code: def test_compile_multi_aggr_sage_conv(device):
      import torch._dynamo as dynamo
  
      x = torch.randn(4, 8, device=device)
      edge_index = torch.tensor(
          [[0, 1, 2, 3], [0, 0, 1, 1]], device=device
      )
  
      conv = SAGEConv(
          in_channels=8,
          out_channels=32,
          aggr=["mean", "sum", "min", "max", "std"],
      ).to(device)
  
      explanation = dynamo.explain(conv)(x, edge_index)
      assert explanation.graph_break_count == 0
  
      compiled_conv = torch.compile(conv)
  
      expected = conv(x, edge_index)
      out = compiled_conv(x, edge_index)
      assert torch.allclose(out, expected, atol=1e-6)
  
file_path: pytorch_geometric/test/nn/conv/test_sage_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import MLPAggregation, SAGEConv
  from torch_geometric.testing import assert_module, is_full_test, onlyLinux, withCUDA, withPackage
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 6 for calling a global function with example usage: test_compile_multi_aggr_sage_conv(device) without return types