chore(format): formatting code

tests/datasets/test_aggregate.py

@@ -30,12 +30,11 @@ from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.feature_utils import features_equal_for_merge
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from tests.fixtures.constants import (
-    DUMMY_CAMERA_FEATURES,
-    DUMMY_DEPTH_CAMERA_FEATURES,
-    DUMMY_REPO_ID,
     DUMMY_CAMERA_FEATURES_WITH_DEPTH,
+    DUMMY_REPO_ID,
 )
 
+
 def assert_episode_and_frame_counts(aggr_ds, expected_episodes, expected_frames):
     """Test that total number of episodes and frames are correctly aggregated."""
     assert aggr_ds.num_episodes == expected_episodes, (
@@ -212,9 +211,7 @@ def assert_depth_keys_preserved(aggr_ds, ds_0, ds_1):
     )
     for key in expected_depth_keys:
         info = aggr_ds.meta.info.features[key].get("info") or {}
-        assert info.get("is_depth_map") is True, (
-            f"Depth marker lost on feature {key!r} after aggregation"
-        )
+        assert info.get("is_depth_map") is True, f"Depth marker lost on feature {key!r} after aggregation"
 
 
 def assert_video_timestamps_within_bounds(aggr_ds):

tests/datasets/test_datasets.py

@@ -1524,7 +1524,7 @@ def test_valid_video_codecs_constant():
     assert "h264_qsv" in VALID_VIDEO_CODECS
     assert "hevc_videotoolbox" in VALID_VIDEO_CODECS
     assert "hevc_nvenc" in VALID_VIDEO_CODECS
-    assert len(VALID_VIDEO_CODECS) == 11    
+    assert len(VALID_VIDEO_CODECS) == 11
 
 
 def test_delta_timestamps_with_episodes_filter(tmp_path, empty_lerobot_dataset_factory):

tests/datasets/test_depth.py

@@ -26,15 +26,16 @@ from lerobot.datasets.image_writer import image_array_to_pil_image, write_image
 from tests.fixtures.constants import (
     DEFAULT_FPS,
     DUMMY_CAMERA_FEATURES,
+    DUMMY_CAMERA_FEATURES_WITH_DEPTH,
+    DUMMY_CHW,
     DUMMY_DEPTH_CAMERA_FEATURES,
     DUMMY_REPO_ID,
-    DUMMY_CAMERA_FEATURES_WITH_DEPTH,
-    DUMMY_CHW
 )
 from tests.fixtures.dataset_factories import add_frames
 
 _, H, W = DUMMY_CHW
 
+
 def _depth_metres_ramp() -> np.ndarray:
     """Linearly-spaced float32 depth in metres covering the default range."""
     return np.linspace(DEFAULT_DEPTH_MIN, DEFAULT_DEPTH_MAX, H * W, dtype=np.float32).reshape(H, W)
@@ -98,9 +99,7 @@ class TestQuantizeDequantize:
         # ``m``: float32 noise (~10 µm in log mode, after ``exp``) — still 200× below the ~2 mm quant step.
         # ``mm`` + tensor stays in float32 (no uint16 round-trip), so allow 1 mm slop.
         atol = 1e-5 if output_unit == "m" else 1.0
-        np.testing.assert_allclose(
-            out.cpu().numpy().astype(np.float64), ref.astype(np.float64), atol=atol
-        )
+        np.testing.assert_allclose(out.cpu().numpy().astype(np.float64), ref.astype(np.float64), atol=atol)
 
     @pytest.mark.parametrize(
         "input_shape,output_shape",
@@ -131,9 +130,7 @@ class TestQuantizeDequantize:
 
     def test_invalid_log_params_raises(self):
         with pytest.raises(ValueError, match=r"depth_min \+ shift must be positive"):
-            quantize_depth(
-                _depth_metres_ramp(), depth_min=1.0, shift=-2.0, use_log=True, video_backend=None
-            )
+            quantize_depth(_depth_metres_ramp(), depth_min=1.0, shift=-2.0, use_log=True, video_backend=None)
 
 
 # ── 2. Image writer depth support ─────────────────────────────────────