code: def test_dense_sage_conv():
      channels = 16
      sparse_conv = SAGEConv(channels, channels, normalize=True)
      dense_conv = DenseSAGEConv(channels, channels, normalize=True)
      assert str(dense_conv) == "DenseSAGEConv(16, 16)"
  
      # Ensure same weights and bias.
      dense_conv.lin_rel = sparse_conv.lin_l
      dense_conv.lin_root = sparse_conv.lin_r
  
      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)
  
      x = torch.cat([x, x.new_zeros(1, channels)], dim=0).view(
          2, 3, channels
      )
      adj = torch.tensor(
          [
              [
                  [0.0, 1.0, 1.0],
                  [1.0, 0.0, 1.0],
                  [1.0, 1.0, 0.0],
              ],
              [
                  [0.0, 1.0, 0.0],
                  [1.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0],
              ],
          ]
      )
      mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.bool)
  
      dense_out = dense_conv(x, adj, mask)
      assert dense_out.size() == (2, 3, channels)
  
      if is_full_test():
          jit = torch.jit.script(dense_conv)
          assert torch.allclose(jit(x, adj, mask), dense_out)
  
      assert dense_out[1, 2].abs().sum().item() == 0
      dense_out = dense_out.view(6, channels)[:-1]
      assert torch.allclose(sparse_out, dense_out, atol=1e-04)
  
imports: import torch
  from torch_geometric.nn import DenseSAGEConv, SAGEConv
  from torch_geometric.testing import is_full_test
label_desc: Use image node_id 1 for calling a global function with example usage: test_dense_sage_conv() without return types