code: def histo_repro(h):
      """Given the histogram h, compute a standardized reproducibility measure
  
      Parameters
      ----------
      h array of shape(xmax+1), the histogram values
  
      Returns
      -------
      hr, float: the measure
      """
      k = np.size(h) - 1
      if k == 1:
          return 0.0
      nf = np.dot(h, np.arange(k + 1)) / k
      if nf == 0:
          return 0.0
      n1k = np.arange(1, k + 1)
      res = 1.0 * np.dot(h[1:], n1k * (n1k - 1)) / (k * (k - 1))
      return res / nf
  
imports: import numpy
  from nipy.io.nibcompat import get_affine
  from nipy.labs.spatial_models.discrete_domain import grid_domain_from_binary_array
label_desc: Use image node_id 1 for calling a global function with example usage: histo_repro(h) and returns: unknown, int, int