code: def test_rect():
      x = torch.randn(6, 8)
      y = torch.tensor([1, 0, 0, 2, 1, 1])
      edge_index = torch.tensor(
          [[0, 1, 1, 2, 4, 5], [1, 0, 2, 1, 5, 4]]
      )
      mask = torch.randint(0, 2, (6,), dtype=torch.bool)
  
      model = RECT_L(8, 16)
      assert str(model) == "RECT_L(8, 16)"
  
      out = model(x, edge_index)
      assert out.size() == (6, 8)
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(6, 6)
          )
          assert torch.allclose(out, model(x, adj.t()), atol=1e-6)
  
      # Test `embed`:
      embed_out = model.embed(x, edge_index)
      assert embed_out.size() == (6, 16)
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(
              embed_out, model.embed(x, adj.t()), atol=1e-6
          )
  
      # Test `get_semantic_labels`:
      labels_out = model.get_semantic_labels(x, y, mask)
      assert labels_out.size() == (int(mask.sum()), 8)
  
      if is_full_test():
          jit = torch.jit.script(model)
          assert torch.allclose(jit(x, edge_index), out, atol=1e-6)
          assert torch.allclose(
              embed_out, jit.embed(x, edge_index), atol=1e-6
          )
          assert torch.allclose(
              labels_out, jit.get_semantic_labels(x, y, mask)
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x, adj.t()), out, atol=1e-6)
              assert torch.allclose(
                  embed_out, jit.embed(x, adj.t()), atol=1e-6
              )
              assert torch.allclose(
                  labels_out, jit.get_semantic_labels(x, y, mask)
              )
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import RECT_L
  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_rect() without return types