code: def test_han_conv_empty_tensor():
      x_dict = {
          "author": torch.randn(6, 16),
          "paper": torch.empty(0, 12),
      }
      edge_index_dict = {
          ("paper", "to", "author"): torch.empty(
              (2, 0), dtype=torch.long
          ),
          ("author", "to", "paper"): torch.empty(
              (2, 0), dtype=torch.long
          ),
          ("paper", "to", "paper"): torch.empty(
              (2, 0), dtype=torch.long
          ),
      }
  
      metadata = (list(x_dict.keys()), list(edge_index_dict.keys()))
      in_channels = {"author": 16, "paper": 12}
      conv = HANConv(in_channels, 16, metadata, heads=2)
  
      out_dict = conv(x_dict, edge_index_dict)
      assert len(out_dict) == 2
      assert out_dict["author"].size() == (6, 16)
      assert torch.all(out_dict["author"] == 0)
      assert out_dict["paper"].size() == (0, 16)
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import HANConv
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import coalesce, to_torch_csc_tensor
label_desc: Use image node_id 3 for calling a global function with example usage: test_han_conv_empty_tensor() without return types