code: def rfft(x, n=None, axis=-1, overwrite_x=False):
      """
      Discrete Fourier transform of a real sequence.
  
      Parameters
      ----------
      x : array_like, real-valued
          The data to transform.
      n : int, optional
          Defines the length of the Fourier transform. If `n` is not specified
          (the default) then ``n = x.shape[axis]``. If ``n < x.shape[axis]``,
          `x` is truncated, if ``n > x.shape[axis]``, `x` is zero-padded.
      axis : int, optional
          The axis along which the transform is applied. The default is the
          last axis.
      overwrite_x : bool, optional
          If set to true, the contents of `x` can be overwritten. Default is
          False.
  
      Returns
      -------
      z : real ndarray
          The returned real array contains::
  
            [y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2))]              if n is even
            [y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)),Im(y(n/2))]   if n is odd
  
          where::
  
            y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k*2*pi/n)
            j = 0..n-1
  
      See Also
      --------
      fft, irfft, scipy.fft.rfft
  
      Notes
      -----
      Within numerical accuracy, ``y == rfft(irfft(y))``.
  
      Both single and double precision routines are implemented. Half precision
      inputs will be converted to single precision. Non-floating-point inputs
      will be converted to double precision. Long-double precision inputs are
      not supported.
  
      To get an output with a complex datatype, consider using the newer
      function `scipy.fft.rfft`.
  
      Examples
      --------
      >>> from scipy.fftpack import fft, rfft
      >>> a = [9, -9, 1, 3]
      >>> fft(a)
      array([  4. +0.j,   8.+12.j,  16. +0.j,   8.-12.j])
      >>> rfft(a)
      array([  4.,   8.,  12.,  16.])
  
      """
      return _pocketfft.rfft_fftpack(x, n, axis, None, overwrite_x)
  
file_path: catboost/contrib/python/scipy/py3/scipy/fftpack/basic.py
imports: from scipy.fft import _pocketfft
  from .helper import _good_shape
label_desc: Use image node_id 3 for calling a global function with example usage: rfft(x, n, axis, overwrite_x) and returns: _pocketfft