code: def random_graph_descriptors():
      """
      Creates a random graph to use, and computes its various descriptors.
      :return: The normalized graph laplacian, the normalized line graph
          laplacian, and the incidence matrix.
      """
      graph = nx.dense_gnm_random_graph(11, 50, seed=1337)
      line_graph = nx.line_graph(graph)
  
      node_laplacian = np.array(
          nx.normalized_laplacian_matrix(graph).todense()
      )
      edge_laplacian = np.array(
          nx.normalized_laplacian_matrix(line_graph).todense()
      )
      incidence = np.array(nx.incidence_matrix(graph).todense())
  
      return node_laplacian, edge_laplacian, incidence
  
file_path: spektral/tests/test_layers/convolutional/test_censnet_conv.py
imports: import enum
  import networkx
  import numpy
  import pytest
  from core import A, F, S, batch_size
  from tensorflow.keras import Input, Model
  from spektral.layers import CensNetConv
label_desc: Use image node_id 1 for calling a global function with example usage: random_graph_descriptors() and returns: node_laplacian,  edge_laplacian,  incidence