Mateusz Tabaka a6076a1fd6 Introduce Quantize-Dequantize to FakeQuantize transformation (#1849)
* Introduce Quantize-Dequantize to FakeQuantize transformation

* Revert changes in DequantizeLinear

* apply code format

* Changes after review:

- description for transformation
- remove NGRAPH_CHECK and move some checks from callback to predicates in pattern
- check if out_low/high are broadcastable for FQ's first input
- fix params to copy_runtime_info

* Add type_matches and type_matches_any predicates

* Use get_single_value

* Changes after review:

- add brief description of transformation
- use get_pattern_value_map instead of get_pattern_map
- change opset1 to opset4
- fix params to copy_runtime_info

* Check result of dynamic_pointer_cast
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.

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