code: def test_ppf_conv():
      x1 = torch.randn(4, 16)
      pos1 = torch.randn(4, 3)
      pos2 = torch.randn(2, 3)
      n1 = F.normalize(torch.rand(4, 3), dim=-1)
      n2 = F.normalize(torch.rand(2, 3), dim=-1)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      local_nn = Seq(Lin(16 + 4, 32), ReLU(), Lin(32, 32))
      global_nn = Seq(Lin(32, 32))
      conv = PPFConv(local_nn, global_nn)
      assert str(conv) == (
          "PPFConv(local_nn=Sequential(\n"
          "  (0): Linear(in_features=20, out_features=32, bias=True)\n"
          "  (1): ReLU()\n"
          "  (2): Linear(in_features=32, out_features=32, bias=True)\n"
          "), global_nn=Sequential(\n"
          "  (0): Linear(in_features=32, out_features=32, bias=True)\n"
          "))"
      )
  
      out = conv(x1, pos1, n1, edge_index)
      assert out.size() == (4, 32)
      assert torch.allclose(
          conv(x1, pos1, n1, adj1.t()), out, atol=1e-3
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(
              conv(x1, pos1, n1, adj2.t()), out, atol=1e-3
          )
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit(x1, pos1, n1, edge_index), out, atol=1e-3
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit(x1, pos1, n1, adj2.t()), out, atol=1e-3
              )
  
      # Test bipartite message passing:
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
  
      out = conv(x1, (pos1, pos2), (n1, n2), edge_index)
      assert out.size() == (2, 32)
      assert torch.allclose(
          conv((x1, None), (pos1, pos2), (n1, n2), edge_index),
          out,
          atol=1e-3,
      )
      assert torch.allclose(
          conv(x1, (pos1, pos2), (n1, n2), adj1.t()), out, atol=1e-3
      )
      assert torch.allclose(
          conv((x1, None), (pos1, pos2), (n1, n2), adj1.t()),
          out,
          atol=1e-3,
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 2)
          )
          assert torch.allclose(
              conv(x1, (pos1, pos2), (n1, n2), adj2.t()), out, atol=1e-3
          )
          assert torch.allclose(
              conv((x1, None), (pos1, pos2), (n1, n2), adj2.t()),
              out,
              atol=1e-3,
          )
  
      if is_full_test():
          assert torch.allclose(
              jit((x1, None), (pos1, pos2), (n1, n2), edge_index), out
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, None), (pos1, pos2), (n1, n2), adj2.t()),
                  out,
                  atol=1e-3,
              )
  
file_path: pytorch_geometric/test/nn/conv/test_ppf_conv.py
imports: import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sequential
  import torch_geometric.typing
  from torch_geometric.nn import PPFConv
  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_ppf_conv() without return types