code: def ifft(a, n=None, axis=-1, norm=None):
      """
      Compute the one-dimensional inverse discrete Fourier Transform.
  
      This function computes the inverse of the one-dimensional *n*-point
      discrete Fourier transform computed by `fft`.  In other words,
      ``ifft(fft(a)) == a`` to within numerical accuracy.
      For a general description of the algorithm and definitions,
      see `numpy.fft`.
  
      The input should be ordered in the same way as is returned by `fft`,
      i.e.,
  
      * ``a[0]`` should contain the zero frequency term,
      * ``a[1:n//2]`` should contain the positive-frequency terms,
      * ``a[n//2 + 1:]`` should contain the negative-frequency terms, in
        increasing order starting from the most negative frequency.
  
      For an even number of input points, ``A[n//2]`` represents the sum of
      the values at the positive and negative Nyquist frequencies, as the two
      are aliased together. See `numpy.fft` for details.
  
      Parameters
      ----------
      a : array_like
          Input array, can be complex.
      n : int, optional
          Length of the transformed axis of the output.
          If `n` is smaller than the length of the input, the input is cropped.
          If it is larger, the input is padded with zeros.  If `n` is not given,
          the length of the input along the axis specified by `axis` is used.
          See notes about padding issues.
      axis : int, optional
          Axis over which to compute the inverse DFT.  If not given, the last
          axis is used.
      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 axis
          indicated by `axis`, or the last one if `axis` is not specified.
  
      Raises
      ------
      IndexError
          If `axis` is not a valid axis of `a`.
  
      See Also
      --------
      numpy.fft : An introduction, with definitions and general explanations.
      fft : The one-dimensional (forward) FFT, of which `ifft` is the inverse
      ifft2 : The two-dimensional inverse FFT.
      ifftn : The n-dimensional inverse FFT.
  
      Notes
      -----
      If the input parameter `n` is larger than the size of the input, the input
      is padded by appending zeros at the end.  Even though this is the common
      approach, it might lead to surprising results.  If a different padding is
      desired, it must be performed before calling `ifft`.
  
      Examples
      --------
      >>> np.fft.ifft([0, 4, 0, 0])
      array([ 1.+0.j,  0.+1.j, -1.+0.j,  0.-1.j]) # may vary
  
      Create and plot a band-limited signal with random phases:
  
      >>> import matplotlib.pyplot as plt
      >>> t = np.arange(400)
      >>> n = np.zeros((400,), dtype=complex)
      >>> n[40:60] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20,)))
      >>> s = np.fft.ifft(n)
      >>> plt.plot(t, s.real, label='real')
      [<matplotlib.lines.Line2D object at ...>]
      >>> plt.plot(t, s.imag, '--', label='imaginary')
      [<matplotlib.lines.Line2D object at ...>]
      >>> plt.legend()
      <matplotlib.legend.Legend object at ...>
      >>> plt.show()
  
      """
      a = asarray(a)
      if n is None:
          n = a.shape[axis]
      inv_norm = _get_backward_norm(n, norm)
      output = _raw_fft(a, n, axis, False, False, inv_norm)
      return output
  
imports: import functools
  from numpy.core import asarray, zeros, swapaxes, conjugate, take, sqrt
  from .None import _pocketfft_internal
  from numpy.core.multiarray import normalize_axis_index
  from numpy.core import overrides
label_desc: Use image node_id 7 for calling a global function with example usage: ifft(a, n, axis, norm) and returns: output