* Add a function to set the qnn output range wrt each elemwise operation.
* Add comments warning for nonsense clamped output in the tflite/tvm results comparison.

* Add test for the qnn_add operator

The tests use fake quant approach so until the tf session tensors remain in float32.
The test data has to be passed in uint8 because of how the tflite/tvm comparison works.
Abs tolerance up to 1 is allowed for the qnn results. For now input_stats are hardcoded
assuming the tests for the other qnn ops will pass the input data in the same range.

* Separate qnn uint8 test function from the fp32 elemwise tests

Isolate qnn uint8 elemwise tests
Remove blank lines

This implementation provides cast to limited number of dtypes
that tflite currently supports for placeholder op. Add INT64 in the
possible dtypes as it appears to be supported accrording to tlfite schema.

The tensorflow zeros_like operation provided in array_ops.py produces directly a tensor with zeros
without a graph, using only the shape and type of the input. This imposes the use of gen_array_ops.py
that produces both a tensor and a graph so a comparison between tflite and tvm can be done.

add test for GREATER

* Fix unittest

* Fix pylint error: Line 915 too long

* Fix the conflicting files

* frontend operator support: space_to_batch_nd

* add test case for frontend operator support: space_to_batch_nd

* add test case for frontend operator support: space_to_batch_nd

* frontend operator support: space_to_batch_nd

* Fix ValueError: don't know how to convert type <class 'numpy.ndarray'> to node

* transpose implementation for tflite.py

* add TRANSPOSE to convert_map

* Fix Unexpected keyword argument 'axis' in function call

* add test for transpose oprator

* Add the parameter 'axes' handling

* add test for transpose oprator

* solve conflict within CONTRIBUTORS.md

* Improve the if condition for empty tuple

* Add one unit test to cover empty tuple

* solve conflict within CONTRIBUTORS.md

* [RFC] Initial support for Tflite operator SPLIT

This patch adds initial support for the tflite operator split. However
I am not yet sure how to handle the axis parameter for the split
operator and support it in the test infrastructure. Putting this up for
an initial review and comment.

The split operator in tflite according to
https://www.tensorflow.org/lite/guide/ops_compatibility

appears to take num_or_size_split as a 0D tensor.

I also note that tflite.split is one of the few operators that returns
multiple outputs and thus the helper routines in the tests needed some
massaging to make this work.

@apivarov , could you please review this ?

Thanks,
Ramana

* Fix the axis parameter

Add more tests

* Address review comments

* Try out frozen_gene's suggestion

* Handle split of 1 element

* int32 is only supported in tflite 1.14, let's check that version here.

* Keep this at python3.5

* Add packaging as a python package to be installed

* Convert TFLite NCHW to NHWC

* Minor comment fix

* Add FULLY_CONNECTED op into tflite frontend, support Inception V4

* Fix comment style in TF Lite tests.

* [HEADER] ASF header dir=include

* [HEADER] ASF Header dir=src

* [HEADER] ASF Header -dir=python

* [HEADER] ASF header dir=topi

* [HEADER] ASF Header dir=nnvm

* [HEADER] ASF Header -dir=tutorials

* [HEADER] ASF Header dir=tests

* [HEADER] ASF Header -dir=docker

* fix whitespace

* [HEADER] ASF Header -dir=jvm

* [HEADER] ASF Header -dir=web

* [HEADER] ASF Header --dir=apps

* [HEADER] ASF Header --dir=vta

* [HEADER] ASF Header -dir=go

* temp

* [HEADER] ASF Header --dir=rust

* [HEADER] Add ASF Header --dir=cmake

* [HEADER] ASF Header --dir=docs

* [HEADER] Header for Jenkinsfile

* [HEADER] ASF Header to toml and md

* [HEADER] ASF Header to gradle

* Finalize rat cleanup

* Fix permission

* Fix java test

* temporary remove nnvm onnx test