How to use the paddlehub.RunConfig function in paddlehub

module = hub.Module(name="senta_bilstm")
    inputs, outputs, program = module.context(trainable=True)

    # Download dataset and use LACClassifyReader to read dataset
    dataset = hub.dataset.ChnSentiCorp()
    reader = hub.reader.LACClassifyReader(
        dataset=dataset, vocab_path=module.get_vocab_path())

    sent_feature = outputs["sentence_feature"]

    # Setup feed list for data feeder
    # Must feed all the tensor of senta's module need
    feed_list = [inputs["words"].name]

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_cuda=args.use_gpu,
        use_pyreader=False,
        use_data_parallel=False,
        num_epoch=args.num_epoch,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=hub.AdamWeightDecayStrategy())

    # Define a classfication finetune task by PaddleHub's API
    cls_task = hub.TextClassifierTask(
        data_reader=reader,
        feature=sent_feature,
        feed_list=feed_list,
        num_classes=dataset.num_labels,
        config=config)

# Construct transfer learning network
    # Use "sequence_output" for token-level output.
    sequence_output = outputs["sequence_output"]

    # Setup feed list for data feeder
    # Must feed all the tensor of ERNIE's module need
    feed_list = [
        inputs["input_ids"].name,
        inputs["position_ids"].name,
        inputs["segment_ids"].name,
        inputs["input_mask"].name,
    ]

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_data_parallel=False,
        use_cuda=args.use_gpu,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())

    # Define a sequence labeling finetune task by PaddleHub's API
    # if add crf, the network use crf as decoder
    seq_label_task = hub.SequenceLabelTask(
        data_reader=reader,
        feature=sequence_output,
        feed_list=feed_list,
        max_seq_len=args.max_seq_len,
        num_classes=dataset.num_labels,
        config=config,
        add_crf=True)

# Must feed all the tensor of ERNIE's module need
    feed_list = [
        inputs["input_ids"].name,
        inputs["position_ids"].name,
        inputs["segment_ids"].name,
        inputs["input_mask"].name,
    ]

    # Select finetune strategy, setup config and finetune
    strategy = hub.AdamWeightDecayStrategy(
        warmup_proportion=args.warmup_proportion,
        weight_decay=args.weight_decay,
        learning_rate=args.learning_rate)

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        eval_interval=300,
        use_data_parallel=args.use_data_parallel,
        use_cuda=args.use_gpu,
        num_epoch=args.num_epoch,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=strategy)

    # Define a regression finetune task by PaddleHub's API
    reg_task = hub.RegressionTask(
        data_reader=reader,
        feature=pooled_output,
        feed_list=feed_list,
        config=config)

    # Finetune and evaluate by PaddleHub's API

with fluid.program_guard(program):
    label = fluid.layers.data(name="label", shape=[1], dtype='int64')

    pooled_output = outputs["pooled_output"]

    cls_task = hub.create_text_classification_task(
        feature=pooled_output, label=label, num_classes=dataset.num_labels)

# Step4
strategy = hub.AdamWeightDecayStrategy(
    learning_rate=5e-5,
    warmup_proportion=0.1,
    warmup_strategy="linear_warmup_decay",
    weight_decay=0.01)

config = hub.RunConfig(
    use_cuda=True, num_epoch=3, batch_size=32, strategy=strategy)

feed_list = [
    inputs["input_ids"].name, inputs["position_ids"].name,
    inputs["segment_ids"].name, inputs["input_mask"].name, label.name
]

hub.finetune_and_eval(
    task=cls_task, data_reader=reader, feed_list=feed_list, config=config)

# Setup feed list for data feeder
    # Must feed all the tensor of module need
    feed_list = [
        inputs["input_ids"].name, inputs["position_ids"].name,
        inputs["segment_ids"].name, inputs["input_mask"].name
    ]

    # Select a finetune strategy
    strategy = hub.AdamWeightDecayStrategy(
        warmup_proportion=args.warmup_proportion,
        weight_decay=args.weight_decay,
        learning_rate=args.learning_rate)

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_data_parallel=args.use_data_parallel,
        use_cuda=args.use_gpu,
        num_epoch=args.num_epoch,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=strategy)

    # Define a sequence labeling finetune task by PaddleHub's API
    # If add crf, the network use crf as decoder
    seq_label_task = hub.SequenceLabelTask(
        data_reader=reader,
        feature=sequence_output,
        feed_list=feed_list,
        max_seq_len=args.max_seq_len,
        num_classes=dataset.num_labels,
        config=config,

# Use ImageClassificationReader to read dataset
    data_reader = hub.reader.ImageClassificationReader(
        image_width=module.get_expected_image_width(),
        image_height=module.get_expected_image_height(),
        images_mean=module.get_pretrained_images_mean(),
        images_std=module.get_pretrained_images_std(),
        dataset=dataset)

    feature_map = output_dict["feature_map"]

    # Setup feed list for data feeder
    feed_list = [input_dict["image"].name]

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_data_parallel=False,
        use_cuda=args.use_gpu,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())

    # Define a reading comprehension finetune task by PaddleHub's API
    task = hub.ImageClassifierTask(
        data_reader=data_reader,
        feed_list=feed_list,
        feature=feature_map,
        num_classes=dataset.num_labels,
        config=config)

    data = ["./test/test_img_daisy.jpg", "./test/test_img_roses.jpg"]
    label_map = dataset.label_dict()

module = hub.Module(name="senta_bilstm")
    inputs, outputs, program = module.context(trainable=True)

    # Download dataset and use LACClassifyReader to read dataset
    dataset = hub.dataset.ChnSentiCorp()
    reader = hub.reader.LACClassifyReader(
        dataset=dataset, vocab_path=module.get_vocab_path())

    sent_feature = outputs["sentence_feature"]

    # Setup feed list for data feeder
    # Must feed all the tensor of senta's module need
    feed_list = [inputs["words"].name]

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_data_parallel=False,
        use_cuda=args.use_gpu,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=hub.AdamWeightDecayStrategy())

    # Define a classfication finetune task by PaddleHub's API
    cls_task = hub.TextClassifierTask(
        data_reader=reader,
        feature=sent_feature,
        feed_list=feed_list,
        num_classes=dataset.num_labels,
        config=config)

    # Data to be predicted
    data = ["这家餐厅很好吃", "这部电影真的很差劲"]

# Use ImageClassificationReader to read dataset
    data_reader = hub.reader.ImageClassificationReader(
        image_width=module.get_expected_image_width(),
        image_height=module.get_expected_image_height(),
        images_mean=module.get_pretrained_images_mean(),
        images_std=module.get_pretrained_images_std(),
        dataset=dataset)

    feature_map = output_dict["feature_map"]

    # Setup feed list for data feeder
    feed_list = [input_dict["image"].name]

    # Setup runing config for PaddleHub Finetune API
    config = hub.RunConfig(
        use_data_parallel=args.use_data_parallel,
        use_cuda=args.use_gpu,
        num_epoch=args.num_epoch,
        batch_size=args.batch_size,
        checkpoint_dir=args.checkpoint_dir,
        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())

    # Define a reading comprehension finetune task by PaddleHub's API
    task = hub.ImageClassifierTask(
        data_reader=data_reader,
        feed_list=feed_list,
        feature=feature_map,
        num_classes=dataset.num_labels,
        config=config)

    # Finetune by PaddleHub's API