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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 std_scale(expr, columns=None, with_means=True, with_std=True, preserve=False, suffix='_scaled', group=None):
 """ Resize a data frame by mean and standard error. :param DataFrame expr: Input DataFrame :param bool with_means: Determine whether the output will be subtracted by means :param bool with_std: Determine whether the output will be divided by standard deviations :param bool preserve: Determine whether input data should be kept. If True, scaled input data will be appended to the data frame with `suffix` :param columns: Columns names to resize. If set to None, float or int-typed columns will be normalized if the column is not specified as a group column. :param group: determine scale groups. Scaling will be done in each group separately. :param str suffix: column suffix to be appended to the scaled columns. :return: resized data frame :rtype: DataFrame """

    time_suffix = str(int(time.time()))

    def calc_agg(expr, col):
        return [
            getattr(expr, col).mean().rename(col + '_mean_' + time_suffix),
            getattr(expr, col).std(ddof=0).rename(col + '_std_' + time_suffix),
        ]

    def do_scale(expr, col):
        c = getattr(expr, col)
        mean_expr = getattr(expr, col + '_mean_' + time_suffix)
        if with_means:
            c = c - mean_expr
            mean_expr = Scalar(0)
        if with_std:
            std_expr = getattr(expr, col + '_std_' + time_suffix)
            c = (std_expr == 0).ifelse(mean_expr, c / getattr(expr, col + '_std_' + time_suffix))
        return c

    return _scale_values(expr, columns, calc_agg, do_scale, preserve=preserve, suffix=suffix, group=group)