def test_partitioned_call_optimized_to_add_op(self, compute_unit, backend):
        """
        The PartitionedCall will be optimized to V2Add op in TF's internal optimization pass (see
        `_run_inline_graph_optimization`), so this test passes even when we haven't implemented
        the `PartitionedCall` op).
        """
        x_shape = [2, 3]
        y_shape = [2, 3]

        @tf.function
        def simple_func(*args):
            output = [args[0] + args[1]]
            return output

        @make_tf_graph([x_shape, y_shape])
        def build_model(x, y):
            return tf.raw_ops.PartitionedCall(
                args=[x, y],
                f=simple_func.get_concrete_function(tf.zeros(x_shape), tf.zeros(y_shape)),
                Tout=[tf.float32]
            )

        model, inputs, outputs = build_model

        input_values = [
            np.zeros(x_shape).astype(np.float32),
            np.zeros(y_shape).astype(np.float32),
        ]

        input_dict = dict(zip(inputs, input_values))

        TensorFlowBaseTest.run_compare_tf(
            model,
            input_dict,
            outputs,
            compute_unit=compute_unit,
            backend=backend
        )

        The PartitionedCall will be optimized to V2Add op in TF's internal optimization pass (see
        `_run_inline_graph_optimization`), so this test passes even when we haven't implemented
        the `PartitionedCall` op).
        
coremltools/converters/mil/frontend/tensorflow2/test/test_v2_ops.py