As the title

* connect stop_gradient with trainable; test=develop

* add value error message; test=develop

* add paddle.fleet.DistributedStrategy for 2.0

* add Pad2D and Leaky_ReLU, test=develop

* update,test=develop

* change name,test=develop

* add unittest and redine docs,test=develop

* test=develop, fix softmax

* add cast transform and its UT for dygraph_to_static. 

* fix test_lac timeout test=develop

* disable it on windows test=develop

* modify pipeline optimizer, test=develop

* show the attr and functions of the Layer,test=develop

* add buffer for dir,test=develop

* fix __dir__,test=develop

* fix doc of Layer.__dir__, test=develop

* add mish op. test=develop

* support tuple/list init for VarBase,test=develop

* fix doc of fluid.dygraph.to_variable,test=develop

* fix doc of fluid.dygraph.to_variable,test=develop

* Add `matrix_nms_op`

test=develop

* Make ci happy

test=develop

* Exit early when no detection

test=develop

* Fix license year

test=develop

* Output index as well

test=develop

* Match nms2 lod behavior and add `return_index` flag

test=develop

* Make CI happy

test=develop

* Fix wording

test=develop

* disable distributed UT temporary，enable it soon, test=develop

test=develop

* fix test_multiprocess_dataloader_static random fail. test=develop

* add sentiment unittest

* add LAC model

* add test=develop

* rename tmp test=develop

* fix timeout test=develop

* undo tmp_var test=develop

* test=develop, fix_embedding

* add new API: MultiStepDecay, a new learing rate strategy, test=develop

* add new API: MultiStepDecay, a new learing rate strategy,test=develop

* add new API: MultiStepDecay, a new learing rate strategy,test=develop

* add base class of LearningRateEpochDecay, and MultiStepDecay, and StepDecay, test=develop

* fix doc to add coverage,test=develop

* Modify tmp var name prefix in dygraph test=develop

* refine comment test=develop

* add new api: optimizer.set_lr, test=develop

* add API doc and example code for optimizer.set_lr,test=develop

* add API doc and example code for optimizer.set_lr,test=develop

* Modified doc to :api_attr: imperative,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.

* fix GPU diff test=develop

* refine code test=develop

* add test_parallel_executor_profiler.py

test=develop

* set exec_strategy.num_threads=1

* skip check_profile_result

* add TODO for test_profiler

test=develop

* 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

This PR added basic support for 'return' grammar in dy2stat. It supports the control flow of 'return'.

The basics idea is using a return value variable to store the early return statements and boolean state variables with if-else to skip the statements after the return statements.

**This PR is very basic support. There are some corner cases I didn't develop/test**. For example, 'return None', 'return different length of variables', 'return non-tensor and tensor together', 'no return statement'. **These corner cases will be done in my next PRs**. Target date is this week.

**Note**: 
1. for the unit test, I changed test_program_translator.py because the StaticCode of `dyfunc_with_if_else` will change. To guarantee the correctness of `dyfunc_with_if_else`, I also run it in `TestRecursiveReturn` in test_return.py.

2. I commented the early return code in bert_dygraph_model.py because 'return different length of variables' is unsupported now. I also know that there are some other models used early return and we didn't enable it in the unit test. I will add support for it in next PRs and then re-enable those tests.

* fix beam_search infershape, test=develop

* fix beam search op unittest, test=develop