code: def test_in_memory_num_classes():
      dataset = MyTestDataset([Data(), Data()])
      assert dataset.num_classes == 0
  
      dataset = MyTestDataset([Data(y=0), Data(y=1)])
      assert dataset.num_classes == 2
  
      dataset = MyTestDataset([Data(y=1.5), Data(y=2.5), Data(y=3.5)])
      with pytest.warns(UserWarning, match="unique elements"):
          assert dataset.num_classes == 3
  
      dataset = MyTestDataset(
          [
              Data(y=torch.tensor([[0, 1, 0, 1]])),
              Data(y=torch.tensor([[1, 0, 0, 0]])),
              Data(y=torch.tensor([[0, 0, 1, 0]])),
          ]
      )
      assert dataset.num_classes == 4
  
      # Test when `__getitem__` returns a tuple of data objects.
      def transform(data):
          copied_data = copy.copy(data)
          copied_data.y += 1
          return data, copied_data, "foo"
  
      dataset = MyTestDataset(
          [Data(y=0), Data(y=1)], transform=transform
      )
      assert dataset.num_classes == 3
  
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 5 for calling a global function with example usage: test_in_memory_num_classes() and returns: data,  copied_data,  str