code: def test_wolfe_linear_search(self):
      x_current = 3
      grad = 2 * (x_current - 5.5)
  
      def square(step):
          x_new = x_current - step * grad
          return (x_new - 5.5) ** 2
  
      def square_deriv(step):
          # Derivative with respect to the step
          return -grad * 2 * ((x_current - step * grad) - 5.5)
  
      x_star = wolfe.line_search(square, square_deriv)
      x_star = self.eval(x_star)
  
      self.assertEqual(square(0), 6.25)
      self.assertAlmostEqual(square(x_star), 0, places=2)
      self.assertAlmostEqual(x_star, 0.5, places=2)
  
file_path: neupy/tests/algorithms/minsearch/test_wolfe.py
imports: import operator
  from functools import reduce
  from collections import namedtuple
  from itertools import product
  import numpy
  from neupy.algorithms.minsearch import wolfe
  from base import BaseTestCase
label_desc: Use image node_id 9 for calling the WolfeInterpolationTestCase obj's underlying member method code with example usage: obj.test_wolfe_linear_search() and returns: unknown, unknown