* fix bug

* refine manual seed

* fix ci problem

* fix unittests

* fix unittest

* set is_init_py=false in manual_seed

* fix unittest

* fix bernoulli_op

* fix(unittest): change random_seed to manual_seed

* 🐞fix(unittest): fix manual_seed

* trigger ci

* fix test_sentiment

* fix test_imperative_save_load

* fix test_uniform_random_op

* fix test_uniform_random_op

* fix test_jit_save_load

* merge develop

* fix manual_seed

* fix manual_seed

* use global engine

* use shared_ptr

* fix double free

* fix bug

* fix bug

* fix bug

* fix test bug

* fix test bug

* fix test bug

* fix ci

* update save_inference_model for hapi

* update save_inference_model to support dygraph

* fix comments

* fix comments

* test=develop

* test, test=develop

* fix dim test, test=develop

* test, test=develop

* add test_export_deploy_model_dynamic

* fix unittest for hapi: save_inference_model

* fix code style

* accept review by guoshengCS

* fix coverage rate

* update doc for save_inference_model and copyright

* change test model back to LeNet() in test_export_deploy_model

* copy jit.save, use LeNet() to test export deploy model

* add return value for dygraph, and fix doc error

* corrected the doc writing

* Delete redundant import and correct import order in sample code.

* remove 'fluid' and add prepare() and fit() in sample code

* correct usage of API 2.0 in sample code

* fix sample code bugs

* fix code style bugs

* fix test_model.py bugs

* set for_inference=True

* correct usage for static.InputSpec

* update doc for model.save

* correct usage of API 2.0

* rename param name for model.save

* correct for_inference as training

* Move paddle/incubate/hapi/metrics to paddle/metric
* Add Precision, Recall and Auc metric

* update Conv2d Conv3d conv2d conv3d api

* add map_style dataset. test=develop

* Rename `Input` into `InputSpec`

* fix argument place of Input api

* add uncombined_weight_to_state_dict API

* Remove paddle.incubate.hapi.loss and reuse the paddle.nn.layer.loss in high level API

* Refine Model

1. Take the network (instance of Layer) as the input of Model.
2. Refine set_dict/load_dict of Layer.
3. Refine Input interface, so update code sample about Input

* add more vision transfrom apis

* add relu for lenet, test=develop

* fix test model, test=develop

* 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

* add vision

* fix predict, test=develop

* add unittest for vision apis, test=develop

* fix typos

* add hapi models api, test=develop

* fix code format, test=develop

* fix typos, test=develop

* fix sample code import, test=develop

* fix sample codes, test=develop

* add decompress, test=develop

* rm darknet, test=develop

* rm debug code, test=develop

* Merge hapi into Paddle

Hapi is a high level API for training and inference.
The main modules include Model, Loss, Metrics, Dataset.
Also includes common modules and models in NLP and computer vision, such as BERT, ResNet.

These modules are developed by:
0YuanZhang0, guoshengCS heavengate, LielinJiang, qingqing01, xyzhou-puck huangjun12, wangxiao1021, zhangyang.