code: def simple_networkx_multigraph():
      num_nodes = 10
      edge_index = torch.tensor(
          [
              [
                  0,
                  0,
                  0,
                  0,
                  1,
                  1,
                  2,
                  2,
                  2,
                  3,
                  3,
                  3,
                  4,
                  4,
                  5,
                  6,
                  6,
                  6,
                  7,
                  7,
                  9,
              ],
              [
                  1,
                  1,
                  1,
                  2,
                  2,
                  3,
                  3,
                  8,
                  8,
                  4,
                  5,
                  6,
                  5,
                  6,
                  7,
                  8,
                  8,
                  9,
                  8,
                  9,
                  8,
              ],
          ]
      ).long()
      x = torch.zeros([num_nodes, 2])
      y = torch.tensor([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]).long()
      for i in range(num_nodes):
          x[i] = np.random.randint(1, num_nodes)
      edge_x = torch.zeros([edge_index.shape[1], 2])
      edge_y = torch.tensor(
          [
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              1,
              1,
              1,
              1,
              1,
              2,
              2,
              2,
              3,
              3,
              3,
              3,
              3,
              3,
          ]
      ).long()
      for i in range(edge_index.shape[1]):
          edge_x[i] = np.random.randint(1, num_nodes)
  
      G = nx.MultiDiGraph()
      G.add_nodes_from(range(num_nodes))
      for i, (u, v) in enumerate(edge_index.T.tolist()):
          G.add_edge(u, v)
  
      graph_x = torch.tensor([[0, 1]])
      graph_y = torch.tensor([0])
      return G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y
  
imports: import numpy
  import networkx
  import random
  import torch
  import itertools
label_desc: Use image node_id 7 for calling a global function with example usage: simple_networkx_multigraph() and returns: G,  x,  y,  edge_x,  edge_y,  edge_index,  graph_x,  graph_y