code: def test_hetero_in_memory_dataset():
      data1 = HeteroData()
      data1.y = torch.randn(5)
      data1["paper"].x = torch.randn(10, 16)
      data1["paper", "paper"].edge_index = torch.randint(
          0, 10, (2, 30)
      ).long()
  
      data2 = HeteroData()
      data2.y = torch.randn(5)
      data2["paper"].x = torch.randn(10, 16)
      data2["paper", "paper"].edge_index = torch.randint(
          0, 10, (2, 30)
      ).long()
  
      dataset = MyTestDataset([data1, data2])
      assert str(dataset) == "MyTestDataset(2)"
      assert len(dataset) == 2
  
      assert len(dataset[0]) == 3
      assert dataset[0].y.tolist() == data1.y.tolist()
      assert dataset[0]["paper"].x.tolist() == data1["paper"].x.tolist()
      assert (
          dataset[0]["paper", "paper"].edge_index.tolist()
          == data1["paper", "paper"].edge_index.tolist()
      )
  
      assert len(dataset[1]) == 3
      assert dataset[1].y.tolist() == data2.y.tolist()
      assert dataset[1]["paper"].x.tolist() == data2["paper"].x.tolist()
      assert (
          dataset[1]["paper", "paper"].edge_index.tolist()
          == data2["paper", "paper"].edge_index.tolist()
      )
  
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 10 for calling a global function with example usage: test_hetero_in_memory_dataset() without return types