code: def parse_args():
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--workers",
          default=4,
          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=8000,
          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(
          "--print-freq",
          default=10,
          type=int,
          metavar="N",
          help="print frequency in epochs",
      )
      parser.add_argument(
          "--dataset",
          default="ljspeech",
          choices=["ljspeech", "libritts"],
          type=str,
          help="select dataset to train with",
      )
      parser.add_argument(
          "--batch-size",
          default=256,
          type=int,
          metavar="N",
          help="mini-batch size",
      )
      parser.add_argument(
          "--learning-rate",
          default=1e-4,
          type=float,
          metavar="LR",
          help="learning rate",
      )
      parser.add_argument(
          "--clip-grad", metavar="NORM", type=float, default=4.0
      )
      parser.add_argument(
          "--mulaw",
          default=True,
          action="store_true",
          help="if used, waveform is mulaw encoded",
      )
      parser.add_argument(
          "--jit",
          default=False,
          action="store_true",
          help="if used, model is jitted",
      )
      parser.add_argument(
          "--upsample-scales",
          default=[5, 5, 11],
          type=List[int],
          help="the list of upsample scales",
      )
      parser.add_argument(
          "--n-bits",
          default=8,
          type=int,
          help="the bits of output waveform",
      )
      parser.add_argument(
          "--sample-rate",
          default=22050,
          type=int,
          help="the rate of audio dimensions (samples per second)",
      )
      parser.add_argument(
          "--hop-length",
          default=275,
          type=int,
          help="the number of samples between the starts of consecutive frames",
      )
      parser.add_argument(
          "--win-length",
          default=1100,
          type=int,
          help="the length of the STFT window",
      )
      parser.add_argument(
          "--f-min",
          default=40.0,
          type=float,
          help="the minimum frequency",
      )
      parser.add_argument(
          "--min-level-db",
          default=-100,
          type=float,
          help="the minimum db value for spectrogam normalization",
      )
      parser.add_argument(
          "--n-res-block",
          default=10,
          type=int,
          help="the number of ResBlock in stack",
      )
      parser.add_argument(
          "--n-rnn",
          default=512,
          type=int,
          help="the dimension of RNN layer",
      )
      parser.add_argument(
          "--n-fc",
          default=512,
          type=int,
          help="the dimension of fully connected layer",
      )
      parser.add_argument(
          "--kernel-size",
          default=5,
          type=int,
          help="the number of kernel size in the first Conv1d layer",
      )
      parser.add_argument(
          "--n-freq",
          default=80,
          type=int,
          help="the number of spectrogram bins to use",
      )
      parser.add_argument(
          "--n-hidden-melresnet",
          default=128,
          type=int,
          help="the number of hidden dimensions of resblock in melresnet",
      )
      parser.add_argument(
          "--n-output-melresnet",
          default=128,
          type=int,
          help="the output dimension of melresnet",
      )
      parser.add_argument(
          "--n-fft",
          default=2048,
          type=int,
          help="the number of Fourier bins",
      )
      parser.add_argument(
          "--loss",
          default="crossentropy",
          choices=["crossentropy", "mol"],
          type=str,
          help="the type of loss",
      )
      parser.add_argument(
          "--seq-len-factor",
          default=5,
          type=int,
          help="the length of each waveform to process per batch = hop_length * seq_len_factor",
      )
      parser.add_argument(
          "--val-ratio",
          default=0.1,
          type=float,
          help="the ratio of waveforms for validation",
      )
      parser.add_argument(
          "--file-path",
          default="",
          type=str,
          help="the path of audio files",
      )
      parser.add_argument(
          "--normalization",
          default=True,
          action="store_true",
          help="if True, spectrogram is normalized",
      )
  
      args = parser.parse_args()
      return args
  
file_path: audio/examples/pipeline_wavernn/main.py
imports: import argparse
  import logging
  import os
  from collections import defaultdict
  from datetime import datetime
  from time import time
  from typing import List
  import torch
  import torchaudio
  from datasets import collate_factory, split_process_dataset
  from losses import LongCrossEntropyLoss, MoLLoss
  from processing import NormalizeDB
  from torch.optim import Adam
  from torch.utils.data import DataLoader
  from torchaudio.models.wavernn import WaveRNN
  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