code: def _imagenet_predict(self, images):
      probs = imagenet_utils.predict(
          self.model,
          images,
          image_transform=self._preprocess_transform(),
      )
      return pytorch_utils.tensor2numpy(probs)
  
file_path: tensorwatch/tensorwatch/saliency/lime_image_explainer.py
imports: from skimage.segmentation import mark_boundaries
  from torchvision import transforms
  import torch
  from .lime import lime_image
  import numpy
  from ..None import imagenet_utils, pytorch_utils, utils
label_desc: Use image node_id 5 for calling the LimeImagenetExplainer obj's underlying member method code with example usage: obj._imagenet_predict(images) and returns: pytorch_utils