code: def test_shadow(num_workers):
      g = dgl.data.CoraFullDataset()[0]
      sampler = dgl.dataloading.ShaDowKHopSampler([5, 10, 15])
      dataloader = dgl.dataloading.DataLoader(
          g,
          torch.arange(g.num_nodes()),
          sampler,
          batch_size=5,
          shuffle=True,
          drop_last=False,
          num_workers=num_workers,
      )
      for i, (input_nodes, output_nodes, subgraph) in enumerate(
          dataloader
      ):
          assert torch.equal(input_nodes, subgraph.ndata[dgl.NID])
          assert torch.equal(
              input_nodes[: output_nodes.shape[0]], output_nodes
          )
          assert torch.equal(
              subgraph.ndata["label"], g.ndata["label"][input_nodes]
          )
          assert torch.equal(
              subgraph.ndata["feat"], g.ndata["feat"][input_nodes]
          )
          if i == 5:
              break
  
imports: import os
  import unittest
  from collections.abc import Iterator, Mapping
  from functools import partial
  import backend
  import dgl
  import dgl.ops
  import numpy
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from utils import parametrize_idtype
label_desc: Use image node_id 3 for calling a global function with example usage: test_shadow(num_workers) without return types