code: def test_to_datapipe():
      x = torch.randn(3, 8)
      edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
      data = Data(x=x, edge_index=edge_index)
      dataset = MyTestDataset([data, data])
  
      dp = dataset.to_datapipe()
  
      assert isinstance(dp, torch.utils.data.IterDataPipe)
      assert len(dp) == 2
  
      assert torch.equal(dataset[0].x, list(dp)[0].x)
      assert torch.equal(dataset[0].edge_index, list(dp)[0].edge_index)
      assert torch.equal(dataset[1].x, list(dp)[1].x)
      assert torch.equal(dataset[1].edge_index, list(dp)[1].edge_index)
  
imports: import copy
  import pytest
  import torch
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Data, HeteroData, InMemoryDataset
  from torch_geometric.datasets import KarateClub
  from torch_geometric.testing import withPackage
  from torch_geometric.transforms import BaseTransform
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 7 for calling a global function with example usage: test_to_datapipe() without return types