input: Write a function to minimize a given function using the Nelder-Mead method.
  assert np.isclose(minimize_func([0, 0])[0], -0.5, atol=1e-4)
  assert np.isclose(minimize_func([1, 1])[0], -0.5, atol=1e-4)
  assert np.isclose(minimize_func([-1, -1])[0], -0.5, atol=1e-4)
output: def minimize_func(x0):
      from scipy.optimize import minimize
      import numpy as np
  
      # Define the function to be minimized
      def func(x):
          return (x[0] - 1)**2 + (x[1] + 1)**2 - 0.5
  
      # Minimize the function using the Nelder-Mead method
      result = minimize(func, x0, method='Nelder-Mead')
      
      return result.x