code: def fftn(a, s=None, axes=None, norm=None):
      """
      Compute the N-dimensional discrete Fourier Transform.
  
      This function computes the *N*-dimensional discrete Fourier Transform over
      any number of axes in an *M*-dimensional array by means of the Fast Fourier
      Transform (FFT).
  
      Parameters
      ----------
      a : 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.
          Repeated indices in `axes` means that the transform over that axis is
          performed multiple times.
      norm : {None, "ortho"}, optional
          .. versionadded:: 1.10.0
  
          Normalization mode (see `numpy.fft`). Default is None.
  
      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.
  
      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.
      ifftn : The inverse of `fftn`, the inverse *n*-dimensional FFT.
      fft : The one-dimensional FFT, with definitions and conventions used.
      rfftn : The *n*-dimensional FFT of real input.
      fft2 : The two-dimensional 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.
  
      See `numpy.fft` for details, definitions and conventions used.
  
      Examples
      --------
      >>> a = np.mgrid[:3, :3, :3][0]
      >>> np.fft.fftn(a, axes=(1, 2))
      array([[[  0.+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]],
             [[  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]]])
      >>> np.fft.fftn(a, (2, 2), axes=(0, 1))
      array([[[ 2.+0.j,  2.+0.j,  2.+0.j],
              [ 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
      >>> [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) + np.random.uniform(0, 1, X.shape)
      >>> FS = np.fft.fftn(S)
      >>> plt.imshow(np.log(np.abs(np.fft.fftshift(FS))**2))
      <matplotlib.image.AxesImage object at 0x...>
      >>> plt.show()
  
      """
  
      return _raw_fftnd(a, s, axes, fft, norm)
  
imports: from __future__ import division, absolute_import, print_function
  import functools
  from numpy.core import array, asarray, zeros, swapaxes, shape, conjugate, take, sqrt
  from numpy.core.multiarray import normalize_axis_index
  from numpy.core import overrides
  from .None import fftpack_lite
  from .helper import _FFTCache
label_desc: Use image node_id 13 for calling a global function with example usage: fftn(a, s, axes, norm) and returns: _raw_fftnd