def METHOD_NAME():
    """
     Creates a test model with a convolution node and quantize/dequantize nodes
    | Starting with:
     |          INPUT         QuantizeLinear (with constant kernel)
     |            |               |
     |     QuantizeLinear     DequantizeLinear
     |            |               |
     |     DequantizeLinear      |
     |                  |      |
     |                   Conv (with bias)
     |                     |
     |                  OUTPUT
    """
    x_scale = onnx.helper.make_tensor("x_scale", onnx.TensorProto.FLOAT, (1,), [1])
    y_scale = onnx.helper.make_tensor("y_scale", onnx.TensorProto.FLOAT, (1,), [1])
    zero_point = onnx.helper.make_tensor("zero_point", onnx.TensorProto.INT8, (1,), [1])
    bias = onnx.helper.make_tensor("bias", onnx.TensorProto.FLOAT, (3,), [1, 1, 1])
    input = onnx.helper.make_tensor_value_info("input", onnx.TensorProto.FLOAT, (3, 3))
    output = onnx.helper.make_tensor_value_info("output", onnx.TensorProto.FLOAT, (1,))
    quantize_linear_node_0 = onnx.helper.make_node(
        "QuantizeLinear",
        ["input", "y_scale", "zero_point"],
        ["quant_linear_0_output"],
        name="quantize_linear_node_0",
    )
    dequantize_linear_node_0 = onnx.helper.make_node(
        "DequantizeLinear",
        ["quant_linear_0_output", "x_scale", "zero_point"],
        ["dequant_linear_0_output"],
        name="dequantize_linear_node_0",
    )
    kernel = onnx.helper.make_tensor(
        "kernel", onnx.TensorProto.FLOAT, (3, 3), [1, 1, 1, 1, 1, 1, 1, 1, 1]
    )
    quantize_linear_node_1 = onnx.helper.make_node(
        "QuantizeLinear",
        ["kernel", "y_scale", "zero_point"],
        ["quant_linear_1_output"],
        name="quantize_linear_node_1",
    )
    dequantize_linear_node_1 = onnx.helper.make_node(
        "DequantizeLinear",
        ["quant_linear_1_output", "x_scale", "zero_point"],
        ["dequant_linear_1_output"],
        name="dequantize_linear_node_1",
    )
    conv_node = onnx.helper.make_node(
        "Conv",
        inputs=["dequant_linear_0_output", "dequant_linear_1_output", "bias"],
        outputs=["conv_node_output"],
        kernel_shape=[3, 3],
        name="conv_node",
    )
    graph = onnx.helper.make_graph(
        nodes=[
            quantize_linear_node_0,
            dequantize_linear_node_0,
            quantize_linear_node_1,
            dequantize_linear_node_1,
            conv_node,
        ],
        name="g",
        inputs=[input],
        outputs=[output],
        initializer=[y_scale, x_scale, bias, zero_point, kernel],
    )
    model = onnx.helper.make_model(graph)
    validate_onnx(model)
    return model