QAT support for LayerNormalization

[tom-arm]

System information

• TensorFlow version (you are using): 2.8
• Are you willing to contribute it (Yes/No): Yes

Motivation
This would be beneficial for models that use this layer - this is for example used in Transformer models.

Describe the feature
Be able to run QAT on a model with the LayerNormalization layer.

Describe how existing APIs don't satisfy your use case (optional if obvious)
As an example, the following code snippet will fail:

import tensorflow as tf
import tensorflow_model_optimization as tfmot

from tensorflow import keras

model = keras.Sequential([
    keras.layers.InputLayer(input_shape=(28, 28)),
    keras.layers.Reshape(target_shape=(28, 28, 1)),
    keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),
    keras.layers.LayerNormalization(axis=3),
    keras.layers.MaxPooling2D(pool_size=(2, 2)),
    keras.layers.Flatten(),
    keras.layers.Dense(10)
])

quant_model = tfmot.quantization.keras.quantize_model(model)