code: def add_ResNet_roi_conv5_head_for_bodyUV(
      model, blob_in, dim_in, spatial_scale
  ):
      """Add a ResNet "conv5" / "stage5" head for body UV prediction."""
      model.RoIFeatureTransform(
          blob_in,
          "_[body_uv]_pool5",
          blob_rois="body_uv_rois",
          method=cfg.BODY_UV_RCNN.ROI_XFORM_METHOD,
          resolution=cfg.BODY_UV_RCNN.ROI_XFORM_RESOLUTION,
          sampling_ratio=cfg.BODY_UV_RCNN.ROI_XFORM_SAMPLING_RATIO,
          spatial_scale=spatial_scale,
      )
      # Using the prefix '_[body_uv]_' to 'res5' enables initializing the head's
      # parameters using pretrained 'res5' parameters if given (see
      # utils.net.initialize_from_weights_file)
      s, dim_in = ResNet.add_stage(
          model,
          "_[body_uv]_res5",
          "_[body_uv]_pool5",
          3,
          dim_in,
          2048,
          512,
          cfg.BODY_UV_RCNN.DILATION,
          stride_init=int(cfg.BODY_UV_RCNN.ROI_XFORM_RESOLUTION / 7),
      )
      return s, 2048
  
file_path: dense-pose/detectron/modeling/body_uv_rcnn_heads.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from caffe2.python import core
  from detectron.core.config import cfg
  import detectron.modeling.ResNet
  import detectron.utils.blob
label_desc: Use image node_id 3 for calling a global function with example usage: add_ResNet_roi_conv5_head_for_bodyUV(model, blob_in, dim_in, spatial_scale) and returns: s,  int