* bug fix for padded load with large inputs

* Update TensorLoad.scala

* Update test_vta_insn.py

* add gcnArch query

* kGcnArch query for cuda is a no-op

* [Relay][Frontend][TF] Use _infer_value_simulated when axes is not a const to Transpose

* uncomment tests

* dummy change to retrigger ci

* [Contrib] Add MKL DNN

* update

* update

A test for qnn_mul has to be added when the qnn elemwise tests (#4282) get merged.

* [Relay][Pass] Add pass to remove unused functions in relay module

* Add tests

* Fix lint

* Fix visit order

* Add pass argument

* Fix

* Fix build

* dummy change to retrigger CI

* dummy change to retrigger ci

* dummy change to retrigger ci

* add volatile override back

* [codegen] remove fp16 function override for cuda

* [DOCKER] Add ONNX runtime dep

* Improve ci script

* Support Proposal operator on CPU.

* PyLint space issue

* PyLint space issue

* Pylint singleton-comparison issue

* WIP Run the TF tutorial on TF2

* Remove debugger statement.

* Complete the support for TF2.0's `resize`.

TF2.0 adds a `half_pixel_centers` attribute to the `resize` function in
the image API. This commit completes the hooks in Relay's TF frontend.

At the point of this commit, no new test yet. Also, this commit
addresses solely the `resize` change. Other commits address other
changes in TF2.0.

* Support TF2.0 in the tutorial by using the compat API.

This looks cleaner than trying to detect the TF version.

* Use the TF compat API, so as to support TF2.0.

This is a direct change, relying on the compat API provided by the TF
team.

This code will last as long as the compat API exists, so a
"proper" support for TF1.x and 2.x will require more work in some
future.

* Partial support for EXPLICIT padding introduced in TF2.0.

Explicit padding is a special case in TF2.0 (see reference linked
below). Some models are serialized with that mode, and break TF support
in TVM.

Support is *partial* as EXPLICIT falls back to set padding on the
Relay op, which only supports 2 values. At some point, padding may need
to be extended to support 4 values, but that is out of scope of this
support commit.

Reference on EXPLICIT padding: https://github.com/tensorflow/tensorflow/commit/ec81825aaf7e848d9f8ddffdf1e0d20aebe9172c#diff-1d1c0bb0a880f85b6164f71dbb2f446e

* Guard on checking for optional TF2.0 attribute.

* Do not expect Relay to implement TF-specific attributes.

The `half_pixel_centers` attribute is a new feature in TF2.0. Earlier
commits of mine mistakenly introduce them in the Relay API. This is
probably not what Relay is expected to support, and the semantics of
`half_pixel_centers` is unclear (to me, at least) at this point.

* Remove unclear comment.

CR https://github.com/dmlc/tvm/pull/4104#discussion_r338705742

Addresses #4104

* Changes after review.

Complying without understanding the rationale for now.

* Fix the arguments set mistakenly.

An argument ignored for the wrong operation.

* 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

* Relay Keras frontent batch_norm op params not handeling well

* add unit test for Relay Frontend Keras batch_norm

* Fix incorrect call to Unicode Win32

* Removed inet_pton call. Win32 already has it.

* Add shape functions

* Fix get_const_tuple

* Fix cpplint

* Fix pylint

* Fix pylint

* rebase and fix

* Check Any for infer type

* Fix expand_dim shape func for zero rank input

* Fix pooling infer type

* Address comment

* Register layout transform attr

* [TF][Relay][Op] Pass module when infer shape

* Fix lint

* Improve style

* Add test

Previously runtime::Module was supported using shared_ptr.
This PR refactors the codebase to use the Object protocol.

It will open doors to allow easier interpolation between
Object containers and module in the future.

the test case was removed in #4181 for some reason
@tqchen @soiferj @zhiics

* Fix tf reshape

* Fix test

* Fix pylint

* Fix pylint

* Add pass manager tutorial

* fix some examples

* retrigger ci

* Update tutorials/dev/relay_pass_infra.py

Co-Authored-By: 雾雨魔理沙 <lolisa@marisa.moe>

* Add ToANormalForm link