human
I wrote the below code

    learning_rate = 0.001
    batch_size = 5
    dim = 3
    (m1, m2, m3) = (10, 15, 22)
    rng = np.random.RandomState([25, 9, 2012])
    dataset1 = DenseDesignMatrix(X=rng.randn(m1, dim))
    dataset2 = DenseDesignMatrix(X=rng.randn(m2, dim))
    dataset3 = DenseDesignMatrix(X=rng.randn(m3, dim))
    def train_with_monitoring_datasets(train_dataset, monitoring_datasets, model_force_batch_size, train_iteration_mode, monitor_iteration_mode):
        model = SoftmaxModel(dim)
        if model_force_batch_size:
            model.force_batch_size = model_force_batch_size
        cost = DummyCost()
        algorithm = SGD(learning_rate, cost, batch_size=batch_size, train_iteration_mode=train_iteration_mode, monitor_iteration_mode=monitor_iteration_mode, monitoring_dataset=monitoring_datasets, termination_criterion=EpochCounter(2))
        train = Train(train_dataset, model, algorithm, save_path=None, save_freq=0, extensions=None)
        train.main_loop()
    no_monitoring_datasets = None
    even_monitoring_datasets = {'valid': dataset2}
    uneven_monitoring_datasets = {'valid': dataset2, 'test': dataset3}
    train_with_monitoring_datasets(train_dataset=dataset1, monitoring_datasets=no_monitoring_datasets, model_force_batch_size=False, train_iteration_mode='sequential', monitor_iteration_mode='sequential')
    train_with_monitoring_datasets(train_dataset=dataset1, monitoring_datasets=no_monitoring_datasets, model_force_batch_size=batch_size, train_iteration_mode='sequential', monitor_iteration_mode='sequential')
    train_with_monitoring_datasets(train_dataset=dataset3, monitoring_datasets=no_monitoring_datasets, model_force_batch_size=False, train_iteration_mode='sequential', monitor_iteration_mode='sequential')
    try:
        train_with_monitoring_datasets(train_dataset=dataset3, monitoring_datasets=no_monitoring_datasets, model_force_batch_size=batch_size, train_iteration_mode='sequential', monitor_iteration_mode='sequential')
        assert False
    except ValueError:
        pass
   ...

What's a good function header?
assistant
#Here's a good function header.

def test_uneven_batch_size():