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

* fix phi kernel

* Revert "fix phi kernel"

This reverts commit eff8891c7efda6e49799edbcaef2ca50379d50ef.

* 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

* Add BaseTransformer for dy2st error message

* Fix return_transformer error

* Polish dy2st error info in runtime

* Fix UT error

* Polish runtime error code

* [Dy2Stat]Polish all API name of _jst

* [Dy2Stat]Refactor convert_shape transformer logic

* clean usless 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

* 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

* 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

* 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()

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

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

* Simplify code for gast.If in is_control_flow_to_transform.
* Move IsControlFlowVisitor to file utils. 
* Don't use convert_call for build-in func in CallTransformer. 
* Optimize api is_control_flow_to_transform. 
* Polish the document of IsControlFlowVisitor.

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