code: def explain(
      self,
      inp,
      ind=None,
      raw_inp=None,
      top_labels=5,
      hide_color=0,
      num_samples=1000,
      positive_only=True,
      num_features=5,
      hide_rest=True,
      pixel_val_max=255.0,
  ):
      explainer = lime_image.LimeImageExplainer()
      explanation = explainer.explain_instance(
          self.preprocess_input(raw_inp),
          self.predict_fn,
          top_labels=5,
          hide_color=0,
          num_samples=1000,
      )
  
      temp, mask = explanation.get_image_and_mask(
          self.preprocess_label(ind) or explanation.top_labels[0],
          positive_only=True,
          num_features=5,
          hide_rest=True,
      )
  
      img = mark_boundaries(temp / pixel_val_max, mask)
      img = torch.from_numpy(img)
      img = torch.transpose(img, 0, 2)
      img = torch.transpose(img, 1, 2)
      return img.unsqueeze(0)
  
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 4 for calling the LimeImageExplainer obj's underlying member method code with example usage: obj.explain(inp, ind, raw_inp, top_labels, hide_color, num_samples, positive_only, num_features, hide_rest, pixel_val_max) and returns: img