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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 compute_mean_returns_spread(mean_returns, upper_quant, lower_quant, std_err=None):
 """ Computes the difference between the mean returns of two quantiles. Optionally, computes the standard error of this difference. Parameters mean_returns : pd.DataFrame DataFrame of mean period wise returns by quantile. MultiIndex containing date and quantile. See mean_return_by_quantile. upper_quant : int Quantile of mean return from which we wish to subtract lower quantile mean return. lower_quant : int Quantile of mean return we wish to subtract from upper quantile mean return. std_err : pd.DataFrame, optional Period wise standard error in mean return by quantile. Takes the same form as mean_returns. Returns ------- mean_return_difference : pd.Series Period wise difference in quantile returns. joint_std_err : pd.Series Period wise standard error of the difference in quantile returns. if std_err is None, this will be None """


    mean_return_difference = mean_returns.xs(upper_quant,
                                             level='factor_quantile') \
        - mean_returns.xs(lower_quant, level='factor_quantile')

    if std_err is None:
        joint_std_err = None
    else:
        std1 = std_err.xs(upper_quant, level='factor_quantile')
        std2 = std_err.xs(lower_quant, level='factor_quantile')
        joint_std_err = np.sqrt(std1**2 + std2**2)

    return mean_return_difference, joint_std_err