review: fix dead-code bug, add thread safety, atomic writes, smaller cleanups

**Critical: video_for_episode was unreachable dead code.**
``video_for_episode`` was indented inside ``_decode_pyav_direct``, after
its ``return`` statement — Python parsed it as a nested function that
never executed. Module 1's ``_episode_video_block`` calls
``self.frame_provider.video_for_episode(record, target_count)`` on the
``use_video_url=False`` path, which would have AttributeError'd on any
real dataset. Tests passed only because they used ``_StubFrameProvider``
/ ``_NullProvider`` which have the method. Moved it to be a proper
method of ``VideoFrameProvider`` (right after ``frames_at``).

**Thread safety on VideoFrameProvider.**
The executor runs Module 1/2/3 phases under a ``ThreadPoolExecutor``, so
the per-instance ``_cache`` dict and the one-shot ``_warned_decode_fail``
flag were exposed to concurrent reads/writes. Added a ``threading.Lock``
field, wrapped cache reads/writes and the warn-flag check-and-set in
``with self._lock:``. Stub fixtures unaffected.

**episode_clip_path is now a method of VideoFrameProvider.**
Used to be a free function reaching into ``provider._meta.episodes`` and
``provider._meta.get_video_file_path`` from outside the class. As a
method it just uses ``self._meta``. The only caller (Module 1) updated;
no external callers.

**Atomic write in LanguageColumnsWriter.**
``pq.write_table(new_table, path)`` was overwriting the parquet shard
in place — a crash mid-write would corrupt the file. Now writes to a
sibling ``.tmp`` and ``Path.replace`` atomically.

**Smaller items:**
* ``executor.py`` docstring opened with "four phases" but listed six.
  Now says "six phases" to match.
* ``[annotations]`` extra in ``pyproject.toml`` now includes
  ``openai>=1.40,<2.0``. Default ``VlmConfig.backend`` is ``"openai"``,
  so without it ``_make_openai_client`` would ImportError on a fresh
  ``uv sync --extra annotations``.
* ``_snap_to_frame`` was duplicated identically in
  ``plan_subtasks_memory.py`` and ``interjections_and_speech.py``.
  Promoted to ``snap_to_frame`` in ``reader.py`` (next to
  ``EpisodeRecord``); both modules now import it. Backwards-compat alias
  not needed — no external callers.
* ``EpisodeRecord.frames_df()`` was re-reading the full parquet on every
  call. Now memoizes via a private dataclass field so repeat calls from
  different modules pay the cost once. Method signature unchanged.
* ``_extract_first_json_object`` had a redundant ``and not escape`` guard
  that was dead because the prior block already handled and reset
  ``escape``. Replaced with a comment explaining the invariant.

**Pre-existing lint cleanups surfaced once these files entered
pre-commit's scope:**
* dead local ``client = clients[0]`` in ``_make_openai_client`` (the
  real round-robin uses ``clients[rr_counter[...]]``).
* ``cmd = ... if "{port}" in cmd else f"...{port}"`` ternary collapse in
  ``_spawn_parallel_inference_servers``.
* ``seek_pts = 0 if stream.time_base is None else int(...)`` ternary
  collapse in ``_decode_pyav_direct``.
* ``# nosec B310`` on the localhost ``urllib.request.urlopen`` probe in
  ``_server_is_up`` — the URL is the user-configured local-server endpoint
  the CLI itself spawned, not arbitrary user input.

**Test added.**
``tests/annotations/test_frames.py`` pins the regression on
``VideoFrameProvider``: asserts ``video_for_episode`` and
``episode_clip_path`` are callable methods (not nested dead code or
free functions), and that the ``_lock`` field is a real
``threading.Lock``.

Sweep: 64 passed, 2 failed (same pre-existing module-impl bugs as
before this commit). Pre-commit clean.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

pyproject.toml

@@ -201,13 +201,15 @@ hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpci
 async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
 peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
 
-# Annotation pipeline (lerobot-annotate). vllm is the preferred backend on
-# Linux, with a transformers fallback elsewhere. Distributed execution is
-# delegated to Hugging Face Jobs (see examples/annotation/run_hf_job.py),
-# so this pipeline pulls no cluster-scheduler dependency.
+# Annotation pipeline (lerobot-annotate). vllm is the preferred backend
+# on Linux, with a transformers fallback elsewhere; openai is the default
+# backend and talks to any OpenAI-compatible server (``vllm serve`` /
+# ``transformers serve`` / hosted endpoints). Distributed execution is
+# delegated to Hugging Face Jobs (see examples/annotation/run_hf_job.py).
 annotations = [
     "lerobot[dataset]",
     "lerobot[transformers-dep]",
+    "openai>=1.40,<2.0",
     "vllm>=0.6.0,<1.0.0; sys_platform == 'linux'",
 ]