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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def outline_segments(self, mask_background=False):
 """ Outline the labeled segments. The "outlines" represent the pixels *just inside* the segments, leaving the background pixels unmodified. Parameters mask_background : bool, optional Set to `True` to mask the background pixels (labels = 0) in the returned image. This is useful for overplotting the segment outlines on an image. The default is `False`. Returns ------- boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray` An image with the same shape of the segmentation image containing only the outlines of the labeled segments. The pixel values in the outlines correspond to the labels in the segmentation image. If ``mask_background`` is `True`, then a `~numpy.ma.MaskedArray` is returned. Examples -------- array([[0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0], [0, 2, 0, 0, 2, 0], [0, 2, 0, 0, 2, 0], [0, 2, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0]]) """


        from scipy.ndimage import grey_erosion, grey_dilation

        # mode='constant' ensures outline is included on the image borders
        selem = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
        eroded = grey_erosion(self.data, footprint=selem, mode='constant',
                              cval=0.)
        dilated = grey_dilation(self.data, footprint=selem, mode='constant',
                                cval=0.)

        outlines = ((dilated != eroded) & (self.data != 0)).astype(int)
        outlines *= self.data

        if mask_background:
            outlines = np.ma.masked_where(outlines == 0, outlines)

        return outlines