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

def get_data(n_images, axes, shape):
        red_n = rng.choice(len(axes)-1)+1
        red_axes = ''.join(rng.choice(tuple(axes),red_n,replace=False))
        keepdims = rng.choice((True,False))

        def _gen():
            for i in range(n_images):
                x = rng.uniform(size=shape)
                y = np.mean(x,axis=tuple(axes_dict(axes)[a] for a in red_axes),keepdims=keepdims)
                yield x, y, axes, None
        return RawData(_gen, n_images, ''), red_axes, keepdims

def get_data(n_images, axes, shape):
        def _gen():
            for i in range(n_images):
                x = rng.uniform(size=shape)
                y = 5 + 3*x
                yield x, y, axes, None
        return RawData(_gen, n_images, '')

def _create(img_size,img_axes,patch_size,patch_axes):
        U,V = (rng.uniform(size=(n_images,)+img_size) for _ in range(2))
        X,Y,XYaxes = create_patches (
            raw_data            = RawData.from_arrays(U,V,img_axes),
            patch_size          = patch_size,
            patch_axes          = patch_axes,
            n_patches_per_image = n_patches_per_image,
            save_file           = save_file
        )
        (_X,_Y), val_data, _XYaxes = load_training_data(save_file,verbose=True)
        assert val_data is None
        assert _XYaxes[-1 if backend_channels_last else 1] == 'C'
        _X,_Y = (move_image_axes(u,fr=_XYaxes,to=XYaxes) for u in (_X,_Y))
        assert np.allclose(X,_X,atol=1e-6)
        assert np.allclose(Y,_Y,atol=1e-6)
        assert set(XYaxes) == set(_XYaxes)
        assert load_training_data(save_file,validation_split=0.5)[2] is not None
        assert all(len(x)==3 for x in load_training_data(save_file,n_images=3)[0])