code: def parse_args():
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--type",
          metavar="T",
          default="mfcc",
          choices=["waveform", "mfcc"],
          help="input type for model",
      )
      parser.add_argument(
          "--freq-mask",
          default=0,
          type=int,
          metavar="N",
          help="maximal width of frequency mask",
      )
      parser.add_argument(
          "--win-length",
          default=400,
          type=int,
          metavar="N",
          help="width of spectrogram window",
      )
      parser.add_argument(
          "--hop-length",
          default=160,
          type=int,
          metavar="N",
          help="width of spectrogram window",
      )
      parser.add_argument(
          "--time-mask",
          default=0,
          type=int,
          metavar="N",
          help="maximal width of time mask",
      )
      parser.add_argument(
          "--workers",
          default=0,
          type=int,
          metavar="N",
          help="number of data loading workers",
      )
      parser.add_argument(
          "--checkpoint",
          default="",
          type=str,
          metavar="PATH",
          help="path to latest checkpoint",
      )
      parser.add_argument(
          "--epochs",
          default=200,
          type=int,
          metavar="N",
          help="number of total epochs to run",
      )
      parser.add_argument(
          "--start-epoch",
          default=0,
          type=int,
          metavar="N",
          help="manual epoch number",
      )
      parser.add_argument(
          "--reduce-lr-valid",
          action="store_true",
          help="reduce learning rate based on validation loss",
      )
      parser.add_argument(
          "--normalize",
          action="store_true",
          help="normalize model input",
      )
      parser.add_argument(
          "--progress-bar",
          action="store_true",
          help="use progress bar while training",
      )
      parser.add_argument(
          "--decoder",
          metavar="D",
          default="greedy",
          choices=["greedy"],
          help="decoder to use",
      )
      parser.add_argument(
          "--batch-size",
          default=128,
          type=int,
          metavar="N",
          help="mini-batch size",
      )
      parser.add_argument(
          "--n-bins",
          default=13,
          type=int,
          metavar="N",
          help="number of bins in transforms",
      )
      parser.add_argument(
          "--optimizer",
          metavar="OPT",
          default="adadelta",
          choices=["sgd", "adadelta", "adam", "adamw"],
          help="optimizer to use",
      )
      parser.add_argument(
          "--scheduler",
          metavar="S",
          default="reduceonplateau",
          choices=["exponential", "reduceonplateau"],
          help="optimizer to use",
      )
      parser.add_argument(
          "--learning-rate",
          default=0.6,
          type=float,
          metavar="LR",
          help="initial learning rate",
      )
      parser.add_argument(
          "--gamma",
          default=0.99,
          type=float,
          metavar="GAMMA",
          help="learning rate exponential decay constant",
      )
      parser.add_argument(
          "--momentum",
          default=0.8,
          type=float,
          metavar="M",
          help="momentum",
      )
      parser.add_argument(
          "--beta_1",
          default=0.9,
          type=float,
          metavar="BETA_1",
          help="beta_1",
      )
      parser.add_argument(
          "--beta_2",
          default=0.999,
          type=float,
          metavar="BETA_2",
          help="beta_2",
      )
      parser.add_argument(
          "--weight-decay",
          default=1e-5,
          type=float,
          metavar="W",
          help="weight decay",
      )
      parser.add_argument(
          "--eps", metavar="EPS", type=float, default=1e-8
      )
      parser.add_argument(
          "--rho", metavar="RHO", type=float, default=0.95
      )
      parser.add_argument(
          "--clip-grad", metavar="NORM", type=float, default=0.0
      )
      parser.add_argument(
          "--dataset-root",
          type=str,
          help="specify dataset root folder",
      )
      parser.add_argument(
          "--dataset-folder-in-archive",
          type=str,
          help="specify dataset folder in archive",
      )
      parser.add_argument(
          "--dataset-train",
          default=["train-clean-100"],
          nargs="+",
          type=str,
          help="select which part of librispeech to train with",
      )
      parser.add_argument(
          "--dataset-valid",
          default=["dev-clean"],
          nargs="+",
          type=str,
          help="select which part of librispeech to validate with",
      )
      parser.add_argument(
          "--distributed",
          action="store_true",
          help="enable DistributedDataParallel",
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="random seed"
      )
      parser.add_argument(
          "--world-size",
          type=int,
          default=8,
          help="the world size to initiate DPP",
      )
      parser.add_argument(
          "--jit", action="store_true", help="if used, model is jitted"
      )
  
      args = parser.parse_args()
      logging.info(args)
      return args
  
imports: import argparse
  import logging
  import os
  import string
  from datetime import datetime
  from time import time
  import torch
  import torchaudio
  from ctc_decoders import GreedyDecoder
  from datasets import collate_factory, split_process_librispeech
  from languagemodels import LanguageModel
  from torch.optim import Adadelta, Adam, AdamW, SGD
  from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from torchaudio.functional import edit_distance
  from torchaudio.models.wav2letter import Wav2Letter
  from transforms import Normalize, UnsqueezeFirst
  from utils import count_parameters, MetricLogger, save_checkpoint
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args