code: def graph_norm(graph, feature):
      """Implementation of graph normalization
  
      Reference Paper: BENCHMARKING GRAPH NEURAL NETWORKS
  
      Each node features is divied by sqrt(num_nodes) per graphs.
  
      Args:
          graph: the graph object from (:code:`Graph`)
  
          feature: A tensor with shape (num_nodes, feature_size).
  
      Return:
          A tensor with shape (num_nodes, hidden_size)
      """
  
      nodes = paddle.ones(shape=[graph.num_nodes, 1], dtype="float32")
      norm = graph_pool(graph, nodes, pool_type="sum")
      norm = paddle.sqrt(norm)
      norm = paddle.gather(norm, graph.graph_node_id)
      return feature / norm
  
file_path: PGL/pgl/nn/functional/graph_op.py
imports: import paddle
  import paddle.nn
  import pgl
  import pgl.math
label_desc: Use image node_id 3 for calling a global function with example usage: graph_norm(graph, feature) and returns: unknown