diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py
index bd9ca1b2..1e000439 100644
--- a/lerobot/common/datasets/lerobot_dataset.py
+++ b/lerobot/common/datasets/lerobot_dataset.py
@@ -462,7 +462,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             download_videos (bool, optional): Flag to download the videos. Note that when set to True but the
                 video files are already present on local disk, they won't be downloaded again. Defaults to
                 True.
-            video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec.  
+            video_backend (str | None, optional): Video backend to use for decoding videos. Defaults to torchcodec.
                 You can also use the 'pyav' decoder used by Torchvision.
         """
         super().__init__()
@@ -707,9 +707,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         item = {}
         for vid_key, query_ts in query_timestamps.items():
             video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
-            frames = decode_video_frames(
-                video_path, query_ts, self.tolerance_s, self.video_backend
-            )
+            frames = decode_video_frames(video_path, query_ts, self.tolerance_s, self.video_backend)
             item[vid_key] = frames.squeeze(0)
 
         return item
diff --git a/lerobot/common/datasets/video_utils.py b/lerobot/common/datasets/video_utils.py
index 27a60943..9e042d92 100644
--- a/lerobot/common/datasets/video_utils.py
+++ b/lerobot/common/datasets/video_utils.py
@@ -29,30 +29,32 @@ from datasets.features.features import register_feature
 from PIL import Image
 from torchcodec.decoders import VideoDecoder
 
+
 def decode_video_frames(
     video_path: Path | str,
     timestamps: list[float],
     tolerance_s: float,
     backend: str = "torchcodec",
 ) -> torch.Tensor:
-        """
-        Decodes video frames using the specified backend.
-        
-        Args:
-            video_path (Path): Path to the video file.
-            query_ts (list[float]): List of timestamps to extract frames.
-        
-        Returns:
-            torch.Tensor: Decoded frames.
+    """
+    Decodes video frames using the specified backend.
+
+    Args:
+        video_path (Path): Path to the video file.
+        query_ts (list[float]): List of timestamps to extract frames.
+
+    Returns:
+        torch.Tensor: Decoded frames.
+
+    Currently supports torchcodec on cpu and pyav.
+    """
+    if backend == "torchcodec":
+        return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)
+    elif backend == "pyav":
+        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
+    else:
+        raise ValueError(f"Unsupported video backend: {backend}")
 
-        Currently supports torchcodec on cpu and pyav.
-        """
-        if backend == "torchcodec":
-            return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)
-        elif backend == "pyav":
-            return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
-        else:
-            raise ValueError(f"Unsupported video backend: {backend}")
 
 def decode_video_frames_torchvision(
     video_path: Path | str,