Apply suggestions from code review

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>

Author: daniil-lyakhov

prototype_source/openvino_quantizer.rst

@@ -15,18 +15,18 @@ Introduction
 
     This is an experimental feature, the quantization API is subject to change.
 
-This tutorial demonstrates how to use `OpenVINOQuantizer` from `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf/tree/develop>`_ in PyTorch 2 Export Quantization flow to generate a quantized model customized for the `OpenVINO torch.compile backend <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_ and explains how to lower the quantized model into the `OpenVINO <https://docs.openvino.ai/2024/index.html>`_ representation.
-`OpenVINOQuantizer` unlocks the full potential of low-precision OpenVINO kernels due to the placement of quantizers designed specifically for the OpenVINO.
+This tutorial demonstrates how to use ``OpenVINOQuantizer`` from `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf/tree/develop>`_ in PyTorch 2 Export Quantization flow to generate a quantized model customized for the `OpenVINO torch.compile backend <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_ and explains how to lower the quantized model into the `OpenVINO <https://docs.openvino.ai/2024/index.html>`_ representation.
+``OpenVINOQuantizer`` unlocks the full potential of low-precision OpenVINO kernels due to the placement of quantizers designed specifically for the OpenVINO.
 
-The PyTorch 2 export quantization flow uses the torch.export to capture the model into a graph and performs quantization transformations on top of the ATen graph.
+The PyTorch 2 export quantization flow uses ``torch.export`` to capture the model into a graph and performs quantization transformations on top of the ATen graph.
 This approach is expected to have significantly higher model coverage, improved flexibility, and a simplified UX.
 OpenVINO backend compiles the FX Graph generated by TorchDynamo into an optimized OpenVINO model.
 
 The quantization flow mainly includes four steps:
 
 - Step 1: Capture the FX Graph from the eager Model based on the `torch export mechanism <https://pytorch.org/docs/main/export.html>`_.
 - Step 2: Apply the PyTorch 2 Export Quantization flow with OpenVINOQuantizer based on the captured FX Graph.
-- Step 3: Lower the quantized model into OpenVINO representation with the API `torch.compile <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_.
+- Step 3: Lower the quantized model into OpenVINO representation with the `torch.compile <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_ API.
 - Optional step 4: : Improve quantized model metrics via `quantize_pt2e <https://openvinotoolkit.github.io/nncf/autoapi/nncf/experimental/torch/fx/index.html#nncf.experimental.torch.fx.quantize_pt2e>`_ method.
 
 The high-level architecture of this flow could look like this: