How to use the tsfresh.feature_extraction.feature_calculators.abs_energy function in tsfresh

def time_series_abs_energy(x):
    """
    Returns the absolute energy of the time series which is the sum over the squared values

    .. math::

        E = \\sum_{i=1,\ldots, n} x_i^2

    :param x: the time series to calculate the feature of
    :type x: pandas.Series
    :return: the value of this feature
    :return type: float
    """
    return ts_feature_calculators.abs_energy(x)

"autocorr5": feature_calculators.autocorrelation(sig, 5),
        "autocorr10": feature_calculators.autocorrelation(sig, 10),

        "autocorr_abs_01": feature_calculators.autocorrelation(x=np.abs(sig), lag=1),
        "autocorr_abs_02": feature_calculators.autocorrelation(x=np.abs(sig), lag=2),
        "autocorr_abs_03": feature_calculators.autocorrelation(x=np.abs(sig), lag=3),
        "autocorr_abs_05": feature_calculators.autocorrelation(x=np.abs(sig), lag=5),
        "autocorr_abs_10": feature_calculators.autocorrelation(x=np.abs(sig), lag=10),

        # Trend error
        "trend_stderr": feature_calculators.linear_trend(x=sig, param=[{"attr": "stderr"}])[0][1],

        "abs_change": feature_calculators.absolute_sum_of_changes(x=sig),
        "mean_change": np.mean(diff),
        "ratio_diff": (diff[diff >= 0].sum() + eps) / (diff[diff < 0].sum() + eps),
        "abs_energy": feature_calculators.abs_energy(x=sig - np.mean(sig)),
        "agg_autocorr_mean":
            feature_calculators.agg_autocorrelation(x=sig, param=[{"f_agg": "mean", "maxlag": 10}])[0][
                1],
        "agg_autocorr_std":
            feature_calculators.agg_autocorrelation(x=sig, param=[{"f_agg": "std", "maxlag": 10}])[0][
                1],
        "agg_autocorr_abs_mean":
            feature_calculators.agg_autocorrelation(x=np.abs(sig), param=[{"f_agg": "mean", "maxlag": 10}])[0][1],
        "agg_autocorr_abs_std":
            feature_calculators.agg_autocorrelation(x=np.abs(sig), param=[{"f_agg": "std", "maxlag": 10}])[0][1],

        "binned_entropy": feature_calculators.binned_entropy(x=sig, max_bins=250),
        "cid_ce_normed": feature_calculators.cid_ce(x=sig, normalize=True),
    }

    mfcc = librosa.feature.mfcc(sig.astype(np.float64) - the_mean, n_mfcc=mfcc_size).mean(axis=1)

As in tsfresh `abs_energy `_ \
        Returns the absolute energy of the time series which is the sum over the squared values\


        .. math::

            E=\\sum_{i=1,\ldots, n}x_i^2


        :param x: the time series to calculate the feature of
        :type x: pandas.Series
        :return: the value of this feature
        :rtype: float
        """
        _energy = feature_calculators.abs_energy(x)
        logging.debug("abs energy by tsfresh calculated")
        return _energy

def get_function(self):
        return abs_energy