code: def test_sample_adj():
      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])
      value = torch.arange(row.size(0))
      adj_t = SparseTensor(
          row=row, col=col, value=value, sparse_sizes=(6, 6)
      )
  
      out, n_id = sample_adj(
          adj_t, torch.arange(2, 6), num_neighbors=-1
      )
  
      assert n_id.tolist() == [2, 3, 4, 5, 0, 1]
  
      row, col, val = out.coo()
      assert row.tolist() == [0, 0, 0, 0, 1, 2, 2, 3, 3]
      assert col.tolist() == [2, 3, 4, 5, 4, 0, 3, 0, 2]
      assert val.tolist() == [7, 8, 5, 6, 9, 10, 11, 12, 13]
  
      out, n_id = sample_adj(
          adj_t, torch.arange(2, 6), num_neighbors=2, replace=True
      )
      assert out.nnz() == 8
  
      out, n_id = sample_adj(
          adj_t, torch.arange(2, 6), num_neighbors=2, replace=False
      )
      assert out.nnz() == 7
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_sample_adj() without return types