* [D2SCinn]Support deliver skip_gc_vars into Graph

* fix unittest

* fix copy

* delete FLAGS_enable_eager_mode

* 移除 declarative 接口

* 修改 CI报错，清理注释

* move translated layer

* fix build bugs

* fix unittest bug

* trigger ci

* del array_write & array_read

* fix import err

* fix import err

* fix example codes

* remove non-public apis from __all__

* fix code style

* isort jit

* refine comment

* [API Clean]Clean __all__ to avoid exposing usless API

* fix import

* fix typo

* remove tracedLayer unittest

* [Dy2St] refactor convert_print to display Tensor in compile time

* refactor: rm shrink_memory

* refactor: rm create_array

* refactor: rm array_length

* [Fluid Clean]Migrate utils files and delete dygraph_to_static dir

* fix setup.py.in

* fix import

* fix unittest

* fix code style

* fix unittest

* [Fluid clean]Migrate base/call/print et.al transformer into paddle.jit

* fix phi kernel

* Revert "fix phi kernel"

This reverts commit eff8891c7efda6e49799edbcaef2ca50379d50ef.

* [Fluid Clean]Migrate if/while/return/break transformer into paddle.jit

* migrate call_transformer

* migrate call_transformer

* [Fluid Clean] Migrate program_translate.py/jit.py into paddle.jit dir

* [CodeStyle][py2][U004] unecessary explicit `object` inheritance in class definition

* fix an increment

* update config

* re-blacken python code

* temporarily disable date and diff_py_file

* skip a format

* [CodeStyle] remove all future import

* revert test_error.py

* restore future import in example code

* unify the size and size() by jst

* fix bugs

* bug fix.

* fix bugs

* change all_close -> np.testing.assert_allclose

* Pybind Fucniton and hide ExecutorEngine and PEEngine

* Remove FunctionNames in compilation_unit

* [JIT]Layer support eager dygraph mode and polish Function interface

* remove usless code

* fix #define

* change NameVisitor to FunctionScopeAnalysis

* polish the logic of undefined var in while_loop. create vars after body execution

* replace old NameVisitor in while and fix all CI

* Togather with CreateVariableTransformer

* add create_variable_transformer

* fix bugs

* merge

* fix some error, TODO: ForNodePreTransform ahead

* merge for unite PR

* fix conflict with base_transformer PR

* fix ci errors, fix [for i in range()] error

* fix according to code review

* Pybind JitLayer VarBase Function and add python UT

* Add multi program load UT

* Fix UT place error

* Update jit.save param name

* Remove some comments

* Polish cmakelists

* Polish JitLayer in Python

* Fix comments

* [Dy2Stat]Polish all API name of _jst

* [Dy2Stat]Refactor convert_shape transformer logic

* clean usless unittest

* [Dy2Stat]Support nonlocal mechanism in IF ast transformer

* support prune return vars in cond

* fix unittest

* fix unittest

* fix static check

* use yapf to format all python file

* yapf exclude two unittests file for they rely on writing and reading file, and format will break them

* disable diff_py_file because too many diff files cause command following failed

* Pre-save hooks of jit.save

1. Added pre_save_hooks features to jit.save.
2. Added related unittests

* Added jit pre_save_hooks functions's alias to paddle.jit and copyright.

* Make jit.save_pre_hook style be consisent with Paddle's rule.

* Fixed arguments passing bug in run_save_pre_hooks

* Added API Documents

* Move clear and run_pre_save_hooks as internal methonds only.

* Made register_save_pre_hook as an internal function.

