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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def create_turnover_tear_sheet(factor_data, turnover_periods=None):
 """ Creates a tear sheet for analyzing the turnover properties of a factor. Parameters factor_data : pd.DataFrame - MultiIndex A MultiIndex DataFrame indexed by date (level 0) and asset (level 1), containing the values for a single alpha factor, forward returns for each period, the factor quantile/bin that factor value belongs to, and (optionally) the group the asset belongs to. - See full explanation in utils.get_clean_factor_and_forward_returns turnover_periods : sequence[string], optional Periods to compute turnover analysis on. By default periods in 'factor_data' are used but custom periods can provided instead. This can be useful when periods in 'factor_data' are not multiples of the frequency at which factor values are computed i.e. the periods are 2h and 4h and the factor is computed daily and so values like ['1D', '2D'] could be used instead """


    if turnover_periods is None:
        turnover_periods = utils.get_forward_returns_columns(
            factor_data.columns)

    quantile_factor = factor_data['factor_quantile']

    quantile_turnover = \
        {p: pd.concat([perf.quantile_turnover(quantile_factor, q, p)
                       for q in range(1, int(quantile_factor.max()) + 1)],
                      axis=1)
            for p in turnover_periods}

    autocorrelation = pd.concat(
        [perf.factor_rank_autocorrelation(factor_data, period) for period in
         turnover_periods], axis=1)

    plotting.plot_turnover_table(autocorrelation, quantile_turnover)

    fr_cols = len(turnover_periods)
    columns_wide = 1
    rows_when_wide = (((fr_cols - 1) // 1) + 1)
    vertical_sections = fr_cols + 3 * rows_when_wide + 2 * fr_cols
    gf = GridFigure(rows=vertical_sections, cols=columns_wide)

    for period in turnover_periods:
        if quantile_turnover[period].isnull().all().all():
            continue
        plotting.plot_top_bottom_quantile_turnover(quantile_turnover[period],
                                                   period=period,
                                                   ax=gf.next_row())

    for period in autocorrelation:
        if autocorrelation[period].isnull().all():
            continue
        plotting.plot_factor_rank_auto_correlation(autocorrelation[period],
                                                   period=period,
                                                   ax=gf.next_row())

    plt.show()
    gf.close()