Assumes that all arguments of the function are Tensors which will be batched
along their first dimension.

Arguments that are captured, are not batched. The session.run call which does
the concatenation, will use the values of the captured tensors available to it.
Therefore, typical uses of captured tensors should involve values which remain
unchanged across session.run calls. Inference is a good example of this.

        decorator.
    *   Added a `tf.keras.layers.experimental.preprocessing.HashedCrossing`
        layer which applies the hashing trick to the concatenation of crossed
        scalar inputs. This provides a stateless way to try adding feature
        crosses of integer or string data to a model.
    *   Removed `keras.layers.experimental.preprocessing.CategoryCrossing`.