code: def _fp_recip_private(
      prot, x: "TFEPrivateTensor", nonsigned, precision: int = 1
  ):
      """
      Approxiamtedly compute 1/x from x.
  
      Apply the quintic iteration from
      http://numbers.computation.free.fr/Constants/Algorithms/inverse.html
      """
      with tf.name_scope("fp_reciprocal"):
          sgf, exp = __fp_normalize(
              prot, x, nonsigned
          )  # x = sgf / exp, then 1 / x = 1/sgf * exp
          one = prot.define_constant(1.0)
          two = prot.define_constant(2, apply_scaling=False)
          # 2.9281928 - 2 * s approxiates 1/s with small relative errors
          # in the interval s \in [0.5, 1.)
          # By using integer factor '2', we can save one truncation.
          inv_sgf = 2.9281928 - two * sgf
          appr_recip = inv_sgf * exp  # ~ 1/x
          for _ in range(precision):
              """One iteration should give us very good approximation
              (10^{-5} relative error ratio).
              More iterations, more precision."""
              res = one - x * appr_recip
              res2 = res * res
              appr_recip = appr_recip + appr_recip * (one + res2) * (
                  res + res2
              )  # quintic iteration
          return appr_recip
  
imports: from math import ceil
  from math import log2
  import numpy
  import tensorflow
  from ..protocol import TFEPrivateTensor
  from ..protocol import TFEPublicTensor
label_desc: Use image node_id 7 for calling a global function with example usage: _fp_recip_private(prot, x, nonsigned, precision) and returns: appr_recip