code: def test_pdn_conv():
      x = torch.randn(4, 16)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      edge_attr = torch.randn(edge_index.size(1), 8)
  
      conv = PDNConv(16, 32, edge_dim=8, hidden_channels=128)
      assert str(conv) == "PDNConv(16, 32)"
  
      out = conv(x, edge_index, edge_attr)
      assert out.size() == (4, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, edge_attr, (4, 4)
          )
          assert torch.allclose(conv(x, adj.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index, edge_attr), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x, adj.t()), out, atol=1e-6)
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import PDNConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_pdn_conv() without return types