* Make dynamic_decode support dygraph and expose to API 2.0
test=develop

* update info about BeamSearchDecoder and dynamic_decode

* remove all APIs in paddle.text, expose BeamSearchDecoder and dynamic_decode

* update example code

* delete test_text.py, decode.py, update some doc, fix example code float64

* delete decode import from paddle.nn

* fix unittest bugs

* use dygraph.Embedding instead of nn.Embedding, add paddle.enbale_static()

* update, correct doc

* move dynamic_decode, BeamSearchDecoder API to paddle.nn

* fix code style

* update unittest param, delete import pf text.py

* set dtype of beamsearchtest float64

* update example code of BeamSearchDecoder, dynamic_decode
Co-authored-by: NLiuChiaChi <709153940@qq.com>

* Move hapi form paddle/incubate to paddle

* Remove vision/datasets/utils.py and clean code

* Add sample code for conll05

* Print pull path when saving model

* Fix sample code after paramter_list of SGD is changed to parameters

* Fix bug in wmt16 datase

* Add hapi.text and corresponding unit test.
test=develop

* Remove hapi.text apis' reuse parameter args for coverage.
test=develop

* Fix TransformerCell and TransformerBeamSearchDecoder example codes.
test=develop

* Fix example codes in hapi.text.
test=develop

* Add some apis in hapi.text into example code white list.
test=develop

* Fix example code of DynamicDecode in hapi.text.
text=develop

* Rename Model.self as model in test_text.py
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`