This adds model_alias support for native pytorch models.
Model aliases can be used in `_infer` or within the inference processor. This way the alias can be atomically changed without down time to another deployed model.
Restrictions:
- Model alias changes need to be done between two models of the same kind (e.g. pytorch -> pytorch)
- Model alias change is not allowed between a model that is deployed to a model that is not
- Model alias change is not allowed between a model that deployed AND allocated to a model that is deployed but NOT allocated (not assigned to any nodes).
- A deployment cannot be stopped (without supplying the `force` parameter) when the model has a model alias that is used by a pipeline.
closes: https://github.com/elastic/elasticsearch/issues/90960
This commit adds a new `_ml/trained_models/{model_id}/_infer` API. This api works for both native NLP models and supervised models trained via Data Frame analytics.
The format of the API is the same as the old `_ml/trained_models/{model_id}/deployment/_infer`. Taking a `docs` and an `inference_config` parameter.
This PR also deprecates the old experimental `_ml/trained_models/{model_id}/deployment/_infer` API.
The biggest difference is that the response now nests all results under an "inference_results" object.
closes: https://github.com/elastic/elasticsearch/issues/86032
