Ambiguous DCN layer

I would like to ask for some help regarding stacked Cross layers.

From this tutorial, the calling of a simple DCN layer looks like this:

if use_cross_layer:
      self._cross_layer = tfrs.layers.dcn.Cross(
          projection_dim=projection_dim,
          kernel_initializer="glorot_uniform")

......

x = tf.concat(embeddings, axis=1)

# Build Cross Network
if self._cross_layer is not None:
    x = self._cross_layer(x)

From the documentation of the DCN Cross layer:

# after embedding layer in a functional model:
input = tf.keras.Input(shape=(None,), name='index', dtype=tf.int64)
x0 = tf.keras.layers.Embedding(input_dim=32, output_dim=6)
x1 = Cross()(x0, x0)
x2 = Cross()(x0, x1)
logits = tf.keras.layers.Dense(units=10)(x2)
model = tf.keras.Model(input, logits)

I really don't get how it is possible to properly call the Cross layer first with one variable then with two. The second looks more reasonable for me according to the DCN paper and the tutorial.

What I would like to do is to stack multiple Cross layers and properly feed them with the data from the embedding layers.

self.cross_layers = []

for i in range(n_cross_layers):
    self.cross_layers.append(tfrs.layers.dcn.Cross(
        projection_dim=projection_dim,
        #kernel_regularizer='glorot_uniform'
    ))


def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:

    embedding = tf.concat(
        [self.embeddings[key](inputs[key])
         for key in self.embedding_features],
        axis=1
    )
    embedding = tf.concat([
        embedding,
        tf.stack([inputs[feature] for feature in self.features], axis=1),
    ], axis=1)

    if self.cross_layers:
        x1 = self.cross_layers[0](embedding, embedding)

        for cross_layer in self.cross_layers[1:]:
            x1 = cross_layer(embedding, x1)

        embedding = x1

    embedding = self.dense_layers(embedding, training)

    return embedding

Start with the TensorFlow Recommenders Cross layer API documentation and the linked DCN tutorial, focusing on the differing call signatures and the stacked-layer example in the issue. Clarify how the first and subsequent Cross layers should be called, and document a complete, unambiguous stacking pattern so users can tell when the guidance is complete.