* Handle 0 and -1 in shape inputs for dynamic reshape op

* Flag to control semantics of zero values in dynreshape op

* Support for no-arg op construction

* Add a-ops.

* Two const init variations to try to solve windows link error

* Try export for windows

* Create outputs as needed, test

* Remove attribute helpers

* another windows try

* windows

* windows

* Partial node cleanup

* Review comments
Node destruction that doesn't blow out the stack on deep graphs

* Review comments

* Update src/ngraph/node.hpp
Co-Authored-By: Adam Procter <adam.m.procter@intel.com>

* Have huge test check that nodes were deleted

* Switch constructors to use Output instead of Node

* Review comments

* Add some comments

Avoid negative values in int64 initialization for cases where int64 parameters are used as indices (#3004)

* handle case where a node's output is connected multiple inputs of another node

* fix creation of the FunctionCall to have the correct outputs

* fix per review comment

* Cleanup how compile flags set and used by nGraph and external projects.
Set C++11 through CMake and pass it down to external projects.
Prefer CMake variables such as CMAKE_POSITION_INDEPENDENT_CODE and
CMAKE_CXX_STANDARD instead of explicitly setting compiler dependent
flags.
Create json compilation database for external projects.
CMAKE_CXX_FLAGS is used as common global options for nGraph and external
projects.
add_compile_options() is used for local options for current and sub
directories.
add_definitions() is used for setting definitions for current and sub
directories.
Note: Global options are not passed down to some external projects.
Note: mkl-dnn resets CMAKE_CXX_FLAGS internally.
Note: TBB and MLSL are not CMake based.
Noet: Eigen and json is header only library.

* Fix error.

* Fix error. (second attempt)

* Cleanup code.

* Allow check for undefined macro.

* Try to fix cldnn issue.

* Set type for CMake arguments.

* Pass C++ standard to protobuf.

* Pass C++ standard down to TBB.

* Change how Clang specific flags are handled.

* Fix error.

* Workaround for compile error on Baidu's PDPD docker.

* Fix windows build error.

* Initial implementation of implicit broadcasting for eltwise ops. Only Add supported

* Addressed PR feedback

* cleanup

* Rename Bcast to Broadcast

* Autobroadcasting support for rest of elementwise ops

* Serializer support for autobroadcast

* Added missing autob serialization for Minimum

* Added execution unit tests and more op types to implicit broadcast elimination

* Addressed PR feedback

* Fixes windows build issue

* RVO optimization per PR feedback

* serialize constant faster

* more speedup

* Draft of FakeQuantize operation along with UTs.

* Add FakeQuantize to implemented operators on IGPU.

* Get back FakeQuantize op case to switch.

* Fix compilation errors.

* Skip test for INTERPRETER backend and disable type_prop tests.

* Initial implementation covering the most basic case

* Cleanup of fake_quantize_with_clip UT

* Reformat the cpu unit tests manifest and unlock anothe fake quant UT

* Handle the clipping case by subtracting input_low from quantization input

* Clip the input data before quantization to avoid Selects

* UT manifest fix

* Obsolete comment removed

* Code formatting

* Broadcast input data for non-scalar in/out params

* Code formatting

* Enable the type prop tests for FakeQuantize

* Dequant the data without using the Dequantize op (fixes an edge case)

* Move reshape from utils to builder.

* Add aliases to functions in old place and describe changes.

* ShuffleChannels implementation

* Validation of ShuffleChannels params

* Implementation of ShuffleChannels decompose_op()

* Formatting adjustments

* Corrected implementation and validation of op params

* Basic test of ShuffleChannels

* Negative axis value test

* Default params for the ShuffleChannels op

* ShuffleChannels test with floats

* ShuffleChannels validation unit tests

* PR comments

* Compilation error fix

* PR feedback and cleanup

* Code formatting adjustment

* Negative axis value documentation

* Docs update (PR feedback)

* PR feedback: shape and axis validation

* Modify axis semantics on shuffle op

* Revert "PR feedback: shape and axis validation"

This reverts commit 21b708e710b91da2a7e37a69c0da1f31c7743b47.

