input: 
  def optimize_objective_function():
      """Given an objective function to be minimized by the Scipy library's minimize function using the Nelder-Mead method.
      Initialize the optimization process with a starting point of x0 = [0.0, 0.0, 0.0]. The objective function is represented as obj_func(x), 
      where x is the parameter vector to be optimized. The user needs to define obj_func(x) externally.
      The output of this code is the optimized parameters in a list. 
      >>> optimize_objective_function()
      [0.2345, 0.6789, 0.3456]
      """
  
output:     from scipy.optimize import minimize
  
      def obj_func(x):
          # Example objective function, replace with actual function
          return x[0]**2 + x[1]**2 + x[2]**2
  
      x0 = [0.0, 0.0, 0.0]
      result = minimize(obj_func, x0, method='Nelder-Mead')
      return result.x.tolist()