def METHOD_NAME(self):
    tf.compat.v1.disable_eager_execution()
    x = tf.compat.v1.placeholder(tf.float32, [1, 224, 224, 3], name="input")
    if tf.version.VERSION <= "2.1.0":
        x = tf.nn.relu(x)
    conv_weights = tf.compat.v1.get_variable(
        "weights", [3, 3, 3, 32], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv = tf.nn.conv2d(x, conv_weights, strides=[1, 1, 1, 1], padding="SAME")
    normed = tf.nn.bias_add(
        conv,
        tf.constant(
            [
                3.0,
                1.2,
                1.0,
                2,
                3,
                4,
                5,
                6,
                7,
                8,
                0,
                1,
                4.0,
                5.2,
                8.1,
                2,
                4,
                5,
                8,
                9,
                10,
                12,
                11,
                2,
                5.0,
                7.2,
                3.2,
                3,
                4,
                5,
                7,
                8,
            ]
        ),
    )
    relu = tf.nn.relu(normed, name="Relu_0")
    op_wise_sequences = TensorflowQuery(
        local_config_file=os.path.join(os.path.dirname(__file__), "../../neural_compressor/adaptor/tensorflow.yaml")
    ).get_eightbit_patterns()
    with tf.compat.v1.Session() as sess:
        sess.run(tf.compat.v1.global_variables_initializer())
        output_graph_def = graph_util.convert_variables_to_constants(
            sess=sess, input_graph_def=sess.graph_def, output_node_names=[relu.name.split(":")[0]]
        )
        output_graph_def = QuantizeGraphHelper.remove_training_nodes(
            output_graph_def, protected_nodes=[relu.name.split(":")[0]]
        )
        inputs = [x.name.split(":")[0]]
        outputs = [relu.name.split(":")[0]]
        op_wise_config = {
            "Conv2D": (False, "minmax", False, 7.0),
        }
        int8_graph_def, _, _ = QuantizeGraphForIntel(
            output_graph_def, inputs, outputs, op_wise_config, op_wise_sequences, "cpu"
        ).do_transform()
    correct_graph_def = BiasCorrection(int8_graph_def, output_graph_def).do_transformation()
    self.assertEqual(len(correct_graph_def.node), len(int8_graph_def.node))