dimensions if "s" is given.
  * **norm** (*str**, **optional*) --

    Normalization mode. For the backward transform ("ifftn()"),
    these correspond to:

    * ""forward"" - no normalization

    * ""backward"" - normalize by "1/n"

    * ""ortho"" - normalize by "1/sqrt(n)" (making the IFFT
      orthonormal)

    Where "n = prod(s)" is the logical IFFT size. Calling the
    forward transform ("fftn()") with the same normalization mode
    will apply an overall normalization of "1/n" between the two
    transforms. This is required to make "ifftn()" the exact
    inverse.

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

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



x = torch.rand(10, 10, dtype=torch.complex64)
ifftn = torch.fft.ifftn(x)



The discrete Fourier transform is separable, so "ifftn()" here is
   equivalent to two one-dimensional "ifft()" calls: