* fix multiple documentation error, test=document_fix

* fix more rst syntax errors, test=document_fix

* fix format issues in docstring, test=document_fix

* fix expand && concat/transpose to new api

* update uniform_random_op

* update xpu_header

Reduce exception type so that if covert_to_static failed, it reports right error message.

[Dy2Stat] Fix bug: Do not use gast.Subscript to replace gast.Name in when transforming for_enumerate_loop (#29310)

* test=kunlun

[inplace] Add ShareHolderWith for class Variable and SharePlaceholderWith in VarBase.detach() to share the same Tensor/SelectedRows (#29267)

* basic impl of type promote

* add comment & another testcase

* fix complex bugs & support python op promote type

* fix failed unittests & polish code

* add unittest for coverage

* change to only promote complex type

* polish code details

* polish several comments

* fix gpu emb out of range

Change-Id: I5794ac73bd634d5ea069a6fbbd914274b6d6b7bf

* fix doc

Change-Id: I5a3350b2930a9ab2f52116c192b087307faf8fdf

* use has_grad instead of train_mode

* add vlog for debug

* fix ut

* fix ut

* improve performance of elementwise_sum_grad

* fix tensorrt output shape error

* fix unittest tensorrt_engine_op_test

* fix code style for unitest

[Dy2stat] Add a decorator paddle.jit.not_to_static to support that not to convert a function in Dynamic-to-Static. (#29253)

Usage scenarios：A function could have run successfully in static mode,  you can use it to decorate a function in the following cases:
  1. An unknown error occurs in the dynamic-to-static conversion process of the function;
  2. In the internal implementation of the function, it has two branches: dynamic branch and static branch;
  3. Users don't want to convert the function in the process of dynamic to static.

* fix doc, test=document_fix

* fix DATA_HOME path in win

* fix nll_loss doc;test=document_fix;

* remove numpy and set_device;test=document_fix;

* remove numpy;test=document_fix;

* move temporal_shift to functional

* add the weight decay func for the momentum op

* Add the multi_precision function in Momentum Optimizer.

* Make sure that the initial value of master weights are same with the fp16 weights.

* add static loss scaling.

* add the rescale_grad function in the pure fp16 training.

* use the original momentum updating method.

* Polish some codes, such as variable names.

* add docstring for apis.

* update the var creation details of _create_master_weight.

* not modify codes about imperative momentum updating.

* Fix the error of test_dist_sparse_tensor_load_momentum UT.

* add unit test for multi precision fp16 training.

* add more unit tests for CI.

* Use lower threshold values for allclose comparing in test_multi_precision_fp16_train UT.

* For CI Coverage Checking.