chore(dataset): add check dataset shape

tests/datasets/test_dataset_writer.py

@@ -27,6 +27,7 @@ pytest.importorskip("datasets", reason="datasets is required (install lerobot[da
 
 from lerobot.configs import VideoEncoderConfig
 from lerobot.datasets.dataset_writer import _encode_video_worker
+from lerobot.datasets.feature_utils import get_hf_features_from_features
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.utils import DEFAULT_IMAGE_PATH
 from tests.fixtures.constants import DEFAULT_FPS, DUMMY_REPO_ID
@@ -190,12 +191,7 @@ def test_save_multiple_episodes(tmp_path):
 
 
 def test_save_episode_with_zero_width_feature(tmp_path):
-    """save_episode() succeeds when a feature has a zero-width dimension (shape=(0,)).
-
-    Regression test for https://github.com/huggingface/lerobot/issues/3654: such a
-    feature previously crashed stats computation with
-    "ValueError: cannot reshape array of size 0 into shape (0)".
-    """
+    """A one-dimensional empty numeric feature round-trips and has no statistics."""
     features = {
         **SIMPLE_FEATURES,
         "target": {"dtype": "float32", "shape": (0,), "names": None},
@@ -204,19 +200,26 @@ def test_save_episode_with_zero_width_feature(tmp_path):
     dataset = LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=features, root=root)
     for _ in range(4):
         dataset.add_frame(_make_frame(features))
-    dataset.save_episode()  # previously raised ValueError on the zero-width 'target' feature
+    dataset.save_episode()
     dataset.finalize()
 
     assert dataset.meta.total_episodes == 1
     assert dataset.meta.total_frames == 4
 
-    # The zero-width feature round-trips back as an empty vector and is excluded from stats.
     reloaded = LeRobotDataset(repo_id=DUMMY_REPO_ID, root=root)
     target = np.asarray(reloaded[0]["target"])
     assert target.shape == (0,)
     assert "target" not in (reloaded.meta.stats or {})
 
 
+@pytest.mark.parametrize("shape", [(0, 2), (2, 0), (1, 0, 2)])
+def test_multidimensional_zero_width_feature_rejected(shape):
+    features = {"target": {"dtype": "float32", "shape": shape, "names": None}}
+
+    with pytest.raises(ValueError, match="Multidimensional features with a zero-width dimension"):
+        get_hf_features_from_features(features)
+
+
 # ── clear / lifecycle ────────────────────────────────────────────────