[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/Convert_Model_From_Kaldi.md

@@ -64,13 +64,13 @@ Examples of CLI Commands
 
 * If you want to remove the last SoftMax layer in the topology, launch the model conversion with the ``remove_output_softmax`` flag:
 
-.. code-block:: cpp
+  .. code-block:: cpp
 
      mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --remove_output_softmax
 
-Model conversion API finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
+  Model conversion API finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
 
-.. note:: Model conversion can remove SoftMax layer only if the topology has one output.
+  .. note:: Model conversion can remove SoftMax layer only if the topology has one output.
 
 * You can use the *OpenVINO Speech Recognition* sample application for the sample inference of Kaldi models. This sample supports models with only one output. If your model has several outputs, specify the desired one with the ``output`` option.
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -164,21 +164,21 @@ Command-Line Interface (CLI) Examples Using TensorFlow-Specific Parameters
 
 * Launching model conversion for Inception V1 frozen model when model file is a plain text protobuf:
 
-.. code-block:: sh
+  .. code-block:: sh
 
      mo --input_model inception_v1.pbtxt --input_model_is_text -b 1
 
 
 * Launching model conversion for Inception V1 frozen model and dump information about the graph to TensorBoard log dir ``/tmp/log_dir``
 
-.. code-block:: sh
+  .. code-block:: sh
 
      mo --input_model inception_v1.pb -b 1 --tensorboard_logdir /tmp/log_dir
 
 
 * Launching model conversion for BERT model in the SavedModel format, with three inputs. Specify explicitly the input shapes where the batch size and the sequence length equal 2 and 30 respectively.
 
-.. code-block:: sh
+  .. code-block:: sh
 
      mo --saved_model_dir BERT --input mask,word_ids,type_ids --input_shape [2,30],[2,30],[2,30]
 
@@ -189,7 +189,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.keras.Model``
 
-.. code-block:: python
+  .. code-block:: python
 
      model = tf.keras.applications.ResNet50(weights="imagenet")
      ov_model = convert_model(model)
@@ -197,7 +197,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.keras.layers.Layer``. Requires setting the "input_shape".
 
-.. code-block:: python
+  .. code-block:: python
 
      import tensorflow_hub as hub
 
@@ -206,7 +206,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.Module``. Requires setting the "input_shape".
 
-.. code-block:: python
+  .. code-block:: python
 
      class MyModule(tf.Module):
         def __init__(self, name=None):
@@ -221,7 +221,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.compat.v1.Graph``
 
-.. code-block:: python
+  .. code-block:: python
 
      with tf.compat.v1.Session() as sess:
         inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
@@ -234,7 +234,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.compat.v1.GraphDef``
 
-.. code-block:: python
+  .. code-block:: python
 
      with tf.compat.v1.Session() as sess:
         inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
@@ -247,7 +247,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.function``
 
-.. code-block:: python
+  .. code-block:: python
 
      @tf.function(
         input_signature=[tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32),
@@ -259,7 +259,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.compat.v1.session``
 
-.. code-block:: python
+  .. code-block:: python
 
      with tf.compat.v1.Session() as sess:
         inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
@@ -271,7 +271,7 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 
 * ``tf.train.checkpoint``
 
-.. code-block:: python
+  .. code-block:: python
 
      model = tf.keras.Model(...)
      checkpoint = tf.train.Checkpoint(model)

docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_GluonCV_Models.md

@@ -21,13 +21,13 @@ This article provides the instructions and examples on how to convert `GluonCV S
 
 2. Run model conversion API, specifying the ``enable_ssd_gluoncv`` option. Make sure the ``input_shape`` parameter is set to the input shape layout of your model (NHWC or NCHW). The examples below illustrate running model conversion for the SSD and YOLO-v3 models trained with the NHWC layout and located in the ``<model_directory>``:
 
-* **For GluonCV SSD topologies:**
+   * **For GluonCV SSD topologies:**
 
      .. code-block:: sh
 
         mo --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data --output_dir <OUTPUT_MODEL_DIR>
 
-* **For YOLO-v3 topology:**
+   * **For YOLO-v3 topology:**
 
      * To convert the model:
 
@@ -39,7 +39,7 @@ This article provides the instructions and examples on how to convert `GluonCV S
 
        .. code-block:: sh
 
-       mo --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "front/mxnet/yolo_v3_mobilenet1_voc.  json" --output_dir <OUTPUT_MODEL_DIR>
+          mo --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params  --input_shape [1,255,255,3] --transformations_config "front/mxnet/   yolo_v3_mobilenet1_voc.  json" --output_dir <OUTPUT_MODEL_DIR>
 
 
 @endsphinxdirective

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_DeepSpeech_From_Tensorflow.md

