code: def test_ego_k_hop_sample_adj():
      rowptr = torch.tensor([0, 3, 5, 9, 10, 12, 14])
      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])
      _ = SparseTensor(row=row, col=col, sparse_sizes=(6, 6))
  
      nid = torch.tensor([0, 1])
      fn = torch.ops.torch_sparse.ego_k_hop_sample_adj
      out = fn(rowptr, col, nid, 1, 3, False)
      rowptr, col, nid, eid, ptr, root_n_id = out
  
      assert nid.tolist() == [0, 1, 2, 3, 0, 1, 2]
      assert rowptr.tolist() == [0, 3, 5, 7, 8, 10, 12, 14]
      #      row             [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]
      assert eid.tolist() == [0, 1, 2, 3, 4, 5, 6, 9, 0, 1, 3, 4, 5, 6]
      assert ptr.tolist() == [0, 4, 7]
      assert root_n_id.tolist() == [0, 5]
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_ego_k_hop_sample_adj() without return types