input: Write a function to optimize the given objective function using the Nelder-Mead method with scipy's minimize function. The function should initialize the optimization with a starting point x0 and return the optimized values of x.
  assert optimize() == expected_optimized_values_for_example_function
  assert optimize() == expected_optimized_values_for_second_example_function
  assert optimize() == expected_optimized_values_for_another_example_function
  assert optimize() == expected_optimized_values_for_empty_input_case
  assert optimize() == expected_optimized_values_for_error_case
output: def optimize(objective_function, x0):
      from scipy.optimize import minimize
      result = minimize(objective_function, x0, method='Nelder-Mead')
      return result.x