Release notes 2.10 (#2640)

### Changes

- Added v2.10.0 template;

### Reason for changes

- Upcoming release;

### Related tickets

- 138131;

#### For the contributors:

1. Please add your changes (as the commit to the branch) to the list
according to the template and previous notes;
2. Do not add tests-related notes;
3. Provide the list of the PRs (for all your notes) in the comment for
the discussion;

---------

Co-authored-by: Liubov Talamanova <[email protected]>
Co-authored-by: Daniil Lyakhov <[email protected]>
Co-authored-by: Nikita Savelyev <[email protected]>

ReleaseNotes.md

@@ -1,5 +1,57 @@
 # Release Notes
 
+## New in Release 2.10.0
+
+Post-training Quantization:
+
+- Features:
+  - Introduced the subgraph defining functionality for the `nncf.IgnoredScope()` option.
+  - Introduced limited support for the batch size of more than 1. MobilenetV2 [PyTorch example](examples/post_training_quantization/torch/mobilenet_v2) was updated with batch support.
+- Fixes:
+  - Fixed issue with the `nncf.OverflowFix` parameter absence in some scenarios.
+  - Aligned the list of correctable layers for the FastBiasCorrection algorithm between PyTorch, OpenVINO and ONNX backends.
+  - Fixed issue with the `nncf.QuantizationMode` parameters combination.
+  - Fixed MobilenetV2 ([PyTorch](examples/post_training_quantization/torch/mobilenet_v2), [ONNX](examples/post_training_quantization/onnx/mobilenet_v2), [OpenVINO](examples/post_training_quantization/openvino/mobilenet_v2)) examples for the Windows platform.
+  - (OpenVINO) Fixed [Anomaly Classification example](examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control) for the Windows platform.
+  - (PyTorch) Fixed bias shift magnitude calculation for fused layers.
+  - (OpenVINO) Fixed removing the ShapeOf graph which led to an error in the `nncf.quantize_with_accuracy_control()` method.
+- Improvements:
+  - `OverflowFix`, `AdvancedSmoothQuantParameters` and `AdvancedBiasCorrectionParameters` were exposed into the `nncf.*` namespace.
+  - (OpenVINO, PyTorch) Introduced scale compression to FP16 for weights in `nncf.compress_weights()` method, regardless of model weights precision.
+  - (PyTorch) Modules that NNCF inserted were excluded from parameter tracing.
+  - (OpenVINO) Extended the list of correctable layers for the BiasCorrection algorithm.
+  - (ONNX) Aligned BiasCorrection algorithm behaviour with OpenVINO in specific cases.
+- Tutorials:
+  - [Post-Training Optimization of PhotoMaker Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/photo-maker/photo-maker.ipynb)
+  - [Post-Training Optimization of Stable Diffusion XL Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-diffusion-xl/stable-diffusion-xl.ipynb)
+  - [Post-Training Optimization of KerasCV Stable Diffusion Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-diffusion-keras-cv/stable-diffusion-keras-cv.ipynb)
+  - [Post-Training Optimization of Paint By Example Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/paint-by-example/paint-by-example.ipynb)
+  - [Post-Training Optimization of aMUSEd Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/amused-lightweight-text-to-image/amused-lightweight-text-to-image.ipynb)
+  - [Post-Training Optimization of InstantID Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/instant-id/instant-id.ipynb)
+  - [Post-Training Optimization of LLaVA Next Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llava-next-multimodal-chatbot/llava-next-multimodal-chatbot.ipynb)
+  - [Post-Training Optimization of AnimateAnyone Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/animate-anyone/animate-anyone.ipynb)
+  - [Post-Training Optimization of YOLOv8-OBB Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov8-optimization/yolov8-obb.ipynb)
+  - [Post-Training Optimization of LLM Agent](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-agent-langchain/llm-agent-langchain.ipynb)
+
+Compression-aware training:
+
+- Features:
+  - (PyTorch) `nncf.quantize` method now may be used as quantization initialization for Quantization-Aware Training. Added a [Resnet18-based example](examples/quantization_aware_training/torch/resnet18) with the transition from the Post-Training Quantization to a Quantization-Aware Training algorithm.
+  - (PyTorch) Introduced extractors for the fused Convolution, Batch-/GroupNorm, and Linear functions.
+- Fixes:
+  - (PyTorch) Fixed `apply_args_defaults` function issue.
+  - (PyTorch) Fixed `dtype` handling for the compressed `torch.nn.Parameter`.
+  - (PyTorch) Fixed `is_shared` parameter propagation.
+- Improvements:
+  - (PyTorch) Updated command creation behaviour to reduce the number of adapters.
+  - (PyTorch) Added option to insert point for models that wrapped with `replace_modules=False`.
+- Deprecations/Removals:
+  - (PyTorch) Removed the `binarization` algorithm.
+  - NNCF installation via `pip install nncf[<framework>]` option is now deprecated.
+- Requirements:
+  - Updated PyTorch (2.2.1) and CUDA (12.1) versions.
+  - Updated ONNX (1.16.0) and ONNXRuntime (1.17.1) versions.
+
 ## New in Release 2.9.0
 
 Post-training Quantization: