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

def test_trainer_2(data):
    trainer = Trainer()
    with pytest.raises(RuntimeError, match='no model for training'):
        trainer.fit(*data[1])

    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])

def test_trainer_prediction_1(data):
    model = deepcopy(data[0])
    trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=200)
    trainer.extend(TensorConverter())
    trainer.fit(*data[1], *data[1])

    trainer = Trainer(model=model).extend(TensorConverter())
    y_p = trainer.predict(data[1][0])
    assert np.any(np.not_equal(y_p, data[1][1].numpy()))
    assert np.allclose(y_p, data[1][1].numpy(), rtol=0, atol=0.2)

    y_p, y_t = trainer.predict(*data[1])
    assert np.any(np.not_equal(y_p, y_t))
    assert np.allclose(y_p, y_t, rtol=0, atol=0.2)

    val_set = DataLoader(TensorDataset(*data[1]), batch_size=50)
    y_p, y_t = trainer.predict(dataset=val_set)
    assert np.any(np.not_equal(y_p, y_t))
    assert np.allclose(y_p, y_t, rtol=0, atol=0.2)

    with pytest.raises(RuntimeError, match='parameters  and  are mutually exclusive'):
        trainer.predict(*data[1], dataset='not none')

def test_trainer_prediction_2():
    model = _Net(n_feature=2, n_hidden=10, n_output=2)

    n_data = np.ones((100, 2))
    x0 = np.random.normal(2 * n_data, 1)
    y0 = np.zeros(100)
    x1 = np.random.normal(-2 * n_data, 1)
    y1 = np.ones(100)

    x = np.vstack((x0, x1))
    y = np.concatenate((y0, y1))
    s = np.arange(x.shape[0])
    np.random.shuffle(s)
    x, y = x[s], y[s]

    trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=CrossEntropyLoss(), epochs=200)
    trainer.extend(TensorConverter(x_dtype=torch.float32, y_dtype=torch.long, argmax=True))
    trainer.fit(x, y)

    y_p, y_t = trainer.predict(x, y)
    assert y_p.shape == (200,)
    assert np.all(y_p == y_t)

    # trainer.reset()
    val_set = DataLoader(ArrayDataset(x, y, dtypes=(torch.float, torch.long)), batch_size=20)
    trainer.extend(TensorConverter(x_dtype=torch.float32, y_dtype=torch.long, auto_reshape=False))
    y_p, y_t = trainer.predict(dataset=val_set)
    assert y_p.shape == (200, 2)

    y_p = np.argmax(y_p, 1)
    assert np.all(y_p == y_t)

def test_trainer_1(data):
    trainer = Trainer()
    assert trainer.device == torch.device('cpu')
    assert trainer.model is None
    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._init_states is None
    assert trainer._optimizer_state is None
    assert trainer.total_epochs == 0
    assert trainer.total_iterations == 0
    assert trainer.training_info is None
    assert trainer.loss_type is None
    assert trainer.loss_func is None

    trainer = Trainer(optimizer=Adam(),

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')

assert trainer.device == torch.device('cpu')
    assert trainer.model is None
    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._init_states is None
    assert trainer._optimizer_state is None
    assert trainer.total_epochs == 0
    assert trainer.total_iterations == 0
    assert trainer.training_info is None
    assert trainer.loss_type is None
    assert trainer.loss_func is None

    trainer = Trainer(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)