code: def run(data_dir: str, export_dir: str, qat: bool):
      """Runs demo."""
      data = object_detector.Dataset.from_coco_folder(data_dir)
      train_data, rest_data = data.split(0.6)
      validation_data, test_data = rest_data.split(0.5)
  
      hparams = object_detector.HParams(
          batch_size=1, export_dir=export_dir
      )
      options = object_detector.ObjectDetectorOptions(
          supported_model=object_detector.SupportedModels.MOBILENET_V2,
          hparams=hparams,
      )
      model = object_detector.ObjectDetector.create(
          train_data=train_data,
          validation_data=validation_data,
          options=options,
      )
      loss, coco_metrics = model.evaluate(test_data, batch_size=1)
      print(f"Evaluation loss:{loss}, coco_metrics:{coco_metrics}")
      if qat:
          qat_hparams = object_detector.QATHParams(batch_size=1)
          model.quantization_aware_training(
              train_data, validation_data, qat_hparams
          )
          qat_loss, qat_coco_metrics = model.evaluate(
              test_data, batch_size=1
          )
          print(
              f"QAT Evaluation loss:{qat_loss}, coco_metrics:{qat_coco_metrics}"
          )
  
      model.export_model()
  
file_path: mediapipe/mediapipe/model_maker/python/vision/object_detector/object_detector_demo.py
imports: import os
  from absl import app
  from absl import flags
  from absl import logging
  from mediapipe.model_maker.python.vision import object_detector
label_desc: Use image node_id 2 for calling a global function with example usage: run(data_dir, export_dir, qat) without return types