np.tile(
          np.array([[normalize, normalize]]), (pred.shape[0], 1)
      )
      x = [1.0 * i / num_step for i in range(num_step)]
      y = []
      for thr in x:
          _, avg_acc, _ = keypoint_pck_accuracy(
              pred, gt, mask, thr, nor
          )
          y.append(avg_acc)
  
      auc = 0
      for i in range(num_step):
          auc += 1.0 / num_step * y[i]
      return auc
  
imports: import cv2
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 19 for calling a global function with example usage: keypoint_auc(pred, gt, mask, normalize, num_step) and returns: auc