code: def maskrcnn_upXconv_head(
      feature, num_category, num_convs, norm=None
  ):
      """
      Args:
          feature (NxCx s x s): size is 7 in C4 models and 14 in FPN models.
          num_category(int):
          num_convs (int): number of convolution layers
          norm (str or None): either None or 'GN'
  
      Returns:
          mask_logits (N x num_category x 2s x 2s):
      """
      assert norm in [None, "GN"], norm
      l = feature
      with argscope(
          [Conv2D, Conv2DTranspose],
          data_format="channels_first",
          kernel_initializer=tfv1.variance_scaling_initializer(
              scale=2.0,
              mode="fan_out",
              distribution="untruncated_normal",
          ),
      ):
          # c2's MSRAFill is fan_out
          for k in range(num_convs):
              l = Conv2D(
                  "fcn{}".format(k),
                  l,
                  cfg.MRCNN.HEAD_DIM,
                  3,
                  activation=tf.nn.relu,
              )
              if norm is not None:
                  l = GroupNorm("gn{}".format(k), l)
          l = Conv2DTranspose(
              "deconv",
              l,
              cfg.MRCNN.HEAD_DIM,
              2,
              strides=2,
              activation=tf.nn.relu,
          )
          l = Conv2D(
              "conv",
              l,
              num_category,
              1,
              kernel_initializer=tf.random_normal_initializer(
                  stddev=0.001
              ),
          )
      return l
  
file_path: tensorpack/examples/FasterRCNN/modeling/model_mrcnn.py
imports: import tensorflow
  from tensorpack import tfv1
  from tensorpack.models import Conv2D, Conv2DTranspose, layer_register
  from tensorpack.tfutils.argscope import argscope
  from tensorpack.tfutils.scope_utils import under_name_scope
  from tensorpack.tfutils.summary import add_moving_summary
  from .backbone import GroupNorm
  from config import config
label_desc: Use image node_id 2 for calling a global function with example usage: maskrcnn_upXconv_head(feature, num_category, num_convs, norm) and returns: l