code: def add_roi_Xconv1fc_gn_head(model, blob_in, dim_in, spatial_scale):
      """Add a X conv + 1fc head, with GroupNorm"""
      hidden_dim = cfg.FAST_RCNN.CONV_HEAD_DIM
      roi_size = cfg.FAST_RCNN.ROI_XFORM_RESOLUTION
      roi_feat = model.RoIFeatureTransform(
          blob_in,
          "roi_feat",
          blob_rois="rois",
          method=cfg.FAST_RCNN.ROI_XFORM_METHOD,
          resolution=roi_size,
          sampling_ratio=cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO,
          spatial_scale=spatial_scale,
      )
  
      current = roi_feat
      for i in range(cfg.FAST_RCNN.NUM_STACKED_CONVS):
          current = model.ConvGN(
              current,
              "head_conv" + str(i + 1),
              dim_in,
              hidden_dim,
              3,
              group_gn=get_group_gn(hidden_dim),
              stride=1,
              pad=1,
              weight_init=("MSRAFill", {}),
              bias_init=("ConstantFill", {"value": 0.0}),
          )
          current = model.Relu(current, current)
          dim_in = hidden_dim
  
      fc_dim = cfg.FAST_RCNN.MLP_HEAD_DIM
      model.FC(current, "fc6", dim_in * roi_size * roi_size, fc_dim)
      model.Relu("fc6", "fc6")
      return "fc6", fc_dim
  
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 5 for calling a global function with example usage: add_roi_Xconv1fc_gn_head(model, blob_in, dim_in, spatial_scale) and returns: str,  fc_dim