docs: def _rolling_window(a, window, axis=-1):
      """
      Make an ndarray with a rolling window along axis.
  
      Parameters
      ----------
      a : array_like
          Array to add rolling window to
      axis: int
          axis position along which rolling window will be applied.
      window : int
          Size of rolling window
  
      Returns
      -------
      Array that is a view of the original array with a added dimension
      of size w.
  
      Examples
      --------
      >>> x=np.arange(10).reshape((2,5))
      >>> np.rolling_window(x, 3, axis=-1)
      array([[[0, 1, 2], [1, 2, 3], [2, 3, 4]],
             [[5, 6, 7], [6, 7, 8], [7, 8, 9]]])
  
      Calculate rolling mean of last dimension:
      >>> np.mean(np.rolling_window(x, 3, axis=-1), -1)
      array([[ 1.,  2.,  3.],
             [ 6.,  7.,  8.]])
  
      This function is taken from https://github.com/numpy/numpy/pull/31
      but slightly modified to accept axis option.
      """
      axis = _validate_axis(a, axis)
      a = np.swapaxes(a, axis, -1)
  
      if window < 1:
          raise ValueError(
              "`window` must be at least 1. Given : {}".format(window))
      if window > a.shape[-1]:
          raise ValueError("`window` is too long. Given : {}".format(window))
  
      shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
      strides = a.strides + (a.strides[-1],)
      rolling = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides,
                                                writeable=False)
      return np.swapaxes(rolling, -2, axis)