I'm aware that keras serves as a high-level interface to TensorFlow.
But it seems to me that keras can do many functionalities on its own (data input, model creation, training, evaluation).
Furthermore, some of TensorFlow's functionality can be ported directly to keras (e.g. it is possible to use a tf metric or loss function in keras).
My question is, what does TensorFlow offer that can't be reproduced in keras?