code: def test_dense_mincut_pool():
      batch_size, num_nodes, channels, num_clusters = (2, 20, 16, 10)
      x = torch.randn((batch_size, num_nodes, channels))
      adj = torch.ones((batch_size, num_nodes, num_nodes))
      s = torch.randn((batch_size, num_nodes, num_clusters))
      mask = torch.randint(
          0, 2, (batch_size, num_nodes), dtype=torch.bool
      )
  
      x_out, adj_out, mincut_loss, ortho_loss = dense_mincut_pool(
          x, adj, s, mask
      )
      assert x_out.size() == (2, 10, 16)
      assert adj_out.size() == (2, 10, 10)
      assert -1 <= mincut_loss <= 0
      assert 0 <= ortho_loss <= 2
  
      if is_full_test():
          jit = torch.jit.script(dense_mincut_pool)
  
          x_jit, adj_jit, mincut_loss, ortho_loss = jit(x, adj, s, mask)
          assert x_jit.size() == (2, 10, 16)
          assert adj_jit.size() == (2, 10, 10)
          assert -1 <= mincut_loss <= 0
          assert 0 <= ortho_loss <= math.sqrt(2)
  
imports: import math
  import torch
  from torch_geometric.nn import dense_mincut_pool
  from torch_geometric.testing import is_full_test
label_desc: Use image node_id 1 for calling a global function with example usage: test_dense_mincut_pool() without return types