code: def autocov(x, **kwargs):
      
      # only remove the mean once, if needed
      debias = kwargs.pop('debias', True)
      axis = kwargs.get('axis', -1)
      if debias:
          x = remove_bias(x, axis)
      kwargs['debias'] = False
      return crosscov(x, x, **kwargs)
docs: Returns the autocovariance of signal s at all lags.
  
      Parameters
      ----------
  
      x : ndarray
      axis : time axis
      all_lags : {True/False}
         whether to return all nonzero lags, or to clip the length of r_xy
         to be the length of x and y. If False, then the zero lag correlation
         is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2
  
      Returns
      -------
  
      cxx : ndarray
         The autocovariance function
  
      Notes
      -----
  
      Adheres to the definition
  
      .. math::
  
      C_{xx}[k]=E\{(X[n+k]-E\{X\})(X[n]-E\{X\})^{*}\}
  
      where X is a discrete, stationary (ergodic) random process