class_docstr: A basic wrapper for machine learning models.
  
  This wrapper should contain the model itself and any additional configuration or
  resources required to run the model/make predictions. It offers a standard interface
  to interact with models regardless of their implementation or framework, e.g. to
  save/load a model to file or make a prediction.
class_docstr_tok: ["A","basic","wrapper","for","machine","learning","models",".","This","wrapper","should","contain","the","model","itself","and","any","additional","configuration","or","resources","required","to","run","the","model\/make","predictions",".","It","offers","a","standard","interface","to","interact","with","models","regardless","of","their","implementation","or","framework",",","e.g",".","to","save\/load","a","model","to","file","or","make","a","prediction","."]
docstr: Deploys the model through a REST API. kwargs are forwarded to uvicorn.
docstr_tok: ["Deploys","the","model","through","a","REST","API",".","kwargs","are","forwarded","to","uvicorn","."]
code: def deploy(self, **kwargs) -> None:
      """Deploys the model through a REST API. kwargs are forwarded to uvicorn."""
      app = self._create_fastapi()
      uvicorn.run(app, **kwargs)
  
file_path: traintool/traintool/model_wrapper.py
imports: from pathlib import Path
  from fastapi import FastAPI
  import uvicorn
  import numpy
  from abc import ABC, abstractmethod
  from datetime import datetime
  from pydantic import BaseModel
label_desc: Use image node_id 7 for calling the ModelWrapper obj's underlying member method code with example usage: obj.deploy() without return types