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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 q_gram(self, qrange=qtransform.DEFAULT_QRANGE, frange=qtransform.DEFAULT_FRANGE, mismatch=qtransform.DEFAULT_MISMATCH, snrthresh=5.5, **kwargs):
 """Scan a `TimeSeries` using the multi-Q transform and return an `EventTable` of the most significant tiles Parameters qrange : `tuple` of `float`, optional `(low, high)` range of Qs to scan frange : `tuple` of `float`, optional `(low, high)` range of frequencies to scan mismatch : `float`, optional maximum allowed fractional mismatch between neighbouring tiles snrthresh : `float`, optional lower inclusive threshold on individual tile SNR to keep in the table **kwargs other keyword arguments to be passed to :meth:`QTiling.transform`, including ``'epoch'`` and ``'search'`` Returns ------- qgram : `EventTable` a table of time-frequency tiles on the most significant `QPlane` See Also -------- TimeSeries.q_transform for a method to interpolate the raw Q-transform over a regularly gridded spectrogram gwpy.signal.qtransform for code and documentation on how the Q-transform is implemented gwpy.table.EventTable.tile to render this `EventTable` as a collection of polygons Notes ----- Only tiles with signal energy greater than or equal to `snrthresh ** 2 / 2` will be stored in the output `EventTable`. The table columns are ``'time'``, ``'duration'``, ``'frequency'``, ``'bandwidth'``, and ``'energy'``. """

        qscan, _ = qtransform.q_scan(self, mismatch=mismatch, qrange=qrange,
                                     frange=frange, **kwargs)
        qgram = qscan.table(snrthresh=snrthresh)
        return qgram