def METHOD_NAME(self):
    x_data = np.random.rand(1024, 1024).astype(np.float32)
    y_data = np.random.rand(1024, 1024).astype(np.float32)
    import tensorflow.compat.v1 as tf
    x = tf.placeholder(tf.float32, shape=[1024, 1024], name="x")
    y = tf.constant(y_data, dtype=tf.float32, shape=[1024, 1024])
    z = tf.matmul(x, y)
    bias = np.random.rand(1024).astype(np.float32)
    z = tf.nn.bias_add(z, bias)
    z = tf.nn.relu(z, name="op_to_store")
    with tf.Session() as sess:
        sess.run(z, feed_dict={x: x_data, y: y_data})
        output_graph_def = sess.graph.as_graph_def()
    set_random_seed(9527)
    config = PostTrainingQuantConfig(
        quant_level=1,
        recipes={"smooth_quant": True, "smooth_quant_args": {"alpha": 0.5}},
        calibration_sampling_size=[1024],
    )
    from neural_compressor.data import Datasets
    dataset = Datasets("tensorflow")["dummy"](shape=(1024, 1024), label=True)
    dataloader = DataLoader(framework="tensorflow", dataset=dataset, batch_size=1024)
    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, 1)