math formula fixes 2021.2 (#3514)

* math formula fix

* ops math formula fix

Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com>

docs/MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md

@@ -1582,9 +1582,9 @@ OI, which means that Input changes the fastest, then Output.
 
 **Mathematical Formulation**
 
-    \f[
-        output[:, ... ,:, i, ... , j,:, ... ,:] = input2[:, ... ,:, input1[i, ... ,j],:, ... ,:]
-    \f]
+\f[
+    output[:, ... ,:, i, ... , j,:, ... ,:] = input2[:, ... ,:, input1[i, ... ,j],:, ... ,:]
+\f]
 
 
 **Inputs**
@@ -5086,7 +5086,9 @@ t \in \left ( 0, \quad tiles \right )
 
 Output tensor is populated by values computes in the following way:
 
-    output[i1, ..., i(axis-1), j, i(axis+1) ..., iN] = top_k(input[i1, ...., i(axis-1), :, i(axis+1), ..., iN]), k, sort, mode)
+\f[
+output[i1, ..., i(axis-1), j, i(axis+1) ..., iN] = top_k(input[i1, ...., i(axis-1), :, i(axis+1), ..., iN]), k, sort, mode)
+\f]
 
 So for each slice `input[i1, ...., i(axis-1), :, i(axis+1), ..., iN]` which represents 1D array, top_k value is computed individually. Sorting and minimum/maximum are controlled by `sort` and `mode` attributes.
 

docs/ops/activation/HSwish_4.md

@@ -9,9 +9,9 @@
 **Detailed description**: For each element from the input tensor calculates corresponding
 element in the output tensor with the following formula: 
 
-    \f[
-    HSwish(x) = x \frac{min(max(x + 3, 0), 6)}{6}
-    \f]
+\f[
+HSwish(x) = x \frac{min(max(x + 3, 0), 6)}{6}
+\f]
 
 The HSwish operation is introduced in the following [article](https://arxiv.org/pdf/1905.02244.pdf).
 

docs/ops/activation/Mish_4.md

@@ -26,9 +26,9 @@
 
    For each element from the input tensor calculates corresponding
     element in the output tensor with the following formula:
-    \f[
-    Mish(x) = x*tanh(ln(1.0+e^{x}))
-    \f]
+\f[
+Mish(x) = x*tanh(ln(1.0+e^{x}))
+\f]
 
 **Examples**
 

docs/ops/activation/Sigmoid_1.md

@@ -14,9 +14,9 @@
 
    For each element from the input tensor calculates corresponding
     element in the output tensor with the following formula:
-    \f[
-    sigmoid( x ) = \frac{1}{1+e^{-x}}
-    \f]
+\f[
+sigmoid( x ) = \frac{1}{1+e^{-x}}
+\f]
 
 **Inputs**:
 

docs/ops/activation/SoftPlus_4.md

@@ -9,9 +9,9 @@
 **Detailed description**: For each element from the input tensor calculates corresponding
 element in the output tensor with the following formula:
 
-    \f[
-    SoftPlus(x) = ln(e^{x} + 1.0)
-    \f]
+\f[
+SoftPlus(x) = ln(e^{x} + 1.0)
+\f]
 
 **Attributes**: *SoftPlus* operation has no attributes.
 

docs/ops/activation/Swish_4.md

@@ -9,9 +9,9 @@
 **Detailed description**: For each element from the input tensor calculates corresponding
 element in the output tensor with the following formula: 
 
-    \f[
-    Swish(x) = x / (1.0 + e^{-(beta * x)})
-    \f]
+\f[
+Swish(x) = x / (1.0 + e^{-(beta * x)})
+\f]
 
 The Swish operation is introduced in the [article](https://arxiv.org/pdf/1710.05941.pdf).
 

docs/ops/pooling/AvgPool_1.md

@@ -78,9 +78,9 @@
 
 **Mathematical Formulation**
 
-    \f[
-    output_{j} = \frac{\sum_{i = 0}^{n}x_{i}}{n}
-    \f]
+\f[
+output_{j} = \frac{\sum_{i = 0}^{n}x_{i}}{n}
+\f]
 
 **Example**
 

docs/ops/pooling/MaxPool_1.md

@@ -70,9 +70,9 @@
 
 **Mathematical Formulation**
 
-    \f[
-    output_{j} = MAX\{ x_{0}, ... x_{i}\}
-    \f]
+\f[
+output_{j} = MAX\{ x_{0}, ... x_{i}\}
+\f]
 
 **Example**