How to use the amici.petab_objective.rdatas_to_measurement_df function in amici

# import to pyabc
    importer = pyabc.petab.AmiciPetabImporter(
        petab_problem, amici_model, solver)
    model = importer.create_model(return_rdatas=True)

    # simulate
    problem_parameters = petab_problem.x_nominal_free_scaled
    ret = model(problem_parameters)

    llh = ret['llh']

    # extract results
    rdatas = ret['rdatas']
    chi2 = sum(rdata['chi2'] for rdata in rdatas)
    simulation_df = amici.petab_objective.rdatas_to_measurement_df(
        rdatas, amici_model, importer.petab_problem.measurement_df)
    petab.check_measurement_df(
        simulation_df, importer.petab_problem.observable_df)
    simulation_df = simulation_df.rename(
        columns={petab.MEASUREMENT: petab.SIMULATION})
    simulation_df[petab.TIME] = simulation_df[petab.TIME].astype(int)

    # check if matches
    chi2s_match = petabtests.evaluate_chi2(chi2, gt_chi2, tol_chi2)
    llhs_match = petabtests.evaluate_llh(llh, gt_llh, tol_llh)
    simulations_match = petabtests.evaluate_simulations(
        [simulation_df], gt_simulation_dfs, tol_simulations)

    # log matches
    logger.log(logging.INFO if chi2s_match else logging.ERROR,
               f"CHI2: simulated: {chi2}, expected: {gt_chi2},"

importer = pypesto.petab.PetabImporter.from_yaml(
        yaml_file, output_folder=output_folder)
    model = importer.create_model()
    obj = importer.create_objective(model=model)

    # the scaled parameters
    problem_parameters = importer.petab_problem.x_nominal_scaled

    # simulate
    ret = obj(problem_parameters, sensi_orders=(0,), return_dict=True)

    # extract results
    rdatas = ret['rdatas']
    chi2 = sum(rdata['chi2'] for rdata in rdatas)
    llh = - ret['fval']
    simulation_df = amici.petab_objective.rdatas_to_measurement_df(
        rdatas, model, importer.petab_problem.measurement_df)
    petab.check_measurement_df(
        simulation_df, importer.petab_problem.observable_df)
    simulation_df = simulation_df.rename(
        columns={petab.MEASUREMENT: petab.SIMULATION})
    simulation_df[petab.TIME] = simulation_df[petab.TIME].astype(int)

    # check if matches
    chi2s_match = petabtests.evaluate_chi2(chi2, gt_chi2, tol_chi2)
    llhs_match = petabtests.evaluate_llh(llh, gt_llh, tol_llh)
    simulations_match = petabtests.evaluate_simulations(
        [simulation_df], gt_simulation_dfs, tol_simulations)

    # log matches
    logger.log(logging.INFO if chi2s_match else logging.ERROR,
               f"CHI2: simulated: {chi2}, expected: {gt_chi2},"