How to use the xenonpy.model.nn.wrap.L1.batch_norm function in xenonpy

def test_generator1d_4():
    g = Generator1d(290, 1, n_neuron=[100, 90, 80, 70, 60, 50], drop_out=(0.2, 0.3, 0.4),
                    batch_normalize=(None, L1.batch_norm()))
    m = g(5, n_models=20, replace=True)
    assert len(list(m)) == 20, "when 'replace=False' is OK"

def test_generator1d_1():
    g = Generator1d(290, 1, n_neuron=(100, 70, 50), drop_out=(0.2, 0.3, 0.4),
                    batch_normalize=(None, L1.batch_norm()))
    m = g(1)
    assert len(list(m)) == 18, '3x3x2'

def test_generator1d_2():
    g = Generator1d(290, 1, n_neuron=[100, 70, 50], drop_out=(0.2, 0.3, 0.4),
                    batch_normalize=(None, L1.batch_norm()))
    m = g(1, n_models=10)
    assert len(list(m)) == 10, '0 < n_models <= 3x3x2'

def test_generator1d_3():
    g = Generator1d(290, 1, n_neuron=[100, 70, 50], drop_out=(0.2, 0.3, 0.4),
                    batch_normalize=(None, L1.batch_norm()))
    m = g(1, n_models=20)
    with pytest.raises(ValueError):
        len(list(m)) == 18

def __init__(self, n_features: int, n_predict: int, *,
                 output_layer=L1.linear(),
                 n_neuron,
                 drop_out=(0.0,),
                 layer_func=(L1.linear(),),
                 act_func=(nn.ReLU(),),
                 batch_normalize=(L1.batch_norm(),)
                 ):
        """
        Parameters
        ----------
        n_features: int
            Input dimension.
        n_predict: int
            Output dimension.
        output_layer: func
            Output layer.
        n_neuron: [int]
            Number of neuron.
        drop_out: [float]
            Dropout rate.
        layer_func: [func]
            Layer functions. such like: :meth:`~.L1.linear`.

def __init__(self, n_in, n_out, *,
                 drop_out=0.,
                 layer_func=L1.linear(bias=True),
                 act_func=nn.ReLU(),
                 batch_nor=L1.batch_norm(eps=1e-05, momentum=0.1, affine=True)
                 ):
        """
        Parameters
        ----------
        n_in: int
            Size of each input sample.
        n_out: int
            Size of each output sample
        drop_out: float
            Probability of an element to be zeroed. Default: 0.5
        layer_func: func
            Layers come with PyTorch.
        act_func: func
            Activation function.
        batch_nor: func
            Normalization layers