code: def test_rai_insights_object_detection_fridge(
      self, num_masks, mask_res
  ):
      data = load_fridge_object_detection_dataset()
      model = retrieve_fridge_object_detection_model(
          load_fridge_weights=True
      )
      task_type = ModelTask.OBJECT_DETECTION
      class_names = np.array(
          ["can", "carton", "milk_bottle", "water_bottle"]
      )
      run_rai_insights(
          model,
          data[:2],
          ImageColumns.LABEL,
          task_type,
          class_names,
          num_masks=num_masks,
          mask_res=mask_res,
          test_error_analysis=True,
      )
  
imports: import sys
  import numpy
  import PIL
  import pytest
  from common_vision_utils import FRIDGE_MULTILABEL_TARGETS, ImageTransformEnum, ImageTransforms, create_dummy_model, create_image_classification_pipeline, create_pytorch_vision_model, gridify_fridge_multilabel_labels, load_flowers_dataset, load_fridge_dataset, load_fridge_object_detection_dataset, load_imagenet_dataset, load_imagenet_labels, load_mnist_dataset, load_multilabel_fridge_dataset, retrieve_fridge_object_detection_model, retrieve_or_train_fridge_model
  from ml_wrappers import wrap_model
  from ml_wrappers.common.constants import Device
  from rai_vision_insights_validator import validate_rai_vision_insights
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai_vision import ModelTask, RAIVisionInsights
  from responsibleai_vision.common.constants import ExplainabilityDefaults, ImageColumns, ImageModes
label_desc: Use image node_id 7 for calling the TestRAIVisionInsights obj's underlying member method code with example usage: obj.test_rai_insights_object_detection_fridge(num_masks, mask_res) without return types