chore(format): format code

src/lerobot/configs/__init__.py

@@ -34,8 +34,8 @@ from .types import (
 from .video import (
     VALID_VIDEO_CODECS,
     VIDEO_ENCODER_INFO_KEYS,
-    VideoEncoderConfig,
     DepthEncoderConfig,
+    VideoEncoderConfig,
     camera_encoder_defaults,
     depth_encoder_defaults,
 )

src/lerobot/datasets/dataset_writer.py

@@ -53,9 +53,9 @@ from .io_utils import (
     write_info,
 )
 from .utils import (
+    DEFAULT_DEPTH_PATH,
     DEFAULT_EPISODES_PATH,
     DEFAULT_IMAGE_PATH,
-    DEFAULT_DEPTH_PATH,
     update_chunk_file_indices,
 )
 from .video_utils import (

src/lerobot/datasets/image_writer.py

@@ -56,11 +56,9 @@ def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True)
     Other shapes / channel counts raise ``NotImplementedError`` or
     ``ValueError``.
     """
-    #TODO(CarolinePascal): 4 dimensions RGB-D images
+    # TODO(CarolinePascal): 4 dimensions RGB-D images
     if image_array.ndim not in (2, 3):
-        raise ValueError(
-            f"The array has {image_array.ndim} dimensions, but 2 or 3 is expected for an image."
-        )
+        raise ValueError(f"The array has {image_array.ndim} dimensions, but 2 or 3 is expected for an image.")
 
     # Squeeze 3D single-channel inputs to 2D so depth maps work whether the
     # caller emits (H, W), (1, H, W), or (H, W, 1).

src/lerobot/robots/so_follower/so_follower.py

@@ -72,7 +72,7 @@ class SOFollower(Robot):
         for cam in self.cameras:
             features[cam] = (self.cameras[cam].height, self.cameras[cam].width, 3)
             if getattr(self.cameras[cam], "use_depth", False):
-                features[f"{cam}_depth"] = (self.cameras[cam].height, self.cameras[cam].width,1)
+                features[f"{cam}_depth"] = (self.cameras[cam].height, self.cameras[cam].width, 1)
         return features
 
     @cached_property

src/lerobot/utils/feature_utils.py

@@ -69,7 +69,7 @@ def hw_to_dataset_features(
         for key, ftype in hw_features.items()
         if ftype is float or (isinstance(ftype, PolicyFeature) and ftype.type != FeatureType.VISUAL)
     }
-    #TODO(CarolinePascal): we should not rely on the shape to determine if a feature is a camera !
+    # TODO(CarolinePascal): we should not rely on the shape to determine if a feature is a camera !
     cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
 
     if joint_fts and prefix == ACTION: