code: def get_point_coords_from_point_annotation(instances):
      """
      Load point coords and their corresponding labels from point annotation.
  
      Args:
          instances (list[Instances]): A list of N Instances, where N is the number of images
              in the batch. These instances are in 1:1
              correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask,
              ...) associated with each instance are stored in fields.
      Returns:
          point_coords (Tensor): A tensor of shape (N, P, 2) that contains the coordinates of P
              sampled points.
          point_labels (Tensor): A tensor of shape (N, P) that contains the labels of P
              sampled points. `point_labels` takes 3 possible values:
              - 0: the point belongs to background
              - 1: the point belongs to the object
              - -1: the point is ignored during training
      """
      point_coords_list = []
      point_labels_list = []
      for instances_per_image in instances:
          if len(instances_per_image) == 0:
              continue
          point_coords = instances_per_image.gt_point_coords.to(
              torch.float32
          )
          point_labels = instances_per_image.gt_point_labels.to(
              torch.float32
          ).clone()
          proposal_boxes_per_image = (
              instances_per_image.proposal_boxes.tensor
          )
  
          # Convert point coordinate system, ground truth points are in image coord.
          point_coords_wrt_box = get_point_coords_wrt_box(
              proposal_boxes_per_image, point_coords
          )
  
          # Ignore points that are outside predicted boxes.
          point_ignores = (
              (point_coords_wrt_box[:, :, 0] < 0)
              | (point_coords_wrt_box[:, :, 0] > 1)
              | (point_coords_wrt_box[:, :, 1] < 0)
              | (point_coords_wrt_box[:, :, 1] > 1)
          )
          point_labels[point_ignores] = -1
  
          point_coords_list.append(point_coords_wrt_box)
          point_labels_list.append(point_labels)
  
      return (
          cat(point_coords_list, dim=0),
          cat(point_labels_list, dim=0),
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_point_coords_from_point_annotation(instances) and returns: cat,  cat