 : sequence of ints, optional
        Axes over which to compute the FFT.  If not given, the last ``len(s)``
        axes are used, or all axes if `s` is also not specified.
    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 by a combination of `s` and `a`,
        as explained in the parameters section above.
        The length of the last axis transformed will be ``s[-1]//2+1``,
        while the remaining transformed axes will have lengths according to
        `s`, or unchanged from the input.

    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
    --------
    irfftn : The inverse of `rfftn`, i.e. the inverse of the n-dimensional FFT
         of real input.
    fft : The one-dimensional FFT, with definitions and conventions used.
    rfft : The one-dimensional FFT of real input.
    fftn : The n-dimensional FFT.
    rfft2 : The two-dimensional FFT of real input.

    Notes
    -----
    The transform for real input is performed over the last transformation
    axis, as by `rfft`, then the transform over the remaining axes is
    performed as by `fftn`.  The order of the output is as for `rfft` for the
    final transformation axis, and as for `fftn` for the remaining
    transformation axes.

    See `fft` for details, definitions and conventions used.

    Examples
    --------
    >>> a = np.ones((2, 2, 2))
    >>> np.fft.rfftn(a)
    array([[[8.+0.j,  0.+0.j], # may vary
            [0.+0.j,  0.+0.j]],
           [[0.+0.j,  0.+0.j],
            [0.+0.j,  0.+0.j]]])

    >>> np.fft.rfftn(a, axes=(2, 0))
    array([[[4.+0.j,  0.+0.j], # may vary
            [4.+0.j,  0.+0.j]],
           [[0.+0.j,  0.+0.j],
            [0.+0.j,  0.+0.j]]])

    r