text: import torch
  from torch_sparse import SparseTensor
  
  from torch_geometric.nn import ClusterGCNConv
  
  
  def test_cluster_gcn_conv():
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      row, col = edge_index
      adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
  
      conv = ClusterGCNConv(16, 32, diag_lambda=1.)
      assert conv.__repr__() == 'ClusterGCNConv(16, 32, diag_lambda=1.0)'
      out = conv(x, edge_index)
      assert out.size() == (4, 32)
      assert torch.allclose(conv(x, adj.t()), out, atol=1e-5)
  
      t = '(Tensor, Tensor) -> Tensor'
      jit = torch.jit.script(conv.jittable(t))
      assert jit(x, edge_index).tolist() == out.tolist()
  
      t = '(Tensor, SparseTensor) -> Tensor'
      jit = torch.jit.script(conv.jittable(t))
      assert torch.allclose(jit(x, adj.t()), out, atol=1e-5)
  