Hi, I'm not the maintainer of this package, but since I have some experience with it, I'll try to answer this question.

The way I've done this is to use the spark-tensorflow-connector (or you can use spark-tfrecord if you want partition by), and output the data frame into SequenceExample format. And when you build the dataset, you can use SEQ instead of ELWC in the dataset builder. (Note that to have the plugin correctly recognize whether a feature is an example feature, you need to wrap it into an array of array of some primitive types)

But, if you want ELWC format in spark, what I've also done instead is to construct ELWC examples by using apply function, and for each partition you write them into one tfrecord file and upload it to your cloud storage.