diff --git a/examples/neptune_plugin/neptune_plugin/neptune_example.py b/examples/neptune_plugin/neptune_plugin/neptune_example.py
index f74d2e580..b6a7949b8 100644
--- a/examples/neptune_plugin/neptune_plugin/neptune_example.py
+++ b/examples/neptune_plugin/neptune_plugin/neptune_example.py
@@ -62,6 +62,7 @@
 # %% [markdown]
 # First, we use a task to download the dataset and cache the data in Flyte:
 
+
 # %%
 @task(
     container_image=image,
@@ -85,6 +86,7 @@ def get_dataset() -> Tuple[np.ndarray, np.ndarray]:
 # in Neptune's integrations. In this example, we pass the `Run` object into Neptune's
 # XGBoost callback.
 
+
 # %%
 @task(
     container_image=image,
@@ -133,6 +135,7 @@ def train_model(max_depth: int, X: np.ndarray, y: np.ndarray):
 # With Flyte's dynamic workflows, we can scale up multiple training jobs with different
 # `max_depths`:
 
+
 # %%
 @dynamic(container_image=image)
 def train_multiple_models(max_depths: List[int], X: np.ndarray, y: np.ndarray):