code: def test_re_net(tmp_path):
      dataset = MyTestEventDataset(tmp_path, seq_len=4)
      loader = DataLoader(dataset, 2, follow_batch=["h_sub", "h_obj"])
  
      model = RENet(
          dataset.num_nodes,
          dataset.num_rels,
          hidden_channels=16,
          seq_len=4,
      )
  
      if is_full_test():
          jit = torch.jit.export(model)
  
      logits = torch.randn(6, 6)
      y = torch.tensor([0, 1, 2, 3, 4, 5])
  
      mrr, hits1, hits3, hits10 = model.test(logits, y)
      assert 0.15 < mrr <= 1
      assert hits1 <= hits3 and hits3 <= hits10 and hits10 == 1
  
      for data in loader:
          log_prob_obj, log_prob_sub = model(data)
          if is_full_test():
              log_prob_obj_jit, log_prob_sub_jit = jit(data)
              assert torch.allclose(log_prob_obj_jit, log_prob_obj)
              assert torch.allclose(log_prob_sub_jit, log_prob_sub)
          model.test(log_prob_obj, data.obj)
          model.test(log_prob_sub, data.sub)
  
file_path: pytorch_geometric/test/nn/models/test_re_net.py
imports: import torch
  from torch_geometric.datasets.icews import EventDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import RENet
  from torch_geometric.testing import is_full_test
label_desc: Use image node_id 1 for calling a global function with example usage: test_re_net(tmp_path) without return types