def METHOD_NAME(self, test_method):
    trainer_config = {
        "devices": 1,
        "num_nodes": 1,
        "accelerator": "gpu",
        "logger": False,
        "precision": 16,
    }
    tensor_model_parallel_size = 1
    pipeline_model_parallel_size = 1
    model_file = '/home/TestData/nlp/megatron_gpt/125M/megatron_gpt.nemo'
    # trainer required for restoring model parallel models
    trainer = Trainer(strategy=NLPDDPStrategy(), **trainer_config)
    assert (
        trainer_config["devices"] * trainer_config['num_nodes']
        == tensor_model_parallel_size * pipeline_model_parallel_size
    ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size"
    model = MegatronGPTModel.restore_from(restore_path=model_file, trainer=trainer)
    model.freeze()
    # has to turn off activations_checkpoint_method for inference
    try:
        model.model.language_model.encoder.activations_checkpoint_method = None
    except AttributeError:
        pass
    self.model = model