class_docstr: Implementation of graph normalization
  
  Reference Paper: BENCHMARKING GRAPH NEURAL NETWORKS
  
  Each node features is divied by sqrt(num_nodes) per graphs.
class_docstr_tok: ["Implementation","of","graph","normalization","Reference","Paper",":","BENCHMARKING","GRAPH","NEURAL","NETWORKS","Each","node","features","is","divied","by","sqrt","(","num_nodes",")","per","graphs","."]
code: def __init__(self):
      super(GraphNorm, self).__init__()
      self.graph_pool = GraphPool(pool_type="sum")
  
imports: import warnings
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  from pgl.math import segment_sum, segment_softmax, segment_topk, segment_pool
  from pgl.utils.transform import filter_adj
label_desc: Use image node_id 1 to create a new GraphNorm object from inherited base classes: nn with example: obj = GraphNorm()