padding is done in that dimension. Default: "s =
        [input.size(d) for d in dim]"
  * **dim** (*Tuple**[**int**]**, **optional*) -- Dimensions to be
    transformed. Default: last two dimensions.

  * **norm** (*str**, **optional*) --

    Normalization mode. For the forward transform ("rfft2()"),
    these correspond to:

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

    * ""backward"" - no normalization

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

    Where "n = prod(s)" is the logical FFT size. Calling the
    backward transform ("irfft2()") with the same normalization
    mode will apply an overall normalization of "1/n" between the
    two transforms. This is required to make "irfft2()" the exact
    inverse.

    Default is ""backward"" (no normalization).

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



t = torch.rand(10, 10)


