def METHOD_NAME(cfg) -> None:
    # trainer required for restoring model parallel models
    trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer)
    if (
        cfg.tensor_model_parallel_size < 0
        or cfg.pipeline_model_parallel_size < 0
        or cfg.get('pipeline_model_parallel_split_rank', -1) < 0
    ):
        save_restore_connector = NLPSaveRestoreConnector()
        if os.path.isdir(cfg.gpt_model_file):
            save_restore_connector.model_extracted_dir = cfg.gpt_model_file
        model_config = MegatronGPTModel.restore_from(
            restore_path=cfg.gpt_model_file,
            trainer=trainer,
            return_config=True,
            save_restore_connector=save_restore_connector,
        )
        with open_dict(cfg):
            cfg.tensor_model_parallel_size = model_config.get('tensor_model_parallel_size', 1)
            cfg.pipeline_model_parallel_size = model_config.get('pipeline_model_parallel_size', 1)
            cfg.pipeline_model_parallel_split_rank = model_config.get('pipeline_model_parallel_split_rank', 0)
    # Load an adapter model,  must be provided in config
    if cfg.get("adapter_model_file", None) is not None:
        # Update frozen GPT model path in case it has changed
        ia3_tuning_cfg = MegatronGPTInfusedAdapterModel.restore_from(
            cfg.adapter_model_file, trainer=trainer, return_config=True
        )
        with open_dict(ia3_tuning_cfg):
            ia3_tuning_cfg.language_model_path = cfg.gpt_model_file
        # Now load prompt learning model with frozen gpt model base
        model = MegatronGPTInfusedAdapterModel.restore_from(
            restore_path=cfg.adapter_model_file, trainer=trainer, override_config_path=ia3_tuning_cfg
        )
    # Or load regular GPT model
    else:
        raise NotImplementedError(
            "This script is meant for inference from an Adapter Tuned GPT Model, for inference from a Megatron GPT model, refer to ../megatron_gpt_eval.py"
        )
    model.freeze()
    # Have to turn off activations_checkpoint_method for inference
    try:
        model.model.language_model.encoder.activations_checkpoint_method = None
    except AttributeError:
        pass
    try:
        model.frozen_model.model.language_model.encoder.activations_checkpoint_method = None
    except AttributeError:
        pass
    max_input_length = model.frozen_model.cfg.encoder_seq_length - cfg.inference.tokens_to_generate
    # check whether the DDP is initialized
    if parallel_state.is_unitialized():
        def dummy():
            return
        if trainer.strategy.launcher is not None:
            trainer.strategy.launcher.launch(dummy, trainer=trainer)
        trainer.strategy.setup_environment()
    _, dataloader = model.build_virtual_prompt_dataset(
        data=cfg.data_paths,
        batch_size=cfg.get("batch_size", 1),
        max_seq_length=max_input_length,
        min_seq_length=model.cfg.data.get('min_seq_length', 1),
        add_bos=cfg.inference.add_BOS,
        add_eos=False,
        for_train=False,
        tokens_to_generate=cfg.inference.tokens_to_generate,
        drop_last=False,
        shuffle=False,
        num_workers=cfg.get("num_workers", 1),
    )
    config = OmegaConf.to_container(cfg.inference)
    model.set_inference_config(config)
    response = trainer.predict(model, dataloader)
    print("***************************")
    if cfg.pred_file_path is not None:
        with open(cfg.pred_file_path, "w", encoding="utf-8") as f:
            for batch in response:
                for sentence in batch['sentences']:
                    s = ' '.join(sentence.split('\n'))
                    f.write(s + "\n")
        print("predictions saved to {}".format(cfg.pred_file_path))
    else:
        print(response)
    print("***************************")