input: 
  def optimize_objective_function():
      """
      Optimize the given objective function using the Nelder-Mead method from the scipy.optimize module.
      
      >>> optimize_objective_function()
      array([0., 0., 0.])
      """
      from scipy.optimize import minimize
  
      def obj_func(x):
          # Example objective function: x^2 + y^2 + z^2
          return x[0]**2 + x[1]**2 + x[2]**2
  
      def optimize():
          x0 = [0.0, 0.0, 0.0]
          res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
          return res.x
  
      return optimize()
  
  # Example usage
  print(optimize_objective_function())
  
output:     from scipy.optimize import minimize
  
      def obj_func(x):
          # Example objective function: x^2 + y^2 + z^2
          return x[0]**2 + x[1]**2 + x[2]**2
  
      def optimize():
          x0 = [0.0, 0.0, 0.0]
          res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})
          return res.x
  
      return optimize()