def METHOD_NAME():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--feats-dir", type=str, help="folder contains wav.scp for training"
    )
    parser.add_argument(
        "--n-clusters", default=100, type=int, help="number of clusters for K-Means"
    )
    parser.add_argument("--nj", default=1, type=int, help="only support mfcc")
    parser.add_argument("--seed", default=0, type=int)
    parser.add_argument("--fs", type=int, default=16000)
    parser.add_argument("--feature-type", type=str, default="mfcc")
    parser.add_argument("--hubert-model-url", type=str, default=None)
    parser.add_argument("--hubert-model-path", type=str, default=None)
    parser.add_argument(
        "--portion", type=float, default=1.0, help="Using a subset of the data."
    )
    group = parser.add_argument_group(description="K-means model.")
    group.add_argument("--km-path", type=str, help="path for k-means model.")
    group.add_argument("--init", default="k-means++")
    group.add_argument("--max-iter", default=100, type=int)
    group.add_argument("--batch-size", default=10000, type=int)
    group.add_argument("--tol", default=0.0, type=float)
    group.add_argument("--max-no-improvement", default=100, type=int)
    group.add_argument("--n-init", default=20, type=int)
    group.add_argument("--reassignment-ratio", default=0.0, type=float)
    return parser