title: How are the non-trainable parameters in the summary coming from preprocessing.Normalization in tensorflow.keras.layers.experimental calculated?
body: <p>I have been using <code>preprocessing.Normalization</code> in <code>tensorflow.keras.layers.experimental</code> to normalize data. Here, when I use one input ([None,1]), I get 3 non-trainable parameters in the summary. When I use nine inputs ([None,9]), I get 19 non-trainable parameters. These examples, summary, code and complete data can be found <a href="https://www.tensorflow.org/tutorials/keras/regression" rel="nofollow noreferrer">here</a>. How the &quot;9&quot; and the &quot;19&quot; are calculated?</p>
  <p>I have seen a <a href="https://stackoverflow.com/questions/42521005/how-the-number-of-parameters-associated-with-batchnormalization-layer-is-2048">similar question</a> for <code>BatchNormalization</code> but it is likely that it is not the same case for the above-mentioned normalization.
  I would like to know in more detail what they mean and how are they calculated, is it related somehow to the mean, standard deviation and bias? What is the theory behind it? Can someone point to a paper?</p>
  <p>Thanks.</p>