code: def golden_search(f, maxstep=50, maxiter=1024, tol=1e-5):
      """
      Identify best step for function in specific direction.
  
      Parameters
      ----------
      f : func
  
      maxstep : float
          Defaults to ``50``.
  
      maxiter : int
          Defaults to ``1024``.
  
      tol : float
          Defaults to ``1e-5``.
  
      Returns
      -------
      float
          Identified optimal step.
      """
      with tf.name_scope("golden-search"):
          golden_ratio = asfloat((math.sqrt(5) - 1) / 2)
          tol = asfloat(tol)
  
          def interval_reduction(a, b, c, d):
              fc = f(c)
              fd = f(d)
  
              is_c_smaller_than_d = tf.less(fc, fd)
  
              new_a = tf.where(is_c_smaller_than_d, a, c)
              new_b = tf.where(is_c_smaller_than_d, d, b)
              new_c = tf.where(
                  is_c_smaller_than_d, d - golden_ratio * (d - a), d
              )
              new_d = tf.where(
                  is_c_smaller_than_d, c, c + golden_ratio * (b - c)
              )
  
              return [new_a, new_b, new_c, new_d]
  
          a = tf.constant(asfloat(0))
          b = maxstep
          c = b - golden_ratio * (b - a)
          d = a + golden_ratio * (b - a)
  
          a, b, c, d = tf.while_loop(
              cond=lambda a, b, c, d: tf.greater(tf.abs(c - d), tol),
              body=interval_reduction,
              loop_vars=[a, b, c, d],
              back_prop=False,
              maximum_iterations=maxiter,
          )
          return (a + b) / 2
  
imports: import math
  import numpy
  import tensorflow
  from neupy.utils import asfloat
label_desc: Use image node_id 2 for calling a global function with example usage: golden_search(f, maxstep, maxiter, tol) and returns: unknown, list