def METHOD_NAME(self):
    x = tf.compat.v1.placeholder(tf.float32, [1, 56, 56, 16], name="input")
    top_relu = tf.nn.relu(x)
    conv_weights = tf.compat.v1.get_variable(
        "weight", [3, 3, 16, 16], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv = tf.nn.conv2d(top_relu, conv_weights, strides=[1, 2, 2, 1], padding="VALID")
    normed = tf.compat.v1.layers.batch_normalization(conv)
    relu = tf.nn.relu(normed)
    sq = tf.squeeze(relu, [0])
    reshape = tf.reshape(sq, [729, 16])
    conv_weights2 = tf.compat.v1.get_variable(
        "weight2", [16, 729], initializer=tf.compat.v1.random_normal_initializer()
    )
    matmul = tf.matmul(reshape, conv_weights2)
    # normed2 = tf.compat.v1.layers.batch_normalization(matmul)
    bias = tf.compat.v1.get_variable("bias", [729], initializer=tf.compat.v1.random_normal_initializer())
    normed2 = tf.nn.bias_add(matmul, bias, name="bias_add")
    relu6 = tf.nn.relu6(normed2)
    reshape2 = tf.reshape(relu6, [1, 729, 729, 1], name="op_to_store")
    out_name = reshape2.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, 56, 56, 16), label=True)
        quantizer.calib_dataloader = common.DataLoader(dataset)
        quantizer.eval_dataloader = common.DataLoader(dataset)
        quantizer.model = output_graph_def
        output_graph = quantizer.fit()
        found_reshape = False
        for i in output_graph.graph_def.node:
            if i.op == "Reshape":
                found_reshape = True
                break
        self.assertEqual(found_reshape, True)