code: def cli_main():
      parser = ArgumentParser()
      parser.add_argument(
          "--checkpoint-path",
          default=None,
          type=pathlib.Path,
          help="Path to checkpoint to use for evaluation.",
      )
      parser.add_argument(
          "--exp-dir",
          default=pathlib.Path("./exp/"),
          type=pathlib.Path,
          help="Directory to save checkpoints and logs to. (Default: './exp/')",
      )
      parser.add_argument(
          "--dataset-path",
          type=pathlib.Path,
          help="Path to L3DAS22 datasets.",
          required=True,
      )
      parser.add_argument(
          "--batch_size",
          default=4,
          type=int,
          help="Batch size for training. (Default: 4)",
      )
      parser.add_argument(
          "--gpus",
          default=1,
          type=int,
          help="Number of GPUs per node to use for training. (Default: 1)",
      )
      parser.add_argument(
          "--epochs",
          default=100,
          type=int,
          help="Number of epochs to train for. (Default: 100)",
      )
      args = parser.parse_args()
      run_train(args)
  
file_path: audio/examples/dnn_beamformer/train.py
imports: import pathlib
  from argparse import ArgumentParser
  import lightning.pytorch
  from datamodule import L3DAS22DataModule
  from lightning.pytorch.callbacks import ModelCheckpoint
  from lightning.pytorch.loggers import TensorBoardLogger
  from model import DNNBeamformer, DNNBeamformerLightningModule
label_desc: Use image node_id 2 for calling a global function with example usage: cli_main() without return types