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]])

    rn