code: # Testing Setup of Multilayer Extraction
  import networkx as nx
  import pandas as pd
  import numpy as np
  import math
  import itertools as it
  from . import adjacency_to_edgelist
  from . import expectation_CM
  from . import initialization
  from . import score
  import matplotlib.pyplot as plt
  
  # Gen default testing graph
  g1 = nx.planted_partition_graph(5, 25, 0.5, 0.05)
  graph = nx.generators.complete_graph(9)
  # Gen adjacency matrix for complete graph
  adjacency = [[[0, 1, 1, 1, 1, 1, 1, 1, 1],
               [1, 0, 1, 1, 1, 1, 1, 1, 1],
               [1, 1, 0, 1, 1, 1, 1, 1, 1],
               [1, 1, 1, 0, 1, 1, 1, 1, 1],
               [1, 1, 1, 1, 0, 1, 1, 1, 1],
               [1, 1, 1, 1, 1, 0, 1, 1, 1],
               [1, 1, 1, 1, 1, 1, 0, 1, 1],
               [1, 1, 1, 1, 1, 1, 1, 0, 1],
               [1, 1, 1, 1, 1, 1, 1, 1, 0]]]
  print(adjacency)
  
  # Gen edgelist from adjacency matrix
  edgelist = adjacency_to_edgelist.adjacency_to_edgelist(adjacency)
  
  print(edgelist)
  
  # Gen Expectation.CM from edgelist
  expectation_CM = expectation_CM.expectation_CM(edgelist)
  
  nx.draw(expectation_CM[0])
  plt.show()
  
  # Gen initialization outputs
  initial = initialization.initialization(graph, 0.05, 1, 9)
  
  print(initial)
  
  # Gen score
  # test_score = score.score(adjacency, initial['vertex_set'], initial['layer_set'], 9)
  
  n = 9
  vertex_set = initial['vertex_set']
  layer_set = initial['layer_set']
  adjacency_score = expectation_CM
  super_mod = None
  if len(layer_set) < 1 or len(vertex_set) < 1:
      print(0)
  if len(layer_set) == 1:
      super_mod = adjacency_score[layer_set[0][0]]
  if len(layer_set) > 1:
      super_mod = nx.empty_graph(n)
      for i in range(0, layer_set):
          super_mod = nx.union(super_mod, adjacency_score[i])
  
  super_mod_subgraph = super_mod.subgraph(map(int, vertex_set[0]))
  
  edge_weight_tuples = nx.get_edge_attributes(super_mod_subgraph, 'weight')
  edge_weights = pd.DataFrame({'edge': list(edge_weight_tuples.keys()), 'weight': list(edge_weight_tuples.values())})
  for _, weights in edge_weights.iterrows():
      if math.isnan(weights['weight']):
          weights['weight'] = 0
          
  modularity_score = np.sum(edge_weights['weight'])
  modularity_score = [0 for i in modularity_score if i < 0]
  
  tot_mod = np.sum(modularity_score)
  obs_score = (tot_mod ** 2) / ((n ** 2 * it.combinations(range(0, len(vertex_set)), 2)) * (len(layer_set)))
  
  print(obs_score)
  
  print(score.score(edgelist, vertex_set, layer_set, n))
  
apis: numpy.sum matplotlib.pyplot.show