inverse.
    Default is ""backward"" (normalize by "1/n").

Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
-[ Example ]-



T = torch.rand(10, 10)
ihfftn = torch.fft.ihfftn(T)
ihfftn.size()
   torch.Size([10, 6])



Compared against the full output from "ifftn()", we have all
   elements up to the Nyquist frequency.



ifftn = torch.fft.ifftn(t)
torch.allclose(ifftn[..., :6], ihfftn)
   True



The discrete Fourier transform is separable, so "ihfftn()" here is
   equivalent to a combination of "ihfft()" and "ifft()":



two_iffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0)
torch.allclose(ihfftn, two_iffts)
   True


