# How to use a ragged tensor with a convolutional layer?

I have textual data of various lengths for which ragged tensors seems well suited. For instance my data could look as follows :

x = tf.ragged.constant([[1,2,3,4,5,6,7], [5,6,1,2]])


I would like provide this ragged tensor to a model composed by some convolutional filters, let's say at least one filter as follows :

model = Sequential([Embedding(alphabet_size, embedding_dim),
Conv1D(filters=10, kernel_size=10),
GlobalMaxPooling1D()])


I tried to use tf.ragged.map_flat_values however I am not sure that it does what I would like, i.e : embedding each text line of the batch, convolving it, and then taking the max over each text line.

Is there a workaround to make this model work on (very) variable lengths texts (except of course using 3d tensors batches)?