* Fix save in eval mode

* remove assert statement

* fix test_partial_program failed

* add more test

* modify back into _train_program

* Add env value to  log to stdout; 2.Add logger name

* Optimize log messages in dygraph-to-static

* Replace logging.warn and warnings.warn with logging_utils.warn

* Fix Python Version compatibility

* add import six

[Dy2Stat-ErrorMessage]Optimize error value to improve readability when error raised in runtime.  (#25970)

* don't remove op_callstack

* [Dy2Stat-ErrorMessage] Optimize error value to improve readability if error raised in run-time. 
  1. update op_callstack with original information;
  2. simplify error value to improve readability if error raised in run-time.

* Fix error in Python3. 

* fix eval() sync problem

* add unittest

* modify according reviewer

* fix unittest failed

* remove op call stack test=develop

* fix typo test=develop

Support Various-Length Return Grammar in Dy2stat. This PR is a follow-up of https://github.com/PaddlePaddle/Paddle/pull/25176 .

The basic idea is putting no-value placeholder variables at `return` statement to make all `return` statement have same length, after that the static graph can have fixed fetch output (code at return_transformer.py). Then remove those no-value placeholder when we finally return dygraph result (code at partial_program.py).

However, various length return in Bert model is still not supported. The dy2stat can change the code as I wish but some ops which check shape at compile time (e.g. Reshape, MatMul) will throw error because of the no-value-placeholder may not have the required shape. Is this a matter? To me, those no-value placeholder will be replaced as really values meeting shape requirements at run time, so I think the solution should be some way to do the compile-time checking. By the way, every time when we have dynamic shape, it often causes problem in dy2stat. We should find a way to handle it in the future.

Fixing various return in Bert is my TODO thing and I will also find some other existing models for verification.

* support to save varBase created in __init__ test=develop

* polish code test=develop

* refine to_static_var test=develop

* refine warning test=develop

* add unitteset for to_static_var test=develop

* fix logger test=develop

* polish buffers en doc test=develop

* fix param_guard test=develop

* refine en doc test=develop

* add cycle_gan_model

* align train test=develop

* modify image_size into 64 to avoid TimeOut test=develop

* TODO in GPU test=develop

* support nested input and output test=develop

* remove code of convert type(output) in unittest test=develop

* add warning test=develop

* support save_inference_model in program_translator test=develop

* fix compatibility with OrderedDict.values() in python3 test=develop

* synchronized random_seed test=develop

* Polish Error Message 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