def METHOD_NAME(test_case, args):
    train_data_loader = OFRecordDataLoader(
        ofrecord_root=args.ofrecord_path,
        mode="train",
        dataset_size=args.train_dataset_size,
        batch_size=args.train_batch_size,
    )
    alexnet_module = alexnet()
    alexnet_module.to(args.device)
    of_cross_entropy = flow.nn.CrossEntropyLoss()
    of_cross_entropy.to(args.device)
    of_sgd = flow.optim.SGD(
        alexnet_module.parameters(), lr=args.learning_rate, momentum=args.mom
    )
    class AlexNetGraph(flow.nn.Graph):
        def __init__(self):
            super().__init__()
            self.alexnet = alexnet_module
            self.cross_entropy = of_cross_entropy
            self.add_optimizer(of_sgd)
        def build(self, image, label):
            logits = self.alexnet(image)
            loss = self.cross_entropy(logits, label)
            loss.backward()
            return loss
    alexnet_graph = AlexNetGraph()
    print("repr(alexnet_graph) before run: \n", repr(alexnet_graph))
    # debug graph build
    alexnet_graph.debug(1, op_repr_with_py_stack=True, max_py_stack_depth=4)
    alexnet_module.train()
    image, label = train_data_loader()
    image = image.to(args.device)
    label = label.to(args.device)
    loss = alexnet_graph(image, label)
    print("repr(alexnet_graph) after run: \n", repr(alexnet_graph))