code: def get_image_and_mask(self, label, positive_only=True, hide_rest=False,
                             num_features=5, min_weight=0.):
          
          if label not in self.local_exp:
              raise KeyError('Label not in explanation')
          segments = self.segments
          image = self.image
          exp = self.local_exp[label]
          mask = np.zeros(segments.shape, segments.dtype)
          if hide_rest:
              temp = np.zeros(self.image.shape)
          else:
              temp = self.image.copy()
          if positive_only:
              fs = [x[0] for x in exp
                    if x[1] > 0 and x[1] > min_weight][:num_features]
              for f in fs:
                  temp[segments == f] = image[segments == f].copy()
                  mask[segments == f] = 1
              return temp, mask
          else:
              for f, w in exp[:num_features]:
                  if np.abs(w) < min_weight:
                      continue
                  c = 0 if w < 0 else 1
                  mask[segments == f] = 1 if w < 0 else 2
                  temp[segments == f] = image[segments == f].copy()
                  temp[segments == f, c] = np.max(image)
                  for cp in [0, 1, 2]:
                      if c == cp:
                          continue
                      # temp[segments == f, cp] *= 0.5
              return temp, mask
docs: Init function.
  
          Args:
              label: label to explain
              positive_only: if True, only take superpixels that contribute to
                  the prediction of the label. Otherwise, use the top
                  num_features superpixels, which can be positive or negative
                  towards the label
              hide_rest: if True, make the non-explanation part of the return
                  image gray
              num_features: number of superpixels to include in explanation
              min_weight: TODO
  
          Returns:
              (image, mask), where image is a 3d numpy array and mask is a 2d
              numpy array that can be used with
              skimage.segmentation.mark_boundaries