code: def test_rai_insights_image_downloader_object_detection(self):
      data = load_fridge_object_detection_dataset(True)
      model = retrieve_fridge_object_detection_model(
          load_fridge_weights=True
      )
      task_type = ModelTask.OBJECT_DETECTION
      test = data[:3]
      label = ImageColumns.LABEL
      serializer = ObjectDetectionPipelineSerializer()
  
      get_data = (
          "test_rai_vision_insights_save_and_load_scenarios"
          ".FakeImageDownloader.get_data"
      )
      with patch(get_data) as mock_images_df:
          mock_images_df.return_value = test.copy()
          run_and_validate_serialization(
              model,
              test,
              task_type,
              FRIDGE_CLASS_NAMES,
              label,
              serializer,
              ignore_test_data=True,
              image_downloader=FakeImageDownloader,
          )
  
imports: import shutil
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from unittest.mock import patch
  import numpy
  import PIL
  import pytest
  from common_vision_utils import DummyFlowersPipelineSerializer, ImageClassificationPipelineSerializer, ObjectDetectionPipelineSerializer, create_dummy_model, create_image_classification_pipeline, load_flowers_dataset, load_fridge_object_detection_dataset, load_imagenet_dataset, load_imagenet_labels, retrieve_fridge_object_detection_model
  from rai_vision_insights_validator import run_and_validate_serialization
  from responsibleai_vision import ModelTask, RAIVisionInsights
  from responsibleai_vision.common.constants import ImageColumns
label_desc: Use image node_id 5 for calling the TestRAIVisionInsightsSaveAndLoadScenarios obj's underlying member method code with example usage: obj.test_rai_insights_image_downloader_object_detection() without return types