code: def smoothed_sens(counts, noise_eps, l, beta):
      """Compute beta-smooth sensitivity.
  
      Args:
        counts: array of scors
        noise_eps: noise parameter
        l: moment of interest
        beta: smoothness parameter
      Returns:
        smooth_sensitivity: a beta smooth upper bound
      """
      k = 0
      smoothed_sensitivity = sens_at_k(counts, noise_eps, l, k)
      while k < max(counts):
          k += 1
          sensitivity_at_k = sens_at_k(counts, noise_eps, l, k)
          smoothed_sensitivity = max(
              smoothed_sensitivity,
              math.exp(-beta * k) * sensitivity_at_k,
          )
          if sensitivity_at_k == 0.0:
              break
      return smoothed_sensitivity
  
file_path: privacy/research/pate_2017/analysis.py
imports: import os
  import math
  import numpy
  from six.moves import xrange
  import tensorflow.compat.v1
  import maybe_download
label_desc: Use image node_id 6 for calling a global function with example usage: smoothed_sens(counts, noise_eps, l, beta) and returns: smoothed_sensitivity