def METHOD_NAME():
    config = load_yaml(os.path.join("configs", "defaults.yaml"))
    return OmegaConf.merge(
        config,
        {
            "distributed": {},
            "run_type": "train_val",
            "training": {
                "trainer": "lightning",
                "detect_anomaly": False,
                "evaluation_interval": 4,
                "log_interval": 2,
                "update_frequency": 1,
                "fp16": False,
                "batch_size": 1,
                "batch_size_per_device": None,
                "lr_scheduler": False,
                "tensorboard": False,
                "num_workers": 0,
                "max_grad_l2_norm": 1,
                "exit_on_nan_losses": True,
            },
            "optimizer": {"type": "adam_w", "params": {"lr": 5e-5, "eps": 1e-8}},
            "scheduler": {
                "type": "warmup_linear",
                "params": {"num_warmup_steps": 8, "num_training_steps": 8},
            },
            "trainer": {
                "type": "lightning",
                "params": {
                    "gpus": 1 if torch.cuda.is_available() else 0,
                    "num_nodes": 1,
                    "enable_checkpointing": False,
                    "deterministic": True,
                    "benchmark": False,
                    "gradient_clip_val": 0.0,
                    "val_check_interval": 4,
                    "log_every_n_steps": 2,
                    "progress_bar_refresh_rate": 0,
                    "accumulate_grad_batches": 1,
                    "precision": 32,
                    "num_sanity_val_steps": 0,
                    "limit_val_batches": 1.0,
                    "logger": False,
                },
            },
        },
    )