
norm (str, optional) --Normalization mode. For the backward transform ("ifft2()"),
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 ("fft2()") with the same normalization mode
will apply an overall normalization of "1/n" between the two
transforms. This is required to make "ifft2()" 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)
ifft2 = torch.fft.ifft2(x)



The discrete Fourier transform is separable, so "ifft2()" here is
   equivalent to two one-dimensional "ifft()" calls: