def METHOD_NAME(device="cpu"):
    experiment_dir = pathlib.Path(__file__).resolve().parent
    hparams_file = experiment_dir / "hyperparams.yaml"
    data_folder = "../../samples/ASR"
    data_folder = (experiment_dir / data_folder).resolve()
    # Load model hyper parameters:
    with open(hparams_file) as fin:
        hparams = load_hyperpyyaml(fin)
    # Dataset creation
    train_data, valid_data = data_prep(data_folder)
    # Trainer initialization
    auto_brain = AutoBrain(
        hparams["modules"],
        hparams["opt_class"],
        hparams,
        run_opts={"device": device},
    )
    # Training/validation loop
    auto_brain.fit(
        range(hparams["N_epochs"]),
        train_data,
        valid_data,
        train_loader_kwargs=hparams["dataloader_options"],
        valid_loader_kwargs=hparams["dataloader_options"],
    )
    # Evaluation is run separately (now just evaluating on valid data)
    auto_brain.evaluate(valid_data)
    # Check if model overfits for integration test
    assert auto_brain.train_loss < 0.08