code: def test_arma_conv():
      x = torch.randn(4, 16)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = ARMAConv(16, 32, num_stacks=8, num_layers=4)
      assert str(conv) == "ARMAConv(16, 32, num_stacks=8, num_layers=4)"
      out = conv(x, edge_index)
      assert out.size() == (4, 32)
      with pytest.raises(RuntimeError):  # No 3D feature tensor support.
          assert torch.allclose(conv(x, adj1.t()), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x, adj2.t()), out)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x, adj2.t()), out, atol=1e-6)
  
file_path: pytorch_geometric/test/nn/conv/test_arma_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import ARMAConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_csc_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_arma_conv() without return types