diff --git a/generative/networks/nets/autoencoderkl.py b/generative/networks/nets/autoencoderkl.py
index 366c8ee2..7989d13f 100644
--- a/generative/networks/nets/autoencoderkl.py
+++ b/generative/networks/nets/autoencoderkl.py
@@ -612,7 +612,9 @@ def __init__(
             num_res_blocks = ensure_tuple_rep(num_res_blocks, len(num_channels))
 
         if len(num_res_blocks) != len(num_channels):
-            raise ValueError("`num_res_blocks` should be a single integer or a tuple of integers with the same length as `num_channels`.")
+            raise ValueError(
+                "`num_res_blocks` should be a single integer or a tuple of integers with the same length as `num_channels`."
+            )
 
         self.encoder = Encoder(
             spatial_dims=spatial_dims,
diff --git a/generative/networks/nets/diffusion_model_unet.py b/generative/networks/nets/diffusion_model_unet.py
index 38b532ae..54dfa277 100644
--- a/generative/networks/nets/diffusion_model_unet.py
+++ b/generative/networks/nets/diffusion_model_unet.py
@@ -1655,7 +1655,9 @@ def __init__(
             num_res_blocks = ensure_tuple_rep(num_res_blocks, len(num_channels))
 
         if len(num_res_blocks) != len(num_channels):
-            raise ValueError("`num_res_blocks` should be a single integer or a tuple of integers with the same length as `num_channels`.")
+            raise ValueError(
+                "`num_res_blocks` should be a single integer or a tuple of integers with the same length as `num_channels`."
+            )
 
         self.in_channels = in_channels
         self.block_out_channels = num_channels
diff --git a/generative/networks/schedulers/ddim.py b/generative/networks/schedulers/ddim.py
index 480e79e3..d1395113 100644
--- a/generative/networks/schedulers/ddim.py
+++ b/generative/networks/schedulers/ddim.py
@@ -103,7 +103,6 @@ def __init__(
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0
 
-
         self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].astype(np.int64))
 
         self.clip_sample = clip_sample
diff --git a/generative/networks/schedulers/pndm.py b/generative/networks/schedulers/pndm.py
index 2502c517..0a1f2018 100644
--- a/generative/networks/schedulers/pndm.py
+++ b/generative/networks/schedulers/pndm.py
@@ -117,11 +117,11 @@ def __init__(
         self.cur_sample = None
         self.ets = []
 
-
         self._timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
 
         # default the number of inference timesteps to the number of train steps
         self.set_timesteps(num_train_timesteps)
+
     def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
diff --git a/tests/test_scheduler_pndm.py b/tests/test_scheduler_pndm.py
index 0e8b49e1..ee0cda29 100644
--- a/tests/test_scheduler_pndm.py
+++ b/tests/test_scheduler_pndm.py
@@ -39,7 +39,6 @@ def test_add_noise_2d_shape(self, input_param, input_shape, expected_shape):
         noisy = scheduler.add_noise(original_samples=original_sample, noise=noise, timesteps=timesteps)
         self.assertEqual(noisy.shape, expected_shape)
 
-
     @parameterized.expand(TEST_CASES)
     def test_step_shape(self, input_param, input_shape, expected_shape):
         scheduler = PNDMScheduler(**input_param)