Cherry pick of pull request 3422

docs/ops/activation/Clamp_1.md

@@ -30,7 +30,7 @@
 
 **Outputs**:
 
-*   **1**: Multidimensional output tensor with shape and type matching the input tensor. Required.
+*   **1**: Multidimensional output tensor with shape and type matching the input tensor.
 
 **Detailed description**:
 

docs/ops/activation/Elu_1.md

@@ -33,4 +33,4 @@ elu(x) = \left\{\begin{array}{ll}
 
 **Outputs**:
 
-*   **1**: Result of Elu function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
+*   **1**: Result of Elu function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.

docs/ops/activation/Exp_1.md

@@ -14,4 +14,4 @@
 
 **Outputs**:
 
-*   **1**: Result of Exp function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
+*   **1**: Result of Exp function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.

docs/ops/activation/GELU_2.md

@@ -28,6 +28,10 @@ Similarly, the following Gelu approximation (typical for the TensorFlow*) is rec
 
 *   **1**: Multidimensional input tensor. Required.
 
+**Outputs**:
+
+*   **1**: Floating point tensor with shape and type matching the input tensor.
+
 **Example**
 
 ```xml
@@ -46,4 +50,4 @@ Similarly, the following Gelu approximation (typical for the TensorFlow*) is rec
     </output>
 </layer>
 
-```
+```

docs/ops/activation/Mish_4.md

@@ -16,7 +16,7 @@
 
 **Outputs**:
 
-*   **1**: Floating point tensor with shape and type matching the input tensor. Required.
+*   **1**: Floating point tensor with shape and type matching the input tensor.
 
 **Types**
 
@@ -47,4 +47,4 @@
         </port>
     </output>
 </layer>
-```
+```

docs/ops/activation/Sigmoid_1.md

@@ -24,7 +24,7 @@
 
 **Outputs**:
 
-*   **1**: Result of Sigmoid function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
+*   **1**: Result of Sigmoid function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
 
 **Example**
 
@@ -44,4 +44,4 @@
     </output>
 </layer>
 
-```
+```

docs/ops/arithmetic/Sinh_1.md

@@ -16,7 +16,7 @@
 
 **Outputs**
 
-* **1**: The result of element-wise sinh operation. A tensor of type T.
+* **1**: The result of element-wise sinh operation. A tensor of type *T*.
 
 **Types**
 
@@ -47,4 +47,4 @@ a_{i} = sinh(a_{i})
         </port>
     </output>
 </layer>
-```
+```

docs/ops/arithmetic/Tanh_1.md

@@ -14,12 +14,12 @@
 
 **Outputs**:
 
-*   **1**: Result of Tanh function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
+*   **1**: Result of Tanh function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
 
 **Detailed description**
 
 For each element from the input tensor calculates corresponding
 element in the output tensor with the following formula:
 \f[
 tanh ( x ) = \frac{2}{1+e^{-2x}} - 1 = 2sigmoid(2x) - 1
-\f]
+\f]

docs/ops/convolution/BinaryConvolution_1.md

@@ -35,4 +35,5 @@ The operation has the same attributes as a regular *Convolution* layer and sever
 
 **Outputs**:
 
-*   **1**: output tensor containing float values. Required.
+*   **1**: output tensor containing float values.
+

docs/ops/detection/Proposal_4.md

@@ -153,7 +153,7 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
 
 *   **1**: tensor of type *T* and shape `[batch_size * post_nms_topn, 5]`.
 
-*   **2**: tensor of type *T* and shape `[batch_size * post_nms_topn]` with probabilities. *Optional*.
+*   **2**: tensor of type *T* and shape `[batch_size * post_nms_topn]` with probabilities.
 
 **Types**
 
@@ -191,4 +191,4 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
         </port>
     </output>
 </layer>
-```
+```

docs/ops/detection/ROIPooling_1.md

@@ -63,7 +63,7 @@ Batch indices must be in the range of `[0, N-1]`.
 
 **Outputs**:
 
-*   **1**: 4D output tensor of shape `[NUM_ROIS, C, pooled_h, pooled_w]` with feature maps of type *T*. Required.
+*   **1**: 4D output tensor of shape `[NUM_ROIS, C, pooled_h, pooled_w]` with feature maps of type *T*.
 
 **Types**
 
@@ -77,4 +77,4 @@ Batch indices must be in the range of `[0, N-1]`.
         <input> ... </input>
         <output> ... </output>
     </layer>
-```
+```

docs/ops/detection/ReorgYolo_1.md

@@ -26,7 +26,7 @@
 
 **Outputs**:
 
-*   **1**: 4D output tensor of the same type as input tensor and shape `[N, C*stride*stride, H/stride, W/stride]`. Required.
+*   **1**: 4D output tensor of the same type as input tensor and shape `[N, C*stride*stride, H/stride, W/stride]`.
 
 **Example**
 

docs/ops/shape/Squeeze_1.md

@@ -14,6 +14,10 @@
 
 *   **2**: 0D or 1D tensor of type *T_SHAPE* with dimensions indices to squeeze. Values could be negative. *Optional*.
 
+**Outputs**:
+
+*   **1**: Tensor with squeezed values of type *T*.
+
 **Types**
 
 * *T*: supported type.
@@ -65,4 +69,4 @@
         </port>
     </output>
 </layer>
-```
+```

docs/ops/shape/Unsqueeze_1.md

@@ -14,6 +14,10 @@
 
 *   **2**: OD or 1D tensor of type *T_SHAPE* with dimensions indices to be set to 1. Values could be negative. *Required*.
 
+**Outputs**:
+
+*   **1**: Tensor with unsqueezed values of type *T*.
+
 **Types**
 
 * *T*: supported type.
@@ -65,4 +69,4 @@
         </port>
     </output>
 </layer>
-```
+```

docs/ops/sort/NonMaxSuppression_5.md

@@ -72,7 +72,7 @@ class must not exceed `max_output_boxes_per_class`.
 
 *   **2**: `selected_scores` - tensor of type *T_THRESHOLDS* and shape `[number of selected boxes, 3]` containing information about scores for each selected box as triplets `[batch_index, class_index, box_score]`.
 
-*   **3**: `valid_outputs` - 1D tensor with 1 element of type *T_IND* representing the total number of selected boxes. Optional.
+*   **3**: `valid_outputs` - 1D tensor with 1 element of type *T_IND* representing the total number of selected boxes.
 
 Plugins which do not support dynamic output tensors produce `selected_indices` and `selected_scores` tensors of shape `[min(num_boxes, max_output_boxes_per_class) * num_batches * num_classes, 3]` which is an upper bound for the number of possible selected boxes. Output tensor elements following the really selected boxes are filled with value -1.
 

docs/ops/sort/TopK_3.md

@@ -51,7 +51,7 @@
 
 *   **1**: Output tensor of type *T* with top *k* values from the input tensor along specified dimension *axis*. The shape of the tensor is `[input1.shape[0], ..., input1.shape[axis-1], k, input1.shape[axis+1], ...]`.
 
-*   **2**: Output tensor with top *k* indices for each slice along *axis* dimension of type *T_IND*. The shape of the tensor is the same as for the 1st output, that is `[input1.shape[0], ..., input1.shape[axis-1], k, input1.shape[axis+1], ...]`
+*   **2**: Output tensor with top *k* indices for each slice along *axis* dimension of type *T_IND*. The shape of the tensor is the same as for the 1st output, that is `[input1.shape[0], ..., input1.shape[axis-1], k, input1.shape[axis+1], ...]`.
 
 **Types**