code: def normalize_sparse_adj(mx):
      """symmetric normalized Laplacian"""
      rowsum = np.array(mx.sum(1))
      r_inv_sqrt = np.power(rowsum, -0.5).flatten()
      r_inv_sqrt[np.isinf(r_inv_sqrt)] = 0.0
      r_mat_inv_sqrt = sparse.diags(r_inv_sqrt)
  
      return mx.dot(r_mat_inv_sqrt).transpose().dot(r_mat_inv_sqrt)
  
imports: import os
  import pickle
  from collections import OrderedDict
  import numpy
  import torch
  import torch.nn
  import yaml
  from scipy import sparse
  from sklearn import preprocessing
  from graph4nlp.pytorch.data.data import GraphData
label_desc: Use image node_id 8 for calling a global function with example usage: normalize_sparse_adj(mx) and returns: mx