code: def test_rai_insights_automl_image_classification_fridge(self):
      data = load_fridge_dataset()
      task_type = ModelTask.IMAGE_CLASSIFICATION
      class_names = data[ImageColumns.LABEL.value].unique().tolist()
      try:
          model = get_automl_images_mlflow_model(class_names)
      except Exception as exp:
          print(
              "Failed to retrieve or load automl"
              " images mlflow model: {}".format(exp)
          )
      run_rai_insights(
          model,
          data[:3],
          ImageColumns.LABEL,
          task_type,
          class_names,
          test_explainer=True,  # enabled as gradcam is faster
      )
  
file_path: responsible-ai-toolbox/responsibleai_vision/tests/test_rai_vision_automl_images_insights.py
imports: import copy
  import json
  import os
  import sys
  import tempfile
  import pytest
  import torch
  from common_vision_utils import load_fridge_dataset
  from test_rai_vision_insights import run_rai_insights
  from responsibleai_vision import ModelTask
  from responsibleai_vision.common.constants import ImageColumns
label_desc: Use image node_id 1 for calling the TestRAIVisionInsightsAutoMLImages obj's underlying member method code with example usage: obj.test_rai_insights_automl_image_classification_fridge() without return types