code: def get_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--dense_model_name",
          type=str,
          help="Dense model to be used",
          default="hubert-base-ls960",
          choices=["hubert-base-ls960", "cpc-big-ll6k"],
      )
      parser.add_argument(
          "--vocab_size", type=int, help="Unit vocab size", default=50
      )
      parser.add_argument("--epochs", type=int, default=150)
      parser.add_argument(
          "--batch_size",
          type=int,
          help="Batch size for K-means training",
          default=32,
      )
      parser.add_argument("--seed", type=int, default=13)
      parser.add_argument(
          "--model_type",
          choices=["baseline", "discrete", "continuous"],
          default="baseline",
      )
  
      args = parser.parse_args()
  
      return args
  
file_path: textlesslib/examples/speaker_probing/train.py
imports: import torch
  import argparse
  from textless.data.quantized_datasets import QuantizedLibriSpeech
  from torch.utils.data import DataLoader
  import torch.nn.functional
  from probes import ContinuousClassifier, DiscreteClassifier, ConstantBaseline
  from textless.data.speech_encoder import SpeechEncoder
  from textless import dispatch_dense_model, dispatch_quantizer
label_desc: Use image node_id 3 for calling a global function with example usage: get_args() and returns: args