diff --git a/tests/tests_nn/test_transformers.py b/tests/tests_nn/test_transformers.py
index 22902711..145dafbe 100644
--- a/tests/tests_nn/test_transformers.py
+++ b/tests/tests_nn/test_transformers.py
@@ -15,91 +15,91 @@ def create_input(shape):
     return data
 
 
-# @pytest.mark.parametrize(
-#     "shape",
-#     [
-#         [3, 2, 32, 32],
-#         [3, 2, 16, 16],
-#     ],
-# )
-# @pytest.mark.parametrize(
-#     "embedding_dim",
-#     [20],
-# )
-# @pytest.mark.parametrize(
-#     "patch_size",
-#     [140],
-# )
-# @pytest.mark.parametrize(
-#     "encoder_depths, encoder_num_heads, bottleneck_depth, bottleneck_num_heads",
-#     [
-#         [(2, 2, 2), (1, 2, 4), 1, 8],
-#         [(2, 2, 2, 2), (1, 2, 4, 8), 2, 8],
-#     ],
-# )
-# @pytest.mark.parametrize(
-#     "patch_norm",
-#     [True, False],
-# )
-# @pytest.mark.parametrize(
-#     "win_size",
-#     [8],
-# )
-# @pytest.mark.parametrize(
-#     "mlp_ratio",
-#     [2],
-# )
-# @pytest.mark.parametrize(
-#     "qkv_bias",
-#     [True, False],
-# )
-# @pytest.mark.parametrize(
-#     "qk_scale",
-#     [None, 0.5],
-# )
-# @pytest.mark.parametrize(
-#     "token_projection",
-#     [AttentionTokenProjectionType.LINEAR, AttentionTokenProjectionType.CONV],
-# )
-# @pytest.mark.parametrize(
-#     "token_mlp",
-#     [LeWinTransformerMLPTokenType.FFN, LeWinTransformerMLPTokenType.MLP, LeWinTransformerMLPTokenType.LEFF],
-# )
-# def test_uformer(
-#     shape,
-#     patch_size,
-#     embedding_dim,
-#     encoder_depths,
-#     encoder_num_heads,
-#     bottleneck_depth,
-#     bottleneck_num_heads,
-#     win_size,
-#     mlp_ratio,
-#     patch_norm,
-#     qkv_bias,
-#     qk_scale,
-#     token_projection,
-#     token_mlp,
-# ):
-#     model = UFormerModel(
-#         patch_size=patch_size,
-#         in_channels=2,
-#         embedding_dim=embedding_dim,
-#         encoder_depths=encoder_depths,
-#         encoder_num_heads=encoder_num_heads,
-#         bottleneck_depth=bottleneck_depth,
-#         bottleneck_num_heads=bottleneck_num_heads,
-#         win_size=win_size,
-#         mlp_ratio=mlp_ratio,
-#         qkv_bias=qkv_bias,
-#         qk_scale=qk_scale,
-#         patch_norm=patch_norm,
-#         token_projection=token_projection,
-#         token_mlp=token_mlp,
-#     )
-#     data = create_input(shape).cpu()
-#     out = model(data)
-#     assert list(out.shape) == shape
+@pytest.mark.parametrize(
+    "shape",
+    [
+        [3, 2, 32, 32],
+        [3, 2, 16, 16],
+    ],
+)
+@pytest.mark.parametrize(
+    "embedding_dim",
+    [20],
+)
+@pytest.mark.parametrize(
+    "patch_size",
+    [140],
+)
+@pytest.mark.parametrize(
+    "encoder_depths, encoder_num_heads, bottleneck_depth, bottleneck_num_heads",
+    [
+        [(2, 2, 2), (1, 2, 4), 1, 8],
+        [(2, 2, 2, 2), (1, 2, 4, 8), 2, 8],
+    ],
+)
+@pytest.mark.parametrize(
+    "patch_norm",
+    [True, False],
+)
+@pytest.mark.parametrize(
+    "win_size",
+    [8],
+)
+@pytest.mark.parametrize(
+    "mlp_ratio",
+    [2],
+)
+@pytest.mark.parametrize(
+    "qkv_bias",
+    [True, False],
+)
+@pytest.mark.parametrize(
+    "qk_scale",
+    [None, 0.5],
+)
+@pytest.mark.parametrize(
+    "token_projection",
+    [AttentionTokenProjectionType.LINEAR, AttentionTokenProjectionType.CONV],
+)
+@pytest.mark.parametrize(
+    "token_mlp",
+    [LeWinTransformerMLPTokenType.FFN, LeWinTransformerMLPTokenType.MLP, LeWinTransformerMLPTokenType.LEFF],
+)
+def test_uformer(
+    shape,
+    patch_size,
+    embedding_dim,
+    encoder_depths,
+    encoder_num_heads,
+    bottleneck_depth,
+    bottleneck_num_heads,
+    win_size,
+    mlp_ratio,
+    patch_norm,
+    qkv_bias,
+    qk_scale,
+    token_projection,
+    token_mlp,
+):
+    model = UFormerModel(
+        patch_size=patch_size,
+        in_channels=2,
+        embedding_dim=embedding_dim,
+        encoder_depths=encoder_depths,
+        encoder_num_heads=encoder_num_heads,
+        bottleneck_depth=bottleneck_depth,
+        bottleneck_num_heads=bottleneck_num_heads,
+        win_size=win_size,
+        mlp_ratio=mlp_ratio,
+        qkv_bias=qkv_bias,
+        qk_scale=qk_scale,
+        patch_norm=patch_norm,
+        token_projection=token_projection,
+        token_mlp=token_mlp,
+    )
+    data = create_input(shape).cpu()
+    out = model(data)
+    assert list(out.shape) == shape
 
 
 @pytest.mark.parametrize(