def METHOD_NAME(self, height_factor, width_factor):
    np.random.seed(1337)
    num_samples = 2
    orig_height = 5
    orig_width = 8
    channels = 3
    input = {
        "images": tf.random.uniform(
            shape=[num_samples, orig_height, orig_width, channels],
        ),
        "segmentation_masks": tf.random.uniform(
            shape=[num_samples, orig_height, orig_width, 1],
            minval=0,
            maxval=2,
        ),
    }
    layer = RandomZoom(height_factor, width_factor)
    actual_output = layer(input)
    expected_output = {
        "images": tf.random.uniform(
            shape=[num_samples, orig_height, orig_width, channels],
        ),
        "segmentation_masks": tf.random.uniform(
            shape=[num_samples, orig_height, orig_width, 1],
            minval=0,
            maxval=2,
        ),
    }
    # Check output shape of images
    self.assertAllEqual(
        expected_output["images"].shape, actual_output["images"].shape
    )
    # Check output shape of segmentation masks
    self.assertAllEqual(
        expected_output["segmentation_masks"].shape,
        actual_output["segmentation_masks"].shape,
    )