def METHOD_NAME(self):
    config_dict = {
        "train_batch_size": 2,
        "steps_per_print": 1,
        "optimizer": {
            "type": "Adam",
            "params": {
                "lr": 0.00015,
                "weight_decay": 0.01
            }
        },
        "gradient_clipping": 1.0,
        "fp16": {
            "enabled": True,
            "loss_scale": 0,
            "initial_scale_power": 16
        },
        "curriculum_learning": {
            "enabled": True,
            "curriculum_type": "seqlen",
            "min_difficulty": 2,
            "max_difficulty": 10,
            "schedule_type": "fixed_linear",
            "schedule_config": {
                "total_curriculum_step": 8,
                "difficulty_step": 2
            }
        }
    }
    hidden_dim = 10
    ground_truths = {1: 2, 2: 4, 3: 4, 4: 6, 5: 6, 6: 8, 7: 8, 8: 10, 9: 10, 10: 10}
    model = Curriculum_SimpleModel(hidden_dim)
    model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters())
    data_loader = random_dataloader(model=model, total_samples=20, hidden_dim=hidden_dim, device=model.device)
    for n, batch in enumerate(data_loader):
        loss, seqlen = model(batch[0], batch[1])
        model.backward(loss)
        model.step()
        if n + 1 in ground_truths:
            true_seqlen = ground_truths[n + 1]
            assert seqlen == true_seqlen, f"Incorrect curriculum schedule"