torch.fft.rfftn
torch.fft.rfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor
Computes the N-dimensional discrete Fourier transform of real
   "input".
The FFT of a real signal is Hermitian-symmetric, "X[i_1, ..., i_n]
   = conj(X[-i_1, ..., -i_n])" so the full "fftn()" output contains
   redundant information. "rfftn()" instead omits the negative
   frequencies in the last dimension.
Note:
 Supports torch.half on CUDA with GPU Architecture SM53 or
 greater. However it only supports powers of 2 signal length in
 every transformed dimensions.

Parameters:
      * input (Tensor) -- the input tensor
  * **s** (*Tuple**[**int**]**, **optional*) -- Signal size in the
    transformed dimensions. If given, each dimension "dim[i]" will
    either be zero-padded or trimmed to the length "s[i]" before
    computing the real FFT. If a length "-1" is specified, no
    padding is done in that dimension. Default: "s =
