[AINode] Integrate PatchTST-FM-R1 as builtin forecasting model

### Search before asking

- [x] I searched in the [issues](https://github.com/apache/iotdb/issues) and found nothing similar.

### Motivation

Description:

Summary: This issue tracks the integration of IBM Research's [PatchTST-FM-R1](https://huggingface.co/ibm-research/patchtst-fm-r1) into IoTDB AINode's built-in model registry, following the contribution guide documented [here](https://iotdb.apache.org/UserGuide/latest-Table/AI-capability/AINode_Upgrade_apache.html#_6-contributing-open-source-time-series-large-models-to-iotdb-ainode).

Motivation: PatchTST-FM-R1 is a SOTA patch-based Transformer foundation model for time series forecasting. Adding it expands the range of zero-shot forecasting options available to IoTDB users without requiring model training.

Implementation Plan:

Create iotdb-core/ainode/iotdb/ainode/core/model/patchtst/ directory with a PatchTSTFMPipeline extending

ForecastPipeline
Register the model in BUILTIN_HF_TRANSFORMERS_MODEL_MAP in

model_info.py
Add integration test
Reference: PR #16903 (Chronos2 integration) as the implementation pattern.

### Solution

_No response_

### Alternatives

_No response_

### Are you willing to submit a PR?

- [x] I'm willing to submit a PR!

Commencez par PR #16903 pour étudier le modèle d’intégration de Chronos2, puis lisez le guide de contribution de AINode et examinez model_info.py ainsi que le point d’entrée ForecastPipeline. Ajoutez le répertoire et le pipeline PatchTST, enregistrez le modèle dans BUILTIN_HF_TRANSFORMERS_MODEL_MAP et vérifiez que le test d’intégration réussit.

Rédigé par le modèle d'indexation à partir du texte de l'issue.