code: def test_appnp_dropout():
      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))
  
      # With dropout probability of 1.0, the final output equals to alpha * x:
      conv = APPNP(K=2, alpha=0.1, dropout=1.0)
      assert torch.allclose(0.1 * x, conv(x, edge_index))
      assert torch.allclose(0.1 * x, conv(x, adj1.t()))
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(0.1 * x, conv(x, adj2.t()))
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import APPNP
  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 2 for calling a global function with example usage: test_appnp_dropout() without return types