* move to platform

* "move init from framework to platform"

* "remove used init"

* "fix ci"

* "fix ci"

* "fix generic"

* "fix ci"

* "fix ci"

* "fix ci"

* "disable fragile test"

disable the LODTensor warning for now

* add lod_tensor util and modify pybind

* refind pybind LoDTensor API and modify LoDTensor and DataFeeder test

* fix test error

* fix detection map op test

* fix reorder_lod_tensor test

* fix seq_concat_op

* fix chunk evel op test

* fix target assign op

* fix warp ctc op

* address comments step 1: reverse reset_lod op

* step 2: modify op test

* add warning message

* remove has_valid_lod

* add back has_valid_lod

* address comments

* add exception catching trial

* wip

* use executor.complete to end trainer

* fix build

* fix build with distribute off

* fix typo

* fix cmake typo

* fix build

replace use_event with use_cuda, because use_event means the program running with CUDA, so use_cuda maybe more intuitive.

* Use Builder Chain to decorate new builders. It is easy to extend
  builders.
* Make graphviz path as a build strategy, not a FLAGS.

Fix a bug in save_inference_model and prune when the program is initailized by load_inference_model (#10011)

* Fix bug in save_inference_model and prune when the program is initialized by load_inference_program.

* Save the transpiled program instead.

* remove net op and cond_op

* fix cpplint

* fix dependency

* delete backward_test; fix compile

* disable batch_norm backward

* rm test_net.py

* make batchnorm test independent of backward.cc

* make test_layer_norm_op independent of backward.cc

* make test_layer_norm_op independent of backward.cc

* delete unused code

* clean up

* Support testing during training by ParallelExecutor.

* Add unit test.

* Improve the interface.

* Follow comments.

* cpplint test and add tesnor_py_test.cc

* Update

* Update