How to use LayerNormalization layer in a Keras sequential Model?

I am just getting into Keras and Tensor flow. Im having a lot of problems adding an input normalization layer in a sequential model. Now my model is ;

 model = tf.keras.models.Sequential()
 model.add(keras.layers.Dense(256, input_shape=(13, ), activation='relu'))
 model.add(tf.keras.layers.LayerNormalization(axis=-1 , center=True , scale=True))
 model.add(keras.layers.Dense(128, activation='relu'))
 model.add(keras.layers.Dense(64, activation='relu'))
 model.add(keras.layers.Dense(64, activation='relu'))
 model.add(keras.layers.Dense(1))
 model.summary()

My doubts are whether I should first perform an adapt function and how to use it in the sequential model. Thanks to all!!

I'm trying to figure this out as well. According to this example, adapt is not necessary.

model = tf.keras.models.Sequential([
  # Reshape into "channels last" setup.
  tf.keras.layers.Reshape((28,28,1), input_shape=(28,28)),
  tf.keras.layers.Conv2D(filters=10, kernel_size=(3,3),data_format="channels_last"),
  # LayerNorm Layer
  tf.keras.layers.LayerNormalization(axis=3 , center=True , scale=True),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_test, y_test)

Also, make sure you want a LayerNormalization. If I understand correctly, that normalizes every input on its own. Batch normalization may be more appropriate. See this for more info.