How to use the summit.get_run_string function in summit

res_dir += '_'.join([run_name, compiler, architecture, str(n_node)]) + '/'
        runtime_param_list = []
        # Deep copy as we change the attribute n_cell of
        # each instance of class test_element
        test_list_n_node = copy.deepcopy(test_list)
        job_time_min = time_min(len(test_list))
        batch_string = get_batch_string(test_list_n_node, job_time_min, module_Cname[architecture], n_node)
        # Loop on tests
        for count, current_run in enumerate(test_list_n_node):
            current_run.scale_n_cell(n_node)
            runtime_param_string  = ' amr.n_cell=' + ' '.join(str(i) for i in current_run.n_cell)
            runtime_param_string += ' amr.max_grid_size=' + str(current_run.max_grid_size)
            runtime_param_string += ' amr.blocking_factor=' + str(current_run.blocking_factor)
            runtime_param_string += ' max_step=' + str( current_run.n_step )
            # runtime_param_list.append( runtime_param_string )
            run_string = get_run_string(current_run, architecture, n_node, count, bin_name, runtime_param_string)
            batch_string += run_string
            batch_string += 'rm -rf plotfiles lab_frame_data diags\n'

        submit_job_command = get_submit_job_command()
        # Run the simulations.
        run_batch_nnode(test_list_n_node, res_dir, bin_name, config_command, batch_string, submit_job_command)
    os.chdir(cwd)
    # submit batch for analysis
    if os.path.exists( 'read_error.txt' ):
        os.remove( 'read_error.txt' )
    if os.path.exists( 'read_output.txt' ):
        os.remove( 'read_output.txt' )
    process_analysis(args.automated, cwd, compiler, architecture, args.n_node_list, start_date)

# read the output file from each test and store timers in
# hdf5 file with pandas format