code: def test_transform(self):
      train_transform = Lambda(lambda k: 1)
      test_transform = Lambda(lambda k: 0)
      dataset = ActiveLearningDataset(
          MyDataset(train_transform),
          make_unlabelled=lambda x: (x[0], -1),
          pool_specifics={"transform": test_transform},
      )
      dataset.label(np.arange(10))
      pool = dataset.pool
      assert np.equal(
          [i for i in pool], [(0, -1) for i in np.arange(10, 100)]
      ).all()
      assert np.equal(
          [i for i in dataset], [(1, i) for i in np.arange(10)]
      ).all()
  
      with pytest.raises(ValueError) as e:
          ActiveLearningDataset(
              MyDataset(train_transform),
              pool_specifics={"whatever": 123},
          ).pool
  
      # Test warnings related to stochasticity of the pool.
      with warnings.catch_warnings(record=True) as w:
          _ = ActiveLearningDataset(
              MyDataset(Lambda(lambda k: np.random.rand()))
          )
          assert (
              len(w) > 0
              and issubclass(w[-1].category, UserWarning)
              and "It seems that data augmentation is not disabled when iterating on the pool."
              in str(w[-1].message)
          )
  
      with warnings.catch_warnings(record=True) as w:
          _ = ActiveLearningDataset(
              MyDataset(Lambda(lambda k: np.random.rand())),
              pool_specifics={"transform": test_transform},
          )
          assert len(w) == 0
  
imports: import pickle
  import unittest
  import unittest.mock
  import warnings
  import datasets
  import numpy
  import pytest
  import torch.utils.data
  from torchvision.transforms import Lambda
  from baal.active import ActiveLearningDataset
label_desc: Use image node_id 8 for calling the ActiveDatasetTest obj's underlying member method code with example usage: obj.test_transform() without return types