torch.fft.fftfreq
torch.fft.fftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
Computes the discrete Fourier Transform sample frequencies for a
   signal of size "n".
Note:
 By convention, "fft()" returns positive frequency terms first,
 followed by the negative frequencies in reverse order, so that
 "f[-i]" for all 0 < i \leq n/2` in Python gives the negative
 frequency terms. For an FFT of length "n" and with inputs spaced
 in length unit "d", the frequencies are:

    f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n)

Note:
 For even lengths, the Nyquist frequency at "f[n/2]" can be
 thought of as either negative or positive. "fftfreq()" follows
 NumPy's convention of taking it to be negative.

Parameters:
      * n (int) -- the FFT length
  * **d** (*float**, **optional*) -- The sampling length scale.
