class_docstr: cache dataset and persistent dataset should behave in the same way when used with different loader settings.
  loader's are tested with two epochs.
code: def test_hash_as_key(self, transform, expected_shape):
      test_image = nib.Nifti1Image(
          np.random.randint(0, 2, size=[128, 128, 128]).astype(float),
          np.eye(4),
      )
      with tempfile.TemporaryDirectory() as tempdir:
          test_data = []
          for i in ["1", "2", "2", "3", "3"]:
              for k in ["image", "label", "extra"]:
                  nib.save(
                      test_image,
                      os.path.join(tempdir, f"{k}{i}.nii.gz"),
                  )
              test_data.append(
                  {
                      k: os.path.join(tempdir, f"{k}{i}.nii.gz")
                      for k in ["image", "label", "extra"]
                  }
              )
  
          dataset = CacheDataset(
              data=test_data,
              transform=transform,
              cache_num=4,
              num_workers=2,
              hash_as_key=True,
          )
          self.assertEqual(len(dataset), 5)
          # ensure no duplicated cache content
          self.assertEqual(len(dataset._cache), 3)
          self.assertEqual(len(dataset._hash_keys), 3)
          self.assertEqual(dataset.cache_num, 3)
          data1 = dataset[0]
          data2 = dataset[1]
          data3 = dataset[-1]
          # test slice indices
          data4 = dataset[0:-1]
          self.assertEqual(len(data4), 4)
  
          if transform is None:
              self.assertEqual(
                  data1["image"], os.path.join(tempdir, "image1.nii.gz")
              )
              self.assertEqual(
                  data2["label"], os.path.join(tempdir, "label2.nii.gz")
              )
              self.assertEqual(
                  data3["image"], os.path.join(tempdir, "image3.nii.gz")
              )
          else:
              self.assertTupleEqual(
                  data1["image"].shape, expected_shape
              )
              self.assertTupleEqual(
                  data2["label"].shape, expected_shape
              )
              self.assertTupleEqual(
                  data3["image"].shape, expected_shape
              )
              for d in data4:
                  self.assertTupleEqual(
                      d["image"].shape, expected_shape
                  )
  
          test_data2 = test_data[:3]
          dataset.set_data(data=test_data2)
          self.assertEqual(len(dataset), 3)
          # ensure no duplicated cache content
          self.assertEqual(len(dataset._cache), 2)
          self.assertEqual(dataset.cache_num, 2)
  
imports: from __future__ import annotations
  import os
  import sys
  import tempfile
  import unittest
  import nibabel
  import numpy
  from parameterized import parameterized
  from monai.data import CacheDataset, DataLoader, PersistentDataset, SmartCacheDataset
  from monai.transforms import Compose, Lambda, LoadImaged, RandLambda, ThreadUnsafe, Transform
  from monai.utils.module import pytorch_after
label_desc: Use image node_id 2 for calling the TestCacheThread obj's underlying member method code with example usage: obj.test_hash_as_key(transform, expected_shape) without return types