Under the unified IR. We will allow a single IRModule
to store different function variants, such as relay::Function,
ExternFunc, and low-level function.

This PR changes relay::Function -> BaseFunc in the module file
to support multiple function variants.

Identity is not the same thing as equality in Python so use ==/!= to compare str, bytes, and int literals. In Python >= 3.8, these instances will raise __SyntaxWarnings__ so it is best to fix them now. https://docs.python.org/3.8/whatsnew/3.8.html#porting-to-python-3-8

% __python__
```
>>> dtype = "float"
>>> dtype += "16"
>>> dtype == "float16"
True
>>> dtype is "float16"
False
>>> 0 == 0.0
True
>>> 0 is 0.0
False
```

#4682 Tried to fix a Python syntax error but did not go far enough because there are _three sets_ of embedded quotes.
This PR solves the syntax error by using Python's triple quoted strings on the outside and then double quotes in the middle and then single quotes on the inside.

* [TOPI][RELAY][OP] add op crop_and_resize

* fix pylint

* incorporate comments

* fix ci

This PR moves a few base types from relay and low-level Expr into the ir sub-folder.
These classes will serve as a common type system across the stack.

Rationale:

- PrimExpr for low-level expressions
- RelayExpr for advanced features, including Function definition.
- Introduce BaseFunc to host all functions, including future PrimFunc(low-level expr functions, subject to discussion).

This is a minimum change we can do to unify the classes into a common hierarchy.
The main data structure that are variant specific will still be kept in the sub-namespaces.
We only include classes that is needed to allow a common Module class.
- BaseFunc
- GlobalVar
- Type definition part of ADT

We will only need the BaseFunc and their checked_type to decide the calling convention
across the function variants.

* replace TensorObj and TensorValue with NDArray

* NodeBase to Object in Python

* rebase

* Add output_padding to generic

* Add output_padding to the reference impl

* Add output_padding to arm_cpu

* Add output_padding to the test

* Add output_padding for cuda

* Add output_padding for x86

* Make use of the new output_padding argument in Relay

* Adjust conv2d_transpose Relay test

* Fix lint errors

* Fix the VTA declaration of conv2d_transpose

* support for output padding in conv2d transpose

* some output padding will break IR pass

* Fix new conv2d_transpose test

* Update tophub

* Fix conv1d output_padding too.

* Fix the conv1d_transpose reference function.

* Fix the cuda impl

* fix the topi test for conv1d

* Update the versions in tophub.py

Co-authored-by: Thierry Moreau <tmoreau@octoml.ai>

* 'fskip' will not always return false

fskip returns false at the end of PackedFunc, discards return true in 'cast' case

* Update build_module.cc

[flake8](http://flake8.pycqa.org) testing of https://github.com/apache/incubator-tvm on Python 3.8.0

$ __flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics__
```
./apps/vta_rpc/start_rpc_server_to_tracker.py:18:18: E999 SyntaxError: invalid syntax
PROJROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/../../" && pwd )"
                 ^
```

* [Relay][Frontend][TFlite] Add parses support for SLICE

* TFlite 1.13: convertor gives nonsense output when size[i]==-1
* TF parser: SLICE need fixing for size[i]==-1 -> gives wrong output
  bcs of indices

* Set end[i] = input_tensor_shape[i] as suggested in PR review

* Add another test to cover size=-1 case

https://github.com/actions/checkout/releases

* Update topi.cc

fix topi.nn.global_pool layout="NHWC"

* add topi.nn.global_pool layout=NHWC test

* [REFACTOR][IR] tvm::Expr -> PrimExpr(Primitive Expr)

As part of unified IR, we will need to unify relay::Expr
and the current tvm::Expr under the same base type.

From the techinical point of view. tvm::Expr is a "primitive"
expression that only contains POD types and handles and does
not do life-cycle management.

This PR renames Expr->PrimExpr to clarify that.
We will send a subsequent PR to introduce the base expr class.

* Remove legacy VarExpr and ExprHash/Equal

* [Relay][Frontend][Tflite] Add parses support for unary elemwise ops

* Add generic method to convert unary functions: abs, exp, ceil, floor
  log, sin, cos, sqrt, rsqrt, neg
* Add relevant tests

* Delete excessive underscores as requested in PR review

* Change parameter name as suggested in PR review

* [REFACTOR] relay::Module Def -> TypeDef

The term Def was not very clear about what is the object of interest(could be function def or type def).
Changes the term to TypeDef to be more explicit.

* Update include/tvm/relay/module.h

Co-Authored-By: Wei Chen <ipondering.weic@gmail.com>

Co-authored-by: Wei Chen <ipondering.weic@gmail.com>

* Added 1D pooling to Topi

* Added 1D pooling relay op and tests.

* Added onnx parsing and tests for maxpool1d and averagepool1d

* formatting

* moved partial import.

* Fixed typo.

* [AutoTVM] Use vm compile in extracting task from relay

* update

* restructure vm compiler to reduce task extraction time

* x

* fix

* update doc

* udpate doc

* lint

* [RUNTIME] Fix windows build after the latest dso module change.

Switch to shared_ptr to get around a problem in latest MSVC.

* [CI] Add github action for win mac build.

Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com>

* [REFACTOR][IR] Variable -> VarNode

* [REFACTOR][IR] Add/Sub/Mul/Div -> AddNode/SubNode etc.

* [REFACTOR][IR] Min/Max/FloorDiv/FloorMod -> MinNode/MaxNode etc.

* [REFACTOR][IR] EQ/NE/LT/LE/GT/GE/Select -> EQNode/NENode etc.

* [REFACTOR][IR] Add Node suffix to Select/Call/Load/Ramp/Shuffle/Let

* [REFACTOR][IR] Add node suffix to IntImm/UIntImm/FloatImm/StringImm

* [REFACTOR][IR] Add Node suffix to Any, AttrStmt, AssertStmt

* [REFACTOR][IR] Add Node suffix to Store/Provide/Allocate/Free

* [REFACTOR][IR] Add Node suffix to ProducerConsumer

* Fix lint

* style updates, test fixes

* [RUNTIME][DSO] Improve TVMBackendPackedCFunc to allow return value.

Previously the signature of LibraryModule's PackedFunc does not support return value.
This wasn't a limitation for our current usecase but could become one
as we start to generate more interesting functions.

This feature also start to get interesting as we move towards unified
object protocol and start to pass object around.
This PR enhances the function signature to allow return values.

We also created two macros TVM_DLL_EXPORT_PACKED_FUNC and TVM_DLL_EXPORT_TYPED_FUNC
to allow manual creation of functions that can be loaded by a LibraryModule.

Examples are added in apps/dso_plugin_module.
The change to TVMBackendPackedCFunc is backward compatible,
as previous function will simply ignore the return value field.

* address review comments

* Make Relay Keras frontend support networks created using
   Tensorflow (1.13) Keras implementation (tf.Keras)
 * Modify Keras frontend tests to run from a class rather than a
   function based script
 * Adjust Keras frontend tests to run with both 'Keras' and 'tf.Keras'
 * Change "TestKeras.test_forward_merge" to validate instances by
   class name rather than instance type

* Fix typos on Docker image versions that we are currently running
   as part of CI

 * Add version comment in the same pattern for ci_lint image

* As a result of backwards incompatible changes released in pillow 7.0,
   torchvision crashes if you just "pip install pillow", as we do in
   a few places.

 * This patch sets pillow<7 to be installed in Dockerfiles and support
   material as tutorials and documentation.

* Improve commentary for operator fusion.

* Attempt to clarify what well formed checker is doing