<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def fft(self, nfft=None):
 """Compute the one-dimensional discrete Fourier transform of this `TimeSeries`. Parameters nfft : `int`, optional length of the desired Fourier transform, input will be cropped or padded to match the desired length. If nfft is not given, the length of the `TimeSeries` will be used Returns ------- out : `~gwpy.frequencyseries.FrequencySeries` the normalised, complex-valued FFT `FrequencySeries`. See Also -------- :mod:`scipy.fftpack` for the definition of the DFT and conventions used. Notes ----- This method, in constrast to the :func:`numpy.fft.rfft` method it calls, applies the necessary normalisation such that the amplitude of the output `~gwpy.frequencyseries.FrequencySeries` is correct. """

        from ..frequencyseries import FrequencySeries
        if nfft is None:
            nfft = self.size
        dft = npfft.rfft(self.value, n=nfft) / nfft
        dft[1:] *= 2.0
        new = FrequencySeries(dft, epoch=self.epoch, unit=self.unit,
                              name=self.name, channel=self.channel)
        try:
            new.frequencies = npfft.rfftfreq(nfft, d=self.dx.value)
        except AttributeError:
            new.frequencies = numpy.arange(new.size) / (nfft * self.dx.value)
        return new