* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* rewrite all code examples, test=document_fix

* refine arguments, test=document_fix

* fix desc format error, test=document_fix

* capitalize the first letter, test=document_fix

* refine api desc, test=document_fix

* fix wrong COPY-FROM label in Model docs, test=document_fix

* refine returns, test=document_fix

* refine returns, test=document_fix

* add a blank line in code block, test=document_fix

* use yapf to format all python file

* yapf exclude two unittests file for they rely on writing and reading file, and format will break them

* disable diff_py_file because too many diff files cause command following failed

* update 2.0 public api in vision

* fix some flake8 errors

* Fix slow data reading, In the old version, one epoch read time of this data set was about 5371 seconds(MacBook Pro Retina, 13-inch, Early 2015 2.7 GHz), and a batch took 211 seconds, It's too painful to use. Now decompress the data in advance (about 10 seconds). Each epoch of reading takes about 3 seconds(MacBook Pro Retina, 13-inch, Early 2015 2.7 GHz), and a batch takes 0.017 seconds more.

* Run CI, test=allcase

* fix qq group number. test=document_fix

 fix qq group number. test=document_fix

* fix qq group number. test=document_fix 

fix qq group number. test=document_fix

* fix test_datasets unittest

* update document of paddle.vision.dataset, test=document

* update document of paddle.vision.dataset, test=document

* lazy import for scipy

* rm unused check

* return pil image as default according backend

* 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 map_style dataset. 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.