code: def _autocov(s, **kwargs):
      
      # only remove the mean once, if needed
      debias = kwargs.pop('debias', True)
      axis = kwargs.get('axis', -1)
      if debias:
          s = _remove_bias(s, axis)
      kwargs['debias'] = False
      return _crosscov(s, s, **kwargs)
docs: Returns the autocovariance of signal s at all lags.
  
      Adheres to the definition
      sxx[k] = E{S[n]S[n+k]} = cov{S[n],S[n+k]}
      where E{} is the expectation operator, and S is a zero mean process