code: def mask_rcnn_fcn_head_v1upXconvs_gn(
      model, blob_in, dim_in, spatial_scale, num_convs
  ):
      """v1upXconvs design: X * (conv 3x3), convT 2x2, with GroupNorm"""
      current = model.RoIFeatureTransform(
          blob_in,
          blob_out="_mask_roi_feat",
          blob_rois="mask_rois",
          method=cfg.MRCNN.ROI_XFORM_METHOD,
          resolution=cfg.MRCNN.ROI_XFORM_RESOLUTION,
          sampling_ratio=cfg.MRCNN.ROI_XFORM_SAMPLING_RATIO,
          spatial_scale=spatial_scale,
      )
  
      dilation = cfg.MRCNN.DILATION
      dim_inner = cfg.MRCNN.DIM_REDUCED
  
      for i in range(num_convs):
          current = model.ConvGN(
              current,
              "_mask_fcn" + str(i + 1),
              dim_in,
              dim_inner,
              group_gn=get_group_gn(dim_inner),
              kernel=3,
              pad=1 * dilation,
              stride=1,
              weight_init=(cfg.MRCNN.CONV_INIT, {"std": 0.001}),
              bias_init=("ConstantFill", {"value": 0.0}),
          )
          current = model.Relu(current, current)
          dim_in = dim_inner
  
      # upsample layer
      model.ConvTranspose(
          current,
          "conv5_mask",
          dim_inner,
          dim_inner,
          kernel=2,
          pad=0,
          stride=2,
          weight_init=(cfg.MRCNN.CONV_INIT, {"std": 0.001}),
          bias_init=const_fill(0.0),
      )
      blob_mask = model.Relu("conv5_mask", "conv5_mask")
  
      return blob_mask, dim_inner
  
file_path: dense-pose/detectron/modeling/mask_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.modeling.ResNet
  import detectron.utils.blob
label_desc: Use image node_id 7 for calling a global function with example usage: mask_rcnn_fcn_head_v1upXconvs_gn(model, blob_in, dim_in, spatial_scale, num_convs) and returns: blob_mask,  dim_inner