How to use the pypesto.OptimizeOptions function in pypesto

elif library == 'pyswarm':
        optimizer = pypesto.PyswarmOptimizer(options=options)
    else:
        raise ValueError("This code should not be reached")

    optimizer.temp_file = os.path.join('test', 'tmp_{index}.csv')

    dim = len(objective.x_ids)
    lb = -2 * np.ones((1, dim))
    ub = 2 * np.ones((1, dim))
    pars = objective.amici_model.getParameters()
    problem = pypesto.Problem(objective, lb, ub,
                              x_fixed_indices=fixed_pars,
                              x_fixed_vals=[pars[idx] for idx in fixed_pars])

    optimize_options = pypesto.OptimizeOptions(
        allow_failed_starts=False,
        startpoint_resample=True,
    )

    pypesto.minimize(problem, optimizer, n_starts, options=optimize_options)

def test_error_leastsquares_with_ssigma():
    petab_problem = petab.Problem.from_yaml(
        folder_base + "Zheng_PNAS2012/Zheng_PNAS2012.yaml")
    petab_problem.model_name = "Zheng_PNAS2012"
    importer = pypesto.PetabImporter(petab_problem)
    obj = importer.create_objective()
    problem = importer.create_problem(obj)

    optimizer = pypesto.ScipyOptimizer('ls_trf', options={'max_nfev': 50})
    with pytest.raises(RuntimeError):
        pypesto.minimize(
            problem=problem, optimizer=optimizer, n_starts=1,
            options=pypesto.OptimizeOptions(allow_failed_starts=False)
        )

if library == 'scipy':
        optimizer = pypesto.ScipyOptimizer(method=solver,
                                           options=options)
    elif library == 'ipopt':
        optimizer = pypesto.IpoptOptimizer()
    elif library == 'dlib':
        optimizer = pypesto.DlibOptimizer(method=solver,
                                          options=options)
    elif library == 'pyswarm':
        optimizer = pypesto.PyswarmOptimizer(options=options)

    lb = 0 * np.ones((1, 2))
    ub = 1 * np.ones((1, 2))
    problem = pypesto.Problem(objective, lb, ub)

    optimize_options = pypesto.OptimizeOptions(
        allow_failed_starts=allow_failed_starts)

    result = pypesto.minimize(
        problem=problem,
        optimizer=optimizer,
        n_starts=1,
        startpoint_method=pypesto.startpoint.uniform,
        options=optimize_options
    )

    assert isinstance(result.optimize_result.list[0]['fval'], float)

def check_history(self):
        kwargs = {
            'objective': self.obj,
            'ub': self.ub,
            'lb': self.lb,
        }
        if self.fix_pars:
            kwargs = {**kwargs, **{
                'x_fixed_indices': self.x_fixed_indices,
                'x_fixed_vals': self.x_fixed_indices
            }}
        self.problem = pypesto.Problem(**kwargs)

        optimize_options = pypesto.OptimizeOptions(
            allow_failed_starts=False
        )

        self.history_options.trace_save_iter = 1

        for storage_file in ['tmp/traces/conversion_example_{id}.csv', None]:
            self.history_options.storage_file = storage_file

            result = pypesto.minimize(
                problem=self.problem,
                optimizer=self.optimizer,
                n_starts=1,
                startpoint_method=pypesto.startpoint.uniform,
                options=optimize_options,
                history_options=self.history_options
            )