我正在尝试校准一个模型，该模型包含12个未知参数，但只有5个参数作为输入。我正在为它构建一个类，例如：

class model:

def __init__(self,input_1,input_2,input_3,...):
    self.input_1=input_1
    self.input_2=input_2
    self.input_3=input_3
    .
    . 
    . #Only inputs here, no pmts
Then I've defined the functions as:

def integral(self, input_1, pmt1, pm2, pm3.... )

integral,err= quad(f(input,pmt1,pmt2,pmt3)) #I'm simplifying here, the function is longer

    return integral

def model_price(self,input_1,input_2,input_3):

     price = pm3 + self.integral(self)/input_3

   return price

def loss_function(self):

   err = (self.input_1 - self.model_price)
   pen = 0 

   return err + pen

def minimization(self,x0,bnds):

 params = {"pm1": {"x0": 0.0746, "lbub": [1e-4,4.9]},  #1
              "pmt2": {"x0": 0.3369, "lbub": [1e-4,4]}, #2
              "pm3": {"x0": 0.3369, "lbub": [1e-4,4]}, #3
                  ....
           }

    x0 = [param["x0"] for key, param in params.items()]
    bnds = [param["lbub"] for key, param in params.items()]
    results =minimize(self.loss_function(), x0, tol = 1e-3, method='SLSQP', options={'maxiter': 1e3 }, bounds=bnds)
            
    return results

当我尝试使用但不起作用时，我尝试在函数外部传递x0和bounds。我总是得到：

TypeError: integral() missing 12 required positional arguments: 'pmt1, pmt2, pmt3,