code: def run_train(args):
      seed_everything(1337)
      checkpoint_dir = (
          args.exp_dir / f"checkpoints_{args.dataset}_{args.model_name}"
      )
      checkpoint = ModelCheckpoint(
          checkpoint_dir,
          monitor="val_loss",
          mode="min",
          save_top_k=5,
          save_weights_only=False,
          verbose=True,
      )
      train_checkpoint = ModelCheckpoint(
          checkpoint_dir,
          monitor="train_loss",
          mode="min",
          save_top_k=5,
          save_weights_only=False,
          verbose=True,
      )
      callbacks = [
          checkpoint,
          train_checkpoint,
      ]
      trainer = Trainer(
          default_root_dir=args.exp_dir,
          max_steps=args.max_updates,
          num_nodes=args.num_nodes,
          devices=args.gpus,
          accelerator="gpu",
          strategy="ddp_find_unused_parameters_true",
          precision=args.precision,
          accumulate_grad_batches=args.accumulate_grad_batches,
          gradient_clip_val=args.clip_norm,
          use_distributed_sampler=False,
          callbacks=callbacks,
          reload_dataloaders_every_n_epochs=1,
      )
      if args.model_name not in [
          "hubert_pretrain_base",
          "hubert_pretrain_large",
          "hubert_pretrain_xlarge",
      ]:
          raise ValueError(
              "Expect model_name to be one of 'hubert_pretrain_base', 'hubert_pretrain_large', 'hubert_pretrain_xlarge'."
              f"Found {args.model_name}."
          )
      model = getattr(torchaudio.models, args.model_name)()
      loss_fn = partial(
          hubert_loss,
          masked_weight=args.masked_weight,
          unmasked_weight=args.unmasked_weight,
          feature_weight=args.feature_weight,
      )
      optimizer = torch.optim.AdamW(
          model.parameters(),
          lr=args.learning_rate,
          betas=args.betas,
          eps=args.eps,
          weight_decay=args.weight_decay,
      )
      lr_scheduler = LinearDecayLRScheduler(
          optimizer, args.warmup_updates, args.max_updates
      )
      lightning_module = HuBERTModule(
          model,
          loss_fn,
          optimizer,
          lr_scheduler,
      )
      data_module = HuBERTDataModule(
          dataset_path=args.dataset_path,
          dataset="librispeech",
          feature_type="mfcc",
          seconds_per_batch=args.seconds_per_batch,
          train_shuffle=True,
          num_workers=10,
      )
      trainer.fit(lightning_module, datamodule=data_module)
  
imports: import logging
  import pathlib
  from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, RawDescriptionHelpFormatter
  from functools import partial
  from typing import Dict, Tuple
  import torch
  import torchaudio.models
  from lightning.pytorch import seed_everything, Trainer
  from lightning.pytorch.callbacks import ModelCheckpoint
  from .data_modules import HuBERTDataModule
  from .lightning_modules import SSLPretrainModule
  from .losses import hubert_loss
  from .lr_schedulers import LinearDecayLRScheduler
label_desc: Use image node_id 2 for calling a global function with example usage: run_train(args) without return types