tf.keras and tfp.tfp.layers. are not compatible

hello fellow developers, it appears that the tf.keras and tfp.tfp.layers. are not compatible
i have this code="
**num_inducing_points = 40
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=[1], dtype=x.dtype),
tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False),
tfp.layers.VariationalGaussianProcess(
num_inducing_points=num_inducing_points,
kernel_provider=RBFKernelFn(dtype=x.dtype),
event_shape=[1],
inducing_index_points_initializer=tf.constant_initializer(
np.linspace(*x_range, num=num_inducing_points,
dtype=x.dtype)[..., np.newaxis]),
unconstrained_observation_noise_variance_initializer=(
tf.constant_initializer(
np.log(np.expm1(1.)).astype(x.dtype))),
),
])

//Do inference.
batch_size = 32
loss = lambda y, rv_y: rv_y.variational_loss(
y, kl_weight=np.array(batch_size, x.dtype) / x.shape[0])
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=loss)
model.fit(x, y, batch_size=batch_size, epochs=1000, verbose=False)

//Make predictions.
yhats = [model(x_tst) for _ in range(100)]**
"

and i get the following
error output=
"**:7: UserWarning: layer.add_variable is deprecated and will be removed in a future version. Please use the layer.add_weight() method instead.
self._amplitude = self.add_variable(
:12: UserWarning: layer.add_variable is deprecated and will be removed in a future version. Please use the layer.add_weight() method instead.
self._length_scale = self.add_variable(

ValueError Traceback (most recent call last)

in <cell line: 2>()
1 num_inducing_points = 40
----> 2 model = tf.keras.Sequential([
3 tf.keras.layers.InputLayer(input_shape=[1], dtype=x.dtype),
4 tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False),
5 tfp.layers.VariationalGaussianProcess(

1 frames

/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py in add(self, layer, rebuild)
93 layer = origin_layer
94 if not isinstance(layer, Layer):
---> 95 raise ValueError(
96 "Only instances of keras.Layer can be "
97 f"added to a Sequential model. Received: {layer} "

ValueError: Only instances of keras.Layer can be added to a Sequential model. Received: <tensorflow_probability.python.layers.distribution_layer.VariationalGaussianProcess object at 0x7acd5e77fc70> (of type <class 'tensorflow_probability.python.layers.distribution_layer.VariationalGaussianProcess'>)**"

Start by reproducing the provided notebook example that adds tfp.layers.VariationalGaussianProcess to a tf.keras.Sequential model, including the displayed ValueError. Check the layer and Keras compatibility involved; done means the example can construct the model without the incompatibility error.