def METHOD_NAME(self):
    x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
    top_relu = tf.nn.relu(x)
    paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
    x_pad = tf.pad(top_relu, paddings, "CONSTANT")
    conv1_weights = tf.compat.v1.get_variable(
        "weight_conv1", [3, 3, 16, 16], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv1 = tf.nn.conv2d(x_pad, conv1_weights, strides=[1, 2, 2, 1], padding="VALID")
    matmul_weights = tf.compat.v1.get_variable(
        "weight_matmul", [28 * 28 * 16, 7 * 7 * 32], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv1_reshaped = tf.reshape(conv1, shape=[-1, 28 * 28 * 16])
    matmul = tf.matmul(conv1_reshaped, matmul_weights)
    reshape = tf.reshape(matmul, (1, 7, 7, 32))
    conv2_weights = tf.compat.v1.get_variable(
        "weight_conv2", [7, 7, 32, 1], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv2 = tf.nn.conv2d(reshape, conv2_weights, strides=[1, 2, 2, 1], padding="VALID")
    leaky_relu = tf.nn.leaky_relu(conv2, name="op_to_store")
    out_name = leaky_relu.name.split(":")[0]
    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=[out_name]
        )
    set_random_seed(9527)
    config = PostTrainingQuantConfig(
        quant_level=1,
        recipes={"smooth_quant": True, "smooth_quant_args": {"alpha": 0.6}},
        calibration_sampling_size=[500],
    )
    from neural_compressor.data import Datasets
    dataset = Datasets("tensorflow")["dummy"](shape=(100, 56, 56, 16), label=True)
    dataloader = DataLoader(framework="tensorflow", dataset=dataset)
    from neural_compressor import Metric
    top1 = Metric(name="topk", k=1)
    output_graph = fit(
        model=output_graph_def,
        conf=config,
        calib_dataloader=dataloader,
        eval_dataloader=dataloader,
        eval_metric=top1,
    )
    mul_count = 0
    for i in output_graph.graph_def.node:
        if i.op == "Mul":
            mul_count += 1
    self.assertEqual(mul_count, 3)