code: def test_to_superpixels(tmp_path):
      import torchvision.transforms as T
      from torchvision.datasets.mnist import (
          MNIST,
          read_image_file,
          read_label_file,
      )
  
      raw_folder = osp.join(tmp_path, "MNIST", "raw")
      processed_folder = osp.join(tmp_path, "MNIST", "processed")
  
      os.makedirs(raw_folder, exist_ok=True)
      os.makedirs(processed_folder, exist_ok=True)
      for resource in resources:
          path = download_url(resource, raw_folder)
          extract_gz(path, osp.join(tmp_path, raw_folder))
  
      test_set = (
          read_image_file(
              osp.join(raw_folder, "t10k-images-idx3-ubyte")
          ),
          read_label_file(
              osp.join(raw_folder, "t10k-labels-idx1-ubyte")
          ),
      )
  
      torch.save(test_set, osp.join(processed_folder, "training.pt"))
      torch.save(test_set, osp.join(processed_folder, "test.pt"))
  
      dataset = MNIST(tmp_path, download=False)
  
      dataset.transform = T.Compose([T.ToTensor(), ToSLIC()])
  
      data, y = dataset[0]
      assert len(data) == 2
      assert data.pos.dim() == 2 and data.pos.size(1) == 2
      assert data.x.dim() == 2 and data.x.size(1) == 1
      assert data.pos.size(0) == data.x.size(0)
      assert y == 7
  
      loader = DataLoader(dataset, batch_size=2, shuffle=False)
      for batch, y in loader:
          assert batch.num_graphs == len(batch) == 2
          assert batch.pos.dim() == 2 and batch.pos.size(1) == 2
          assert batch.x.dim() == 2 and batch.x.size(1) == 1
          assert batch.batch.dim() == 1
          assert batch.ptr.dim() == 1
          assert (
              batch.pos.size(0)
              == batch.x.size(0)
              == batch.batch.size(0)
          )
          assert y.tolist() == [7, 2]
          break
  
      dataset.transform = T.Compose(
          [T.ToTensor(), ToSLIC(add_seg=True, add_img=True)]
      )
  
      data, y = dataset[0]
      assert len(data) == 4
      assert data.pos.dim() == 2 and data.pos.size(1) == 2
      assert data.x.dim() == 2 and data.x.size(1) == 1
      assert data.pos.size(0) == data.x.size(0)
      assert data.seg.size() == (1, 28, 28)
      assert data.img.size() == (1, 1, 28, 28)
      assert data.seg.max().item() + 1 == data.x.size(0)
      assert y == 7
  
      loader = DataLoader(dataset, batch_size=2, shuffle=False)
      for batch, y in loader:
          assert batch.num_graphs == len(batch) == 2
          assert batch.pos.dim() == 2 and batch.pos.size(1) == 2
          assert batch.x.dim() == 2 and batch.x.size(1) == 1
          assert batch.batch.dim() == 1
          assert batch.ptr.dim() == 1
          assert (
              batch.pos.size(0)
              == batch.x.size(0)
              == batch.batch.size(0)
          )
          assert batch.seg.size() == (2, 28, 28)
          assert batch.img.size() == (2, 1, 28, 28)
          assert y.tolist() == [7, 2]
          break
  
imports: import os
  import os.path
  import torch
  from torch_geometric.data import download_url, extract_gz
  from torch_geometric.loader import DataLoader
  from torch_geometric.testing import onlyOnline, withPackage
  from torch_geometric.transforms import ToSLIC
label_desc: Use image node_id 1 for calling a global function with example usage: test_to_superpixels(tmp_path) without return types