* fix norm api doc, test=develop

* fix error message, test=develop

* fix api norm, test=develop

* add adagrad, test=develop

* fix bug, test=develop

* fix bug, test=develop

* add spetral_norm, test=develop

* fix adagrad, test=develop

* merge , test=develop

* add local_response_norm in nn.functional and nn.layer, test=develop

* update layers to functional api, test=develop

* fix ci coverage, test=develop

* fix unittests bug, test=develop

* fix 2.0api, test=develop

* fix, test=develop

* fix norm bug, test=develop

* fix norm bug, test=develop

* fix norm bug, test=develop

* fix norm bug, test=develop

* fix norm bug, test=develop

* add cnvert,test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* fix syncbn, test=develop

* fix test,test=develop

* fix unittest,test=develop

* add SyncBatchNorm,test=develop

* define fluid alias in paddle.tensor and paddle.nn dir test=develop

* from layer import loss in paddle.nn dir test=develop

* add instance_norm op for dygraph, test=develop

* add error message, test=develop

* fix high level API(Inference) bug

* patch the unit tests

* Use for_test=True in the Fluid Trainer to clone the test program

* fix typo

* Should do the samething to the inferencer

* add Inference.infer

* optimize code

* update no_test_word2vec_new_api.py

* update trainer

* split check_and_get_place

* use inference_program to save inference model in Trainer

* update demo

* update save_inference_model

* clean code

* Load/save the params from the params_path

* Switch to use load_persistables and save_persistables

* Instaed of setup the executor to run program and scope. Pass the program to the load_persistables

- The trainer and inferencer will load params from disk if param_path
  argument is not None in their constructor.

- Remove params.py, we will expose core.Scope to the user if needed
  (e.g., for GAN). Currently we will not expose it, unless we clearly
  know doing so can support GAN.

- Add `save_params` to Trainer (a TODO item).

- rename "network" to "program"

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

* Fix lots of trainer_config_helpers bug, and complete unittest for `layers.py`