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: Loads the model from the out dir.
docstr_tok: ["Loads","the","model","from","the","out","dir","."]
code: def _load(self) -> None:
      """Loads the model from the out dir."""
      pass
  
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 3 for calling the ModelWrapper obj's underlying member method code with example usage: obj._load() without return types