def METHOD_NAME(self):
    x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
    paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0]])
    x_pad = tf.pad(x, paddings, "CONSTANT")
    conv_weights = tf.constant(np.random.random((3, 3, 16, 16)).astype(np.float32), name="y")
    conv = tf.nn.conv2d(x_pad, conv_weights, strides=[1, 2, 2, 1], padding="VALID")
    sque = tf.squeeze(conv)
    bias_add = tf.nn.bias_add(sque, tf.constant(np.random.random((16)).astype(np.float32), name="bias_add"))
    sigmoid = tf.nn.sigmoid(bias_add, name="op_to_store")
    out_name = sigmoid.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.adaptor.tf_utils.graph_rewriter.generic.move_squeeze_after_relu import (
        MoveSqueezeAfterReluOptimizer,
    )
    convert_graph = MoveSqueezeAfterReluOptimizer(output_graph_def).do_transformation()
    move_sigmoid = False
    for node in convert_graph.node:
        if node.op == "Squeeze" and node.input[0] == "op_to_store":
            move_sigmoid = True
    self.assertEqual(move_sigmoid, True)