<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def filter(self, *filt, **kwargs):
 """Filter this `TimeSeries` with an IIR or FIR filter Parameters *filt : filter arguments 1, 2, 3, or 4 arguments defining the filter to be applied, - an ``Nx1`` `~numpy.ndarray` of FIR coefficients - an ``Nx6`` `~numpy.ndarray` of SOS coefficients - ``(numerator, denominator)`` polynomials - ``(zeros, poles, gain)`` - ``(A, B, C, D)`` 'state-space' representation filtfilt : `bool`, optional filter forward and backwards to preserve phase, default: `False` analog : `bool`, optional if `True`, filter coefficients will be converted from Hz to Z-domain digital representation, default: `False` inplace : `bool`, optional if `True`, this array will be overwritten with the filtered version, default: `False` **kwargs other keyword arguments are passed to the filter method Returns ------- result : `TimeSeries` the filtered version of the input `TimeSeries` Notes ----- IIR filters are converted either into cascading second-order sections (if `scipy >= 0.16` is installed), or into the ``(numerator, denominator)`` representation before being applied to this `TimeSeries`. .. note:: When using `scipy < 0.16` some higher-order filters may be unstable. With `scipy >= 0.16` higher-order filters are decomposed into second-order-sections, and so are much more stable. FIR filters are passed directly to :func:`scipy.signal.lfilter` or :func:`scipy.signal.filtfilt` without any conversions. See also -------- scipy.signal.sosfilt for details on filtering with second-order sections (`scipy >= 0.16` only) scipy.signal.sosfiltfilt for details on forward-backward filtering with second-order sections (`scipy >= 0.18` only) scipy.signal.lfilter for details on filtering (without SOS) scipy.signal.filtfilt for details on forward-backward filtering (without SOS) Raises ------ ValueError if ``filt`` arguments cannot be interpreted properly Examples -------- We can design an arbitrarily complicated filter using :mod:`gwpy.signal.filter_design` And then can download some data from LOSC to apply it using `TimeSeries.filter`: We can plot the original signal, and the filtered version, cutting off either end of the filtered data to remove filter-edge artefacts """

        # parse keyword arguments
        filtfilt = kwargs.pop('filtfilt', False)

        # parse filter
        form, filt = filter_design.parse_filter(
                filt, analog=kwargs.pop('analog', False),
                sample_rate=self.sample_rate.to('Hz').value,
        )
        if form == 'zpk':
            try:
                sos = signal.zpk2sos(*filt)
            except AttributeError:  # scipy < 0.16, no SOS filtering
                sos = None
                b, a = signal.zpk2tf(*filt)
        else:
            sos = None
            b, a = filt

        # perform filter
        kwargs.setdefault('axis', 0)
        if sos is not None and filtfilt:
            out = signal.sosfiltfilt(sos, self, **kwargs)
        elif sos is not None:
            out = signal.sosfilt(sos, self, **kwargs)
        elif filtfilt:
            out = signal.filtfilt(b, a, self, **kwargs)
        else:
            out = signal.lfilter(b, a, self, **kwargs)

        # format as type(self)
        new = out.view(type(self))
        new.__metadata_finalize__(self)
        new._unit = self.unit
        return new