* add scale_op in model save step is not necessary, just fix the prune method to support static graph and inplace op

* fix jit.save, no need to add scale_op to each outputvar anymore.
fix prune_with_input, now it supports inplace op

* temporarily disable test_trt_dynamic_shape.TRTDynamicShapeOutOfBound2Test

* use iwyu clean include second time, test=develop

* add new macro BOOST_GET_SAFELY & unittests, test=develop

* add different macro type, test=develop

* fix get macro type in executor, test=develop

* four macro part change backup

* using one macro for all case, test=develop

* revert attribute change, test=develop

* change to three func to solve gcc4.8 bug, test=develop

* polish some details, test=develop

* prune train program by fetch_list, test=develop

* add unittest for prune, test=develop

* fix pruned feed, test=develop

* support ParallelExecutor and feed prune, test=develop

* add comments, test=develop

* update unittest, test=develop

* update unittests, test=develop

* remove debug code, test=develop

* support cond in clone, test=develop

* support cond in prune, test=develop

* support multiple minimize, test=develop

* support cache, test=develop

* fix _copy_param_info_from, test=develop

* support python2 str, test=develop

* remove debug code, test=develop

* fix bug of caching CompiledProgram, test=develop

* fix multi_device issue, test=develop

* tmp

* support tuple in fetch_list and overriding use_prune, test=develop

* dont use nonlocal in python2, test=develop

* remove nonlocal, test=develop

* code clean, test=develop

* code clean, test=develop

* feed list, test=develop

* test adam, test=develop

* follow comments, test=develop

* reduce duplicate code, test=develop

* update comments, test=develop

* support cond in clone, test=develop

* refine code, test=develop

* refine code, test=develop

* follow comments, test=develop

* refine code, test=develop

* Set states of recurrent op as dependent vars in prune of save inference model

This PR will fix the save/load inference model problem of RNN models.

The reason of the bug is that save_inferenc_model will prune OPs that doesn't contribute to Output. But in recurrent_op, States are not Output, OPs refers States will be pruned. 

This fix adds States of recurrent_op as dependent var so that OPs referring States won't be pruned. 

* Fix bug: add feed_vars to the prune function

* Fic CPPLint issues with data_transform

* Fic CPPLint issues with prune.cc

* fix is_test issue

* add paddle enforce

* fix bug

* add new func

* small fix

* address comments

* Move framework.proto to proto namespace

* Fix compile

* Fix compile

* Fix Compile

* is_training to is_test in dropout op

* handle dropout and batch_norm operator when prune pdesc in testing mode

* handle dropout and batch_norm operator when prune pdesc in testing mode

* add get_inference_program method

* fix dropout op

* fix ci

* test data after each batch training

* refine code

* refine test_book3

* fix ci

* follow comments

* Add `dump_to_file()` for ProgrameDescBind in pybind

* Update

* Add utility.py

* typo

* Fix bugs

* Move add_feed/fetch_components to untility.py

* Compelete dump

* Follow comments

* Change output of Prune() from inference to pointer

* Expose Prune() to Python

* Compelete save/load API of inference model

* Fix errors

* Debuging

* Compelete unit tests

* follow comments