code: def test_my_static_graph_conv():
      x1 = torch.randn(3, 4, 8)
      x2 = torch.randn(3, 2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      value = torch.randn(edge_index.size(1))
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(edge_index, value, (4, 4))
  
      conv = MyConv(8, 32)
      out = conv(x1, edge_index, value)
      assert out.size() == (3, 4, 32)
      assert torch.allclose(conv(x1, edge_index, value, (4, 4)), out)
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(conv(x1, adj.t()), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(edge_index, value, (4, 2))
  
      conv = MyConv((8, 16), 32)
      out1 = conv((x1, x2), edge_index, value)
      out2 = conv((x1, None), edge_index, value, (4, 2))
      assert out1.size() == (3, 2, 32)
      assert out2.size() == (3, 2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, value, (4, 2)), out1
      )
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(conv((x1, x2), adj.t()), out1)
          assert torch.allclose(conv((x1, None), adj.t()), out2)
  
imports: import copy
  from typing import Tuple, Union
  import pytest
  import torch
  from torch import Tensor
  from torch.nn import Linear
  import torch_geometric.typing
  from torch_geometric.nn import MessagePassing, aggr
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size, SparseTensor
  from torch_geometric.utils import add_self_loops, scatter, spmm, to_torch_csc_tensor
label_desc: Use image node_id 5 for calling a global function with example usage: test_my_static_graph_conv() without return types