def METHOD_NAME(self):
    x = tf.compat.v1.placeholder(tf.float32, [1, 128, 128, 16], name="input")
    conv_weights = tf.compat.v1.get_variable(
        "weight", [2, 2, 16, 16], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv_bias = tf.compat.v1.get_variable("bias", [16], initializer=tf.compat.v1.random_normal_initializer())
    x = tf.nn.relu(x)
    sqrt = tf.math.sqrt(x)
    relu_sqrt = tf.nn.relu(sqrt)
    conv = tf.nn.conv2d(relu_sqrt, conv_weights, strides=[1, 2, 2, 1], padding="SAME", name="last")
    normed = tf.compat.v1.layers.batch_normalization(conv)
    relu = tf.nn.relu(normed)
    conv1 = tf.nn.conv2d(x, conv_weights, strides=[1, 2, 2, 1], padding="SAME", name="last")
    conv_bias = tf.nn.bias_add(conv1, conv_bias)
    concat = tf.concat([relu, conv_bias], 1)
    final_node = tf.nn.relu(concat, name="op_to_store")
    out_name = final_node.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]
        )
        from neural_compressor.experimental import Quantization, common
        quantizer = Quantization("fake_yaml.yaml")
        dataset = quantizer.dataset("dummy", shape=(100, 128, 128, 16), label=True)
        quantizer.calib_dataloader = common.DataLoader(dataset)
        quantizer.eval_dataloader = common.DataLoader(dataset)
        quantizer.model = output_graph_def
        output_graph = quantizer.fit()
        quantized_concat = False
        for i in output_graph.graph_def.node:
            if i.op == "QuantizedConcatV2":
                quantized_concat = True
        self.assertEqual(quantized_concat, False)