code: def test_to_dense():
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      edge_attr = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
      num_nodes = edge_index.max().item() + 1
      x = torch.randn((num_nodes, 4))
      pos = torch.randn((num_nodes, 3))
      y = torch.randint(0, 4, (num_nodes,), dtype=torch.long)
  
      transform = ToDense()
      assert str(transform) == "ToDense()"
      data = Data(
          x=x, pos=pos, edge_index=edge_index, edge_attr=edge_attr, y=y
      )
      data = transform(data)
      assert len(data) == 5
      assert data.x.tolist() == x.tolist()
      assert data.pos.tolist() == pos.tolist()
      assert data.y.tolist() == y.tolist()
      assert data.adj.size() == (num_nodes, num_nodes)
      assert data.adj.tolist() == [
          [0, 1, 2, 3],
          [4, 0, 0, 0],
          [5, 0, 0, 0],
          [6, 0, 0, 0],
      ]
      assert data.mask.tolist() == [1, 1, 1, 1]
  
      transform = ToDense(num_nodes=5)
      assert str(transform) == "ToDense(num_nodes=5)"
      data = Data(
          x=x, pos=pos, edge_index=edge_index, edge_attr=edge_attr, y=y
      )
      data = transform(data)
      assert len(data) == 5
      assert data.x.size() == (5, 4)
      assert data.x[:4].tolist() == x.tolist()
      assert data.x[4].tolist() == [0, 0, 0, 0]
      assert data.pos.size() == (5, 3)
      assert data.pos[:4].tolist() == pos.tolist()
      assert data.pos[4].tolist() == [0, 0, 0]
      assert data.y.size() == (5,)
      assert data.y[:4].tolist() == y.tolist()
      assert data.y[4].tolist() == 0
      assert data.adj.size() == (5, 5)
      assert data.adj.tolist() == [
          [0, 1, 2, 3, 0],
          [4, 0, 0, 0, 0],
          [5, 0, 0, 0, 0],
          [6, 0, 0, 0, 0],
          [0, 0, 0, 0, 0],
      ]
      assert data.mask.tolist() == [1, 1, 1, 1, 0]
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_to_dense() without return types