torch.fft.rfftfreq
torch.fft.rfftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
Computes the sample frequencies for "rfft()" with a signal of size
   "n".
Note:
 "rfft()" returns Hermitian one-sided output, so only the positive
 frequency terms are returned. For a real FFT of length "n" and
 with inputs spaced in length unit "d", the frequencies are:

    f = torch.arange((n + 1) // 2) / (d * n)

Note:
 For even lengths, the Nyquist frequency at "f[n/2]" can be
 thought of as either negative or positive. Unlike "fftfreq()",
 "rfftfreq()" always returns it as positive.

Parameters:
      * n (int) -- the real FFT length
  * **d** (*float**, **optional*) -- The sampling length scale.
    The spacing between individual samples of the FFT input. The
    default assumes unit spacing, dividing that result by the
