* Fix stop_gradient in RunProgramOp

* fix reference

(cherry picked from PR #36095)

PR主要功能：支持C++开发注册GeneratePass，简化针对fusion等子图优化场景开发方式。

对于__getattr__重载后不满足条件的参数，全部抛出AttributeError异常，达到与未重载版本一致。

(cherry picked from PR #36229)

* update func name

* skip cpu

* update unittest

* update unittest

* fix raw optim

* pre-commit test file
Co-authored-by: Nsneaxiy <sneaxiy@126.com>
Co-authored-by: Nsneaxiy <sneaxiy@126.com>

add roi align, cherry-pick #35102

向PaddlePaddle中的线性代数库添加eig算子，该算子计算一般方阵的特征分解。
cherry-pick 自#35674.

* add get_device_name and get_device_capability

* fix docs

* fix docs

* fix decs

* test=document_fix

* test=document_fix

* test=document_fix

* test=document_fix

* add roi pool

* rename input as x

根据线性代数库的API暴露规则修改multi_dot的API暴露规则：
1、在python/paddle/tensor/linalg.py 路径下实现
2、在python/paddle/linalg.py 下import并加入__all__列表
3、在python/paddle/tensor/init.py下引入并加入tensor_method_func列表
4、删除了pythonpaddle/init.py的import

ATT, cherry-pick #36160

* Initial Commit

* fix py2 error

* fix wrong words and doc

* test=document_fix

* fix _gpuDeviceProperties

* fix unique unstack dim 0

* fix unique_op format

remove recent linalg api in paddle.init;
add args 'name' in some new linalg api interface

ATT, cherry-pick #36098

cherry-pick from #35352

Add new detection api paddle.vision.ops.psroi_pool and paddle.vision.ops.PSRoIPool

cherry-pick #35858、#35895

The AdamW optimizer modify the op from adamw to adam in pr35521, this is a inappropriate modify. Modify adam to adamw in AdamW.

When users use gumbel_softmax, they can use paddle.seed() in python for fixed seed.

cherry-pick #35916，CPU前向计算将Eigen替换为Lapack，修改linalg暴露规则

This PR added det and slogdet API to release/2.2
It is cherry-pick from #34992 and #36013

[cherry-pick] Add function comments and instructions to the Primitive API

This PR supports linalg.solve calculation for linear algorithm module of Paddle. One may call paddle.linalg.solve to use it.

* add randperm_op_npu

* fix test_set_value_op_npu

* fix pad tuple

* fix format

1、Split function GradScaler::minimize() to GradScaler::step() + GradScaler::update()
2、Add GradScaler::unscale_(optimizer)