code: def test_heat_conv(concat):
      x = torch.randn(4, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      edge_attr = torch.randn((4, 2))
      node_type = torch.tensor([0, 0, 1, 2])
      edge_type = torch.tensor([0, 2, 1, 2])
  
      conv = HEATConv(
          in_channels=8,
          out_channels=16,
          num_node_types=3,
          num_edge_types=3,
          edge_type_emb_dim=5,
          edge_dim=2,
          edge_attr_emb_dim=6,
          heads=2,
          concat=concat,
      )
      assert str(conv) == "HEATConv(8, 16, heads=2)"
  
      out = conv(x, edge_index, node_type, edge_type, edge_attr)
      assert out.size() == (4, 32 if concat else 16)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, edge_attr, (4, 4)
          )
          assert torch.allclose(
              conv(x, adj.t(), node_type, edge_type), out, atol=1e-5
          )
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit(x, edge_index, node_type, edge_type, edge_attr),
              out,
              atol=1e-5,
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit(x, adj.t(), node_type, edge_type), out, atol=1e-5
              )
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import HEATConv
  from torch_geometric.testing import is_full_test, withPackage
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_heat_conv(concat) without return types