How to use the stardist.utils._is_power_of_2 function in stardist

def _compute_receptive_field(self, img_size=None):
        # TODO: good enough?
        from scipy.ndimage import zoom
        if img_size is None:
            img_size = tuple(g*(128 if self.config.n_dim==2 else 64) for g in self.config.grid)
        if np.isscalar(img_size):
            img_size = (img_size,) * self.config.n_dim
        img_size = tuple(img_size)
        # print(img_size)
        assert all(_is_power_of_2(s) for s in img_size)
        mid = tuple(s//2 for s in img_size)
        dummy = np.empty((1,)+img_size+(1,), dtype=np.float32)
        x = np.zeros((1,)+img_size+(self.config.n_channel_in,), dtype=np.float32)
        z = np.zeros_like(x)
        x[(0,)+mid+(slice(None),)] = 1
        y  = self.keras_model.predict([x,dummy])[0][0,...,0]
        y0 = self.keras_model.predict([z,dummy])[0][0,...,0]
        grid = tuple((np.array(x.shape[1:-1])/np.array(y.shape)).astype(int))
        assert grid == self.config.grid
        y  = zoom(y, grid,order=0)
        y0 = zoom(y0,grid,order=0)
        ind = np.where(np.abs(y-y0)>0)
        return [(m-np.min(i), np.max(i)-m) for (m,i) in zip(mid,ind)]