* init grad op checker

* can run

* add GradeChecker class

* use get_numeric_gradient

* refine code

* add softmax and cross entropy auto grad test

* use close to judge op_grad and numeric_grad

* add cpu and gpu compare

* add comments

* add support_gpu

* fix allclose

* fix name error and symplify code

* optimize gradient checker

* add test_cross_entropy_op

* update gradient_checker.py

* optimize code

* use random.uniform instead of random.random

* fix type bug

* optimize check_grad

* put SupportGPU into OperatorBase

* typo

* add support_gpu

* fix allclose

* fix name error and symplify code

* put everything in paddle wheel

* update

* update

* fix unitest

* with platform specs

* finish forward debug

It can be run both CPU/GPU. configure attributes are:

* min: the min value of uniform random
* max: the max value of uniform random
* dims: the dimension of output tensor
* seed: the random seed of uniform random. 0 means generate a seed each
        time.

* init cross entropy graident

* add crossentropy grad op

* remove details

* fix static compile

Make ci pass

Currently use `Operator("fc", X="x", W='w1', B='b1')` as operator
creation method.

Fix #3198

Rename operator.py to op.py because it is conflict with protobuf

* because it uses `numpy.flip` in `test_image.py`

* init softmax grad op

* add compute code

* export Backward to python

* update test ,export op.type to python

* update python test, fix compute bug

* update unit test

* use eigen

* optimize eigen code

* add gpu test

* register softmax_grad GPU kernel and fix test bug

* typo

* follow comments

* export Backward to python

Add get_numeric_gradient API and its unittest.