review: address CarolinePascal feedback

- language timestamps: float64 -> float32 to match LeRobotDataset frame
  timestamps (Arrow struct + HF feature)
- dataset_metadata: hoist `.language` imports to module top — language.py
  has no lerobot imports, so there is no circular-import risk
- dataset_metadata: add a `meta.tools` setter that persists the catalog to
  info.json and reloads `meta.info`
- feature_utils: validate the `language` dtype instead of returning "" —
  warn (non-fatal) when a non-empty value is written at record time
- centralize the scalar-unwrap helper as `lerobot.utils.utils.unwrap_scalar`,
  shared by render_messages_processor and language_render
- docs: move `## Layer 2 — recipe anatomy` ahead of the resolver sections,
  which describe recipe bindings rather than dataset layout
- language_render: note in EMITTED_AT_TOLERANCE_S that persistent rows change
  on a human-action timescale, not the camera frame rate

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

docs/source/language_and_recipes.mdx

@@ -40,7 +40,7 @@ frame the row sits on already provides it):
 role: string
 content: string | null
 style: string | null
-timestamp: float64        # persistent rows only
+timestamp: float32        # persistent rows only
 camera: string | null     # observation.images.* feature key, view-dependent rows only
 tool_calls: list[Json] | null
 ```
@@ -64,6 +64,23 @@ The language stack itself has three internal modules backing layer 1:
 
 `LeRobotDataset` stays recipe-agnostic. It passes `language_persistent` and `language_events` through when present, and unannotated datasets keep their existing behavior.
 
+## Layer 2 — recipe anatomy
+
+Recipes are YAML files backed by `TrainingRecipe` and `MessageTurn`. They
+declare which annotation rows to pull (via `bindings`) and how to compose them
+into chat turns (`messages`).
+
+```yaml
+messages:
+  - { role: user, content: "${task}", stream: high_level }
+  - { role: assistant, content: "${subtask}", stream: low_level, target: true }
+```
+
+A recipe can also branch into a weighted **blend** of sub-recipes. At sample
+time, exactly one branch is selected deterministically from the sample index,
+so different frames train different objectives (e.g. memory updates vs.
+low-level execution vs. VQA) without any Python wiring.
+
 ### Temporal semantics
 
 Persistent styles are active after emission until replaced:
@@ -112,23 +129,6 @@ ask_vqa_top:
 
 Add one such sub-recipe per camera the dataset records.
 
-## Layer 2 — recipe anatomy
-
-Recipes are YAML files backed by `TrainingRecipe` and `MessageTurn`. They
-declare which annotation rows to pull (via `bindings`) and how to compose them
-into chat turns (`messages`).
-
-```yaml
-messages:
-  - { role: user, content: "${task}", stream: high_level }
-  - { role: assistant, content: "${subtask}", stream: low_level, target: true }
-```
-
-A recipe can also branch into a weighted **blend** of sub-recipes. At sample
-time, exactly one branch is selected deterministically from the sample index,
-so different frames train different objectives (e.g. memory updates vs.
-low-level execution vs. VQA) without any Python wiring.
-
 ## Layer 3 — training format
 
 Rendered samples use HF-style chat messages plus LeRobot sidecars: