code: def get_point_coords_wrt_image(boxes_coords, point_coords):
      """
      Convert box-normalized [0, 1] x [0, 1] point cooordinates to image-level coordinates.
  
      Args:
          boxes_coords (Tensor): A tensor of shape (R, 4) that contains bounding boxes.
              coordinates.
          point_coords (Tensor): A tensor of shape (R, P, 2) that contains
              [0, 1] x [0, 1] box-normalized coordinates of the P sampled points.
  
      Returns:
          point_coords_wrt_image (Tensor): A tensor of shape (R, P, 2) that contains
              image-normalized coordinates of P sampled points.
      """
      with torch.no_grad():
          point_coords_wrt_image = point_coords.clone()
          point_coords_wrt_image[:, :, 0] = point_coords_wrt_image[
              :, :, 0
          ] * (boxes_coords[:, None, 2] - boxes_coords[:, None, 0])
          point_coords_wrt_image[:, :, 1] = point_coords_wrt_image[
              :, :, 1
          ] * (boxes_coords[:, None, 3] - boxes_coords[:, None, 1])
          point_coords_wrt_image[:, :, 0] += boxes_coords[:, None, 0]
          point_coords_wrt_image[:, :, 1] += boxes_coords[:, None, 1]
      return point_coords_wrt_image
  
file_path: detectron2/projects/PointRend/point_rend/point_features.py
imports: import torch
  from torch.nn import functional
  from detectron2.layers import cat, shapes_to_tensor
  from detectron2.structures import BitMasks, Boxes
label_desc: Use image node_id 6 for calling a global function with example usage: get_point_coords_wrt_image(boxes_coords, point_coords) and returns: point_coords_wrt_image