code: def compute_rpn_bbox_loss(
      rpn_target_deltas, rpn_pred_deltas, rpn_match
  ):
      """
      :param rpn_target_deltas:   (b, n_positive_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd)))).
      Uses 0 padding to fill in unsed bbox deltas.
      :param rpn_pred_deltas: predicted deltas from RPN. (b, n_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd))))
      :param rpn_match: (n_anchors). [-1, 0, 1] for negative, neutral, and positive matched anchors.
      :return: loss: torch 1D tensor.
      """
      if 0 not in torch.nonzero(rpn_match == 1).size():
          indices = torch.nonzero(rpn_match == 1).squeeze(1)
          # Pick bbox deltas that contribute to the loss
          rpn_pred_deltas = rpn_pred_deltas[indices]
          # Trim target bounding box deltas to the same length as rpn_bbox.
          target_deltas = rpn_target_deltas[
              : rpn_pred_deltas.size()[0], :
          ]
          # Smooth L1 loss
          loss = F.smooth_l1_loss(rpn_pred_deltas, target_deltas)
      else:
          loss = torch.FloatTensor([0]).cuda()
  
      return loss
  
imports: import utils.model_utils
  import utils.exp_utils
  from cuda_functions.nms_2D.pth_nms import nms_gpu
  from cuda_functions.nms_3D.pth_nms import nms_gpu
  from cuda_functions.roi_align_2D.roi_align.crop_and_resize import CropAndResizeFunction
  from cuda_functions.roi_align_3D.roi_align.crop_and_resize import CropAndResizeFunction
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils
label_desc: Use image node_id 2 for calling a global function with example usage: compute_rpn_bbox_loss(rpn_target_deltas, rpn_pred_deltas, rpn_match) and returns: loss