code: def test_rai_insights_invalid_max_evals(self, max_evals):
      data = load_imagenet_dataset()
      pred = create_image_classification_pipeline()
      task_type = ModelTask.IMAGE_CLASSIFICATION
      class_names = load_imagenet_labels()
      with pytest.raises(
          ValueError, match="max_evals must be greater than 0"
      ):
          run_rai_insights(
              pred,
              data[:1],
              ImageColumns.LABEL,
              task_type,
              class_names,
              image_mode=ImageModes.RGB,
              test_explainer=True,
              max_evals=max_evals,
          )
  
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 3 for calling the TestRAIVisionInsights obj's underlying member method code with example usage: obj.test_rai_insights_invalid_max_evals(max_evals) without return types