However, migrating to a keras-like approach, you can work thinking about "objects with variables inside" and then the graph will be built for you by the abstraction introduced by `tf.keras.Model`.
However, for automatic differentiation, graph is always required (as you can see from the example that uses eager)
Can you explain what you like so much about manipulating graphs, over just writing statements like z= x⋆y, where ⋆ is some tensor operation?
Really, the graph makes me feel like I'm stacking Lego bricks with chopsticks, rather than with my bare hands ;)
The same description (the graph) can be taken and used in every other language. I can move a trained model to production just picking a single file (a `tf.train.SavedModel` IIRC), give it a tag and I'm ready to go, with different models with a native support for the "tagging" (hence the model versioning).