input: 
  from scipy.optimize import minimize 
  
  def optimize(): 
      """ You will be given an objective function, the goal is to optimize said 
      function using scipy.optimize.minimize.
      >>> optimize()
      [1.19287935 0.76350782 1.34701517]
      """
      # Define the objective function to minimize
      def obj_func(x):
          return (x[0] - 1) ** 2 + (x[1] - 0.5) ** 2 + (x[2] - 1.5) ** 2
      
      # Initial guess for the optimization problem
      x0 = [0.0, 0.0, 0.0] 
      # Use the Nelder-Mead method to minimize the objective function
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x
  
  print(optimize())
  
output:     # Define the objective function to minimize
      def obj_func(x):
          return (x[0] - 1) ** 2 + (x[1] - 0.5) ** 2 + (x[2] - 1.5) ** 2
      
      # Initial guess for the optimization problem
      x0 = [0.0, 0.0, 0.0] 
      # Use the Nelder-Mead method to minimize the objective function
      res = minimize(obj_func, x0, method='nelder-mead', options={'xtol': 1e-8, 'disp': True}) 
      return res.x