def METHOD_NAME():
    # Compile simulated quantize once but support any type of quantization.
    data = np.random.uniform(low=-64, high=64, size=[2, 5]).astype("float32")
    # Test scalar float32 to uint8.
    scale_np = np.asarray([0.5]).astype("float32")
    zp_np = np.asarray([127]).astype("int32")
    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["uint8"])
    quant_args = {"out_zero_point": zp_np[0], "out_scale": scale_np[0]}
    q_out = quantize_test_driver(
        in_dtype="float32",
        quant_args=quant_args,
        axis=-1,
        out_dtype="uint8",
        in_data=data,
    )
    # Create variables with undefined shape and run with scalar inputs.
    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
    scale = relay.var("scale", shape=[relay.Any()], dtype="float32")
    zp = relay.var("zp", shape=[relay.Any()], dtype="int32")
    dtype = relay.var("dtype", shape=[], dtype="int32")
    vm = build_simulated_quantize(input_data, scale, zp, dtype)
    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
    allclose_with_rounding(sim_q_out.numpy(), q_out)
    # Now test float32 to int32 compilation.
    # Get the reference quantize output.
    q_out = quantize_test_driver(
        in_dtype="float32",
        quant_args=quant_args,
        axis=-1,
        out_dtype="int32",
        in_data=data,
    )
    # Run the simulated quantize without recompiling and confirm results match.
    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["int32"])
    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
    allclose_with_rounding(sim_q_out.numpy(), q_out)