def METHOD_NAME(self, tmpdir):
    config_dict = {
        "train_batch_size": 2,
        "steps_per_print": 1,
        "optimizer": {
            "type": "Lamb",
            "params": {
                "lr": 0.00015
            }
        },
        "gradient_clipping": 1.0,
        "fp16": {
            "enabled": True
        },
        "scheduler": {
            "type": "OneCycle",
            "params": {
                "cycle_first_step_size": 1000,
                "cycle_first_stair_count": 500,
                "cycle_second_step_size": 1000,
                "cycle_second_stair_count": 500,
                "decay_step_size": 1000,
                "cycle_min_lr": 0.0001,
                "cycle_max_lr": 0.0010,
                "decay_lr_rate": 0.001,
                "cycle_min_mom": 0.85,
                "cycle_max_mom": 0.99,
                "decay_mom_rate": 0.0
            }
        }
    }
    args = args_from_dict(tmpdir, config_dict)
    hidden_dim = 10
    models = [SimpleModel(hidden_dim, empty_grad=False) for _ in range(2)]
    # Load & verify optimizer states
    checkpoint_correctness_verification(config_dict,
                                        models=models,
                                        hidden_dim=hidden_dim,
                                        tmpdir=tmpdir,
                                        load_optimizer_states=True)
    # Ignore optimizer states
    checkpoint_correctness_verification(config_dict,
                                        models=models,
                                        hidden_dim=hidden_dim,
                                        tmpdir=tmpdir,
                                        load_optimizer_states=False)