add solution drafts, add component decomposition , add spec for other docs

docs/02_components/04_Model_Registry/spec.md

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+# Model Registry Component
+
+## Detailed Description
+The **Model Registry** is a centralized manager for all deep learning models (SuperPoint, LightGlue, AnyLoc, LiteSAM). It abstracts the loading mechanism, supporting both **TensorRT** (for production/GPU) and **PyTorch/ONNX** (for fallback/CPU/Sandbox).
+
+It implements the "Factory" pattern, delivering initialized and ready-to-infer model wrappers to the Layer components. It also manages GPU resource allocation (e.g., memory growth).
+
+## API Methods
+
+### `load_model`
+- **Input:** `model_name: str` (e.g., "superpoint"), `backend: str` ("tensorrt" | "pytorch" | "auto")
+- **Output:** `ModelWrapper`
+- **Description:** Loads the specified model weights.
+    - If `backend="auto"`, attempts TensorRT first; if fails (or no GPU), falls back to PyTorch.
+    - Returns a wrapper object that exposes a uniform `infer()` method regardless of backend.
+- **Test Cases:**
+    - Load "superpoint", backend="pytorch" -> Success.
+    - Load invalid name -> Error.
+    - Load "tensorrt" on CPU machine -> Fallback or Error (depending on strictness).
+
+### `unload_model`
+- **Input:** `model_name: str`
+- **Output:** `void`
+- **Description:** Frees GPU/RAM resources associated with the model.
+- **Test Cases:**
+    - Unload loaded model -> Memory released.
+
+### `list_available_models`
+- **Input:** `void`
+- **Output:** `List[str]`
+- **Description:** Returns list of models registered and available on disk.
+
+## Integration Tests
+- **Load All:** Iterate through all required models and verify they load successfully in the sandbox environment (likely PyTorch mode).
+
+## Non-functional Tests
+- **Warmup Time:** Measure time from `load_model` to first successful inference.
+- **Switching Overhead:** Measure latency if models need to be swapped in/out of VRAM (though ideally all stay loaded).
+