Switch to clDNN version with conformance fix for 3 ONNX models (DenseNet-121, Inception-v2, ResNet-50) (#2982)

* Cleanup section

* Add updated illustrations for pattern_matcher and tensor_descriptor

* Add subsection link to be consistent

* update a few files to build on windows

* more fixes

* Switch some get_inputs uses to use the newer inputs

* Review comments

* Added CTCGreedyDecoder layer op

* Added comment on seq_len validation checks

* API defined

* add unit test for save/load with INTERPRETER

* Update per review comments

* fix compiler error

* Added accessor methods for layer op attributes

* style fixes and addressed PR feedback

* [ONNX] Unit test models for QLinearMatMul

* [ONNX] Extended types support for NgraphTestCase

* [ONNX] Move the value comparators to the NgraphTestCase class

* Add test cases

* Add shape checking

* disable GPU tests

* make private members protected in hybrid classes

* allow overriding the passes

* [Fused] LeakyRelu op

* Add LeakyRelu to serializer

* Add unit tests

* Fix merge branch 'master' into mkarzyns/fused_leaky_relu

* Change broadcasting rules to NumPy style

* Remove std:: and ngraph:: prefixes

* Rename CPU Runtime LeakyRelu to CPULeakyRelu

* Style apply

* Fix cpu_fusion.fuse_leaky_relu test

* Use eigen's tanh in the fused sigmoid multiply kernel (#2946)

* Merge branch 'master' into mkarzyns/fused_leaky_relu

* Add LeakyRelu to Intel GPU backend op list

* Add LeakyRelu to Intel GPU backend op list

* create tensor for the primary backend

* move private objects to protected

* Move zero padded conv fusions from CPUFusion to CoreFusion.

* Address PR feedback: move unit tests to core_fusion.

* Remove functions from cpu which were moved to core

* Fix a typo

* Remove unused function

* Update version, clean up ToC, add more detail to section on inspecting graphs...

* Minor adjustments to version module
* Move distributed training page to extras since there's not much there
* Fix links that break when doing that
* Consistent casing on section titles
* Orphan governance page so we don't have blank/empty links
* Update release notes with new version module structure

* PR feedback

* update visualize tree file extenstions and output formats

* fix runtime error

* constexpr ctor for EnumMask

* added pass properties to core passes.

* change fusion type to have better type safety.

* refactor to use enum mask.

* remove extra code.

* added constants for FusionType backward compatibility.

* spelling.

* grammar fix.

* Virtualize some things that crash when layout descriptor is missing

* More shape specialization

* (very bare) skeleton for dyn elimination

* Miscellaneous

* Lift i32->int64-only restriction on constant folding for Convert

* Add constant folding for ShapeOf, and some tests for new constant folders

* Tests for DynElimination

* Rename specialize_shapes to specialize_function, and add a unit test for value substitution

* Roll back overeager API change in dyn slice bprop (it has to handle right-indexed axes; bummer)

* Add a test for dynamic usage of transpose op

* Fix warning/error about variable shadowing

* Strengthen checks in apply_permutation

* Propagate Constant shapes through Transpose

* Add CHANGE_DYNAMIC_STATE where appropriate

* PR feedback, and fix unit test failure

* Fix PR reference in comment

* PR comments

* Comments for helper funcs

* Remove unique_ptr indirection for the AlignedBuffers

* Fix incorrect indexing of AlignedBuffer vector (whoops\!)

* Remove unnecessary CHANGE_DYAMIC_STATEs

* De-update pass property unit test for const folding

* Replace mystery runes with all_pass_property_off

* Split op skeleton

* Two ways to construct a fused Split to be able to use it in onnx importer

* refactor: move the util::split() helper functions to the core

* Split's decompose_op() implementation using a helper function

* Use fused Split in the onnx_importer

* Code formatting

* PR feedback

* Split helpers moved to ngraph/builder

* Basic UT - split a 1D tensor to 3 equal parts

* UT: Split 2D tensor into variable length parts

* Code formatting

* Catch the proper type of exception in the onnx_importer split()

* Initialize members in the correct order

* Type prop tests for Split

* Code formatting

* PR feedback