code: def ihfftn(
      x,
      s=None,
      axes=None,
      norm=None,
      overwrite_x=False,
      workers=None,
      *,
      plan=None,
  ):
      """
      Compute the N-D inverse discrete Fourier Transform for a real
      spectrum.
  
      This function computes the N-D inverse discrete Fourier Transform
      over any number of axes in an M-D real array by means of the Fast
      Fourier Transform (FFT). By default, all axes are transformed, with the
      real transform performed over the last axis, while the remaining transforms
      are complex.
  
      Parameters
      ----------
      x : array_like
          Input array, taken to be real.
      s : sequence of ints, optional
          Shape (length along each transformed axis) to use from the input.
          (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.).
          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.
          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 `x`.
  
      See Also
      --------
      hfftn : The forward N-D FFT of Hermitian input.
      hfft : The 1-D FFT of Hermitian input.
      fft : The 1-D FFT, with definitions and conventions used.
      fftn : The N-D FFT.
      hfft2 : The 2-D FFT of Hermitian input.
  
      Notes
      -----
  
      The transform for real input is performed over the last transformation
      axis, as by `ihfft`, then the transform over the remaining axes is
      performed as by `ifftn`. The order of the output is the positive part of
      the Hermitian output signal, in the same format as `rfft`.
  
      Examples
      --------
      >>> import scipy.fft
      >>> x = np.ones((2, 2, 2))
      >>> scipy.fft.ihfftn(x)
      array([[[1.+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]]])
      >>> scipy.fft.ihfftn(x, axes=(2, 0))
      array([[[1.+0.j,  0.+0.j], # may vary
              [1.+0.j,  0.+0.j]],
             [[0.+0.j,  0.+0.j],
              [0.+0.j,  0.+0.j]]])
  
      """
      return (Dispatchable(x, np.ndarray),)
  
imports: from scipy._lib.uarray import generate_multimethod, Dispatchable
  import numpy
label_desc: Use image node_id 19 for calling a global function with example usage: ihfftn(x, s, axes, norm, overwrite_x, workers) and returns: Dispatchable