* Added transformation config to support automl efficientdet-4 model
* Added configuration file to convert Automl EfficientDet model
* Updated unit test for Pack
* Added instruction on how to convert EfficientDet Tensorflow model
* Updated documentation on how to convert EfficientDet model
* Updated a documentation with instruction on how to convert Automl EfficientDet.
* Generate TensorIterator without back edges from TensorFlow models
* Added a check in the MarkSubgraphsWithCorrectLayout to not fail when port is not connected
* Updated the 'protobuf2nx' to consume the graph protobuf message
* Cleanup TI from the IRv7 specific code
* Do not run some front transformations recursively
* Draft support for the ONNX Loop operation when 'cond' = True
* LoopToTI transformation changes
* Added draft of Loop operation and parser for ONNX Loop operation body
* Updated Loop body parser + added shape and type infer for the Loop operation
* Fixes for ONNX Loop operation parser
* Moved Loop parsing to Loop op extractor. Added generation of external edges for the Loop body ops
* Added support for ThresholdedRelu using decomposition
* Added support for Min ONNX operation
* Draft fixes for port_map generation for the Loop
* Rename transformation file and fix BOM
* Fixed shape inference for Loop scan outputs (axis is not None)
* Fixed shape inference for ONNX Loop operation
* Refactor checks in the TensorIteratorMerge transformation
* Code refactoring. Enabled commented transformations
* Documentation update for ONNX Loop, ThresholdedRelu and Min
* Fixed typo in the Loop front transformation where execution condition input is connected. Other refactorings
* Fixed in the Loop extractor
* Added printing 'internal_layer_id' attribute in the graph dumper
* Updated calculation of iterations number for the Loop
* Added missing code
* Fixed output port shapes and types generation for Loop operation
* Update function names and variable names in the Loop operation
* Fixed type inference for iteration count input
* Added removal of input/output ports of the Loop if they are not used
* Fixed renumbering Loop operations input/output ports to keep mandatory
* Fixed ThresholdedReluDecomposition transformation
* Updated MO IR Reader to know about Loop operation. But it is still not supported by the MO IR Reader
* Added unit test for Slice op shape infer (reverse the sequence of elements)
* Reverted changes in the ONNX loader function call to protobuf2nx
* Enable Reshape0DToSqueeze transformation recursively
* Refactored Loop operation support implementation
* Changed ThresholdedReluDecomposition to generate Const with shape [1] instead of scalar
* Code style and wording fixes
* Restored accidentally removed 'return' statement in the TI shape infer function
* Fixed comments
* Fixed comment
Co-authored-by: Evgeny Lazarev <elazarev.nnov@gmail.com>
* [MO] [Kaldi] Added TDNN Component
* TdnnComponent replacer graphical comment updated
* Added SpecAugmentTimeMaskComponent
* some refactor of memoryoffset shape_infer
* moved memoryoffset splitting to the middle stage
* some corrections
- set `need_shape_inferenc`=False in split_memoryoffset
- use cycle instead of pattern in tdnn_replacer
* separated splitting of MemoryOffsets in LSTM and TDNN blocks
* set transpose_weights=True in TdnnComponent
* Corrected Supported_Frameworks_Layers
* corrected comments
* separate naming for tdnn and lstm memoryoffset splits
* corrected BOM file
* corrected generaldropout_ext.py and removed 'has_default' for tdnn_component
* corrections after PR review
* renamed LSTM -> recurrent; added setting element_size for paired nodes of tdnn_memoffset and othe minor changes
* Update split_tdnn_memoryoffset.py
* corrected partial infer with new API in elemental.py and split_tdnn_memoryoffset.py
* Added Caffe Slice_ext
* Added TFSlice, AttributedSlice (both with extractors and replacers), corrected SliceConverter and added unittests for all cases
* added comments to each type of Slice operation; optimized shape inference; moved mxlice inside of slice.py; renamed slice_replacers
* removed type annotation for get_shape_after_slice routine
* replaced zeros_like with zeros
* Corrected preserving node names, renamed attributes names, added tests fro slice_replacer onnx phase
* Renamed slice_replacers.py
* added more unittest cases
* added type annotations, moved to more relevant place routines for shape calculation, and some other minor corrections
* corrected a typo `normalize_slice_indices` comment
* corrected shape calculation for Nonconstant inputs
* corrected a few typos
* corrected type declarations
* corrected shape inference with rounding
* refactored unit-tests for front transforms of Slice
* added error raising for negative and zero shapes
* removed magic_num
* corrected AttributedSlice, clarified comments
* fixed unit-test for AttributedSliceToSlice
* typo in type hints corrected
* removed supported_attrs
* returned back default None for attrs of Slice
* Remove unnnecessary ir_version checks in the MO
* Cleaned up 'backend_attrs_v2' function
* Small clean up from the 'TFCustomSubgraphCall'
* Clean up the MO extractor attributes mapping
* Renamed PreluOp to PReLU
* Removed back phase transformations related to IRv7
* Fixed setting value for the input port using the 'set_value' method
* Removed front and middle phase transformations related to IRv7
* Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations
* Final cleanup of the deprecated IR v7 related code
* Removed 'blobs_as_input' usage in the Model Optimizer.
* Removed function '_fuse_add' from the Model Optimizer since it is not used anymore.
* Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO
* Disabled failing gpu_engine.user_context test
