code: def normalized_laplacian(A, symmetric=True):
      r"""
      Computes a  normalized Laplacian of the given adjacency matrix as
      \(\I - \D^{-1}\A\) or \(\I - \D^{-1/2}\A\D^{-1/2}\) (symmetric normalization).
      :param A: rank 2 array or sparse matrix;
      :param symmetric: boolean, compute symmetric normalization;
      :return: the normalized Laplacian.
      """
      if sp.issparse(A):
          I = sp.eye(A.shape[-1], dtype=A.dtype)
      else:
          I = np.eye(A.shape[-1], dtype=A.dtype)
      normalized_adj = normalized_adjacency(A, symmetric=symmetric)
      return I - normalized_adj
  
imports: import copy
  import warnings
  from functools import partial
  import numpy
  import tensorflow
  from scipy import linalg
  from scipy import sparse
  from scipy.sparse.linalg import ArpackNoConvergence
label_desc: Use image node_id 5 for calling a global function with example usage: normalized_laplacian(A, symmetric) and returns: unknown