* migrate fill_constant to paddle.tensor

* move fill_constant to paddle.tensor and repalce the reference

* add missing fill_constant replacement

* fix typro

* remove unused import fill_constant

* fix zeros import error

* fix circle import

* fix layers.zeros

* fix unitest

* fix unitests

* fix unitest

* use paddle.full replace fill_constant in samplecode

* fix sample code

* recovery xpu test

* recovery xpu test

* fix circle import

* fix utils import error

* fix utils error

* fix circle import

* redo

* fix circle import

* fix prim fill constant import

* fix type error

* fix increase error

* fix test error

* fix fill_constant

* 移除 declarative 接口

* 修改 CI报错，清理注释

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

* remove reshape in nn.py under fluid

* remove reshape reference

* fix test case

* fix test case in distribution.uniform

* remove fluid reshape reference

* 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

* fixed1

* fix 2

* fixre

* test complext var

* delete logic no needed

* fix to_tensor_static

* code style

* del

* fix_shape

[CodeStyle][NPU] use np.testing.assert_allclose instead of self.assertTrue(np.allclose(...)) (part 1) (#44988)

* autofix

* try resolve precision issues

* revert some changes

* clean some `err_msg`

* 0.0001 -> 1e-4

* update commented assert code

* try to fix some shape errors

* `numpy` -> `np`

* empty commit, trigger kunlun ci, test=kunlun

* empty commit, retrigger kunlun ci, test=kunlun

* empty commit, trigger kunlun ci, try fix npu memcpy_h2d, test=kunlun

* try fix npu import error, test=kunlun

* 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

* [Dy2Stat]Polish all API name of _jst

* [Dy2Stat]Refactor convert_shape transformer logic

* clean usless unittest

* Refine covnert_var_shape in dy2st

* Fix UT error

* Return paddle.shape only when has_negative and fix test_tensor_shape error

* Fix test_jit_save_load error

* 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

* support assign x.shape to dict['key'] in dy2st

* remove replace_dot

* refine unit test

* fix error when tensor_shape_transformer. Before in stmt like `if len(paddle.shape(x)[0]) > 0`, `paddle` will be used as a variable

* handle other call like `fluid.layers.mean` and `fluid.layers.shape`

* add unit test

For python, if users write `a, b = var`, the `__getitem__` method will iterate through 0, 1, 2 ... until `__getitem__` throws an IndexError, then stop. The var[0], var[1] will be given to a, b respectively. If more values are given, the unpack size would cause error.

We didn't raise the IndexError in the past and we add statement in `__getitem__` to raise IndexError here to support grammar like `a, b = var` in this PR.

As the title

* Fix bug with static_convert_var_shape

* replace dot with dash

Fix Read-Only Attribute as while_loop Output:

Usually, our convert_while_loop will be like:
```
    [a, b, c] = paddle.jit.dy2static.convert_while_loop(
            condition_name, body_name, [a, b, c])
```
where a, b, c are in loop_var_names.

However, if loop_var_names contains property such as foo.x, we cannot
assign the attribute as output of convert_while_loop because Python
property is a kind of read-only attribute. To handle the case, we replace
the attributes which are output of convert_while_loop with generated
variables, then if we know the attribute is not read-only at runtime, we
assign the attribute. The created statements are like:
```
    [a, b, __attribute_variable_1] = paddle.jit.dy2static.convert_while_loop(
            condition_name, body_name, [a, b, foo.x])
    if not isinstance(getattr(type(foo), x, None), property): foo.x = __attribute_variable_1
```

* fix eval_if_exist_else_none bug

* fix typo

* fix typo

* fix test_op_num unittest

**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.

As the title, when slice_node like 1:3 being passed to idx of convert_var_shape, it will cause syntax error because a function cannot take this as argument. This PR fixed it.

* fix tensor shape bug

* fix op_num

* clean code

1. When x is Variable, call nn.shape(x) only in following cases:
 1）The shape of x is used in control flow condition.
 2）The dim to be used is negetive
2. When x is Variable, but x.shape or x.shape[idx] doesn't contain negetive value, don't convert to paddle.shape()

Add support for using tuple as tensor.shape (For example: a, b, c, d = x.shape)

Refine Dy2stat APIs to 2.0rc

After discussion, we accepted 3 key points from reviewers:

1. In 2.0rc we changed dygraph_to_static folder to dy2static
2. Keep the three files: convert_call_func.py, convert_operators.py, variable_trans_func.py
3. Remove convert_operators path when users import convert_xxx. 

After this PR, users can import convert_xxx APIs by:

`import paddle.jit.dy2static.convert_xxx`

The file structure will be:

```
jit
    dy2static
          convert_operators.py
          convert_func_call.py
          variable_trans_func.py
```

Detail changed API in files:

In python/paddle/jit/dygraph_to_static/convert_call_func.py:
from ...fluid.dygraph.dygraph_to_static.convert_call_func import convert_call  #DEFINE_ALIAS

In python/paddle/jit/dygraph_to_static/convert_operators.py:
from ...fluid.dygraph.dygraph_to_static.convert_operators import cast_bool_if_necessary  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_assert  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_ifelse  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_len  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_logical_and  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_logical_not  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_logical_or  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_print  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_var_dtype  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_var_shape  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.convert_operators import convert_while_loop  #DEFINE_ALIAS

In python/paddle/jit/dygraph_to_static/variable_trans_func.py:
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import create_fill_constant_node  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import create_static_variable_gast_node  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import data_layer_not_check  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import to_static_variable  #DEFINE_ALIAS
from ...fluid.dygraph.dygraph_to_static.variable_trans_func import to_static_variable_gast_node  #DEFINE_ALIAS

[Dy2Static]Convert var.shape stmt and Convert the return variables of Tensor-dependent 'if' staments to Tensor if it not  (#24911)

* Support int and long: int or long -> six.integer_types. 

* Modify test_tensor_shape: fix bug and modify comment. 

* Support convert_var_shape to convert var.shape stmt

* Modify code in ifelse_simple_func.py because don't support return non-Tensor in Tensor-dependent 'if' stament currently. 

* Convert the return variables of Tensor-dependent 'if' staments to Tensor if it not. test=develop

* support to train in static

* support to independent decorator

* remove in_dygraph_mode condition in ProgramTranslator

* fix import param_guard and add train/eval test=develop

* Modify into ShareVarsFromScope and rm __all__ in partial_program test=develop

This PR does exact the thing as the title. The reason is that we plan to develop 4 decorators

"dygraph_to_static_code"
"dygraph_to_static_program"
"dygraph_to_static_func"
"dygraph_to_static_output"

 The 4 decorators will emphasize different part when translating dygraph to static graph. Decorator name "dygraph_to_static_graph" is too big for the function it implements.

* Support Tensor.shape in control flow if/for/while and separate TensorShapeTransformer from BasicApiTransformer. test=develop

* support basic tensor.shape. 

* Support tensor.shape with dependencies.