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 by a combination of `s` or `a`,
        as explained in the parameters section above.

    Raises
    ------
    ValueError
        If `s` and `axes` have different length.
    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.
    fftn : The forward *n*-dimensional FFT, of which `ifftn` is the inverse.
    ifft : The one-dimensional inverse FFT.
    ifft2 : The two-dimensional inverse FFT.
    ifftshift : Undoes `fftshift`, shifts zero-frequency terms to beginning
        of array.

    Notes
    -----
    See `numpy.fft` for definitions 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 `ifftn` is called.

    Examples
    --------
    >>> a = np.eye(4)
    >>> np.fft.ifftn(np.fft.fftn(a, axes=(0,)), axes=(1,))
    array([[ 1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],
           [ 0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j],
           [ 0.+0.j,  0.+0.j,  1.+0.j,  0.+0.j],
           [ 0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j]])


    Create and plot an image with band-limited frequency content:

    >>> import matplotlib.pyplot as plt
    >>> n = np.zeros((200,200), dtype=complex)
    >>> n[60:80, 20:40] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20, 20)))
    >>> im = np.fft.ifftn(n).real
    >>> plt.imshow(im)
    <matplotlib.image.AxesImage object at 0x...>
    >>> plt.show()

    r@