diff --git a/test/test_datasets.py b/test/test_datasets.py
index b3088686ba3..05f0f72d291 100644
--- a/test/test_datasets.py
+++ b/test/test_datasets.py
@@ -206,9 +206,6 @@ def inject_fake_data(self, tmpdir, config):
 
         return num_images_per_category * len(categories)
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self)
-
 
 class WIDERFaceTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.WIDERFace
@@ -486,9 +483,6 @@ def test_class_to_idx(self):
             actual = dataset.class_to_idx
             assert actual == expected
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self)
-
 
 class CIFAR100(CIFAR10TestCase):
     DATASET_CLASS = datasets.CIFAR100
@@ -503,9 +497,6 @@ class CIFAR100(CIFAR10TestCase):
         categories_key="fine_label_names",
     )
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self)
-
 
 class CelebATestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.CelebA
@@ -901,9 +892,6 @@ def _create_annotation_file(self, root, name, video_files):
         with open(pathlib.Path(root) / name, "w") as fh:
             fh.writelines(f"{str(file).replace(os.sep, '/')}\n" for file in sorted(video_files))
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self, config=dict(output_format="TCHW"))
-
 
 class LSUNTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.LSUN
@@ -1073,9 +1061,6 @@ def _create_split_files(self, root, video_files, fold, train):
 
         return num_train_videos if train else (num_videos - num_train_videos)
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self, config=dict(output_format="TCHW"))
-
 
 class OmniglotTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.Omniglot
@@ -1487,9 +1472,6 @@ def _magic(self, dtype, dims):
     def _encode(self, v):
         return torch.tensor(v, dtype=torch.int32).numpy().tobytes()[::-1]
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self)
-
 
 class FashionMNISTTestCase(MNISTTestCase):
     DATASET_CLASS = datasets.FashionMNIST
@@ -1641,9 +1623,6 @@ def test_classes(self, config):
             assert len(dataset.classes) == len(info["classes"])
             assert all([a == b for a, b in zip(dataset.classes, info["classes"])])
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self)
-
 
 class ImageFolderTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.ImageFolder
@@ -1665,9 +1644,6 @@ def test_classes(self, config):
             assert len(dataset.classes) == len(info["classes"])
             assert all([a == b for a, b in zip(dataset.classes, info["classes"])])
 
-    def test_transforms_v2_wrapper(self):
-        datasets_utils.check_transforms_v2_wrapper(self)
-
 
 class KittiTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.Kitti