code: def test_shadow_k_hop_sampler():
      row = torch.tensor([0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 4, 4, 5, 5])
      col = torch.tensor([1, 2, 3, 0, 2, 0, 1, 4, 5, 0, 2, 5, 2, 4])
      edge_index = torch.stack([row, col], dim=0)
      edge_weight = torch.arange(row.size(0))
      x = torch.randn(6, 16)
      y = torch.randint(3, (6,), dtype=torch.long)
      data = Data(
          edge_index=edge_index, edge_weight=edge_weight, x=x, y=y
      )
  
      train_mask = torch.tensor([1, 1, 0, 0, 0, 0], dtype=torch.bool)
      loader = ShaDowKHopSampler(
          data,
          depth=1,
          num_neighbors=3,
          node_idx=train_mask,
          batch_size=2,
      )
      assert len(loader) == 1
  
      batch1 = next(iter(loader))
      assert batch1.num_graphs == len(batch1) == 2
  
      assert batch1.batch.tolist() == [0, 0, 0, 0, 1, 1, 1]
      assert batch1.ptr.tolist() == [0, 4, 7]
      assert batch1.root_n_id.tolist() == [0, 5]
      assert (
          batch1.x.tolist()
          == x[torch.tensor([0, 1, 2, 3, 0, 1, 2])].tolist()
      )
      assert batch1.y.tolist() == y[train_mask].tolist()
      row, col = batch1.edge_index
      assert row.tolist() == [0, 0, 0, 1, 1, 2, 2, 3, 4, 4, 5, 5, 6, 6]
      assert col.tolist() == [1, 2, 3, 0, 2, 0, 1, 0, 5, 6, 4, 6, 4, 5]
      e_id = torch.tensor([0, 1, 2, 3, 4, 5, 6, 9, 0, 1, 3, 4, 5, 6])
      assert batch1.edge_weight.tolist() == edge_weight[e_id].tolist()
  
      adj_t = SparseTensor(
          row=edge_index[0], col=edge_index[1], value=edge_weight
      ).t()
      data = Data(adj_t=adj_t, x=x, y=y)
  
      loader = ShaDowKHopSampler(
          data,
          depth=1,
          num_neighbors=3,
          node_idx=train_mask,
          batch_size=2,
      )
      assert len(loader) == 1
  
      batch2 = next(iter(loader))
      assert batch2.num_graphs == len(batch2) == 2
  
      assert batch1.batch.tolist() == batch2.batch.tolist()
      assert batch1.ptr.tolist() == batch2.ptr.tolist()
      assert batch1.root_n_id.tolist() == batch2.root_n_id.tolist()
      assert batch1.x.tolist() == batch2.x.tolist()
      assert batch1.y.tolist() == batch2.y.tolist()
      row, col, value = batch2.adj_t.t().coo()
      assert batch1.edge_index[0].tolist() == row.tolist()
      assert batch1.edge_index[1].tolist() == col.tolist()
      assert batch1.edge_weight.tolist() == value.tolist()
  
imports: import torch
  from torch_geometric.data import Data
  from torch_geometric.loader import ShaDowKHopSampler
  from torch_geometric.testing import withPackage
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_shadow_k_hop_sampler() without return types