code: def train_inbody(app):
      res = app.train(
          request={
              "model": "inbody",
              "max_epochs": 10,
              "dataset": "Dataset",  # PersistentDataset, CacheDataset
              "train_batch_size": 1,
              "val_batch_size": 1,
              "multi_gpu": False,
              "val_split": 0.1,
          }
      )
      print(res)
      logger.info("All Done!")
  
imports: import logging
  import os
  from datetime import timedelta
  from typing import Dict
  import lib.configs
  import schedule
  from timeloop import Timeloop
  import monailabel
  from monailabel.config import settings
  from monailabel.datastore.cvat import CVATDatastore
  from monailabel.interfaces.app import MONAILabelApp
  from monailabel.interfaces.config import TaskConfig
  from monailabel.interfaces.datastore import Datastore
  from monailabel.interfaces.tasks.infer_v2 import InferTask
  from monailabel.interfaces.tasks.scoring import ScoringMethod
  from monailabel.interfaces.tasks.strategy import Strategy
  from monailabel.interfaces.tasks.train import TrainTask
  from monailabel.tasks.activelearning.random import Random
  from monailabel.utils.others.class_utils import get_class_names
  from monailabel.utils.others.generic import create_dataset_from_path, strtobool
label_desc: Use image node_id 10 for calling a global function with example usage: train_inbody(app) without return types