Add GELU layer for vision transformers

* add gelu and gelu approximation

* drop setKernelParams

dnn: add layer normalization for vision transformers

* add layer norm onnx parser, impl and tests

* add onnx graph simplifier for layer norm expanded

* handle the case when constants are of type Initializer

* add test case for layer norm expanded with initializers

* use CV_Assert & CV_CheckType in place of CV_Assert_N; use forward_fallback for OCL_FP16

* use const ref / ref in parameters of invoker::run; extract inner const if from nested loop; use size_t in place of ull

* template hasBias

* remove trailing whitespace

* use pointer parameter with null check; move normSize division & mean_square division outside of loop; use std::max to ensure positive value before std::sqrt

* refactor implementation, optimize parallel_for

* disable layer norm expanded

* remove the removal of layer norm optional outputs

Add acos, acosh, asin, asinh, atan, atanh, cos, cosh, erf, hardswish, sin, sinh, softplus, softsign, tan layers

dnn : int8 quantized layers support in onnx importer

* added quantized layers support in onnx importer

* added more cases in eltwise node, some more checks

* added tests for quantized nodes

* relax thresholds for failed tests, address review comments

* refactoring based on review comments

* added support for unsupported cases and pre-quantized resnet50 test

* relax thresholds due to int8 resize layer

Add Normalize subgraph, fix Slice, Mul and Expand

* Add Normalize subgraph, support for starts<0 and axis<0 in Slice, Mul broadcasting in the middle and fix Expand's unsqueeze

* remove todos

* remove range-based for loop

* address review comments

* change >> to > > in template

* fix indexation

* fix expand that does nothing

ONNX diagnostic tool

* Final

* Add forgotten Normalize layer to the set of supported types

* ONNX diagnostic tool corrections

* Fixed CI test warnings

* Added code minor corrections
Co-authored-by: NSergey Slashchinin <sergei.slashchinin@xperience.ai>

Fix loading issue for Faster RCNN model from #16783

* Add a reproducer with multi-output Gather

* Fix an issue with ONNX graph simplifier

* fix build

* Move checks to correct class

* Minor changes for better code appearence

* Added ReduceSum to ONNX importer

* Fix comments

* Fix Mul

* Added ONNX BatchNorm subgraph

* Move removing constant inputs to addConstantNodesForInitializers

* Added initializers to ONNXGraphWrapper

* Supported FullyConnected layer with two inputs

* Skipped test

* Fix conditions

* Added OpenCL support

* Supported ReduceMean3D

* Supported Expand layer

* Fix warning

* Added Normalize subgraph

* refactoring

* Used addLayer

* Fix check

* Used addLayer

* Skip failed test

* Added normalize1 subgraph

* Fix comments