def METHOD_NAME(self, min_zoom, max_zoom, mode, keep_size, align_corners=None):
    for p in TEST_NDARRAYS_ALL:
        init_param = {
            "prob": 1.0,
            "min_zoom": min_zoom,
            "max_zoom": max_zoom,
            "mode": mode,
            "keep_size": keep_size,
            "dtype": torch.float64,
            "align_corners": align_corners,
        }
        random_zoom = RandZoom(**init_param)
        random_zoom.set_random_state(1234)
        im = p(self.imt[0])
        call_param = {"img": im}
        zoomed = random_zoom(**call_param)
        # test lazy
        # TODO: temporarily skip "nearest" test
        if mode == InterpolateMode.BILINEAR:
            test_resampler_lazy(
                random_zoom, zoomed, init_param, call_param, seed=1234, atol=1e-4 if USE_COMPILED else 1e-6
            )
        test_local_inversion(random_zoom, zoomed, im)
        expected = [
            zoom_scipy(channel, zoom=random_zoom._zoom, mode="nearest", order=0, prefilter=False)
            for channel in self.imt[0]
        ]
        expected = np.stack(expected).astype(np.float32)
        assert_allclose(zoomed, p(expected), atol=1.0, type_test=False)