code: def _constraints(self, X, missing_mask, S, error_tolerance):
          
          ok_mask = ~missing_mask
          masked_X = cvxpy.multiply(ok_mask, X)
          masked_S = cvxpy.multiply(ok_mask, S)
          abs_diff = cvxpy.abs(masked_S - masked_X)
          close_to_data = abs_diff <= error_tolerance
          constraints = [close_to_data]
          if self.require_symmetric_solution:
              constraints.append(S == S.T)
  
          if self.min_value is not None:
              constraints.append(S >= self.min_value)
  
          if self.max_value is not None:
              constraints.append(S <= self.max_value)
  
          return constraints
docs: Parameters
          ----------
          X : np.array
              Data matrix with missing values filled in
  
          missing_mask : np.array
              Boolean array indicating where missing values were
  
          S : cvxpy.Variable
              Representation of solution variable