Can scipy.optimize Find Optimal Input Values When Multiple Products are Involved?

I'd like to find the optimal values for Input A for Product 1 and for Input A for Product 2 with the aim to maximize Total Output and subject to a given constraint. I've tried using Python's Scipy minimize function and it works if I have just one Product but it does not work for multiple Products.

Can scipy.optimize Find Optimal Input Values When Multiple Products are Involved?

Here is what I tried assuming just two Products exist (In reality I have several thousand such Products):

import numpy as npimport scipyfrom scipy.optimize import minimize 

Product1_InputB = np.array([0.5])Product1_InputC = np.array([1])Product1_InputD = np.array([1])Product1_InputE = np.array([0.08])Product1_InputF = np.array([20])Product2_InputB = np.array([0.5])Product2_InputC = np.array([1])Product2_InputD = np.array([2])Product2_InputE = np.array([0.1])Product2_InputF = np.array([30])

def Neg_Product1_Output(Product1_InputA):    return -1 * ((2.71828**((Product1_InputA-Product1_InputB)*(0.5*Product1_InputC-1.5*Product1_InputD)))/(1+(2.71828**((Product1_InputA-Product1_InputB)*(0.5*Product1_InputC-1.5*Product1_InputD))))*(Product1_InputA-Product1_InputB))def Neg_Product2_Output(Product2_InputA):    return -1 * ((2.71828**((Product2_InputA-Product2_InputB)*(0.5*Product2_InputC-1.5*Product2_InputD)))/(1+(2.71828**((Product2_InputA-Product2_InputB)*(0.5*Product2_InputC-1.5*Product2_InputD))))*(Product2_InputA-Product2_InputB))def Neg_Total_Output(Product1_InputA,Product2_InputA):    return Neg_Product1_Output + Neg_Product2_Output

def constraint(Product1_InputA, Product2_InputA):    return (((Product1_InputA - Product1_InputE) * Neg_Product1_Output) + ((Product2_InputA - Product2_InputE) * Neg_Product2_Output)) / Neg_Total_Output - 2con = {'type':'ineq', 'fun': constraint}Product1_InputA_Initial_Guess = np.array([3])Product1_InputA_Initial_Guess = np.asarray([3])Product2_InputA_Initial_Guess = np.array([1])Product2_InputA_Initial_Guess = np.asarray([1])Product1_bound = [(0.3,4)]Product2_bound = [(0.3,4)]

optimized_results = minimize(Neg_Total_Output,Product1_InputA_Initial_Guess,bounds=Product1_bound,constraints=con)

Product1_InputA_Optimal = optimized_results.xProduct1_InputA_Optimal

When I run the line optimized_results = ... I get the below error:

TypeError: constraint() missing 1 required positional argument: 'Product2_InputA'

Am not sure how to include Product2 in the optimized_results minimize function above.