input: Write a function to optimize the given objective function using the scipy.optimize module. The function should take an initial guess and return the optimized values for the variables.
  assert optimize([0.0, 0.0, 0.0])==[optimized_value_1, optimized_value_2, optimized_value_3]
  assert optimize([1.0, 1.0, 1.0])==[optimized_value_4, optimized_value_5, optimized_value_6]
  assert optimize([0.5, 0.5, 0.5])==[optimized_value_7, optimized_value_8, optimized_value_9]
  assert optimize([-1.0, -1.0, -1.0])==[optimized_value_10, optimized_value_11, optimized_value_12]
  assert optimize([2.0, 2.0, 2.0])==[optimized_value_13, optimized_value_14, optimized_value_15]
output: def optimize(initial_guess):
      from scipy.optimize import minimize
      def objective_function(x):
          return x[0]**2 + x[1]**2 + x[2]**2
      result = minimize(objective_function, initial_guess)
      return result.x