The PR: https://github.com/PaddlePaddle/Paddle/pull/24651 seems causes new random failure of unit test test_parallel_executor_seresnext_base_cpu. The reason is that smaller batch size causes random optimization of neural network. I distinguished cpu/gpu batch size to fix the unittest.

* support nested input and output test=develop

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

* add warning test=develop

添加loss自适应的学习率衰减策略。

* Fix bug of different dtype in dygraph math_op_patch, test=develop

* support np.dtype and str, test=develop

* add unit test, test=develop

* [dy2stat] Support len

* add convert_call func

* refine code test=develop

* Revert "Fix test_bert on GPU (#24692)"

This reverts commit 62222bf4.

* fix random bug in bert unittest, test=develop

* Support AucRunner in PaddleBox
* update some code style

* 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)

* 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)

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. 

* test_seresnet support gpu test=develop

* Set the Flag by core.globals test=develop

* modify into set_flags test=develop

* API/OP(sequence_expand_as) error message enhancement.
test=develop
Co-authored-by: NFrostML <380185688@qq.com>

* initial test

* for enumerate basic implement, test=develop

* update unittests, test=develop

* refine unittests to adapt new training mode, test=develop

* refactor for node stmts parsing code, test=develop

* self-review & polish details, test=develop