code: def relative_deviation(
      h1, h2
  ):  # 18 us @array, 42 us @list \w 100 bins
      r"""
      Calculate the deviation between two histograms.
  
      The relative deviation between two histograms :math:`H` and :math:`H'` of size :math:`m` is
      defined as:
  
      .. math::
  
          d_{rd}(H, H') =
              \frac{
                  \sqrt{\sum_{m=1}^M(H_m - H'_m)^2}
                }{
                  \frac{1}{2}
                  \left(
                      \sqrt{\sum_{m=1}^M H_m^2} +
                      \sqrt{\sum_{m=1}^M {H'}_m^2}
                  \right)
                }
  
      *Attributes:*
  
      - semimetric (triangle equation satisfied?)
  
      *Attributes for normalized histograms:*
  
      - :math:`d(H, H')\in[0, \sqrt{2}]`
      - :math:`d(H, H) = 0`
      - :math:`d(H, H') = d(H', H)`
  
      *Attributes for not-normalized histograms:*
  
      - :math:`d(H, H')\in[0, 2]`
      - :math:`d(H, H) = 0`
      - :math:`d(H, H') = d(H', H)`
  
      *Attributes for not-equal histograms:*
  
      - not applicable
  
      Parameters
      ----------
      h1 : sequence
          The first histogram.
      h2 : sequence
          The second histogram, same bins as ``h1``.
  
      Returns
      -------
      relative_deviation : float
          Relative deviation between the two histograms.
      """
      h1, h2 = __prepare_histogram(h1, h2)
      numerator = math.sqrt(scipy.sum(scipy.square(h1 - h2)))
      denominator = (
          math.sqrt(scipy.sum(scipy.square(h1)))
          + math.sqrt(scipy.sum(scipy.square(h2)))
      ) / 2.0
      return numerator / denominator
  
imports: import math
  import scipy
label_desc: Use image node_id 10 for calling a global function with example usage: relative_deviation(h1, h2) and returns: unknown