How to use the paddlehub.dataset function in paddlehub

def predict(args):
    # Load Paddlehub  pretrained model
    module = hub.Module(name=args.module)
    input_dict, output_dict, program = module.context(trainable=True)

    # Download dataset
    if args.dataset.lower() == "flowers":
        dataset = hub.dataset.Flowers()
    elif args.dataset.lower() == "dogcat":
        dataset = hub.dataset.DogCat()
    elif args.dataset.lower() == "indoor67":
        dataset = hub.dataset.Indoor67()
    elif args.dataset.lower() == "food101":
        dataset = hub.dataset.Food101()
    elif args.dataset.lower() == "stanforddogs":
        dataset = hub.dataset.StanfordDogs()
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    # 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]

parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
args = parser.parse_args()
# yapf: enable.

if __name__ == '__main__':
    # Load Paddlehub ERNIE 2.0 pretrained model
    module = hub.Module(name="ernie_v2_eng_base")
    inputs, outputs, program = module.context(
        trainable=True, max_seq_len=args.max_seq_len)

    # Download dataset and use MultiLabelReader to read dataset
    dataset = hub.dataset.Toxic()
    reader = hub.reader.MultiLabelClassifyReader(
        dataset=dataset,
        vocab_path=module.get_vocab_path(),
        max_seq_len=args.max_seq_len)

    # Setup feed list for data feeder
    feed_list = [
        inputs["input_ids"].name,
        inputs["position_ids"].name,
        inputs["segment_ids"].name,
        inputs["input_mask"].name,
    ]

    # Construct transfer learning network
    # Use "pooled_output" for classification tasks on an entire sentence.
    # Use "sequence_output" for token-level output.

lstm_h, c = fluid.layers.dynamic_lstm(
        input=fc0, size=hid_dim * 4, is_reverse=False)
    lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
    lstm_max_tanh = fluid.layers.tanh(lstm_max)
    fc = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')

    return fc


if __name__ == '__main__':
    # Step1: load Paddlehub elmo pretrained model
    module = hub.Module(name="elmo")
    inputs, outputs, program = module.context(trainable=True)

    # Step2: Download dataset and use LACClassifyReade to read dataset
    dataset = hub.dataset.ChnSentiCorp()

    reader = hub.reader.LACClassifyReader(
        dataset=dataset, vocab_path=module.get_vocab_path())
    word_dict_len = len(reader.vocab)

    word_ids = inputs["word_ids"]
    elmo_embedding = outputs["elmo_embed"]

    # Step3: switch program and build network
    # Choose the net which you would like: bow, cnn, gru, bilstm, lstm
    switch_main_program(program)

    # Embedding layer
    word_embed_dims = 128
    word_embedding = fluid.layers.embedding(
        input=word_ids,

def finetune(args):
    # Load Paddlehub  pretrained model
    module = hub.Module(name=args.module)
    input_dict, output_dict, program = module.context(trainable=True)

    # Download dataset
    if args.dataset.lower() == "flowers":
        dataset = hub.dataset.Flowers()
    elif args.dataset.lower() == "dogcat":
        dataset = hub.dataset.DogCat()
    elif args.dataset.lower() == "indoor67":
        dataset = hub.dataset.Indoor67()
    elif args.dataset.lower() == "food101":
        dataset = hub.dataset.Food101()
    elif args.dataset.lower() == "stanforddogs":
        dataset = hub.dataset.StanfordDogs()
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    # 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(),

def finetune(args):
    # Load Paddlehub  pretrained model
    module = hub.Module(name=args.module)
    input_dict, output_dict, program = module.context(trainable=True)

    # Download dataset
    if args.dataset.lower() == "flowers":
        dataset = hub.dataset.Flowers()
    elif args.dataset.lower() == "dogcat":
        dataset = hub.dataset.DogCat()
    elif args.dataset.lower() == "indoor67":
        dataset = hub.dataset.Indoor67()
    elif args.dataset.lower() == "food101":
        dataset = hub.dataset.Food101()
    elif args.dataset.lower() == "stanforddogs":
        dataset = hub.dataset.StanfordDogs()
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    # 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]

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint_dir", type=str,                 default=None, help="Directory to model checkpoint")
parser.add_argument("--use_gpu",        type=ast.literal_eval,    default=True, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--batch_size",     type=int,                 default=1,    help="Total examples' number in batch when the program predicts.")
args = parser.parse_args()
# yapf: enable.

if __name__ == '__main__':
    # loading Paddlehub senta pretrained model
    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())

parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
args = parser.parse_args()
# yapf: enable.

if __name__ == '__main__':
    # Load Paddlehub ERNIE Tiny pretrained model
    module = hub.Module(name="ernie_tiny")
    inputs, outputs, program = module.context(
        trainable=True, max_seq_len=args.max_seq_len)

    # Download dataset and use accuracy as metrics
    # Choose dataset: GLUE/XNLI/ChinesesGLUE/NLPCC-DBQA/LCQMC
    dataset = hub.dataset.ChnSentiCorp()

    # For ernie_tiny, it use sub-word to tokenize chinese sentence
    # If not ernie tiny, sp_model_path and word_dict_path should be set None
    reader = hub.reader.ClassifyReader(
        vocab_path=module.get_vocab_path(),
        max_seq_len=args.max_seq_len,
        sp_model_path=module.get_spm_path(),
        word_dict_path=module.get_word_dict_path())

    # Construct transfer learning network
    # Use "pooled_output" for classification tasks on an entire sentence.
    # Use "sequence_output" for token-level output.
    pooled_output = outputs["pooled_output"]

    # Setup feed list for data feeder
    # Must feed all the tensor of module need

parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
args = parser.parse_args()
# yapf: enable.

if __name__ == '__main__':
    # Load Paddlehub ERNIE pretrained model
    module = hub.Module(name="ernie")
    inputs, outputs, program = module.context(
        trainable=True, max_seq_len=args.max_seq_len)

    # Download dataset and use ClassifyReader to read dataset
    dataset = hub.dataset.NLPCC_DBQA()
    reader = hub.reader.ClassifyReader(
        dataset=dataset,
        vocab_path=module.get_vocab_path(),
        max_seq_len=args.max_seq_len)

    # Construct transfer learning network
    # Use "pooled_output" for classification tasks on an entire sentence.
    # Use "sequence_output" for token-level output.
    pooled_output = outputs["pooled_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,

def predict(args):
    # Load Paddlehub  pretrained model
    module = hub.Module(name=args.module)
    input_dict, output_dict, program = module.context(trainable=True)

    # Download dataset
    if args.dataset.lower() == "flowers":
        dataset = hub.dataset.Flowers()
    elif args.dataset.lower() == "dogcat":
        dataset = hub.dataset.DogCat()
    elif args.dataset.lower() == "indoor67":
        dataset = hub.dataset.Indoor67()
    elif args.dataset.lower() == "food101":
        dataset = hub.dataset.Food101()
    elif args.dataset.lower() == "stanforddogs":
        dataset = hub.dataset.StanfordDogs()
    else:
        raise ValueError("%s dataset is not defined" % args.dataset)

    # 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

import paddle.fluid as fluid
import paddlehub as hub

# Step1
module = hub.Module(name="ernie")
inputs, outputs, program = module.context(trainable=True, max_seq_len=128)

# Step2
dataset = hub.dataset.ChnSentiCorp()
reader = hub.reader.ClassifyReader(
    dataset=dataset, vocab_path=module.get_vocab_path(), max_seq_len=128)

# Step3
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,