class_docstr: Lime: Explaining the predictions of any machine learning classifier
  https://github.com/marcotcr/lime
code: def __init__(
      self, model, image_size, normalize_mean, normalize_std, device
  ):
      self._model = model
      self.device = device
      # dataset
      self._image_size = image_size
      self._normalize_mean = normalize_mean
      self._normalize_std = normalize_std
      self._explainer = lime_image.LimeImageExplainer()
      # lime configs
      # number of images that will be sent to classification function
      self._num_samples = 100
      self._top_labels = 5
      self._hide_color = 0
  
imports: from lime import lime_image
  from skimage.segmentation import mark_boundaries
  from singa_auto.model import utils
label_desc: Use image node_id 1 to create a new Lime object  with example: obj = Lime(model, image_size, normalize_mean, normalize_std, device)