* remove input requirment in dygraph Model

* correct unittest

* upadte save inference model in dygraph without input

* fix unittets for test_model.py

* solve conflicts

* solve conflicts

* delete http.log

* fix test_model.py bug, correct initialization of MyModel

* fix unittests bugs

* set paddle manual seed for unittest

* fix Model bugs, because inputs can be list or dict when it is provided.

* add random seed for test_export_deploy_model

* delete redundant codes, because  calls

* Code optimization, error information optimization

* refine vision models

* reproduce summary api

* update model.save_inference_model

* update doc for _save_inference_model, delete useless class in unittests

* make users not be able to set model._inputs be None

* update usage of Model class in unittests

* fix bugs of _verify_spec

* fix bugs of _verify_spec

* add unittest to increase coverage rate

* delete http.log

* update doc for save, remove requirments and limitations for using

* update doc for class Model

* fix summary rnn

* fix kl_div bug when input shape is [1] and reduction is batchmean

* fix conv output_size has no default value bug
* fix summary bug

* add summary for hapi

* replace fluid.optimizer.set_dict with optimizer.set_state_dict

* replace fluid.optimizer.set_dict with optimizer.set_state_dict

* add coverage rate

* Increase coverage rate, fix code style

* Increase coverage rate, fix code style

* add fit to generate optimizer.state_dict() to save .pdopt to increase coverage rate

* delete http.log

* 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

* 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

* Rename `Input` into `InputSpec`

* fix argument place of Input 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 relu for lenet, test=develop

* fix test model, 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.