code: def acorr_lpc(x, axis=-1):
      
      if not np.isrealobj(x):
          raise ValueError("Complex input not supported yet")
  
      maxlag = x.shape[axis]
      nfft = int(2 ** nextpow2(2 * maxlag - 1))
  
      if axis != -1:
          x = np.swapaxes(x, -1, axis)
      a = _acorr_last_axis(x, nfft, maxlag)
      if axis != -1:
          a = np.swapaxes(a, -1, axis)
      return a
docs: Compute autocorrelation of x along the given axis.
  
      This compute the biased autocorrelation estimator (divided by the size of
      input signal)
  
      Notes
      -----
          The reason why we do not use acorr directly is for speed issue.