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.compat.v1.get_variable(
        "weight", [3, 3, 16, 16], initializer=tf.compat.v1.random_normal_initializer()
    )
    conv = tf.nn.conv2d(x_pad, conv_weights, strides=[1, 2, 2, 1], padding="VALID")
    conv_bias = tf.compat.v1.get_variable(
        "bias", [16], dtype=tf.float32, initializer=tf.compat.v1.random_normal_initializer()
    )
    conv_bias = tf.math.add(conv, conv_bias)
    relu6 = tf.nn.relu6(conv_bias, name="op_to_store")
    out_name = relu6.name.split(":")[0]
    with tf.compat.v1.Session() as sess:
        sess.run(tf.compat.v1.global_variables_initializer())
        constant_graph = 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.model = constant_graph
        q_model = quantizer.fit()
        framework_specific_info = {
            "device": "cpu",
            "workspace_path": "saved",
            "random_seed": 1978,
            "inputs": ["input"],
            "outputs": ["op_to_store"],
            "approach": "post_training_static_quant",
            "format": "default",
            "backend": "default",
        }
        adaptor = TensorFlowAdaptor(framework_specific_info)
        adaptor.set_tensor(q_model, {"bias": np.random.random(16)})
        from tensorflow.core.framework import attr_value_pb2
        from tensorflow.python.framework import dtypes
        for node in q_model.graph_def.node:
            if node.name == "bias":
                self.assertEqual(node.attr["dtype"], attr_value_pb2.AttrValue(type=dtypes.float32.as_datatype_enum))