class_docstr: A CoreSaliency class that computes saliency masks using the XRAI method.
class_docstr_tok: ["A","CoreSaliency","class","that","computes","saliency","masks","using","the","XRAI","method","."]
code: def _make_baselines(self, x_value, x_baselines):
      # If baseline is not provided default to im min and max values
      if x_baselines is None:
          x_baselines = []
          x_baselines.append(np.min(x_value) * np.ones_like(x_value))
          x_baselines.append(np.max(x_value) * np.ones_like(x_value))
      else:
          for baseline in x_baselines:
              if baseline.shape != x_value.shape:
                  raise ValueError(
                      "Baseline size {} does not match input size {}".format(
                          baseline.shape, x_value.shape
                      )
                  )
      return x_baselines
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  from .base import CoreSaliency
  from .integrated_gradients import IntegratedGradients
  import numpy
  from skimage import segmentation
  from skimage.morphology import dilation
  from skimage.morphology import disk
  from skimage.transform import resize
label_desc: Use image node_id 3 for calling the XRAI obj's underlying member method code with example usage: obj._make_baselines(x_value, x_baselines) and returns: x_baselines