code: def test_pan_conv():
      x = torch.randn(4, 16)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      adj2 = SparseTensor.from_edge_index(
          edge_index, sparse_sizes=(4, 4)
      )
  
      conv = PANConv(16, 32, filter_size=2)
      assert str(conv) == "PANConv(16, 32, filter_size=2)"
      out1, M1 = conv(x, edge_index)
      assert out1.size() == (4, 32)
  
      out2, M2 = conv(x, adj1.t())
      assert torch.allclose(out1, out2, atol=1e-6)
      assert torch.allclose(M1.to_dense(), M2.to_dense())
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          out3, M3 = conv(x, adj2.t())
          assert torch.allclose(out1, out3, atol=1e-6)
          assert torch.allclose(M1.to_dense(), M3.to_dense())
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import PANConv
  from torch_geometric.testing import withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_csc_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_pan_conv() without return types