code: def test_gps_conv(norm, attn_type):
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]])
      batch = torch.tensor([0, 0, 1, 1])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = GPSConv(
          16,
          conv=SAGEConv(16, 16),
          heads=4,
          norm=norm,
          attn_type=attn_type,
      )
      conv.reset_parameters()
      assert str(conv) == (
          f"GPSConv(16, conv=SAGEConv(16, 16, aggr=mean), "
          f"heads=4, attn_type={attn_type})"
      )
  
      out = conv(x, edge_index)
      assert out.size() == (4, 16)
      assert torch.allclose(conv(x, adj1.t()), out, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x, adj2.t()), out, atol=1e-6)
  
      out = conv(x, edge_index, batch)
      assert out.size() == (4, 16)
      assert torch.allclose(conv(x, adj1.t(), batch), out, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(
              conv(x, adj2.t(), batch), out, atol=1e-6
          )
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import GPSConv, SAGEConv
  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_gps_conv(norm, attn_type) without return types