[DOCS] Fixing formatting issues in articles (#17994)

* fixing-formatting

docs/Documentation/inference_modes_overview.md

@@ -17,7 +17,7 @@ OpenVINO Runtime offers multiple inference modes to allow optimum hardware utili
 The remaining modes assume certain levels of automation in selecting devices for inference. Using them in the deployed solution may potentially increase its performance and portability. The automated modes are:
 
 * :doc:`Automatic Device Selection (AUTO) <openvino_docs_OV_UG_supported_plugins_AUTO>`
-* :doc:``Multi-Device Execution (MULTI) <openvino_docs_OV_UG_Running_on_multiple_devices>`
+* :doc:`Multi-Device Execution (MULTI) <openvino_docs_OV_UG_Running_on_multiple_devices>`
 * :doc:`Heterogeneous Execution (HETERO) <openvino_docs_OV_UG_Hetero_execution>`
 * :doc:`Automatic Batching Execution (Auto-batching) <openvino_docs_OV_UG_Automatic_Batching>`
 

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md

@@ -184,7 +184,7 @@ Converting a YOLACT Model to the OpenVINO IR format
    mo --input_model /path/to/yolact.onnx
 
 
-**Step 4**. Embed input preprocessing into the IR:
+**Step 5**. Embed input preprocessing into the IR:
 
 To get performance gain by offloading to the OpenVINO application of mean/scale values and RGB->BGR conversion, use the following model conversion API parameters:
 

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md

@@ -47,8 +47,6 @@ If you have another implementation of CRNN model, it can be converted to OpenVIN
 
    * For  Windows, add ``/path/to/CRNN_Tensorflow/`` to the ``PYTHONPATH`` environment variable in settings.
 
-
-
 2. Edit the ``tools/demo_shadownet.py`` script. After ``saver.restore(sess=sess, save_path=weights_path)`` line, add the following code:
 
    .. code-block:: python

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_NCF_From_Tensorflow.md

@@ -27,8 +27,7 @@ This tutorial explains how to convert Neural Collaborative Filtering (NCF) model
 
    where ``rating/BiasAdd`` is an output node.
 
-3. Convert the model to the OpenVINO format. If you look at your frozen model, you can see that
+3. Convert the model to the OpenVINO format. If you look at your frozen model, you can see that it has one input that is split into four ``ResourceGather`` layers. (Click image to zoom in.)
-it has one input that is split into four ``ResourceGather`` layers. (Click image to zoom in.)
 
    .. image::  ./_static/images/NCF_start.svg
 

docs/OV_Runtime_UG/Python_API_inference.md

@@ -74,6 +74,7 @@ Example usage:
 
 "Postponed Return" is a practice to omit overhead of ``OVDict``, which is always returned from
 synchronous calls. "Postponed Return" could be applied when:
+
 * only a part of output data is required. For example, only one specific output is significant 
   in a given pipeline step and all outputs are large, thus, expensive to copy.
 * data is not required "now". For example, it can be later extracted inside the pipeline as

docs/OV_Runtime_UG/deployment/conditional_compilation_deployment.md

@@ -161,7 +161,7 @@ Considering that JIT kernels can be affected by L1/L2/L3 cache size and the numb
 
 - L2/L3 cache emulation
 
-  Hack the function of get cache size:
+  Hack the function of get cache size
 
     ``unsigned int dnnl::impl::cpu::platform::get_per_core_cache_size(int level)``
 

docs/OV_Runtime_UG/supported_plugins/GNA.md

@@ -78,13 +78,6 @@ Starting with the 2021.4.1 release of OpenVINO™ and the 03.00.00.1363 version
 In this mode, the GNA driver automatically falls back on CPU for a particular infer request if the HW queue is not empty. 
 Therefore, there is no need for explicitly switching between GNA and CPU.
 
-
-
-
-
-
-
-
 .. tab-set::
 
    .. tab-item:: C++
@@ -110,9 +103,6 @@ Therefore, there is no need for explicitly switching between GNA and CPU.
          :fragment: [ov_gna_exec_mode_hw_with_sw_fback]
 
 
-
-
-
 .. note:: 
    
    Due to the "first come - first served" nature of GNA driver and the QoS feature, this mode may lead to increased 

docs/OV_Runtime_UG/supported_plugins/GPU_RemoteTensor_API.md

@@ -490,7 +490,7 @@ To see pseudo-code of usage examples, refer to the sections below.
 See Also
 #######################################
 
-* ov::Core
+* ``:ref:`ov::Core <doxid-classov-1-1-core>```
-* ov::RemoteTensor
+* ``:ref:`ov::RemoteTensor <doxid-classov-1-1-remote-tensor>```
 
 @endsphinxdirective

docs/install_guides/configurations-for-intel-gna.md

@@ -68,13 +68,13 @@ What’s Next?
 
 Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C++.
 
-Developing in Python:
+* Developing in Python:
 
   * `Start with tensorflow models with OpenVINO™ <notebooks/101-tensorflow-to-openvino-with-output.html>`__
   * `Start with ONNX and PyTorch models with OpenVINO™ <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
   * `Start with PaddlePaddle models with OpenVINO™ <notebooks/103-paddle-to-openvino-classification-with-output.html>`__
 
-Developing in C++:
+* Developing in C++:
 
   * :doc:`Image Classification Async C++ Sample <openvino_inference_engine_samples_classification_sample_async_README>`
   * :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`

docs/install_guides/configurations-for-intel-gpu.md

@@ -83,7 +83,7 @@ What’s Next?
 You can try out the toolkit with:
 
 
-`Python Quick Start Example <notebooks/201-vision-monodepth-with-output.html>`_ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+* `Python Quick Start Example <notebooks/201-vision-monodepth-with-output.html>`_ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
 
   Visit the :ref:`Tutorials <notebook tutorials>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
@@ -91,8 +91,7 @@ You can try out the toolkit with:
   * `Basic image classification program with Hello Image Classification <notebooks/001-hello-world-with-output.html>`__
   * `Convert a PyTorch model and use it for image background removal <notebooks/205-vision-background-removal-with-output.html>`__
 
-
+* `C++ Quick Start Example <openvino_docs_get_started_get_started_demos.html>`__ for step-by-step instructions on building and running a basic image classification C++ application.
- `C++ Quick Start Example <openvino_docs_get_started_get_started_demos.html>`__ for step-by-step instructions on building and running a basic image classification C++ application.
 
   Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
 

docs/install_guides/installing-openvino-brew.md

@@ -30,7 +30,7 @@ See `Installing Additional Components <#optional-installing-additional-component
    
      * `Homebrew <https://brew.sh/>`_
      * `CMake 3.13 or higher <https://cmake.org/download/>`__ (choose "macOS 10.13 or later"). Add ``/Applications/CMake.app/Contents/bin`` to path (for default installation). 
-     * `Python 3.7 - 3.11 <https://www.python.org/downloads/mac-osx/>`__ (choose 3.7 - 3.10). Install and add it to path.
+     * `Python 3.7 - 3.11 <https://www.python.org/downloads/mac-osx/>`__ . Install and add it to path.
      * Apple Xcode Command Line Tools. In the terminal, run ``xcode-select --install`` from any directory to install it.
      * (Optional) Apple Xcode IDE (not required for OpenVINO™, but useful for development)