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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 spectrogram(self, stride, fftlength=None, overlap=None, window='hann', method=DEFAULT_FFT_METHOD, nproc=1, **kwargs):
 """Calculate the average power spectrogram of this `TimeSeries` using the specified average spectrum method. Each time-bin of the output `Spectrogram` is calculated by taking a chunk of the `TimeSeries` in the segment `[t - overlap/2., t + stride + overlap/2.)` and calculating the :meth:`~gwpy.timeseries.TimeSeries.psd` of those data. As a result, each time-bin is calculated using `stride + overlap` seconds of data. Parameters stride : `float` number of seconds in single PSD (column of spectrogram). fftlength : `float` number of seconds in single FFT. overlap : `float`, optional number of seconds of overlap between FFTs, defaults to the recommended overlap for the given window (if given), or 0 window : `str`, `numpy.ndarray`, optional window function to apply to timeseries prior to FFT, see :func:`scipy.signal.get_window` for details on acceptable formats method : `str`, optional FFT-averaging method, see *Notes* for more details nproc : `int` number of CPUs to use in parallel processing of FFTs Returns ------- spectrogram : `~gwpy.spectrogram.Spectrogram` time-frequency power spectrogram as generated from the input time-series. Notes ----- The accepted ``method`` arguments are: - ``'bartlett'`` : a mean average of non-overlapping periodograms - ``'median'`` : a median average of overlapping periodograms - ``'welch'`` : a mean average of overlapping periodograms """

        # get method
        method_func = spectral.get_method(method)

        # calculate PSD using UI method
        return spectral.average_spectrogram(
            self,
            method_func,
            stride,
            fftlength=fftlength,
            overlap=overlap,
            window=window,
            **kwargs
        )