* Replace dygraph_to_static_func with @declarative or program_translator.get_func in test_list.py

* Add comments in ConditionalBlock.

* Support list pop last item. 

* Support pop the i-th item. 

* Support an empty tensor array as Input in assign op and set the kernel type is float.

* add print transformer & unify print format, test=develop

* remove using of dygraph_to_static_func, test=develop

* remove python stdout capture, test=develop

* fix compatibility problems for PY2, test=develop

* fix detail error, test=develop

* fix type analysis bug, test=develop

* fix print tuple compatible error in PY2, test=develop

* replace get_func to declarative, test=develop

* fix detail bug, test=develop

* fix some detail problems, test=develop

* change visit_call in print transformer, test=develop

* lod_rank_table error message enhance. test=develop

test=develop test=document_fix

 fix compare and logical ops cond/out parameter doc bug，correct the dtype message 

* OP clip, merge_lod_tensor, convert/elementwise error message enhancement. test=develop

As the title

As the title.

* optimize compare and logical ops error info
* add out and cond dtype test

* lod_tensor_to_array error message enhance. test=develop

API(has_inf, has_nan, cumsum, fsp_matrix, increment) error message enhancement, test=develop (#23769)

* API(argsort, argmax, argmin, cast, diag) add error message，and the same time add the test cases for those apis.

This PR enhances error messages of several API/OPs:

ParallelExecutor (python && C++)
Executor (python && C++)
StaticRNN (python)
IfElse (python)
cond (python)
split_lod_tensor (python && C++)

API/OP (ConditionalBlock) error message enhancement (#23480)

* API (Switch) error message enhancement. 

* fix bug: dtype of out in api isfinite is set incorrectly. The dtype should be bool. 

* support tensor array create read write in dygraph
test=develop

* minor fix
test=develop

* support tensor_array_to_tensor
test=develop

* add while loop case and err msg
test=develop

* refine ut
test=develop

* Polish error Info in while_loop test=develop

* support to modify dict and list in while_loop test=develop

* polish code test=develop

* dygraph support cond op
test=develop

* unittest coverage
test=develop

* fix coverage
test=develop

* fix for coverage
test=develop

* refine TypeError msg
test=develop

* remove restrict
test=develop

* dygraph support while_loop op
test=develop

* refine assign
test=develop

As the title

Modify english document and unittest of while_loop

As the title.

This PR makes assign op support LoDTensorArray and enable the loop_vars in
while_loop to support tuple or list.

* append optimize op in the grad block of current block if current block is in control flow. test=develop

* add conditional grad op when optimizer used in control flow. test=develop

* add comment and modify typo. test=develop

* fix append_backward to support control flow. test=develop

* add test. test=develop

* fix copy_var_to_parent_block and conditional_block_grad. test=develop

* fix bug: revert to append conditional_block_grad vars to sub grad block. test=develop

* fix bug: revert to assign var to parent block even if var already is in parent block

* fix bug: consider outputs is empty. test=develop

* move _rename_grad_ out. test=develop

* modify code according to reviews from Huihuang. test=develop

* modify code according to reviews from Jinle. test=develop

As the title

Add basic while_loop

Add tests to use dy/dx to make sure the gradient values calculated by the control flow backward is correct. Also fixed bugs detected by those tests.

Fix bugs:

1. Unlike sum_op, optimizer ops don't allow uninitialized input tensor. But in conditional_block_grad_op, since the conditional_block may not run, the output gradient tensor may be uninitialized, which will cause the optimizer op error. To fix it, we should let optimizer ops support uninitialized input like sum_op or assign the uninitialized gradient to 0 when the conditional_block_grad_op doesn't run. I found there are about 10+ optimizer ops. **To be simpler, I just assign output gradient of the conditional_block_grad_op to 0 in this PR**. But it can be further explored whether we can make optimizer ops like sum_op to support uninitialized input tensor because theoretically we can speed up without the assigning in conditional_block_grad_op.

2. Infer parameter shapes during append_backward. I didn't know that all our parameters are in global block. When op_desc is inferring shapes at the sub-block, it may not know the shape of gradients of parameters whose shape information is at global block. I fixed it by inferring shapes of gradients from forward var.

This PR also did some code clean up:
1. Print the var name when sgd_op catches shape error so that it is easier to debug
2. Fix a typo: dicta -> dict