chore(format): formatting code

src/lerobot/datasets/lerobot_dataset.py

@@ -155,9 +155,10 @@ class LeRobotDataset(torch.utils.data.Dataset):
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
             episode_filter (Callable[[dict], bool] | None, optional): Predicate over per-episode
-                metadata rows used to select episodes; evaluated against ``meta/`` only, so
-                non-matches skip ``data/`` and ``videos/`` downloads. Intersected with ``episodes``
-                when both are set. Example: ``lambda ep: ep["length"] >= 100``. Defaults to None.
+                metadata rows used to select episodes. Evaluated against ``meta/`` without ``stats`` keys
+                (e.g.``task_index``, ``episode_index``, ``length``, ``from_timestamp``, ``to_timestamp``).
+                Intersected with ``episodes`` when both are set. Example: ``lambda ep: ep["length"] >= 100``.
+                Defaults to None.
             image_transforms (Callable | None, optional):
                 Transform applied to visual modalities inside `__getitem__` after image decoding / tensor
                 conversion. This works for both image-backed and video-backed observations and can later be
@@ -222,13 +223,19 @@ class LeRobotDataset(torch.utils.data.Dataset):
         self.root = self.meta.root
         self.revision = self.meta.revision
 
-        if episodes is not None and any(episode>=self.meta.total_episodes or episode<0 for episode in episodes):
-            logger.warning(f"Some episodes in the provided episodes list are out of range for this dataset ({self.meta.total_episodes}).")
-            
+        if episodes is not None and any(
+            episode >= self.meta.total_episodes or episode < 0 for episode in episodes
+        ):
+            logger.warning(
+                f"Some episodes in the provided episodes list are out of range for this dataset ({self.meta.total_episodes})."
+            )
+
         if episode_filter is not None:
             resolved = self.meta.filter_episodes(episode_filter, candidates=episodes)
             if not resolved:
-                raise ValueError(f"The episode filter did not match any episode. Make sure the filter and episodes list are valid and compatible.")
+                raise ValueError(
+                    "The episode filter did not match any episode. Make sure the filter and episodes list are valid and compatible."
+                )
             logger.info(f"The episode filter matched {len(resolved)} episode(s).")
             episodes = resolved
         self.episodes = episodes

tests/datasets/test_datasets.py

@@ -1717,11 +1717,10 @@ def test_episode_filter_intersects_with_episodes(tmp_path, lerobot_dataset_facto
     """When both episodes and episode_filter are given to LeRobotDataset, the result is their intersection."""
     dataset = lerobot_dataset_factory(root=tmp_path / "test", total_episodes=8, total_frames=200)
     lengths = dataset.meta.episodes["length"]
-    threshold = sorted(lengths)[len(lengths) // 2]
     candidates = [0, 2, 4, 6]
+    candidate_lengths = [lengths[i] for i in candidates]
+    threshold = sorted(candidate_lengths)[len(candidate_lengths) // 2]
     expected_eps = [i for i in candidates if lengths[i] >= threshold]
-    if not expected_eps:
-        pytest.skip("multinomial draw produced no candidate above threshold")
 
     filtered = LeRobotDataset(
         dataset.repo_id,