code: def regional_probability_map(graph, xxx_todo_changeme):
      r"""
      Regional term based on a probability atlas.
  
      An implementation of a regional term, suitable to be used with the
      `~medpy.graphcut.generate.graph_from_voxels` function.
  
      Takes an image/graph/map as input where each entry contains a probability value for
      the corresponding GC graph node to belong to the foreground object. The probabilities
      must be in the range :math:`[0, 1]`. The reverse weights are assigned to the sink
      (which corresponds to the background).
  
      Parameters
      ----------
      graph : GCGraph
          The graph to add the weights to.
      probability_map : ndarray
          The label image.
      alpha : float
          The energy terms alpha value, balancing between boundary and regional term.
  
      Notes
      -----
      This function requires a probability atlas image of the same shape as the original image
      to be passed along. That means that `~medpy.graphcut.generate.graph_from_labels` has to
      be called with ``regional_term_args`` set to the probability atlas image.
      """
      (probability_map, alpha) = xxx_todo_changeme
      probability_map = scipy.asarray(probability_map)
      probabilities = numpy.vstack(
          [
              (probability_map * alpha).flat,
              ((1 - probability_map) * alpha).flat,
          ]
      ).T
      graph.set_tweights_all(probabilities)
  
file_path: medpy/medpy/graphcut/energy_voxel.py
imports: import sys
  import numpy
  import scipy
  import math
label_desc: Use image node_id 1 for calling a global function with example usage: regional_probability_map(graph, xxx_todo_changeme) without return types