code: def test_dense_graph_conv(aggr):
      channels = 16
      sparse_conv = GraphConv(channels, channels, aggr=aggr)
      dense_conv = DenseGraphConv(channels, channels, aggr=aggr)
      assert str(dense_conv) == "DenseGraphConv(16, 16)"
  
      # Ensure same weights and bias.
      dense_conv.lin_rel = sparse_conv.lin_rel
      dense_conv.lin_root = sparse_conv.lin_root
  
      x = torch.randn((5, channels))
      edge_index = torch.tensor(
          [[0, 0, 1, 1, 2, 2, 3, 4], [1, 2, 0, 2, 0, 1, 4, 3]]
      )
  
      sparse_out = sparse_conv(x, edge_index)
      assert sparse_out.size() == (5, channels)
  
      adj = to_dense_adj(edge_index)
      mask = torch.ones(5, dtype=torch.bool)
  
      dense_out = dense_conv(x, adj, mask)[0]
  
      assert dense_out.size() == (5, channels)
      assert torch.allclose(sparse_out, dense_out, atol=1e-04)
  
      if is_full_test():
          jit = torch.jit.script(dense_conv)
          assert torch.allclose(jit(x, adj, mask), dense_out)
  
imports: import pytest
  import torch
  from torch_geometric.nn import DenseGraphConv, GraphConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.utils import to_dense_adj
label_desc: Use image node_id 1 for calling a global function with example usage: test_dense_graph_conv(aggr) without return types