def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset_yaml', type=str, default='examples/pytorch/semantic_parsing/graph2tree/geo/config/new_dynamic_graphsage_undirected.yaml')
    parser.add_argument('--learning-rate', type=float, default=0.001)
    parser.add_argument('--gpuid', type=int, default=1, help='which gpu to use. -1 = use CPU')
    parser.add_argument('--seed', type=int, default=123, help='torch manual random number generator seed')
    parser.add_argument('--init-weight', type=float, default=0.08, help='initailization weight')
    parser.add_argument('--weight-decay', type=float, default=0)
    parser.add_argument('--max-epochs', type=int, default=200, help='number of full passes through the training data')
    parser.add_argument('--min-freq', type=int, default=1, help='minimum frequency for vocabulary')
    parser.add_argument('--grad-clip', type=int, default=5, help='clip gradients at this value')
    parser.add_argument('--batch-size', type=int, default=20, help='the size of one mini-batch')
    parser.add_argument('--share-vocab', type=bool, default=True, help='whether to share vocab')
    parser.add_argument('--pretrained_word_emb_name', type=str, default='6B', help='')
    parser.add_argument('--pretrained_word_emb_url', type=str, default=None, help='')
    parser.add_argument('--pretrained_word_emb_cache_dir', type=str, default='.vector_cache', help='')
    parser.add_argument('--beam-size', type=int, default=4, help='the beam size of beam search')
    cfg = parser.parse_args()
    our_args = get_yaml_config(cfg.dataset_yaml)
    template = get_basic_args(graph_construction_name=our_args['graph_construction_name'], graph_embedding_name=our_args['graph_embedding_name'], decoder_name=our_args['decoder_name'])
    update_values(to_args=template, from_args_list=[our_args, vars(cfg)])
    return template
def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset_yaml', type=str, default='examples/pytorch/semantic_parsing/graph2tree/geo/config/new_dynamic_graphsage_undirected.yaml')
    parser.add_argument('--learning-rate', type=float, default=0.001)
    parser.add_argument('--gpuid', type=int, default=1, help='which gpu to use. -1 = use CPU')
    parser.add_argument('--seed', type=int, default=123, help='torch manual random number generator seed')
    parser.add_argument('--init-weight', type=float, default=0.08, help='initailization weight')
    parser.add_argument('--weight-decay', type=float, default=0)
    parser.add_argument('--max-epochs', type=int, default=200, help='number of full passes through the training data')
    parser.add_argument('--min-freq', type=int, default=1, help='minimum frequency for vocabulary')
    parser.add_argument('--grad-clip', type=int, default=5, help='clip gradients at this value')
    parser.add_argument('--batch-size', type=int, default=20, help='the size of one mini-batch')
    parser.add_argument('--share-vocab', type=bool, default=True, help='whether to share vocab')
    parser.add_argument('--pretrained_word_emb_name', type=str, default='6B', help=)
    parser.add_argument('--pretrained_word_emb_url', type=str, default=None, help=)
    parser.add_argument('--pretrained_word_emb_cache_dir', type=str, default='.vector_cache', help=)
    parser.add_argument('--beam-size', type=int, default=4, help='the beam size of beam search')
    cfg = parser.parse_args()
    our_args = get_yaml_config(cfg.dataset_yaml)
    template = get_basic_args(graph_construction_name=our_args['graph_construction_name'], graph_embedding_name=our_args['graph_embedding_name'], decoder_name=our_args['decoder_name'])
    update_values(to_args=template, from_args_list=[our_args, vars(cfg)])
    return template