code: def test_neighbor_sample():
      adj = SparseTensor.from_edge_index(torch.tensor([[0], [1]]))
      colptr, row, _ = adj.csc()
  
      # Sampling in a non-directed way should not sample in wrong direction:
      out = neighbor_sample(
          colptr, row, torch.tensor([0]), [1], False, False
      )
      assert out[0].tolist() == [0]
      assert out[1].tolist() == []
      assert out[2].tolist() == []
  
      # Sampling should work:
      out = neighbor_sample(
          colptr, row, torch.tensor([1]), [1], False, False
      )
      assert out[0].tolist() == [1, 0]
      assert out[1].tolist() == [1]
      assert out[2].tolist() == [0]
  
      # Sampling with more hops:
      out = neighbor_sample(
          colptr, row, torch.tensor([1]), [1, 1], False, False
      )
      assert out[0].tolist() == [1, 0]
      assert out[1].tolist() == [1]
      assert out[2].tolist() == [0]
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_neighbor_sample() without return types