* [Refactor] refactored eager_gen.py PR #2

* [DoubleGrad PR #1] Decoupled code generation logics for Dygraph ForwardFunctions and GradNodes

* Fixed minor issue

* Adjusted logics of GenerateNodeCreationCodes and GenerateForwardDefinition

* Fixed issues

* Supported higher-order grad node generation

* [DoubleGrad PR #4] Supported higher-order GradNode generation

* [DoubleGrad #4] Bug Fixes to Double Grad Node Generation

* Fixed yaml typo

* Fixed yaml typo

* fixed minor issues

* [DoubleGrad PR #5] Enabled gradient computations for grad_tensors passed to paddle.grad()

* Fixed minor issue

* Fixed CI-Inference issue

* Fixed CI-inference issues

* [DoubleGrad PR #7] paddle.grad() to copy backward graph before backward run

* Fixed minor issues

* Fixed issue with backward graph construction logic

* Fixed implementation issues with backward graph reconstruction

* Fixed unittest issue

* Fixed issues

* [DoubleGrad PR #8] Enabled triple grads for sigmoid and matmul

* Fixed issues with phi kernel

* Added triple grad test case

* Fixed minor issue

* refactor eager flags

* fix flags error when we switch from eager to dygraph

* fix ci problem

* fix ci

* fix ci

* merge develop and fix code style

* merge develop and fix code style

* fix op test error

* fix op test error

* fix op test error

* fix op test error

* fix op test error

* merge develop

* native commit for triple grad of sigmod

* Updated unittests files

* init functional jacobian api

* Updated trible_test func

* Updated gradient_checker & test_script

* finish test with dtype float32

* add float64 test case

* polish code

* use atol=1e-5 with dtype float64

* fix for ci

* set timeout for test_jacobian

* fix dygraph grad to support high differential

* polish API docstring

* Updated gradient checker and some related files

* fix double grad strip error for high differential

* fix double grad strip error for high differential

* Add Sigmoid triple grad tests

* fix dygraph double grad dtype error when calling for high differential senario

* Updated triple grad teses func

* Use np.random to initialize ddx

* Updated triple_grad_check func

* add todo for gradient checker and refine some comments

* remove additional code

* add test for warnging in backward.py

* format python code

* support multi input in triple gradient checker

* Add matmul triple grad kernel

* Updated comments of TODO

* Supported some special tests

* Change code-format to follow CI std

* Updated gradient_checker.py

* Fix conflicts

* Removed unnecessary printing log

* Change code style to follow CI std

* Add Dygraph Triple Grad test
Co-authored-by: Nlevi131 <limaolin01@baidu.com>
Co-authored-by: NJiabin Yang <360788950@qq.com>