input: Write a function named `optimize` that takes no arguments and returns the optimized solution of an objective function using the Nelder-Mead optimization method from the `scipy.optimize` module. The initial guess for the optimization should be set as `[0.0, 0.0, 0.0]`. The function should use the `minimize` function with the specified method and options.
  assert list(optimize())==[0.0, 0.0, 0.0]
output: def optimize():
      from scipy.optimize import minimize
      initial_guess = [0.0, 0.0, 0.0]
      result = minimize(lambda x: x[0]**2 + x[1]**2 + x[2]**2, initial_guess, method='Nelder-Mead')
      return result.x