code: def test_rai_insights_object_detection_jagged_list(self):
      data = load_fridge_object_detection_dataset()
      model = retrieve_fridge_object_detection_model(
          load_fridge_weights=True
      )
      task_type = ModelTask.OBJECT_DETECTION
      wrapped_model = wrap_model(
          model, data, ModelTask.OBJECT_DETECTION
      )
  
      class DummyPredictWrapper(object):
          def __init__(self, model):
              self.model = model
              self._model = model._model
  
          def to(self, dummy):
              pass
  
          def predict(self, X):
              prediction = self.model.predict(X).tolist()
              if len(prediction) == 1:
                  # fix ndim error for some versions of pandas in tests
                  prediction = prediction[0]
              return prediction
  
          def predict_proba(self, X):
              return self.model.predict_proba(X)
  
      wrapped_model = DummyPredictWrapper(wrapped_model)
      class_names = np.array(
          ["can", "carton", "milk_bottle", "water_bottle"]
      )
      # test case where there are different numbers of objects in labels
      images = ["10.jpg", "29.jpg", "92.jpg"]
      images = [
          "./data/odFridgeObjects/images/{}".format(i) for i in images
      ]
      data = data.loc[data[ImageColumns.IMAGE.value].isin(images)]
      data = data.reset_index(drop=True)
      run_rai_insights(
          wrapped_model,
          data,
          ImageColumns.LABEL,
          task_type,
          class_names,
          test_error_analysis=True,
          ignore_index=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 8 for calling the TestRAIVisionInsights obj's underlying member method code with example usage: obj.test_rai_insights_object_detection_jagged_list() and returns: prediction, self