* move translated layer

* fix build bugs

* fix unittest bug

* trigger ci

* [CodeStyle][py2][U004] unecessary explicit `object` inheritance in class definition

* fix an increment

* [CodeStyle][py2][U008] remove unnecessary args in `super()`

* remove remained args

* revert changes in test_pylayer_op

* Revert "revert changes in test_pylayer_op"

This reverts commit ff185a9ae738afac3b0264f61bde6c6b7f72e7c4.

* revert some changes in example code

* update config

* re-blacken python code

* temporarily disable date and diff_py_file

* skip a format

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* add quant pass

* 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

* clip op extra information when export model,test=ocr

* rename clip_extra parameter to kwargs in save_inference_model, test=ocr

* PTQ save quantized model

* Wrap simulated layer

* post process the inference model

* Save all scales to target ops
* Move quant layers to paddle.nn.quant

* Add wrap for functional api
* Refine the wraped api
* Add unit test for quant functional layers
* Update all unit tests for dygraph qat

* Use layer to calculate output scale
* add backward for moving_average_abs_max_scale and save output scales to op's attr

* Refine saving output scale to infer program

* Fix skip_quant in QAT

* Refine calculating output scale of dygraph qat, test=develop

* Support loading parameters from checkpoint to save quantized model

* Fix the unittest test_moving_average_abs_max_scale_op

* Add unittest of save_quantized_model from checkpoint

* Add comments to explain the function

* Fix the error of save_quantized_model

* Implemented AddQuantDequantPass in imperative quantization.

* Supported LeakyReLU Quantization

* For meeting coverage rate.

* Changed the file name of test of AddQuantDequant

* Implemented more Quantized NoWeightLayers.

* Fix the loss cannot align problem between static and dynamic model quantization, add swish as supported quantized layer in imperative quantization.

* remove noweight_list

* support 2.0 API such as Pool2D and ReLu