input: Write a function to create an optimized solution for the objective function using scipy's minimize function with the Nelder-Mead method. The function should take no input and return the optimized values for the variables x.
  
  assert optimize()==[array([-2.0313085 ,  0.08783679,  4.09984736])]
  assert optimize()==[array([ 0.12349063,  3.45678901, -2.3456789 ])]
output: 
  def optimize():
      from scipy.optimize import minimize
      import numpy as np
      
      def objective(x):
          return x[0]**2 + x[1]**2 + x[2]**2
      
      initial_guess = np.array([1.0, 1.0, 1.0])
      result = minimize(objective, initial_guess, method='Nelder-Mead')
      
      return [result.x]
  