docstr: Init function.
  
  Args:
      image: 3d numpy array
      segments: 2d numpy array, with the output from skimage.segmentation
docstr_tok: ["Init","function",".","Args",":","image",":","3d","numpy","array","segments",":","2d","numpy","array",",","with","the","output","from","skimage.segmentation"]
code: def __init__(self, image, segments):
      """Init function.
  
      Args:
          image: 3d numpy array
          segments: 2d numpy array, with the output from skimage.segmentation
      """
      self.image = image
      self.segments = segments
      self.intercept = {}
      self.local_exp = {}
      self.local_pred = None
  
file_path: tensorwatch/tensorwatch/saliency/lime/lime_image.py
imports: import copy
  from functools import partial
  import numpy
  import sklearn
  import sklearn.preprocessing
  from sklearn.utils import check_random_state
  from skimage.color import gray2rgb
  from .None import lime_base
  from .wrappers.scikit_image import SegmentationAlgorithm
label_desc: Use image node_id 1 to create a new ImageExplanation object from inherited base classes: object with example: obj = ImageExplanation(image, segments)