Transition to a newer NNCF API for PyTorch model quantization

[nikita-savelyevv]

Post-Training Quantization for PyTorch backend with nncf.create_compressed_model() API is obsolete and should be replaced with nncf.quantize() call which is used for OV backend.

What does this PR do?

Replace nncf.create_compressed_model() call with nncf.quantize() call for quantization of PyTorch models.

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• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you make sure to update the documentation with your changes?
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[nikita-savelyevv]

@AlexKoff88 @alexsu52 could you please review this PR?

[AlexKoff88]

@echarlaix, as we agreed, this is the first PR in the series of changes to deprecate legacy NNCF functions and move all the actual optimization functionality under OVQuantizer API. Please take a look.

[echarlaix]

Looks good thanks @nikita-savelyevv

[echarlaix]

not used for quantization (but now only for the save_onnx_model parameter) so not sure we need to create an instance when not provided, we can only give save_onnx_model a default value instead. Also no need to save the configuration after the quantization + export steps for the same reason : would remove saving the config here

optimum-intel/optimum/intel/openvino/quantization.py

Line 444 in c2d267a

ov_config.save_pretrained(save_directory)

[nikita-savelyevv]

Made the suggested changes, also removed ov_config from list of arguments altogether. In the future will bring it back to pass quantization parameters through it.