def METHOD_NAME(self):
    input_constant_name = "input_constant"
    relu_name = "relu"
    float_graph_def = graph_pb2.GraphDef()
    input_constant = QuantizeGraphHelper.create_constant_node(
        input_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[1, 2, 6, 1]
    )
    float_graph_def.node.extend([input_constant])
    relu_node = QuantizeGraphHelper.create_node("Relu", relu_name, [input_constant_name])
    QuantizeGraphHelper.set_attr_dtype(relu_node, "T", dtypes.float32)
    float_graph_def.node.extend([relu_node])
    b_constant_name = "b_constant"
    conv2d_name = "conv2d_1"
    b_constant = QuantizeGraphHelper.create_constant_node(
        b_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[1, 2, 3, 4]
    )
    float_graph_def.node.extend([b_constant])
    conv2d_node = QuantizeGraphHelper.create_node("Conv2D", conv2d_name, [relu_name, b_constant_name])
    QuantizeGraphHelper.set_attr_dtype(conv2d_node, "T", dtypes.float32)
    float_graph_def.node.extend([conv2d_node])
    bias_add_name = "bias_add"
    offset_constant_name = "offset_constant"
    offset_constant = QuantizeGraphHelper.create_constant_node(
        offset_constant_name, value=[1, 2, 3, 4, 5, 6], dtype=dtypes.float32, shape=[6]
    )
    float_graph_def.node.extend([offset_constant])
    bias_add_node = QuantizeGraphHelper.create_node("BiasAdd", bias_add_name, [conv2d_name, offset_constant_name])
    QuantizeGraphHelper.set_attr_dtype(bias_add_node, "T", dtypes.float32)
    float_graph_def.node.extend([bias_add_node])
    bn_scale_name = "bn_scale"
    bn_scale_node = QuantizeGraphHelper.create_constant_node(
        bn_scale_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[12, 1]
    )
    bn_offset_name = "bn_offset"
    bn_offset_node = QuantizeGraphHelper.create_constant_node(
        bn_offset_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[12, 1]
    )
    bn_mean_name = "bn_mean"
    bn_mean_node = QuantizeGraphHelper.create_constant_node(
        bn_mean_name,
        value=[
            1,
            2,
        ],
        dtype=dtypes.float32,
        shape=[
            2,
        ],
    )
    bn_var_name = "bn_var"
    bn_var_node = QuantizeGraphHelper.create_constant_node(bn_var_name, value=[], dtype=dtypes.float32, shape=[0])
    fused_bn_node_name = "bn"
    fused_bn_node = QuantizeGraphHelper.create_node(
        "FusedBatchNormV3",
        fused_bn_node_name,
        [bias_add_name, bn_scale_name, bn_offset_name, bn_mean_name, bn_var_name],
    )
    QuantizeGraphHelper.set_attr_dtype(fused_bn_node, "T", dtypes.float32)
    QuantizeGraphHelper.set_attr_dtype(fused_bn_node, "U", dtypes.float32)
    float_graph_def.node.extend([fused_bn_node, bn_scale_node, bn_offset_node, bn_mean_node, bn_var_node])
    post_relu_name = "post_relu"
    post_relu_node = QuantizeGraphHelper.create_node("Relu", post_relu_name, [fused_bn_node_name])
    float_graph_def.node.extend([post_relu_node])
    post_graph = FoldBatchNormNodesOptimizer(float_graph_def).do_transformation()
    bn_not_fused = False
    for i in post_graph.node:
        if i.op == "FusedBatchNormV3":
            bn_not_fused = True
            break
    self.assertEqual(bn_not_fused, True)