code: def get_parser():
      parser = argparse.ArgumentParser(
          description="Compute and dump log mel fbank features."
      )
      parser.add_argument(
          "--feature_type",
          type=str,
          choices=["logmel", "hubert", "w2v2", "cpc"],
          default=None,
          help="Acoustic feature type",
      )
      parser.add_argument(
          "--manifest_path",
          type=str,
          default=None,
          help="Manifest file containing the root dir and file names",
      )
      parser.add_argument(
          "--out_features_path",
          type=str,
          default=None,
          help="Features file path to write to",
      )
      parser.add_argument(
          "--checkpoint_path",
          type=str,
          help="Pretrained acoustic model checkpoint",
      )
      parser.add_argument(
          "--layer",
          type=int,
          help="The layer of the pretrained model to extract features from",
          default=-1,
      )
      parser.add_argument(
          "--sample_pct",
          type=float,
          help="Percent data to use for K-means training",
          default=0.1,
      )
      parser.add_argument(
          "--out_features_path",
          type=str,
          help="Path to save log mel fbank features",
      )
      return parser
  
imports: import argparse
  import logging
  from examples.textless_nlp.gslm.speech2unit.pretrained.utils import get_and_dump_features
label_desc: Use image node_id 1 for calling a global function with example usage: get_parser() and returns: parser