code: def test_compile_graph_breaks(Model, device):
      import torch._dynamo as dynamo
  
      x = torch.randn(3, 8, device=device)
      edge_index = torch.tensor(
          [[0, 1, 1, 2], [1, 0, 2, 1]], device=device
      )
  
      kwargs = {}
      if Model in {GCN, GAT}:
          # Adding self-loops inside the model leads to graph breaks :(
          kwargs["add_self_loops"] = False
  
      if Model in {PNA}:  # `PNA` requires additional arguments:
          kwargs["aggregators"] = [
              "sum",
              "mean",
              "min",
              "max",
              "var",
              "std",
          ]
          kwargs["scalers"] = [
              "identity",
              "amplification",
              "attenuation",
          ]
          kwargs["deg"] = torch.tensor([1, 2, 1])
  
      model = Model(
          in_channels=8,
          hidden_channels=16,
          num_layers=2,
          **kwargs,
      ).to(device)
  
      explanation = dynamo.explain(model)(x, edge_index)
      assert explanation.graph_break_count == 0
  
imports: import os
  import os.path
  import random
  import sys
  import warnings
  import pytest
  import torch
  import torch.nn.functional
  import torch_geometric.typing
  from torch_geometric.data import Data
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import SAGEConv
  from torch_geometric.nn.models import GAT, GCN, GIN, PNA, EdgeCNN, GraphSAGE
  from torch_geometric.profile import benchmark
  from torch_geometric.testing import onlyFullTest, onlyLinux, onlyNeighborSampler, onlyOnline, withCUDA, withPackage
label_desc: Use image node_id 15 for calling a global function with example usage: test_compile_graph_breaks(Model, device) without return types