[DOCS] Table directive update + Get Started fix for master (#18203)

docs/optimization_guide/dldt_deployment_optimization_tput.md

@@ -29,16 +29,19 @@ In general, most throughput-oriented inference applications should:
 * Decompose the data flow into a collection of concurrent inference requests that are aggressively scheduled to be executed in parallel:
 
   * Setup the configuration for the *device* (for example, as parameters of the ``ov::Core::compile_model``) via either previously introduced :doc:`low-level explicit options <openvino_docs_deployment_optimization_guide_tput_advanced>` or :doc:`OpenVINO performance hints <openvino_docs_OV_UG_Performance_Hints>` (**preferable**):
-
-
-    .. tab:: C++
-
+    
+    .. tab-set::
+    
+       .. tab-item:: C++
+          :sync: cpp
+    
           .. doxygensnippet:: docs/snippets/ov_auto_batching.cpp
              :language: cpp
              :fragment: [compile_model]
-
-    .. tab:: Python
-
+    
+       .. tab-item:: Python
+          :sync: py
+    
           .. doxygensnippet:: docs/snippets/ov_auto_batching.py
              :language: python
              :fragment: [compile_model]

docs/optimization_guide/nncf/filter_pruning.md

@@ -47,7 +47,7 @@ In this step, NNCF-related imports are added in the beginning of the training sc
          :fragment: [imports]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -69,7 +69,7 @@ of optimization methods (`"compression"` section).
          :fragment: [nncf_congig]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -104,7 +104,7 @@ optimization.
          :fragment: [wrap_model]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -127,7 +127,7 @@ of the original model.
          :fragment: [tune_model]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -150,7 +150,7 @@ fine-tuning that will inform optimization methods to do some adjustments to func
          :fragment: [distributed]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -172,7 +172,7 @@ the case of PyTorch and frozen graph - for TensorFlow 2.
          :fragment: [export]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -198,7 +198,7 @@ To save model checkpoint use the following API:
          :fragment: [save_checkpoint]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python
@@ -220,7 +220,7 @@ To restore the model from checkpoint you should use the following API:
          :fragment: [load_checkpoint]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/pruning_tf.py
          :language: python

docs/optimization_guide/nncf/ptq/basic_quantization_flow.md

@@ -42,7 +42,7 @@ The transformation function is a function that takes a sample from the dataset a
          :language: python
          :fragment: [dataset]
 
-   .. tab-item::  PyTorch
+   .. tab-item:: PyTorch
       :sync: pytorch
       
       .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_torch.py
@@ -57,7 +57,7 @@ The transformation function is a function that takes a sample from the dataset a
          :fragment: [dataset]
 
    .. tab-item:: TensorFlow
-      :sync: tensorflow
+      :sync: tf
 
       .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_tensorflow.py
          :language: python
@@ -80,7 +80,7 @@ Once the dataset is ready and the model object is instantiated, you can apply 8-
          :language: python
          :fragment: [quantization]
 
-   .. tab-item::  PyTorch
+   .. tab-item:: PyTorch
       :sync: pytorch
       
       .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_torch.py
@@ -95,7 +95,7 @@ Once the dataset is ready and the model object is instantiated, you can apply 8-
          :fragment: [quantization]
 
    .. tab-item:: TensorFlow
-      :sync: tensorflow
+      :sync: tf
 
       .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_tensorflow.py
          :language: python
@@ -113,7 +113,7 @@ After that the model can be converted into the OpenVINO Intermediate Representat
          :language: python
          :fragment:  [inference]
 
-   .. tab-item::  PyTorch
+   .. tab-item:: PyTorch
       :sync: pytorch
       
       .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_torch.py
@@ -128,7 +128,7 @@ After that the model can be converted into the OpenVINO Intermediate Representat
          :fragment:  [inference]
 
    .. tab-item:: TensorFlow
-      :sync: tensorflow
+      :sync: tf
 
       .. doxygensnippet:: docs/optimization_guide/nncf/ptq/code/ptq_tensorflow.py
          :language: python

docs/optimization_guide/nncf/qat.md

@@ -36,7 +36,7 @@ In this step, you add NNCF-related imports in the beginning of the training scri
          :fragment: [imports]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -59,7 +59,7 @@ specific to the DL framework. It will be used at the model creation step to init
          :fragment: [nncf_congig]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -86,7 +86,7 @@ will be shown below.
          :fragment: [wrap_model]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -110,7 +110,7 @@ you can skip this step which means that the post-training optimization will be a
          :fragment: [tune_model]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -134,7 +134,7 @@ the fine-tuning that will inform optimization methods to do some adjustments to
          :fragment: [distributed]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2       
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -156,7 +156,7 @@ the case of PyTorch and frozen graph - for TensorFlow 2.
          :fragment: [export]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -186,7 +186,7 @@ To save model checkpoint use the following API:
          :fragment: [save_checkpoint]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python
@@ -208,7 +208,7 @@ To restore the model from checkpoint you should use the following API:
          :fragment: [load_checkpoint]
          
    .. tab-item:: TensorFlow 2
-      :sync: tensorflow        
+      :sync: tf2        
 
       .. doxygensnippet:: docs/optimization_guide/nncf/code/qat_tf.py
          :language: python