code: def test_simple_conv(aggr, combine_root):
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = SimpleConv(aggr, combine_root)
      assert str(conv) == "SimpleConv()"
  
      num_aggrs = 1 if isinstance(aggr, str) else len(aggr)
      output_size = sum([8] * num_aggrs) + (
          8 if combine_root == "cat" else 0
      )
  
      out = conv(x1, edge_index)
      assert out.size() == (4, output_size)
      assert torch.allclose(conv(x1, edge_index, size=(4, 4)), out)
      assert torch.allclose(conv(x1, adj1.t()), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x1, adj2.t()), out)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1, edge_index), out)
          assert torch.allclose(jit(x1, edge_index, size=(4, 4)), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj2.t()), out)
  
      # Test bipartite message passing:
      if combine_root != "self_loop":
          adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
  
          out = conv((x1, x2), edge_index)
          assert out.size() == (2, output_size)
          assert torch.allclose(
              conv((x1, x2), edge_index, size=(4, 2)), out
          )
          assert torch.allclose(conv((x1, x2), adj1.t()), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              adj2 = SparseTensor.from_edge_index(
                  edge_index, sparse_sizes=(4, 2)
              )
              assert torch.allclose(conv((x1, x2), adj2.t()), out)
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import SimpleConv
  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_simple_conv(aggr, combine_root) without return types