input: Write a function to optimize a given objective function using the Nelder-Mead method.
  assert np.allclose(optimize_function(objective, [1.0, 1.0]), scipy.optimize.minimize(objective, [1.0, 1.0], method='nelder-mead', options={'xtol': 1e-8, 'disp': True}).x)
  assert np.allclose(optimize_function(objective, [2.0, 2.0]), scipy.optimize.minimize(objective, [2.0, 2.0], method='nelder-mead', options={'xtol': 1e-8, 'disp': True}).x)
output: def optimize_function(objective, initial_guess):
      import numpy as np
      from scipy.optimize import minimize
  
      result = minimize(objective, initial_guess, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
      return result.x