code: def test_arga():
      model = ARGA(
          encoder=lambda x: x, discriminator=lambda x: T([0.5])
      )
      model.reset_parameters()
  
      x = torch.tensor([[1.0, -1.0], [1.0, 2.0], [2.0, 1.0]])
      z = model.encode(x)
  
      assert float(model.reg_loss(z)) > 0
      assert float(model.discriminator_loss(z)) > 0
  
      if is_full_test():
          jit = torch.jit.export(model)
          assert torch.allclose(jit.encode(x), z)
          assert float(jit.reg_loss(z)) > 0
          assert float(jit.discriminator_loss(z)) > 0
  
file_path: pytorch_geometric/test/nn/models/test_autoencoder.py
imports: import torch
  from torch import Tensor
  from torch_geometric.data import Data
  from torch_geometric.nn import ARGA, ARGVA, GAE, VGAE
  from torch_geometric.testing import is_full_test
  from torch_geometric.transforms import RandomLinkSplit
label_desc: Use image node_id 3 for calling a global function with example usage: test_arga() without return types