* Add pr checker for ut

test=develop

* Change approvers for ut spec checking

test=document_fix

* Change approvers for ut spec checking

test=develop
test=document_fix

* Add comments

test=develop
test=document_fix

* Refine error message

test=develop
test=document_fix

* Support AucRunner in PaddleBox
* update some code style

* polish reader error message, test=develop

* fix detail error, test=develop

* reset activation dcudnn change, test=develop

* remove pow in speed up in dequantize_log test=develop

* remove pow in speed up in dequantize_log test=develop

* fix unittest test=develop

* support NHWC in Pool2D, test=develop

* add unittest, test=develop

* fix unittest, test=develop

* fix typo, test=develop

* follow comments, test=develop

* refine comments, test=develop

DataLoader makes the data diff even if the data of reader is the same on CUDA place. This PR doesn't use DataLoader to pass the test. we will use DataLoader back after we fix it.

[Dy2Stat]Support to transform sequence assignments and multi-target assignments to normal assignments (#24643)

* Update isCached() to be thread freindly
test=develop

* Add isCached() check inside Softmax handler
test=develop

* Fix PaddleEnforce() message
test=develop

test=develop

* test=develop update interpolate & upsample api

As discussed with QA, we will use p4 machine for unit test and the GPU on those machine may not have enough GPU, which can cause "test_parallel_executor_seresnext_base_gpu" failed. So I decrease the batch size.

* fix bug of varbase.__getitem__, test=develop

* fix bug of float and other type, test=develop

API/OP(sequence_first_step, sequence_last_step, sequence_mask, beam_search, beam_search_decode) error message enhancement (#24590)

The background is that buddy_allocator_test depends on some big GPU allocations and current machine may not have it. It can cause random failure on cloud machine. After discussion with Shuo Tian, we decided to make buddy_allocator_test exclusive.

In the past, the test_cond will fail with 2% probability and easy to re-produce.

Now I re-run 300 times and no failure occurs. The probability of still has the failure is (1 - 2%) ^ 300 ~= 0.00004. We can say the random failure disappears. Maybe someone fixed some bugs in PE.
  

* add scale for static runner outputs, test=develop

* fix import relation, test=develop

* remove len limit, test=develop

* Refactor code for dump_field & dump_param: abstracting the common function in base class.
* Support dump randomly & random with lineid
* Support specify the random interval, which avoids printing too much logs.

* fix bug with `if Tensor` in is_control_flow test=develop

* remove continue test=develop

* Support lambda and add unittest test=develop

* Support to create LoDTensorArray in control flow (cond and while_loop)

* Fix bug: return LoDTensorArray in while_loop

* Change code in list_transformer.py to accommodate the new features.