code: def _lowest_mu(x, fun_prediction, fun_prediction_args,
                 x_bounds, x_types, minimize_constraints_fun):
      '''
      Calculate the lowest mu
      '''
      # This is only for step-wise optimization
      x = lib_data.match_val_type(x, x_bounds, x_types)
  
      mu = sys.maxsize
      if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
          mu, _ = fun_prediction(x, *fun_prediction_args)
      return mf _lowest_mu(x, fun_prediction, fun_prediction_args,
                 x_bounds, x_types, minimize_constraints_fun):
      '''
      Calculate the lowest mu
      '''
      # This is only for step-wise optimization
      x = lib_data.match_val_type(x, x_bounds, x_types)
  
      mu = sys.maxsize
      if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
          mu, _ = fun_prediction(x, *fun_prediction_args)
      return mu
docs: Calculate the lowest mu