code: def test_stochastic_blockmodel_graph():
      torch.manual_seed(12345)
  
      block_sizes = [2, 2, 4]
      edge_probs = [
          [0.25, 0.05, 0.02],
          [0.05, 0.35, 0.07],
          [0.02, 0.07, 0.40],
      ]
  
      edge_index = stochastic_blockmodel_graph(
          block_sizes, edge_probs, directed=False
      )
      assert edge_index.tolist() == [
          [2, 3, 4, 4, 5, 5, 6, 7, 7, 7],
          [3, 2, 5, 7, 4, 7, 7, 4, 5, 6],
      ]
  
      edge_index = stochastic_blockmodel_graph(
          block_sizes, edge_probs, directed=True
      )
      assert edge_index.tolist() == [
          [0, 1, 3, 5, 6, 6, 7, 7],
          [3, 3, 2, 4, 4, 7, 5, 6],
      ]
  
imports: import numpy
  import torch
  from torch_geometric.utils import barabasi_albert_graph, erdos_renyi_graph, stochastic_blockmodel_graph
label_desc: Use image node_id 2 for calling a global function with example usage: test_stochastic_blockmodel_graph() without return types