code: def test_interpreter(self):
      interpreter = image_gradient_maps.VanillaGradients()
      model = ClassificationTestModel()
      self.assertTrue(
          interpreter.is_compatible(
              model, lit_dataset.NoneDataset({"test": model})
          )
      )
  
      pil_image = PILImage.new(mode="RGB", size=(300, 200))
      inp = {"image": image_utils.convert_pil_to_image_str(pil_image)}
      run_output = interpreter.run(
          inputs=[inp], model=model, dataset=lit_dataset.Dataset()
      )[0]
  
      self.assertIn("grads", run_output)
      self.assertIsInstance(run_output["grads"], str)
  
imports: from absl.testing import absltest
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.components import image_gradient_maps
  from lit_nlp.lib import image_utils
  import numpy
  from PIL import Image
label_desc: Use image node_id 1 for calling the ImageGradientsMapsTest obj's underlying member method code with example usage: obj.test_interpreter() without return types