code: def add_fast_rcnn_outputs(model, blob_in, dim):
      """Add RoI classification and bounding box regression output ops."""
      # Box classification layer
      model.FC(
          blob_in,
          "cls_score",
          dim,
          model.num_classes,
          weight_init=gauss_fill(0.01),
          bias_init=const_fill(0.0),
      )
      if not model.train:  # == if test
          # Only add softmax when testing; during training the softmax is combined
          # with the label cross entropy loss for numerical stability
          model.Softmax("cls_score", "cls_prob", engine="CUDNN")
      # Box regression layer
      num_bbox_reg_classes = (
          2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
      )
      model.FC(
          blob_in,
          "bbox_pred",
          dim,
          num_bbox_reg_classes * 4,
          weight_init=gauss_fill(0.001),
          bias_init=const_fill(0.0),
      )
  
file_path: dense-pose/detectron/modeling/fast_rcnn_heads.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from detectron.core.config import cfg
  from detectron.utils.c2 import const_fill
  from detectron.utils.c2 import gauss_fill
  from detectron.utils.net import get_group_gn
  import detectron.utils.blob
label_desc: Use image node_id 1 for calling a global function with example usage: add_fast_rcnn_outputs(model, blob_in, dim) without return types