code: def test_zip_loader(filter_per_worker):
      x = torch.arange(100)
      edge_index = torch.randint(0, 100, (2, 1000))
      data = Data(x=x, edge_index=edge_index)
  
      loaders = [
          NeighborLoader(data, [5], input_nodes=torch.arange(0, 50)),
          NeighborLoader(data, [5], input_nodes=torch.arange(50, 95)),
      ]
  
      loader = ZipLoader(
          loaders, batch_size=10, filter_per_worker=filter_per_worker
      )
  
      assert str(loader) == (
          "ZipLoader(loaders=[NeighborLoader(), " "NeighborLoader()])"
      )
      assert len(loader) == 5
      assert loader.dataset == range(0, 45)
  
      for i, (batch1, batch2) in enumerate(loader):
          n_id1 = batch1.n_id[: batch1.batch_size]
          n_id2 = batch2.n_id[: batch2.batch_size]
  
          if i < 4:
              assert batch1.batch_size == 10
              assert batch2.batch_size == 10
              assert torch.equal(
                  n_id1, torch.arange(0 + i * 10, 10 + i * 10)
              )
              assert torch.equal(
                  n_id2, torch.arange(50 + i * 10, 60 + i * 10)
              )
          else:
              assert batch1.batch_size == 5
              assert batch2.batch_size == 5
              assert torch.equal(
                  n_id1, torch.arange(0 + i * 10, 5 + i * 10)
              )
              assert torch.equal(
                  n_id2, torch.arange(50 + i * 10, 55 + i * 10)
              )
  
imports: import pytest
  import torch
  from torch_geometric.data import Data
  from torch_geometric.loader import NeighborLoader, ZipLoader
  from torch_geometric.testing import onlyNeighborSampler
label_desc: Use image node_id 1 for calling a global function with example usage: test_zip_loader(filter_per_worker) without return types