[3.3.1] Normalization broken

[Luux]

Describe the bug
Setting use_batch_norm or use_weight_norm to True results in "AttributeError: Only one normalization can be specified at once."
use_layer_norm works = True works as expected. This occurs in keras-tcn==3.3.1, it also works as expected in keras-tcn==3.3.0.

Paste a snippet

import tensorflow as tf

from tcn import TCN

# define input shape
max_len = 100
max_features = 50

# make model
model = tf.keras.models.Sequential(
    layers=[
        tf.keras.layers.Embedding(max_features, 16, input_shape=(max_len,)),
        TCN(
            nb_filters=12,
            dropout_rate=0.5,
            kernel_size=6,
            dilations=[1, 2, 4],
            use_weight_norm=True,
        ),
        tf.keras.layers.Dense(units=1, activation="sigmoid"),
    ]
)

results in

Traceback (most recent call last):
  File "build_simple_tcn.py", line 13, in <module>
    TCN(
  File [...]/miniconda3/envs/3.8/lib/python3.8/site-packages/tcn/tcn.py", line 235, in __init__
    raise ValueError('Only one normalization can be specified at once.')
ValueError: Only one normalization can be specified at once.

Dependencies
Specify which version of tensorflow you are running.
tensorflow==2.3.0

[Luux]

Oh. This is not really a bug. The exception works as expected. However, in 3.3.1, use_layer_norm defaulted to True for some reason, while both older versions and the current master do not have any normalization activated per default.