How to use the xenonpy.model.training.Adam function in xenonpy

with pytest.raises(TypeError, match='parameter `m` must be a instance of '):
        trainer.model = {}

    trainer.model = data[0]
    assert isinstance(trainer.model, torch.nn.Module)
    with pytest.raises(RuntimeError, match='no loss function for training'):
        trainer.fit(*data[1])

    trainer.loss_func = MSELoss()
    assert trainer.loss_type == 'train_mse_loss'
    assert trainer.loss_func.__class__ == MSELoss
    with pytest.raises(RuntimeError, match='no optimizer for training'):
        trainer.fit(*data[1])

    trainer.optimizer = Adam()
    assert isinstance(trainer.optimizer, torch.optim.Adam)
    assert isinstance(trainer._optimizer_state, dict)
    assert isinstance(trainer._init_states, dict)

    trainer.lr_scheduler = ExponentialLR(gamma=0.99)
    assert isinstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ExponentialLR)

def test_persist_1(data):
    model = deepcopy(data[0])
    trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=200)
    trainer.extend(TensorConverter(), Persist('model_dir'))
    trainer.fit(*data[1], *data[1])

    persist = trainer['persist']
    checker = persist._checker
    assert isinstance(persist, Persist)
    assert isinstance(checker.model, torch.nn.Module)
    assert isinstance(checker.describe, dict)
    assert isinstance(checker.files, list)
    assert set(checker.files) == {'model', 'init_state', 'model_structure', 'describe', 'training_info', 'final_state'}

    trainer = Trainer.load(checker)
    assert isinstance(trainer.training_info, pd.DataFrame)
    assert isinstance(trainer.model, torch.nn.Module)
    assert isinstance(trainer._training_info, list)
    assert trainer.optimizer is None

assert isinstance(trainer.training_info, pd.DataFrame)
    assert isinstance(trainer.model, torch.nn.Module)
    assert isinstance(trainer._training_info, list)
    assert trainer.optimizer is None
    assert trainer.lr_scheduler is None
    assert trainer.x_val is None
    assert trainer.y_val is None
    assert trainer.validate_dataset is None
    assert trainer._optimizer_state is None
    assert trainer.total_epochs == 0
    assert trainer.total_iterations == 0
    assert trainer.loss_type is None
    assert trainer.loss_func is None

    trainer = Trainer.load(from_=checker.path,
                           optimizer=Adam(),
                           loss_func=MSELoss(),
                           lr_scheduler=ExponentialLR(gamma=0.99),
                           clip_grad=ClipValue(clip_value=0.1))
    assert isinstance(trainer._scheduler, ExponentialLR)
    assert isinstance(trainer._optim, Adam)
    assert isinstance(trainer.clip_grad, ClipValue)
    assert isinstance(trainer.loss_func, MSELoss)

assert trainer.x_val is None
    assert trainer.y_val is None
    assert trainer.validate_dataset is None
    assert trainer._optimizer_state is None
    assert trainer.total_epochs == 0
    assert trainer.total_iterations == 0
    assert trainer.loss_type is None
    assert trainer.loss_func is None

    trainer = Trainer.load(from_=checker.path,
                           optimizer=Adam(),
                           loss_func=MSELoss(),
                           lr_scheduler=ExponentialLR(gamma=0.99),
                           clip_grad=ClipValue(clip_value=0.1))
    assert isinstance(trainer._scheduler, ExponentialLR)
    assert isinstance(trainer._optim, Adam)
    assert isinstance(trainer.clip_grad, ClipValue)
    assert isinstance(trainer.loss_func, MSELoss)

def test_trainer_fit_4(data):
    model = deepcopy(data[0])
    trainer = Trainer(model=model,
                      optimizer=Adam(),
                      loss_func=MSELoss(),
                      clip_grad=ClipValue(0.1),
                      lr_scheduler=ReduceLROnPlateau(),
                      epochs=10)

    count = 1
    for i in trainer(*data[1]):
        assert isinstance(i, dict)
        assert i['i_epoch'] == count
        if count == 3:
            trainer.early_stop('stop')
        count += 1

    assert trainer.total_epochs == 3
    assert trainer._early_stopping == (True, 'stop')