class_docstr: Lime: Explaining the predictions of any machine learning classifier
  https://github.com/marcotcr/lime
code: def explain(self, images):
      img_boundry = []
      for img in images:
          explanation = self._explainer.explain_instance(
              img,
              self.batch_predict,
              self._top_labels,
              self._hide_color,
              self._num_samples,
          )
          temp, mask = explanation.get_image_and_mask(
              explanation.top_labels[0],
              positive_only=True,
              num_features=5,
              hide_rest=False,
          )
          # (M, N, 3) array of float
          img_boundry = mark_boundaries(temp / 255.0, mask)
      return img_boundry * 255
  
imports: from lime import lime_image
  from skimage.segmentation import mark_boundaries
  from singa_auto.model import utils
label_desc: Use image node_id 3 for calling the Lime obj's underlying member method code with example usage: obj.explain(images) and returns: unknown