* Add the imperative quantization aware training.
*  This is the python part of Imperative QAT. test=develop

* Save output threshold by argname_index, test=develop

test=develop

test=develop

* add user defined func test=develop

* update test=develop

* update test=develop

* fix name conflicts test=develop

* add unittest test=develop

* change 2018 to 2020 test=develop

* add comment test=develop

* add comment for function test=develop

* fix details test=develop

* fix details test=develop

* Post_training_quantization supports optimize model by fusing, test=develop

Update DNNL QAT document 2.0-alpha

[Fix bug] Init scale node in OutScaleForTrainingPass and enable test_quantization_scale_pass UT (#24393)

* Init scale node in OutScaleForTrainingPass, test=develop
* Enable test_quantization_scale, test=develop

* Enabled quantize all and skip missing in QAT

* update local data preprocess doc

* update for 2.0 QAT

test=develop

test=document_fix

* update benchmark data

test=develop

test=document_fix
Co-authored-by: NWojciech Uss <wojciech.uss@intel.com>

* QAT: support range-based quantization and scales from attribute

* added support for channelwise

* Weight quantization support channel_wise_abs_max method to achieve higher accuracy

* Update for QAT INT8 MKL-DNN document, added info on VNNI in Windows, benchmark results added and updated

* Collect output scale for quantized op and fused op
* Post_training_quantizaion sets batch_generator to support lod tensor

* Post_training_quantizaion supports min_max methon