def METHOD_NAME(self):
    model = EfficientNetLiteBackbone(
        stackwise_kernel_sizes=[3, 3, 5, 3, 5, 5, 3],
        stackwise_num_repeats=[1, 2, 2, 3, 3, 4, 1],
        stackwise_input_filters=[32, 16, 24, 40, 80, 112, 192],
        stackwise_output_filters=[16, 24, 40, 80, 112, 192, 320],
        stackwise_expansion_ratios=[1, 6, 6, 6, 6, 6, 6],
        stackwise_strides=[1, 2, 2, 2, 1, 2, 1],
        width_coefficient=1.0,
        depth_coefficient=1.0,
        include_rescaling=True,
    )
    model_output = model(self.input_batch)
    save_path = os.path.join(
        self.get_temp_dir(), "efficientnet_lite_backbone.keras"
    )
    model.save(save_path)
    restored_model = keras.models.load_model(save_path)
    # Check we got the real object back.
    self.assertIsInstance(restored_model, EfficientNetLiteBackbone)
    # Check that output matches.
    restored_output = restored_model(self.input_batch)
    self.assertAllClose(model_output, restored_output)