chore(format): format code

src/lerobot/datasets/factory.py

@@ -128,7 +128,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
     if cfg.dataset.use_imagenet_stats:
         for key in dataset.meta.camera_keys:
             if key in dataset.meta.depth_keys:
-                continue    # Exclude depth keys from ImageNet stats
+                continue  # Exclude depth keys from ImageNet stats
             for stats_type, stats in IMAGENET_STATS.items():
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -86,6 +86,7 @@ def check_chw_float32(frame: torch.Tensor) -> None:
     c, h, w = frame.shape
     assert c < h and c < w, f"expect channel first images, but instead {frame.shape}"
 
+
 def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     check_chw_float32(chw_float32_torch)
     hwc_uint8_numpy = (chw_float32_torch * 255).type(torch.uint8).permute(1, 2, 0).numpy()

src/lerobot/utils/visualization_utils.py

@@ -108,9 +108,7 @@ def log_rerun_data(
                         rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
                 else:
                     if arr.shape[-1] == 1:
-                        img_entity = (
+                        img_entity = rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
-                            rr.DepthImage(arr, colormap=rr.components.Colormap.Viridis)
-                        )
                     else:
                         img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
                     rr.log(key, entity=img_entity, static=True)