code: def test_point_gnn_conv():
      x = torch.randn(6, 8)
      pos = torch.randn(6, 3)
      edge_index = torch.tensor(
          [[0, 1, 1, 1, 2, 5], [1, 2, 3, 4, 3, 4]]
      )
      adj1 = to_torch_csc_tensor(edge_index, size=(6, 6))
  
      conv = PointGNNConv(
          mlp_h=MLP([8, 16, 3]),
          mlp_f=MLP([3 + 8, 16, 8]),
          mlp_g=MLP([8, 16, 8]),
      )
      assert str(conv) == (
          "PointGNNConv(\n"
          "  mlp_h=MLP(8, 16, 3),\n"
          "  mlp_f=MLP(11, 16, 8),\n"
          "  mlp_g=MLP(8, 16, 8),\n"
          ")"
      )
  
      out = conv(x, pos, edge_index)
      assert out.size() == (6, 8)
      assert torch.allclose(conv(x, pos, adj1.t()), out, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(6, 6)
          )
          assert torch.allclose(conv(x, pos, adj2.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, pos, edge_index), out, atol=1e-6)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit(x, pos, adj2.t()), out, atol=1e-6
              )
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import MLP, PointGNNConv
  from torch_geometric.testing import is_full_test
  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_point_gnn_conv() without return types