code: def test_compile():
      import torch._dynamo as dynamo
  
      class Model(torch.nn.Module):
          def forward(self, x: Tensor, edge_index: EdgeIndex) -> Tensor:
              row, col = edge_index[0], edge_index[1]
              x_j = x[row]
              out = scatter(x_j, col, dim_size=edge_index.num_cols)
              return out
  
      x = torch.randn(3, 8)
      # Test that `num_cols` gets picked up by making last node isolated.
      edge_index = EdgeIndex(
          [[0, 1, 1, 2], [1, 0, 0, 1]], sparse_size=(3, 3)
      )
  
      model = Model()
      expected = model(x, edge_index)
      assert expected.size() == (3, 8)
  
      explanation = dynamo.explain(model)(x, edge_index)
      assert explanation.graph_break_count <= 0
  
      compiled_model = torch.compile(model)
      out = compiled_model(x, edge_index)
      assert torch.allclose(out, expected)
  
imports: import os.path
  import warnings
  from typing import List, Optional
  import pytest
  import torch
  from torch import Tensor, tensor
  import torch_geometric
  from torch_geometric import EdgeIndex
  from torch_geometric.edge_index import SUPPORTED_DTYPES, ReduceType, SortReturnType, _scatter_spmm, _torch_sparse_spmm, _TorchSPMM, set_tuple_item
  from torch_geometric.profile import benchmark
  from torch_geometric.testing import onlyCUDA, onlyLinux, withCUDA, withoutExtensions, withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import scatter
label_desc: Use image node_id 35 for calling a global function with example usage: test_compile() and returns: out