@@ -19,7 +19,7 @@ To download the model, follow the instruction below:
 
 * For UNIX-like systems, run the following command:
 
-.. code-block:: sh
+  .. code-block:: sh
 
      wget -O - https://github.com/mozilla/DeepSpeech/archive/v0.8.2.tar.gz | tar xvfz -
      wget -O - https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/deepspeech-0.8.2-checkpoint.tar.gz | tar xvfz -

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/ShapeInference.md

@@ -56,9 +56,9 @@ When using the ``reshape`` method, you may take one of the approaches:
 
 
 1. You can pass a new shape to the method in order to change the input shape of
-the model with a single input. See the example of adjusting spatial dimensions to the input image:
+   the model with a single input. See the example of adjusting spatial dimensions to the input image:
 
-.. tab-set::
+   .. tab-set::
 
       .. tab-item:: C++
          :sync: cpp
@@ -75,13 +75,13 @@ the model with a single input. See the example of adjusting spatial dimensions t
             :fragment: simple_spatials_change
 
 
-To do the opposite - to resize input image to match the input shapes of the model, 
+   To do the opposite - to resize input image to match the input shapes of the model,
-use the :doc:`pre-processing API <openvino_docs_OV_UG_Preprocessing_Overview>`.
+   use the :doc:`pre-processing API <openvino_docs_OV_UG_Preprocessing_Overview>`.
 
 
 2. You can express a reshape plan, specifying the input by the port, the index, and the tensor name:
 
-.. tab-set::
+   .. tab-set::
 
       .. tab-item:: Port
 

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/Device_Plugins.md

@@ -29,9 +29,9 @@ Feature Support Matrix
 
 The table below demonstrates support of key features by OpenVINO device plugins.
 
- ========================================================================================= ============================ =============== ===============
+========================================================================================= ============================ =============== ===============
  Capability                                                                                CPU                         GPU             GNA            
- ========================================================================================= ============================ =============== ===============
+========================================================================================= ============================ =============== ===============
  :doc:`Heterogeneous execution <openvino_docs_OV_UG_Hetero_execution>`                     Yes                         Yes             No            
  :doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>`           Yes                         Yes             Partial       
  :doc:`Automatic batching <openvino_docs_OV_UG_Automatic_Batching>`                        No                          Yes             No            
@@ -42,7 +42,7 @@ The table below demonstrates support of key features by OpenVINO device plugins.
  :doc:`Preprocessing acceleration <openvino_docs_OV_UG_Preprocessing_Overview>`            Yes                         Yes             No            
  :doc:`Stateful models <openvino_docs_OV_UG_model_state_intro>`                            Yes                         No              Yes            
  :doc:`Extensibility <openvino_docs_Extensibility_UG_Intro>`                               Yes                         Yes             No            
- ========================================================================================= ============================ =============== ===============
+========================================================================================= ============================ =============== ===============
 
 For more details on plugin-specific feature limitations, see the corresponding plugin pages.
 

docs/OV_Runtime_UG/supported_plugins/GNA.md

@@ -78,14 +78,7 @@ 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-set::
 
    .. tab-item:: C++
       :sync: cpp
@@ -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

@@ -428,12 +428,12 @@ on waiting for the completion of inference. The pseudo-code may look as follows:
 Limitations
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
- - Some primitives in the GPU plugin may block the host thread on waiting for the previous primitives before adding its kernels
+- Some primitives in the GPU plugin may block the host thread on waiting for the previous primitives before adding its kernels
   to the command queue. In such cases, the ``ov::InferRequest::start_async()`` call takes much more time to return control to the calling thread
   as internally it waits for a partial or full network completion.
   Examples of operations: Loop, TensorIterator, DetectionOutput, NonMaxSuppression
- - Synchronization of pre/post processing jobs and inference pipeline inside a shared queue is user's responsibility.
+- Synchronization of pre/post processing jobs and inference pipeline inside a shared queue is user's responsibility.
- - Throughput mode is not available when queue sharing is used, i.e., only a single stream can be used for each compiled model.
+- Throughput mode is not available when queue sharing is used, i.e., only a single stream can be used for each compiled model.
 
 Low-Level Methods for RemoteContext and RemoteTensor Creation
 #####################################################################
@@ -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

@@ -68,17 +68,17 @@ 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 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 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>`__
+  * `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:`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>`
+  * :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`
-* :doc:`Hello Reshape SSD C++ Sample <openvino_inference_engine_samples_hello_reshape_ssd_README>`
+  * :doc:`Hello Reshape SSD C++ Sample <openvino_inference_engine_samples_hello_reshape_ssd_README>`
 
 @endsphinxdirective
 

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)