With TF 2.0 preview, "old-style" variational layers error with "Inputs to eager execution function cannot be Keras symbolic tensors"

Hi,

I have unit tests for Convolution1DReparameterization , Convolution1DReparameterization etc. that basically look like this

x = tf.ones(shape = [150,1])
y = tf.ones(shape = [150])

model = tf.keras.Sequential(
  [tfpl.DenseReparameterization(
      units = 512,
      activation = "relu"),
   tfpl.DenseReparameterization(
      units = 1)])

model.compile(optimizer = 'adam', loss = "mse")
model.fit(x, y, steps_per_epoch = 1)

These run fine with TF 1, but with TF 2 preview I get

_SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'sequential/dense_reparameterization/divergence_kernel:0' shape=() dtype=float32>, <tf.Tensor 'sequential/dense_reparameterization_1/divergence_kernel:0' shape=() dtype=float32>]

Would you happen to know what is the cause here?
Are these "old-style" variational layers supposed to run with TF 2?

Many thanks in advance!!

Start with the provided tf.keras.Sequential reproducer using DenseReparameterization and the existing unit tests for the variational layers. Run it with the TensorFlow 2 preview and compare the eager-execution failure with TensorFlow 1 behavior. Done means the old-style layers have a verified TensorFlow 2 outcome and the relevant tests pass or clearly capture the supported behavior.