code: def graph_norm(gw, feature):
      """Implementation of graph normalization
  
      Reference Paper: BENCHMARKING GRAPH NEURAL NETWORKS
  
      Each node features is divied by sqrt(num_nodes) per graphs.
  
      Args:
          gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)
  
          feature: A tensor with shape (num_nodes, hidden_size)
  
      Return:
          A tensor with shape (num_nodes, hidden_size)
      """
      nodes = L.fill_constant(
          [gw.num_nodes, 1], dtype="float32", value=1.0
      )
      norm = graph_pooling(gw, nodes, pool_type="sum")
      norm = L.sqrt(norm)
      feature_lod = op.nested_lod_reset(feature, gw.graph_lod)
      norm = L.sequence_expand_as(norm, feature_lod)
      norm.stop_gradient = True
      return feature_lod / norm
  
file_path: PGL/legacy/pgl/layers/graph_op.py
imports: import paddle.fluid
  import paddle.fluid.layers
  from pgl import graph_wrapper
  from pgl.utils import paddle_helper
  from pgl.utils import op
label_desc: Use image node_id 2 for calling a global function with example usage: graph_norm(gw, feature) and returns: unknown