Simplify model import code

## Feature Request
As a Python user, I would like to simplify the code required to load an inference model from Hugging Face (or private source) into Elasticsearch.

## Use Case
Today eland is the way we load inference models into Elasticsearch. Many users will only use eland to load models, making it as simple to use will help the end-user experience.

Currently, in python, it requires several lines of code
eg:
```
hf_model_id='sentence-transformers/msmarco-MiniLM-L-12-v3'
tm = TransformerModel(hf_model_id, "text_embedding")
es_model_id = tm.elasticsearch_model_id()
tmp_path = "models"
Path(tmp_path).mkdir(parents=True, exist_ok=True)
model_path, config, vocab_path = tm.save(tmp_path)
ptm = PyTorchModel(es, es_model_id)
ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)
```

This could be abstracted into a simple function call such as `eland.ml.pytorch.import_model` and take in the minimum required parameters. Something like

```
def import_model(
es: Elasticsearch,
model_id: str,
task_type: str = 'text_embedding',
access_token: Union[str, bool] = False
) -> str:
```
where it would return the model_id as it is known in Elasticsearch so it can be used later in the code to call inference

cc: @joshdevins

Start by tracing TransformerModel, PyTorchModel, and PyTorchModel.import_model, then compare them with the proposed eland.ml.pytorch.import_model entry point. Done means a minimal call can load a Hugging Face or private-source model, accept the stated parameters, and return the Elasticsearch model ID.

Written by the indexing model from the issue text.