How to use the stardist.models.base.StarDistPadAndCropResizer function in stardist

axes     = self._normalize_axes(img, axes)
        axes_net = self.config.axes

        _permute_axes = self._make_permute_axes(axes, axes_net)
        x = _permute_axes(img) # x has axes_net semantics

        channel = axes_dict(axes_net)['C']
        self.config.n_channel_in == x.shape[channel] or _raise(ValueError())
        axes_net_div_by = self._axes_div_by(axes_net)

        grid = tuple(self.config.grid)
        len(grid) == len(axes_net)-1 or _raise(ValueError())
        grid_dict = dict(zip(axes_net.replace('C',''),grid))

        normalizer = self._check_normalizer_resizer(normalizer, None)[0]
        resizer = StarDistPadAndCropResizer(grid=grid_dict)

        x = normalizer.before(x, axes_net)
        x = resizer.before(x, axes_net, axes_net_div_by)

        def predict_direct(tile):
            sh = list(tile.shape); sh[channel] = 1; dummy = np.empty(sh,np.float32)
            prob, dist = self.keras_model.predict([tile[np.newaxis],dummy[np.newaxis]], **predict_kwargs)
            return prob[0], dist[0]

        if np.prod(n_tiles) > 1:
            tiling_axes   = axes_net.replace('C','') # axes eligible for tiling
            x_tiling_axis = tuple(axes_dict(axes_net)[a] for a in tiling_axes) # numerical axis ids for x
            axes_net_tile_overlaps = self._axes_tile_overlap(axes_net)
            # hack: permute tiling axis in the same way as img -> x was permuted
            n_tiles = _permute_axes(np.empty(n_tiles,np.bool)).shape
            (all(n_tiles[i] == 1 for i in range(x.ndim) if i not in x_tiling_axis) or