def METHOD_NAME(self):
    # vector case
    self.run_layer_test(
        layers.BatchNormalization,
        init_kwargs={
            "center": True,
            "scale": True,
        },
        call_kwargs={"training": True},
        input_shape=(2, 3),
        expected_output_shape=(2, 3),
        expected_num_trainable_weights=2,
        expected_num_non_trainable_weights=2,
        expected_num_seed_generators=0,
        expected_num_losses=0,
        supports_masking=True,
    )
    self.run_layer_test(
        layers.BatchNormalization,
        init_kwargs={
            "center": False,
            "scale": False,
        },
        call_kwargs={"training": True},
        input_shape=(2, 3),
        expected_output_shape=(2, 3),
        expected_num_trainable_weights=0,
        expected_num_non_trainable_weights=2,
        expected_num_seed_generators=0,
        expected_num_losses=0,
        supports_masking=True,
    )
    # image case, with regularizers
    self.run_layer_test(
        layers.BatchNormalization,
        init_kwargs={
            "center": True,
            "scale": True,
            "beta_regularizer": "l2",
            "gamma_regularizer": "l2",
        },
        call_kwargs={"training": True},
        input_shape=(2, 4, 4, 3),
        expected_output_shape=(2, 4, 4, 3),
        expected_num_trainable_weights=2,
        expected_num_non_trainable_weights=2,
        expected_num_seed_generators=0,
        expected_num_losses=2,  # we have 2 regularizers.
        supports_masking=True,
    )