[AZ-338] [AZ-283] C2 NetVLAD mandatory simple-baseline VprStrategy

NetVLAD is the C2 comparative baseline per the engine rule (every
production-default backbone ships with a simple-baseline alongside).
Runs on the C7 PyTorch FP16 runtime (NOT TRT) so a TRT engine compile
bug cannot simultaneously break NetVLAD AND UltraVPR.

Production changes:
- c2_vpr/net_vlad.py — NetVladStrategy + module-level create() factory.
  Constructor wires InferenceRuntimeCut + DescriptorIndexCut +
  NetVladBackbonePreprocessor + DescriptorNormaliser + FaissBridge.
  embed_query pipeline: preprocess -> runtime.infer -> dual-stage
  normalisation (intra-cluster THEN global L2) -> VprQuery.
  retrieve_topk delegates one-line to FaissBridge.
- c2_vpr/_net_vlad_architecture.py — Arandjelovic et al. 2016 NetVLAD
  layer over torchvision VGG16 features + optional Linear PCA
  projection to descriptor_dim (default 4096; published Pittsburgh
  reference uses K*D=64*512=32768 raw + Linear(32768, 4096) PCA).
- c2_vpr/_preprocessor_net_vlad.py — OpenCV-based image preprocessor:
  decode -> centre-crop square -> resize (480, 480) -> ImageNet
  normalisation -> FP16 NCHW. Calibration is not consumed (NetVLAD
  is calibration-agnostic per published preprocessing chain).
- c2_vpr/inference_runtime_cut.py — NEW AZ-507 consumer-side cut
  mirroring C7 InferenceRuntime; lets c2_vpr stay AZ-507-clean.
- c2_vpr/config.py — added netvlad_descriptor_dim: int = 4096 knob.
- helpers/descriptor_normaliser.py — added intra_cluster_normalise
  (DescriptorNormaliser v1.0.0 -> v1.1.0; backward-compatible add).
- runtime_root/vpr_factory.py — added _register_strategy_architecture
  helper that binds (MODEL_NAME, architecture_factory(descriptor_dim))
  to C7's architecture registry before delegating to the strategy's
  create() factory. Keeps the c7 import at L4, preserves AZ-507.
- fdr_client/records.py — registered vpr.embed_query,
  vpr.backbone_error, vpr.preprocess_error record kinds.

Tests:
- tests/unit/c2_vpr/test_net_vlad.py — 31 tests covering all 11 ACs +
  preprocessor contract + architecture factory + constructor
  validation + FDR record emission.
- tests/unit/test_az283_descriptor_normaliser.py — +8 tests for the
  new intra_cluster_normalise.
- tests/unit/test_az272_fdr_record_schema.py — +3 fixture payloads.

Full unit suite: 1608 passed / 80 env-skipped (+43 new tests).
Per-batch code review (batch_46_review.md): PASS_WITH_WARNINGS
(4 Low-severity hygiene findings; no Critical/High/Medium).

Architectural notes:
- The spec implied c2_vpr.net_vlad.create() registers the architecture
  with C7. That violates AZ-507 (no cross-component imports). Resolved
  by exposing MODEL_NAME + architecture_factory(descriptor_dim) on the
  strategy module and having the composition root perform the C7 bind.
- C7 PyTorch runtime API names in the spec (forward, load_engine)
  were outdated; aligned implementation with the live v1.0.0 Protocol
  (infer, compile_engine + deserialize_engine). Spec hygiene flagged
  in review F2.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/gps_denied_onboard/fdr_client/records.py

@@ -314,6 +314,46 @@ KNOWN_PAYLOAD_KEYS: Final[dict[str, frozenset[str]]] = {
             "latency_us",
         }
     ),
+    # AZ-338 / E-C2: emitted by each concrete C2 ``VprStrategy`` on every
+    # successful ``embed_query(...)`` call (post-flight forensic record
+    # of the embedding pipeline; complements ``vpr.retrieve_topk`` for the
+    # subsequent FAISS lookup). ``frame_id`` echoes
+    # ``NavCameraFrame.frame_id``; ``backbone_label`` is the strategy's
+    # lowercase ``BUILD_VPR_<variant>`` token (e.g. ``"net_vlad"``);
+    # ``descriptor_dim`` is the strategy's stable embedding dim;
+    # ``latency_us`` is the strategy-internal ``Clock.monotonic_ns``
+    # delta around the backbone forward, in integer microseconds.
+    "vpr.embed_query": frozenset(
+        {
+            "frame_id",
+            "backbone_label",
+            "descriptor_dim",
+            "latency_us",
+        }
+    ),
+    # AZ-338 / E-C2: emitted when ``embed_query`` raises a
+    # :class:`VprBackboneError` (forward-pass failure: CUDA OOM, TRT
+    # engine deserialize mismatch, etc.). One record per occurrence;
+    # logged at ERROR alongside.
+    "vpr.backbone_error": frozenset(
+        {
+            "frame_id",
+            "backbone_label",
+            "error_type",
+            "error_message",
+        }
+    ),
+    # AZ-338 / E-C2: emitted when ``embed_query`` raises a
+    # :class:`VprPreprocessError` (image decode / shape / dtype
+    # violation in the per-strategy preprocessor).
+    "vpr.preprocess_error": frozenset(
+        {
+            "frame_id",
+            "backbone_label",
+            "error_type",
+            "error_message",
+        }
+    ),
 }
 
 KNOWN_KINDS: Final[frozenset[str]] = frozenset(KNOWN_PAYLOAD_KEYS.keys())