* Support append method and initialized value for List in ControlFlow

* polish error msg and en doc

* fix code style

* Support getitem by Bool index

* delete some debug info of bool index

* support the case that the shape of bool index is different from indexed tensor

* support setitem by bool index

* add the unittest for throwing exception

* merge conflict

* add check for int tensor when index is bool

* polish code

* fix unittest on windows

* refine pybind interface

* support statistic MemSize of AllocatorPool

* Replace mutex into atomic

This PR adds fused transformer python related files. It defines interface of fused transformer.

Fused transformer implements an optimized version of transformer layer (in python/paddle/nn/layer/transformer.py). In this PR, four layers (functions) are defined:
(1) FusedMultiHeadAttention: multi-head attention layer
(2) FusedFeedForward: feed forward layer
(3) FusedTransformerEncoderLayer: transformer encoder layer
(4) FusedTransformer: transformer layer

* Add model for ResNet50 for Dy2stat AMP training

* fix timeout

* fix dataloader

* [NPU] fix cmake for ascend ci, test=develop

* update paddle_build.sh scripts, test=allcase

* Revert "Revert "Add copy from tensor (#34406)" (#35173)"

This reverts commit 32c1ec42.

* add template instantiation

[Background]
Expansion in code size can be irreversible in the long run, leading to huge release packages which
not only hampers user experience but also exceeds a hard limit of pypi.

In such, NV_FATBIN section takes up 86% of the compiled dylib size, owing to the vast number of GPU
arches supported.

This PR aims to prune this NV_FATBIN.

[Solution]
In the new release strategy, two types of whl packages will be involved:

Cubin PIP package:
PIP package maintains a smaller window for GPU arches support, containing
sm_60, sm_70, sm_75, sm_80 cubins, covering Pascal - Ampere arches

JIT release package:
This is a backup for Cubin PIP package, containing compute_35, compute_50, compute_60,
compute_70, compute_75, compute_80, with best performance and GPU arches coverage.

However, it takes around 10 min to install due to the JIT compilation.

[How to use]
The new release strategy is disabled by default.
To compile for Cubin PIP package, add this to cmake: -DCUBIN_RELEASE_PIP
To compile for JIT release package, add this to cmake: -DJIT_RELEASE_WHL

* add_op_unittest

* [NPU] Add log_loss op

* [NPU] Add log_loss op

* [NPU] Add log_loss op

* tmp

* Tile - Assign - Crop

* Finish the set value npu kernel and test case in npu

* improve the error message

* Modify according to zhangliujie

* code review

* add maxunppol2d op, test=develop

* fix typo, test=develop

* fix unpool unitest, test=develop

* fix unpool code-example, test=develop

* fix for unpool_op_unittest,test=develop

* fix example code, test=develop

* add noqa:F401, test=develop

* fix converage, test=develop

* fix unitest for unpool, test=develop

* rename unpool2d to unpool, test=develop

* rename unpool2d to unpool, test=develop

* sparse_momentum_op is used to save w@GRAD memory for gather_op when gather from a large parameter