diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py
index cc8dd677da..8b4b400aab 100644
--- a/monai/transforms/croppad/array.py
+++ b/monai/transforms/croppad/array.py
@@ -397,9 +397,7 @@ def __init__(
                     data=roi_center, output_type=torch.Tensor, dtype=torch.int16, wrap_sequence=True
                 )
                 roi_size, *_ = convert_to_dst_type(src=roi_size, dst=roi_center, wrap_sequence=True)
-                roi_start_torch = maximum(
-                    roi_center - floor_divide(roi_size, 2), torch.zeros_like(roi_center)  # type: ignore
-                )
+                roi_start_torch = maximum(roi_center - floor_divide(roi_size, 2), torch.zeros_like(roi_center))  # type: ignore
                 roi_end_torch = maximum(roi_start_torch + roi_size, roi_start_torch)
             else:
                 if roi_start is None or roi_end is None:
diff --git a/monai/transforms/utils_create_transform_ims.py b/monai/transforms/utils_create_transform_ims.py
index 22e3d5f2d2..1c052e53fe 100644
--- a/monai/transforms/utils_create_transform_ims.py
+++ b/monai/transforms/utils_create_transform_ims.py
@@ -520,13 +520,7 @@ def create_transform_im(
     create_transform_im(RandKSpaceSpikeNoise, dict(prob=1, intensity_range=(10, 13)), data)
     create_transform_im(
         RandKSpaceSpikeNoised,
-        dict(
-            keys=CommonKeys.IMAGE,
-            global_prob=1,
-            prob=1,
-            common_sampling=True,
-            intensity_ranges={CommonKeys.IMAGE: (13, 15)},
-        ),
+        dict(keys=CommonKeys.IMAGE, global_prob=1, prob=1, common_sampling=True, intensity_range=(13, 15)),
         data,
     )
     create_transform_im(GibbsNoise, dict(alpha=0.8), data)
@@ -677,4 +671,8 @@ def create_transform_im(
         KeepLargestConnectedComponentd, dict(keys=CommonKeys.LABEL, applied_labels=1), data_binary, is_post=True, ndim=2
     )
     create_transform_im(RandGridDistortion, dict(num_cells=3, prob=1.0, distort_limit=(-0.1, 0.1)), data)
-    create_transform_im(RandGridDistortiond, dict(keys=keys, num_cells=5, prob=1.0, distort_limit=(-0.1, 0.1)), data)
+    create_transform_im(
+        RandGridDistortiond,
+        dict(keys=keys, num_cells=4, prob=1.0, distort_limit=(-0.2, 0.2), mode=["bilinear", "nearest"]),
+        data,
+    )
diff --git a/tests/test_rotated.py b/tests/test_rotated.py
index cd27dd5406..91918513b8 100644
--- a/tests/test_rotated.py
+++ b/tests/test_rotated.py
@@ -92,7 +92,7 @@ def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, al
             self.segn[0, 0], np.rad2deg(angle), (0, 2), not keep_size, order=0, mode=_mode, prefilter=False
         )
         expected = np.stack(expected).astype(int)
-        self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 130)
+        self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 160)
 
 
 class TestRotated3DXY(NumpyImageTestCase3D):
@@ -121,7 +121,7 @@ def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, al
             self.segn[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=0, mode=_mode, prefilter=False
         )
         expected = np.stack(expected).astype(int)
-        self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 130)
+        self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 160)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_transchex.py b/tests/test_transchex.py
index 716d3cc52e..e178cb5184 100644
--- a/tests/test_transchex.py
+++ b/tests/test_transchex.py
@@ -16,6 +16,7 @@
 
 from monai.networks import eval_mode
 from monai.networks.nets.transchex import Transchex
+from tests.utils import skip_if_quick
 
 TEST_CASE_TRANSCHEX = []
 for drop_out in [0.4]:
@@ -42,7 +43,8 @@
                                 TEST_CASE_TRANSCHEX.append(test_case)
 
 
-class TestPatchEmbeddingBlock(unittest.TestCase):
+@skip_if_quick
+class TestTranschex(unittest.TestCase):
     @parameterized.expand(TEST_CASE_TRANSCHEX)
     def test_shape(self, input_param, expected_shape):
         net = Transchex(**input_param)