code: def ifft2(a, s=None, axes=(-2, -1), norm=None):
      """
      Compute the 2-dimensional inverse discrete Fourier Transform.
  
      This function computes the inverse of the 2-dimensional discrete Fourier
      Transform over any number of axes in an M-dimensional array by means of
      the Fast Fourier Transform (FFT).  In other words, ``ifft2(fft2(a)) == a``
      to within numerical accuracy.  By default, the inverse transform is
      computed over the last two axes of the input array.
  
      The input, analogously to `ifft`, should be ordered in the same way as is
      returned by `fft2`, i.e. it should have the term for zero frequency
      in the low-order corner of the two axes, the positive frequency terms in
      the first half of these axes, the term for the Nyquist frequency in the
      middle of the axes and the negative frequency terms in the second half of
      both axes, in order of decreasingly negative frequency.
  
      Parameters
      ----------
      a : array_like
          Input array, can be complex.
      s : sequence of ints, optional
          Shape (length of each axis) of the output (``s[0]`` refers to axis 0,
          ``s[1]`` to axis 1, etc.).  This corresponds to `n` for ``ifft(x, n)``.
          Along each axis, if the given shape is smaller than that of the input,
          the input is cropped.  If it is larger, the input is padded with zeros.
          if `s` is not given, the shape of the input along the axes specified
          by `axes` is used.  See notes for issue on `ifft` zero padding.
      axes : sequence of ints, optional
          Axes over which to compute the FFT.  If not given, the last two
          axes are used.  A repeated index in `axes` means the transform over
          that axis is performed multiple times.  A one-element sequence means
          that a one-dimensional FFT is performed.
      norm : {"backward", "ortho", "forward"}, optional
          .. versionadded:: 1.10.0
  
          Normalization mode (see `numpy.fft`). Default is "backward".
          Indicates which direction of the forward/backward pair of transforms
          is scaled and with what normalization factor.
  
          .. versionadded:: 1.20.0
  
              The "backward", "forward" values were added.
  
      Returns
      -------
      out : complex ndarray
          The truncated or zero-padded input, transformed along the axes
          indicated by `axes`, or the last two axes if `axes` is not given.
  
      Raises
      ------
      ValueError
          If `s` and `axes` have different length, or `axes` not given and
          ``len(s) != 2``.
      IndexError
          If an element of `axes` is larger than than the number of axes of `a`.
  
      See Also
      --------
      numpy.fft : Overall view of discrete Fourier transforms, with definitions
           and conventions used.
      fft2 : The forward 2-dimensional FFT, of which `ifft2` is the inverse.
      ifftn : The inverse of the *n*-dimensional FFT.
      fft : The one-dimensional FFT.
      ifft : The one-dimensional inverse FFT.
  
      Notes
      -----
      `ifft2` is just `ifftn` with a different default for `axes`.
  
      See `ifftn` for details and a plotting example, and `numpy.fft` for
      definition and conventions used.
  
      Zero-padding, analogously with `ifft`, is performed by appending zeros to
      the input along the specified dimension.  Although this is the common
      approach, it might lead to surprising results.  If another form of zero
      padding is desired, it must be performed before `ifft2` is called.
  
      Examples
      --------
      >>> a = 4 * np.eye(4)
      >>> np.fft.ifft2(a)
      array([[1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j], # may vary
             [0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j],
             [0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
             [0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j]])
  
      """
      return _raw_fftnd(a, s, axes, ifft, norm)
  
imports: import functools
  from numpy.core import asarray, zeros, swapaxes, conjugate, take, sqrt
  from .None import _pocketfft_internal
  from numpy.core.multiarray import normalize_axis_index
  from numpy.core import overrides
label_desc: Use image node_id 18 for calling a global function with example usage: ifft2(a, s, axes, norm) and returns: _raw_fftnd