How to use the csbdeep.data.PadAndCropResizer function in csbdeep

def test_resizer(axes):
    rng = np.random.RandomState(42)

    resizer = PadAndCropResizer()
    checker = NoResizer()

    for _ in range(50):

        imdims = list(rng.randint(20,40,size=len(axes)))
        div_by = list(rng.randint(1,20,size=len(axes)))

        u = np.empty(imdims,np.float32)
        if any(s%div_n!=0 for s, div_n in zip(imdims, div_by)):
            with pytest.raises(ValueError):
                checker.before(u, axes, div_by)

        v = resizer.before(u, axes, div_by)
        assert all (
            s_v >= s_u and s_v%div_n==0
            for s_u, s_v, div_n in zip(u.shape, v.shape, div_by)

    def predict(self, img, axes, resizer=PadAndCropResizer(), n_tiles=None):
        """
        Apply the network to sofar unseen data. This method expects the raw data, i.e. not normalized.
        During prediction the mean and standard deviation, stored with the model (during data generation), are used
        for normalization.

        Parameters
        ----------
        img     : array(floats)
                  The raw images.
        axes    : String
                  Axes of the image ('YX').
        resizer : class(Resizer), optional(default=PadAndCropResizer())
        n_tiles : tuple(int)
                  Number of tiles to tile the image into, if it is too large for memory.

        Returns