title: A Tensorflow training agnostic to Eager and Graph modes
body: <p>I spend some of my time coding novel (I wish) RNN cells in Tensorflow. 
  To prototype, I use eager mode (easier to debug). 
  In order to train, I migrate the code to a graph (runs faster).</p>
  
  <p>I am looking for a wrapper code/example that can run forward pass and training in a way that will be agnostic to the mode I run it - eager or graph, as much as possible. I have in mind a set of functions/classes, to which the particular neural network/optimizer/data can be inserted, and that these set of functions/classes could run in both modes with minimal changes between the two. In addition, it is of course good that it would be compatible with many types of NN/optimizers/data instances.</p>
  
  <p>I am quite sure that many had this idea.
  I wonder if something like this is feasible given the current eager/graph integration in TF.</p>