* [Dy2Stat]Polish @to_static temporary file directory

* [Dy2Stat]Polish @to_static temporary file directory

* refine temp.name

* fix typo

* fix typo

* [CodeStyle] remove all future import

* revert test_error.py

* restore future import in example code

* add numpy api err msg

* fix bug

* fix unittest

* add set_state_dict err

* rewrite numpy_api_check

* add define

* change err msg

* fix test

* move import statement

* use re replace judge by case

* simplify re

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* fix select_input with different shape errors:
1. select_input_with_buildin_type directly return non-undefinedvar branch when meeting undefined var
2. the output shape of select_input is inferred from inputs.

* reverse the logic in select_input

* 1. fix non-local error while dealing push_pop names
2. escape "'" in push_pop_names to avoid syntax errors.
3. unified the non-local stmt creation processes in getter and setter.
4. split the nonlocal_names and getter/setter names.

* fix bugs

* 1. revert setter and getter, push_pop_names must have non-local

* fix bugs.

* code format

* [Dy2Stat]Filter UserWarings while @to_static

* only filter DeprecationWarning

* fix unittest

* 1. make list transformer into jit form.
2. fix some bugs in tensor_array, such as append.
3. enhance the function analysis visitor to recognize push/pop.
4. add setter/getter helper to deal with 2+ name sets.

* fix ci errors:
1. add to_tensor_array logic in convert_cond
2. fix IfExpr error.
3. fix erros while return_names or push_pop_names is None
4. fix slice error in a[i]=1 where a is tensor_array
5. add pop interface in Variable

* while support for python container.
It is convenient to convert more dynamic graph codes into static graphs.

* cond support python container

* 1. make select_input output shape = input[1]
2. add warning in while_loop risky assign

* fix 2 problem in GPT export:
1. a bug in while_op no_need_copy_var, which causes gpu memory leakage
2. a bug in undefined_var where the stop_gradient should be False.

* change name by code review

* format

* Make sure that the output of whilep must exist in the input

* insert assign in block(0)

* add unittest.

* 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

* Polish visit function in transformer

* Call generic_visit first in visit_While/For

* Remove comments

* Polish utils.py, move some transformer to base_transformer

* add support for control flow block analysis

* move FunctionNameLivenessAnalysis into utils

* pass test_ifelse.py

* remove duplicate data_layer_not_check

* pass the test_ifelse.py

* fix unittest error .

* fix all ci error in first version

* temporay disable CreateVariableTransformer

* fix ci errors

* fix function name liveness analysis bugs

* modifty def cond

* fix

* fix ci error - v2

* fix by code review

* change return_name_ids -> return_name

* Add BaseTransformer for dy2st error message

* Fix return_transformer error

* Polish dy2st error info in runtime

* Fix UT error

* Polish runtime error code

* [Dy2Stat]Enhance nonlocal machanism while returning single var

* [Dy2Stat]Enhance nonlocal machanism while returning single var

* merge and add base support for non-local for

* for and while non-local support

* fix ci errors: v1

* fix bug

* fix

* fix code

* fix

* fix

* fix

* [Dy2Stat]Polish all API name of _jst

* [Dy2Stat]Support nonlocal mechanism in IF ast transformer

* support prune return vars in cond

* fix unittest

* fix unittest

* fix static check

* [Dy2Stat]Remove fluid.layers in transformer code

* remove useless return_name_vars

* fix unittest

* fix unittest

* fix unittest

* 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

* [Dy2Stat]Replace paddle.jit.dy2stat with _jst

* [Dy2Stat]Replace paddle.jit.dy2stat with _jst

* refine code style

* refine code style

* remove outer comment when dy2stat

* remove all comment

* add unit test

* support `for i in [1,2,3]` statements in dy2stat

* add test case

* fix ci

* remove wrong code

* Support Py3 type annotations in @to_static

* support type hint for args in func

* support type hint assign

* if annotation and value(Constant) are diffent type, we use value type

* polish type_from_annotation()

* code format

* code format

* remove useless commentary

* fix review
Co-authored-by: NAurelius84 <zhangliujie@baidu.com>

* integrated gast library

* integrated gast library

* fix unittest and remove ast2.py

* remove 'gast' from __all__ in __init__.py

* add copyright in other files

* fix copyright

* support type hint

* fix unittest

* del py2 code2

* fix test timeout

* support non-tensor type

* fix unittest failed

* add unittest with prune

* rm unused code

* coverage

* fix two or

* support DictCmp and zip grammar

* fix code style

Our old `loop_body` function may return single element when `loop_vars` just contains only 1 element, which can cause bug. The key point of this PR is forcing `loop_body` functions always return tuple.

Dy2stat didn't support tuple as iteration variable in the past. This PR added there main cases:

       1). Non-enumerate case: for var1, var2 in var|var.numpy() will be re-written as:
          for FOR_ITER_TUPLE_PREFIX_x in var | var.numpy():
            var1 = FOR_ITER_TUPLE_PREFIX_x[0]
            var2 = FOR_ITER_TUPLE_PREFIX_x[1]
        2). Enumerate out tuple case: for t in enumerate(var|var.numpy) will be rewritten as:
          for FOR_ITER_TUPLE_INDEX_PREFIX_x, FOR_ITER_TUPLE_PREFIX_x in enumerate(var|var.numpy):
            t = (FOR_ITER_TUPLE_INDEX_PREFIX_x, FOR_ITER_TUPLE_PREFIX_x)
        3). Enumerate inner tuple case: for i, (var1, (var2, va3)) in enumerate(var|var.numpy()) will
        be re-written as:
          for i, FOR_ITER_TUPLE_PREFIX_x in var | var.numpy():
            var1 = FOR_ITER_TUPLE_PREFIX_x[0]
            var2 = FOR_ITER_TUPLE_PREFIX_x[1][0]
            var3 = FOR_ITER_TUPLE_PREFIX_x[1][1]

* add paddle.

* add unittest

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