code: def dctn(
      x, type=2, shape=None, axes=None, norm=None, overwrite_x=False
  ):
      """
      Return multidimensional Discrete Cosine Transform along the specified axes.
  
      Parameters
      ----------
      x : array_like
          The input array.
      type : {1, 2, 3, 4}, optional
          Type of the DCT (see Notes). Default type is 2.
      shape : int or array_like of ints or None, optional
          The shape of the result. If both `shape` and `axes` (see below) are
          None, `shape` is ``x.shape``; if `shape` is None but `axes` is
          not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
          If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
          If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
          length ``shape[i]``.
          If any element of `shape` is -1, the size of the corresponding
          dimension of `x` is used.
      axes : int or array_like of ints or None, optional
          Axes along which the DCT is computed.
          The default is over all axes.
      norm : {None, 'ortho'}, optional
          Normalization mode (see Notes). Default is None.
      overwrite_x : bool, optional
          If True, the contents of `x` can be destroyed; the default is False.
  
      Returns
      -------
      y : ndarray of real
          The transformed input array.
  
      See Also
      --------
      idctn : Inverse multidimensional DCT
  
      Notes
      -----
      For full details of the DCT types and normalization modes, as well as
      references, see `dct`.
  
      Examples
      --------
      >>> from scipy.fftpack import dctn, idctn
      >>> rng = np.random.default_rng()
      >>> y = rng.standard_normal((16, 16))
      >>> np.allclose(y, idctn(dctn(y, norm='ortho'), norm='ortho'))
      True
  
      """
      shape = _good_shape(x, shape, axes)
      return _pocketfft.dctn(x, type, shape, axes, norm, overwrite_x)
  
file_path: catboost/contrib/python/scipy/py3/scipy/fftpack/realtransforms.py
imports: from scipy.fft import _pocketfft
  from .helper import _good_shape
label_desc: Use image node_id 1 for calling a global function with example usage: dctn(x, type, shape, axes, norm, overwrite_x) and returns: _pocketfft