code: def main():
      logger.setLevel(logging.INFO)
      hparams_file, run_opts, overrides = sb.parse_arguments(
          sys.argv[1:]
      )
  
      sb.utils.distributed.ddp_init_group(run_opts)
  
      with open(hparams_file) as fin:
          hparams = load_hyperpyyaml(fin, overrides)
      hparams.update(run_opts)
  
      sb.create_experiment_directory(
          experiment_directory=hparams["output_folder"],
          hyperparams_to_save=hparams_file,
          overrides=overrides,
      )
  
      # Update precision to bf16 if the device is CPU and precision is fp16
      if (
          run_opts.get("device") == "cpu"
          and hparams.get("precision") == "fp16"
      ):
          hparams["precision"] = "bf16"
  
      from librispeech_prepare import prepare_librispeech
  
      run_on_main(
          prepare_librispeech,
          kwargs={
              "data_folder": hparams["data_folder"],
              "tr_splits": hparams["train_splits"],
              "dev_splits": hparams["dev_splits"],
              "te_splits": hparams["test_splits"],
              "save_folder": hparams["output_folder"],
              "merge_lst": hparams["train_splits"],
              "merge_name": "train.csv",
              "skip_prep": hparams["skip_prep"],
          },
      )
  
      # Part that matters starts here.
      train_dataset, valid_loader, train_loader_kwargs = dataio_prepare(
          hparams
      )
  
      brain = W2V2Brain(
          modules=hparams["modules"],
          opt_class=hparams["optimizer"],
          hparams=hparams,
          run_opts=run_opts,
          checkpointer=hparams["checkpointer"],
      )
  
      brain.fit(
          brain.hparams.epoch_counter,
          train_dataset,
          valid_loader,
          train_loader_kwargs=train_loader_kwargs,
          progressbar=False,
      )
  
imports: import logging
  import sys
  import time
  from functools import partial
  import speechbrain
  import torch
  import torch.nn.functional
  from torch.nn.parallel import DistributedDataParallel
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain import Stage
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.dataloader import SaveableDataLoader
  from speechbrain.dataio.sampler import DynamicBatchSampler
  from speechbrain.lobes.models.wav2vec import w2v_mask_collate_fn
  from speechbrain.lobes.models.wav2vec import sample_negatives
  from speechbrain.core import AMPConfig
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types