* update 2.0 public api in all left files

* reverse device.py all list;
fix some flake8 errors

**Problem**
In our old shape transformer logic, if user write:
```
s = tensor.shape
...
y = paddle.some_api(s)
```
Dy2stat will change it to
```
...
y = paddle.some_api(convert_var_shape(tensor))
```
However it will cause fatal bug if user changes the shape of `x` after assign. For example:
```
s = tensor.shape
...
tensor = paddle.some_change_shape_api(tensor)
...
y = paddle.some_api(s)
```
Then the Dy2stat will get wrong result because the code is translated into:
```
tensor = paddle.some_change_shape_api(tensor)
...
y = paddle.some_api(convert_var_shape(tensor)) # tensor shape has been changed, not origin `s` value
```

**Solution Logic**

It can not be solved in the old logic, so I refactoring tensor_shape_transformer logic. Now we will use `s` to store shape attribute and generate a var `s__STATIC_CONVERT_VAR_SHAPE_SUFFIX` to store static shape API `shape(tensor)`
```
s = tensor.shape
...
y = paddle.some_api(s)
```
Dy2stat will change it to
```
s = tensor.shape
s__STATIC_CONVERT_VAR_SHAPE_SUFFIX = shape(tensor)
...
y = paddle.some_api(choose_shape_attr_or_api(s, s__STATIC_CONVERT_VAR_SHAPE_SUFFIX ))
```
In this case, the code is consistent with origin dygraph meaning and it fixed the change after assign bug.

**Code Key Note**

To help reviewers, the key change of this PR is changing `self.name_to_var_shape` from "mapping name to shape node" to "mapping name to its STATIC_CONVERT_VAR_SHAPE_SUFFIX name", then if a variable name has the SUFFIX, we can choose to use attribute shape or shape api. Other changes go with the key change.

**Consideration**
The issue of this PR is that we store extra static `shape` API result, will it harms the speed of Dy2stat? In some cases it will, but we argue that the benefit would be greater than the cost.

1. The extra calling to static `shape` API will happen when coder assign among shape variables. Take the following dygraph code as an instance:
```
s1 = tensor.shape
s2 = s1
s3 = s2
...
```
Then we called extra static `shape` APIs again and again, however users seldom write code like this.

2. If the shape variable is used a lot, for example:
```
s = tensor.shape
y1 = paddle.some_api1(s)
y2 = paddle.some_api2(s)
y3 = paddle.some_api3(s)
```
Our old logic will create 3 shape APIs but now just 1. This is more common user code pattern. In fact, if reviewers take a look at the current unit test in this PR, you could see the op numbers decrease after this PR. So we argue that this PR can also improve speed in this code pattern.

[Dy2stat] Add a decorator paddle.jit.not_to_static to support that not to convert a function in Dynamic-to-Static. (#29253)

Usage scenarios：A function could have run successfully in static mode,  you can use it to decorate a function in the following cases:
  1. An unknown error occurs in the dynamic-to-static conversion process of the function;
  2. In the internal implementation of the function, it has two branches: dynamic branch and static branch;
  3. Users don't want to convert the function in the process of dynamic to static.

This PR fixes several problems in dy2stat for Deoldify model in PaddleGan.

In model, software engineer wrote if x.shape == y.shape, the Tenser shape is a tuple in dygraph so the == returns True/False, but in static graph the == becomes element-wise comparison, which is a different behavior. In this PR we reduce the element-wise comparison result.

If software engineer write computations which uses parameters in hooks, the static graph can loss the parameter variable because we put param_guard at forward of a Layer. In this PR we made param_guard cover pre-hook and post-hook.

In PaddleGan, software engineer calculated some parameter values in __init__ by running some dygraph code. Those code also run during dy2stat. So some variables may be assign as a VarBase (Tensor) first and then Variable, which raised an error. We fixed the bug in this PR by handling the case.

TODO: We just added testcase for the 1. shape comparison. Should add test case for 2. and 3. But since we are chasing 2.0RC, I will do it in the near future PR

[Dy2Stat] Fix bug: the return statement should be transformed to an equivalent Paddle/Python if statement, which depends on if conditions of the return stmt. (#29165)

* Support pop for dict in dy2stat

* Move convert_pop to convert_operators.py and polish convert_pop