* merge recent changes

* fix setting pipline

[New API] add API paddle.vision.transforms.RandomErasing and paddle.vision.transforms.erase (#42280)

* add api RandomErasing
* add numpy/PIL backend support and UT
* fix doc and optimize UT
* add seed

* remove stats_md

* add entry to authors.md

* Add peak memory log for CI

* Change VLOG to std::cout

* Move print code to test_runner.py and paddle_gtest_main.cc

* Fix typo

* Fix conflicts

* Updata message format

* Fix CI errors

* Add FLAGS_enable_gpu_memory_usage_log

* Fix CI errors

* update xpu depends
*test=kunlun

* minor
*test=kunlun
Co-authored-by: Nroot <root@yq01-sys-hic-p40-0091.yq01.baidu.com>

* fix bug of batch_norm_grad kernel with fp16

* format code

* enable graph-engine to return all id

* change vector's dimension

* change vector's dimension

* enlarge returned ids dimensions

* add actual_val

* change vlog

* fix bug

* bug fix

* bug fix

* fix display test

* singleton of gpu_graph_wrapper

* change sample result's structure to fit training

* recover sample code

* fix

* secondary sample

* add graph partition

* fix pybind
Co-authored-by: NDesmonDay <908660116@qq.com>

* fix paddle-ort python bug

* fix paddle-ort python bug

This PR hotfixed the `test_cond.py` in CUDA 11.2

The reason of the bug is that the `fill_constant` op returns wrong value in the modified test case `test_extremely_simple_net_with_op_in_condition`, SWEs can use `layers.Print(a)` and `layers.Print(b)` in the test case to reproduce it and they can see the `fill_constant` returns something `e-50` instead of `1.23` and `1.25`

This PR hotfixed the bug by comparing `b` value instead of actual number, which makes sure the `cond` logic is right. **However, the PR didn't fix `fill_constant`**. We would let the SWEs who are working here to find the op bug and fix it.

* Fix test_cudnn_norm_conv and test_cudnn_bn_add_relu in CUDA11.2

* no throw in V100 for some cases

* remove useless lod copy

* fix test failed

* revert meta change

* revert tensor change

* add Tensor support for sub-functions of colorjitter
* add UT

* enable graph-engine to return all id

* change vector's dimension

* change vector's dimension

* enlarge returned ids dimensions

* Extend python einsum interface to make einsum_v2 support multi-operands and switch it to default.

* add opt_einsum dependence

* add yaml and support eager model

* fix by code review

* [Eager] Remove enable_legacy_dygraph setting

* Add more tests

* add double yaml

* add inline func