Pymoo Optimization problem for binary variables and constraints

I am very new to this nice optimization package pymoo. After going through the documents, I still can't implement this package for my specific problem. Here are the objective functions and constraints:

Objective:

       1) min(f1); 2) min(f2)

Constraints

      1)x =0 or 1; 2) sum(x)=9

Here is my current code:

class MyProblem(Problem):

  def __init__(self):
      super().__init__(n_var=13,
                     n_obj=2,
                     n_constr=1,
                     xl=0,
                     xu=1)
  def first_objective_function(self, x):
      y = ''my main function''
      return y
    
  def second_objective_function(self, x):
          y = ''my second main function''
          return y

  def _evaluate(self, X, out, *args, **kwargs):
          f1 = self.first_objective_function(X)
          f2 = self.second_objective_function(X)
          g = ???????
          out["F"] = np.column_stack([f1, f2])
          out["G"] = np.column_stack([g])

my_problem = MyProblem()

algorithm = NSGA2(
           pop_size=40,
           n_offsprings=10,
           sampling=get_sampling("bin_random"),
           crossover=get_crossover("bin_hux", prob=0.9, eta=15),
           mutation=get_mutation("bin_bitflip", eta=20),
           eliminate_duplicates=True)

res = minimize(my_problem,
           algorithm,
           ('n_gen', 100),
           verbose=False)

My question is how to define my constraint equation. The sum(X) equals a value, not less than a value.

The second question is, am I using the correct sampling, cross-over, and mutation algorithm for this problem? I hope the number of 1 in the x is fixed.