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Some preprocessing layers have an internal state that can be computed based on a sample of the training data. The stateful preprocessing layers are:
          i) TextVectorization. It holds a mapping between string tokens and integer indices
          ii) StringLookup and IntegerLookup. They hold a mapping between input values and integer indices.
          iii) Normalization. It holds the mean and standard deviation of the features.
          iv) Discretization. It holds information about value bucket boundaries.

The keras modules which are needed for Model.ipynb and required to quickly implement high-performance neural networks with the TensorFlow backend, are:
         import numpy as np
         import keras
         from keras.models import Sequential
         from keras.layers import Dense, Dropout, Flatten, Activation
         from keras.layers import Conv2D, MaxPooling2D
         from keras import backend as K
         from keras.layers.normalization import BatchNormalization

For each of the Numeric features, a Normalization() layer is used to ensure that the mean of each feature is 0 and its standard deviation is 1.
          preprocessing.Normalization(axis = -1, mean = None, variance = None, **kwargs)

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