code: def fftn(
      x,
      s=None,
      axes=None,
      norm=None,
      overwrite_x=False,
      workers=None,
      *,
      plan=None,
  ):
      """
      Compute the N-D discrete Fourier Transform.
  
      This function computes the N-D discrete Fourier Transform over
      any number of axes in an M-D array by means of the Fast Fourier
      Transform (FFT).
  
      Parameters
      ----------
      x : array_like
          Input array, can be complex.
      s : sequence of ints, optional
          Shape (length of each transformed axis) of the output
          (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
          This corresponds to ``n`` for ``fft(x, n)``.
          Along any 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.
      axes : 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
          Normalization mode (see `fft`). Default is "backward".
      overwrite_x : bool, optional
          If True, the contents of `x` can be destroyed; the default is False.
          See :func:`fft` for more details.
      workers : int, optional
          Maximum number of workers to use for parallel computation. If negative,
          the value wraps around from ``os.cpu_count()``.
          See :func:`~scipy.fft.fft` for more details.
      plan : object, optional
          This argument is reserved for passing in a precomputed plan provided
          by downstream FFT vendors. It is currently not used in SciPy.
  
          .. versionadded:: 1.5.0
  
      Returns
      -------
      out : complex ndarray
          The truncated or zero-padded input, transformed along the axes
          indicated by `axes`, or by a combination of `s` and `x`,
          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 `x`.
  
      See Also
      --------
      ifftn : The inverse of `fftn`, the inverse N-D FFT.
      fft : The 1-D FFT, with definitions and conventions used.
      rfftn : The N-D FFT of real input.
      fft2 : The 2-D FFT.
      fftshift : Shifts zero-frequency terms to centre of array.
  
      Notes
      -----
      The output, analogously to `fft`, contains the term for zero frequency in
      the low-order corner of all axes, the positive frequency terms in the
      first half of all axes, the term for the Nyquist frequency in the middle
      of all axes and the negative frequency terms in the second half of all
      axes, in order of decreasingly negative frequency.
  
      Examples
      --------
      >>> import scipy.fft
      >>> x = np.mgrid[:3, :3, :3][0]
      >>> scipy.fft.fftn(x, axes=(1, 2))
      array([[[ 0.+0.j,   0.+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]],
             [[ 9.+0.j,   0.+0.j,   0.+0.j],
              [ 0.+0.j,   0.+0.j,   0.+0.j],
              [ 0.+0.j,   0.+0.j,   0.+0.j]],
             [[18.+0.j,   0.+0.j,   0.+0.j],
              [ 0.+0.j,   0.+0.j,   0.+0.j],
              [ 0.+0.j,   0.+0.j,   0.+0.j]]])
      >>> scipy.fft.fftn(x, (2, 2), axes=(0, 1))
      array([[[ 2.+0.j,  2.+0.j,  2.+0.j], # may vary
              [ 0.+0.j,  0.+0.j,  0.+0.j]],
             [[-2.+0.j, -2.+0.j, -2.+0.j],
              [ 0.+0.j,  0.+0.j,  0.+0.j]]])
  
      >>> import matplotlib.pyplot as plt
      >>> rng = np.random.default_rng()
      >>> [X, Y] = np.meshgrid(2 * np.pi * np.arange(200) / 12,
      ...                      2 * np.pi * np.arange(200) / 34)
      >>> S = np.sin(X) + np.cos(Y) + rng.uniform(0, 1, X.shape)
      >>> FS = scipy.fft.fftn(S)
      >>> plt.imshow(np.log(np.abs(scipy.fft.fftshift(FS))**2))
      <matplotlib.image.AxesImage object at 0x...>
      >>> plt.show()
  
      """
      return (Dispatchable(x, np.ndarray),)
  
imports: from scipy._lib.uarray import generate_multimethod, Dispatchable
  import numpy
label_desc: Use image node_id 9 for calling a global function with example usage: fftn(x, s, axes, norm, overwrite_x, workers) and returns: Dispatchable